A Literature Review on the Potential of the Rfid to Tackle Inventory Management Issues

8/12/2019 A Literature Review on the Potential of the Rfid to Tackle Inventory Management Issues

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A LITERATURE REVIEW ON THE IMPACT OFINVENTORY DATA RECORD INACCURACIESON INVENTORY MANAGEMENT AND THEPOTENTIAL OF THE RFID TECHNOLOGY TOTACKLE THIS ISSUE

Evren SAHIN, Yves DALLERY

La b o rat o ire Gnie Ind ustriel,

Ec ole Ce ntrale Pa ris

Grand e Voie d es Vigne s, 92295 Chate nay

Ma lab ry, te l : + 00 33 1 41 13 16 07, + 00 33

1 41 13 15 33, e m a il : [email protected],

[email protected]

Abstract:The impact of the use of RFID onsupply chain operations has beenexplored in several investigations. One of

the main benefits of RFID that is identifiedby these studies is its ability to improvevisibility in supply chains by enabling abetter synchronization between thephysical flow of goods and the informationflow representing it. In this work, our focus issteered on data record inaccuracies thatlead to discrepancies between these twoflows in inventory systems (warehouses ordistribution centers); our aim is to presentan overview of investigations that examinethe inventory data inaccuracy issue and

explore the way that this issue can betackled by using an advanced itemidentification and data capture systemsuch as RFID.

Key words: inventory record inaccuracies,RFID, supply chain management

Introduction:

Nowadays, more and more companies

use several systems such as enterpriseresource planning, supply chain executionor warehouse management systems inorder to plan and control their materialflows effectively. Many of these systemshave, however, yielded poor results due tothe unavailability of accurate information.It is therefore critical to the success ofthese companies to have accurateinformation on the identity, status andlocation of entities (items, cases or pallets)during the various transactions that take

place in their supply chains.One of the main questions practitionersface today is how to justify the investments

that enable to improve visibility in supplychains. Nevertheless, there is still littleresearch inthe supply chain managementliterature that deals with this issue.Recently, there has been a renewedinterest in this topic due to the

development of advanced data capturetechnologies such as the RFID technology.To our knowledge, in the literatureexploring the RFID technology, besidesresearch related to the technical aspectsof the technology (hardware, software,languages, etc..), several investigationshave been conducted on how companiescan benefit from the use of RFID as anenabling technology to support moreintelligent automation and control (forinstance, [1] discusses how flow shops can

be adapted by intelligent software agentsmaking distributed decisions meanwhile,[2] highlights the impact RFID technologywill have in materials handling systems)and improved operations in supply chains.In this paper, our attention is focused onstudies that aim to quantify the benefits ofRFID on supply chain management.Among these benefits, there areimprovements such as the reduction inwaiting times, the acceleration of supplychain flows or the elimination of non value

adding material handling activities thatare quantifiable quite easily. There areother types of benefits for which a simplemodeling approach may not be enough,meaning that more sophisticated analysesshould be carried out. Assessing thebenefit that would stem from having moreaccurate information on the identity,quantity and location of products stored inan inventory is an example of suchbenefits. The focus of our paper is on thisspecific point, namely, we are seeking an

answer to the question how can wemeasure the value of item levelinformation provided by this newtechnology in managing inventorysystems?.

The investigations that have beendeveloped in the inventory datainaccuracy literature focus on one or acombination of the four following points:(1) examining the parameters thatinfluence the impact of inaccuracies onproduct availability and other supply chain

performance measures (2) studying theroot causes of inventory inaccuracy and
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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their influence on supply chainperformance (3) determining appropriateinventory counting policies (when toconduct inventory counts, how manyproducts to count) (4) determining how toadjust safety stocks and replenishment

policies in order to adjust for inventoryinaccuracies. Our aim is to provide anoverview of investigations pertaining to thisstream of literature. Our paper is organizedas follows: section 1 begins with presentingexamples of investigations that model thebusiness value expected fromtechnologies that are used in supplychains in general. This is followed by anoverview of the literature that is interestedin quantifying the benefits stemming fromthe use of the bar code technology in

particular. Section 2 proposes a review ofthe quantitative investigations dealing withthe inventory inaccuracy issue: we beginby classifying these investigations and thenprovide a brief description of some modelsdeveloped. Finally Section 3 concludes thepaper.

