Improving collaborative forecasting performance in the food supply chain

Abstract

The dynamic structure of the Food Supply Chain (FSC) distinguishes itself from other supply chains. Providing food to customers in a healthy and fresh manner necessitates a significant effort on the part of manufacturers and retailers. In practice, while these partners collaboratively forecast time-sensitive and / or short-life product-groups (e.g. perishable, seasonal, promotional and newly launched products), they confront significant challenges which prevent them from generating accurate forecasts and conducting long-term collaborations. Partners’ challenges are not limited only to the fluctuating demand of time-sensitive product-groups and continuously evolving consumer choices, but are also largely related to their conflicting expectations. Partners’ contradictory expectations mainly occur during the practices of integration, forecasting and information exchange in the FSC. This research specifically focuses on the Collaborative Forecasting (CF) practices in the FSC. However, CF is addressed from the manufacturers’ point of view, when they collaboratively forecast perishable, seasonal, promotional and newly launched products with retailers in the FSC. The underlying reasons are that while there is a paucity of research studying CF from the manufacturers’ standpoint, associated product-groups decay at short notice and their demand is influenced by uncertain consumer behaviour and the dynamic environment of FSC. The aim of the research is to identify factors that have a significant influence on the CF performance. Generating accurate forecasts over the aforementioned product-groups and sustaining long-term collaborations (one year or more) between partners are the two major performance criteria of CF in this research. This research systematically reviews the literature on Collaborative Planning, Forecasting and Replenishment (CPFR), which combines the supply chain practices of upstream and downstream members by linking their planning, forecasting and replenishment operations. The review also involves the research themes of supply chain integration, forecasting process and information sharing. The reason behind reviewing these themes is that partners’ CF is not limited to forecasting practices, it also encapsulates the integration of chains and bilateral information sharing for accurate forecasts. A single semi-structured interview with a UK based food manufacturer and three online group discussions on the business oriented social networking service of LinkedIn enrich the research with pragmatic and qualitative data, which are coded and analysed via software package QSR NVivo 9. Modifying the results of literature review through the qualitative data makes it possible to develop a rigorous conceptual model and associated hypotheses. Then, a comprehensive online survey questionnaire is developed to be delivered to food manufacturers located in the UK & Ireland, North America and Europe. An exploratory data analysis technique using Partial Least Squares (PLS) guides the research to analyse the online survey questionnaire empirically. The most significant contributions of this research are (i) to extend the body of literature by offering a new CF practice, aiming to improve forecast accuracy and long-term collaborations, and (ii) to provide managerial implications by offering a rigorous conceptual model guiding practitioners to implement the CF practice, for the achievement of accurate forecasts and long-term collaborations. In detail, the research findings primarily emphasise that manufacturers’ interdepartmental integration plays a vital role for successful CF and integration with retailers. Effective integration with retailers encourages manufacturers to conduct stronger CF in the FSC. Partners’ forecasting meetings are another significant factor for CF while the role of forecasters in these meetings is crucial too, implying forecasters’ indirect influence on CF. Complementary to past studies, this research further explores the manufacturers’ various information sources that are significant for CF and which should be shared with retailers. It is also significant to maintain the quality level of information whilst information is shared with retailers. This result accordingly suggests that the quality level of information is obliquely important for CF. There are two major elements that contribute to the literature. Firstly, relying on the particular product-groups in the FSC and examining CF from the manufacturers’ point of view not only closes a pragmatic gap in the literature, but also identifies new areas for future studies in the FSC. Secondly, the CF practice of this research demonstrates the increasing forecast satisfaction of manufacturers over the associated product-groups. Given the subjective forecast expectations of manufacturers, due to organisational objectives and market dynamics, demonstrating the significant impact of the CF practice on the forecast satisfaction leads to generalising its application to the FSC. Practitioners need to avail themselves of this research when they aim to collaboratively generate accurate forecasts and to conduct long-term collaborations over the associated product-groups. The benefits of this research are not limited to the FSC. Manufacturers in other industries can benefit from the research while they collaborate with retailers over similar product-groups having a short shelf life and / or necessitating timely and reliable forecasts. In addition, this research expands new research fields to academia in the areas of the supply chain, forecasting and information exchange, whilst it calls the interest of academics to particular product-groups in the FSC for future research. Nevertheless, this research is limited to dyad manufacturer-retailer forecast collaborations over a limited range of product-groups. This is another opportunity for academics to extend this research to different types of collaborations and products. Key words: Food industry; Collaborative forecasting; Forecast satisfaction; External integration; Internal integration; Group forecasting; Forecasters; Information types; Information quality