Exploring Collaborative Forecasting in the UK Food Supply Chain

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Exploring Collaborative Forecasting in the UK Food

Supply Chain

Can Eksoz ([email protected]),

Brunel University, UK

Afshin Mansouri


Des Doran


Abstract The study reported in this paper investigates manufacturer-retailer collaborations in the UK Food Supply Chain, and aims to identify prime factors influencing the duration and accuracy of Collaborative Forecasting (CF). A literature survey, semi-structured interviews and group discussions are conducted to develop a conceptual framework, and to generate a number of hypotheses which are planned to be tested by survey statistically. This paper aims to (a) present the methodological approach of the study adopted to develop a new framework as contributions to theory, and to (b) share initial hypotheses generated from literature and a number of interviews. Keywords: UK Food Supply Chain, Collaborative Forecasting Process, Information Sharing Introduction

Existing barriers on the duration of collaboration and accurate forecasting in practice are challenges for Collaborative Forecasting (CF) (Aviv, 2007; Småros, 2007; Vlachos and Bourlakis, 2006; Småros, 2005; Aviv, 2002).

CF as an element of Collaborative Planning, Forecasting and Replenishment (CPFR) is employed by supply chain partners to build joint forecasting, and to improve communication, demand transparency and forecast accuracy in supply chains (Aviv, 2007). CF has demonstrated its capabilities in complex relationships such as reducing inventory losses, operational costs, cycle times, bullwhip effects, and improving overall supply chain performance (Aviv, 2007; Tien-Hsiang Chang et al., 2007; Fliedner, 2006; Aviv, 2002). However, in the food industry, CF cannot be exercised adequately because of the different expectations of partners from collaboration (Xiao et al., 2009; Småros, 2007; Aviv, 2001). For instance, the contrary information sharing (IS) and demand forecasting approaches of partners have been stressed as significant barriers of CF (Aviv, 2007; Småros, 2007; Vlachos and Bourlakis, 2006). Accordingly, CF requires further research in the food industry to reveal its potential in supporting both academia and practice (Xiao et al., 2009; Aviv, 2007; Småros, 2007; Aviv, 2002).

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The aim of this study is to examine manufacturer-retailer collaborations in the UK Food Supply Chain, and to identify key factors that impact CF performance. The literature survey, semi-structured interviews, including both practitioners and researchers, and social media supported group discussions (via Linked-In) are employed to gather primary data and to construct a conceptual framework with associated hypotheses. The research seeks to address the following research questions (RQ): R.Q 1. What initiatives do have an impact on agile IS (AIS) within and between

upstream and downstream supply chain members?

R.Q 2. What factors do influence undistorted IS (UIS) within and between upstream

and downstream supply chain members?

R.Q 3. What principles do have an effect on the choice of joint forecasting methods

(JFM) between upstream and downstream supply chain members?

R.Q 4. What determinants affect group decision making (GDM) between upstream and

downstream supply chain members?

The following section of this paper summarizes CF and Food Supply Chain literature and is followed by the research methodology. This is followed by the development of a preliminary conceptual framework with associated hypotheses. Finally, concluding remarks and future research directions are presented.

Literature Review

The Role of CF in the Food Supply Chain

CPFR and partnerships related academic research conducted in various industries have found both positive and negative features of CF. For instance, the dominant capability of CPFR compared to Vendor Managed Inventory (VMI) has been reported by Sari (2008) in managing uncertain demand in a four-stage supply chain. Furthermore, increased cooperative sales and profits, efficient control of expenditures and customer services are identified as potential benefits of CF (Sari, 2008; Aviv, 2007; Aviv, 2002). However, to implement effective CF, partners primarily need close information-forecast relationships, improved collaboration, joint forecasting and regular data recording (Taylor and Fearne, 2006; Småros, 2002; Mentzer, Min and Zacharia, 2000). Adequate provision of such prerequisites is a challenge in the food industry (Småros, 2007; Vlachos and Bourlakis, 2006).

