A case study on how an e-tailer can use a multiplecriteria ABC analysis to identify risk in the selection

of suppliers

Master thesis

Joel Strand and Louise Strandänger

Spring 2016

ISRN: LIU-IEI-TEK-A--16/02581--SE

A case study on how an e-tailer can use a multiplecriteria ABC analysis to identify risk in the selection

of suppliers

Examensarbete i industriell ekonomi om 30 hp, vid utbildningen tillcivilingenjör i industriell ekonomi.Ämne: ProduktionsekonomiPresentationsdatum: 2016-06-07Publiceringsdatum (elektronisk version): 2016-06-20URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-02581Språk: Engelska

Examinator: Veronica Lindström, ieiHandledare: Helene Lidestam, iei

ISRN: LIU-IEI-TEK-A--16/02581--SE

Tekniska högskola vid Linköpings universitetInstitutionen för ekonomisk och industriell utvecklingAvdelningen för produktionsekonomi

Tekniska Högskolan581 83 Linköping

Abstract

Purpose – The purpose of this master thesis is to explore how an e-tailer selling bulky items canuse a multiple criteria ABC analysis to make its purchasing process more effective, while balancingrichness and reach, with the performance measurements of profitability, total asset turnover andinventory turnover. The purpose will be accomplished through a single case study on an e-taileractive on the Swedish furniture and home furnishing market.Methodology – This thesis applies a multiple criteria ABC-analysis to a single case study. Theapproach is semi-deductive as theory is combined with interviews on how to match and adapttheory about inventory control and purchasing with the specific requirements of an e-tailer sellingbulky items.Findings – This thesis has resulted in a set of recommendations that aim to make the purchasingprocess of an e-tailer more effective. That is, capital and inventory space will be better allocatedto the e-tailer’s more profitable items. Among other things, this thesis shows how dead articlescan be identified and how a purchaser can prioritize more profitable articles over less profitableones when making procurement decisions. The other recommendations are for the e-tailer toinvestigate the possibilities of decoupling the supply chain by keeping stock at the suppliers’premises, to match the supplier reliability with their importance in the supply chain, and lastlyto explore possibilities of drop shipment. Further, the main finding is that a comparison betweenthe A-, B-, and C-classes and the reliability of the suppliers, highlights a gap and a possible risk.Put differently, the importance of a specific item for the business should be reflected in the choiceof supplier and the multiple criteria ABC analysis is the tool to illustrate the importance.Keywords – E-commerce, E-tailer, richness, reach, transaction cost, ABC analysis, multiplecriteria ABC, MCABC, inventory turnover ratio, supplier selection, purchasingPaper type – Masters thesis

Sammanfattning

Syfte – Syftet med detta examensarbete är att undersöka hur en e-handelsdetaljist som säljerskrymmande artiklar kan använda en flerdimensionell ABC-analys för att göra sin inköpsprocessmer effektiv och balansera richness och reach, med mätetal som lönsamhet, kapitalomsättnings-hastighet och lageromsättningshastighet. Syftet kommer att uppfyllas genom en fallstudie på ene-handelsdetaljist verksam på den svenska möbel- och heminredningsmarknaden.Metod – Denna fallstudie använder sig av en flerdimensionell ABC-analys. Tillvägagångssättet ärsemi-deduktivt då intervjuer och teori om hur lagerstyrning och inköp kan matchas och anpassastill ett företags specifika behov.Resultat – Den här uppsatsen har resulterat i en rad åtgärder som syftar till att göra en e-handlares inköpsprocess mer effektiv. På så vis att kapital och lageryta bättre allokeras till e-handlarens lönsamma artiklar. Bland annat visar den här uppsatsen hur döda artiklar kan iden-tifieras och hur inköparen kan prioritera mer lönsamma artiklar över olönsamma vid inköp. Deandra åtgärdena handlar om att undersöka möjligheter att frikoppla försörjningskedjan genomatt lagra produkter hos leverantören, att matcha leverantörernas pålitlighet och deras betydelsei försörjningskedjan, och slutligen att utforska möjligheter att utöka drop shipment. Det främstabidraget är att eventuella felprioriteringar och risker blir tydliga genom en jämförelse mellan A-,B- och C-klasserna och leverantörernas pålitlighet. Med andra ord bör den affärsmässiga inverkansom respektive artikel har på e-handlarens resultat avspegla sig i valet av leverantör. En flerdi-mensionell ABC-analys kan användas för att påvisa respektive artikels affärsmässiga inverkan.Publikationstyp – Examensarbete för utbildning till civilingenjör (masteruppsats).

Preface

This thesis is written by two students, enrolled at different universities. Louise is a student atKTH and Joel is a student at Linköping University. The thesis will be published twice but with adifferent front page and International Standard Technical Report Number (ISRN) or löpnummer.

Publication of KTH: 2016:84Publication of LiU: LIU-IEI-TEK-A--16/02581--SE

Joel Strand and Louise StrandängerJune 15, 2016, Stockholm

Terminology

Abbreviation Full name

AHP Analytical Hierarchy ProcessCODP Customer Order Decoupling PointEBIT Earnings before interests and taxesEOQ Economic order quantityERP Enterprise Resource PlanningITR Inventory turnover ratioMCABC-analysis Multiple Criteria ABC AnalysisMTO Make to orderOrder lead time The time which elapses between placing an order and the delivery

of the goodsROCE Return on capital employedROA Return on assetsROE Return on equityROS Return on salesSNI Swedish Standard Industrial Classification (swe. Svensk Närings-

grensindelning)

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Contents

Terminology i

1 Introduction 11.1 The Context – Retrospective and Contemporary Perspectives on Trade . . . . . . . 11.2 Introducing the Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Discussing the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.4 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.4.1 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.5 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.6 Delimitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.7 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Frame of References 72.1 Reduced Arbitrage from Information Asymmetry . . . . . . . . . . . . . . . . . . . 7

2.1.1 Richness and Reach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 The Connection Between Profitability and Capital Investments . . . . . . . . . . . 92.3 Supply and Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.4 Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.5 Service Level and Safety Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.6 Lot Sizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.7 Order Point System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.8 ABC Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.9 Multiple Criteria ABC Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.9.1 Dollar Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.9.2 Replenishment Lead Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.9.3 Other Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.10 Value Proposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.11 Purchasing in a Strategic Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.11.1 Category Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.11.2 Vertical Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.11.3 Limiting the Amount of Suppliers . . . . . . . . . . . . . . . . . . . . . . . 20

3 Methodology 233.1 Research Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.2 Methodological Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.2.1 Deductive and Inductive Reasoning . . . . . . . . . . . . . . . . . . . . . . . 243.2.2 Research Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.2.3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.2.4 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.3 Methodology for Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.3.1 Transaction Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.3.2 Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

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3.4 Methodology for Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.4.1 Criticism of the Analysis Method . . . . . . . . . . . . . . . . . . . . . . . . 28

3.5 Discussion of Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.5.1 Construct Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.5.2 Internal Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.5.3 External Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.5.4 Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.6 Source Criticism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4 Empirical Findings 314.1 Competitive Advantage and Value Proposition . . . . . . . . . . . . . . . . . . . . 314.2 Financial Position . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.2.1 Sector Positioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.2.2 Income Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334.2.3 Balance Sheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.3 Inventory Control in the Value Chain . . . . . . . . . . . . . . . . . . . . . . . . . . 354.3.1 Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.3.2 Safety Inventory and Service Level . . . . . . . . . . . . . . . . . . . . . . . 354.3.3 Current Lot Sizing Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.3.4 Purchasing Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.3.5 Inventory Location and Warehouse Management . . . . . . . . . . . . . . . 364.3.6 Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.3.7 Suppliers and Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.3.8 Suppliers for Drop Shipment . . . . . . . . . . . . . . . . . . . . . . . . . . 384.3.9 Supplier Grade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.4 Data Extraction for the ABC Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 404.4.1 The Storage of Inventory Data at Auctus . . . . . . . . . . . . . . . . . . . 404.4.2 Data Structure and Characteristics . . . . . . . . . . . . . . . . . . . . . . . 414.4.3 An Example from the Multiple Criteria ABC File . . . . . . . . . . . . . . 43

5 Analysis, Constructing and Performing an MCABC Analysis 455.1 Design of the Multiple Criteria ABC Analysis . . . . . . . . . . . . . . . . . . . . . 45

5.1.1 Criterion – Dollar Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455.1.2 Criterion – Order Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . 465.1.3 Criterion – Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465.1.4 Criterion – Replenishment Lead Time . . . . . . . . . . . . . . . . . . . . . 465.1.5 Imposed Restrictions on the Multiple Criteria ABC Analysis . . . . . . . . 475.1.6 Answering Research Question 1 . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.2 Summary of the Multiple Criteria ABC Analysis . . . . . . . . . . . . . . . . . . . 475.2.1 A-articles – Capital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515.2.2 A-articles – Inventory Space . . . . . . . . . . . . . . . . . . . . . . . . . . . 525.2.3 B-articles – Capital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535.2.4 B-articles – Inventory Space . . . . . . . . . . . . . . . . . . . . . . . . . . . 545.2.5 C-articles – Capital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545.2.6 C-articles – Inventory Space . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

6 Proposed Solutions 596.1 Staying on Top in the E-commerce Competition . . . . . . . . . . . . . . . . . . . . 59

6.1.1 Make Prioritization Clear in the Purchasing Process . . . . . . . . . . . . . 596.1.2 Eliminate Dead Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 606.1.3 Decouple the Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . 606.1.4 Determine Safety Stock According to Product Importance and Supplier

Ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 616.1.5 Match Supplier Reliability and Their Importance in the Supply Chain . . . 62

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6.1.6 Explore Possibilities of Extended Drop Shipment . . . . . . . . . . . . . . . 626.1.7 Answering Research Question 2 . . . . . . . . . . . . . . . . . . . . . . . . . 63

7 Conclusions and Future Work 657.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

7.1.1 Generalizability of the Results . . . . . . . . . . . . . . . . . . . . . . . . . 657.1.2 Empirical Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667.1.3 Theoretical Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

Appendices 73

A Number of Articles per Supplier 75

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List of Figures

2.1 The trade-off between richness and reach. . . . . . . . . . . . . . . . . . . . . . . . 82.2 Illustration of DuPont identity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.3 Normal distribution of replenishment lead time. . . . . . . . . . . . . . . . . . . . . 132.4 The theoretical Pareto distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.1 Illustration of the research process. . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.1 Illustration of value chain for Auctus. . . . . . . . . . . . . . . . . . . . . . . . . . 324.2 The Purchasing Process at Auctus. . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.3 The Pareto distribution of Auctus’ suppliers. . . . . . . . . . . . . . . . . . . . . . 374.4 The process from customer order to accounting. . . . . . . . . . . . . . . . . . . . . 41

5.1 Illustration of the supplier’s reliability and importance for A-articles. . . . . . . . . 525.2 Illustration of the supplier’s reliability and importance for B-articles. . . . . . . . . 535.3 Illustration of the supplier’s reliability and importance for C-articles. . . . . . . . . 55

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viii

List of Tables

2.1 Customer order decoupling point, for different production approaches. . . . . . . . 112.2 Illustration of the MCABC, part I. . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.3 Illustration of the MCABC, part II. . . . . . . . . . . . . . . . . . . . . . . . . . . 172.4 Illustration of the MCABC, part III. . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.1 Sector key figures from Swedish statistics. . . . . . . . . . . . . . . . . . . . . . . . 334.2 Income statement of Auctus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334.3 Balance sheet of Auctus. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344.4 The top four most frequent suppliers to Auctus. . . . . . . . . . . . . . . . . . . . . 384.5 Drop shipment by top four suppliers. . . . . . . . . . . . . . . . . . . . . . . . . . . 394.6 The qualitative grading of supplier lead time reliability. . . . . . . . . . . . . . . . 394.7 An example of raw transaction data from Auctus. . . . . . . . . . . . . . . . . . . . 424.8 Snapshot from the purchasing support file. . . . . . . . . . . . . . . . . . . . . . . . 424.9 Snapshot from the file which will be the foundation of the MCABC analysis. . . . 43

5.1 The result of the MCABC analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . 495.2 The MCABC analysis, focusing on maximum variable. . . . . . . . . . . . . . . . . 505.3 The MCABC analysis of the A-article, separated into product categories. . . . . . 505.4 The MCABC analysis of the B-article, separated into product categories. . . . . . 505.5 The MCABC analysis of the C-article, separated into product categories. . . . . . 50

A.1 A complete list of suppliers and the number of items they provide. . . . . . . . . . 75

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Chapter 1

Introduction

This master thesis begins with a presention of the context of an e-commerce retail business (e-tailer) and the differences between a store with online presence only and a business with physicalstores only.1 The chapter then continues with introduction of the case and the challenges that itis facing. Finally, the purpose and the research questions with limitations and delimitations arepresented.

1.1 The Context – Retrospective and Contemporary Per-spectives on Trade

Through all times, humans have traded with each other to maximize their wealth. Specializationand comparative advantage (Britannica Academic 2016) among humans and entities make it moreefficient to focus on one activity, and trade the outcome of this activity to gain possession ofother essential goods and services. In ancient times, this was done through barter trade and lateron, different monetary systems started to act as mediators and securities of value, lowering thetransaction cost of trade. The most recent defiant of transaction costs is the industrial revolution ofthe Internet; transactions between humans and entities are more easily performed than ever before.Çetinkaya and Lee (2000) claim that e-commerce constitutes a paradigm shift and that it can beclassified as a disruptive innovation due to its impact on how people do business today. Since theinvention of hypertext and the World Wide Web, or just the web, transaction costs of trade havedecreased more, especially in the case of online banking and online retailing (Gunasekaran, Marri,et al. 2002).2 Further, the web testifies to the falsity of traditional thoughts about the trade-offbetween “Reach and Richness” (Evans and Wurster 1997, p. 73). Where richness is about theinformation and reach is about the amount of people that are exchanging the information (Evansand Wurster 1997).

The advent of the web and the reduced transaction costs have gotten more and more entities tointegrate the web into their business models. But the demand, and ability to provide greater reachand richness, put the supply chain, and more specifically the inventory control and purchasing,to the test. More articles need to be kept in stock for the e-tailers to provide the demandedrichness. The competitive advantage of a retailer is still to match suppliers and customers toreduce their search cost, which the Internet enables with a greater reach than a brick-and-mortarretailer.

1The term e-tailer is used in several publications, for example Burt and Sparks (2003), Grewal and Levy (2007),and Beldad et al. (2010).

2Transaction cost is defined by Coase (1937), research for which he was awarded the Sveriges Riksbank Prize inEconomic Sciences in Memory of Alfred Nobel.

1

Theories about inventory control and purchasing management handle items with predictable de-mand well and use marketing to affect the customer behavior. This is useful when the storecompetes on location, and the customer faces an extra search cost if he or she is to find anotherstore. Then, the customer might be more susceptible to the advertisement in the physical store.But on the Internet, where the customer might more easily compare different options to a neg-ligible search cost, the demand for a certain good is not managed in the same way. Evans andWurster (2000) argue that an e-tailer will not experience the same advantage of intensively adver-tising only one or a few products; as the customers might see through the offer when they have theability to compare the same product with several other e-tailers. Evans and Wurster continue bywriting that the strategy of the e-tailer will then be to advertise a greater amount of products ina more neutral way, as the online comparison will make the customer choose the best alternativepossible.

A more unpredictable demand and the greater number of unique articles facing an e-tailer has thepotential of binding an unnecessary amount of capital, if the inventory control and the purchasingprocess is not adapted well enough to fit the situation. Moreover, the bulkiness of an article isnot paid enough attention – unless this dimension is considered, an e-tailer might add unnecessaryfixed costs in the form of extra inventory space. The challenge of holding the products that thecustomers want, when the customers want them is still the topical issue of inventory control andpurchasing. Under a more unpredictable demand, and for a great number of items, this becomesan even more intricate matter.

1.2 Introducing the Case

This thesis will use a single case approach in order to deepen the knowledge about inventorycontrol for e-taliers with operations encompassing bulky articles. The specific case firm operatesas an e-tailer, selling furniture and home furnishings. The company wishes to remain anonymous,which is why the company is given the fictive name Auctus (growth in Latin). The reason for thisprecaution is that the thesis will contain authentic information about the company, using a fictivename will obstruct the identification, and thereby minimize the risk of harming the business whilethis thesis will still be able to fulfill its academic obligations.

Auctus has experienced a period of vigorous growth, since the establishment less than a decadeago. Scott and Bruce (1987) and Churchill and Lewis (1983) assert that, in an initial phase of acompany’s existence, attaining a greater market share and a broader sales base is crucial for theits survival. Establishing a trustworthy value stream, and a unique value proposition – that is, thevalue that a firm creates for its customers – is the main goal in this early phase according to Ma-hadevan (2000). These phenomena can be observed in the case of Auctus. The hunt for increasedsales might have an accelerating effect on the costs as the focus on growth and a generous valueproposition neglects the perspective on controlling costs connected to business activities. Lateron, once the business has proven its profitability, consolidating costs and improving cost efficiencybecome more vital as sales are sufficiently large for the firm’s existence not to be jeopardizedin a near future, according to Scott and Bruce (1987) and Churchill and Lewis (1983). In theautumn of 2015 Auctus announced for students to help with cost control, which is in line with thetheoretical prediction of a company’s growth.

Today, Auctus is in need for a more systematic approach to inventory control and purchasing. Atpresent, there are three main symptoms that can be traced to the performance of the inventorycontrol and purchasing. Firstly, supply delivery sometimes cannot unload goods at the Auctuswarehouse due to the fact that the warehouse is out of available space. There is a lack of commu-nication between the purchasing management and the warehouse management, as the purchasingmanagement procures and purchase whatever articles they think will answer to the customer de-mand, no matter what space that is currently available in the warehouse. Secondly, a large part

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of Auctus’ sales are induced by discount campaigns. This tool is especially common for productsthat have not sold well in the past. Due to the independence of the purchasing department, andsometimes misleading information from the purchasing computer software, the purchase manage-ment are lured into initiating purchase orders for products running on a discount campaigns. Thesoftware then indicates an abnormally high demand and a dangerously low stock level, when thereal reason for the discount campaign was to get rid of the product from the inventory. The resultis that a product, which was about to be removed from the online store once the stock level wasclose to zero, is purchased once more. Finally, Auctus has a lower inventory turnover ratio thancomparable companies in the same sector (SNI 47919).3 This finding is supported by Auctus’ in-come statement – the trend over the past three years indicates a greater increase in Raw materialand consumables than in Net sales. The two income statement items are diverging when they –in the ideal case – should converge, or at least keep a constant relationship. The divergence ofthe income statement items indicates that Auctus has sold more at the expense of a larger, andpossibly more inefficient designed inventory. A first action to understand why the symptoms arisewould be to categorize the inventory according to a multiple criteria ABC analysis, which is anestablished tool for inventory management. The multiple criteria ABC analysis will be used as abase for a further investigation on how to come to terms with the symptoms.

1.3 Discussing the Problem

New sales channels and new ways of communicating have led to a broader scope of informationbeing attainable to a greater amount of people, that is, a substantial extension of richness andreach. Apart from the benefits that these dimensions bring to the consumers, they also createchallenges for online businesses. The reduced asymmetry of information due to the possibilityfor customers to easily compare products, assortments, delivery lead times and prices online,imply that a successful e-tailer needs to offer low prices and a broad range of products with highresponsiveness. Greater pressure to provide extensive richness increases the pressure to keep morearticles in stock, thereby tying up a larger portion of a company’s capital. Housing larger-sizedproducts augment inventory costs even more, why a clever inventory management and purchasingstrategy is required. Structures, systems, and strategic alliances, therefore, require a design thatsecures a proper execution of the value proposition. This thesis seeks to propose solutions for howto systematically balance richness and reach, by using a multiple criteria ABC analysis as a basisfor decisions concerning the selection of suppliers and how to treat the relationships strategically.The proposed solutions will make the purchasing process more effective. Where effective refersthe process’ exposure to unnecessary risk. The underlying idea is that businesses make moneyby taking on risk, for example in the supply chain, that the customer is willing to pay for, butthere might exist other risks in the business that the customers are unwilling to pay for, so calledunnecessary risk. When this unnecessary risk is reduced one can argue that the purchasing processoperates in a more effective manner.

