Effective use of product quality information in food supply chain logistics

Food supply chains have inherent characteristics, such as variability in product quality and quality decay, which put specific demands on logistics decision making. Furthermore, food supply chain organization and control has changed significantly in the past decades by factors such as scale intensification and globalization. In practice, these characteristics and developments frequently lead to supply chain problems, such as high levels of product waste, product quality problems, and high logistics costs. Recent technological developments have created the opportunity to gather, process, and communicate more information on the status of processes and products to support logistics decision making, providing business opportunities to realize performance improvements, and add extra value by differentiating products to specific market segments. This will, however, require the development of effective logistics management strategies that ensure the supply of products of appropriate quality in a cost-effective way to each stage of the supply chain. This thesis studies the use of product quality information in logistics decision making in food supply chains, captured in the following central research question: <strong>How can the effectiveness of logistics decision making in food supply chains be improved using advanced product quality information?</strong> This research question is investigated using four case studies: two in the context of the European Q-porkchains project (i.e. in pork supply chains), and two in the context of the European Veg-i-Trade project (i.e. in fruit- and vegetable supply chains). In these cases we investigated the impact of variability in product quality and quality decay on chain processes and studied if use of product quality information can improve logistics decision making regarding product sourcing and process design. In each case decision support models were developed – in close cooperation with industrial partners - to quantify the impact. <strong><em>Case study 1: Process design for advanced sorting of meat products</em></strong> The first case study, presented in chapter 2, considers the process design of a meat processing company that seeks to add value by sorting meat products for a specific product quality feature. The relation between product sorting, processing efficiency and process design is investigated using a discrete event simulation model. Results indicate that increasing sorting complexity by use of advanced product quality information results in a reduction of processing efficiency, whereas use of production buffers was found to mitigate negative effects of high sorting complexity. The simulation allows practitioners facing segmented customer demand to assess which scenario offers the best trade-off between benefits and drawbacks resulting from efforts to improve responsiveness and flexibility. <strong><em>Case study 2: Livestock sourcing decisions</em></strong> The second case study considers a meat processing company that faces quality feature variation in animals delivered to its slaughterhouses. To support sourcing decisions and ensure that the right product quality is received at its slaughterhouses two stochastic programming models are developed that exploit product quality data gathered during earlier deliveries. The presented implementations reveal that uncertainty in supplied product quality can be reduced using historical farmer delivery data, which improves processing performance. <strong><em>Case study 3: Product sourcing in international strawberry supply chains</em></strong> The third case study relates to an international strawberry distributor that faces frequent product quality problems and substantial product waste. Different sourcing strategies were tested using a combination of both a slow, but cheap transport mode (i.e. sea and truck), and a faster, but more expensive mode (i.e. plane). The performance of these sourcing strategies is examined using a discrete-continuous chain simulation that includes microbiological growth models to predict quality decay. Simulation results reveal that standard cost parameters (that do not take quality decay into account) result in substantial product waste, but if cost for expected shelf-life losses are included in the order policies the effectiveness of product sourcing for the considered supply chain is improved. <strong><em>Case study 4: Use of form postponement for food waste reduction</em></strong> The fourth case study concerns an international lettuce supply chain that struggles with effective product sourcing. Form postponement (FP) is a supply chain strategy which delays processing steps until a demand is realized. This allows a reduction of the total inventory in the supply chain. We studied supply chain scenarios that differ in where and when in the supply chain whole crop lettuce is converted into processed lettuce products. A discrete-continuous chain simulation model revealed that application of FP reduced both product waste and age and improves point-of-sale product quality. <strong>Integrated findings</strong> The findings of this thesis demonstrate that decision makers can improve logistics decisions and reduce food waste by using product quality information and predicting changes in product quality. The developed quantitative decision support models provided essential insights into trade-offs resulting from information-based supply chain performance improvement strategies. The presented case studies demonstrate that supply chain flexibility and responsiveness is required to reduce the impact of product variability and product quality decay. Increasing responsiveness and flexibility typically comes at the expense of other performance dimensions. This research demonstrates the potential of use of product quality information in food supply chain logistics, which may contribute to the effectiveness of food supply chains by improving consumer satisfaction, reducing overall costs, and reducing food waste.