While the European grocery sector was examined by Småros (2007), the reasons of short-lived and inaccurate CF have been attributed to different views of partners regarding IS and forecasting practices, as well as poor internal operations of manufacturers in terms of IS, forecasting and production procedures. In addition, partners’ different thoughts on the collaboration have been discussed in the Greek Food Supply Chain investigation by Vlachos and Bourlakis (2006) who identified trust, commitment, information exchange, category management and distribution related adverse approaches as major barriers of productive collaboration. Notwithstanding the shared reasons of incompetent CF among food practitioners, researchers have called for more investigation to provide further insights about CF in the food industry by addressing manufacturer-retailer collaborations and partners’ IS, forecasting and production processes (Småros, 2007; Vlachos and Bourlakis, 2006).

On the other hand, Xiao et al., (2009) developed a new framework by incorporating CPFR with Collaborative Transportation Management (CTM) in order to improve the forecast accuracy of seasonal, perishable and dairy products in the manufacturer-farmer collaborations. Despite the positive features of that framework in terms of increased

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efficiency in transportation, communication and purchasing, it cannot be easily exercised in practice because of its complexity (Xiao et al., 2009). Further, Ahumada and Villalobos (2009) studied the roles of production and distribution planning in the agri-food industry, and highlighted that the production and distribution models cannot cater for major requirements of perishable products, including shelf availability and freshness. The main reasons are that these models do not facilitate strong IS relationships, environmental adaptation and risk management (Ahumada and Villalobos, 2009). Accordingly, ongoing problems relating to demand management of relevant products have increased the importance of academic research (Ahumada and Villalobos, 2009). In this direction, further research needs to be conducted both in the food sector and CF to enhance current practices of IS, forecasting and production so that fresh products can be sold to customers (Ahumada and Villalobos, 2009; Xiao et al., 2009).

Manufacturer and Retailer Relationships in the UK Food Supply Chain

The sustainability of collaboration among partners relies on their internal and external harmony (Nakano, 2009). For instance, it has been suggested that partners need to adapt strategic collaborations to maintain their relationships and achieve common benefits in the long run (Mentzer, Min and Zacharia, 2000). In other words, if partners’ structural compatibility and management approaches are coordinated in a common way, they can retain long-term relationships and achieve their strategic objectives. It is known that for efficient CF, partners need to have strong collaboration with each other (Taylor and Fearne, 2006; Småros, 2002). There is evidence accentuating that manufacturers and retailers need commitment, trust, interdependence, sufficient IS and common organizational compatibility for sustainable CF (Chang et al., 2007; Zhou and Benton Jr, 2007; Vlachos and Bourlakis, 2006). However, firms’ cultural differences and poor collaboration in IS, inventory and delivery management diminish their willingness of succeeding long-lived and accurate CF (Taylor and Xiao, 2010; Aviv, 2007; Aviv, 2002). Furthermore, existing CF barriers are more widespread and complicated within the European settled manufacturer-retailer collaborations (Fildes et al., 2009; Syntetos et al., 2009; Aviv, 2007; Småros, 2007; Taylor and Fearne, 2006; Småros, 2002).

Aviv (2007) analyzed CF in a decentralized supply chain to evaluate its impact on partnerships and identified that improved and accelerated IS has a vital role in the efficiency of CF. In addition to this, it is argued that partners need to be satisfied with reliable information which is shared on time and correctly, for trustworthy collaboration and better forecasting (Zhou and Benton Jr, 2007; Aviv, 2001). Småros (2007) highlighted that manufacturers’ inadequate skills with regard to IS have led them to lose information collected from retailers and prevent them from using demand-forecast information correctly.