1.4 Purpose

The purpose is to explore how an e-tailer selling bulky articles can use a multiple criteria ABCanalysis to make its purchasing process more effective while balancing richness and reach, withthe performance measurements of profitability, total asset turnover, and inventory turnover. Thepurpose will be accomplished through a single case study on an e-tailer, active on the Swedishfurniture and home furnishing market.

3SNI 47919 is defined by Swedish statistics as Postorderhandel och detaljhandel på Internet med övriga varor.

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1.4.1 Research Questions

The following research questions are a deconstruction of the purpose. Research question 1 focuseson construct validity, hence, how the multiple criteria should be designed to measure the purchasingprocess and make it more effective. Research question 2 exists to investigate what the actualactions should be to facilitate a more effective purchasing process.

1. What should be the design of a multiple criteria ABC analysis to evaluate the purchasingprocess of bulky articles?

2. What will be the recommendations for the purchasing process of an e-talier to more effectivelybalancing richness and reach with the financial performance measurements?

1.5 Limitations

A limitation is an external restriction, a synonym would be the specific circumstances of this casewhich the authors cannot change. Here it is important to highlight that research question 1 askshow the multiple criteria analysis should be designed to evaluate the purchasing process of bulkyarticles. The design of the multiple criteria ABC analysis requires that yet unknown circumstancesabout data characteristics are considered. As a consequence, such circumstances about data will becommunicated in chapter 5 ‘‘Analysis, Constructing and Performing an MCABC Analysis’’.

• Data that is available today goes back to May 1 of 2013, why sophisticated statistical predic-tions based on such a short time period will not be reliable. Instead Auctus uses a movingaverage as an approximation of the demand.

• Data available is of a varying quality, which will affect the working process of this thesis. Inexactly what way will be answered through research question number one.

1.6 Delimitations

Delimitation are the restrictions made by the authors. This is different from the external restric-tions imposed on this thesis, presented above as limitations.

• This thesis treats Auctus’ different stock point as one, but this will not affect the purposewhile it will ease the authors’ analysis process. The reason for this is that the purchasingis a central function and the majority of articles are stores in a few locations geographicallyclose to each other.

• The second symptom mentioned in the introduction of this thesis – the symptom aboutdiscount activities and perceived demand – will not be touched upon in this thesis. Thesymptom is more likely to be solved by investigating a different enterprise resource planning(ERP) system, than performing a multiple criteria ABC analysis.

1.7 Outline

The remaining part of this thesis consists of seven chapters, which are briefly explained be-low.

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Chapter 2 – Frame of References Theories are presented about e-commerce along withthe special implications that this type of context brings, and the identified gap in the literature.Literature concerning inventory control and purchasing is also described and compared.

Chapter 3 – Methodology The chapter begins with a presentation of the research process asan overview of all the incorporated steps, and where in the process that the research questionsare answered. Later on, the choices of research methodologies are explained and motivated andfinally the quality of the research is discussed.

Chapter 4 – Empirical findings The chapter includes a compilation of the empirical findings,beginning with a more detailed description of the case company, its context and operations. Inthis chapter, the case company’s reasoning and current situation regarding inventory control andpurchasing are explained as well as the results from the multiple criteria ABC analysis.

Chapter 5 – Analysis, Constructing and Performing an MCABC Analysis The chapterconnects the empirical findings to the theories from the literature review in a multiple criteria ABCanalysis. The results from the multiple criteria ABC analysis are then evaluated together withthe qualitative data.

Chapter 6 – Proposed Solutions The chapter discussion of the analysis made in Chapter 5and proposals of what measures that an e-tailer in Auctus’ situation should take in order to tacklethe issues highlighted in the analysis.

Chapter 7– Conclusions and Future Work The last chapter connects the proposed solu-tions to the purpose of the thesis and thereby presents the conclusions. Future research is alsodiscussed.

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Chapter 2

Frame of References

This chapter of the thesis will present the theoretical framework. It starts with an introduction ofthe e-tailer environment (Section 2.1) and compare it to the traditional retailers operating thruoghtraditional physical shops. Next, the basics of firm profitability will be explained in Section 2.2.The remaining parts of this chapter will be used to discuss the traditional theories of inventorycontrol and purchasing that are relevant to bridge the discussed problem.

2.1 Reduced Arbitrage from Information Asymmetry

Evans and Wurster (1997) write that customer relationships and supplier relationships – actuallyevery kind of relationship in the value chain – in reality, is the possessed information about;for example, a customer, a company, a supplier or a product. Information asymmetry oftendetermines the bargaining power in a buyer-seller relation. One of the most famous articlesabout information asymmetry is “The Market for ’Lemons’: Quality Uncertainty and the MarketMechanism” by Akerlof (1970). In the article, Akerlof shows that the market for buying a used caris very much determined by the asymmetry of information between the buyer and the seller. Theseller can exploit the asymmetric information about the true condition of the car, and the buyerfaces the option of trusting the seller or investigating every other option in the market. Wherethe last alternative has traditionally been a time-consuming task. Many businesses exploit thisinformation asymmetry; Evans and Wurster argue that the asymmetry in many cases constitutesthe competitive advantage of a company. This has been true as long as the information is restrainedto flow where the information carrier goes, like a sales person or a direct email. Once actors inthe value chain and the market are electronically connected, Evans and Wurster explain thatinformation can travel by itself.

2.1.1 Richness and Reach

The definition of reach by Evans and Wurster (1997) is the number of people exchanging infor-mation with each other, while richness is separated into three categories:

• Bandwidth – the quantity of information transferable from sender to receiver in a givenmoment.

• Customized – How well adapted the information is to the receiver. Compare a TV commercialand a face-to-face sales pitch.

• Interactivity – Is the communication a dialogue or a monologue?

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Grewal and Levy (2007) highlight other aspects of reach of businesses online. The Internet makesit possible for a company to reach more customers, and in turn, more customers are able to reachthem. Not only does e-commerce imply that a larger number of people are able to access theinformation about a company’s offerings, but they are able to do so at any moment of the dayor at any place. The advantages of richness and reach compensate for possible disadvantagesof the online channels such as lack of personalized human contact, assessment the products inreality before acquiring them, and eventual additional costs for after-sales service, such as returns.The lack of pre-purchase trial affects standardized products, such as books and music less, thannon-standardized products, for example, clothes, toys or furniture.

The definitions of richness and reach imply that ‘‘rich’’ information has to be presented in a specialway, due to constraints of bandwidth, customization, and interactivity. Rich information has forexample been presented at customer evenings or special advisory meeting. Figure 2.1 shows thetrade-off between richness and reach. However, new ways of communicating, such as the Internetand the web make it possible to provide rich information and reach many people at the same time.(Evans and Wurster 1997)

Reach

Richness

Traditionaltrade-off

New ways of combiningrichness and reach

Figure 2.1: The trade-off between richness and reach. The Figure is inspired by (Evans and Wurster2000, p. NA, Figure 3-3).

While many customers are able to visit a brick-and-mortar store, it is hard to beat the twenty-fourseven availability from a computer screen located anywhere around the globe. At the same time,information is getting richer as the customers accept ‘‘cookies’’ to be used for customization ofoffers. Schafer et al. (2001) state that greater richness does not have to mean that a companyneeds to necessarily develop more products to meet the many and diversified needs of customers,however, online sales have opened up an opportunity to provide customers with more choices.They compare this to a superstore selling thousands of books, as opposed to an online store wherethe customers may choose from millions of books. Grewal and Levy (2007) also emphasize theimpact and importance of providing a vast range of articles as an e-tailer. The authors describethis in relation to the disadvantages that a buyer experiences when making their purchases viathe Internet versus at a physical location.

Gunasekaran and Yusuf (2002) as well as Gunasekaran, Hung Lai, et al. (2008) have made similarobservations about the increased customer influence that is becoming more present in the 21st

century. Richness and reach have driven companies to focus even more on customer demand,increasing the pressure on agile value streams. They state that in order to achieve agility in anorganization, proactive planning is needed to meet changes in market and customer demands,maximize customer service level while minimizing the cost of goods. The objectives of this areto increase competitive advantages in a global market, and better the chances of long-term sur-vival.

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2.2 The Connection Between Profitability and Capital In-vestments

The main goal of a company is to create a return for its investors. For a firm to achieve long-termsuccess and create the desired return, it needs a positive net profit, which is calculated as:

Net Profit = EBIT− Interest− Taxes (2.1)

Where EBIT is defined as:

EBIT = Revenue− (Operating Expenses+Non-Operating Income) (2.2)

Skärvad and Olsson (2013) write that the net profit can be increased in two ways. On the one,hand the company may enhance its net sales, on the other hand the company can reduce itsexpenses. The focus might shift over the lifetime of a company.

It is natural for an average large company to achieve a bigger net profit than an average smallcompany does. Because a large company can invest more resources, the output of it should behigher. This does not say anything about how efficiently the input is used. Hence, to broadenthe financial analysis of a company, not only absolute figures, like the net profit, are important,but relative performance measurements too. A DuPont analysis provides such a tool of relativeperformance. The DuPont identity deconstructs the profitability of a company, where the netprofit is one component. The DuPont identity (Figure 2.2), also shows the connection of inventoryand profitability. That connection is especially important to this thesis.

DirectLabor

FactoryOverhead Material Finished

GoodsWork inProcess

Semi-finished

RawMaterials

GeneralExpenses

Admin.Expenses

SellingExpenses

Costs ofGoods Cash Accounts

ReceivableTotal

Inventory

Sales Total Costs FixedAssets

CurrentAssets

Profit Sales Sales TotalAssets

ProfitMargin Asset Turns

ROA

− +

÷ ÷

×

Figure 2.2: Illustration of DuPont identity, depicting how the income statement (left leg) and the balancesheet (right leg) construct the performance measurement return on asset (ROA). The Figure is inspiredby (Sanderson 1997, p. 18.29, Figure 18.6).

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The relative performance measurement, return on assets (ROA), indicates how well the assetsare used to generate a return to the investors. The relationship between return on assets andinvestments in inventory is explained by the DuPont identity and the following equation, byOlhager (2000), will highlight it.

ROA =ProfitSales

× SalesTotal Assets

= Profit margin×Asset Turn (2.3)

The return on asset can be deconstructed even further, following the DuPont identity upstream.Despite a company having an acceptable profit margin, ROA might be unsatisfactory due to a lowasset turnover. In a retail business the asset turnover is mainly improved through focusing on thecurrent assets and more specifically an increase of the inventory turnover ratio (ITR), equationby Olhager (2000):

ITR =(Net) SalesInventory

(2.4)

The asset turnover is directly connected to the profitability of an entity, which has been shown withEquation (2.3). Hence, the purpose of this thesis is in line with the well spread and recognized theidea of profit maximization as the overall goal for a firm. While the main RQ of this thesis seeksto maximize the net profit through cost control by focusing on improving the inventory turnoverrate while maintaining the current service level.

2.3 Supply and Demand

Managing material is about balancing requirements of supply and demand. If the demand isgreater than the supply, then a manufacturing or purchasing order must be initiated to meet thedemand. If the opposite situation occurs, planned and released orders will have to be delayed orthe demand manipulated with a discount campaign, for example. By keeping an inventory, Jonsson(2008) state that it is possible to decouple supply and demand, hence, lower the probability oreliminate the unwanted and costly situation when supply and demand differ. Further, Sanderson(1997) describes six functions of inventory that all aim to decouple supply from demand, Silveret al. (1998) describe six functions too, but do not use the same terminology or meaning. Olhager(2000), on the other hand, describes seven types of functions for an inventory to fulfill. Olhageralso makes a general comment about inventory – that there is an immense amount of reasons forkeeping an inventory, and the name of the inventory is, therefore, multifold as well. This is whythe authors of this thesis have made a selection of the inventory functions presented by Olhagerand Sanderson. The selected inventory functions are supposed to be important to the specificcircumstances of this thesis and the case company Auctus. This thesis will touch upon Bufferstock inventory (or safety stock) in Section 2.5, and lot size inventory in Section 2.6.

2.4 Forecasting

The aim of inventory control, according to Axsäter (1991), is to maintain an appropriately sizedinventory. Further, Axsäter argues that the replenishment lead time is usually too long comparedto the promised customer order lead time. The consequence – which has been touched upon inTable 2.1 on the facing page – is that the company needs to hold an inventory and make use offorecasts, in the attempt to predict the demand. De Lurgio et al. (1997) state that forecastingsystems have the combined purpose of meeting short-term demand and evaluating the futurecustomer requests for the development of new products. That is why it should be integrated intothe daily operations, as well as the strategic planning.

Olhager (2012) says that the Customer Order Decoupling Point (CODP) marks the position inthe value chain where the products are linked to a specific customer. Olhager also refer to the

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researcher consensus view of the CODP marking the transition from anticipated demand to actualdemand, presented in Table 2.1, for various production approaches. Hence, upstream the CODPposition, forecasting is the tool used to estimate the demand. While downstream the CODPposition, deterministic demand can be used to control the value creating processes in the valuechain. In his article, Olhager (2012) states that retailers can be seen as applying Make-to-stock(MTS). Even though the production, in this case, equals the purchasing process. As can be seenin Table 2.1, the CODP for an MTS approach is positioned far down the value chain close tothe customer. Therefore, forecasting of upstream demand is important, especially to a retailer.

Table 2.1: Customer order decoupling point, for different production approaches. The dotted linesindicate upstream activities, the solid lines indicate downstream activities. Inspired by (Olhager 2012, p.38, Figure 1.).

Customer orderdecoupling point

Engineer Fabricate Assemble Deliver

Make-to-stock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CODPAssemble-to-order

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CODP

Make-to-order . . . . . . . . . . . . . . . . . CODPEngineer-to-order CODP

De Lurgio et al. (1997) highlight some of the issues that often occur in forecasting systems. Firstly,it needs to be kept in mind that shipments and demand are not exactly the same, since the numberof shipments can be affected by stockouts, delays or substituted products and so on. Thus, it isimportant to consider these factors when determining the demand. Secondly, promotions anddiscounts need to be registered in order to evaluate the influence it has had on the demand.Thirdly, the impact of promotions and outliers should be documented separately to facilitate theanalysis of demand figures. Also, De Lurgio et al. (1997) say that all of the forecasts should bemade independently of other articles. Lastly, the detection of the different phases in the productlife cycle is important. Such a feature in a forecasting system is based on statistical methods,which means that accurate input data is required.

According to Axsäter (1991), many forecasting methods are based on an extrapolation of pastdemand, while this can lead to inaccurate results. Known factors, which not yet has had aninfluence on customer demand, might have a high impact in the future. Such factors are difficultto include in a computer-based system, why there sometimes is a need to manually determine futuredemand. Therefore, the system should be constructed in a way that allows manual adjustments ofthe forecasts. Some situations when this might be needed, is when there are changes in price, newrules and regulations take effect, or when new products are introduced to the market for whichthere is no historical data. However, De Lurgio et al. (1997) claim that methods using historicaldata are the most common when it comes to forecasting methods. The assumption then is thatyesterday’s demand patterns paint a good picture of what the demand will look like tomorrow. DeLurgio et al. (1997) define some of the most customary methods, which are presented below.

Simple moving averages are calculated several times over a varying time period. For example, onemay wish to compute the average over a certain period of a year, for products that are particularlypopular over that time period. Equation (2.5)

Ft+1 = At =Yt + Yt−1 + ...+ Yt−n+2 + Yt−n+1

n=

∑ni=t−n+1 Yi

n(2.5)

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where

At = average through periodtFt+1 = forecast for periodt+1

Yt = sales in periodtYt−1 = sales in time periodt−1

As an alternative to the simple moving average, there is the weighted moving average, wheredifferent time periods are considered more important than others. A common separation is togive the latest time period more weight than previous ones, since the more recent figures could beassumed to be a better estimation of the current demand. The formula for the weighted movingaverage is presented below:

At = wtnYt + wt−1nYt−1 + ...wt−n+1Yt−n+1 =

n∑i=1

wt−i+1Yt−i+1 (2.6)

where

wt = weighting factor for the time t ,1 = wt + wt−1...+ wt−i+1

2.5 Service Level and Safety Inventory

Inventory can have the function of a buffer, also known as safety stock. The safety stock shouldcover the differences between actual demand and forecasted demand. It might also aim to dealwith the problem of differences between actual and planned lead time. In addition, a safetystock reduces the probability of stockout and helps the company to achieve a high service level.(Sanderson 1997)

To decide on a proper level of safety stock, it can be set in accordance with a service level orshortage cost model. The most common way is to use a specific service level (SL). The SL canbe defined in various ways (cf., Olhager 2000; Sürie and Reuter 2015; Anupindi et al. 2014),but the SL most frequently occurring relates average lead time demand (LTD) and reorder pointlevel (ROP). In text Olhager (2000) and Sürie and Reuter (2015) translates the service level into:SERV. 1 or Cycle service level, which indicates the probability that an order can be completelymet by the inventory under an order cycle. A more formal expression of the SL is:

SL = P (LTD ≤ ROP ) (2.7)

Where LTD is the average lead time demand and ROP is defined as:

ROP = Average lead time demand+ Safety Stock = LTD + ISafety (2.8)

Anupindi et al. (2014) write that LTD is commonly assumed to be normally distributed. Anextension of Equation (2.7) result in the following expression:

SL = P (LTD ≤ ROP ) = P (Z ≤ z) (2.9)

In this case Z represents the average lead time demand, which is a stochastic variable with mean 0and standard deviation 1. The safety stock is implicitly represented by z, via eq. (2.8). Figure 2.3

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ROPLTD

fLTD(x)

LTD

Figure 2.3: Under the assumption that the replenishment lead time corresponds to a normal distribution;the shaded area is then the probability that the reordering point will be sufficient to avoid stock out in adesired ratio of the outcomes. The figure is inspired by (Anupindi et al. 2014, p. 180, Figure 2).

on the facing page shows a visual illustration of Equation (2.9). The shaded area equals theprobability of the average lead time demand (LTD) being less than, or equal to, the reorderingpoint.

The safety stock, or safety inventory is expressed by Anupindi et al. (2014), as:

Isafety = z × σLTD ⇔ z =IsafetyσLTD

(2.10)

These equations presented above, eqs. (2.8) to (2.10), can hence be used to calculate service levelSL from safety inventory Isafety and vice versa, as long as average lead time demand LTD andthe standard deviation of average lead time demand σLTD is known.

Previously the replenishment lead time was assumed to be deterministic and not stochastic. Butthis is not always true and Anupindi et al. (2014) presents the equation, dealing with two inde-pendent and stochastic variables.1

σ2LTD = Lσ2

D +Dσ2L

⇔ σLTD =√Lσ2

D +Dσ2L (2.11)

Where L is the replenishment lead time, σL is the standard deviation of the replenishment leadtime, D is the demand during the period, and σD is the standard deviation of the demand.Calculating the service level and the safety inventory is no different once the standard deviationof the average lead time demand is calculated.

2.6 Lot Sizing

Lot size inventory or cycle stock connects to the quantity used between refilling the inventory.The goal of lot size inventory is to minimize the total costs of carrying the inventory and thecost of purchase for each order, to do this one has to take into account the trade-off between thetwo. By decreasing the cost of replenishment, it is possible to order a smaller lot size and reducethe inventory investment. (Sanderson 1997) In other words, increase or optimize the ITR. Someexamples are presented below

1The authors of this thesis have replaced Anupindi et al.’s (2014) demand rate R with the more commonly useddemand D, to better fit this thesis.

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Economic order quantity (EOQ) is a very useful formula when the correct data is available. (Ol-hager 2000) In this case and thesis, such data will not be attainable and the EOQ formula willthus be left outside this theoretical framework.