1 Evaluating the Benefits of UsingTechnologies in Supply Chains:

1.1Assessing the Business Value ofTechnologies Used in Supply Chains inGeneral

As global competition intensifies inresponse to tougher trading conditions,supply chain members from manufacturerto retailer are striving to attain processefficiencies that enable them to drivedown costs and provide competitiveadvantage. [3] states that technologieshave the potential to support theinformation flow and affect many of the

dimensions of supply chain managementsuch as cost, quality, delivery, flexibility andultimately profits of the firm. They supportthe communication and coordination ofthe economic ac tivities between separateunits of an organization and collaborationalong the supply chain by enabling betterinformation processing, sharing [4] andfaster responsiveness by making availableonline, real-time information networkedaround the organization and giving fullsupply chain visibility. Therefore, since 1998,

companies have spent $14.9 billion onsupply chain software; for instance,automotive manufacturers spend roughly

60% of their IT budgets for theimprovement of their supply chainmanagement systems [5].

Supply chain managers, seeking evidencethat technology investment efforts

produce better firm performance, havefaced the following questions: (1) what arethe operational gains accrued by supplychain actors? Does the technology payoff? (2) How can we quantify the benefit?

Investigations developed to quantify thebenefits of deploying a technology areeither case oriented empirica l studies orquantitative models including financialmodels, simulation, stochastic models orheuristics. For instance, some researchershave examined the positive effect of IT/IS

investments on firm productivity ([6]) byproposing a general modeling frameworkor by conducting empirical studies ([7]).Several quantitative methods usingtraditional financial approaches such aspayback period, return on assets, return oninvestment, discounted cash flow methods([8]), return on equity ([9]), net presentvalue (NPV) analysis ([10]) have also beenused for selecting new technologies. Oneof the main problems when trying to applyone of these financial techniques for

assessing a particular investment seems tobe the difficulty in identifying andmeasuring the expected benefits of aproposed technology. Business processmodeling and simulation are mechanismsused by several authors for experimentingwith alternative investments, assessing thebenefits introduced by particular systemand process configurations, and helpingthe management to take decisionsconcerning investments ([11]).

Other authors have formulated the

investment decision as a mathematicalmodel built upon well known operationsmanagement models. For instance, [12]develops a model for the justification ofthe acquisition of a new technology byevaluating its effect on the inventory setupcosts in a lot size reorder point (r,Q) model.[13] proposes a multi-objectivemathematical model for the effectiveacquisition and justification of IT/IS forsupply chain management, the maincontribution of this paper being the

development of a multi-objectivemathematical model that effectively


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incorporates cost, quality, flexibility andtime goals. [9] presents an approach toinvestigate the effects of IT on technicalefficiency in a firms production processthrough a two-stage analytical study witha firm-level data set.

In addition to these investigations, othertypes of methodologies have also beenproposed. For instance, authors such as[14] have applied system dynamics. Thistechnique is relatively easy to apply,accommodates subjective judgment andchange, and leads to improved systemspecifications. However, we argue that it isnot rigorous enough to support low levelanalysis and assessment. Moreover, adetailed modeling is difficult or impossible

given the existing formalism and softwaretools [14].

Finally, [15] addresses the recentdevelopments in the application of fuzzyconcepts to IT/IS justification processeswhile [16] proposes interpretativeapproaches which include exploratorymethods and meta-methodologies.

1.2Assessing the Benefits of the Bar CodeTechnology

The bar code system is among data

capture technologies that have beenwidely used in almost every industry. Byleveraging the barcode technology, thegrocery industry, for instance, was able torealize hard and soft savings of 2.76% and2.89% respectively (as percentages ofrevenue).

