Maverick Picking: Impact & Insights from Real-Life Data

Order picking is the process of retrieving items from a warehouse to fulfill customer orders. It is recognized as the most expensive warehouse operation, especially for picker-to-parts systems in which order pickers travel through the warehouse to collect products. Typically, order pickers are assumed to follow a pre-determined route, but in reality, deviations from these routes often occur. This phenomenon, referred to as maverick picking, introduces uncertainty to the picker routing problem and can lead to longer picking times than anticipated. Existing literature has highlighted the detrimental effect maverick picking may have on operational performance through qualitative research and simulation experiments on artificial data. However, a quantitative justification based on real-life data for their conclusions is still lacking. Moreover, recent studies have demonstrated that failure to comprehend and incorporate human behavior in order picking models may lead to discontent, chronic stress, employee turnover, and burn-outs among order pickers. Fortunately, many warehouses possess readily available data on the picking process. Therefore, we propose a data-driven approach to quantitatively assess the prevalence of maverick picking and identify the underlying patterns contributing to its occurrence. This approach serves two main purposes: firstly, this enables an analysis of the real-life impact of maverick picking, and secondly, it facilitates the development of improved order picking planning models that incorporate these new insights. In this seminar, we will discuss initial findings from the application of our approach to a real-life data set. As order picker deviations can manifest in various ways, we will focus on identifying and quantifying both deviations from the planned pick order of items (e.g., skipped locations because of a congested aisle) and deviations from the expected times at which picks are performed (e.g., alternative travel paths).

Aïcha Leroy is a PhD candidate at the research group Logistics of Hasselt University specializing in data-driven research in the field of logistics and warehousing. She holds a Bachelor's degree in Business Engineering with a focus on Business Information Systems and a Master's degree in Business Engineering, specializing in Operational Management and Logistics, both from Hasselt University. During her academic journey, Aïcha developed a keen interest in the application of data-driven techniques within the logistics sector. This passion was further ignited during her internship at a prominent logistics company, where she witnessed the significant potential of data-driven approaches in optimizing warehouse operations. Motivated by her dedication to data-driven research in logistics, Aïcha embarked on a PhD to expand her knowledge in this domain. Her current research is centered around analyzing the order picking process in warehousing. By utilizing data-driven techniques, she aims to explore the impact of order picker deviations (a phenomenon called maverick picking) on efficiency and effectiveness. Notably, Aïcha emphasizes the importance of considering the human aspects of this process to ensure a comprehensive approach to optimization.