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Long-term and Accurate Collaborative Forecasting Between Supply Chain Members:

An Empirical Study in the UK Food Sector

Can Eksoz and Afshin Mansouri

Brunel Business School, Brunel University, Uxbridge, Middlesex, UB8 3PH, UK

The 32nd Annual International Symposium on Forecasting

24-27 June 2012 - Boston, USA

Abstract:

Accurately forecasting the fluctuated demand in the volatile food market is a joint

responsibility for supply chain members. Collaborative Forecasting (CF) as a common

practice has been used by these members for better predictions of demand. However, their

conflicts of interest on information sharing and demand forecasting prohibit them to establish

long-term relationships and to generate accurate forecasts in CF. This study examines the

collaborations by upstream-downstream members in the UK food supply chain, and aims to

explore tangible parameters having an impact on the long-term and accurate CF. The paper

presents the results of detailed literature research, a conceptual framework and initial

propositions underpinned by primary data collected through an interview with a supply chain

manager. Initial results indicate that partners’ integration, judgmental adjustments and

planning and scheduling skills are the leading determinants that improve CF performance by

influencing its’ duration and accuracy.

Keywords: UK, Food Supply Chain; CPFR; Collaborative Forecasting; Information Sharing;

Integration, Judgmental Adjustments; Forecast Errors; Production Planning and Scheduling

1. Introduction

Collaborative Planning, Forecasting and Replenishment (CPFR) is one of the efficient

practices that underpins the process management and information sharing (IS) activities

among partners for better demand visibility in supply chains (Siefert, 2003). The history of

CPFR dates back to 1996s, when Wal-Mart and Warner-Lambert in the USA incorporated

three sub-stages of planning, forecasting and replenishment in a joint project (Ireland and

Crum, 2005). Several reasons were mentioned for the development of CPFR such as reducing

inventory, comparing sales and forecasts, taking on time decisions, and beyond these

providing homogeneity among supply chain members (Ireland and Crum, 2005).

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A number of studies were conducted on CPFR and its’ components in various industries

(Sari, 2008; Aviv, 2007; Danese, 2007; Småros, 2007; Småros, 2005; Aviv, 2002; Småros,

2002; Aviv, 2001). Despite notable advantages, there are still remarkable hurdles obstructing

both upstream and downstream supply chain members to fully benefit from CPFR in the

industries with intermittent demand and uncontrollable events, such as food industry

(Ahumada and Villalobos, 2009; Xiao et al., 2009; Aviv, 2007; Småros, 2007; Vlachos and

Bourlakis, 2006; Småros, 2005).

In the food sector, there are short-shelf life goods that require substantial care to manage their

forecasting, production, distribution, inventory and shelf availability (Ahumada and

Villalobos, 2009; Xiao et al., 2009). Through the combination of CPFR and Collaborative

Transportation Management (CTM) a new framework was developed by Xiao et al., (2009)

with the aim of improving the forecasts of perishable products. The developed framework has

improved the transportation process, communication and reduced the inventory level of

partners. However, the complexity of this framework hinders its application in practice to

predict demand of short-life span products accurately (Xiao et al., 2009). In addition, lack of

decision making procedures, unsteady partnerships, inadequate product visibility and

information sharing (IS) among partners are the issues that need to be addressed so that the

demands could be predicted accurately (Ahumada and Villalobos, 2009; Xiao et al., 2009).

In another study, Aviv (2002) examined aspects of Vendor Managed Inventory (VMI) and

CF in the forecasting exercise of retailers and suppliers. Notwithstanding the benefits of CF

on the cost and customer service management, the author argued that practitioners cannot

maintain CF in the long haul. Generally speaking, close communication, technology,

common willingness, trust and commitment, short lead times, joint business planning and

continuous process improvement between partners have been identified as major

prerequisites of successful CF (Chang et al., 2007; Taylor and Fearne, 2006; Aviv, 2002;

Aviv, 2001; Mentzer, Min and Zacharia, 2000). However, it is not clear how firms can

sustain a long-lived collaboration by overcoming with volatile demands and environmental

ambiguities in the food supply chain (Xiao et al., 2009; Småros, 2007; Vlachos and

Bourlakis, 2006).

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Småros’s (2007) study of the European grocery sector identified the partners’ ongoing

conflicts in the IS and forecasting process (FP) as one of the primary reasons for short-lived

and inaccurate CF. In addition to this, poor integration and IS within and between partners

have been emerged as substantial obstacles that prompt for further empirical study to clarify

their influence on the duration and/or accuracy of CF (Småros, 2007). Manufacturers’ IS

skills and the role of partners in close relationships are also other issues that awaiting answer

from academics for better CF (Aviv, 2007; Zhou and Benton Jr, 2007; Fliedner, 2006;

Småros, 2002).

This study focuses on the Collaborative Forecasting (CF) practice in the UK Food Sector. In

this way, upstream and downstream supply chain members’ internal and external

collaboration processes are analyzed to explore important factors that influence performance

of CF. To extend the CF period and to improve the forecast accuracy among partners are

prime performance criteria of CF for the study. This paper aims to contribute to the literature

providing initial propositions derived from the literature research and examined through an

interview. The rest of the paper is organized as follows. The following section provides the

results of literature review and put forward research questions and propositions. Next, a

conceptual framework is developed based on these propositions. The proposed research

methodology is then presented with details of the approach to generate hypotheses and test

them. Afterwards, expected managerial implications are discussed. Finally, the conclusion

section summarizes the paper.