Estimated Order Quantity is one of the most unstructured methods of determining lot sizes isestimated order quantity. It is based on tacit knowledge and experience of what quantities thatare suitable for the specific occasion. (Jonsson 2008)

The lot-for-lot method implies that there actually is no determined lot sizing in reality. Instead,quantities are assessed and re-sized to meet the required quantities each time an item is ordered.The lot-for-lot method is mostly used for customer order controlled material flows, costly productsor components in contexts with small set-up costs. (Jonsson 2008)

In the case of using estimated run-out time, the lot size is determined so that the supply is sufficientto meet the demand for several planning periods, for example, weeks or days. As in the methodfor estimated order quantity, the decisions can be based on experienced estimations; nevertheless,they can also be economically calculated. Either way, the order quantity is defined as a run-out-time stated in a number of periods, and adjusted for each order according to the demands for therelevant periods. (Jonsson 2008)

2.7 Order Point System

Order point systems are the most common method for handling stocks of independent articles.(Olhager 2000) In a recent survey conducted by Jonsson and Mattsson (2014), it is stated that75% of the distributing companies in Sweden use this type of tools for inventory control. Thesystem’s function is to decide and signal when it is time to place a new order for an item based onstock level, demand, and associated costs. The stock-level not only refers to the physical inventory,but also encompasses outstanding orders – orders that have already been made, and back-orders– orders for items that are out of stock at the supplier. Aggregated quantity is usually called thestock position, calculated as in formula Equation (2.12). (Axsäter 1991)

stock position = physical stock level+ outstanding orders− back orders (2.12)

The stock position can be monitored either continuously or periodically, the former meaning thatan order is placed as soon as the stock level has dropped to a certain point. The latter methodmeans that the stock level for an item is reviewed only at certain times, and then possibly orderedif needed. The approaches are suitable for different cases. Periodical monitoring increases the needfor safety stock while continuous inspection decreases the requirement, and is preferred when it issought to coordinate orders of different products. In general, this alternative is better economically,since it yields less cost for the control, especially for items with high turnover rate. On the otherhand, continuous inspection of an article with a low turnover rate only implicates higher needlesscosts. (Axsäter 1991)

2.8 ABC Analysis

Flores (1987) states that the foundation of ABC analysis can be traced back to at least the timewhen Pareto published his famous paper Cours d’économie politique (1896) where he observedinequality of the wealth distribution of economies, also known as the 80/20-rule, see Figure 2.4on the facing page for a visual explanation. The Pareto ratio can also be found in a majorityof companies, where approximately 20 percent of the products represent 80 percent of the totalannual dollar usage and vice versa. This relationship proposes that the different items shouldbe treated differently, and more attention should be put on the most revenue-generating articles.

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(Silver 1991; Olhager 2000) An example of dividing the different classes is to identify A-items as thetop 20% of the products, the next 30% as B-items, and the bottom 50% as C-items. However, thevalues of these boundaries and the numbers of them are not fixed and can be adjusted accordingto the context. As a final step, it needs to be assured whether the corresponding managerialimplications are possible from an economic perspective. (Millstein et al. 2014)

20%

80%

Number of Items

Total

Value

Figure 2.4: This plot depicts the theoretical pareto distribution, in this fictive case, 80% of the value isprovided by 20% of the items.

A simple classification can be made, usually concentrating only on dollar-usage or cost in aninventory context. However, in many cases, several criteria need to be included in order formanagement to make the right decisions. Flores and Clay Whybark’s (1986) Joint Criteria Matrixis an example of a tool assessing items based on two criteria. Lead time inaccuracies, the certaintyof supply, and impact of stock out of the items may be critical factors to take into account (Flores1987). Another concern may be the complex connections between the items, such as dependentarticles, meaning for example that an A-article is always sold in conjunction with a C-article.Newly introduced products should be classified as A-articles to evaluate their progress. (Aronssonet al. 2004)

2.9 Multiple Criteria ABC Analysis

In his article from 2007, Ng presents a model for handling a multiple criteria inventory classifi-cation, which transforms all the criteria values into a single scalar score. This model can includemore categories than for example the Joint Criteria Matrix introduced by Flores and Clay Why-bark (1986). Ng’s model is also claimed to be less time consuming when treating a vast number ofarticles than for example Ramanathan’s (2006) model, which requires the solving for the optimalweight through linear optimization for every article individually. Further, the simplicity of Ng’s(2007) model makes it easy to implement in a spreadsheet program.

Ng’s model classifies the number of products I, considering the number of categories J . The valueof the item i under the criteria j is denoted as yij . All measures are presumed to relate positivelyto the final score of a product. Therefore, negatively related measures need to be converted beforeusing them in the model.

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Before beginning the model process, proposed by Ng, it is required to turn all the measures intothe same base so that they are comparable. This is done by using:

xij =yij −mini=1,2,...,I(yij)

maxi=1,2,...,I(yij)−mini=1,2,...,I(yij)(2.13)

Equation (2.13) transforms all measures to a number between 0 and 1. Then, the analyst needs tomake a ranking of the criteria. Ng admits that this step is not completely objective, but states thatobjectivity in this case is less important than in Analytic Hierarchy Process (AHP), for example.In Ng’s model, the decision maker only needs to decide if a criterion is more important thananother one, i.e. the sequence. This is a simplification from many other multiple criteria ABCanalyses (MCABC), which requires an exact weight assigned to each criterion.

The positive weight wij is defined as the contributing weight of performance for the ith articleunder the jth criterion. Further, it is assumed that the different criteria are related as follows;wi1 ≥ wi2 ≥ . . . ≥ wiJ .

Ng’s model includes the following steps:

1. Compute all the partial averages, 1j

∑jk=1 xik , j=1,2,...,J.

2. Localize the maximum among the partial averages. The matching value is the score Si ofthe ith item.

3. Order the items according to their score.

4. Divide the items into groups in accordance with the principles of ABC analysis.

An example of Ng’s model is presented in Tables 2.2 to 2.4. Before starting any step of the model,the data needs transformation and Table 2.2 summarizes the initial manipulation of data, as itlists the items i of a pet shop with corresponding measures for each criterion yik. Their dollarusage is denoted as yi1, order frequency as yi2 and lead time as yi3. Equation (2.13) is used totransform these measures, which are presented as xik where k ∈ {1, .., 3}.

Table 2.2: A silly example of Ng’s model, yik are random numbers.

Pet storeitem, i

Dollar us-age, yi1

Order fre-quency,yi2

Leadtime, yi3

Dollar us-age, xi1

Order fre-quency,xi2

Leadtime, xi3

Transformed numbers, eq. (2.13)

Tame Impala 17 158 48 23 0,979 0,978 0,765Vicuña 9 465 9 27 0,524 0,070 1,000Lama 15 805 44 10 0,899 0,884 0,000Hamster 15 200 38 15 0,863 0,744 0,294Hippopotamus 13 888 18 23 0,786 0,279 0,765Blackbird 627 32 16 0,000 0,605 0,353Parrot 7 166 49 22 0,387 1,000 0,706Zebra 5 691 25 19 0,300 0,442 0,529Cow 15 953 6 26 0,908 0,000 0,941Chimpanzee 17 509 30 22 1,000 0,674 0,706

In Table 2.3 on the facing page, the results of step 1 and 2 in Ng’s model are presented; thecalculated partial averages of the criteria, and the maximum value, which is equal to the score Si.The formulas for each partial average is shown at the top of the corresponding columns.

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Table 2.3: The partial averages and maximum, form the example in Table 2.2.

Pet store item 11xi1

12 (xi1 + xi2)

13 (xi1 + xi2 + xi3) Max( 1j

∑jk=1 xik)

Tame Impala 0,979 0,978 0,907 0,979Vicuña 0,524 0,297 0,531 0,531Lama 0,899 0,891 0,594 0,899Hamster 0,863 0,804 0,634 0,863Hippopotamus 0,786 0,532 0,610 0,786Blackbird 0,000 0,302 0,319 0,319Parrot 0,387 0,694 0,698 0,698Zebra 0,300 0,371 0,424 0,424Cow 0,908 0,454 0,616 0,908Chimpanzee 1,000 0,837 0,793 1,000

Finally, step 3 and 4 are illustrated in Table 2.4, where the items are arranged according to theirtallied scores. These are then divided into groups of A, B and C articles, based on suitable limitsfor the different classifications.

Table 2.4: A sorted list of the maximum values, A-items equals 20% of the top animals, B-items equals30% of the animals, and C-items equals the last 50% of the animals.

Pet store item Max Classification

Chimpanzee 1,000 ATame Impala 0,979 ACow 0,908 BLama 0,899 BHamster 0,863 BHippopotamus 0,786 CParrot 0,698 CVicuña 0,531 CZebra 0,424 CBlackbird 0,319 C

As mentioned above, and suggested by theory, products from different classifications should bemanaged differently. The general idea is that A-items should receive the most amount of attentionand be individually monitored, due to their high ranking in importance. (Silver 1991; Sanderson1997) This means higher demand on accurate inventory documentation, and attempts should bemade to decrease lot sizes, as well as shorten lead times. Sanderson (1997) and Aronsson et al.(2004) says that A-items should be ordered frequently and corresponding safety stocks should bekept low if they are expensive items. Strong relationships with the suppliers should be soughtafter, to assure that supplies are consistent and sufficient. (Aronsson et al. 2004) Due to theirvitality, top management should be frequently informed about the performance of the A-items,and parameters should be closely monitored and re-assessed. Estimations of demand and attemptsto influence it can also be beneficial from an inventory control perspective. Further, stockouts ofA-items should be avoided, or at least, asses regarding the impact of a possible shortage. However,it needs to be kept in mind that although an article may receive a high score in a multiple criteriaABC, the underlying reasons need to be recognized. For example, article X may have a highdemand and low value while article Y has low demand and high value, these two may need to bemanaged in very dissimilar ways. (Silver 1991)

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B-items are the second most important articles, and therefore, need a reasonable but appreciableamount of resources spent on them. (Silver 1991) This is due to the nature of this item classification– not pertaining to either end of the extremes, there are not a lot of available literature on how tomonitor and attend them, apart from that it should not be as detailed as the control for A-items.Nor should it be as loose and generic as for C-items.

Finally, the C-items – the lowest in the ranking, should be handled in a much simpler manner.For C-items, it is recommended to find as many common traits as possible, for example, usagerates, seasonal patterns, common suppliers and lead times. Thereby, decisions for several articlescan be processed simultaneously. (Silver 1991) Aronsson et al. (2004) suggests that these itemsshould be ordered less frequently, but have a larger safety stock than that of A-items. Further,no classifications are static and can change over time. It is therefore required to re-evaluate themfrom time to time. (Silver 1991)

The following paragraphs define and explain relevant criteria on which a multiple criteria ABCanalysis can be based upon, more specifically the criteria that have been elected for this particularresearch problem. The criteria are described in order for the reader to better comprehend theimpacts and implications that these choices might have.

2.9.1 Dollar Usage

Olhager (2000) defines the dollar usage for a product is defined as:

dollar usage = yearly usage× unit cost (2.14)

This measurement is commonly used to separate different articles since they can require differentplanning tools and managerial approaches. For example, a high volume often means that thevolume also is more even. Another implication might be differences in the frequency of stocktaking.Articles with a high dollar usage should be audited more often since they represent a larger portionof the capital tied up in inventory. Stocktaking frequently also means that it can be made fordifferent classifications at different times – so-called cyclic stocktaking, which an be a better wayof using resources.

2.9.2 Replenishment Lead Time

There are several types of lead times. However, in the multiple criteria ABC analysis of thisreport, the focus lies on the replenishment lead time, which Silver (1991) define as the time fromwhich the company places an order until the goods are delivered to the physical inventory. Amathematical definition of replenishment lead time variability is give in section 2.5.

2.9.3 Other Criteria

Based on the description of the purchasing process, presented later in Section 4.3.4 on page 35,bulkiness and order frequency are believed to be important criteria by Auctus. In this thesis, thedefinition of bulkiness is the size of a specific item. It can be measured by the pallet type that aproduct is stored on, or the volume of the product.

Order frequency is defined as the total number of orders of a specific item since the product wasintroduced, but it does not contain any information about what quantity each order holds. Itis interesting to compare this variable to the total quantity demanded of a specific item. A loworder frequency and a high demanded quantity differentiate a product from another article with

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the equal amount of orders and quantity. In the first case, one order holds a high quantity; in thesecond case, every order holds only the quantity of one article.

2.10 Value Proposition

Mohr and Sengupta (2015) state that an important part of forming a firm’s strategy is to definewhat value it should create for its customers, also known as the value proposition. Not only isit important to concentrate on what customers want, but also what value that competitors offer.That should include companies that pose a threat both directly and indirectly since competitioncan arise outside of an industry’s boundaries.

Further, Mohr and Sengupta (2015) state that it needs to be secured that the strategy can beexecuted well. This puts pressure on acquiring the right competencies, suitable structures andsystems, distribution decisions, pricing, and where to promote the products. However, the au-thors stress the importance of staying flexible and not locking oneself in an inflexible program.Fast changes in the market may redefine what value proposition that customers consider to beimportant, and thus the composition of execution requirements as well. Another component inthis mix is effective management of alliances and partnerships, which also includes the vendorselection which is discussed in section 2.11.

2.11 Purchasing in a Strategic Perspective

Many researchers agree that purchasing has taken on an increasingly more important role in supplychain management (Chen et al. 2004; Knoppen and Sáenz 2015; Kraljic 1983). Knoppen and Sáenz(2015) highlight the purchasing function’s impact on a company’s long-term goals. Not only is itthe key to attaining economically advantageous procurement deals, but it also contributes to thecompany strategy by its influence on new solutions and adaptions to customer needs. Dependingon a firm’s profile and competitive advantage, the purchasing function can take on different roles.For instance, if a company competes through low prices, this needs to be reflected in the purchasingoperations. Knoppen and Sáenz (2015) state that if strategic purchasing is not implemented in asatisfying manner, it may have a negative impact on performance due to decreased competitiveness,both in a short- and long-term perspective.

Kraljic (1983) explains that companies sourcing globally have to accept the uncertainties that comewith it. Instead of passively dodging situations, firms must instead take a more active stance andlearn how to turn challenges into opportunities. Therefore, Kraljic argues that purchasing shouldbe treated as a strategic part of management instead of merely an operating function. Kraljic(1983) stresses the strategical view on purchasing by renaming it as supply management and saysthat the greater the uncertainty of for example supplier relationships and physical availability ofitems, the more important the supply management. Further, Kraljic claims that the importanceof supply strategy depends on two factors, which are: (i) the importance of purchasing in termsof its impact on strategy, the total costs of materials and their impact on profitability, and soon; and (ii) the complexity of the supply market including scarcity of products, development paceof technology or new substitutions, entry barriers, logistics costs or complexity, or number ofcompetitors. In a study made by Thompson (1996), it was concluded that effective purchasingshould have a long-term cost focus, meaning that efforts should be directed at evaluating thetotal cost of acquisition rather than attempting to optimize every single transaction. Weber et al.(1991) state that it is impossible to provide low cost, quality products, without suppliers that fulfillthe organization’s requirements. In addition, in the case of many firms, purchases from suppliersconstitute a large portion of their total operating cost. Thereby, electing the right suppliers and

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protecting the relationships with them is essential. However, the many criteria that need to beevaluated, make it a complex and difficult task.

2.11.1 Category Management

Monczka et al. (2015) write that category management is defined as the process of identifyingstakeholder requirements and needs and comparing these to external supplier capabilities. Thecategories from the business point of view are products or services the business wishes to source,they are bundled in categories based on similar requirements or needs. Category managementthus aligns internal requirements such as wanted supplier capacity and operational risk with sup-plier market conditions forming a category strategy. The category strategy contains a plan fornegotiation contracts, how to evaluate and monitor the suppliers. Monczka et al. (2015) continuesto explain that a strategy for category management seeks to reduce the risk for the purchasingbusiness but also improve category performance in the dimensions important to the stakehold-ers. That is, as stated before, the categories are formed on the basis of a need. By identifyingthose requirements or needs and form them into a category which should be meet by externalsuppliers, the strategy has the potential of improving the purchasing along the dimension of theidentified need. The category strategy shall strengthen the value proposition of the business asthe category strategy will be in line with the overall business strategy. The forming of categoriesmost likely requires top management’s involvement but once the categories are set, the categorymanagement, i.e. selection and evaluation of suppliers should be carried out by the purchasingdepartment.

2.11.2 Vertical Integration

According to Buvik and John (2000), vertical integration is beneficial when acting in an envi-ronment of fast-changing or uncertain demand. Chen et al. (2004) stress three important fac-tors:

• Fostering close working relationships with a smaller amount of suppliers.

• Promoting open communication between supply chain-partners.

• Developing long-term strategic relationships with suppliers in order to obtain mutual gains.

The author claims that these factors together make a base for customer responsiveness, which iscrucial in the markets of today with its rapidly changing customer demands. They state that open,informal sales channels are crucial for the development of tacit knowledge, which is vital from astrategic standpoint since it can help to better understand complex competitive subjects. Criticalalliances with partners can also facilitate reaching strategic goals. In these alliances, accurate andrelevant information is exchanged in a timely manner.

2.11.3 Limiting the Amount of Suppliers

Chen et al. (2004) also state that strategic purchasing is vital when establishing close relationshipswith a restricted amount of suppliers, which in turn has been shown to contribute to significantrevenue gains and cost savings. Additionally, they discuss the greater risk that comes with limitingthe number of suppliers, due to decreased flexibility and even supplier opportunism because ofthe high investments in those relationships. However, the authors highlight that working closelywith suppliers contributes to greater trust among partners, dependability, and cooperation. Ifthe suppliers are aware of the dependability, these factors might lead to suppliers not acting inan opportunistic way, and exploiting the relationship for example by increasing prices without nounderlying reason. Further, Chen et al. (2004) claim that closer relationships with suppliers often

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mean that they are long-term rather than “transactional based”, and highlight its contribution togreater cooperation, reduction of functional conflict and enhance decision making.

Cousins (1999) elaborates the reasoning of risks and opportunities that come with narrowing downthe supplier base. Strategic decisions must be taken with caution so that the end result is not justgiving suppliers more power, while in reality, the number of suppliers has not decreased at all. Heunderlines that a reduction has to take place at the same time as relationships are being furtherdeveloped, in order to expand advantages to not only occur in a short-term perspective. However,Cousins (1999) states that the advantages may include facilitating management, spreading risks,and sharing resources. Thus, fewer and improved relationships may lead to using resources moreefficiently, which leads to cost reductions.

An evaluation of these two factors can help determine what type of strategy that can be beneficialin order to use its purchasing power and suppliers, and to reduce risk. In addition, this assessmentcan help assess new sourcing opportunities in terms of vulnerability, threats strengths or newpossibilities.

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Chapter 3

Methodology

This chapter introduces the methodology of this thesis. In Section 3.1, the research process of thisthesis is described. Next, in Section 3.2, the scientific position of this thesis is explained. Later on,the methodology for data collection and methodology for analysis will be introduced in Sections 3.3to 3.4. The method for analysis is especially important, as it also specifies what outcome thatcan be expected from the analysis, given the scope and purpose of this thesis. Ultimately, themethodology chapter discusses the methodology and the sources from a scientific point of view, insections 3.5 to 3.6.

3.1 Research Process

The work process of this thesis and wherein the process the different research questions are an-swered is illustrated by Figure 3.1 on the next page. The different boxes will be explained in theparagraphs below.

Identify Company Issues Initially, the case company Auctus, announced that it was in needof improving its cost control, but it had not yet determined exactly what should be examined tosolve its issues. When the task of investigating this widely formulated problem was assigned tothe authors, the first step in the research process was to conduct interviews with employees fromthe case company to specify their cost control problem. From the interviews, a clearer pictureof the problem, as well as the underlying issues, was obtained. This represents the first box inFigure 3.1 called Identify Company Issue.

Theory Search 1 The theory search that was initiated during the identification of the companyissue proceeds and forms a research strategy of using a multiple criteria ABC analysis to tackle thecost control issues connected to inventory control. This is called Theory Search 1 in the processfigure.

Data Collection – Empirical Findings A compilation was made of the qualitative datafrom the interviews and financial information about the company. The empirical findings werestructured in the same way as the analysis to facilitate the evaluation and comparison of dataand theories. Based on the first theory collection and the data collection, it became clear how todesign the multiple criteria ABC analysis to meet the purpose of this thesis. This is illustrated bythe box called RQ 1.

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MCABC Analysis The multiple criteria ABC analysis (MCABC) is performed and the cate-gories of the analysis are scrutinized and evaluated in the perspective of the symptoms describedin the introduction, i.e. capital tied up in inventory and inventory space. Furthermore, and centralto this thesis, the different categories are matched to the suppliers in each category to evaluatethe risk and impact of Auctus’ supplier selection.

