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The proliferation of (big) data provides numerous opportunities for service advances in practice, yet research on using data to advance service is at a nascent stage in the literature. Many studies have discussed phenomenological benefits of data to service. However, limited research describes managerial issues behind such benefits, although a holistic understanding of the issues is essential in using data to advance service in practice and provides a basis for future research. The purpose of this paper is to address this research gap.

“Using data to advance service” is about change in organizations. Thus, this study uses action research methods of creating real change in organizations together with practitioners, thereby adding to scientific knowledge about practice. The authors participated in five service design projects with industry and government that used different data sets to design new services.

Drawing on lessons learned from the five projects, this study empirically identifies 11 managerial issues that should be considered in data-use for advancing service. In addition, by integrating the issues and relevant literature, this study offers theoretical implications for future research.

“Using data to advance service” is a research topic that emerged originally from practice. Action research or case studies on this topic are valuable in understanding practice and in identifying research priorities by discovering the gap between theory and practice. This study used action research over many years to observe real-world challenges and to make academic research relevant to the challenges. The authors believe that the empirical findings will help improve service practices of data-use and stimulate future research.

The aim of this research was to understand how governmental stakeholder engagement facilitates the sustainability of industrial engineering (IE) projects. A model for governmental stakeholder engagement activities is presented.

The authors relied on a single-case study of a mining project in Northern Europe, where a novel collaboration and engagement approach with governmental stakeholders was piloted in the project's front-end phase. The analysis focused on the collaborative practices through which the IE project investor engaged governmental stakeholders during the project's front-end phase and how the engagement contributed to solving challenges in the early planning and permitting process and achieving project plans that balanced economic, social and environmental aspects.

The findings show how four collaborative engagement practices reduced uncertainty and equivocality related to the legal sustainability requirements, enabled the development of sustainable design solutions and overall accelerated the permitting process without compromising the quality of final project plans.

The findings can be used to plan governmental stakeholder engagement and understand related challenges that need to be overcome. The study highlights the need to develop established practices and guidelines for governmental stakeholder engagement.

This study complements prior research on stakeholder engagement and project sustainability by developing an understanding of how governmental stakeholder engagement can be a key mechanism enabling the sustainability of IE project's end product. This research contributes to stakeholder theory by elaborating on a new stakeholder role, intermediary stakeholder.

The purpose of this study is to explore the benefits and barriers for learning in industrial PhD education through the perspectives of industrial PhD students. A work-integrated learning (WIL) approach is applied to highlight key issues that university and industry need to consider promoting mutual learning.

The empirical context is a Swedish university profiling WIL offering PhD programs in three disciplines for industrial PhD students from both the private and public sectors. Data was gathered using qualitative methods; 19 semistructured interviews with industrial PhD students.

Findings show that industrial PhD students are developing practical and transferable skills, hence, contributing to research of interest for academia and work–life. Identified benefits for learning include proximity and access to data, project and networks and contextual understanding and tacit knowledge. Barriers for learning are the perceived limited understanding of employers, the dilemma of balancing and switching between different roles, lack of belonging and identity, deficient collaboration agreements and ethical dilemmas.

Contributes insights into an industrial PhD education transforming along with societal needs promoting a future workforce of researchers with skills, new work practices and learning capabilities applicable in the work–life of contemporary society.

This study contributes to the emerging field of studies of alternative doctoral educations by identifying benefits and barriers for learning and providing recommendations for how university and industry may promote learning in a resilient industrial PhD education collaboration.

The purpose of this paper is to progress operations management theory and practice by organising contributions to knowledge production, in industrial sustainability, from disparate researcher communities. It addresses the principal question “What scholarly dialogues can be explicated in the emerging research field of industrial sustainability?” and sub-questions: what are the descriptive characteristics of the evidence base? and what thematic lines of scientific inquiry underpin the body of knowledge?

Using an evidenced-based approach, a systematic review (SR) of 574 articles from 62 peer-reviewed scientific journals associated with industrial sustainability is conducted.

This paper distinguishes three prevailing dialogues in the field of industrial sustainability, and uses Kuhn’s theory of paradigms to propose its pre-paradigmatic scientific status. The three dialogues: “productivity and innovation”, “corporate citizenship” and “economic resilience” are conjectured to privilege efficiency strategies as a mode of incremental reductionism. Industrial sustainability espouses the grand vision of a generative, restorative and net positive economy, and calls for a future research trajectory to address institutional and systemic issues regarding scaling-up and transition, through transformative strategies.

The review is limited by the nature of the inquiries addressed in the literatures by specific researcher communities between 1992 and 2014.

This study performs the first SR in the field of industrial sustainability, synthesises prevailing scholarly dialogues and provides an evaluation of the scientific status of the field.

Introducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.

In this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.

The practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.

The dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.

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.

The process of fashion design varies between market segments, yet these variations have not yet been properly explored. This study aims to examine the fashion design process as practised at the mass-market level, as this is the most vibrant and the largest market segment in terms of production volumes and sales.

It is observed that 15 semi-structured interviews were conducted with mass-market fashion designers. Key activities of the mass-market design process were identified and a comparative analysis was conducted with the general design process.

The mass-market design process is found to prioritise profits rather than aesthetic aspects, with the buyer exercising more power than the designer. This hinders creativity, which, in turn, may impede a move towards more environmentally benign designs.

The clothing industry is responsible for high environmental impacts and many of these impacts arise through decisions made in the design stage. In particular, the mass-market for clothing because of its high volume of sales and fast throughput, accounts for a great deal of the impact. However, little is understood about the design process that is practised in the mass-fashion market. This paper fills the gap by developing a framework that describes the mass-market design process. Understanding the design process will enable progress to be made towards achieving the United Nations Sustainable Development Goal 12: Responsible Consumption and Production.

The paper aims to raise awareness in the industry of design automation tools, especially in early design phases, by demonstrating along a case study the seamless integration of a prototypically implemented optimization, supporting design space exploration in the early design phase and an in operational use product configurator, supporting the drafting and detailing of the solution predominantly in the later design phase.

Based on the comparison of modeled as-is and to-be processes of ascent assembly designs with and without design automation tools, an automation roadmap is developed. Using qualitative and quantitative assessments, the potentials and benefits, as well as acceptance and usage aspects, are evaluated.

Engineers tend to consider design automation for routine tasks. Yet, prototypical implementations support the communication and identification of the potential for the early stages of the design process to explore solution spaces. In this context, choosing from and interactively working with automatically generated alternative solutions emerged as a particular focus. Translators, enabling automatic downstream propagation of changes and thus ensuring consistency as to change management were also evaluated to be of major value.

A systematic validation of design automation in design practice is presented. For generalization, more case studies are needed. Further, the derivation of appropriate metrics needs to be investigated to normalize validation of design automation in future research.

Integration of design automation in early design phases has great potential for reducing costs in the market launch. Prototypical implementations are an important ingredient for potential evaluation of actual usage and acceptance before implementing a live system.

There is a lack of systematic validation of design automation tools supporting early design phases. In this context, this work contributes a systematically validated industrial case study. Early design-phases-support technology transfer is important because of high leverage potential.