On the other hand, there is evidence supporting retailers’ performance in the forecasting process, if they have the capabilities of generating accurate forecasts (Taylor and Xiao, 2010). Further, some studies have demonstrated that due to existing customer-forecast data of retailers, their participation in CF increase the accuracy (Aviv, 2007; Aviv, 2002; Aviv, 2001). However, Småros’s (2007) examinations between a retailer and meat producer advocated that joining retailers to CF did not have any impact on forecasting results. Clarifying these contradictory views and raising understanding of the role of retailers in CF can justify further research. Moreover, conflicting views of manufacturers and retailers relating to lead times, the range and quality of products, and inventory-purchasing management are other obstacles of CF

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which prompt the authors to consider the same level of importance for retailers and manufacturers in CF (Småros, 2007; Aviv, 2002; Småros, 2002),

The studies conducted in the European supply chain have stressed the weak forecasting capabilities of retailers as a barrier in accurate forecasting (Småros, 2002). It has been demonstrated that European based partners cannot implement successful forecast collaboration because of the questions they have about their roles, the complexities in information systems and IS, lack of inventory-shelf availability management, and inadequate time spending in CF (Fildes et al., 2009; Taylor and Fearne, 2006; Småros, 2002). Beyond the ongoing challenges in partners’ relationships, Småros (2007) has called for further research underlining the manufacturer-retailer collaborations to examine their internal and external processes including IS, forecasting, production and relevant operations with regard to CF.

Research Methodology

Research Design and Concept

This study adopts positivist research philosophy with critical perspective to demonstrate its results in an objective manner, and to strengthen them with evidence based opinions and interpretations. It is likely that interpreting the results may lead to problems on the objectivity and therefore negatively affect their reliability. To cope with these potential problems and bias, the study is based on the descriptive approach (Wilson, 2010).

With the consideration of descriptive design; first, the study seeks to contribute to knowledge, and to engender future research guiding academics for better understanding of CF in the food industry. Second, the study seeks to reveal applicable insights for practitioners in the decision making procedures of CF. The study follows a systematic approach to gather reliable and sufficient data (Flynn et al., 1990) focusing on CF problems including: Information Sharing (IS); Forecasting Process (FP); Supply Chain Management (SCM); seasonal and/or perishable goods of the UK food sector, and the liabilities of both upstream and downstream members in collaboration.

There is an approach that adopts a positivistic philosophy to design empirical research with the deductive approach based on existing theories and observations (Wilson, 2010). From a different point of view, Flynn et al. (1990) has supported the importance of the inductive approach for theory building by seeking information from practice and opinions to add exploratory insights to both knowledge and practice. By considering these approaches, triangulation is adopted through the data collection process to underpin reliable results (Flynn et al., 1990; Jick, 1979). In this line, the study initially aims to adopt the inductive approach to develop the hypotheses and conceptual framework by conducting the literature research, semi-structured interviews and group discussions. Afterwards, to test the reliability and validity of the framework and associated hypotheses, the deductive approach will be used by employing survey and statistical analysis. Population and Sampling Selection

Most empirical studies concentrate on individual or organizational level analysis (Flynn et al., 1990). For instance, Syntetos et al. (2009) investigated the benefits of Judgmental Forecasting (JF) for intermittent demand in a single Pharmaceutical company in the UK. Furthermore, there are other examples of research that considered specific countries, industries and/or particular partnerships (Taylor and Xiao, 2010; Fildes et al., 2009; Småros, 2007; Vlachos and Bourlakis, 2006). The current study specifically focuses on the manufacturers and retailers in the UK Food Supply Chain having notable hurdles in

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their collaborations and CF practices. In this line, the major product categories of the study consist of perishable, seasonal, meat, poultry, seafood, dairy and relevant short-life products.

