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Both technological and human-centric perspectives need to be acknowledged when combining lean production practices and Industry 4.0 (I4.0) technologies. This study aims to explore and explain how lean production practices and I4.0 technologies may coexist to enhance the human-centric perspective of manufacturing operations in the era of Industry 5.0 (I5.0).

The research approach is an explorative and longitudinal case study. The qualitative data collection encompasses respondents from different job functions and organizational levels to cover the entire organization. In total, 18 interviews with 19 interviewees and five focus groups with a total of 25 participants are included.

Identified challenges bring forth that manufacturing organizations must have the ability to see beyond lean production philosophy and I4.0 to meet the demand for a human-centric perspective in socially sustainable manufacturing in the era of Industry 5.0.

The study suggests that while lean production practices and I4.0 practices may be considered separately, they need to be integrated as complementary approaches. This underscores the complexity of managing simultaneous organizational changes and new digital initiatives.

The research presented illuminates the elusive phenomena comprising the combined aspects of a human-centric perspective, specifically bringing forth implications for the co-existence of lean production practices and I4.0 technologies, in the transformation towards I5.0.

The study contributes to new avenues of research within the field of socially sustainable manufacturing. The study provides an in-depth analysis of the human-centric perspective when transforming organizations towards Industry 5.0.

This paper addresses a gap in research literature in the fields of blockchain technology (BC), supply chain network dynamics (SC) and network effect phenomena (NE). Extant BC and SC literature describes the potential benefits to be reaped through the adoption of BC technology. While BC technology does not yet meet the researched expectations of adoption, performance and efficacy, the authors analyze the three inter-related fields (BC, SC and NE) to bridge this gap in theory.

This paper begins with a research review correlating the technological fundamentals of BC technology into fundamental value propositions for SC logistics contexts. The authors review the gap between these theoretical technological functions and the current ecosystem of BC applications. With an overarching understanding of BC in SC contexts, this paper then explores the phenomena of NE and attempts to synthesize various interrelated aspects of the three fields (BC, SC and NE). Research frameworks from extant literature are used for cross-comparing legacy software/information system solutions with potential and existing BC-based solutions. Case studies are utilized to support this analysis.

Several key considerations and themes are identified to better inform practitioner and researcher decision-making. Novel insights pertain to BC platform architecture and application modularity, integrated governance and decision-making capabilities, and the automation capabilities that arise from a healthy application and smart contract ecosystem.

The core contribution is the synthesis of network effect theory with SC phenomena and BC theory and the exploration of how these three fields are inter-related in the maturation of BC technology. Specifically, the authors deepen insights from extant literature by contextualizing findings with relevant interdisciplinary theoretical frameworks.

The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the sustainability targets for 2030–2050 increasingly become a tougher challenge, society, company managers and policymakers require more support from AI and IT in general. How can the AI-based and IT-based smart decision-support tools help implementation of circular economy principles from micro to macro scales?

This chapter provides a conceptual framework about the current status and future development of smart decision-support tools for facilitating the circular transition of smart industry, focussing on the implementation of the industrial symbiosis (IS) practice. IS, which is aimed at replacing production inputs of one company with wastes generated by a different company, is considered as a promising strategy towards closing the material, energy and waste loops. Based on the principles of a circular economy, the utility of such practices to close resource loops is analyzed from a functional and operational perspective. For each life cycle phase of IS businesses – e.g., opportunity identification for symbiotic business, assessment of the symbiotic business and sustainable operations of the business – the role played by decision-support tools is described and embedding smartness in these tools is discussed.

Based on the review of available tools and theoretical contributions in the field of IS, the characteristics, functionalities and utilities of smart decision-support tools are discussed within a circular economy transition framework. Tools based on recommender algorithms, machine learning techniques, multi-agent systems and life cycle analysis are critically assessed. Potential improvements are suggested for the resilience and sustainability of a smart circular transition.

A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

The purpose of this paper is to provide a comprehensive survey of the literature about the use of agent-based simulation (ABS) in the study of organizational behavior, decision making, and problem-solving. It aims at contributing to the consolidation of ABS as a field of applied research in management and organizational studies.

The authors carried out a non-systematic search in literature published between 2000 and 2016, by using the keyword “agent-based” to search through Scopus’ business, management and accounting database. Additional search criteria were devised using the papers’ keywords and the categories defined by the divisions and interest groups of the Academy of Management. The authors found 181 articles for this survey.

