Improving Order-Picking Performance in E-Commerce Warehouses through Entropy-Based Hierarchical Scattering

Nilendra Singh Pawar (), Subir S. Rao and Gajendra K. Adil
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Nilendra Singh Pawar: S. P. Jain Institute of Management and Research, Mumbai 400058, India
Subir S. Rao: S. P. Jain Institute of Management and Research, Mumbai 400058, India
Gajendra K. Adil: Indian Institute of Technology Bombay, Mumbai 400506, India

Sustainability, 2024, vol. 16, issue 14, 1-27

Abstract: The high service expectations of e-commerce customers are placing unprecedented demands on e-commerce warehouse workers, leading to higher fatigue and health-related disorders among these workers. Order picking in retail e-commerce fulfilment warehouses (REFWs) is highly labour-intensive and physically demanding activity. This is mainly due to the prevalence of single-unit orders and the expectation of quick order servicing. One strategy to reduce picking effort is the adoption of a scattered storage assignment policy, which spreads the inventory of each product across the entire warehouse. This paper proposes a new, hierarchical approach for the scattering of stock, along with an entropy-based measure for scattering. This measure overcomes some significant limitations of the existing scattering measures and captures the extent of scattering more effectively. We developed a storage assignment heuristic for the scattering of stock and conducted a simulation study to demonstrate its effectiveness in reducing the order-picking effort. Some valuable managerial insights were obtained using a simulation with different warehouse designs and operating parameters. This research also illustrates that the adoption of scattered storage requires careful consideration of the nature of the demand pattern in the warehouse.

Keywords: storage policy; scattered storage assignment; dispersed storage assignment; e-commerce fulfilment warehouse; entropy; warehouse simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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