With regards to the samples, the study appeals manufacturers which are the Food and Drink Federation members and produce targeted product categories. Further, a single retailer will be incorporated into the study with sufficiently large number of stores in the UK. Regarding the manufacturers, their size, number of employees, annual sales and product range are considered as indicative factors through the sample selection criteria. For the selection of stores from the retailer, probability sampling technique is employed to mitigate potential bias. In this respect, the study specifically focuses on hypermarkets which sell a full range of aforementioned product categories under a single roof. More specifically, it is planned to follow stratified sampling technique because, it is assumed that the stores should be separated into subgroups regarding their location, weather conditions, and the ethnic groups with their income level who live around the stores. It is assumed in this study that these factors play a crucial role on the demand and forecast variation of relevant products. This is why the retailer will be examined at store level which will create an opportunity to eliminate IS, FP related problems in the retailer (See Zotteri, Kalchschmidt and Caniato (2005) as an example). Although this store sampling strategy can effectively support the study in terms of accuracy (Wilson, 2010), it may cause difficulty in obtaining detailed information for the whole retailer. In case of gathering inadequate information, the study follows the large-scale data collection approach which is presented within the following subsection.

Data Collection and Analysis

The literature survey, semi-structured interviews and group discussions, in the professional network of Linked-In, are conducted for data collection in this study. The NVivo software is used to analyze the interview and group discussion results. In a later stage, the survey questionnaire will be used to test the hypotheses using Statistical Package for the Social Sciences (SPSS) Program. It is notable to clarify that the study is still in the data collection process, but the preliminary conceptual framework and hypotheses were generated based on the existing data from literature survey and a number of interviews conducted with both practitioners and academics.

In terms of the data collection methods, there are similar studies that applied alternative methods including survey, simulation, historical data of organizations, case study and observations to obtain data (Flynn, Huo and Zhao, 2010; Fildes et al., 2009; Småros, 2007; Sanders and Manrodt, 2003; Hill and Scudder, 2002). However, semi-structured interviews played an important role in the data collection process of forecasting and food industry related studies. For instance, Davis and Mentzer (2007) and Vlachos and Bourlakis (2006) conducted semi-structured interviews to gather reliable data supporting their survey. By employing semi-structured interviews the study also expects to highlight the interviewees’ individual/intellectual bias and beliefs to strengthen the solidity of findings (Wilson, 2010). For interviews, a number of academics who conducted relevant studies, and the managers of relevant organizations will be approached. These managers will be sampled from departments of supply chain/logistics, marketing/forecasting or production, for manufacturers. The major reason for considering both academics and practitioners as interviewees is to collect multiple views and to add a new dimension to findings with various opinions. Otherwise, potential subjective bias is likely to occur which can be marked as the limitation of the study (Zacharia, Nix and Lusch, 2011). On the other hand, group discussions will be conducted based on the progress of the study in Linked-In as one of

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the most common social media for professional exchange of ideas. The target groups include for instance: Forecasting Net, International Institute of Forecasters and Sales & Operations Planning-CPFR. This approach will enable the study to gain from the experiences and comments of different members of the network, who are from a wide range of countries and industries, but are experts in relevant practices.

The study aims to test interactions among several independent variables, and to investigate their impacts on the dependent variables that represent the long-term and accurate CF. T-test is regarded for the examination of clustered and individual independent variables, which will lead the study to make an objective evaluation of their relevant interactions. Furthermore, the survey includes various multiple choice questions, rank-orders, closed, Likert-scale and categorical questions, which will be distributed among supply chain/logistics, marketing/forecasting and production managers of manufacturers and the store managers of the retailer. It is therefore believed that employing Chi-Square test increases validity of results regarding hypotheses which are specifically relevant to the retail stores and manufacturers (Wilson, 2010). The ANOVA F - test will be used within the analysis process to strengthen the independent variables’ counter interactions and their influence on the dependent variables (Flynn et al., 1990). Throughout the analysis process, it is likely that interaction among some variables can result in unexpected impact on the dependent variables. In this case, Multiple Linear Regression will be used to support the study by analyzing linear relationship of independent variables with the dependent variables (Wilson, 2010). To construct the conceptual framework, Cluster Analysis will be employed to classify the gathered data concerning relevant variables and to strengthen the validity of results (Flynn et al., 1990). Reliability and Validity

To test the reliability of semi-structured interview results, test-retest process will be conducted at times by requesting interviewees’ secondary comments with reorganized interview questions representing the results of first interviews. In this manner, the correlation coefficient will assist the study to compare the respondents’ initial and actual views, which will enable to confirm the reliability of opinions (Flynn et al., 1990). On the other hand, the study will analyze the internal consistency of the survey results, since different scales will be used looking for similar answers from respondents. The Cronbach’s Alpha will be employed through the combination of these scales to ensure that they are dependable to reveal concrete results (Flynn et al., 1990).

