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Beschreibung:
The COVID-19 pandemic and resulting non-pharmaceutical interventions aspiring to reduce the spread of the virus, e.g., full or partial lockdowns, as well as social distancing measures, lead to increasing at-home consumption and panic buying. The resulting demand peaks for non-cooled perishable items hit the distribution systems of traditional brick-and-mortar retailers and have led to various out-of-stock situations on the shelves of '' urope's retailers. If the impact of demand peaks during the COVID-19 pandemic on grocery retail warehouses are unaware, this can result in out-of-stock situations in the supermarkets. In this paper, we use a process-based discrete-event simulation model to develop and apply a simulation approach to shed light on the underlying mechanisms of grocery retail warehouses in order to predict the future behavior of the examined system and prepare for such external demand shocks. Our results show that both investigated scenarios of volume peaks have a great impact on waiting times for truck drivers and the time-depending utilization level of the warehouse dispatch area. We then derived optimal shift distributions by developing a supply chain resilience strategy varying the output quantities for order picking. Moreover, by that, we could reduce the utilization level in the warehouse dispatch area by nearly 20 percent. Our model can inform managers about the consequences of demand peaks on grocery retail warehouses. Furthermore, our methodology can be transferred to one-time disruptions, as well as to multi-wave disruptions besides COVID-19.