2. Literature Review

2.1. Research Questions

With the intention of improving CF performance, the study specifically adopts IS and FP

conflicts of upstream–downstream supply chain members as prime domains. This section

aims to (i) elaborate on the potential ways for mitigating these conflicts, and to (ii) present

research questions on how to contribute to both long-lived and accurate CF.

2.1.1. Collaborative Information Sharing (CIS)

According to the basics of CPFR, achieving effective CF relies on its preceding stage of joint

business planning which aims to construct a front-end agreement among participants (Ireland

and Crum, 2005). However, CF oriented notable studies support the vital impact of IS on the

long-lived forecast collaboration, as long as partners are satisfied with the timing, frequency

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and quality levels of IS (Sari, 2008; Aviv, 2007; 2001). The reason is that maintaining strong

IS facilitates more accurate predictions via clear demand visibility (Fliedner, 2006).

However, achieving mutual satisfaction between partners is not easy. It requires intensive

coordination and mutual understanding about prime expectations from the shared data (Sari,

2008; Aviv, 2002). For that reason, our argument is that both upstreams and downstreams

should initially join forces on their IS process, which is called collaborative information

sharing (CIS), beyond the development of a mutual business plan to satisfy themselves with

time, quality and frequency based IS.

Apparently, firms face difficulties to predict the demands of agricultural foods accurately.

Taylor and Fearne (2006), for instance, developed a framework to manage the variability in

consumer demand occurring due to seasonality and unpredictable events, such as weather

changes in the UK supply chain. Their studies gave significant importance to the information

transfer of partners because of the multidimensionality in the demand management. In

addition, the authors stressed the positive influence of present data, the final form of data, for

accurate forecasts, while it is shared among partners fluently. However, due to complex

information systems and undiscovered challenges it cannot be transferred within and among

partners on time (Taylor and Fearne, 2006). On the other hand, one of the CF problems that

cause different opinions on the value of information has been linked to the manufacturers’

internal IS process (Småros, 2007). There are also further evidence of the positive effect of

on time IS on CF (Aviv, 2001), and regarding manufacturers’ slow response to retailers as a

drawback of CF (Aviv, 2007). Beyond these evidences, further research is called on IS to

explore this process within and between partners (Taylor and Fearne, 2006). Accordingly,

existing evidences orientate the study to assume that for successful CIS, firms initially should

retain agile IS (AIS), delivering the present data promptly and fluently, within themselves

and towards their partners. Thus, the study put forth the following question:

Q.1. What initiatives do have an impact on AIS within and between upstream and

downstream supply chain members?

The quality of information shared among partners change based on the strength of their

collaboration. However, staying in long-lived collaboration can only be achieved in strategic

partnerships which require both operational and strategic IS (Mentzer, Min and Zacharia,

2000). Some studies also support that to share assortment, price changes and promotions

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increase the accuracy and reduce the time spent in FP, which motivates partners to stay in

collaboration in a long run (Småros, 2002). In practice, firms have ongoing challenges in

choosing exact required information, managing their quality and settling them in FP (Zotteri,

Kalchschmidt and Caniato, 2005). For that reason, relevant papers have suggested that the

customer-forecast information and planning basis sources should be open to access among

partners for better supply chain transparency and profit as well (Taylor and Xiao, 2010; Zhou

and Benton Jr, 2007; Småros, 2002). However, partners’ attitudes or poor IS skills (lack of

trust, opportunistic behaviours, extra-enthusiasm or late responses) alter the content of

information by adding or subtracting illusive knowledge, which weaken the quality and

usability of shared data and lead to the loss of its value, which is called distorted IS (Taylor

and Xiao, 2010; Taylor and Fearne, 2006). It can therefore be argued that partners should

consolidate their CIS with undistorted IS (UIS) in order to be able to obtain exact required

actual raw data within a common fulfilment for longer forecast collaboration. In doing so, the

study aims to explore:

Q.2. What fundamentals do influence UIS within and between upstream and downstream

supply chain members?

In summary, existing evidences suggest that the primary step in improving CF performance is

to improve the collaborative information sharing (CIS) (Aviv, 2007; Småros, 2007; Taylor

and Fearne, 2006; Taylor, 2006). Since, delivering customer forecast data on time, AIS, by

securing its present value, UIS, increase the satisfaction and commitment among partners

which lead them to retain long-term forecast collaborations. This is why the first two

questions consider AIS and UIS.