Theory Search 2 As the multiple criteria ABC analysis was completed and Auctus’ situationbecoming clearer, the focus was directed at improving the sourcing strategies of Auctus. Therefore,a search for suitable theories about purchasing was initiated.

Proposed Solutions Connecting the literature gathered in the second search for theories withthe results from the analysis, the authors presented their proposed solutions for how to managethe purchasing process more efficiently and how the supplier relationships should be handled inorder to match the e-tailer’s value proposition. The second research question of this thesis is toprovide recommendations on how to fulfill the purpose; these recommendations correspond to theproposed solutions. Hence, RQ 2 is answered in the chapter of proposed solutions.

Conclusion Finally, it is reflected upon how the proposed solutions of this thesis match thepurpose.

IdentifyCompanyIssue

TheorySearch 1

DataCollection– EmpiricalFindings

MCABCAnalysis

TheorySearch 2

ProposedSolutions Conclusions

RQ 1 RQ 2

Figure 3.1: This figure shows how the authors of this thesis will try to answer the research questions.The figure is not an illustration of how to, for example, combine theory with empirical findings in theanalysis.

3.2 Methodological Approach

3.2.1 Deductive and Inductive Reasoning

The logic of research can be either deductive or inductive and is part of the classifications research.Collis and Hussey (2009) define deductive research as studies in which conceptual and theoreticalstructures have been formulated and tested by empirical observation while inductive research iswhen one or several theories are elaborated from studying phenomena in reality.

Ghauri and Grønhaug (2010) emphasize that conclusions drawn from inductive research, as in thecase of most qualitative studies, are not true per se as they are based on a few empirical obser-vations. On the other hand, conclusions from deductive research, frequently used in quantitativestudies, do not always coincide with reality although they are logical. These two types of logic

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are not completely decoupled and are often switched in-between in a research process, making ititerative.

This thesis applies established theories to a real case, but will also generate inductive conclu-sions from interviews about this specific case. The approach of this thesis is in line with Ghauriand Grønhaug’s (2010) idea about switching between the two of them, a semi deductive ap-proach.

3.2.2 Research Paradigm

There are two main research paradigms, each with a specific view on how to conduct research.Positivism represents the belief that reality exists independently of humans, and the goal is to un-veil the reality with theories supported by empirical findings through qualitative methods. Hence,deductive reasoning and a positivistic approach are linked to each other as it begins with a generalbelief and ends up in a particular example. (Collis and Hussey 2009; Williamson 2002) Further,Hair et al. (2011) write that a positivistic position argues for the reality to be discovered in anobjective and unbiased way. Positivism is not without criticism – Collis and Hussey (2009) re-ports about the impossibility to separate people from their social context, explaining the complexphenomenon with a single number are deceptive and ‘‘A highly structured research design im-pose constraints on the results and may ignore other relevant findings’’ (Collis and Hussey 2009,p. 56). Smith (1983) refers to the ideas of Dilthey who argued about the incongruity of findinguniversal laws, describing the interaction between a social object in a constantly shifting envi-ronment. Instead, the paradigm of interpretivism emerged, which houses the inherent idea ofreality as a social construction (Hair et al. 2011). Interpretivism is said to be compatible withinductive reasoning, which is opposite to the deductive approach linked to positivism, as an in-ductive approach starts with a particular example and ends with a general belief. This is donewith qualitative methods. (Williamson 2002) While positivism focuses on measuring the socialphenomenon, interpretivism aims to explore and gain interpretive perspective of the phenomenon.The interpretivism paradigm brings the conception of neither truth nor reality exist in vacuity butas a part of a social construction (Collis and Hussey 2009; Hair et al. 2011).

A modern modification to positivism argues that an objective reality exists but admit to thedifficulties in describing or analyzing that reality without being biased by sociocultural or psy-chological factors interfering with the interpretation of reality (Hair et al. 2011). This modifiedapproach is called post-positivism. Williamson (2002) refers to post-positivism in other words anddescribes it as a combination of quantitative and qualitative methods.

In this thesis, we take a post-positivistic position, as we will try to apply mathematical axiomsand theories to management problems. The experimental design, i.e. the case, is limited by socialconstraints regarding what parameters to be manipulated and what is realistic to do in the socialcontext of our case firm.

3.2.3 Case Study

Although case studies require a lot of time and proficiency in interviewing techniques, along withthe caution that is needed when formulating generalizable conclusions from a sample of cases, theoutcome can prove to be very important. Case studies can generate new and innovative ideas, beused to elaborate new theories or the refinement and testing of them, as well as being very helpfulto professionals within different fields, which are the actual users of the investigations. Accordingto Voss et al. (2002), case studies is one of the most potent methods used to conduct investi-gations in the area of operations management, especially when elaborating new theories. Casestudies within operations management have lead to crucial progress, for example, lean productionand other manufacturing strategies. Additionally, the authors also imply that it is a suitable

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method for this discipline since it provides an opportunity to investigate both the physical andhuman components of an organization. Other sources (Ghauri and Grønhaug 2010; Collis andHussey 2009) also confirm that this type of investigation is especially suitable when there aremany variables to take into consideration and the event or phenomenon is strongly affected by itssurrounding context.

Depending on the circumstances, different sorts of case studies may be chosen. Collis and Hussey(2009) define these as exploratory, descriptive, illustrative, experimental and explanatory. Casestudies can further be grouped into either single-case studies or multiple-case studies, implyingthat a decision has to be made which kind should be used in order to satisfactory answer theresearch questions. (Yin 2003) Using a single case can improve the depth of the study, but at thesame time, it might affect the generalizability negatively. However, triangulation of the collectionof data can enhance the validity of the research. On the other hand, multiple-case studies riskreducing the depth of the investigation, while it can increase external validity and prevent observerbias. (Voss et al. 2002)

As mentioned before, at the time when this research was conducted, Auctus had – and is stillexperiencing a phase of growth, and sought help to control their costs and adapt their organizationto make it more efficient. Thus, Auctus reached out for assistance with a very broad problemdescription concerning the improvement of their inventory management. After contacting thecompany, and several discussions about Auctus’ situation, the task was assigned to the authors ofthis thesis.

Although e-commerce is growing increasingly popular, the business proposition of Auctus is quiteuncommon due to the nature of the products that they offer. It is more common that pure e-tailerssell other types of products, such as clothing or electronic devices, but they are still rare withinthe furnishing segment. Because of the company’s uniqueness, the decision was made to conduct asingle-case study of the firm. Further, the single-case method is also believed to bestow the studywith more in-depth and detailed results and conclusions.

3.2.4 Literature Review

The Royal Institute of Technology and Linköping University’s resources have been consulted forsecondary sources for this research, such as books and articles about purchasing and inventorycontrol. The search also included the libraries’ access to online databases like Emerald Insight,JSTOR, and Harvard Business Review to name a few. Statistics Sweden’s records were used toget a hold of sector key figures. In the search for literature, the main keywords that were usedwere: purchasing, e-commerce, inventory control, and multiple criteria ABC analysis.

3.3 Methodology for Data Collection

This section is divided into two parts. The first part considers quantitative data and the secondpart treats qualitative data.

3.3.1 Transaction Data

The positivist approach and deductive reasoning require a literature review where a theory or a setof theories is identified, a theory is a set of interrelated variables (Collis and Hussey 2009). Thisstudy will organize quantitative data collection in line with the variables presented in the frameof references, thus, meeting the requirements presented by Collis and Hussey about scientific datacollection.

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Secondary data will already be coded by delivery from Auctus. The way data is coded is describedin detail in the empirical chapter and more specifically Section 4.4.2 on page 41. In short, theauthors entered the database of Auctus and extracted, via SQL, the requested data.

3.3.2 Interviews

Under a positivist approach, interviews are structured and the interview follows a predeterminedscheme (Collis and Hussey 2009). But the reasoning of this research is post-positivistic, and thus,the interviews are semi-structured. Easterby-Smith et al. (2002) describe the following situationwhen semi-structure is beneficial:

• The construct used by the interviewee is crucial to understand in order to fully appreciatethe respondent’s answers.

• One goal of the interview is to understand the world of the respondent so that the researchermay interact with it; intentionally or unintentionally.

• The situation is complicated and needs a step-by-step logic.

• The subject matter is of a confidential or commercially sensitive nature.

• The respondent might be reluctant about the issue in other situations than a one-to-onesituation.

In this case, Auctus wants secrecy to hide company sensitive information, and their current inven-tory management needs to be understood in a step-by-step logic for this thesis to contribute thebest to an improvement. Further, the authors of this thesis need to understand the respondents’construct to evaluate the current situation and formulate future suggestions. Once the context ofthe inventory has been mapped, a quantitative data collection is applied to the problem. For thisthesis, numerical secondary data will be collected from the database of Auctus.

In order to understand the context, challenges and different functions of the case company, inter-views were conducted with key employees of Auctus. The authors of this thesis have had a closecollaboration with the case company and the interviews were conducted at various points in timeduring the research as new challenges or questions emerged, or if there was a need for confirmingobservations and findings from the quantitative data analysis. There are different levels of struc-ture of an interview. In structured interviews, little or almost no room is given to the respondentto answer the question freely and the interviewer already knows the possible answer options. Theother extreme is unstructured interviews, where the dialogue permits the respondent to answerwith maximal freedom. Semi-structured interviews are, as the name implies, a mixture of thetwo opposites that is beneficial when there is a need to add new questions to investigate otherpertinent areas of the subject. (Collis and Hussey 2009) In addition, Easterby-Smith et al. (2002)state that this level of structure is suitable when there is a need to identify the assessments thatthe respondent makes and uses as a basis for his or her decision making. Also, semi-structuredinterviews are applicable when there is a need for mapping of actions were the liaison betweenthem is not evident.

The respondents of the interviews in this research are key employees from relevant areas of the firm;the CEO, the head of the IT Department, and employees from the Purchasing Department. Thepurpose of these dialogues is to obtain in-depth descriptions of how the daily operations are carriedout and what the main issues of Auctus are, why semi-structured interviews are conducted.

In an initial phase of the research process, interviews are held with the CEO, treating the or-ganizational structure and functions, the history of the company, and challenges and the actionsthat have been taken to overcome them. As the research progresses, further in-depth informationis needed regarding the functions and processes within the firm. The interviews with the head

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of the IT Department generate information about how stock positions are monitored, detaileddescriptions of how Auctus’ ERP is used, as well as communication and IT challenges.

From the Purchasing Department, two employees are inquired about their daily work tasks andreasoning when making decisions concerning the procurement of the products in the company’sassortment. In particular, they are asked about their prioritization of different parameters andwhat they perceive as their main defiance. As discussed later on in Section 4.3.7 on page 37,the reliability of the suppliers’ lead time is crucial for the purchase planning, and therefore theemployees from the purchasing department are asked to rank them according to reliability. Veryunreliable is allocated at the lower end of the scale and highly reliable at the higher end of thescale. This is not a completely exact measure of the supplier lead time variability, but since theevaluation is made by employees with much experience of the operations and supplier relationships,they are considered to be satisfactory. However, it must be noticed that some relative judgmentmight be reflected in the ranking, and thus, the scores might not be completely independent ofeach other.

3.4 Methodology for Analysis

To make the prioritization and management of articles and suppliers easier, a simple analysiswould be to just investigate which suppliers deliver the largest number of items to Auctus andbase the purchasing strategy on these most frequently used manufacturers. But such a simplifiedanalysis would fail to account for the articles’ individual impact on profitability and how efficientthey are concerning the generation of income to Auctus. That is why the multiple criteria ABCanalysis is a better tool to help clarify which items can be managed collectively and how it shouldbe done.

The multiple criteria ABC analysis will constitute the base for the analysis. Each class of themultiple criteria ABC analysis will be analyzed separately in the perspective of the symptomsdescribed in the introduction, that is capital tied up in inventory and inventory space. Furthermoreand central to this thesis, the different classes are of different importance to the studied company.When the different categories are matched to the supplier reliability of each supplier delivering anarticle, this will highlight potential risks of the supplier selection.

3.4.1 Criticism of the Analysis Method

By using the identified symptoms of capital tied up in inventory and lack of inventory spaceas some kind of filter in the analysis, this thesis restricts its external validity to only includecompanies with similar problems. In other situations, the proposed solutions and the conclusionsmight be miss leading. The used analysis approach will strengthen the construct validity of theresearch.

In Section 2.9 on page 15 where Ng’s model is presented, the issue of subjectivity in the rankingof criteria is discussed. In this thesis, the design of the multiple criteria ABC analysis has reliedon the subjective assessments of the employees at Auctus when determining the order of theseparameters. Although, following the literature review, the prioritization of the criteria can bejustified.

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3.5 Discussion of Method

Yin (2003) states that there are four dimensions that determine the quality of a case study. Theseare construct validity, internal validity, external validity and reliability, and a description of eachcan be found in the following paragraphs.

3.5.1 Construct Validity

Construct validity, or simply validity, as it is sometimes referred to Collis and Hussey (2009),and concerns the election of operational measures for the issues under examination Yin (2003).In order to make sure that the construct validity meets the quality requirements, the researchshould clearly identify what phenomena that are being studied and it should be secured that theyare connected to the objectives. Yin (2003) gives suggestions on actions that can be taken toimprove the construct validity. These are using various sources of evidence, determining a chainof evidence, and lastly, let key informants audit a draft of the report.

This master thesis uses a developed version of the ABC analysis, called multiple criteria ABCanalysis. The ABC analysis is widely used and known for its suitability in organizing a vastamount of articles and form a base for future improvements. Hence, the construct validity of thisthesis is adequate.

3.5.2 Internal Validity

Internal validity is most clearly linked to explanatory studies, where the researcher seeks to identifythe links between causes and effects. Thus, descriptive and exploratory studies are not suitable tobe evaluated regarding internal validity. In case studies, internal validity also concerns the drawingof conclusions. This is not only limited to the last step in the research process but concerns everyevent that can not be directly observed. The internal validity can be enhanced using patternmatching, explanation-building, logic models, or by addressing rival explanations. (Yin 2003) Inevery step of the way, the used theories and models hove been compared to alternatives and thepotential criticism of them have been discussed. Because of the fact that the purchasing decisionsare based on a lot of different parameters, it makes the purchasing decisions quite complex inreality. Thereby, clearly defining what the causes and effects are for each order can be difficult todetermine without error.

3.5.3 External Validity

The generalizability or external validity of a research study regards whether its results can beapplied to other cases or contexts, according to Collis and Hussey (2009) and Yin (2003). Yinsays that many critics claim that single case studies have low external validity, comparing it tosurvey research, for example. However, in survey research, the results are based on statisticalgeneralizations, while the results of case studies are derived from analytical generalizations. Com-paring results from a case to another can be difficult, why researchers often struggle with choosinga case, or set of cases that can be considered representative for a larger number of examples. Yinsays that instead, attempts should be focused on relating the results to theories.

Organizational configurations are connected to terms such as typologies, generic strategies, andstrategic orientation. Ketchen, Jr. et al. (1993) describe organizational configuration as groups offirms that share a common set of characteristics that often appear together, forming a reoccurringprofile among many entities. The common factors make a holistic approach suitable for treatingthe strategic management issues that reoccur in many firms.

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This thesis cannot ascertain that the conclusion is widely applicable for other e-tailers using bulkyarticles, as this thesis has included only one observation of the total population. On the other hand,by applying accepted theories (Theoretical Framework) to this single case and propose solutionsfounded in current knowledge strengthens the external validity. Further, in this particular report,the purpose is restricted to e-taliers selling bulky articles. This restriction will also fortify theexternal validity as the conclusion might more easily be generalized to a smaller population thanevery entity.

3.5.4 Reliability

Research is considered reliable when the same results are obtained if the study would be madeseveral times. Detailed descriptions and explanations of the procedures included in the study helpimprove the reliability. Positivist studies often have high reliability, whereas it is not as importantin interpretive studies, or it may be construed differently. Positivist studies are not as concernedwith the reliability of qualitative measures while more emphasis is put on whether observationsmade in other studies can be explained and understood. The purpose of reliability is to reducesources of errors and biases in the research. (Yin 2003)

Since the interviews are not completely structured, the reliability can be questioned to some extent,however, the authors of this thesis believe that discussing the same topics again would generatevery similar results The data collection and the sequential steps of the analysis are described indetail, which reinforces the reliability.

3.6 Source Criticism

A majority of the theories used in this research project can be found in most textbooks regardinginventory control. The basic concepts of lot sizing, safety stocks, and inventory stocks are widelyaccepted and make the foundation for inventory management. Even ABC analyses are a commonlyused tool for prioritizing the articles in a warehouse. However, in this thesis, a particular versionof a multiple criteria ABC is used. The model in question is the one developed by Ng (2007).There are other similar models that seek to optimize ABC classification available, but Ng’s modelwas finally chosen due to its uncomplicated and comprehensible characteristics and simplicity inimplementation, while still maintaining a level of detail in the analysis.

One concern about the model is the many simplifications of the real situation that need to be madein order to apply the model to the problem. Thus, questions can be raised about the adequacyof using Ng’s work to describe and dissect such a complicated issue, and if it really is helpful.Nevertheless, when dealing with such a vast number of items, devoting the same amount of timeand effort to all the different products would be impossible and a waste of resources. Therefore,a level of aggregation needs to be in place to manage the operations successfully.

30

Chapter 4

Empirical Findings

The ambition of this meta communication is to stress the thesis structure so that the readeridentifies the intended joint motion through the text. The chapter’s onset, Section 4.1, presentAuctus’ competitive advantage and value proposition. Next, the financial situation is describedin Section 4.2. The adjoining section will narrate on the operational components of the inventorycontrol in value chain (4.3). The first sub section introduce forecasting (4.3.1), secondly, thecurrent state of service level and safety inventory is described in Section 4.3.2. Once Section 4.3 isdescribed, the thesis moves on to describe the purchasing process, suppliers and their contributionto building Auctus’ inventory. The empirical chapter is enclosed by Section 4.4 – a descriptionof how Auctus creates the data founding the ABC-analysis (4.4.1), moreover how the data isextracted and compiled into a structure ready for analysis (4.4.2).

4.1 Competitive Advantage and Value Proposition

The firm is a fast growing online retailer in the segment of furniture and home furnishings. Thecompany holds an inventory but provides drop shipment from manufacturers to customers for aspecific set of articles. Auctus houses no product design or manufacturing, thus finished goods arebought from suppliers and sold in Auctus’ online store or through the previously mentioned dropshipment strategy. Figure 4.1 on the next page depicts the value chain of Auctus, acting as anintermediator between its suppliers (S1 − Si) and end customers (C1 −Cj), the dotted line aboveshall illustrate the drop shipment process. In reality, not only one supplier but several are usedby Auctus for drop shipment.

The competitive advantages of Auctus are the low prices and the relatively short delivery leadtime they offer their customers (2-8 days) by holding inventory. The purchase lead time, measuredas the time from when Auctus sends an order until delivery from the manufacturer, shifts from28 to 45 days. Hence, the inventory that Auctus holds decouples supply and demand, and makesit possible to deliver what the customer wants at a small premium, compared to if the customerwould purchase the goods directly from the manufacturer. The reason for the customer to buyfrom Auctus instead of buying from the manufacturer directly, is due to lower transaction costsfor both the customer and the manufacturer, where Auctus provides an easier way of shoppingwhen it acts as a mediator of finished goods.1 A customer may visit a ‘‘bricks and mortar’’ storeto purchase a similar product, but then has to pay more for the value of being able to touch theproduct and bringing it home directly.

1 Transaction costs, in this case, may be: searching for information, language barriers, shipping, wait for theproduct to arrive, handle warranty issues, dealing with currencies.

31

S1

S2

S3

...

Si

Auctus C3

C2

C1

...

Cj

Drop Shipment

Figure 4.1: This figure shows how Auctus connects suppliers with customers and thereby lowers bothparties transaction cost. A dashed line for drop shipment is also drawn, to bring about the whole businessmodel of Auctus.

