In pursuit of humanised order picking planning: methodological review, literature classification and input from practice

Abstract

At the core of every high-performing warehouse is an efficient order picking (OP) system. To attain such a system, policy choices should be carefully aligned with subjects responsible for the actual picking within the established system. Despite recent advancements in automating the picking process due to Industry 4.0, human operators will continue to play a crucial role in the future of warehousing. However, unlike robots, human operators have specific skills, conduct, and perceptions, which are only partly accounted for in current planning models. This review adopts a multimethod approach to identify and analyse how these phenomena are currently integrated into OP planning problems. In addition, we assess the relevance and adequacy of human factors modelling in academic literature with practice-based insights gathered via semi-structured interviews. This leads to five major human factors integration constructs and dedicated recommendations on how to refine them. We then take the analysis one step further and make suggestions on how to integrate these constructs with leading research methodologies in the context of Industry 5.0. The results highlight the prevalent need to increasingly account for psychosocial phenomena and their impact on operational performance. Future research opportunities provide a substantiated foundation to assist in human-centric work design.

Storage Location Assignment for Improving Human–Robot Collaborative Order-Picking Efficiency in Robotic Mobile Fulfillment Systems

“…However, the factors of human pickers in human-robot collaboration draw relatively less attention in this field. A few studies quantify the dynamic physical working rate of human pickers into the optimization model in the context of order picking [31]. Wang et al [32] formulate dynamic human pickers' working rate to design a work schedule in human-robot coordinated order picking.…”

Industrial Internet of Things (IIoT) and Other Industry 4.0 Technologies in Spare Parts Warehousing in the Oil and Gas Industry: A Systematic Literature Review

“…With optimal planning for this type of system, maintenance-dependent industries can develop functioning frameworks for ideal IoT data utilization. In a warehouse setting, order picking is the costliest of all activities, partly due to the localization of relevant goods [42]. Order picking 4.0 entails using the technologies and interconnectivity principle of Industry 4.0, of which IoT is an important part.…”

Minimization of metabolic energy expenditure in collaborative order picking

“…Order-picking literature mainly focuses on routing, layout design, storage assignment, order batching, and zoning problems by only considering temporal or economic values rather than ergonomic values, which are mentioned very infrequently [13]. Limiting our focus on ergonomic problems in order picking, we address interested readers in order-picking problems to review papers [10,19,20].…”

“…However, ergonomics is relatively less mentioned in academic studies [7] even though order pickers are exposed to high ergonomic disorders due to abnormal postures, excessive force, and task repetition [6][7]. Moreover, operational models did not include human factors sufficiently [10]. Apparently, ignoring ergonomics in order picking operations results in an incomplete representation of real-world practice [1] and leads to the most common occupational disease for order pickers, musculoskeletal disorders [11].…”

“…Despite technological advances, traditional order picking (TOP) is still significantly used in small-or middlesized warehouses [10][11][12], which is characterized by walking long distances (see Fig. 1.a).…”