In the logistics arena, green warehousing has been achieving increasing attention from both practitioners and academia. On the one hand, practitioners have started to search for solutions to decrease the environmental impact of their logistics facilities and incorporate practices towards greener warehousing processes. On the academic side, a rising – though limited – number of papers have been found addressing the impact of the green warehousing practices in place, together with the related effects on warehouse consumption and environmental performance. In this context, conditioned warehouses represent a key challenge due to their temperature constraints and the ever-demanding logistics performances, and related studies are still lacking. This paper aims to address this research gap by proposing a simulation-based approach where multiple scenarios of a real conditioned logistics facility are discussed, grounded on a conceptual framework of green warehousing practices selection process. Three different scenarios are proposed, and the related performances are examined in terms of energy consumption and CO2eq emissions. Implications of the results are discussed and streams for future investigation are identified.

Manual picker-to-parts order picking systems remain predominant in retail warehousing and have been identified as one of the comparatively most labor-intensive processes. While previous studies have delved into the effects of work intensity and worker experience on performance, they have typically examined each construct separately while neglecting workload-related experience. Given that the interaction remains under-explored, we here investigate how workload-related experience could possibly mitigate the negative performance effects of work intensity. We obtain a unique longitudinal real-world retail warehouse data set including 1,739,352 storage location visits performed by 74 order pickers from January to April 2023. We apply a mixed-effects model allowing for random intercepts for each order picker and utilize order picking task performance time as our dependent variable. We find that work intensity increases task performance time at increasing rates and that workload-related experience can mitigate this effect. Our research informs operations managers under which conditions they can capitalize on the positive effects of workload-related experience while mitigating the negative consequences of work intensity.

Despite the evident connections between Demand Forecasting and Inventory Control, both, researchers and practitioners tend to perform and analyze those tasks separately. Yet, the application of appropriate Demand Forecast-ing Methods promises meeting inventory-related target values while reducing Inventory Costs. A significant difficulty consists in identifying the appropri-ate Demand Forecasting Method. Thus, practitioners require a framework that supports the decision process of selecting said method. Depending on the chosen Forecasting Method, different configurations of the Inventory Control Policy might be suitable. The aim of this work is to facilitate the complex task of connecting the Forecasting Method Selection and Inventory Control Policy Configuration for a group of numerous and heterogeneous products. Thus, a simple framework that generates recommendations regard-ing the appropriate Forecasting Method and Inventory Policy will be devised and empirically tested. However, due to capacity restrictions, only two of the three suggested Forecast Methods will be investigated further. The applica-tion of the framework regarding sales data of a biotech company shows that it enables a significant reduction of stockouts which translates to higher ser-vice levels. The proposed methods therefore contribute to efficient and eco-nomically sustainable warehouse operations and inventory control manage-ment concepts.