4.2 Financial Position

4.2.1 Sector Positioning

Table 4.1 on the facing page shows the key figures of Auctus and the sector (SNI, 47919) to whichit belongs, according to Retriever Business (2014). The sector data from Statistics Sweden (SCB)consists of companies with 20 employees or more (≥ 20). The reported number of employees ofAuctus does not correspond to the actual number of labor employed to operate the business. Theoperation of the warehouse is outsourced to a staffing agency. Therefore is the data with morethan 20 employees relevant to this case. Every key figure but ITR is reported in a standardizedway, but ITR is commonly used in the operations management literature and, therefore, it iscalculated by the authors and included in Table 4.1 on the next page.2

Auctus denoted by x20yy in the table, returns more than the second quartile (≥ Q2) or much more(≥ Q3) than the sector when the return is measured as ROE, ROA, ROS or profit margin. Theasset turnover is in line with the sector. The finding is that the ratio between inventory and netsales in percent is much higher (≥ Q3) for Auctus compared to the sector. Hence, the ITR isamong the bottom 25% in the sector.

2The Swedish de facto standard is called BAS Nyckeltal (BAS nyckeltal : för bättre analys och effektivareekonomistyrning 2010).

32

Table 4.1: These are sector key figures based on Swedish Standard Industrial Classification or in Swedishsvensk näringsgrensindelning (SNI) 47919, corresponding to ‘‘Other retail sale via mail order houses orvia Internet’’, for companies with 20 employees or more. Auctus is denoted as x20Y Y in the tabel tosave space. The data is extracted from SCB (2013) and the financial data about Auctus, comes fromRetriever Business (2014).

2014 (13 obs.) 2013 (13 obs.) 2012 (10 obs.)Sector quartile Sector quartile Sector quartile

Q1 Q2 Q3 x2014 Q1 Q2 Q3 x2013 Q1 Q2 Q3 x2012

Employees 42 46 74 39 32 39 75 15 26 50 79 5ROE (%) 0,3 6,2 47,1 72,01 4 13 56,1 48,16 7,7 19,4 50,8 16,84ROA (%) 2,3 5,6 13,2 26,45 2,2 7,4 27,4 16,41 4,6 9 13,8 10,91ROS (%) 0,5 2,8 7 8,35 0,6 2,9 5,1 5,71 1 3,9 5,1 9,99Profit margin (%) 0 2 6,1 8,36 0,5 1,7 8 6,18 1,1 2,8 5,1 10,94Equity ratio (%) 14 35 51 36,48 16 40 54 30,44 20 35 55 63,71Asset turnover 1,8 3,3 3,5 3,16 2,1 2,7 3,6 2,66 1,7 2,6 3,7 1(Stock+WIP)

net sales (%) 9 13 21 17,1 11 13 14 17,22 9 13 15 5,77

Notice: 1(Stock + WIP)/net sales=ITR, according to eq. (2.4) on page 10. The recip-

rocal also turn the quartiles around. A high ITR is desirable, hence ITRQ1is the

best performance.

ITR 11,1 7,7 4,8 5,85 9,1 7,7 7,1 5,81 11,1 7,7 6,7 17,33

4.2.2 Income Statement

By December 2014 Auctus employed 39 people, an increase of 160% compared to the year before.In Table 4.2 a summary of available financial information is found. Auctus experiences a steadilyimproved net profit. However, attention must be paid to the increased fractional distance betweennet sales growth and the fractional growth of raw materials and consumables. According to currentfinancial information, raw materials and consumables constitute a constantly higher growth thannet sales does, which will excavate the net profit over time if it continues.

Table 4.2: This is a summary of the income statement over the last three years for Auctus. The financialinformation is expressed as kSek. Source: Retriever Business (2014).

2014 2013 2012

Revenue 207 616 (+146%) 84 517 (+789%) 9 512Net sales* 191 189 (+126%) 84 517 (+789%) 9 466Raw materials and consumables 117 579 (+135%) 50 105 (+907%) 4 977

......

...Other operating income 16 427 0 46Other operating expenses 59 552 (+132%) 25 654 (+882%) 2 612

......

...EBIT 15 968 (+231%) 4 824 (+410%) 946

......

...Net profit 12 351 (+240%) 3 637 (+384%) 751

*Net sales = Revenue−Other operating income.

33

4.2.3 Balance Sheet

Regarding the assets and liabilities of Auctus, a concentrated version is shown by Table 4.3. Thefinished goods inventory is the greatest asset of Auctus, and accounts payable is the greatestliability. Both of them are connected to inventory, and hence the core business of Auctus. Thecompany holds almost the equal amount of cash to solve accounts payable. The most importantlearning from this balance sheet is the increased spread of fractional growth between finished goodsinventory and total assets. Finished goods inventory constitutes 7%, 47%, and 58% of total assetsover the years 2012, 2013, and 2014 respectively.3 This increased amount of capital tied up ininventory is in line with the findings of the income statement.

Table 4.3: This is a summary of the balance sheet over the last three years for Auctus. The financialinformation is expressed as kSek. Source: Retriever Business (2014).

2014 2013 2012

Fixed assets

......

...∑Fixed asset 3 630 1 075 1 141

Current assets

Finished Goods Inventory 32 692 (+125%) 14 557 (+2556%) 546Cash 20 004 (+66%) 12 041 (+133%) 5 170

......

...∑Current asset 56 781 (+85%) 30 747 (+268%) 8 357

Total asset 60 411 (+90%) 31 822 (+235%) 9 498

Equity

......

...∑Equity 22 038 (+127%) 9 688 (+60%) 6 051

Liabilities

Accounts payable 20 407 (+49%) 13 685 (+635%) 1 863...

......∑

Current liabilities 38 372 (+73%) 22 135 (+542%) 3 447∑Equity and liabilities 60 411 (+90%) 31 822 (+235%) 9 498

In Auctus’ case, it is obvious that previous years’ growth would not have been possible withoutan increased inventory, but the trend is that stock levels are soaring more than the net sales; thusthis issue needs to be addressed, and is also the reason for Auctus seeking advice. They currentlyuse an ERP system but lack an efficient way of managing its purchasing process and inventory.The current situation includes a lot of manual calculations, which are time-consuming.

Finally, Equation (2.3) on page 10 shows how the asset turnover has a direct impact on the ROAof a firm. In this paper, this relation will be utilized to increase Auctus’ inventory turnoverratio (ITR), and thereby improve its asset turnover. This shall be done through a series ofrecommendations connected to the specific classification of the multiple criteria ABC analysis. Ifthe inventory control is to be carried out with greater precision – meaning that the company keepsin stock what the customer wants, will implicitly also improve the firm’s ROA.

3Own calculation(

Finnished Goods InventoryTotal Assets

)based on figures from the balance sheet.

34

4.3 Inventory Control in the Value Chain

4.3.1 Forecasting

Currently, the forecasts at Auctus are determined by calculating the moving averages for the last7, 14, 30 days. The forecasts further find support from qualitative estimations of the purchasingdepartment. The qualitative estimations include variables such as active sales campaigns, up-coming (customer) holidays and seasonal trends. The order quantities also depend on potentialinconsistencies or shifts (variability) in replenishment lead time.

4.3.2 Safety Inventory and Service Level

Auctus currently employs a policy of holding two months of moving average demand as safetyinventory for the best selling products. The purchasing department has noticed a drop in demandwhen an article is out of stock. Due to the replenishment lead time, the customer gets informa-tion about a longer (than usual) customer order lead time. The purchase management believesthat longer waiting time for the customer is a reason to the diminishing demand. The purchasedepartment state that they want a 100% service level on high demand products. Managementdoes, however, not employ any quantitative method to measure or evaluate the service level otherthan qualitative estimations and an idea that high demand products should never be out of stock.The management is ambiguous about less demanded products since they find it hard to balancethe supply (i.e. inventory level) and demand.

4.3.3 Current Lot Sizing Method

As mentioned in Section 4.3.2, Auctus uses the time period that is covered with the current stockas a measurement for the level of their safety stock. When the inventory for a specific productdrops to that quantity, this is communicated to the purchasing department in the daily revisionof stock positions. The order quantity is then estimated based on previous sales figures, futureinfluences of seasonality and trends as well as regional conditions that may have an impact onlead times from the supplier, such as local holidays or other special conditions. Thus, it can beclaimed that Auctus uses Estimated Run-out time as a method for determining the lot sizes to beordered.

4.3.4 Purchasing Process

At present, Auctus uses an ERP system to compile data about stock positions, customer orders,and delivery dates, in addition to other parameters that concern purchasing decisions. This data isused on a daily basis for the procurement planning and long-term decisions, for example whetherto discard a product from their assortment or how much of a product that should be included inthe next order. A summary of the steps in the purchasing process at Auctus, and their order ispresented in Figure 4.2.

In the purchasing process at Auctus, the articles are handled separately and the planning can bedivided into a few sequential steps. Stock positions are reviewed daily with the first step consistingof splitting up the products into groups based on the company that supplies them since it can bebeneficial to order several products from the same contractor at the same time. In this manner,the capacity that a shipping container offers can be used in a more efficient way. This first step isthe block furthest to the left in Figure 4.2 with the text Choose Supplier.

In the next step, Identify Potential Shortages, items for which stocks are running low are singledout. If the last date to order is approaching or has already passed, it is visually signaled by the

35

Choose SupplierIdentifyPotentialShortages

Place Order

CalculateOrder Quantity

Negotiation

Place Fi-nal Order

Figure 4.2: The purchasing process is an iterative process. The purchaser and the supplier negotiate thefinal order several times in order to fill the standard container volume and avoid extra fees.

system, and the items are prioritized according to urgency. The last day to order is calculated bythe program by using the numbers of demand during the last 60 days, lead time and stock positionof that particular item. As mentioned in Section 4.3.2, the goal for the purchasing departmentis to make sure that the inventory can cover two months worth of demand for each article. Foreach order, the purchasers need to manually assess the numbers for the demand during differenttime spans over the past two months and take into account if and in what way campaigns, trendsor seasonal aspects may have affected those figures. In case an article has not been selling asdesired, it is investigated whether the product has been out of stock or if it is a new addition tothe assortment.

The next steps are Calculate Order Quantity and Place Order. Based on the parameters mentionedin the section above, the order quantity is computed, and an order is sent to the supplier. Auctusthen has to await feedback from the supplier. The box Negotiation represents the next phase,in which Auctus and the supplier negotiate order quantities, or if the order can be filled withother products to avoid extra fees for not using the whole capacity of a shipment. For instance,a supplier may add a cost of 15% of the article cost if a shipment is only used up to 50%. Thisnegotiation and adjustment of price and quantities can take up to two weeks depending on whatsupplier Auctus is dealing with. The iterative process of these three steps is depicted in Figure 4.2by the arched arrows between the three boxes and the dotted rectangle around them. For eachorder, a lead time is estimated, which can vary due to the supplier’s occupancy at the time, orspecial circumstances, such as local holidays. The last step is to place a definitive order, which isrepresented by the box Place Final Order to the very right in Figure 4.2.

4.3.5 Inventory Location and Warehouse Management

Auctus uses several warehouses, but they will be considered as one stock point in this thesis. Thecapital tied up is not affected by several stock points, the procurement and purchasing process isnot connected to a specific stock point. Making a difference among the different stock points wouldcomplicate the analysis process of this thesis and could increase the risk of identifying Auctus’true identity.

Today, every warehouse operates through a third party, a staffing agency. Although, this is aboutto change in one of the stock points, in order for Auctus gain better control over the process. Thiswill have an impact on this thesis.

36

4.3.6 Pricing

Currently at Auctus, fixed margins are set for each product category. For some categories, aminimum sales price is determined. Small variations in profit margins exist between the countriesfrom which they are sourced, but these are very small and insignificant in relation to each other.When the acquisition costs are computed, the expenses for the freight are divided equally among allof the articles in the order. The freight of a standard container is at the time of writing this thesis30 000 SEK. In addition, Auctus is charged by the staffing agency that manages the warehousewith a fixed cost for every article handled by the staffing agency. Because of the fixed nature ofthe cost, it will affect every article the same and thereby make it irrelevant to this analysis.

4.3.7 Suppliers and Inventory

Auctus uses 48 suppliers and sells at least 2 513 articles or combinations of them. A combinationof items of a product, like a bed and bed legs, do not have a combined item number, which makesit very difficult to track anything but the individual article.

In Table 4.6 on page 39, the purchasing management’s estimation of the variability of each supplierare displayed. The scale goes from 1 to 10, where a low grade indicates a high variability and lowreliability and a high grade indicates a low variability and high reliability.

In Figure 4.3, the number of articles for sale per supplier are displayed. Currently, four suppliersconstitute 53% of the total number of items for sale, 81% of the articles for sale are suppliedto Auctus from 14 suppliers. The last 19% of the inventory (∼ 477 items) is spread out over48−14 = 34 suppliers. A more detailed table of the supplier distribution is provided in appendix Afor a more detailed review.

0 10 20 30 40

500

1000

1500

2000

2500

Vital suppliers

Number of suppliers

Cum

ulativenu

mbe

rof

articles

(pcs.)

53% of the SKUs are supplied by 4 suppliers

81% of the SKUs are supplied by 14 suppliers

Figure 4.3: The total number of suppliers and the number of items supplied, illustrated in a similarway to a traditional pareto chart. Date of extraction: 2016-02-03, with the following assumptions: DropShipment=FALSE, Own Stock=TRUE, No Expiring Item=TRUE, Expiring Item=False, Web Last Date=2016.

37

A deeper investigation of the top four most frequently employed suppliers by Auctus is carried outin Table 4.4. It shows how the top four suppliers deliver the largest amount of article numbersin many categories. Supplier L000391 delivers 86% of all products representing a bed. Further,the bed articles from supplier L000391 represents 21% of the total number of products for sale inthe online store. Beds come in a wide variety regarding for example width and color. Every bedis however represented by a unique item number in the ERP system. Supplier L000348 provides26% of all armchair items for sale as well as 49% of the sofa articles.

Table 4.4: A deconstruction of the four most frequently employed suppliers. The quantity name in thetable is pieces. Date of extraction: 2016-02-03, with the following assumptions: Drop Shipment=FALSE,Own Stock=TRUE, No Expiring Item=TRUE, Expiring Item=False.

Company IDi (Top 53%)

L000391 L000348 L000584 L000331∑

Top 4∑48

i=1 IDi

Supplier Grade (1–10) 7 9 6 3

Accessories 12 12 0 9 33 91Armchair 0 32 0 18 50 125Bath 0 0 246 0 246 295Bed 539 0 0 6 545 624Chair 0 0 0 0 0 219Lightning 0 0 3 0 3 3N/A 9 3 13 0 25 109Office 0 0 0 0 0 6Other & Storage 0 0 0 0 0 1Outdoor Furniture 0 0 0 0 0 27Sofa 2 277 0 147 426 701Storage 0 0 11 0 11 88Table 0 0 5 0 5 224∑

Article category 562 324 278 180 1 344 2 513 (pcs.)

4.3.8 Suppliers for Drop Shipment

Auctus has a drop shipment assortment in their online store. The drop shipment articles areshipped directly from the manufacturer to the customer, in a make-to-order procedure. The orderlead-time for these articles are longer than for items kept in Auctus’ inventory, but the financialrisk for Auctus is substantially lower as they only need to advertise the product online, and notkeep it in stock. The shipment assortment encompasses 3 867 article identification numbers, seeTable 4.5 on the facing page.

4.3.9 Supplier Grade

In the interviews with the employees of the purchasing department, the insecurities of the supplierlead times were disclosed. It was stated that the actual length of the supplier lead time wasnot the significant matter, but it was rather the variability of the supplier lead time that wasmore important to the purchasing team. Long supplier lead times were not considered a big issuethough they might impair Auctus’ flexibility when it comes to meeting rapid changes in customerdemand. Planning ahead consolidates the long supplier lead times. Instead, more resources hadto be allocated to those who were unreliable. Insecurities in lead times also have an impact onthe order quantities and safety stock, since a larger amount had to be ordered by the purchasing

38

Table 4.5: The shipment assortment for the top four suppliers in stock.

Company ID Number ofItems (pcs.)

L000391 5L000348 29L000584 2L000331 1

Note. Date of extraction: 2016-02-12, with the following assumptions: Drop Shipment=TRUE, OwnStock=FALSE, No Expiring Item=TRUE, Expiring Item=False.

department to make sure that they do not risk a stockout. Since there is no data available thatreveals the true variability of supplier lead times, the purchasers at Auctus was asked to rank thesuppliers according to their reliability on a scale from 1-10, where 1 is very unreliable, and 10 ishighly reliable. As an example, in the interviews it was said that one top supplier usually has verypredictable lead times, and could even state the day and hour that the delivery is expected toarrive to Auctus’ warehouse. Another supplier is able to tell what week that the order is supposedto be delivered to Auctus’ warehouse, but the exact date often exceeds that pre-estimated deliverydate.

Different suppliers have varying levels of flexibility from a purchasing perspective. In Section 4.3.4on page 35, a negotiation phase is described. This phase is nearly non-existent for some suppliers,while for others; it can be more tedious and time-consuming. In general, the European suppliersrequire the least amount of time at this stage, while settling on the conditions for the orders aremore complicated for the Asian suppliers. Some reasons for this difference are communicationdifficulties, response delays, time difference and distance. In addition, the Asian suppliers nor-mally have limitations concerning minimum order quantities, which the European suppliers donot.

Table 4.6: The qualitative grading of supplier lead time reliability.

Supplier ID Supplier Grade Supplier ID Supplier Grade Supplier ID Supplier Grade

L000149 10 L000620 8 L000632 5L000381 10 L000528 8 L000153 4L000378 10 L000605 8 L000331 3L000393 10 L000680 8 L000010 2L000538 10 L000454 8 L000712 2L000550 10 L000464 8 L000720 2L000633 10 L000549 7 L000691 2L000677 10 L000537 7 L000690 2L000697 10 L000565 7 L000689 2L000559 9 L000637 7 L000430 1L000355 9 L000391 7 L000436 1L000348 9 L000622 7 L000433 Not used anymoreL000530 9 L000641 7 L000551 Not used anymoreL000535 9 L000584 6 L000634 Not used anymoreL000542 9 L000614 6 L000675 Not used anymoreL000585 9 L000612 6 L000668 Not used anymore

39

4.4 Data Extraction for the ABC Analysis

This section will describe under what circumstances data has been extracted from Auctus’ serverand what modifications have been applied to prepare the data for the ABC-analysis and furtherinvestigation.

4.4.1 The Storage of Inventory Data at Auctus

Data has been extracted from the server of Auctus through SQL commands. Auctus keeps trackof its stock positions through customer invoices. Every invoice has a document number and everyrow on that invoice corresponds to a movement of an article to the outbound stock point wherethe shipping agent takes on the responsibility for the order. The implication of this approach isthat the current stock level is not observable unless you track the history of that product since itsintroduction. To gain a better understanding of how data is created by Auctus the process froma customer placing an order to the delivery from the warehouse will be described below, first inwords and then as an illustration in Figure 4.4 on the facing page.

• Step 1: The customer enters Auctus’ online store and assembles an order of what he orshe wants to purchase. The Customer Order is transformed into a Sales Order in the ITsystem of Auctus. The customer can only purchase articles with a positive stock positionor those items that are sent directly from the supplier, also know as ‘‘shipment articles’’.The customer can monitor the remaining products, as they will get an email once the stockposition is positive.

• Step 2: The sales order is queued in the system, once it is first in line to be processed thesales order transforms into a Delivery Order waiting for a warehouse staff to act on it. Thedelivery order contains a list of item numbers (not necessarily equal to an article) and thewanted quantity. In Figure 4.4 on the next page the string of numbers representing ItemNo. is replaced by a more illustrative description. The difference between an item numberand an item would, for example, be: A dining set or a sofa set is advertised in the store asone product with one item number. But this is only to make the customer experience easier.Several items, for example, one table, and four chairs, compile a dining set.

• Step 3: When the warehouse staff handles the example order they accept the order on acomputer screen and a Picking Order is printed. The picking order is a transformed versionof the delivery order.

• Step 4: The picking order tells the warehouse staff (from a staffing agency) to go to locationA52 (fictive location) in the warehouse and pick 1 unit. The warehouse staff cannot tell fromthe picking order that the item in location A52 will be the table to the dining set.