Typical goals in implementing a bar codesystem (and more generally AutomaticIdentification and Data Capture systems)are the reduction of data capture errors,the capture of timely data for inventory

control, an enhanced communicationbetween buyers and sellers and theimprovement of customer service ([17]).[18] adds that contemporary AutomaticIdentification and Data Capture systemsmust provide discipline and control that isbased not only upon plans andperformance goals, but also upon thedynamics of actual operations: thesesystems are the major source of real-timefeedback, they allow businesses to monitoroperations, manage resources, and flag

anomalies before they impact throughput.

bar codes originated in the food sector asthe cornerstone technology to automatethe check out process and in that way,reduce labor costs as well as cash registererrors. It was not until the 1983-1987 periodthat bar code usage spread to sectors

served by mass retailers like Kmart andWall Mart.

Several investigations have beenconducted on how supply chains canbenefit from bar code applications. Thisincludes qualitative research that explainsthe concept of the bar code technologyand develops conceptual methods in aneffort to better understand it. Most of time,results developed are validated within thecontext of case studies with enterprises. An

example of such a study would include[19], which, based on ten case studiescarried out in distribution andmanufacturing companies, show how thebar code system can enhance inventorymanagement performance. In all cases,the system, mostly used for work in processtracking and shipment control (whichensures that the right product is shipped tothe right customer) has enhanced theperformance of the company, butquantitative results are not available.Among results achieved are less capitaltied up in inventory, enhanced inventorycontrol, enhanced customer service andempowered employees. [20] develops aconceptual framework for the integrationof a bar code system in inventory andmarketing information systems using thebarcode technology as an enabler foreffective supply chain management.Problems, benefits and solutions regardingthe integration of the bar codetechnology are examined in the context ofa case study. [21] shows how batchprocessing is currently a major drawbackin industrial applications and emphasizesthe role of Radio Frequency systemsintegrated to technologies of automaticidentification, bar-coding, and automaticdata capture in increased inventoryaccuracy and timeliness of real-time datapertaining to internal and external logisticstransactions. Main results obtained in avery large distribution center are increasedproductivity, higher accuracy of theamount and pallet location, reduced cost

of material handling.


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More sophisticated investigations on theimpact of the bar code technology havebeen realized with a specific interest onthe inventory inaccuracy issue. Forinstance, [22] discusses on mechanismsused in bar code equipped facilities to

track and correct errors creatingmismatches in inventory records. Heargues that these activities should berealized in order to investigate and correctthe cause of the error rather than simplycorrecting a number in the system.

2 The Inventory Data Inaccuracy Issuein Supply Chains:

2.1.Description of the ProblemThe quality of information pertaining to thecharacteristics of products is one of thebiggest problems currently faced bysupply chain management. For example,according to [23], an average of 30% ofinformation in retailer systems is incorrect,and other studies show that as much as63% of product descriptions can diverge insupplier and wholesaler systems. In a studyconducted for the Grocery Manufacturersof America, A.T. Kearney Inc. estimates

that retailers and manufacturers each lose$2 million for every $1 billion in sales due tobad data. They predict that eliminatingbad data could save $10 billion per year[24].

The literature in the field of inventorymanagement is vast. However, thecommon assumption underlying mostresearch is that data gathered fromphysical transactions are accurate so thatthe physical flow is perfectly aligned withdata stored in inventory information

systems. Based on recent empiricalobservations, this implicit assumption hasproven to be wrong. In fact, based on astudy done with a leading retailer, [25]report that out of close to 370,000 SKUsinvestigated, more than 65% of theinventory records did not match thephysical inventory at the store-SKU level.Moreover, 20% of the inventory recordsdiffered from the physical stock by six ormore items.

In prac tice, inventory inaccurac ies can beintroduced by several factors generating a

misalignment between the physical flowand the assoc iated information flow:

Theft: this factor affects the physicalinventory level and let the InformationSystem inventory records unchanged.

Misplacement type errors: this type oferrors affects temporally the physicalavailable for sales inventory and let the ISinventory unchanged.

Transac tion type errors: transaction errorsaffect the IS inventory and let the physicalinventory unchanged.

