Fig 4 - uploaded by Dirk Kauke
Content may be subject to copyright.
Source publication
Warehouses are increasingly experiencing a shortage of human resources. The first approaches to counteract this are flexible robot systems that are designed to support human workers. Mobile picking robots, i.e., robots that can pick independently in an order picking system , are increasingly used. At the moment, however, there is no suitable planni...
Contexts in source publication
Context 1
... the following chapter, the question of average performance development will be examined. Figure 4 shows the average performance trends of humans and robots in a 6-meter-wide aisle system with a back cross-aisle. At first, the difference between human and robot performance is noticeable, as shown in Figure 3. Furthermore, it can be seen that the performance curve of the robot is an equable curve, whereas the other graph decreases more rapidly between 10 and 20 humans. ...
Context 2
... a total of 20 robots are in operation, the average output per robot in two shifts is reduced to about 70 orders. Figure 3 and Figure 4 show the course of performance, with an increase in the number of actors of the same type. In other words, how does the humanoperated system behave when more people are added. ...
Citations
... Human performance is one of the critical metrics in human robot interaction (Steinfeld et al., 2006). A hybrid order picking system exists if autonomous systems and human order pickers work together on one shopfloor (Kauke et al., 2022). The human robot collaboration results in a higher need for system coordination and leads to higher attention to human factors in the process (Zhang et al., 2021). ...
... The human robot collaboration results in a higher need for system coordination and leads to higher attention to human factors in the process (Zhang et al., 2021). The research community has investigated the ergonomic (Zhang 333et al., 2021) and economic (L€ offler et al., 2021;Kauke et al., 2022;Winkelhaus et al., 2022) aspects of hybrid order picking systems. Winkelhaus et al. (2022) propose a simulation model for a hybrid order picking system that integrates manual and autonomous order picking; they seek to optimise order picking operations by combining human and robot efforts. ...
... The study identifies crucial factors influencing system performance, providing insights for designing and implementing effective hybrid order picking systems. Furthermore, Kauke et al. (2022) develop a simulation model to examine the impact of the interaction between humans and robots on the overall performance of the order picking system. The discrete-event simulation model demonstrates a correlation relating to an expansion of the agents and performance within the hybrid order picking system. ...
Purpose
Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots).
Design/methodology/approach
Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance.
Findings
We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders.
Originality/value
Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology.
... According to Vijayakumar and Sgarbossa (2021), there have been few research studies on pick and transport robots, mostly presented in conference papers. Among these conference papers, Kauke, Sailer, and Fottner (2022) examined different aisle widths and layouts of the warehouse, where one zone is dedicated to the robot and the other to the order picker. Zhang, Winkelhaus, and Grosse (2021) developed a simulation considering the energy expenditure of order pickers working in close collaboration with a PTR. ...
... Subsequently, Winkelhaus et al. (2022) conducted one of the first simulation studies of a HOPS by focusing on the economic performance of the HOPS compared to a manual and an automated OPS. With an emphasis on the human-robot interaction in order picking, Kauke et al. (2022) simulated the interaction mechanisms to determine their influence on the throughput of OPS. The robot team in the HOPS can perform certain picking tasks to achieve cost advantages, if the frequency of picker blocking is maintained under a certain level by controlling the robot team size and the corresponding assignment rules of the picking tasks (Winkelhaus et al., 2022). ...
... The robot team in the HOPS can perform certain picking tasks to achieve cost advantages, if the frequency of picker blocking is maintained under a certain level by controlling the robot team size and the corresponding assignment rules of the picking tasks (Winkelhaus et al., 2022). In addition, the system performance increases if the warehouse layout is designed to accommodate the HOPS, including the addition of a back cross-aisle and the employment of wide picking aisles for human-robot interaction (Kauke et al., 2022). Note that both simulation studies considered autonomous picking robots, which can independently complete the picking tasks without any assistance from human pickers, thereby defining the physical human--robot interaction in terms of the picker blocking situations that result from humans and robots working in the same area. ...
... Table 1 gives an overview of recent studies on human-robot collaboration in order picking. Among them, only a few studied HOPSs, where robots can autonomously perform the picking tasks and (physically) interact with human pickers (Zhang et al., 2021;Kauke et al., 2022;Winkelhaus et al., 2022). Only two conference papers reporting preliminary results considered ergonomic indicators as an objective besides the economic/processual indicators (Sgarbossa et al., 2020b;Zhang et al., 2021). ...
Warehouses are important nodes in almost every supply chain. Within warehouses, order picking is a crucial task that is extremely time- and cost-intensive. While order picking systems (OPSs) have traditionally been operated manually, new technologies offer opportunities for reducing the workload of warehouse workers. These technologies include autonomous picking robots that can function in combination with human pickers within a shared workspace. This technology enables human–robot collaboration and enhances flexibility in system design, as robots can either support humans or work independently. Research on the advantages of these hybrid OPSs (HOPSs) for improving operational performance is still scarce, however. To contribute to closing this research gap, we propose an agent-based simulation model to investigate how HOPSs reduce the daily workload of human order pickers. The results reveal that HOPSs – if certain assignment rules for the picking tasks are considered – can reduce both the operational costs of the system and human workload compared to a pure manual or a fully automated OPS. Nonetheless, attention should be paid to control the item weight pickers are supposed to handle, as HOPSs reduce the travel distance of human pickers, resulting in a higher frequency of picking activities and an increased ergonomic risk for musculoskeletal disorders.