2.1.2. Reconciled Forecasting Process (RFP)

Småros (2007) investigated different forecasting approaches of partners, and called for

further study concerning partners’ FP. From a different point of view, Nakano (2009)

interestingly argued that manufacturer-supplier relationships outperform manufacturer-

retailer relationships. However, it was attributed to the buyer position of manufacturers in

relationships with suppliers which enables them to receive further information from suppliers

as customers (Nakano, 2009). On the other hand, the roles of retailers in forecast

collaborations were questioned by many studies which revealed overlapped views. Aviv

(2007; 2002; 2001), Sari (2008) and Taylor and Xiao (2010) have emphasized the retailers’

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positive impact on the forecast results because of their closeness to the market. However, as a

counter view the poor FP of retailers, their capabilities and having different outlooks on

forecasting methods and decision making have been stressed as prime barriers to accurate

predictions by Småros (2007; 2002). In this way, our argument is that in order to seek best

possible accuracy partners need to strengthen and incorporate their FP, which is called

reconciled forecasting process (RFP), on the basis of applying effective forecasting methods

and then taking collective decisions (Nakano, 2009; Småros, 2007; 2005).

Regarding the forecasting methods employed in collaborations, a number of studies were

conducted to explore harmonization of some specific methods to come up with better

estimates, especially for irregular or fluctuated demands (Syntetos, Nikolopoulos and Boylan,

2010; Fildes et al., 2009; Syntetos et al., 2009). For instance, Synetos et al., (2009)

questioned the impact of judgmental adjustments (JA), while they are applied on statistical

results for intermittent demand. Their results have shown that JA significantly increase the

accuracy; however, the level of adjustments has a crucial role on the forecast results. Besides,

Fildes et al., (2009) has also revealed that implementing negative (deflated) and large (wide

range) adjustments on the statistical predictions positively increase the accuracy. However,

there are some mysteries with regard to existing data, organizational structure and

participants’ role on the results, while adjustments are made on the statistical results at the

organization level (Önkal, Lawrence and Zeynep Sayim, 2011; Sanders and Manrodt, 2003).

In this direction, we assume it is good opportunity to strengthen the RFP by applying joint

forecasting methods (JFM), including statistical and judgmental methods, which are likely to

satisfy partners in the method preferences (Taylor and Fearne, 2006), and to support the

accuracy by coping with irregular demands in the UK food sector. Therefore, it is questioned

that:

Q.3. What principles do have an effect on the choices of JFM between upstream and

downstream supply chain members?

In terms of the decisions taken in the FP by partners, it is argued that both upstreams and

downstreams face notable hurdles to predict the demand of seasonal, short-life and perishable

foods because of their isolated decision making processes (Xiao et al., 2009; Zotteri,

Kalchschmidt and Caniato, 2005). Chen and Boylan (2008) implemented an empirical study

by comparing group and shrinkage forecast approaches to verify their effects on the short-

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term predictions. The results demonstrated the group based approaches’ dominant role

compared to the shrinkage methods on the accurate forecast decisions. Notwithstanding the

group based methods’ important role on the demand predictions, their influence on forecast

results is likely to vary based on the forms of these methods (Chen and Boylan, 2008). For

that reason, further study needs to clarify their impact on the seasonal forecasts (Chen and

Boylan, 2008). On the other hand, Önkal, Lawrence & Sayim (2011) examined the consensus

group (who employ statistical forecast data) and staticized group (who employ the average of

participant forecast data) decisions for better forecasts. The consequences of this study have

interestingly elicited that the group consensus forecasts produce more accurate results than

the staticized group forecasts, while the employed forecast data is raw statistical data (neither

inflated nor deflated data: undistorted) or negatively distorted statistical data (deflated data).

However, the same success has not been achieved, while the positively distorted statistical

data (inflated data) has been used. These conclusions raise new questions about the role of

group decisions and statistical data used through this stage. Moreover, it is notable to

highlight that these results have been explored in the student level applications (Önkal,

Lawrence and Zeynep Sayim, 2011). Therefore, the results of group decisions in practice are

a brain-teaser for forecasting phenomena, while followed procedures, used data and the roles

of partners are taken into consideration (Önkal, Lawrence and Zeynep Sayim, 2011). Based

on the above, the study argues that intensifying RFP with group decision making (GDM)

encourages partners to take more accurate final forecast decisions together, and to achieve

further understanding about both forecasting knowledge and implications. So, the study

wonders:

Q.4. What determinants do affect GDM between upstream and downstream supply chain

members?

In consequence, the second major argument of this study is that improving the accuracy of

CF relies on the RFP, as long as it is underpinned with partners’ joint forecasting methods

(JFM) choices, and then culminated with their group decision making (GDM) in

collaborations. Summary of the literature for generating the accentuated constructs and their

prime intentions are presented in Table-1. (A).

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(A)

Constructs Intention of the constructs Relevant references

Prime processes of conceptual framework

Collaborative Information

Sharing (CIS)

To consolidate information sharing based operations within and between supply chain members for long-term CF.

(Sari, 2008; Aviv, 2007; Småros, 2007; Fliedner, 2006; Taylor and Fearne, 2006; Ireland and Crum, 2005; Aviv, 2002; Småros, 2002; Aviv, 2001; Mentzer, Min and Zacharia, 2000)

Reconciled Forecasting

Process (RFP)

To harmonize isolated forecasting processes of supply chain members for more accurate demand predictions in CF.

(Taylor and Xiao, 2010; Nakano, 2009; Sari, 2008; Aviv, 2007; Småros, 2007; Småros, 2005; Aviv, 2002; Småros, 2002; Aviv, 2001)

Prime sub-constructs of CIS:

Agile IS (AIS)

To share customer-forecast data within and between partners on time and fluently for a consistent CIS.