Supplier unreliability (i.e. underages/overages in products shipped bysupplier): those errors may affect both theIS and the physical inventory if no control is

performed when receiving the order.

Among existing works that investigate theimpact of such inventory inaccuracies, [26]focuses on how inaccurate work in processcounts can distort the effectiveness of anMRP system. He explains reasons why tomaintain accurate work in processinventory system records and develops aframework to describe the differentsources of inaccuracies: errors usually stemfrom system structure problems, systemdiscipline problems, process variabilityproblems, measurement problems andquality related problems.

Among the other examples illustrating themagnitude of the problem is [27] thatreports that the Naval Supply Depot usingthe Master Stock Record history of asample of 714 items from the 20 000 lineitem types stocked there, found that 25%of the item types had accumulateddiscrepancies that exceeded 24 units afterone year. It also found the distribution of

accumulated errors to be closelyapproximated by a normal distribution.

As presented earlier, the study of [25] alsoreveals that a large retailer with annualsales of roughly $ 11 billion from more than1500 stores worldwide estimates a profitloss of $ 32 million annually due to itsinventory record inaccuracy problem.According to a store level analysis, 65% ofnearly 370 000 inventory records from 37stores of a large retail chain areinaccurate at the time of the physical

inventory audit. Moreover, for 15% of theserecords, the absolute difference between


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system inventory and actual inventoryquantity per SKU was eight or greater. Thisstudy identifies the magnitude and driversof poor inventory records and provides anempirical study of inaccurate inventoryrecords in a retail store at both record and

store levels. It is reported that theprobability of an inventory record beingaccurate can be predicted by productscharacteristics such as item cost orwhether the item is risky or not, annual unitssold, the entity delivering this item ormaterial flow characteristics such as thecomplexity of pallets received / to beprepared (multiple product pallets orhomogeneous pallets), etc..

[28] develops a similar analysis: he finds

that 36% of 200 000 inventory recordssampled from several distribution centersof an organization with a large logisticaloperation were inaccurate.

[29] suggests that differences betweenaccurate and inaccurate inventoryrecords are associated with item cost, useof weight counting for inventory trackingand the number of different storagelocations for a particular item. Accordingto their analysis, low value, high volumeitems are most likely to be in error.

2.2.Impact of inaccuracies on InventoryManagement

The existing investigations in inventorymanagement that deal with inventoryinaccuracies can be classified based onseveral criterions:

(1) the ob jec t ive of the invest iga t ion:i.e. some investigations try to evaluate theimpact of inventory inaccuracies through

empirical studies (or through simulationanalysis) while other investigations try tooptimize the replenishment policy beingused in the inventory system which issubject to inaccuracy problems.

(2) the structure of the supply chainconsidered (warehouse/reta i l store or

cent ra l ised /decent ra l ised supp ly cha in):from an inventory control point of view, thedifference between the two contextsstems from the fact that (i) in a retail store,the customer demand is confronted to the

physical available for sales inventory sincethe customer is physically present in the

retail store (ii) in a warehouse context,customer demand is satisfied based on theInformation System inventory where acommitment is done based on the levelobserved in the IS inventory in the first step.Then, this commitment is confronted to the

physical inventory when delivering theorder. As a consequence, in a warehousecontext, there exists an additionalunderage penalty that does not occur in aretail store.

The second point associated with thiscriterion concerns the number of actorsconsidered in the supply chain. In acentralized supply chain, a uniquedecision maker is concerned withmaximizing the entire supply chains profitwhereas, in a decentralized supply chain

two or more actors act as different partiesand each one tries to maximize his ownprofit.

(3) the na ture of e rrors c a using inve nto ryinaccurac ies :

without any anomalies in the expectedphysical materials flow, the amount ofgoods available in an inventory systemwould increase each time an expectedreplenishment is received and decreaseeach time a demand is satisfied. These

movements of stock can be qualified asknown inputs and known outputs. But,because of the existence of severalunknown outputs and inputs (such as theft,misplaced items or supplier unreliabilities),the real physical flow differs from thenominal one. A perfect synchronizationbetween the physical flow and theassociated data which is recorded ininformation system enables to verify ifevents happened as planned (without anyproduct losses, overages or delays) and to

identify the reasons of deviations. Thissynchronization is possible only if alltransactions (i.e. known transactions aswell as unknown transactions) aredetected by the information system. If thisis not respected, the information systeminventory diverges from the physicalinventory level leading to inaccuracies.

