More and more companies are paying attention to their carbon footprint beyond production emissions. In this work we consider a ‘carbon-aware’ company (either by choice or enforced by regulation) that is reconsidering the transport mode selection decision. Traditionally the trade-off has been between lead time (and corresponding inventory costs) and transportation costs but now emission costs come into the equation. We use a carbon emission measurement methodology based on real-life data and incorporate it into an inventory model. We consider the results for different types of emission regulation (including voluntary targets). We find that even though large emission reductions can be obtained by switching to a different mode, the actual decision depends on the regulation and non-monetary considerations, such as lead time variability.

Increased demand variability in supply chains (the bullwhip effect) has been discussed in the literature. The practical measurement of this effect, however, entails some problems that have not received much attention in the literature and that have to do with the aggregation of data, incompleteness of data, the isolation of demand data for defined supply chains that are part of a greater supply web. This paper discusses these conceptual measurement problems and discusses experiences in dealing with some of these problems in an industrial project. Also presents empirical results of measurements of the bullwhip effect in two supply chains.

Retail store managers may not follow order advices generated by an automated inventory replenishment system if their incentives differ from the cost-minimization objective of the system or if they perceive the system to be suboptimal. We study the ordering behavior of retail store managers in a supermarket chain to characterize such deviations in ordering behavior, investigate their potential drivers, and thereby devise a method to improve automated replenishment systems. Using orders, shipments, and point-of-sale data for 19,417 item–store combinations over five stores, we show that (i) store managers consistently modify automated order advices by advancing orders from peak to nonpeak days, and (ii) this behavior is explained significantly by product characteristics such as case pack size relative to average demand per item, net shelf space, product variety, demand uncertainty, and seasonality error. Our regression results suggest that store managers improve upon the automated replenishment system by incorporating two ignored factors: in-store handling costs and sales improvement potential through better in-stock. Based on these results, we construct a method to modify automated order advices by learning from the behavior of store managers. Motivated by the management coefficients theory, our method is efficient to implement and outperforms store managers by achieving a more balanced handling workload with similar average days of inventory.