(Aviv, 2007; Småros, 2007; Taylor and Fearne, 2006; Aviv, 2001)

Undistorted IS (UIS)

To share raw customer-forecast data within and between partners neither subtracting nor adding knowledge for a useful CIS providing usable and accurate data.

(Taylor and Xiao, 2010; Zhou and Benton Jr, 2007; Taylor and Fearne, 2006; Zotteri, Kalchschmidt and Caniato, 2005; Småros, 2002; Mentzer, Min and Zacharia, 2000)

Prime sub-constructs of RFP:

Joint Forecasting

Methods (JFM)

To apply whether statistical or judgmental or both forecasting methods for a compatible and continuous RFP.

(Önkal, Lawrence and Zeynep Sayim, 2011; Syntetos, Nikolopoulos and Boylan, 2010; Fildes et al., 2009; Syntetos et al., 2009; Taylor and Fearne, 2006; Sanders and Manrodt, 2003)

Group Decision Making (GDM)

To come to an agreement on a single final demand-forecast decision for a dynamic and conclusive RFP.

(Önkal, Lawrence and Zeynep Sayim, 2011; Xiao et al., 2009; Chen and Boylan, 2008; Småros, 2007; Zotteri, Kalchschmidt and Caniato, 2005)

(B)

Propositions Definition of the propositions 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)

Table - 1. (A). Intentions of generating constructs with relevant references

(B). Definitions of propositions with relevant references

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2.2. Propositions

This section presents the propositions which are developed from literature and need to be

answered empirically using statistical analysis. They are accordingly attributed to CIS

(including AIS & UIS) and RFP (including JFM & GDM), which aim to improve long-term

and accurate CF respectively. Definitions of the propositions are shown in Table-1. (B).

which represent their summary of performance measurement items.

2.2.1. Internal & External Integration (II & EI)

There are a number of studies that investigated firms’ internal integration (II) to explore its

impact on various performance metrics (Flynn, Huo and Zhao, 2010; Nakano, 2009). The

results of these studies have shown that effective II positively influences the operational and

business performance of firms and enables them to have a competitive advantage in the

market. Further, it is elucidated that if firms continuously improve their interdepartmental

collaborations, information can be acquired, used and disseminated accurately within and

between firms, which lead them to secure the value of information (UIS) (Nakano, 2009).

However, the information can neither be shared nor used properly among upstream-

downstream supply chain members, and CF is therefore harmed (Småros, 2007; 2005; 2002).

On the other hand, It is discussed that firms initially should have a strong communication and

dynamic processes among their departments to retain successful partnerships (Flynn, Huo and

Zhao, 2010; Nakano, 2009). Despite the existing results, there are further issues that should

be examined to clarify the role of II on various organizational characters, IS and EI by

considering uncertain environmental factors (Flynn, Huo and Zhao, 2010; Nakano, 2009;

Småros, 2007). Based on the above, it is a good opportunity to propose that:

• P1a: II influences AIS within and between upstream and downstream supply chain

members.

• P1b: II influences UIS within and between upstream and downstream supply chain

members.

To conduct continuous and satisfying external integration (EI) in a doubtful environment

among manufacturers and retailers is not easy. It requires intensive interdependence,

openness to data and negotiation, joint processes, additional time spending and mutual vision

(Taylor and Fearne, 2006; Mentzer, Min and Zacharia, 2000). It is known that the lack of

trust, commitment and complex technologies are important barriers of partners’ collaboration

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(Taylor and Fearne, 2006; Vlachos and Bourlakis, 2006). However, a consistent EI helps

partners to stay in synchronization, and to raise their operational and overall supply chain

performance (Flynn, Huo and Zhao, 2010). It is therefore argued that underpinning EI among

partners is likely to help them to mitigate IS related conflicts and then enables them to stay in

a long-lived collaboration. On the other hand, while supply chain members fulfil IS related

needs between each other, it increases their willingness to be more constructive through FP

(Taylor and Fearne, 2006). This motivation then positively influences the operational

activities of manufacturers and retailers including distribution, inventory and capacity

management (Taylor and Fearne, 2006). This is because EI arguably supports the forecasting

and decision making activities of partners to reveal more accurate forecasts. However, there

is no clarification how EI affects collaborations, while partners’ IS and FP based joint

processes, response rates and risk taking capabilities are taken into account (Nakano, 2009;

Davis and Mentzer, 2007). In this line, considered evidences and ambiguities motivated the

authors to propound that:

• P2a: EI influences AIS between upstream and downstream supply chain members.

• P2b: EI influences UIS between upstream and downstream supply chain members.

• P2c: EI influences the choices of JFM between upstream and downstream supply

chain members.

• P2d: EI influences GDM between upstream and downstream supply chain members.

