Session 2: Behavioral Operations 1

Parallel Sessions

Session 2: Behavioral Operations 1 – Chair: René de Koster

Michael Becker-Peth (Erasmus University Rotterdam) – How to benefit from supplier-retailer negotiations: an example of the buyback contract
Operations Management models predict that suppliers can improve their expected profits as well as channel efficiency by using coordinating contracts, such as the buyback contract. These models rely on the assumption of rational expected profit maximizing retailers placing optimal order quantities. A standard way to test these models in the laboratory is to have the supplier propose a contract and the retailer place an order to maximizes the expected profit. In this paper we show that when this process is used with human decision makers, coordinating contracts do no emerge. Human retailers place sub-optimal order quantities and exhibit inequity averse behavior, i.e., reject unfair offers. We show that allowing the player to negotiate can improve the profits of both players when negotiation is successful, especially when parties negotiate over the order quantity in addition to the contract parameters.

Bregje van der Staak (Eindhoven University of Technology) – How redesigning the way planners interact with forecasting systems decreases the forecasting error: An empirical approach
In the domain of sales forecasting, we develop and test a method that combines the wisdom of planners with the knowledge of the statistical forecasting system. By analyzing how planners adjust statistical forecasts, we identify 1) the strengths and weaknesses of planners and 2) use this in order to reduce the forecasting error. In a large data set (N=68,429), we consistently find that planners perform worse than the system when deciding on the magnitude of upward adjustments, but are surprisingly accurate in predicting whether the statistical forecast should go up or down. These findings are taken into account in a newly developed method in which the planner decides on the direction of the adjustment and the magnitude of the downward adjustment, while an algorithm decides on the magnitude of the upward adjustment. This new forecasting approach reduces the forecasting error on average with 17%. We show that using the strength of the planner, while avoiding its weaknesses, significantly reduces the forecasting error, thus improving inventory levels and profits.

XiaoLi Zhang (Erasmus University Rotterdam) – Effects of incentives and feedback on order pickers’ behaviors and performance
We investigate the effects of different incentives (group vs. individual incentives) and feedback (feedback vs. no feedback) on order pickers’ behaviors and productivity and quality performance. We collected the data from an experimental warehouse, where 173 participants were divided into groups of three people who conducted an order picking task for 40 minutes, individually. Our results show that feedback has a significant, positive influence on productivity regardless of incentives, whereas incentives do not make a significant difference. The productivity improvement comes particularly from the top two pickers in each group with individual incentive. In addition, pickers with different abilities respond differently to performance feedback (in terms of their relative ranking in the team, and absolute productivity difference with team-mates): slower pickers speed up drastically, whereas faster pickers speed up slowly or may even slow down. The difference is especially large and significant in the beginning and diminishes with time. No effects on order picking quality were identified. The analyses demonstrate that by using feedback in the process, companies can positively influence workers’ behaviors and performance and slow and fast workers may be affected differently.