Moreover, the Content and Construct validity will be employed to confirm the strength of the survey exploring the relationships of variables effectively. These methods are already popular in such studies which adopt survey in their data collection (Zacharia, Nix and Lusch, 2011; Flynn, Huo and Zhao, 2010; Zhou and Benton Jr, 2007). For Content validity, Delphi method will be conducted by minding the opinions of academics who have expertise in the areas of forecasting and supply chain. In addition, an extensive literature survey will support this process with the modification of questionnaires (Flynn et al., 1990). Construct validity will then be regarded structuring the survey questionnaires via Factor analysis, which will lead to reveal inferable and rational results. Factor Analysis will be performed to further clarify the independent-dependent variables, and to test their interaction.

Preliminary Conceptual Framework and Hypotheses

Through the literature survey, potential factors which have an impact on the long-term forecast collaboration and accurate forecasting were identified. Subsequently, a number

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of small-scale interviews were conducted with both academics and supply chain managers of a UK food manufacturer. The results of these interviews helped to review the reliability of literature sourced findings, and also to reveal further insight with regards to CF performance. In this direction, a preliminary conceptual framework was developed (Figure -1). The framework is then used to define a number of hypotheses.

Figure – 1. The Preliminary Conceptual Framework

The preliminary conceptual framework consists of two prime keystones: collaborative information sharing (CIS) and reconciled forecasting process (RFP). The CIS is devoted to IS activities of partners, and aims to extend the duration of forecast collaboration among partners by improving information transfer within and between firms. This process adopts two prime requirements called agile IS (AIS) and undistorted IS (UIS). AIS seeks to share actual customer-forecast data on time and fluently, and to secure the value of information which has a significant impact on both forecasting and production operations (Aviv, 2007; Småros, 2007). Furthermore, UIS refers to the transfer of knowledge without subtracting and/or adding information which may lead to distortion and harm the forecast accuracy of partners (Taylor and Xiao, 2010). On the other hand, the reconciled forecasting process (RFP) focuses on the isolated forecasting activities of partners, and seeks to improve the accuracy by reconciling their forecasting exercises on the basis of two major practices: joint forecasting methods (JFM) and group decision making (GDM). Because of the overlapped approaches on the selection of forecasting methods by supply chain members (Småros, 2005) JFM enables them to harmonize both statistical and judgmental methods, and to increase the forecast accuracy of short-shelf

Internal Integration

(II)

Agile Information

Sharing (AIS)

Undistorted Information

Sharing (UIS)

Collaborative

Information Sharing (CIS)

Joint Forecasting Methods (JFM)

Group Decision Making (GDM)

Reconciled Forecasting

Process (RFP)

Judgmental Adjustments

(JA)

Forecast Errors (FE)

External Integration (EI)

Production Planning &

Scheduling (PPS)

Long-Term CF Accurate CF

Collaborative Forecasting Performance

(CFP)

H2a-b

H1a-b

H2c-d

H3a H3b-c

H5a-b H5c-d

H4a H4b-c

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life, seasonal and relevant products having volatile demand (Xiao et al., 2009). Finally, GDM is used to estimate demand forecasts via mutual agreement by supply chain members, and to retain the harmony within the collaboration. It is argued that these clustered independent variables have an impact on the long-lived and the accurate CF to improve CF performance (Aviv, 2007; Småros, 2007; Småros, 2005; Aviv, 2002; Småros, 2002; Aviv, 2001). The following hypotheses have been proposed regarding above mentioned variables, namely AIS, UIS, JFM and GDM. Detailed definitions of the hypotheses are presented in Table -1.