Once the picking order is completed the warehouse staff brings the articles to the placespecially designated for delivery. When the staff reaches the area or delivery, they registerthe order as ‘‘ready for shipping’’ in a computer. This action orders the service of shippingfrom a forwarding company.

• Step 5: Once the order is registred by the staff, the delivery order is transformed into abookkeeping transaction. The sales order is transformed into an invoice sent to the customerand also notified in the bookkeeping.

• Step 6: The information from the sales order and the delivery order is saved in several ways,one of them is the file called Item entry ledger where the date of the transaction is savedalong with the quantity of the specific article. The file item entry ledger compiles informationfrom incoming delivery and customer returns too. The activities are separated by Entry typewhich equals a different number from 1-5 depending on what kind of transaction the entry

40

is about: delivery to customer, incoming delivery, customer return and more in this thesisless important entries.

CustomerOrder

SalesOrder

Delivery Order

Item no. QuantityDining set 1Carpet 2

Picking Order

Location QuantityA52 1B42 4G12 2

Shipping

Accounting

1 2 3

4

5

6

Figure 4.4: The figure illustrates the value chain from ordering to delivery and accounting the transaction.

4.4.2 Data Structure and Characteristics

The resulting files of the process, illustrated in Figure 4.4, have been exported to CSV-files throughSQL commands from the database. Due to restrictions of the Random-access memory (RAM) atthe extracting computer, the transaction history was divided into files of about 50 000 transactionseach and reassembled as a data frame in R (R Core Team 2013), which is the open source softwareused to process the data into information.

In Table 4.7 on the next page, ten transactions, from index 34 500 to index 34 510 visualize thestructure. The complete transaction history contains 526 989 different kinds of transactions, fromMay 1 in 2013 to January 31 in 2016. Because of the many transactions, a complete presentationis not appropriate. Table 4.7 on the following page only purpose is to illustrate the organizationof the data. Further, the original file extracted from the SQL database contained 63 columns fromwhich the most relevant ones are presented in the table.

To make a proper multiple criteria ABC analysis, only the items still for sale in the store areconsidered relevant, and expired items are excluded. Articles shipped directly from the supplierto the customer should be ruled out from the analysis as well, as these shipment goods do not tiecapital in the inventory. To make this selection, the file Purchase proposal extended was extracted– a file that possesses the advantage of only showing the articles fulfilling the following conditions:not expiring items and not drop shipment. An example is shown in Table 4.8 on the next page. Thefile originally contained 3 002 items, also called, observations (rows), and 34 variables (columns).This indicates that by February 3rd 2016, 3 002 articles were active in Auctus’ online store. Forevery 3 002 articles, there are 34 columns of information to support the purchase process; some ofthe columns are displayed in Table 4.8 on the following page.

Since some products for sale consists of more than one item, the multiple criteria ABC analysiswill treat the products that customers are actually buying from Auctus’ website, an explanationwill follow. From the purchasing department’s point of view, some products, such as beds orchairs are divided into several articles. For example, an armchair might be separated into thearmchair itself and the legs that belong to it. However, these kinds of products are presented as

41

Table 4.7: An example of raw transaction data from Auctus.

Item No. Posting Date Document No. Quantity Document Type

100-35-61650 2013-11-05 INL514263 4 5100-35-61650 2013-11-05 UT528539 -4 1100-13-14972 2013-11-05 INL514264 1 5100-13-14972 2013-11-05 UT528540 -1 1100-10-17913 2013-11-05 INL514265 1 5100-10-17913 2013-11-05 UT528541 -1 1100-13-16266 2013-11-05 INL514266 1 5100-13-16266 2013-11-05 UT528542 -1 1100-13-61054 2013-11-05 INL514267 1 5100-13-61054 2013-11-05 UT528543 -1 1100-13-61042 2013-11-05 INL514267 1 5

Table 4.8: Snapshot from the purchasing support file.

Supplier ID Item No Date Sold Out Customer orderslast 7 days

Customer orderslast 60 days

L000348 110-50-101118 16-05-03 1 4L000538 100-71-97902 Never 0 0L000538 100-71-97884 16-04-03 0 1L000538 100-71-97893 Never 0 0L000355 110-32-62447 16-04-03 1 7L000355 110-32-62453 16-05-13 0 3L000355 110-32-62459 16-02-23 0 3L000355 110-32-62441 16-03-10 0 5L000355 110-32-62465 Never 0 0L000355 110-32-62471 16-02-11 3 7L000355 100-39-88182 16-06-02 0 1

complete ones to the customers, and the appendages will never be ordered without the product’smain component, which is why the legs are excluded from the CSV file before the analysis wasconducted. If the adjustment is not made, there is a risk that the analysis will focus on productsthat are not ‘‘drivers’’ of customer purchases. The impact of this decision was examined andrevealed that only 71 items were lost by this elimination, which is a very low number comparedto the total of 3002 articles, after the reduction down to 3002− 71 = 2931. The exclusion of itemsequating legs will thus not extensively affect the analysis.

There is a distinct difference between the two files Item entry ledger (Table 4.7) and Purchaseproposal extended (Table 4.8), the first one contains raw data from the bookkeeping and the lastone contains somewhat transformed data to support the purchasers in their daily work. Thecommon denominator of the two files is column Item No., which is a string of numbers unique toevery item. The approach of this thesis has therefore been to match the two files with each otherbased on item number, and transfer the matching rows of each file to a third file, with an arbitraryname since it is only used internally to perform the ABC later on, let us call the file CompiledData.

The file Compiled Data now contains 63 + 36 − 1 = 98 variables (columns). The subtraction ofone variable is a result of the matching process on variable Item No. because there is no need tokeep a doublet of the variable. Further, the 2 931 unique item numbers (articles) of the purchasefile (Table 4.8) corresponded to 232 623 different transactions of the file with transaction data(Table 4.7). The remaining original transactions: 526 989 − 232 623 = 294 366 belong to either

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no longer active items or shipment articles. The file Compiled Data display nothing more than afiltered combination of Table 4.8 and Table 4.7, therefore it will not be presented in the text.

The next step in preparing the data for the ABC-analysis was to extract the relevant variables.Theory and interviews showed that dollar usage, order frequency, supplier reliability, and bulkinessfor each article were of interest. Table 4.9 is not intentionally sorted and shows index 1 to 12 ofthe file, which contains 2448 observations in total. This indicates that from the original 2931items available in the purchase file, and implicitly possible to buy online, there are only 2448articles that have recorded any transactions. This includes supply delivery, customer returns,customer delivery and so forth. There is 2931 − 2448 = 483 unique items that have no recordedactivity at all, since May 2013. Moreover, Number of Distinct Orders is extracted by the pseudocommand:

Count the number of unique <Document numbers> for every<article> WHEN <Document Type == 1>.

The explanation of the code above is that every transaction is connected to a specific documentnumber. The number is unique for every article transaction, pointing to a specific vendor ofcustomer invoice. Further, the variable Document Type is equal to 1 for all customer invoices.When the code is applied to the file Compiled Data only sales transactions (customer invoices)will be selected and counted.

4.4.3 An Example from the Multiple Criteria ABC File

From the file Compiled Data the authors of this thesis have constructed the ABC file, whichmakes the foundation of the ABC analysis. A snapshot from it is presented in Table 4.9. Since themodel suggested by Ng (2007) uses a positive additive objective function, the Volume variable isexpressed in its inverse (reciprocal) form, which means, everything else equal should a less bulkyarticle get a better classification than a more bulky item. The sofa with item number 110-50-61551,in Table 4.9, has been included in 1 833 sales invoices. This particular item occupies 1

0,52 = 1, 9

m3 of inventory space, and its current stock position ties 590 768,10 SEK. Where the dollar usageis calculated as acquisition cost times average inventory quantity – and average inventory quantityis the mean stock position six observations the last year. The reason for that is because Auctusintroduces such a vast amount of items that going back one full year – almost half of the productshad not been introduced yet. By using more observations a more stable average stock positioncan be obtained.

Table 4.9: Snapshot from the file which will be the foundation of the MCABC analysis.

Item No Supplier ID ArticleCategory

Order fre-quency

SKUVolume−1 i.e.(reciprocal)

Dollar Usage (SEK)

110-50-61551 L000348 Sofa 1, 833 0.52 590, 768.10100-46-101306 L000549 Table 388 2.17 360, 137.60110-28-62614 L000331 Sofa 1, 112 0.52 339, 867.60110-28-66942 L000391 Bed 4, 667 12.50 77, 225.33110-28-66969 L000391 Bed 3, 893 12.50 157, 466.10110-28-66948 L000391 Bed 3, 406 5.56 89, 346.17110-28-62617 L000331 Sofa 740 0.52 241, 914.40310-310-80343 L000391 Bed 70 1, 000 109, 023.10110-28-66960 L000391 Bed 2, 755 5.88 87, 440.8840-40-17045 L000331 Accessories 774 833.33 43, 691.64110-28-73509 L000391 Bed 1, 957 12.50 162, 587.30110-28-66975 L000391 Bed 2, 832 6.25 50, 885.77

43

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Chapter 5

Analysis, Constructing andPerforming an MCABC Analysis

The data collection and literature review have led to the identification of relevant criteria andview of how these should be arranged relative to each other in the multiple criteria ABC analysis.An analysis of how the multiple criteria ABC model should be designed to meet the requirementsof an e-tailer selling bulky articles is performed in Section 5.1, then the design is applied to theAuctus case, later on in the analysis. The multiple criteria ABC analysis in Section 5.2 will startwith some general comments on the result and then the different A-, B- and C-classes will beanalyzed further. Moreover, this will be done in the perspective of the symptoms mentioned inthe introduction to this thesis since these issues might be relevant to more e-tailers than Auctus.As a reminder, the symptoms were a too big amount of capital tied up in inventory and lackof inventory space. Hence, A, B and C will be analyzed through the perspective of capital andinventory space.

5.1 Design of the Multiple Criteria ABC Analysis

The answer to the first research question is that the multiple criteria ABC analysis will use dollarusage, order frequency and article volume. The reasons for these choices and their relative prioritywill be explained in the sections below.

5.1.1 Criterion – Dollar Usage

Olhager (2000) states that dollar usage is often utilized to divide articles into different groupssince they can require different ways of management. The purpose of the multiple criteria ABCanalysis is just that; to group items to make management of them more simple, since articleswithin the groups have similar needs. Dollar usage is also directly connected to the capital tiedup in inventory, where Auctus performs less well than the sector, as described in Table 4.1 onpage 33 (SCB 2013). Due to this clear connection, dollar usage is chosen as the primary and mostimportant criterion in the multiple criteria ABC analysis, thereby having the largest impact onthe end result.

The gross margin is the same for each product category and it does not vary much between differentproduct categories. Thereby, the dollar usage is a fair approximation of the profitability of eachproduct – as there is a linear relationship between the acquisition price and the profitability.

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5.1.2 Criterion – Order Frequency

In the interviews with the employees from the purchasing department, the issue with a proper andefficient determination of the demand was emphasized; why the authors of this thesis assessed thatthis had to be taken into consideration. Currently, Auctus uses several moving averages to forecastthe demand, if the moving averages with different time windows display a linear relationship thearticle can be concluded to have a stable order frequency. In that sense, order frequency helpsbalance the criterion dollar usage. Since a high dollar usage alone could mean that an article hasa high acquisition cost, but low quantity, thus not being an article that is purchased frequently bycustomers. The combination takes into consideration both the capital that the articles tie up inthe inventory, as well as the demand, which is connected to the financial performance measures. Asstated in the theory chapter, Section 2.9.3, order frequency helps illustrate how even the demandof a certain product is. Order frequency is chosen as the second criterion, thus with a mediumimpact on the ABC classification. The purpose of this thesis is to reduce the capital tied up ininventory, which is why dollar usage will get a higher priority than order frequency.

5.1.3 Criterion – Volume

What is particularly special for the case of an e-tailer selling bulky articles is the articles’ occupancyof space in the inventory. As explained in the introduction, Section 1.2, lack of inventory spaceis one of the symptoms of Auctus’ communication issues between the purchasing department andinventory management. For that reason, article volume is chosen as the third criterion, aiming atpromoting a prioritization of items with a smaller volume and thereby a better usage of inventoryspace. It is ranked as the third criterion because it is not as clearly connected to the capitaltied up in inventory. Thus, the volume will not have as large of an impact as the two formercriteria.

5.1.4 Criterion – Replenishment Lead Time

Silver (1991) and Flores (1987) argue that replenishment lead time is an important criterion in anABC analysis. In addition, in the interviews replenishment lead time stood out as an importantcriterion to consider in the purchasing process. Unfortunately, Auctus currently does not trackthe replenishment lead time and its variability. To be able to take into account the replenishmentlead time variability, this thesis uses qualitative estimations to obtain a relative appreciation ofthe suppliers’ performance.

There are 48 suppliers in the studied data, but the grading scale consists of 10 discrete steps.This will result in the same grade for many suppliers, thus transforming the criteria into a numberbetween 0.00 and 1.00 by Equation (2.13) on page 16 will display the same distribution (Ng 2007).That is, only 10 values between 0.00 and 1.00 will be used, not the 101 possibilities {0.00, ..., 1.00}that are available when using two decimals. This is an unfavorable behavior, as a lack of spreadin the data may lead to false conclusions or an abnormal impact on the outcome. In this case,using supplier grade as a criterion in the multiple criteria ABC model would have resulted in everyarticle delivered by a grade 10 supplier getting the value 1.00 for the supplier grade criteria. Lateron, in the analysis when calculating the partial average this high value will affect the analysis inan unnatural way. The grading is a qualitative estimation and comparing them with quantitativedata in the multiple criteria ABC model can be problematic. A way to deal with this issue wouldbe to use a more differentiated scale, maybe 1-100; but this would only pass the error on to theperson who are to make the estimations. There is no guarantee that a wider interval will makethe estimation better, more likely arbitrary, as it is difficult to position 48 objects in relation toeach other. The supplier grade will instead be used to match the outcome of the multiple criteriaABC analysis with the supplier reliability. By doing so, the variability of the replenishment lead

46

time will still be considered – but it is now treated as the qualitative indicator it is intended to be– and not pushed into a quantitative criterion of the multiple criteria ABC analysis.

5.1.5 Imposed Restrictions on the Multiple Criteria ABC Analysis

This section is tightly connected to the section about limitations and delimitations but deserves aseparate section. It is difficult to know in advance what data will be available from a case company.The limitations known in advance have been mentioned earlier, but along the process restrictionsto the product, assortment has been made. It is important to mention this in the context of thefirst research question, as the design of the multiple criteria ABC analysis not only consists of aselection of criteria but an investigation about what data is available and thereby how to designthe multiple criteria ABC analysis to meet those circumstances.

Simplifications of the original product assortments have been made according to the followinglist:

1. Only studying items active for sale in the online store.

2. Only studying items kept in Auctus’ inventory, i.e. ignoring drop shipment.

3. Only studying items with available data, i.e. items with a missing definition of volume (m3)are ignored. Otherwise, the multiple criteria ABC analysis will give a deceptive result sincea missing value for a variable is interpreted as 0 by the model (Ng 2007). Hence, the partialaverage for the third variable will be calculated on two values and the third being 0. Thisfalsely indicates that some items should be a C-article.

5.1.6 Answering Research Question 1

The three quantitative criteria; dollar usage, order frequency, and volume constitute the designof the multiple criteria ABC analysis to evaluate the purchasing process. The actual evaluationof the purchasing process takes place when the multiple criteria ABC analysis is compared tothe qualitative criterion of variability in the replenishment lead time. Ideally, the variabilityin the replenishment lead time would be included in the multiple criteria ABC analysis as aquantitative criterion. Initially, the idea was to include variability in the replenishment lead timeas a quantitative criterion, but Auctus did not possess adequate data about the variable. Instead,this thesis used a qualitative estimation and the pros and cons of this choice have been discussedabove. At the same time, using the model of Ng (2007) would put less attention to the lastcriterion, that is, if variability in replenishment lead time had been included in the quantitativemodel it would have attained one-fourth of its weight, due to the partial averages. In the currentdesign of the multiple criteria ABC analysis, the supplier reliability will be given a higher prioritywithout making other criteria less important.

5.2 Summary of the Multiple Criteria ABC Analysis

About the overall comments on the multiple criteria ABC analysis – the result of the multiplecriteria ABC-analysis is summarized in Table 5.1 on page 49 and Table 5.2 on page 50. The analysiswill be deconstructed on the following pages where, A-, B-, and C-classes will be commented onand analyzed separately. As stated before, this will be done in the perspective of the symptomsmentioned in the introduction to this thesis. Those were; a too big amount of capital tied up ininventory and lack of inventory space. The boldface suppliers in the table are the ones mentionedin the empirical findings chapter (Table 4.4). They are marked in boldface to ease the identificationof the four most frequently used suppliers regarding unique items kept in inventory.

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Improvement actions to make the purchasing more effective for a specific supplier can take on twoapproaches. Either, Auctus can choose to deal with every article delivered by that specific supplieror just some items belonging to the A-, B- or C-class. The differences between these two approaches– negotiating every product of a supplier or just a specific class of articles from a supplier – is thatthe second more specific approach of discussing ways of stabilizing the replenishment lead timewill provide an e-tailer like Auctus with more specific information. Auctus is prone to go furtherfor stabilizing the most important items, as identified in Kraljic’s (1983) research. Aronsson et al.(2004) also recommends that the classes should be treated differently with the most focus onthe the A-articles. That is, a differentiation of the Auctus assortment yields a better and moretransparent position for negotiation with their suppliers. This is similar to category management(Monczka et al. 2015). In addition, Knoppen and Sáenz (2015) stress that poor implementationof strategic purchasing might have a negative impact on performance, why it needs full supportfrom the management. Axsäter (1991) writes about lead time and its impact on the capital tiedup in inventory. With longer and unreliable lead times, the need to keep a large amount of articlesin the inventory increases, and thus excavates the financial performance.

Start focusing on Table 5.1, the bold-faced suppliers deliver 116+ 81+ 25+ 16 = 238 or 72, 8% ofall of the A-articles. The same four suppliers also deliver 79 + 69 + 48 + 45 = 241 or 49% of theB-articles and finally 48, 5% of the C-articles. Remember from the empirical findings (Table 4.4)that these suppliers deliver 53% of every unique item. If the distribution of A-, B-, and C-articleswere to be a uniform distribution, that is, every outcome has the same probability of happening,then the top four suppliers would deliver 53% of the A-articles, 53% of the B-articles and 53% ofthe C-articles. This is however not the case. As previously described; the top four most frequentsuppliers are overrepresented in the A-class and underrepresented in the C-class. This bias is mostlikely a result of the purchasing department selection of what item to purchase. If one item froma specific supplier has a good sales track record, then the supplier might purchase other itemsfrom the same supplier on speculation. This can be described as some sort of momentum – theprofitable articles suppliers will be favored over the less profitable items from other suppliers. Thecolumn Supplier Grade has been added to Table 5.1, after the multiple criteria ABC-analysis wasperformed, so that the understanding of the table shall increase once this thesis start to analyzethe relationship of the category of the multiple criteria ABC analysis and the suppliers deliveringthe items.

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Table 5.1: This table shows a summary of the outcome of the multiple criteria ABC analysis. Eachproduct category is summarised (n) into the total number of articles provided per supplier. The sup-plier identification numbers in boldface are corresponding to the top four most frequently used supplier,presented in Table 4.4 on page 38.