2.2.2. Judgmental Adjustments (JA)

Many organizations who do not have reliable information to believe quantitative forecasts

appeal to judgmental methods by referring the subjective assessments of forecasters on

quantitative results (Sanders and Manrodt, 2003). Notwithstanding the dependence of the

accuracy of these methods on the experience and motivation of the forecasters, the direction

and size of adjustments made by forecasters play a crucial role on final decisions (Fildes et

al., 2009; Syntetos et al., 2009). It has been demonstrated that making negative (deflated) and

large (wide range) adjustments outperform positive (inflated) and small (narrow range)

adjustments for more accurate forecasts (Fildes et al., 2009). Furthermore, a number of

studies likewise discussed that organizations’ position in FP and existing production

capacities of manufacturers influence the direction and size of adjustments (Önkal, Lawrence

and Zeynep Sayim, 2011; Sanders and Manrodt, 2003). Interestingly, it is also argued that the

level of trust and interaction among forecasters have an impact on the acceptance of

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adjustments (Lyn M., 2011). Accordingly, these evidences suggest that JA have a relationship

with trust, organizational interaction and balanced production and forecasts, which are

notable factors for successful CF (Småros, 2007). However, it is not explicit how JA

influence the forecast accuracy, while various forms of information collected from an

uncertain environment are used for adjustments (Sanders and Manrodt, 2003). Therefore, the

study aims to clarify the relation between JA and the value of information used in FP, and to

underpin the positive effect of JA on the accuracy. Based on these arguments, the study

supposes that:

• P3a: JA influence UIS between upstream and downstream supply chain members.

• P3b: JA influence the choices of JFM between upstream and downstream supply

chain members.

• P3c: JA influence GDM between upstream and downstream supply chain members.

2.2.3. Forecast Errors (FE)

A number of studies particularly focused on forecast errors (FE) to elucidate their concern

about forecasting, production and supply chain performance (Thomassey, 2010; Kerkkänen,

Korpela and Huiskonen, 2009). For instance, while Kerkkänen, Korpela and Huiskonen

(2009) analyzed the influence of FE in production planning and inventory management, it has

been identified that FE result in wasted capacity, delayed distributions, increased stock and

supply chain costs. From a different point of view, the opinions and decisions of forecasters

have also been indicated as notable causes of FE. The reason is that forecasting is a process

that is repeated regularly and needs various views to consolidate predictions, Therefore,

personal bias of forecasters, especially positive bias, is likely to cause FE (Kerkkänen,

Korpela and Huiskonen, 2009). Furthermore, it is known that if forecast data including FE is

shared within and among partners, it prevents them to generate accurate forecasts because the

used data has lost its real value (i.e. Distorted IS) (Thomassey, 2010). Strangely, the

drawbacks of FE, such as poor production, inventory management, IS and inaccurate

forecasting, are also inhibitors of successful CF (Småros, 2007; Småros, 2005). The study

accordingly proposes that:

• P4a: FE influence UIS within and between upstream and downstream supply chain

members.

• P4b: FE influence the choices of JFM between upstream and downstream supply

chain members.

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• P4c: FE influence GDM between upstream and downstream supply chain members.

2.2.4. Production Planning and Scheduling (PPS)

Exercising effective IS in collaborations increases supply chain performance and helps

partners to observe the market transparently (Zhou and Benton Jr, 2007). It is because

conducting consistent planning, on time production and smooth distribution consolidate IS

among partners (Zhou and Benton Jr, 2007). However, preparing production schedules based

on long-term goals to meet unexpected demands by manufacturers dissents them from

retailers (Småros, 2007). The reason lies in the conflict that manufacturers need more

accurate and long-term forecast data to prepare schedules and plans which looks redundant

for retailers because of their short-term forecasts to manage inventory and purchasing

(Småros, 2005; 2002). Moreover, there are important ascertains attributing long lead times

and lost value of information during IS to manufacturers’ long-term production plans

(Småros, 2007). Despite the highlighted relationships between production and IS, there is not

adequate clarification that guiding practitioners on adopting practices in production planning

and scheduling to equilibrate IS and production in terms of time (Småros, 2007; Taylor and

Fearne, 2006). From a different viewpoint, if forecast data, received from retailers for

production planning, is not reliable and includes FE, it engenders over/under production and

costs in distribution and inventory (Thomassey, 2010). Some studies have supported that

partners should generate forecasts together to conduct effective production catering for

consumer demands (Taylor and Fearne, 2006). However, practitioners have still questions

about how production and CF performance can be increased by combining production

planning and forecasting (Nakano, 2009; Småros, 2007; Taylor and Fearne, 2006). In this

line, existing studies indicate that production planning and scheduling (PPS) skills of

manufacturers play an important role on IS and FP. We therefore argue that PPS capabilities

undertake a vital position in both IS and FP, while demand is uncertain and sector faces

highly unpredictable events. This study therefore propounds that:

• P5a: PPS capabilities influence AIS within upstream and between upstream and

downstream supply chain members.

• P5b: PPS capabilities influence UIS within upstream and between upstream and

downstream supply chain members.

• P5c: PPS capabilities influence the choices of JFM between upstream and

downstream supply chain members.

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• P5d: PPS capabilities influence GDM between upstream and downstream supply

chain members.

3. Conceptual Framework

Initial findings of the study derived from in depth literature review were discussed with a

senior supply chain manager of a UK food manufacturer through an in-depth interview. The

interview results including ongoing CF challenges in practice helped to modify these findings

and to present them as the propositions of the study. Therefore, the study arguably believes

that validity of the propositions has been strengthened by the interview. The above

propositions have been integrated into an initial conceptual framework which is presented in

Figure - 1.