H1a-b: II positively influences both AIS and UIS within and between upstream and downstream supply chain members. H2a-b: EI positively influences both AIS and UIS between upstream and downstream supply chain members. H2c: EI positively influences the choice of JFM between upstream and downstream supply chain members. H2d: EI positively influences GDM between upstream and downstream supply chain members. H3a: JA influences UIS between upstream and downstream supply chain members. H3b: JA influences the choice of JFM between upstream and downstream supply chain members. H3c: JA influences GDM between upstream and downstream supply chain members. H4a: FE negatively influences UIS within and between upstream and downstream supply chain members. H4b: FE negatively influences the choice of JFM between upstream and downstream supply chain members. H4c: FE negatively influences GDM between upstream and downstream supply chain members. H5a-b: PPS capabilities positively influence both AIS and UIS within upstream and between upstream and downstream supply chain members. H5c: PPS capabilities positively influence the choice of JFM between upstream and downstream supply chain members. H5d: PPS capabilities positively influence GDM between upstream and downstream supply chain members.

Table – 1. Definitions of the Hypotheses with Relevant References

Hypotheses Definition of the Hypotheses Relevant References

Internal Integration

(II)

The level of response rate, transparency and process improvement through interdepartmental IS and FP for AIS and UIS.

(Flynn, Huo and Zhao, 2010; Nakano, 2009; Småros, 2007; Småros, 2005)

External Integration

(EI)

The degree of interdependence, flexibility, communication and openness to IS and FP among partners for continuing and reliable CIS and RFP.

(Flynn, Huo and Zhao, 2010; Nakano, 2009; Davis and Mentzer, 2007; Taylor and Fearne, 2006; Vlachos and Bourlakis, 2006; Mentzer, Min and Zacharia, 2000)

Judgmental Adjustments

(JA)

The size (small-large) and direction (positive-negative) of adjustments made on raw statistical customer-forecast data for UIS and a single but accurate final demand-forecast in RFP.

(Lyn M., 2011; Önkal, Lawrence and Zeynep Sayim, 2011; Fildes et al., 2009; Syntetos et al., 2009; Småros, 2007; Sanders and Manrodt, 2003)

Forecast Errors (FE)

The level of differences and/or variations between actual demand and demand-forecasts generated in both partners and RFP.

(Thomassey, 2010; Kerkkänen, Korpela and Huiskonen, 2009; Småros, 2007; Småros, 2005)

Production Planning and Scheduling

(PPS)

The state of time based production equilibrium among IS, FP and production capacity of manufacturers for reliable CIS and decisive RFP.

(Thomassey, 2010; Nakano, 2009; Småros, 2007; Zhou and Benton Jr, 2007; Taylor and Fearne, 2006; Småros, 2005; Småros, 2002)

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Conclusions

The study aims to examine the manufacturer-retailer collaborations in the UK Food Supply Chain, and to identify major factors having an impact on the CF Performance in terms of the duration and accuracy of CF. Systematic literature survey, semi-structured interviews and group discussions, via Linked-In, are conducted to design the conceptual framework and generate hypotheses. Survey questionnaires will then be employed to test the hypotheses. The study seeks to contribute to (a) knowledge by providing insights into the dynamisms of CF in the food supply chain, and (b) managerial implications assisting practitioners in taking informed decisions to sustain long-lived collaborations with more accurate forecasts. In this paper, the methodological approach was provided in details that will be followed throughout the research. Further, the preliminary conceptual framework and hypotheses were presented which were generated from literature survey and a number of semi-structured interviews, conducted with academics and supply chain managers of a UK food manufacturer.

The research provides an opportunity to examine CF in supplier-manufacturer collaborations which will add further understanding to CF from the upstream point of view. In addition, this study examines short-life and/or perishable foods which have volatile demand. The role of different product categories, such as long-life and canned foods, is still not well known in manufacturer-retailer collaborations. These categories could be explored by future research which will provide further insights for CF in the food industry.

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