The existing investigations that address theinventory inaccuracy issue are models thatcombine the criterions presented above.While the complete analysis of this

literature can be found in [30] and [31],the remaining part of the paper gives a


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brief description of some of theinvestigations developed so far.

2.2.1. Investigations evaluating perfor-mance in presence of inventoryinaccuracies

Inventory management deals withinventory planning and control. Aninventory control policy is charac terized bythe answers it provides to the two basicquestions: (i) when to order? (ii) how manyproducts to order? As explained earlier,some of papers are interested inevaluating the effect of inventoryinaccuracies for a predefined inventorypolicy rather than optimizing theparameters of this policy:

In presence of inventory miscounts, when

the lead time is variable, the realizedservice level, measuring parts availabilityfrom shelf, is much lower than theprescribed service level. [32] developsquantitative measures for the evaluation ofthe degradation in service level in acontinuous review (r,Q) inventory policy asa function of the level of inventorymiscount and the lead time variability.

[27] also evaluates the poorer servicelevel resulting from inaccuracies and

performs a sensitivity analysis of thismeasure to several actions (faced with theinaccuracy issue, managers may act indifferent ways; i.e. increase the safetystock; increase the frequency of inventorycounts or initiate efforts to pinpoint thesources of the errors and reduce them)that improves it.

[33] focuses on the individual impacts ofdifferent types of inaccuracies (e.g. theft,unsaleables, poor process quality) on theperformances of a supply chain (monetary

or non-monetary measures). They usesimulation and variance analysis (Anova)as research method. They consider twosettings; in the base case, they set up asupply chain where information on endconsumer demand is available to allechelons in real time, and the physicalflow is perturbed by several factors. Thenthey modify the model so that physicalinventory and information system recordsare aligned in each time period andcompare the two models. In the base

model, inventory information becomesinaccurate due to low process quality,theft, and items becoming unsaleable. In

the modified model, these factors thatcause inventory inaccuracy are stillpresent, but physical inventory andinformation system inventory are alignedat the end of each period. They found thatelimination of inventory inaccuracy can

reduce supply chain costs as well as out ofstock level.

2.2.2. Investigations optimising inventorypolicies in presence of inventoryinaccuracies

[34] proposes a mathematical modelwhich determines an optimal frequencyfor periodical inventory taking processeswithin a supermarket. Deviations ininventory records tend to decrease withincreasing frequency of a periodical

inventory taking process and thus the totalloss incurred also decreases as thefrequency of a periodical inventory takingbecomes large. However, when theinventory taking frequency increases, thetotal cost for periodical inventory takingactivities increases. The expected cost perunit time is formulated, considering thecost for inventory taking activities and theinvestigation cost for the causes ofdeviations.

The investigation of [30] provides acomprehensive analysis of potential errorsthat may occur within an inventory systemwith a special focus on reasons whymismatches occur between the physicalflow and the information flow representingit. She builds a general Newsvendorframework to model the impact of errors inorder to quantify the cost penalty theygenerate and evaluates if theimplementation of an advanced datacapture technology such as RFID is costjustified. In particular, in [36], the authors

derive analytically the optimal inventorypolicy in presence of errors, when bothdemand and errors are uniformlydistributed. Results demonstrate that theexpression of the optimal quantity to orderis not simple and for reasonable errorparameter values, can deviate from beinglinear.

[35] and [37] are among investigationswhere inventory inaccuracies are mainlyintroduced by transaction type errors. To

our knowledge, [35] is the first paper thatdiscusses the inventory inaccuracyproblem in a quantitative manner, the


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objective being to select the type andfrequency of counts and to modify thepredetermined stocking policy by addinga buffer to cover for errors so as tominimize the total cost (inventory holding +inventory counting cost) per unit time

subject to the probability of errors notdepleting this buffer between inventorycounts does not exceed a prescribedlevel. Their setting is a single-item, periodic-review inventory system with a predefinedstationary stocking policy. In other words,they do not consider establishing anoptimal replenishment policy. Instead theytake the control policy such as (s, S) asgiven.

