Work Characteristics in Logistics 4.0 : Conceptualization of a qualitative assessment in order picking

Logistics 4.0 is an emerging phenomenon, where new digital technologies are used to fulfil increasing demands of delivering individualized products against high cost pressure and fast delivery times. Within this development, human factors should not be neglected, since they will remain an important aspect of process efficiency and business success. One logistics process that has undergone a major transformation within Logistics 4.0 regarding manual human work is warehouse order picking. However, the implications of increasing digitalization on human workers in order picking are not yet fully understood. Hence, this contribution grounds on a qualitative assessment of work characteristics in order picking and conceptualizes possible influences of the digital transformation on order pickers. These conceptual interrelations enable to design future workplaces that allow higher job satisfaction, motivation, quality and performance of work as well as lower turnover intentions

Potential of mobile applications in human-centric production and logistics management

With the increasing market penetration of smart devices (smartphones, smartwatches, and tablets), various mobile applications (apps) have been developed to fulfill tasks in daily life. Recently, efforts have been made to develop apps to support human operators in industrial work. When apps installed on commercial devices are utilized, tasks that were formerly done purely manually or with the help of investment-intensive specific devices can be performed more efficiently and/or at a lower cost and with reduced errors. Despite their advantages, smart devices have limitations because embedded sensors (e.g., accelerometers) and components (e.g., cameras) are usually designed for nonindustrial use. Hence, validation experiments and case studies for industrial applications are needed to ensure the reliability of app usage. In this study, a systematic literature review was employed to identify the state of knowledge about the use of mobile apps in production and logistics management. The results show how apps can support human centricity based on the enabling technologies and components of smart devices. An outlook for future research and applications is provided, including the need for proper validation studies to ensure the diversity and reliability of apps and more research on psychosocial aspects of human-technology interaction

Smart lighting systems : state-of-the-art and potential applications in warehouse order picking

Artificial lighting is a constant companion in everyday private and working life, influencing visibility in interior spaces as well as outdoors. In recent years, new technical solutions have extended traditional lighting systems to become ‘smart’. Different types of smart lighting systems are available on the market today, and researchers have concentrated on analysing their usability and efficiency, especially for private households, office buildings and public streets. This paper presents a systematic literature review to analyse the state-of-knowledge of technologies and applications for smart lighting systems. The results of the review show that smart lighting systems have been frequently discussed in the literature, but that their potentials in industrial environments, such as production and logistics, has rarely been addressed in the literature so far. Lighting systems for industrial environments often have very different requirements depending on the working environment and operating conditions. Based on the results of the literature review, this paper contributes to closing this research gap by discussing the usage potential of smart lighting systems to improve the efficiency of warehouse order picking, which is an application that may benefit from various functions smart lighting systems provide. Several propositions are developed that emphasise research opportunities and managerial implications in this context

Job satisfaction : An explorative study on work characteristics changes of employees in Intralogistics 4.0

The increasing trend toward digitalization in logistics poses a significant managerial challenge, particularly by fundamentally changing the traditional, manual workplaces in intralogistics. Although intralogistics processes have, in some cases, already been automated or are supported by smart technologies, humans remain an inevitable part of future intralogistics but with changing work characteristics. This study aims to examine the influences of the transition toward Intralogistics 4.0 on work characteristics of intralogistics employees. First, a systematic literature review on work characteristics and job satisfaction in a broader Logistics 4.0 context was conducted. Thereafter, a qualitative, explorative methodology was employed to examine the perception of work characteristics that impact job outcomes such as job satisfaction, motivation, and performance at different Intralogistics 4.0 maturity levels. The results of semi-structured interviews conducted across seven companies demonstrated the significant, heterogeneous changes of work characteristics related to the type of technology applied in Intralogistics 4.0. Our findings indicate that the development toward Intralogistics 4.0-implemented workplaces does not have a simple or predefined impact on humans; instead, the individual design is relevant and can improve the workplaces with more opportunities for satisfying and motivating jobs

Opportunities for using eye tracking technology in manufacturing and logistics: Systematic literature review and research agenda