Figure – 1. Conceptual Framework

P1a-b

Judgmental

Adjustments (JA)

Forecast Errors

(FE)

External Integration

(EI)

Production Planning

& Scheduling (PPS)

Long-erm CF Accurate CF

Collaborative

Forecasting

Performance (CFP)

P2a-b

P5a-b

P2c-d

P5c-d

P3b-c

P4b-c

Internal

Integration

(II)

Collaborative

Information Sharing (CIS)

Undistorted

Information

Sharing (UIS)

Agile Information

Sharing (AIS)

Reconciled Forecasting

Process (RFP)

Group Decision

Making (GDM)

Joint Forecasting

Methods (JFM)

P3a

P4a

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4. Proposed Research Methodology

4.1. Design and Samples

As mentioned before, this study aims to identify ambiguous determinants that have an impact

on CF performance which play a notable role on the long-lived and accurate CF. As such;

survey method is selected focusing on food manufacturers and a retailer having a broad store

chain in the UK food market. It is common to conduct surveys in forecasting and supply

chain related empirical studies (Vlachos and Bourlakis, 2006; Sanders and Manrodt, 2003;

Flynn et al., 1990). Under the positivism philosophy, survey results are expected to

contribute to both knowledge and management by empirically testing the relationships

defined in the conceptual framework (Wilson, 2010). By way of the triangulation approach,

first step of the study (discussed partly in section 2) takes an inductive approach to generate

hypotheses and a framework based on the literature findings, interviews and group

discussions, which include practical knowledge. The deductive approach is then considered

to test hypotheses in the UK food sector (Jick, 1979).

For the survey, Food and Drink Federation members will be approached to select

manufacturers. Supply chain/logistics, forecasting/marketing and production managers of

manufacturers are targeting respondents for the survey questionnaires. Vegetable, poultry,

meat, seasonal and dairy goods are prime product categories of the study because of their

short-life and irregular demands in the market (Taylor and Fearne, 2006). It is argued that

selecting the various categories enables the study to make comparisons between

manufacturers and products as well. Further, one of the UK based retailers will be

incorporated into the study. The study uses random sampling technique to select a number of

retail stores as a target group. Respective store managers will be contacted as respondents of

the survey. The main intentions to conduct the survey at the store level are to (i) explore

vague effects emerging through IS and FP of the retailer, such as FE and/or poor II, and to (ii)

examine potential influence of location and/or weather on demands and forecasts. A similar

procedure was followed by Zotteri, Kalchschmidt and Caniato (2005) which provided them

with the opportunity to examine how FP and the accuracy are influenced, while data is

aggregated in various levels. It is important to mention that target stores of the retailer are

expected to be at the hypermarket level to ensure their potential to sell all selected product

categories in this study under one roof.

15

4.2. Data Collection

In addition to the literature research, semi-structured interviews and group discussions will be

conducted to collect further information. By applying these qualitative approaches, it is

expected to uncover practical challenges in CF which have not been explored by literature.

In this way, the study aims to combine both literature and practice sourced findings having an

impact on CF performance, and to add further insight for better forecasting and supply chain

management in the food sector. Following subsections provide more details of the data

collection process of the study.

4.2.1. Semi-structured Interviews

It is assumed that semi-structured interviews with academics and practitioners play an

important role to strengthen the propositions of the study. Besides, various data collection

methods for empirical studies such as case study and simulations (e.g. Sari (2008) and

Taylor and Fearne (2006)), interviews and group discussions are popular methods to collect

reliable information (Småros, 2007; Vlachos and Bourlakis, 2006). Supply chain/ logistics,

marketing/forecasting and production managers are presumed as the prime practitioner group

of interviewees. The professional network of Linked-in provides a good way to reach out to

these respondents. Approximately twenty managers are expected to be interviewed through

this process. In addition to practitioners, around twenty academics who conducted similar

studies in relevant areas will be interviewed. The main intention here is to examine probable

contrasting views among practitioners and academics, and to strengthen the reliability of

findings with multiple opinions (Zacharia, Nix and Lusch, 2011).

It is worth to highlight that so far, a face-to-face in-depth interview has been conducted with

a senior supply chain manager of a UK food manufacturer. The firm produces vegetable,

poultry, meat and some seasonal foods, and have strong CF relationships with three main

retailers in the UK. The interview questions are presented in Appendix A. The interview took

about one hour during which, the main points were recorded using note-taking method. In

this way, the study modified the literature sourced findings. The propositions and conceptual

framework presented in Section 2 and 3 are the results of this process.

16

4.2.2. Group Discussions

Group discussions will be proceeded on the professional groups of Linked-in. The

discussions will be updated based on the progress and new findings of the study. Forecasting

and supply chain related Linked-in groups (e.g. Forecasting Net, CPFR Committee, Demand

Planning Net and SCMWorld) will be selected for these discussions which makes it possible

to gather opinions of both academics and practitioners. So far, two discussions were

completed with six participants including managing directors, consultants and academics

from various positions and countries. Discussions among participants are recorded in text

files which will regularly be analyzed by Nvivo as suitable software for qualitative data

analysis. The concluded two discussions are now being analyzed and their results will be

used to refine the propositions and conceptual framework as well as survey design.