[37] study the inventory replenishment

problem with a counting policy of aninventory system subject to transactionerrors. As in [35], the authors assume thattransaction error random variables areadditive and are identically andindependently distributed with zero mean.In particular, they consider a periodic-review, stationary inventory system inwhich transaction errors accumulate untilan inventory count. The manager incurs alinear ordering, holding and backordercost and a fixed cost per count. Theobjective is to decide whether to count ornot and how much to order to minimizethe total cost of ordering and counting.

[38] is among the rare studies that providean optimization procedure for an inventorysystem subject to misplacement, shrinkageand transaction errors. The investigationconsiders a finite horizon, periodic-reviewinventory control problem in whichinventory records are inaccurate. In orderto model the inventory inaccuracy issue,the total demand is grouped under four

streams: paying customer, misplacement,shrinkage and transaction error.Misplacement, shrinkage and transactionerrors are undetected betweenconsecutive inventory audits without atechnology such as RFID. These error termsaccumulate until a physical inventorycount takes place. The manager performsa physical counting of inventory. After theinventory audit, misplaced items arereturned to inventory, accumulated errorterms are set to zero, and the on-hand

inventory record is set equal to actual on-hand inventory. The authors establish aninventory control policy when the

manager observes the actual inventorymovement. They establish an inventorycontrol policy that can be used when thesystem is RFID enabled. Second, to assessthe value of prevention in addition tovisibility provided by RFID, they model the

special case in which the misplaced itemsare returned to stock at the end of eachperiod. Next, they provide a model and apolicy that does not use RFID technologybut can partially compensate for theinventory discrepancy problem. Theyfurther model the imperfect visibility casein the presence of errors due to RFIDreadings. At the end of their paper, theyconduct a numerical study and comparethe models with and without RFID andquantify the value of RFID.

[39] use analytical and simulation basedmodeling to demonstrate how theft typeerrors increase lost sales and result in anindirect cost of losing customers (due tounexpected out of stock) in addition to thedirect cost of losing inventory. Theyconsider a (r,Q) inventory policy where astore replenishment is perturbed by arandom theft. Even when the theft rate isas small as 1% of the average demand,the error accumulating in the inventoryrecord is large enough to disturb thereplenishment process and make 17% ofthe total demand lost. They also observe acontinual rise in the gap between the ISinventory and the physical inventory andshow that the system reaches a pointwhere the inventory record stays abovethe reorder point r and the consequence isthat no order is placed. The authors referto this situation as the replenishmentfreeze. In the second part of their paper,the authors propose several compensationmethods in order to reduce the effect ofthe inventory inaccuracy issue, amongwhich RFID.

2.2.3. Investigations introducing RFID as alever to improve the accuracy of inventoryrecords

Among investigations described before,some of them introduce explicitly the useof the RFID as a lever to improve accuracy(see for instance [30], [38], [39], etc.). Inthese studies, RFID is presented as atechnology which either reduces or

prevents errors. The cost pertaining to theimplementation of RFID is often introduced


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as being a variable cost associated withtagging each individual unit of products.

Among these investigations that considerthe use of RFID, some of them analyzespecifically how in a decentralized supply

chain structure with two actors, costs thatstem from implementing RFID should bedistributed among supply chain partners.In practice, it is often argued that whilemanufacturers are generally mostinterested in tracking cases or pallets ofproducts, retailers are expected to gainthe most benefit from individual producttracking on their shelves. Hence, [40], [41],[42] and [43] are among studies that havebeen developed in order to proposecontract mechanisms which ensure that

net profits are achieved by all supplychain members once RFID is implementedin decentralised two-level supply chainsconsisting of a supplier and a retailer.