A (20%) B (30%) C (50%)

SupplierID

n SupplierGrade

SupplierID

n SupplierGrade

SupplierID

n SupplierGrade

L000391 116 7 L000348 79 9 L000348 201 9L000331 81 3 L000391 69 7 L000584 128 6L000348 25 9 L000584 48 6 L000355 64 9L000378 17 10 L000331 45 3 L000391 63 7L000584 16 6 L000614 36 6 L000538 53 10L000010 15 2 L000637 29 7 L000632 38 5L000620 11 8 L000355 26 9 L000614 32 6L000436 8 1 L000620 21 8 L000393 31 10L000549 6 7 L000585 15 9 L000549 30 7L000153 5 4 L000549 14 7 L000010 28 2L000559 5 9 L000010 12 2 L000585 23 9L000605 5 8 L000605 12 8 L000637 21 7L000393 4 10 L000436 10 1 L000680 21 8L000355 3 9 L000393 9 10 L000430 20 1L000381 3 10 L000430 9 1 L000620 15 8L000454 2 8 L000680 9 8 L000565 11 7L000632 2 5 L000153 7 4 L000153 8 4L000430 1 1 L000550 6 10 L000697 7 10L000538 1 10 L000454 5 8 L000331 5 3L000712 1 2 L000464 5 8 L000454 5 8

L000632 4 5 L000464 5 8L000712 4 2 L000528 5 8L000720 4 2 L000550 3 10L000528 3 8 L000720 2 2L000559 3 9L000565 3 7L000378 2 10L000381 1 10L000433 1 0

∑n . . . . 327 . . . . . . . . . . . . . . . . 491 . . . . . . . . . . . . . . . . 819 . . . . . . .

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Table 5.2: This table shows the result of the multiple criteria ABC analysis, more specifically it illustrateswhich of the three criteria that got the highest value, in the same fashion as explained in Table 2.2 onpage 16. For example, A-articles of Bed are triggered by the variable Dollar (usage) for 40 items.

A B C

Maximum variable Dollar Freq. Vol. Dollar Freq. Vol. Dollar Freq. Vol.

Accessories 0 1 8 0 4 10 4 2 2Armchair 5 1 0 12 11 0 18 20 41Bathroom 0 0 14 0 0 61 78 0 67Bed 40 53 22 27 46 4 19 24 50Chair 4 17 0 0 24 33 3 4 44Office 0 0 0 0 0 0 0 0 5Lighting 0 0 0 0 0 2 1 0 0Carpet 0 0 0 0 0 0 0 0 0Other and storage 0 0 0 1 0 0 0 0 0Outdoor furniture 1 0 0 0 0 0 0 0 0Sofa 129 7 0 104 30 22 161 48 98Storage 1 0 0 0 1 23 2 1 18NA 0 0 2 0 0 3 25 0 21Table 2 18 2 1 25 47 19 9 35

Sum articles perABC-category:

182 97 48 145 141 205 330 108 381

Table 5.3: The MCABC anal-ysis of the A-article, separatedinto product categories.

Article Category n

Sofa 136Bed 115Table 22Chair 21

Bathroom 14Accessories 9Armchair 6

NA 2Outdoor furniture 1

Storage 1∑n 327

Table 5.4: The MCABC anal-ysis of the B-article, separatedinto product categories.

Article Category n

Sofa 156Bed 77Table 73

Bathroom 61Chair 57Storage 24Armchair 23Accessories 14

NA 3Lighting 2

Other and storage 1∑n 491

Table 5.5: The MCABC anal-ysis of the C-article, separatedinto product categories.

Article Category n

Sofa 307Bathroom 145

Bed 93Armchair 79Table 63Chair 51NA 46

Storage 21Accessories 8

Office 5Lighting 1∑

n 819

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5.2.1 A-articles – Capital

Table 5.1 on page 49 displays each supplier as a point, with supplier grade on the y-axis and thetotal number of supplied items (n) on the x-axis. Figure 5.1 shows that most top grade suppliersonly deliver a few A-articles. The most alarming cases in Figure 5.1 are the suppliers placed in thebottom right corner, as these suppliers deliver many articles while they pose a great uncertaintyin replenishment lead time to Auctus.

An example of a miss-match of number of supplied articles n and supplier grade is supplier L000331with grade 3, which delivers 81 A-articles to Auctus, these 81 articles consist of 77 productscategorised as sofa, 3 articles categorised as accessories and 1 article categorised as armchair. Thesituation is illustrated in Figure 5.1 on the following page, where every point represents a supplier.Furthermore, supplier L000391 is a grade 7 supplier and deliver 116 A-items. This supplier isthe most frequently used of all, for the A-articles. Therefore, a grade 7 can be considered anunnecessary risk. There are several suppliers with grade 8 or 9 that also supply sofas, accessoriesand armchairs to Auctus and thus should be considered a replacement to these uncertain suppliers(Chen et al. 2004). In addition, Cousins (1999) highlights the advantages of facilitated managementwith a reduced supplier base, although, much caution is needed to make sure that the measurestaken are beneficial to the company and in line with the value proposition. The multiple criteriaABC analysis can be miss-leading in such a way that sofas are also at the top when it comesto order frequency and volume but due to the choice of prioritizing dollar usage the most thisvariable will be the maximum. With this in mind; order frequency is a common maximum valuefor beds, the reason for the dollar usage of beds to be less frequent as a maximum value might bethat they are sold in a steadier stream, that is, a more predictable demand. Another example ofa miss-match is L000436 that supplies 8 items, divided into 4 chairs and 4 tables, which can alsobe allocated to other suppliers. An alternative approach to a reallocation of suppliers would be tostore articles at the supplier’s site to reduce the replenishment lead time variability to a minimum,Chen et al. (2004) argues that closer relationships with partners can facilitate the fulfillment ofstrategic goals. Further, Buvik and John (2000) suggests vertical integration to tackle uncertaindemand or fast-changing environments. This action would also push the supplier’s CODP furtherup the value stream, in favour of Auctus (Olhager 2012) and it will therefore not be acceptedby the supplier without a premium. Moreover, Weber et al. (1991) make a point of choosingthe right suppliers, they argue that it is impossible to provide low cost and qualitative productswithout supplier’s matching those requirements. Thompson (1996) adds that effective purchasingis characterized by long-term cost focus. For these reasons, it is important for Auctus to choosethe right suppliers for crucial articles.

Table 5.2 on page 50 shows that for sofas in the A-category, dollar usage is the most frequentmaximum variable. This means that dollar usage influences the sofas the most and is the mainreason for sofas to be an A-article. Dollar usage is the average consumption (sales) times theacquisition cost and measures the amount of capital each article tie up in inventory. Based on theoutcome of Table 5.2 sofas tie up a lot of capital, relative to the other article categories. Hence,using a low-grade supplier for the costly sofas is more disadvantageous than doing so for an articlethat is cheap to acquire. The low grade indicates a low reliability and the purchasing departmentthus needs to order for more days at the same time to cover some kind of safety lead time. AsSanderson (1997) states that safety inventory can be considered the coverage for the differencebetween planned and actual lead time.

Furthermore, when filtering the A-articles on what product category each article belongs to (Ta-ble 5.3), it is obvious that sofas are the most important products to Auctus, but the productcategory bed is not far behind. When comparing the article categories of the A-, B- and C-classesa pattern arises. The A-class contains less differentiation of article categories than B- and C-classesdo. At the same time sofas are on average a bulky article while the other article categories, suchas a table, chair, and bathroom are less bulky. This implies that an inventory carrier in the formof a standard container can be filled with A-items in the form of sofas and the remaining space can

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0 20 40 60 80 100

24

68

10

A-articles

Number of supplied articles (n)

Supp

liergrad

e

Figure 5.1: The figure illustrates the importance and reliability of each supplier. Where the supplier isrepresented as a point in the figure. The importance of a supplier increases by the number of articles (n)they supply.

be utilized by less bulky article categories from mainly the B-class if the purchasing departmentdoes not wish to transport empty space.

5.2.2 A-articles – Inventory Space

The multiple criteria ABC model uses volume as a variable and the articles with a low volumewill get a better classification over articles that are more voluminous – other things being equal.Table 5.2 on page 50 shows that 48 out of 327 A-articles (14, 7%) have become A-articles because oftheir advantageous volume. To be classified as an A-article, the item needs to express a maximumvalue which places it among the top 20% of the items; and the variable volume is the third variablein the partial average. Therefore, only the smallest volumes will turn into A-articles, the averagevolumes will not have the ‘‘strength’’ to affect the partial average in such a way that the articlewill be classified as an A-article, more likely a B- or C-article depending on the performance of theother two variables. This discussion aims at proving that if the purchasing department of Auctusfocuses most of their energy on the A-article then they will purchase small and profitable articles.Furthermore, the variable order frequency indicates that the article experiences a steady customerdemand. Order frequency corresponds to the number of sales invoices where a specific article isincluded.

The next important outcome of the initial multiple criteria ABC analysis connects to previoussections argumentation about a decrease of capital tied up in inventory – by focusing on thesupplier grade, in Table 5.1 on page 49. The same goes for a more effective use of inventory space.When Auctus purchases its articles from a low-grade supplier it is exposing itself to an unnecessaryrisk, a risk that will give Auctus no return. Auctus’ business model is to sell furniture and homefurnishing online. The risks Auctus will get paid for are when Auctus hold an inventory and whenAuctus introduce new articles on speculation to try to better satisfy the customer demand andincrease the richness. The dimension of richness has been discussed by Evans and Wurster (1997)and Schafer et al. (2001) to be especially important to e-tailers. The risk of not receiving theordered articles from the supplier – on the agreed time, forces the purchasing department to orderextra quantities big enough to cover the possibility of a late delivery. When Auctus gets pushed

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into ordering bigger quantities this will consume more inventory space than needed if Auctus hadplaced the order with a more reliable supplier, and thus could avoid ordering the extra quantitywith the purpose of covering for the variability in replenishment lead time. The issue of biggerordering quantities is deduced from the equation, Equation (2.11), determining the variability inreplenishment lead time (Anupindi et al. 2014; Jonsson 2008).

5.2.3 B-articles – Capital

Comparing the product categories for A-articles (Table 5.3) and B-articles (Table 5.4), not muchhas changed. Sofas are still the most frequent product category in Table 5.4. The criteria of dollarusage and order frequency are less dominant than volume, this is because smaller products onaverage is cheaper to purchase as they contain less material. This outcome of the B-articles alsodeals with the problem of bulkiness; the average B-article is a small but fairly demanded product.Better than C-articles or dead articles.

When it comes to B-articles, supplier L000331 is still a subject of discussion, in the same way as forthe A-articles. Supplier L000331 delivers, even more, B-articles but the reliability of when Auctuswill receive the ordered goods is poor. For the B-class, where the importance of each article isless – a lower supplier grade can be accepted, as this poses a minor threat to Auctus’ customervalue. This is in line with a shifted perspective of purchasing, from an operating function tosupply management, presented by Kraljic (1983). However, the behavior of the supplier selectionis slightly better for B-articles (Figure 5.2) compared to A-articles.

0 20 40 60 80

02

46

810

B-articles

Number of supplied articles (n)

Supp

liergrad

e

Figure 5.2: The figure illustrates the importance and reliability of each supplier. Where the supplier isrepresented as a point in the figure. The importance of a supplier increases by the number of articles (n)they supply.

This unreliability still forces the purchasing department to order a bigger lot size, since the pur-chasers need to cover for the customer demand during the period of uncertainty. That is an un-necessarily high safety inventory in the ordering point system. (Sürie and Reuter 2015; Anupindiet al. 2014) On the other hand, B-class items are less important than A-class items, therefore, thesafety inventory might be set at a lower level. (Sanderson 1997; Jonsson 2008) When decidingon the safety inventory, Anupindi et al. (2014) presents an equation representing the standarddeviation when accounting for uncertainty in demand and uncertainty in replenishment lead time.

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Translated into a less quantitative but instead more qualitative context, this could be interpretedas considering the A-, B, and C-class, as well as the supplier grading. While accurate data aboutthe actual numbers are missing, the different classes of the multiple criteria ABC analysis ap-proximate the demand for a specific item and the supplier grade approximates the variability inreplenishment lead time. These approximations will, of course, be more of a mental exercise andconsist of few calculations. But to know what factors to account for when deciding on the safetylevel is nonetheless important.

The B-class is a difficult group to manage. Even in previous literature, the recommendations arediffuse for how to treat the items in this class. Silver (1991) states that a lot of attention should bedirected at A-items while C-items should be treated in a much simpler way. This leaves B-itemsin some kind of limbo state, where it is not very clearly defined what management should do withthem, just that it should not be as meticulously managed as the A-articles, yet not as randomlyas the C-articles. From the interviews, it was found that the purchasing department felt quiteconfident with the management of the most evident cases – those articles with a very high andstable demand and the ones that were not as sought after by the customers. Just as in the theorypresented in by Silver (1991), an effective handling of the B-items was not as clear. However,unlike the suggestions from the literature, the purchasing department felt a need for more timebeing spent on managing the purchasing of ‘‘tricky’’ B-articles, as they felt that more time wasrequired to individually assess order quantities and when to purchase them.

5.2.4 B-articles – Inventory Space

The B-articles are mostly influenced by the volume variable. 205 out of 491 articles (41, 8%) inthe B-category experience a maximum value of the partial averages for volume. The analysis ofthis information is that the B-articles are of smaller volume that the A-articles, when comparingTable 5.3 to Table 5.4 one can see that the B-articles are more evenly distributed among the articlecategories than A-class articles. The A-articles are mostly big objects while the multiple criteriaABC analysis have ranked smaller objects from the other product categories as B-articles, thesearticles are apparently not as demanded as sofas or beds, but because of their smaller volume,they will prove valuable anyway from a point of view of an effective use of inventory space. Atthe same time, the dollar usage and order frequency are medium-sized since the resulting categoryis B. The B-articles bind less capital in inventory. One possible way to use the inventory spacefurther is to shift the purchasing to a more reliable supplier or start storing at the supplier’s site tolower the safety inventory needed to handle the variability in replenishment lead time (Anupindiet al. 2014).

5.2.5 C-articles – Capital

As can be seen in Figure 5.3, the C-articles suppliers are positioned in an acceptable manner. Atthe same time Table 5.5 on page 50 shows a distribution among the article categories similar tothat of the B-class.

The analysis of the C-class becomes interesting as the multiple criteria ABC analysis has helpedto identify slow mover articles (swe. hyllvärmare) and articles with no sales track record, thatthe current forecasting method of moving average (Axsäter 1991) has failed to detect. Thesearticles are categorized as C-articles. The previous answer to the first research question entaileda restriction to the analysis of missing data, which have eliminated most of the unsold articles.This is because the unsold articles have experienced minimal attention and therefore there aremissing product information about them, but the fact that they gather dust in the warehouse isindisputable, product information missing in the system or not. Observe that there is a differenceto a slow mover article and an unsold article. The slow mover article does sell but it is an ineffectiveway of creating a return to the investors – the unsold articles – on the other hand, have never

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0 50 100 150 200

24

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10

C-articles

Number of supplied articles (n)

Supp

liergrad

e

Figure 5.3: The figure illustrates the importance and reliability of each supplier. Where the supplier isrepresented as a point in the figure. The importance of a supplier increases by the number of articles (n)they supply.

sold and will, therefore, yield a negative return to the investors. Along the process of writing thisthesis, while processing the quantitative data, the authors of this thesis found these unsold articlestoo important to be left unnoticed. At the same time, there should be no conflict in including theunsold articles in this analysis of C-articles while they have been excluded by the first researchquestion, due to missing data in the variable volume. The main reason for excluding articles withmissing data is that these articles not falsely should be categorized as B- or C-articles when theyare actually performing well, but the approach and model used in this thesis fail to detect that.With that background, excluding articles with inconsistent information from the multiple criteriaABC analysis when focusing on A- and B-articles is the correct way, but these unsold articleswill be C-articles even if there had existed proper information about them, as they have neversold. Because of that, the analysis of capital tied up in inventory for C-articles will present twotracks – one of them using only articles with complete details, the other track includes articleswith unsatisfactory details.

Starting with the complete detail articles – out of the 819 number of unique C-articles in Table 5.1on page 49, 165 have never sold. Because of purchase speculation, this is not a sensation. Toadjust for this speculation, articles with a First web date later than January 1, 2016, have beenneglected. This is because these articles have not had time to prove themselves. But articles withan inventory history longer than two months have been included and will be regarded as unsold ifthey show no sales track record. If this distinction of two months was not done, 4 more observationswould be added, up to 169 in total. Hence, 4 C-articles have been introduced during the periodJanuary to February 2016. The 165 unsold unique articles, with a quantity of 2 303 pieces, tie1 529 557 SEK in inventory, which is the quantity multiplied by the acquisition cost.

When including unsold articles with inconsistent specification of data, the observed number ofarticles rises to 367, with a quantity of 6 068 pieces. Those articles yield a 3 390 648 SEK investmenttied up in inventory, this capital represents bad use of scarce resources. The total amount of capitaltied up in inventory by 2014 was 32 692 kSEK, presented in Table 4.3 on page 34. Hence, unsoldarticles constitute 10, 37% of the total capital tied up in inventory. If these 10, 37% were to be used,it would yield a financial improvement. To be able to calculate the improved ITR performance

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some definitions are necessary:

Inventory1 = (1− x)× Inventory0 (5.1)where,

Inventory1 = Inventory after reduction of capital for unsold articlesInventory0 = Inventory, from 2014-years balance sheet

x = The fraction of unused capital in inventory

The equation for calculating the ITR by Olhager (2000), will result in the following relativeimprovement, as the change are divided by the original figures:

Net Sales0/Inventory1

Net Sales0/Inventory0

− 1 =Net Sales0Inventory1

×Inventory0Net Sales0

− 1Eq.(5.1)

=Inventory0

(1− x)Inventory0− 1

=1

1− x− 1 =

x

x− 1⇒

0, 1037

1− 0, 1037⇒ 11, 57% (5.2)

The result of Equation (5.2) shows that a reduction of capital tied up in inventory by 10, 37% willyield an improvement in the ITR of 11, 57%. When calculating the same with actual figures, thesame result is obtained:

Net Sales0 = 191 189 000

Inventory0 = 32 692 000

Inventory1 = 32 692 000− 3 390 648 = 29 301 352

ITR0 = 5, 848

ITR1 = 6, 525

ITR1

ITR0− 1 =

6, 525

5, 848− 1 = 0, 1157⇒ 11, 57% (5.3)

This capital contraction will affect the ROA but it is more cumbersome to show that analytically.Remember from Figure 2.2 on page 9 that ROA = Profit margin × Asset turnover (Sanderson1997; Olhager 2000). To free the capital and put it to work for a better investment and increasedrichness, the unsold articles need to be sold, which can be done through a discount campaign.A DuPont analysis perspective (Sanderson 1997) of the freed capital gives that; when the unsoldarticles are accounted for, in the balance sheet item Total Inventory, they represent 100% of theiracquisition value, but they must probably be sold below the break even price and the true valueof the unsold inventory will be revealed. The transfer into cash will probably mean a reduction ofCurrent Assets, as Auctus cannot sell the articles to their full price. This boils down to a lowerlevel of Total Assets, but not doing something about the unsold articles will depreciate the valueof the unsold articles further and make the pain worse in the future. In the other branch of theDuPont analysis the profit margin will not be substantially affected by the action of getting ridof the unsold articles, unless the action includes a lot of administrative costs. In the long runhowever, the overhead costs will be used to run a smaller or more efficient inventory. Hence,the profit margin should in the long run be the same or increase after getting rid of the unsoldarticles.

The redundant C-items are a product of the focus on richness (Evans and Wurster 1997; Schafer etal. 2001) and increased customer influence that Gunasekaran and Yusuf (2002) and Gunasekaran,Hung Lai, et al. (2008) have observed. However, providing a broad product range still requirespurchasing decisions to be made in a strategic manner, which might have been lacking whenprocuring these items.

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5.2.6 C-articles – Inventory Space

From Table 5.1 on page 49 the variable volume of C-articles constitutes 46, 5% of the maximumpartial averages. This is probably a consequence of the weak performance of the two previousvariables, dollar usage, and order frequency. The variable volume is the third in line of thepartial averages and should affect the multiple criteria ABC analysis the least, but if the previousvariables have low values then the volume has a higher likelihood to prevail. The analysis of thisinformation is that C-articles mostly consist of less capital intensive, less frequently ordered andsmall articles.