The survey shows that ABS provides a robust and rigorous framework to elaborate descriptions, explanations, predictions and theories about organizations and their processes as well as develop tools that support strategic and operational decision making and problem-solving. The authors show that the areas that report the highest number of applications are operations and logistics (37 percent), marketing (17 percent) and organizational behavior (14 percent).

The paper illustrates the increasingly prominent role of ABS in fields such as organizational behavior, strategy, human resources, marketing and logistics. To-date, this is the most complete survey about ABS in all management areas.

Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations.

After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations).

The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations).

This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).

Learning as the way in which labor acquire new knowledge and skills has important strategic implications for the competitive advantage of an organization. The purpose of this paper is to present an agent-based modeling (ABM) approach to investigate the learning behavior of workers. The effect of interactions among different workers as well as the factors affecting the workers’ learning behavior is assessed using the proposed ABM approach.

For this purpose, the processes through which the competency value of worker is changed are understood and the workers’ learning behavior is modeled, taking account of various influencing factors such as knowledge flow, social ability to teach and forgetting factor.

The proposed model is implemented on a real steel structure project to evaluate its applicability and performance. The variation in the competency value of different workers involved in the project is simulated over time taking account of all the influencing factors using the proposed ABM approach.

In order to assess the effect of interactions among welders as well as the welders’ characteristics on their learning behavior, the competence value of different welders is evaluated.

This research presents an ABM approach to investigate the workers’ learning behavior. To evaluate the performance of the proposed ABM approach, it was implemented on a real steel structure project. The learning behavior of different welders (agents) was simulated taking account of their interactions as well as the factors affecting the welders’ learning behavior. The project involved the welding of a 240-ton steel structure. The initial project duration was estimated as 100 days. In this project, it has been planned to execute the welding process using three different welders namely welder A, B and C.

The purpose of this paper is to study how to effectively allocate water, energy and food (WEF) resources in urban development.

An agent-based model combined with NetLogo simulation model has been used in this paper.

This paper proposes a framework for agent analysis in urban WEF consumption.

Further discussions using empirical data are of great importance.

Apply to form the development model of the city in the future.

A new method of WEF management has been used at the city level.

This paper aims to explore drivers of the effectiveness of risk assessments in risk workshops.

This study uses an agent-based model to simulate risk assessments in risk workshops. Combining the notions of transactive memory and the ideal speech situation, this study establishes a risk assessment benchmark and then investigates real-world deviations from this benchmark. Specifically, this study models limits to information transfer, incomplete discussions and potentially detrimental group characteristics, as well as interaction patterns.

First, limits to information transfer among workshop participants can prevent a correct consensus. Second, increasing the required number of stable discussion rounds before an assessment improves the correct assessment of high but not low likelihood risks. Third, while theoretically advantageous group characteristics are associated with the highest assessment correctness for all risks, theoretically detrimental group characteristics are associated with the highest assessment correctness for high likelihood risks. Fourth, prioritizing participants who are particularly concerned about the risk leads to the highest level of correctness.

This study shows that by increasing the duration of simulated risk workshops, the assessments change – as a rule – from underestimating to overestimating risks, unraveling a trade-off for risk workshop facilitators. Methodologically, this approach overcomes limitations of prior research, specifically the lack of an assessment and process benchmark, the inability to disentangle multiple effects and the difficulty of capturing individual cognitive processes.

This article aims to investigate the impact of (1) the establishment of a refugee settlement, (2) the energy demand of a host and refugee population, (3) the residence time of refugees and (4) interventions in the energy sector on sustainable utilization of the forest.

Refugee movements from the Democratic Republic of Congo and settlement construction in a Zambian host society provide the setting. An agent-based model is developed. It uses survey data from 277 Zambian households, geographic information system coordinates and supplementary data inputs.

The future forest stock remains up to 30 years without an influx of refugees. Refugee developments completely deplete the forest over time. The settlement construction severely impacts the forest, while refugees' energy needs seem less significant. Compared with the repatriation of refugees, permanent integration has no influential impact on forest resources. Interventions in the energy sector through alternative sources slow down deforestation. Once a camp is constructed, tree cutting by hosts causes forest covers to decline even if alternative energy is provided.

The analysis is useful for comparable host–refugee settings and United Nations High Commissioner for Refugees interventions in settlement situations. Forest and energy sector interventions should involve host and refugee stakeholders.

This article adds value through an agent-based model in the Zambian deforestation–refugee context. The study has a pilot character within the United Nation's Comprehensive Refugee Response Framework. It fills a gap in long-term assessments of refugee presence in local host communities.