Purpose -The purpose of this paper is to assess whether an existing sourcing strategy can effectively supply products of appropriate quality with acceptable levels of product waste if applied to an international perishable product supply chain. The authors also analyse whether the effectiveness of this sourcing strategy can be improved by including costs for expected shelf life losses while generating order policies. Design/methodology/approach -The performance of sourcing strategies is examined in a prototype international strawberry supply chain. Appropriate order policies were determined using parameters both with and without costs for expected shelf life losses. Shelf life losses during transport and storage were predicted using microbiological growth models. The performance of the resulting policies was assessed using a hybrid discrete event chain simulation model that includes continuous quality decay. Findings -The study's findings reveal that the order policies obtained with standard cost parameters result in poor product quality and large amounts of product waste. Also, including costs for expected shelf life losses in sourcing strategies significantly reduces product waste and improves product quality, although transportation costs rise.Practical implications -The study shows that in perishable product supply chain design a trade-off should be made between transportation costs, shortage costs, inventory costs, product waste, and expected shelf life losses. Originality/value -By presenting a generically applicable methodology for perishable product supply chain design, the authors contribute to research and practice efforts to reduce food waste. Furthermore, product quality information is included in supply chain network design, a research area that is still in its infancy.

To match products of different quality with end market preferences under supply uncertainty, it is crucial to integrate product quality information in logistics decision making. We present a case of this integration in a meat processing company that faces uncertainty in delivered livestock quality. We develop a stochastic programming model that exploits historical product quality delivery data to produce slaughterhouse allocation plans with reduced levels of uncertainty in received livestock quality. The allocation plans generated by this model fulfill demand for multiple quality features at separate slaughterhouses under prescribed service levels while minimizing transportation costs. We test the model on real world problem instances generated from a data set provided by an industrial partner. Results show that historical farmer delivery data can be used to reduce uncertainty in quality of animals to be delivered to slaughterhouses.

To fulfil segmented consumer demand and add value meat processors seek to exploit quality differences in meat products. Availability of product quality information is of key importance for this. We present a case study where an innovative sensor technology that provides estimates of an important meat quality feature is considered. Process design scenarios that differ with respect to sorting complexity, available product quality information, and use of temporary buffers are assessed 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. Use of production buffers was found to increase processing flexibility and mitigate negative effects of high sorting complexity. This research illustrates how use of advanced product quality information in logistics decision making affects sorting performance, processing efficiency, and the optimal processing design, an area that has so far received little attention in literature.

In practical decision making, one often is interested in solutions that balance multiple objectives. In this study we focus on generating efficient solutions for optimization problems with two objectives and a large but finite number of feasible solutions. Two classical approaches exist, being the constraint method and the weighting method, for which a specific implementation is required for this problem class. This paper elaborates specific straightforward implementations and applies them to a practical allocation problem, in which transportation cost and risk of shortage in supplied livestock quality are balanced.The variability in delivered quality is modelled using a scenario-based model that exploits historical farmer quality delivery data. The behaviour of both implementations is illustrated on this specific case, providing insight in i.) the obtained solutions, ii.) their computational efficiency. Our results indicate how efficient trade-offs in bi-criterion problems can be found in practical problems.