| dc.description.abstract | The significance of operation processes within production and logistics systems for sustaining a company’s competitiveness is underscored (Sgarbossa et al. 2020). These systems encompass equipment/machines and human components working together as a sociotechnical system to execute processes (Abdu et al. 2016). Supporting system performance requires cohesive enhancements across all interconnected components due to their interdependency (Nardo, Forino, & Murino 2020; Boon-itt & Wong 2016; Bolatan, Gozlu, Alpkan, & Zaim 2016; Neumann & Dul 2010; Neumann, Winkelhaus, Grosse, & Glock 2021). Human involvement in numerous operation processes is pivotal, given their adaptability and flexibility surpassing equipment/machines (Sgarbossa et al. 2020). Manual human labor often proves more costeffective than automation for many companies (Kadir, Broberg, & Conceição 2019). Overlooking human factors (HF) in production and logistics systems can lead to deviations from anticipated performance enhancements (Sgarbossa et al. 2020). Sgarbossa et al. (2020) reveals that planning models for managerial decisions in production and logistics systems often overlook or underestimate HF considerations, resulting in adverse effects on system performance, including increased operational time and cost, decreased productivity, and compromised work quality (Sobhani et al. 2017). Inadequate knowledge of HF aspects can lead to operator fatigue, discomfort, injuries, diminished performance, and heightened errors (Kolus et al. 2018). Consequently, enhancing production and logistics system performance entails incorporating HF principles into decision-making for system design and management.
Understanding the interaction between design and operational decisions is pivotal to comprehending performance deviations and their causes. This study delves into two key decision levels: the design level and the operational level. Decisions at the design level prioritize long-term aspects, while operational level decisions primarily concern short-term considerations, which can be altered more readily due to lower investment and effort requirements (de Koster, le-Duc, & Roodbergen 2007; Schmidt & Wilhelm 2010). The interplay between these levels has a substantial impact, influencing not only human-system interaction but also the overall system performance (Vijayakumar et al., 2022). In production and logistics, the Order Picking (OP) system retrieves products from storage to meet customer needs, constituting 55% of warehouse operations. An efficient, flexible OP system is crucial for quicker delivery and improved customer satisfaction. Manual order pickers are preferred due to their adaptability, cognitive, and motor skills, enabling them to handle diverse items, which automation struggles with. Thus, the focus of this research is on OP system. Consequently, incorporating HF aspects of order pickers into decisions at both levels is essential for performance optimization.
In order to achieve this, the study employed a more comprehensive and expansive understanding of the subject area, integrating elements from both qualitative and quantitative research methodologies. The study was divided into three phases:
The initial phase of the study involved a systematic literature review. The primary aim of this review was to gather insights from the latest research that integrated HF into production and logistics systems. Additionally, the literature review laid the foundation for the research questions addressed in this study. The second phase centered around a comprehensive case study. The primary objective of this case study was to conduct a thorough evaluation of a specific real-world scenario. Emphasis was placed on elucidating the key processes and relationships within this scenario. The central goal of this research was to identify and analyze the essential system designs utilized in the case company’s three distinct warehouses. Simultaneously, the study examined how these designs correlated with the HF experiences encountered by the order pickers. The third phase involved the creation of a mathematical model. This model aimed to provide a detailed and accurate depiction of the problem through the utilization of multi-objective integer optimization techniques. The model was designed to allow decision-makers to explore trade-offs between different objectives. It facilitated informed decision-making that would enhance the overall performance of the OP system in terms of cost, energy consumption by order pickers, and error rates.
As a result of these phases, the outcomes of the systematic literature review played a crucial role in shaping a framework that seamlessly integrated HF considerations with production and logistics systems. This framework not only guided the formulation of the three research questions addressed in the study but also served as a source of inspiration for industry practitioners involved in designing such systems. By prioritizing the HF aspects of operators influenced by system design, the framework offered valuable insights to decision-makers, aiding them in optimizing the design of these systems. Consequently, the utilization of these research methods enabled the study to address the three research questions, leading to the following contributions:
RQ1: How do the decisions at the design level affect the operator in an order picking system?
The primary aim of the initial research question (RQ1) is to comprehensively understand the workload encountered by order pickers. This involves identifying effective methods to evaluate their workload, while considering both the aspects of HF and the design level of the OP system considering both picker-to-part and part-to-picker OP method. To achieve this, the researchers introduced a variety of assessment tools specifically tailored to measure and analyze the HF aspects (physical, mental, perceptual, and psychosocial) relevant to order pickers. These tools offer valuable insights into their work conditions and challenges. With the assessment tools in place, the study then proceeds to a real-world case study, detailing how adjustments to specific design attributes within the OP system can influence the HF experiences of order pickers. Through an examination of these modifications and their effects on order pickers, the research contributes to a deeper comprehension of the interplay between system design and HF. This, in turn, facilitates potential enhancements to the well-being and performance of order pickers within their work environment.
RQ2: How can the operators at operational level be modeled based on the decisions at the design level in an order picking system?
The second research question (RQ2) shifts its focus to the role of technology in supporting order pickers within the system. Acknowledging the significance of integrating technology into the OP system, the study’s intention is not to replace human workers but rather to enhance and empower their capabilities. To address RQ2, the researchers delve into the identification of various levels of automation present in a picker-to-parts OP system. This exploration enables them to determine the appropriate degree of technological integration that optimally complements the abilities of order pickers, thus making their tasks more efficient and manageable. Subsequently, the study introduces a mathematical model designed to explore the concept of dynamic warehouse zoning. This involves strategically arranging the warehouse layout to streamline OP tasks and alleviate the workload on order pickers. Through the examination of real-world scenarios and analysis, the researchers assess how these operational changes impact overall performance and the well-being of order pickers. This underscores the importance of judicious technology implementation to effectively support human operators.
RQ3: How do the decisions at the operational level impact the performance outcomes of an order picking system?
The third research question (RQ3) underscores the critical importance of enhancing HF aspects for order pickers and the potential benefits this brings to the performance outcomes of the OP system. The study emphasizes the need for a comprehensive and systematic consideration of HF to elevate overall system performance. Addressing RQ3 involves investigating how analyzing and addressing HF-related concerns can positively influence various dimensions of the OP system, including productivity, quality, and the well-being of order pickers. This exploration is facilitated through the use of the case study previously examined in RQ2. By highlighting the significance of HF aspects and their interconnectedness with system performance, the research aims to illuminate the relationship between human-machine interactions within OP systems. Ultimately, these insights provide valuable guidance for optimizing these systems, aligning operational efficiency with the welfare of the human workforce. | en_US |