4.3. Data Analysis

Information collected through qualitative research (inductive approach), including literature

research, interviews and group discussions, are analyzed by using the NVivo software. By

doing so, the study proposes to (i) employ results of the NVivo software to underpin validity

and reliability of the propositions (Welsh, 2002), and then to (ii) promote them as prime

hypotheses.

After that, hypothesis testing process will be initiated (deductive approach) by employing the

survey, which has two separate sections and includes multiple choice, rank-order, closed,

Likert-scale and categorical questions. The reasons of having two sections and various

question models in the survey are that (i) the survey targets two different samples from the

manufacturers and the retailer, and (ii) answers are sought from three respondents of

manufacturers (supply chain/logistics, forecasting/marketing and production managers) and

one respondent from the retailer (store managers). Statistical package for the social science

(SPSS) program will aid the study to test hypotheses statistically.

Regarding statistical tests, T-test plays an important role to compare variables both separately

and in a group. Since, the propositions are attributed to the group of independent variables

(CIS: AIS & UIS, RFP: JFM & GDM). The variables will therefore be analyzed both one by

one and in a group to explore their significance on the long-term and accurate CF. By

applying two different significance levels (α: 0.01 and 0.05) strength of the relations among

variables will be verified (Flynn et al., 1990). The main product categories of the study

17

(vegetable, poultry, meat, seasonal and dairy goods) are attempted to be analyzed in both

manufacturers and a retailer. Chi-Square test will be employed to explore significant

differences between the categories, and to classify the views of respondents, who keep

different positions in manufacturers. To achieve both long-term and accurate CF the study

will measure the both clustered (CIS, RFP) and the impact of individual independent

variables on them. Two-way ANOVA will help the study to examine whether the variables

have a significant influence on the duration and accuracy of CF, and to explore whether the

variables have a strong interaction between each other. The same principle is likewise applied

to examine the influence of single variables on CIS and RFP. Multiple linear regression will

also be applied to add further explanatory insight to results of ANOVA, especially for

forecasting related variables. Through the survey process, it is likely that the organizational

structure of manufacturers differs, and therefore they cannot provide three comparable

responses of managers. For that reason, Cluster analysis will be employed to classify

managers and analyze the results effectively (Wilson, 2010).

In terms of reliability and variability, the study will test the solidity of existing data. For

instance, with regards to the semi-structured interviews, test-retest reliability will be used

considering the correlation coefficient measurement. After completing the interview process

and data analysis via NVivo, the results will be used to modify and reorganize interview

questions. The reason is that by employing these reformatted questions the same

interviewees’ answers will then be sought. In doing so, the study will compare the two

responses of the interviewees collected in different time periods which will lead to explore

potential changes on answers, and to strengthen results of the interviews. On the other hand,

internal consistency will be used to measure the homogeneity of the survey employing

Cronbach’s alpha (Flynn et al., 1990). In addition, Content validity, by applying the Delphi

method and detailed literature research, and then Construct validity, via Factor Analysis, will

be conducted to ensure that the survey is strong enough to experiment the variables’ relation.

Zhou and Benton (2007) and Flynn, Huo and Zhao (2010) are sample studies that applied

similar techniques in this respect.

18

5. Discussions and Expected Managerial Implications

To develop an applicable conceptual framework for better CF and to leverage its position in

the food industry is a major propensity of this study to contribute to both knowledge and

managerial implications. In this line, the study investigates forecast collaborations among

upstream and downstream members of the UK food supply chain, and aims to identify

important determinants having an impact on the duration and accuracy of CF. Detailed

literature research, semi-structured interviews and group discussions foster the study to obtain

reliable data, and to generate hypotheses. The survey method and statistical analyses will then

be conducted to test hypotheses in practice.

The study dedicated the first and second research questions to clarify ambiguities affecting

the duration of CF by investigating partners’ internal and external IS processes. In order to

achieve this goal, the argument of the study is that partners should correlate their IS

relationships. This can be achieved by constructing the collaborative information sharing

(CIS) process between supply chain members. However, it is discussed that this process

becomes successful, while partners share customer-forecast data within and between each

other on time and fluently, which is called agile IS (AIS). Because of the sensitivity of food

industry and the chosen category of seasonal, short-live or perishable products, real time data

is a prerequisite for more accurate forecasts. Otherwise, neither retailers nor manufacturers

can predict accurate forecasts for products having volatile demand. Apart from the fluctuated

demand, the uncertain environmental events, such as weather changes, should not be

overlooked in this respect. As an additional approach to CIS process, partners should not

subtract or add confusing knowledge to information shared within and between firms, which

is called undistorted IS (UIS). As, these activities directly harm the forecasting, production

and distribution process of partners, and cause excessive supply chain costs. This is why

conducting effective CIS process is attributed to both AIS and UIS. The first and second

questions accordingly seek solutions to improve both AIS and UIS respectively. Preliminary

investigations of the study, including detailed literature research and interview, have enabled

us to explore that first step for the effective CIS process is to have a strong internal

integration (II) within firms. However, judgmental adjustments (JA) arranged by forecasters

based on retail customer-forecast data, and forecast errors (FE) emerges in FP play a pivotal

role for UIS. Further, production planning and scheduling (PPS) capabilities of manufacturers

and external integration (EI) among partners are also significant principles for reliable and

continuous CIS process.