... Collaborative OPS: A collaborative OPS combines advantages of both flexible human OPSs and the highly efficient autonomous OPSs. In a collaborative OPS, autonomous robots as well as human OPSs are applied to a shared workspace, and these cooperate or collaborate for order fulfilment (Kauke et al., 2020). Within such OPSs, high-level automation technologies are applied in conjunction with appropriate technologies for smart operator OPSs. ...
In this chapter, we discuss the smart warehouse concept and the challenges it entails with the increasing digitalization of the supply chain. The principal enabling technologies that play a major role in the progression towards smart warehouses are identified and discussed in the context of the changes occurring across business, industry, and the retail economy. The warehouse processes affected and potential influences of the technologies on warehouse management and operations are described. The chapter focuses on one of the most important process steps in the smart warehouse, order-picking, which is currently subject to major developments and transformations. Using a technological grid, four types of order-picking system are derived, which systematize how technologies can support human operators in warehouses to reduce physical workload and/or improve cognitive ergonomics. The four system types are classified based on supportive (digital) and substitutive (automation) technologies. The impacts of increased digitalization in warehouses on the physical, cognitive, perceptual and psychosocial human factors are examined from a sociotechnical perspective. These manifold influences are exemplified for the case of a collaborative order-picking system and broken down using an analysis framework that can be used for the systematic development of sociotechnical systems in the digitalisation of the supply chain.
... A HOPS exists if autonomous systems and human order pickers work together on one shopfloor (Kauke et al., 2020) for a joint target. These systems can be of various types, for example, parts-to-picker, picker-toparts, and even fully autonomous in combination with manual OPSs. ...
Order picking is a key task in almost all supply chains and has a significant effect on operational efficiency of warehouses. Although most companies still rely on manual order picking, research on diverse possibilities to automate order picking tasks or support human order pickers with technology is increasing rapidly.
This paper conceptualises Order Picking 4.0 (OP 4.0), considering substitutive and supportive technologies. Based on a conceptual background, a framework for OP 4.0 as a sociotechnical system is developed. A systematic literature review is performed to assess the state of knowledge in this field, and prospective research opportunities in OP 4.0 are highlighted.
... Given the different strengths and weaknesses of human operators and automated systems (Fig. 1), a collaborative order picking system that leverages the individual strengths of both could increase the performance of the warehouse. This paper follows the terminology established by Winkelhaus et al. (2021) and refers to hybrid order picking systems (HOPSs) where autonomous systems and human order pickers work together on one shop floor (Kauke et al., 2020) for a joint target, see Fig. 2 for an example . Alternative definitions can be found in Ibrahim et al. (2020). ...
... In their simulation study, Kauke et al. (2020) investigated a HOPS in a small warehouse with only four aisles and concluded that spatial interactions between humans and robots while performing their tasks increase with larger numbers of humans and robots, which has a negative impact on the number of orders picked per agent. Relying more on robot picking, Verbeet et al. (2019) investigated a HOPS in which robots perform all order picking tasks but can call for humans in case of picking failures. ...
Zusammenfassung
Die Kommissionierung ist eine besonders zeit- und kostenintensive Tätigkeit in der Intralogistik, vor allem wenn diese manuell ausgeführt wird. Deswegen kann es für Unternehmen wirtschaftlich interessant sein, autonome Kommissionierroboter, die mit Menschen zusammenarbeiten, in einem hybriden System einzusetzen. Dieser Artikel gibt einen Überblick über die Vorteile der Mensch-Roboter-Zusammenarbeit in der Intralogistik und quantifiziert diese exemplarisch mit Hilfe eines Simulationsmodells. Daneben werden praxisnahe Herausforderungen bei der Implementierung derartiger hybrider Systeme in Bezug auf Menschenzentrierung, Ergonomie, Technologie-Akzeptanz und wirtschaftliche Arbeitsleistung im Sinne der Industrie 5.0 beleuchtet.
Order picking is a key process in supply chains and a determinant of business success in many industries. Order picking is still performed manually by human operators in most companies; however, there are also increasingly more technologies available to automate order picking processes or to support human order pickers.
One concept that has not attracted much research attention so far is hybrid order picking where automated and autonomous robots and human order pickers work together in warehouses within a shared workspace for a joint target. This study presents a simulation model that considers various system characteristics and parameters of hybrid order picking systems, such as picker blocking, to evaluate the performance of such systems. Our results show that hybrid order picking is generally capable of improving pure manual or automated order picking operations in terms of throughput and total costs. Based on the simulation results, promising future research potentials are discussed.
