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Industry 4.0 Process Design—Enhancing Organizational and Social Sustainability

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Management".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 29319

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Special Issue Editors

Dr. Udo Kannengiesser

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Guest Editor

Institute of Business Informatics - Communications Engineering, and Business School, Johannes Kepler University Linz, 4040 Linz, Austria
Interests: Industry 4.0; stakeholder-centred process management; multi-agent systems; design computing; design cognition

Prof. Dr. Alois Zoitl

E-Mail Website
Guest Editor

LIT Cyber-Physical Systems Lab, Johannes Kepler University Linz, 4040 Linz, Austria
Interests: Industry 4.0; holonic manufacturing; product-driven production planning; reconfigurable production systems; adaptive distributed real-time control

Special Issue Information

Dear Colleagues,

Industry 4.0 (I4.0) has been a major buzzword over the last few years, driven by industry associations, academic institutions and political players, conjointly touting it as the new mainstream. However, many companies are hesitant to transform their traditionally grown production operations into the smart, decentralized networks recommended by I4.0 proponents. This is due to the high organizational and investment risks involved and a lack of know-how in I4.0 process design and implementation. In addition, the workforce often opposes I4.0 transformations because they fear losing their jobs or being forced into new processes that they perceive as too rigid and not adapted to their needs. These challenges relate to the organizational and social sustainability of I4.0 processes at a micro level, within the scope of the internal operations of production enterprises. There is insufficient understanding of such micro-level aspects, as most sustainability research has focused on the economic and environmental impact of I4.0 on a macro level. Given the different design layers, interfaces of micro-level structures and behaviours with their macro-level correspondences also need to be put under scrutiny, based on the high connectivity, if not co-location, of actors in I4.0 eco-systems.

The purpose of this Special Issue is to collect conceptual approaches, empirical studies, case studies and literature reviews investigating Industry 4.0 process design pertaining to organizational and social sustainability within production enterprises.

Topics to be explored include, but are not limited to:

Human factors in Industry 4.0
Stakeholder involvement in I4.0 process design
Explainable I4.0 process designs
Incremental design methods for Industry 4.0 processes
Model-driven engineering of I4.0 processes
Process modelling for I4.0 systems
Risk management processes for Industry 4.0
Change management for digital transformation
Technical and organizational interoperability in and across smart factories
Lean Industry 4.0
Continuous improvement approaches for Industry 4.0 processes
Decentralized elicitation of I4.0 processes
Seamless integration of business and production processes
I4.0 servitization and business process integration
Ambidextrous I4.0 process management
Sensemaking support for I4.0 stakeholders
Composable I4.0

Dr. Udo Kannengiesser
Prof. Dr. Alois Zoitl
Guest Editors

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Keywords

Industry 4.0
digital transformation of production systems
smart manufacturing
organizational sustainability
social sustainability
human-centred design
process design
process management

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Published Papers (7 papers)

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Research

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22 pages, 2655 KiB

Open AccessArticle

Designing Socially and Organizationally Sustainable Industry 4.0 Systems: Requirements for Modeling Approaches

by Udo Kannengiesser

Sustainability 2023, 15(20), 14706; https://doi.org/10.3390/su152014706 - 10 Oct 2023

Cited by 1 | Viewed by 1134

Abstract

Industry 4.0 (I4.0) systems are often designed without sufficiently considering the needs of stakeholders and the organizational processes to be supported, leading to solutions that are socially and organizationally unsustainable. In this study, the notions of social and organizational sustainability were viewed from a micro-level perspective, referring to the ability of technology to sustain the concerns of people and work organization within the socio-technical system, as opposed to a macro-level perspective related to concerns outside the system. Through a literature review, this study shows that social and organizational sustainability is covered by principles originally proposed in agile software engineering. A set of core requirements for model-based design approaches were then derived from the agile principles, based on insights from design research and model theory. The requirements include (1) the coverage of function and behavior, (2) simplicity, (3) executability and (4) modularity. They were then used to evaluate an existing modeling approach—subject-oriented process modeling (S-BPM)—to demonstrate their applicability and usefulness. Full article

(This article belongs to the Special Issue Industry 4.0 Process Design—Enhancing Organizational and Social Sustainability)

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29 pages, 6268 KiB

Open AccessArticle

An Employee Competency Development Maturity Model for Industry 4.0 Adoption

by Bertha Leticia Treviño-Elizondo and Heriberto García-Reyes

Sustainability 2023, 15(14), 11371; https://doi.org/10.3390/su151411371 - 21 Jul 2023