When focusing specifically on the unsold items, like in the discussion about capital tied up forC-articles; these articles not only use capital, but also inventory space. Previous discussions aboutwhat data to include and exclude will now be important to keep in mind. When multiplying thewarehouse quantity of each article with the corresponding information about volume the total spaceconsumed by these 165 unsold articles is 523, 46 m3. When including the articles with incompletedata specification the unsold articles consists of 367 articles but they still correspond to 523, 46m3.Because of the specific circumstances of this case, of missing data in the volume variable it mightlook like 165 articles (2 303 pieces in inventory) with complete product information specified, usethe same amount of space as the 367 number of unique articles (6 068 pieces in inventory) discussedabove – but this is nothing but a consequence of missing data. There are 202 (367− 165) articleswith no volume specified, therefore, they will not affect the sum of inventory space used. To givethe reader and Auctus a rough estimation:

Space per item =523, 46

2303= 0, 2273 m3 × item−1

Estimated total space =523, 46

2303× 6068 ' 1379 m3

These unsold articles not only tie up capital but they use the space of 36 standard shipmentcontainers, if one uses the information on Wikipedia1 (L: 6, 1 m, W: 2, 44 m, H: 2, 59 m, Vol: 38, 5m3). One standard shipment container has a fixed ordering cost of 30 000 SEK, and 30 000×36 =1 080 000 SEK. This shows that speculative purchasing comes with a cost, but as the business isconstructed this cost is hard to come by.

1https://en.wikipedia.org/wiki/Twenty-foot_equivalent_unit, accessed: 2016-04-12

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Chapter 6

Proposed Solutions

6.1 Staying on Top in the E-commerce Competition

E-commerce has affected the information asymmetry, to the advantage of the consumers. Assearching and comparing products have become easier for customers, more pressure has been puton e-tailers to reduce prices, increase delivery flexibility, and enhance their product ranges. If acompany wants to be able to perform well and survive in the competitive market, it needs to beable to answer to those customer requirements in a clever and cost-effective way, while at the sametime staying flexible.

In this chapter, a number of solutions of how to manage the purchasing process and the inherentdecisions are presented. The solutions are based on the multiple criteria ABC analysis and designedin such a way that they do not compromise the parameters that are presented in the paragraphabove, thus not impairing the customer value including the richness and reach that e-tailers offer.The multiple criteria ABC analysis can be seen as a tool to help category management, that is,divide items into groups with similar requirements of the suppliers. The solutions aim to make thepurchasing process more effective by reducing the capital tied up in inventory and use inventoryspace more effectively. Firstly, a suggestion of how to prioritize items when placing new orders,based on the classifications is presented. Then, solutions concerning cooperating with suppliers tomove the supplier’s customer order decoupling point upstream, stabilizing lead times by managingthe selection of suppliers in accordance with their ability to provide stable lead times.

6.1.1 Make Prioritization Clear in the Purchasing Process

From the interviews it was found that Auctus can improve their long-term perspective in thepurchasing strategy. A lot of emphasis is put on the daily purchasing decisions, attempting tosolve every order decision in isolation. This is in conflict with what is found in the frame ofreferences in this research, which puts emphasis on acting according to a more long-term plan.The short-term perspective shows in the way that extra articles sometimes are purchased.

Due to the high fixed ordering cost, the purchasing process deals with one supplier at the time andtries to fill the inventory carrier. For example, when re-ordering an item, and the amount does notfill up an entire load, the rest of the space is filled with another product(s). This compensationshould be done in a more structured and strategic manner and a multiple criterion ABC analysiswill help Auctus as well as other e-tailers in a similar situation. When prioritizing A-articlesover B- and C-articles as additional items, the purchasing process will favor profitable and smallarticles over less profitable and bulky articles. Doing the other way around and filling the lastempty space with less income generating articles will only lead to more capital being tied up in

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the inventory since these products are more slow moving. These slow moving articles will alsooccupy inventory space for a longer time and crowd out more profitable articles. This proposedsolution will generate a more effective use of capital and inventory space, while at the same timenot deteriorating richness and reach.

6.1.2 Eliminate Dead Articles

Dollar usage is defined in the theoretical chapter as the yearly usage times acquisition cost, Equa-tion (2.14) on page 18. The yearly usage is interpreted as the sold quantity. A consequence of thisis that the articles that have never sold will have a zero dollar usage and end up in the bottom ofthe multiple criteria ABC analysis. These unsold articles are ‘‘dead’’ articles which only consumescapital and space. A multiple criteria ABC analysis will help the e-tailer to identify these articlesso that appropriate actions can be initiated. Like the rest of the proposed solutions will the mul-tiple criteria ABC analysis facilitate a more effective way of identifying articles with a need forchanged management. Category management has been mentioned before, and the dead articlescan be said to form a unique category. The goal for any e-tailer should be to as fast as possiblesell these articles on discount or discard them. This will save space and facilitate a more effectiveuse of capital, measured as ITR or ROA. The freed capital and space can be used to increase therichness of the e-tailer, by allocating these resources to more demanded items.

6.1.3 Decouple the Supply Chain

One possible solution to tackle the challenge of variability in replenishment lead time is to decouplethe e-tailer demand and the supply from the supplier. By creating a buffer inventory at the siteof the supplier, and get the supplier to make to stock instead of make to order, the risk is pushedupstream the value chain.

In this case, Auctus has already realized the benefits of vertical integration and have a deal with oneof their suppliers (denoted as L000348) to store their goods at the supplier’s premises. However,the supplier ranking shows that this particular supplier has a very good track record of deliveringon time. Therefore, such a deal will at its best be an indifferent monetary decision. A deal ofcreating a finished goods inventory at the site of a top grade supplier before transporting it to thewarehouse of Auctus will turn some of Auctus’ overhead inventory costs into direct costs chargedby the supplier, that is, the end customer price can more easily be calculated as the overheadis smaller. The reason for this transformation is that initially the safety stock connected to thevariability in replenishment lead time claimed additional administration and inventory space, in asituation of a buffer at the supplier, these indirect costs will not occur anymore. Instead, the costhas turned into a direct cost charged by the supplier, and this direct cost can easily be allocatedto each article.

The vertical integration is most desirable in the case of an unpredictable but still importantsupplier; if the supplier were not important, it should be replaced and it will not be worth the in-vestment of creating a deal of storing goods at the supplier’s site. By using the result of a multiplecriteria ABC analysis as a base, the suppliers delivering A-products will form the most impor-tant suppliers. Moreover, the variability in replenishment lead time occurs due to the supplier’sproduction approach make to order and the supplier’s inability to correctly assess the productionlead time and hence the replenishment lead time of their customers, like Auctus. For an e-tailer,like Auctus or others, to persuade their suppliers to make to stock instead of make to order –the e-tailer most likely needs to pay a premium. The authors of this thesis have come up with atheoretical value of this premium:

Cost of buffer = σreduced × C × h (6.1)

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Where,

σreduced = The reduced variability in replenishment lead time, because of the buffer.C = The initial acquisition price of the article.h = carrying charge (Swe. lagerhållningsränta translated by Olhager (2004))

The logic behind Equation (6.1) is that it does not matter where the article is located duringthe period of uncertainty. The e-tailer can continue to order a quantity big enough to cover thedemand during the uncertainty in replenishment lead time, and store the extra quantity in thee-tailer inventory. This will yield a cost equal to the acquisition cost times the carrying chargetimes the extra quantity of time corresponding to the uncertainty in replenishment lead time. Putdifferently, the e-tailer is interested in storing goods at the site of the supplier up to a cost equal tothe value of dealing with the uncertainty them self. Hence, Equation (6.1) shows the theoreticalvalue but Equation (6.2) shows the e-tailer’s willingness to pay for the buffer:

E-tailer willingness to pay ≤ σreduced × C × h (6.2)

This willingness neglects the risk of the forwarding company, but this is made with the assumptionthat the forwarding company is an expert in logistics and the average deviation from expecteddelivery, therefore, should not be significant. Furthermore, a forwarding company is easier toreplace if they do not fulfill their customer obligations, than a supplier.

Finally, since the suppliers currently use make to order, they might lack information and knowledgeof what price to charge for storing finished goods. This asymmetric information can be exploitedby the e-tailer when negotiating the premium of storing. By using this immature service, thee-tailer might get out on top and further increase the value proposition to its customers.

A problem with this proposal is that data might exist but information is missing. That is, thee-tailer does not have the infrastructure or the resources to extract the information from the datathat is needed to support a calculation of their willingness to pay. However, during the interviewswith Auctus the authors of this thesis got the feeling that the purchasing department could givea rough estimate of how late some suppliers used to be. This is reflected in the supplier grade.Therefore the suggestion is to, unless quantitative data is available, use these rough estimationsand multiply them with the acquisition cost to find out the e-tailer willingness to pay for abuffer inventory at the site of the supplier. Another simplification to this way of calculating thewillingness to pay for storage at the supplier’s premises is to use the most important product (anA-class article) from a specific supplier and calculate the maximum storage cost for that article.Then every other article from that supplier can be stored to the same cost. This simplifies thee-tailer’s calculation and negotiation process with the supplier, while it will be accurate for themost important articles supplied from that supplier.

This proposed solution will primarily enhance the financial performance of total asset turnover andinventory turnover for an e-tailer while not impair the richness and reach offered to the customers.The overall value proposition will be strengthened.

6.1.4 Determine Safety Stock According to Product Importance andSupplier Ranking

If a contract about creating a buffer inventory at the supplier’s site is not feasible, then the e-tailermight adjust its own safety stock levels instead. When deciding on the safety inventory level, bothuncertainty in demand and uncertainty in replenishment lead time should be considered. Thiscould be interpreted as considering the A-, B-, and C-classes, as well the supplier grading. Thedifferent classes of the multiple criteria ABC analysis approximates the demand for a specific itemand the supplier grade approximates the variability in replenishment lead time. These approxi-mations will, of course, be more of a mental exercise and consist of few calculations. But to knowwhat factors to account for when deciding on the safety level is nonetheless important.

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Currently, the case e-tailer Auctus uses the same safety stock for every supplier. By differentiatingthe safety stock level on the criteria of supplier reliability, the cost of holding inventory can bereduced while not affecting the e-tailers service level and thereby avoiding to lose customers in thelong run.

6.1.5 Match Supplier Reliability and Their Importance in the SupplyChain

An e-tailer should also consider how well the supplier risk matches how much of their assortmentthat that is sourced from the different manufacturers. If the e-tailer relies a lot on a supplier for alarge quantity of articles, that specific supplier becomes more important to the e-tailer’s business.With increasing importance, the supplier grade should optimally increase as well. Following thesame logic, a larger risk is more acceptable for suppliers that are not used as frequently or deliversfew articles to Auctus. It is unfavorable to use many poorly performing suppliers delivering a fewproducts each compared to use less but more reliable suppliers.

This thesis has shown how a few numbers of suppliers deliver a majority of the articles, in linewith the Pareto rule. By using less but more reliable suppliers, primarily for the A- and B-category of the multiple criteria ABC analysis – as these are the most important ones – while theunreliable suppliers of mainly C-articles will be removed. To remove unreliable and unprofitablesuppliers; where reliability is measured as the supplier grade and profitability is illustrated bythe categories of a multiple criteria ABC analysis will not harm the perceived richness from thecustomer point of view. Because, the resources previously tied to the unreliable suppliers andtheir unprofitable articles can now be allocated to introducing new articles on speculation, hopingfor a high customer demand. This action will thus increase the richness of any e-tailers customeroffer. More specifically it will extend the bandwidth by transferring more information, through theonline store, about more demanded products. By continuously conducting this review of suppliersand their products the richness of the information provided by the e-tailer will be up to date andcompetitive.

A result of the action mentioned above will lead to a reduction of the supplier base, which canimprove existing supplier relationships and cooperation. The consequent concentration of man-ufacturers to a smaller amount of vendors can also mean that the company’s in question ordersmake out a larger percentage of the manufacturer’s occupancy, and thus the manufacturer’s profit.The dependability would then grow simultaneously from the vendor’s perspective and the sourcingfirm would possibly increase its bargaining power. With a stronger position in negotiations, morestability in lead times. This positive external effect generates a win-win situation for the e-tailerand their supplier(s).

6.1.6 Explore Possibilities of Extended Drop Shipment

This thesis has a distinct limitation of only considering articles in kept in stock and ignoringthe drop shipment assortment. However, this suggestion is about pushing some articles from theinventory category into the drop shipment assortment. From a multiple criteria ABC analysisC-articles will be identified and the C-articles supplied by reliable suppliers might come intoconsideration for a changed supply chain. It is important that this idea is not applied to unreliablesuppliers, the unreliable suppliers should be treated accordingly to Section 6.1.5. If drop shipmentis applied to unreliable suppliers of C-articles, then the risk will be pushed onto the end customer.This will harm the customer perception of Auctus’ ability to meet the customer requirements orthe customer value.

The important thing of an e-talier is to keep its richness and reach, where reach is connectedmore to the fact that the business is run online than the actual business. Richness, on the other

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hand, relates more to the actual business – by not totally ignoring C-articles or give them minimalattention like the theory suggests, this suggestion is about redesigning their value chain. Whenthe ‘‘reliable’’ C-articles are delivered in a value chain separated from the conventional supplychain that includes the e-tailer inventory, capital can be freed while not violating the richness.The C-class items now in drop shipment will still be available in the online store but it ties nocapital in inventory. Instead, the freed capital can be used to extend the richness by introducingeven more products in the online store, or by creating a greater profit to the e-talier investors ifthey prefer to receive a return right now instead of future growth.

6.1.7 Answering Research Question 2

By presenting the proposed solutions, the second research question has been answered. In the endof each proposal a discussion about its implication on richness reach and financial performancemeasurements. The overall effectiveness of the purchasing process will increase if implementingthese proposed solutions, this is because Auctus will be exposed to less unnecessary risk in theirsupply chain. In the beginning of this thesis an unnecessary risk was defined as a risk for whichthe company did not get paid for by their customers. Having a less stable supply chain is not arisk the customer is willing to pay for. On the other hand, a risk that the customer would like topay for is the risk of Auctus storing goods in their warehouse and deliver them to the customerwhen the customer wants it.

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Chapter 7

Conclusions and Future Work

7.1 Conclusion

The purpose of this thesis has been to explore how an e-tailer selling bulky articles can use amultiple criteria ABC analysis to make its purchasing process more effective by balancing richnessand reach, with the performance measurements of profitability, total asset turnover and inventoryturnover. The answer, and thus the conclusion of this thesis, is that using a multiple ABC analysisthat takes dollar usage, order frequency, and volume into account, will facilitate the identificationof both the articles that have the biggest positive impact on profitability, as well as the ‘‘dead’’articles that no longer generate any income. The classifications can act as a guide to what articlesto prioritize when filling the final space of a standard container. The classifications can also actas a guide to where resources should be allocated to obtain the highest possible impact from therationalization measures. More importantly, the ABC analysis helps avoid spending time andmoney on improving the supply chain for less profitable articles.

The main finding is that – comparing the A-, B-, and C-classes, with the reliability of the suppliershighlights a gap and a possible risk. The proposed solutions all strive to manage and narrow thatgap for an e-tailer with the possibility of unlimited reach as it operates through the Internet,by introducing buffer stock and (or) consolidate the number of suppliers. Put differently, theimportance of a specific article for the business should be reflected in the choice of supplier andthe multiple criteria ABC analysis is the tool to illustrate the importance. By managing thisgap, there is a possibility to increase the inventory turnover, the asset turnover, and the overallprofitability. This shall not lower, but in some cases, instead reallocate the freed resources toincrease the richness of the business offer.

7.1.1 Generalizability of the Results

Since this is a single case study, the generalizability of the results is restricted. However, other e-tailers that find themselves in a similar situation – selling bulky articles and experiencing a varyingdemand and depending on suppliers with unreliable lead times – can benefit from approachingtheir issues in the same way. In other words, this means that the conclusion of this report canbe generalized to other e-tailer businesses than furniture and home furnishing as well as othermarkets than the Swedish market. This is because identifying risks and mitigating them bymaking the right decisions in the supplier selection is not restricted to a specific market or sector.In addition, e-commerce is growing and companies are now offering increasingly different productsto be purchased via the Internet. Before, size might have been looked upon as a barrier and areason for not selling bulky articles online. Beginning with offering easily manageable products,

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customer demand has intensified, and may possibly continue to increase the pressure to drive thedevelopment towards also sell other types of articles. This type of items may require a more detailedand complex way of managing the purchasing process and inventory, similar to the approach ofthis research.

7.1.2 Empirical Contribution

In this thesis, a single case study has been performed. The study contributes to the pool ofempirical data of e-tailers. The case study has also uncovered issues of a growing e-tailer sellingbulky articles. The proposed solutions in Chapter 6 are focusing on making the purchasing processmore effective and thereby making the company more profitable.

7.1.3 Theoretical Contribution

In this master thesis, the use of the multiple criteria ABC analysis is extended by comparing itwith an extra dimension – the reliability of the suppliers. With that comparison, this researchshows how the multiple criteria ABC classes can be used when determining the constellations ofthe supplier relationships and how they should be adapted according to their importance to thee-tailer. Determining the variability in lead times accurately can be difficult when data is notdocumented in a structured and clear way, or when data is only available for a short time period.In those cases, the simple and comprehensible ranking of suppliers, which has been used in thisresearch, can be a good alternative to measuring their reliability. However, the subjectivity mayaffect the reliability of the ranking, why it should be considered to be an approximation of the realvalue. If possible, it would be beneficial to make several purchasers perform individual rankingsand then compare them in order to get more precise scores.

The multiple criteria analysis in combination with the theories about purchasing contributes to amore structured handling of the supplier relationships. This systematic way of dividing the articlesinto groups according to their contribution to profitability and other situational parameters, suchas order frequency and size of the products, makes it a good tool for decision making concerningpurchasing strategies especially when handling a very large assortment.

In this master thesis, the focus has not been directed at increasing an e-tailer’s ability to improvetheir reach, since it is considered to already be fulfilled by operating online. On the other hand,richness – as in the amount of information presented to the customer, can be enhanced by freecapital from less profitable information investments and reallocate the capital to other investmentswith the aim to improve the provided information, that is the assortment.

7.2 Future Work

As discussed previously, this research is based on a single case study making the generalizabilitylimited. If similar research were to be performed again, using several comparable case firms fora comparison and evaluation would improve the ability to draw general conclusions about thestudied problems and solutions. A way to further test the validity of the results would be tochange the order of the criteria in the multiple criteria ABC analysis, and investigate what impactit would have on the classification. In this study the order of the criteria was set by the theoreticalframework and interviews, however, this must not always be the case. Future studies could exploredifferent ways to combine the criteria.

Much theory about inventory control and purchasing exists for manufacturing companies butless for retail companies and even less for e-tailers. The theories for how to manage supplierrelationships are therefore adapted for that type of business, which can be difficult to use without

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modification in a firm that acts as an intermediator in the value chain. Equivalent theories andmodels should, therefore, be developed for e-tailers.

Theory stresses the issue that companies in the majority of cases do not collect data needed for afull-scale use of existing theories in the field of operations management and more specifically in-ventory control. This studied case is no exception. Future research of e-tailers and their ‘‘portfoliomanagement’’ of suppliers should emphasize qualitative data more to cope with that circumstance.Another approach is to use statistics and simulations to generate data without relying too muchon data provided by the case company.

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Appendices

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Appendix A

Number of Articles per Supplier

Table A.1: A complete list of suppliers and the number of items they provide.

Supplier ID Number of supplieditems

Cumulativesum

L000391 562 562L000348 324 886L000584 278 1, 164L000331 180 1, 344L000355 150 1, 494L000614 72 1, 566L000010 71 1, 637L000632 70 1, 707L000680 67 1, 774L000538 56 1, 830L000549 53 1, 883L000393 52 1, 935L000637 50 1, 985L000585 48 2, 033L000620 47 2, 080L000605 41 2, 121L000430 37 2, 158L000551 32 2, 190L000153 31 2, 221L000622 28 2, 249L000454 25 2, 274L000378 20 2, 294L000436 18 2, 312L000689 18 2, 330L000535 16 2, 346L000691 16 2, 362L000550 15 2, 377L000565 14 2, 391L000528 13 2, 404L000530 12 2, 416L000712 12 2, 428L000677 11 2, 439L000464 10 2, 449

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L000697 9 2, 458L000537 8 2, 466L000559 8 2, 474L000634 8 2, 482L000612 6 2, 488L000720 6 2, 494L000381 4 2, 498L000542 4 2, 502L000690 4 2, 506L000633 2 2, 508L000149 1 2, 509L000433 1 2, 510L000641 1 2, 511L000668 1 2, 512L000675 1 2, 513

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