Workers play essential roles in manufacturing and logistics. Releasing workers from routine tasks and enabling them to focus on creative, value-adding activities can enhance their performance and wellbeing, and it is also key to the successful implementation of Industry 4.0. One technology that can help identify patterns of worker-system interaction is Eye Tracking (ET), which is a non-intrusive technology for measuring human eye movements. ET can provide moment-by-moment insights into the cognitive state of the subject during task execution, which can improve our understanding of how humans behave and make decisions within complex systems. It also enables explorations of the subject’s interaction mode with the working environment. Earlier research has investigated the use of ET in manufacturing and logistics, but the literature is fragmented and has not yet been discussed in a literature review yet. This article therefore conducts a systematic literature review to explore the applications of ET, summarise its benefits, and outline future research opportunities of using ET in manufacturing and logistics. We first propose a conceptual framework to guide our study and then conduct a systematic literature search in scholarly databases, obtaining 71 relevant papers. Building on the proposed framework, we systematically review the use of ET and categorize the identified papers according to their application in manufacturing (product development, production, quality inspection) and logistics. Our results reveal that ET has several use cases in the manufacturing sector, but that its application in logistics has not been studied extensively so far. We summarize the benefits of using ET in terms of process performance, human performance, and work environment and safety, and also discuss the methodological characteristics of the ET literature as well as typical ET measures used. We conclude by illustrating future avenues for ET research in manufacturing and logistics

Evaluation of human workload in a hybrid order picking system

Order picking is a labor-intensive and costly process in supply chains, which is performed manually in most cases. Recently, picking robots have been developed which are capable of working together with human pickers in a shared working space. Such hybrid order picking system can ease human pickers’ workload and provide ergonomic improvements, because it partially automates the order picking process. We propose a simulation model to measure the energy expenditure of human pickers who work with the support of picking robots. The hybrid order picking system is evaluated based on its operational costs, efficiency, and ergonomic characteristics. Preliminary results presented in this study show that there are assignment rules for items to workers and robots that reduce human energy expenditure and costs per pick, as well as maintain average throughput time at a certain level. The aim of this preliminary study is to closely analyze the hybrid order picking system, evaluate managerial implications, and detect research opportunities for future works

Hybrid order picking : A simulation model of a joint manual and autonomous order picking system

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 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

New solution procedures for the order picker routing problem in U-shaped pick areas with a movable depot

This paper develops new solution procedures for the order picker routing problem in U-shaped order picking zones with a movable depot, which has so far only been solved using simple heuristics. The paper presents the frst exact solution approach, based on combinatorial Benders decomposition, as well as a heuristic approach based on dynamic programming that extends the idea of the venerable sweep algorithm. In a computational study, we demonstrate that the exact approach can solve small instances well, while the heuristic dynamic programming approach is fast and exhibits an average optimality gap close to zero in all test instances. Moreover, we investigate the infuence of various storage assignment policies from the literature and compare them to a newly derived policy that is shown to be advantageous under certain circumstances. Secondly, we investigate the efects of having a movable depot compared to a fxed one and the infuence of the efort to move the depot

Reciprocal Learning in Production and Logistics

Integration of AI technologies and learnable systems in production and logistics transforms the concepts of work organization and task assignments to human and machine agents. Thus, the question arises of what intelligent machines and human workers may be able to achieve as teammates. One answer may be guiding and training the workforce at the workplace to cope with emerging skill mismatches, emphasized by concepts of work-based learning. The extension of cyber-physical production systems towards becoming human-centered and social systems enabling human-machine interaction, creates opportunities for human-machine symbiosis by complementing each other's strengths. In this way, the concept of “Reciprocal Learning” (RL) between humans and intelligent machines has emerged, which is still rather ambiguous and lacks a profound knowledge base. Especially in production and logistics, literature is fragmented. Hence, the objective of this paper is to conduct a systematic literature review to elicit and cluster the knowledge base in RL represented by adjacent interdisciplinary fields of research, such as social and computer sciences. This work contributes to the literature by developing a comprehensive knowledge base on the concept of RL enabling to pursue future research directions towards the realization of human-machine symbiosis through RL in production and logistics

Industry 4.0 and the human factor : A systems framework and analysis methodology for successful development

The fourth industrial revolution we currently witness changes the role of humans in operations systems. Although automation and assistance technologies are becoming more prevalent in production and logistics, there is consensus that humans will remain an essential part of operations systems. Nevertheless, human factors are still underrepresented in this research stream resulting in an important research and application gap. This article first exposes this gap by presenting the results of a focused content analysis of earlier research on Industry 4.0. To contribute to closing this gap, it then develops a conceptual framework that integrates several key concepts from the human factors engineering discipline that are important in the context of Industry 4.0 and that should thus be considered in future research in this area. The framework can be used in research and development to systematically consider human factors in Industry 4.0 designs and implementations. This enables the analysis of changing demands for humans in Industry 4.0 environments and contributes towards a successful digital transformation that avoid the pitfalls of innovation performed without attention to human factors. The paper concludes with highlighting future research directions on human factors in Industry 4.0 as well as managerial implications for successful applications in practice