Cited by 4 | Viewed by 5270

Abstract

Industry 4.0 (I4.0) is challenging for organizations, as workers lack digital competencies, and research on new roles is limited. Additionally, existing models for its adoption focus on technology incorporation, process improvement, and organizational transformation. Therefore, the opportunity exists for designing a new model that emphasizes developing employees’ competencies. A systematic literature review was conducted regarding existing models for I4.0 adoption and the desired worker competencies. After examining the gap in the current models and the categorization of their main elements, a new maturity model (MM) for I4.0 adoption, based on the development of employees’ competencies, is proposed. The MM helps practitioners and researchers assess an organization’s I4.0 adoption level in order to improve future actions. A validation process for the MM was implemented through the Delphi method. Additionally, a roadmap to guide workforce development is presented, which considers the digital challenges face by employees in advancing a strategic I4.0 adoption. The proposed roadmap allows for depicting new deployment strategies aligned with digital trends and employees’ commitments to sustaining the implementation efforts. This research recognizes talent, organizational culture, and communication plans as key elements for defining actions for developing the skills and competencies required for embracing the I4.0 enabling technologies. Full article

(This article belongs to the Special Issue Industry 4.0 Process Design—Enhancing Organizational and Social Sustainability)

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27 pages, 7869 KiB

Open AccessArticle

A Digital Twin-Based Distributed Manufacturing Execution System for Industry 4.0 with AI-Powered On-The-Fly Replanning Capabilities

by Jiří Vyskočil, Petr Douda, Petr Novák and Bernhard Wally

Sustainability 2023, 15(7), 6251; https://doi.org/10.3390/su15076251 - 5 Apr 2023

Cited by 14 | Viewed by 3726

Abstract

Industry 4.0 smart production systems comprise industrial systems and subsystems that need to be integrated in such a way that they are able to support high modularity and reconfigurability of all system components. In today’s industrial production, manufacturing execution systems (MESs) and supervisory control and data acquisition (SCADA) systems are typically in charge of orchestrating and monitoring automated production processes. This article explicates an MES architecture that is capable of autonomously composing, verifying, interpreting, and executing production plans using digital twins and symbolic planning methods. To support more efficient production, the proposed solution assumes that the manufacturing process can be started with an initial production plan that may be relatively inefficient but quickly found by an AI. While executing this initial plan, the AI searches for more efficient alternatives and forwards better solutions to the proposed MES, which is able to seamlessly switch between the currently executed plan and the new plan, even during production. Further, this on-the-fly replanning capability is also applicable when newly identified production circumstances/objectives appear, such as a malfunctioning robot, material shortage, or a last-minute change to a customizable product. Another feature of the proposed MES solution is its distributed operation with multiple instances. Each instance can interpret its part of the production plan, dedicated to a location within the entire production site. All of these MES instances are continuously synchronized, and the actual global or partial (i.e., from the instance perspective) progress of the production is handled in real-time within one common digital twin. This article presents three main contributions: (i) an execution system that is capable of switching seamlessly between an original and a subsequently introduced alternative production plan, (ii) on-the-fly AI-powered planning and replanning of industrial production integrated into a digital twin, and (iii) a distributed MES, which allows for running multiple instances that may depend on topology or specific conditions of a real production plant. All of these outcomes are demonstrated and validated on a use-case utilizing an Industry 4.0 testbed, which is equipped with an automated transport system and several industrial robots. While our solution is tested on a lab-sized production system, the technological base is prepared to be scaled up to larger systems. Full article

(This article belongs to the Special Issue Industry 4.0 Process Design—Enhancing Organizational and Social Sustainability)

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28 pages, 3671 KiB

Open AccessArticle

Task Classification Framework and Job-Task Analysis Method for Understanding the Impact of Smart and Digital Technologies on the Operators 4.0 Job Profiles

by Chiara Cimini, David Romero, Roberto Pinto and Sergio Cavalieri

Sustainability 2023, 15(5), 3899; https://doi.org/10.3390/su15053899 - 21 Feb 2023

Cited by 11 | Viewed by 4426

Abstract

There is limited scientific and grey literature studying the phenomenon of how the current job profiles are being affected by Industry 4.0 technologies at the operational level. This paper aims to answer the following question: how can the evolution of Workforce 4.0 job profiles be analyzed from a job-task perspective concerning the adoption of smart and digital technologies in manufacturing companies? To this end, it presents a task classification framework addressing three task classification dimensions, namely: (i) routine/nonroutine tasks, (ii) physical/cognitive tasks, and (iii) individual/social tasks, and a job-task analysis method to analyze the evolution of job profiles due to smart or digital technology adoption at the task level. Both artifacts were created using a state-of-the-art review to ground their conceptualization in the most recent knowledge available on work design and job-task analysis methods and were later evaluated and refined using an action-research approach to increase their applicability and usefulness for academic researchers and practitioners. The applicability of the proposed framework and method was demonstrated in an industrial case study discussing the theoretical and managerial contributions of these two artifacts for the development of Workforce 4.0 job profiles. It was concluded that the proposed framework and method are valuable artifacts that contribute to the limited universe of tools available in the literature to first analyze how operators’ tasks and roles change concerning the adoption of new Industry 4.0 technologies and then identify the requirements of new skills and competencies for the evolving and emerging job profiles on the shop floor. Full article

