The Global Supply Chain Forum (Stanford Global Supply Chain Forum Web Resource, http://www.stanford.edu/groups/scforum) defines supply chain management (SCM) as “Supply chain management is the integration of key business processes from end user through original suppliers that provides products, services and information that add value for customer and other stakeholders.” The rapid development of the Internet has dramatically changed the traditional definitions of manufacturer, suppliers and customers. Newer approaches to supply chain management attempt to organize the supply chain as a network of cooperating intelligent agents, each performing one or more supply chain functions and each coordinating actions with one another. This research is aimed at creating a viable model of a single manufacturer single supplier collaborative supply chain system using a Vendor Managed Inventory (VMI) system. The research further uses known inventory performance parameters to performance benchmark the VMI system with traditional push-pull systems, develop a collaborative forecasting spreadsheet solution and a best alternative ordering policy amongst EOQ, Monthly, JIT and VMI policies under known lead time and a variety of demand distribution functions. / Master of Science

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.

Bullwhip effect is the situation that the order information distortion resulted from time delay and expectation of the demand. Through the order information distortion, the inventory of upstream suppliers raised and fluctuated, thus the operation cost of suppliers increased. Although the down stream customer seems to exempts from such disaster, but the cost increase in the upstream supplier will cause the raising of the purchasing cost of those downstream customers. The raising cost and inefficiency will be the burden of whole supply chain and not single party can exempts such result. Supply chain collaboration is aimed to solve such problems. This study explode the operations of supply chain collaboration from a integration perspective to understand the value of collaborative forecasting for the supply chain operations. Taiwan TFT-LCD industry will be the platform of this study, base on the platform, system dynamics models will be build up and simulated to analyze the effectiveness of collaborative forecasting model under different demand patterns. The result of this study can be the reference when adopting collaboration in Taiwan TFT-LCD industry.