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ABSTRACTPractical experience and scientific research show that there is scope for improving the performance of inventory control systems by taking into account the discrepancies between the actual physical inventory levels and those recorded in the information system (IS). Such discrepancies, which are often referred to as inventory (record) inaccuracies, are a major concern in contemporary supply chains where commitments to orders are usually made based on IS records only. Empirical data obtained in two case studies motivate the development of a multiperiod inventory control model that explicitly accounts for the differences between physical inventory levels and IS stock records. Numerical experiments help derive some key managerial insights. We find that previous important results on the behavior of the optimal order quantity in the retailing environment do not necessarily apply in an e‐retailing/Business‐To‐Business (B2B) context. We adjust and apply the zero balance walk technique to the e‐retailing/B2B case and deduce a simple and efficient learning mechanism about the errors' distributions. We close with an agenda for further research in this area.

PurposeEnsuring high on-shelf availability at low inventory costs remains an important challenge in retailing. Inaccurate inventory records, i.e. discrepancies between the stock records displayed in the inventory system and the stock quantity actually found in the retail store, have been identified as one of the most important drivers of retail stockouts in the past. The purpose of this work is to investigate the causes of positive inventory discrepancies in retailing, i.e. where there is more inventory on-hand than identified by the inventory system.Design/methodology/approachBased on input from retailers, the authors develop a simulation model of a retail store that considers various error-prone processes and study in a full factorial test design how the different operational errors may drive inventory discrepancies, paying special attention to the sources of positive inventory record inaccuracies.FindingsThis makes it possible to gain insights into the process parameters retailers need to adjust to avoid inventory records becoming inaccurate. In addition, the authors analyze how positive inventory discrepancies relate to stockouts to further our understanding of the role so-called phantom products may play in a retailing context.Originality/valueWhile negative inventory discrepancies (where the stock that is available in the store is less than what the system displays) and their sources (theft, shrinkage, etc.) have been discussed quite frequently in the literature, the causes of positive inventory discrepancies (where the available inventory exceeds the system inventory) have received much less attention.

PurposeIn manual order picking systems, temporary workers are often employed to handle demand peaks. While this increases flexibility, it may hamper productivity, as they are usually unfamiliar with the processes and may have little experience. It is important for managers to understand how quickly inexperienced workers arrive at full productivity and which factors support workers in improving their productivity. This paper aims to investigate how learning improves the performance of order pickers, and how their regulatory focus (RF) and monetary incentives, as management actions, influence learning.Design/methodology/approachData was collected in two case studies in controlled field-lab experiments and statistically analysed. This allowed evaluating the validity of hypotheses through an ANOVA, the calculation of correlation coefficients and the application of regression models.FindingsA monetary incentive based on total order picking time and pick errors has a positive influence on order picking time, but not on pick quality. The incentive influences initial productivity, but not the learning rate. A dominant promotion-oriented RF increases the effect of the incentive on initial productivity, but it does not impact worker learning.Practical implicationsThis study contributes to behavioral and human-focused order picking management and supports managers in setting up work plans and developing incentive systems for learning and productivity enhancement, considering worker RF.Originality/valueThis work is among the few to empirically investigate the effect of monetary incentives on learning in interaction with RF. It is the first study to investigate these concepts in an order picking scenario.