3. Conclusion:In this paper, we presented an overview ofinvestigations that have been conductedon how companies can benefit from theuse of RFID as a lever to improve theaccuracy of inventory data records. Suchstudies enable to asses one part of gainsachieved by deploying RFID but are notrepresentative of the overall benefits since,as reported in [44], the implementation ofthis technology can also lead to othertypes of benefits such as the reduction ofinformation asymmetries and the incentiveproblems arising between a downstreamand an upstream party; the acquisition ofadvanced knowledge about actual leadtimes experienced in the supply chain; theincrease in supply system security, etc.

We think that quantitative models that

consider the effect of RFID on supportingreverse logistics or on planning supplychain activities are of special interest.While the need for further investigationconcerning the first point is accelerated bythe recent governmental regulations thatimply the recycling of most of consumergoods such as electronics orpharmaceuticals, the second point refersto the ability of RFID to support theimplementation of a decentralisedapproach when planning supply chain

activities.

References:

1-McFarlane, D.; Auto - ID Based Co ntrol: An O verview;Technical Report; Auto ID Center; 2002 May.

2-Garca, A., McFarlane, D., Fletcher M. and ThorneA.; Auto -ID in Ma terials Hand ling; Tec hnica l Report;

Auto ID Center; 2003 May.3-Brandyberry, A., Rai, A., and White, G .P.;I n t e rmed ia t e pe r f o rmance impac t s o f advanced

manufactur ing systems: an empir ical invest igat ion;Dec isions Sciences; 1999; Vol. 30.

4-Shin, N.; Does in format ion techno logy improvecoordinat ion? An empir ical analysis; LogisticsInformation Mana gement; 1999; Vol. 12(1/2).

5-Alexander, K., Gilliam, T., Gramling, K., Kindy, M.,Moogimane, D., Schultz, M. and Woods M.; Focus onthe Supply Chain: Applying Auto-ID within theDistribution Center; Tec hnica l Report; Auto ID Center;2002.

6-Brynjolfsson, E. and Hitt, L.; Pa rad ox lost? Firm levelevidence on the returns to informat ion systems

spending; Management Science; 1996 April: p. 541-58.

7-Ross, A.; A mul t i-d imensiona l emp iric al exp lorat ionof techno logy inves tment , coord ina t ion and f i rm

pe r f o rmance; Internationa l Journal of PhysicalDistribution & Logistics Management; 2002; Vol. 32(7):p. 591-609.

8-Turner, J .; Organizational performance, size and theuse of da ta processing resources; Center forResearch in Information Systems, New York University;1985.

9-Shao, B.M. and Lin, W.; Technica l efficiency ana lysisof information technology investments: a two-stageempirical investigation; Information & Management;2002; Vol. 39: p. 391-401.

10-Rouwenhorst, B., Reuter, B., Stockrahm, V., VanHoutum, G .J ., Mantel R.J . and Zijm, W.H.M.;Warehouse design and control: Framework andliterature review; European J ournal of OperationalResearch; 2000; Vol. 122: p. 515-533.

11-MacArthur, P.J ., Crosslin, R.L., and Warren, J .R.; Astrategy for evaluating alternative information systemdesigns for business proc ess reengineering;International J ournal of Information Management;1994; Vol. 14: p. 237-51.

12-Keller, G. and Hamid, N.; Justifying NewTechnology Ac quisition Through its Impact on theCost of Running an Inventory Policy; IIE Transac tions;1998; Vol. 20(3).

13-Talluri, S.; An IT/IS ac quisition and justificationmodel for supply-chain management; InternationalJ ournal of Physical Distribution & LogisticsManagement; 2000; Vol. 30(3/4).

14-Wolstenholme, E., Henderson, S., and Gavine, A.;The Evaluation of Management Information Systems:A Dynamic and Holistic Approach; 1993; Wiley.

15-Sharif, A.M. and Irani, Z.; Research note:theoretica l optimization of IT/IS investments; Logistics

Information Management; 1999; Vol. 12(1/2): p. 189-96.


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A Literature Review on the Potential of the Rfid to Tackle Inventory Management Issues