19

On the other hand, the last two research questions seek solutions to harmonize partners’

isolated forecasting processes, and to generate more accurate forecasts for the products

having irregular demands. Therefore, the study suggests that achieving more accuracy in CF

relies on the reconciled forecasting process (RFP) which aims to keep partners in close

interdependence across FP. Due to partners’ overlapped forecasting approaches two prime

requirements have been attributed to this process which are the joint forecasting methods

(JFM) and group decision making (GDM). The major intention for developing JFM as an

initial step of RFP is partners’ different preferences in employing statistical and judgmental

methods separately. Remarkable studies in the area of forecasting have already demonstrated

the positive influence of these methods on the accuracy, while they are used within a

harmony. This is the reason why the authors argue that supply chain members should initially

joint their forecasting methods, and initiate forecasting based negotiations between each

other. However, applying sole JFM is not enough to generate better forecasts because of the

required final concrete forecast decisions, which play a vital role for the accuracy and

profitable collaborations. In this direction, the study aims to investigate the impact of

supplementing RFP with GDM to rise the partners’ discussions on forecasts to the mutual

agreement point, and then to finalize FP with a single final demand-forecast decision. In this

manner, the last two research questions are directly devoted to identify significant principles

that affect the choices of JFM and GDM practices of partners to predict demand as accurately

as possible. By relying on the initial propositions the approach of the study is that if (i)

effective JA are made on customer-forecast data, not having FE; (ii) manufacturers stabilize

their production capacity and forecasts with effective PPS, and (iii) overall relationships are

managed and integrated in an accord, partners can generate more accurate forecasts.

The propositions developed from both literature and practice originated sources which led the

study to explore new initiatives in CF. However, there are some limitations in the current

study. For instance, during the data collection process it is aimed to obtain data from both

manufacturers and a single retailer by employing the survey method. With regard to

manufacturers, expecting responses from supply chain/logistics, marketing/forecasting and

production managers is likely to negatively influence the response rate of the survey. Having

a low response rate plays an important role on the quality of results, and prevents the study to

generalize them to the whole food industry in the UK. On the other hand, choosing a single

retailer limits the study to solely explore that retailer’s CF activities.

20

6. Conclusions

In summary, this paper aims to present the preliminary propositions and the conceptual

framework by investigating the forecast collaborations of upstream and downstream supply

chain members in the UK food sector.

The generated proportions and the framework have been developed based on the literature

investigations and in-depth interview, which has been conducted with a senior supply chain

manager of a UK food manufacturer. It is notable to underline that the manufacturer produces

vegetable, poultry, meat and some seasonal foods in their production band, and have strong

CF relationships with all three major retailers in the UK. Therefore, it is presumed that

interview results include credible information which support the validity of literature

findings. The next step of the study is to conduct the semi-structured interviews with both

practitioners and academics. Additionally, further group discussions are intended to be run on

Linked-in.

It is hoped that the study produces fresh insights for academia. For instance, analyzing the

performance of different forecasting methods is a good opportunity, while various food

categories are considered in CF, such as long-lived and/or canned foods. Further, it is worth

to test the finalized framework’s functionality in different industries. Tourism, clothing and

apparel industries are a good opportunity for this sort of research because of their seasonal,

fashion based and volatile demands in their markets.

21

Appendix. A: In-depth Interview Questions of the Study

Interviewee:

-A senior supply chain manager of a UK food manufacturer

Overview:

-Explanations about the study and description of the conceptual framework.

-Indications of the main objectives based on the literature findings.

General information:

-Request the company background information –history, # of employees, product categories, brands,

production system, annual sales, CF with retailers and the structure of forecast collaborations.

Main Subjects of the Interview:

1. Collaborative Forecasting (CF) and UK Food Sector

Q.1. Do you face with any duration and/ or accuracy related CF problems within the collaborations

conducted with retailers? Can you tell about how do these difficulties influence you and your

relationships with retailers?

Q.2. Do you have further CF problems that lead to conflicts with retailers in CF?

Q.3. In what situations do you face with these problems? And how do you deal with them?

2. Important Factors on CF Performance

(Detailed explanations for findings and descriptions on the conceptual framework)

Q.4. What are your views on referred areas and parameters that correlated with the duration and

accuracy of CF? What is their realness in practice?

Q.5. Do you think there are further different situations or specific factors that put pressure to your

collaborations with regards to the period of collaborations and demand forecasting?

Q.6. How these situations and/or factors influence the forecast accuracy and collaborations?

3. The Role of Various Product Categories in CF

Q.7. What is the existing performance of CF in the information sharing and forecasting process

concerning specific products such as seasonal, short-life, perishable, poultry, meat, vegetables and

new launched categories? Does its performance satisfy you?

4. Information Sharing, Forecasting, Decision Making and Risk Management Processes in CF

Q.8. What approaches do you adopt to consolidate information sharing, forecasting, decision making

and risk management procedures with your partners?

Q.9. How do these processes and approaches, referred by you and retailers, affect your CF

performance? Can you obtain a tangible positive result?

Q.10. What are your overall views while existing CF problems and the initial findings of this study

are harmonized? Have you got further suggestions to the study of considering specific

situations/subjects to investigate further from literatures?

22

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