(This article belongs to the Special Issue Industry 4.0 Process Design—Enhancing Organizational and Social Sustainability)

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22 pages, 7528 KiB

Open AccessArticle

Enriching Socio-Technical Sustainability Intelligence through Sharing Autonomy

by Richard Heininger, Thomas Ernst Jost and Christian Stary

Sustainability 2023, 15(3), 2590; https://doi.org/10.3390/su15032590 - 1 Feb 2023

Cited by 6 | Viewed by 2746

Abstract

We suggest to extend scientific research on sustainability beyond its focus on interactions between natural and social systems to socio-technical systems and the ways in which those interactions affect the challenge of sustainability. In increasingly digitalized settings, socio-technical sustainability intelligence becomes critical for human-centered development of societies worldwide, including the achievement of future organizational success. Human-centered enablers, such as self-awareness, global perspective, and societal consciousness, lay foundation for reflective socio-technical practice in highly dynamic ecosystems that are increasingly backed by Cyber-Physical Systems (CPS). Socio-technical practice requires frameworks and architectures that support active stakeholder engagement throughout design and engineering. In this contribution, we propose sharing autonomy as inherent feature of sustainable socio-technical system development and operation. We introduce an architecture and mechanism for building and handling autonomy as part of socio-technical sustainability intelligence. We exemplify both with a system-relevant logistics use case to illustrate the enrichment of CPS-based socio-technical environments through active stakeholder participation. Full article

(This article belongs to the Special Issue Industry 4.0 Process Design—Enhancing Organizational and Social Sustainability)

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Review

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27 pages, 1611 KiB

Open AccessReview

Sustainable Development and Implementation of Quality Management Excellence Models in Public Organizations: A Systematic Literature Review

by Effrosyni Taraza, Sofia Anastasiadou, Andreas Masouras and Christos Papademetriou

Sustainability 2023, 15(10), 7971; https://doi.org/10.3390/su15107971 - 13 May 2023

Cited by 11 | Viewed by 3566

Abstract

Purpose: The purpose of this study was to determine the effects of the European Foundation for Quality Management Excellence Model and Six Sigma and Lean Six Sigma approaches in public organizations. Design/methodology/approach: A systematic literature review was conducted based on articles from three academic publishers (Emerald, Elsevier/Science Direct and Taylor & Francis). The 88 selected journal articles were published between 2004 and 2022 and documented the results of the quality tools. Findings: The effects of applying the models in the public sector are presented. From the literature review, specific findings were identified regarding the motivations of all areas of education and services and the challenges they face in applying the qualitative tool methodologies. The main topics discussed are the human factors involved in implementing quality tools. Research limitations/implications: An important limitation is that data were drawn from only three major journals and the authors did not always have access to all databases and peer-reviewed journals or to any review articles in languages other than English. Multiple keywords limited the article search, as qualitative tools have been widely used in the private sector but less so in the public sector. Practical implications: The results and limitations detailed in the study and presentation of the 88 articles will motivate academic researchers to further study the application of qualitative tools in the public sector and fill the knowledge gap caused by the limited publications on this topic. Originality/value: The European Foundation for Quality Management Excellence Model and the Six Sigma and Lean Six Sigma approaches have not been widely implemented in the public sector, and literature reviews are limited despite the increasing trend of their use in the sector in recent years. More future research in public administration is needed to determine the effects and limitations of implementing qualitative tools. Full article

(This article belongs to the Special Issue Industry 4.0 Process Design—Enhancing Organizational and Social Sustainability)

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37 pages, 1893 KiB

Open AccessReview

Sustainability and Industry 4.0: Definition of a Set of Key Performance Indicators for Manufacturing Companies

by Giuditta Contini and Margherita Peruzzini

Sustainability 2022, 14(17), 11004; https://doi.org/10.3390/su141711004 - 2 Sep 2022

Cited by 41 | Viewed by 6898

Abstract

Today, sustainability represents a fundamental concept to be developed and implemented in any industrial context. Therefore, it is essential to be able to measure sustainability performance by proper indicators, along the entire lifecycle and the value chain, considering environmental, economic, and social impacts. Moreover, every manufacturing company should have a specific measuring framework to calculate all the specific parameters. In this direction, the modern digital transition and Industry 4.0 (I4.0) technologies are proposing to transform human–machine relations, with a significant impact on social and organizational aspects. At the same time, digitization can help companies to define and implement sustainability by correlating production with proper evaluation metrics. The aim of this research is to provide a complete overview of sustainability Key Performance Indicators (KPIs) based on the Triple Bottom Line concept, referring to the three sustainability areas. Such an overview can be used by companies to set their specific KPIs and metrics to measure their sustainability level, according to their needs. Full article

(This article belongs to the Special Issue Industry 4.0 Process Design—Enhancing Organizational and Social Sustainability)

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