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The purpose of this paper is to assess the differences in importance assigned by manufacturing or service organizations to topics related to operations management and its attendant body of knowledge.

The authors did this by cataloguing and analyzing vacancy announcements related to operations management, presented by manufacturing and services companies in major Brazilian human resources websites.

The results show that manufacturing companies primarily hire personnel with skills in routine process management, quality management, lean manufacturing, ergonomics and work organization. Service companies generally seek professionals with knowledge and experience in logistics, supply chain management and project management.

This study presents some limitations that reduce the power of its conclusions. There is some degree of subjectivity in the interpretation of the contents of the analyzed ads. In order to reduce this problem, the authors who did the tabulation of data marked the situations for which there were some doubts about the classification, discussing them with the other author, until they reached a consensus on the best way to classify each one.

The discussion about the importance assigned by manufacturing and service companies to the topics of operations management is crucial for not only the results obtained, but also to stimulate the debate on topics that comprise or should comprise the body of knowledge of operations management, and the way they are incorporated into business practice. This provides an additional opportunity to reflect on the potential of operations management in supporting business managers now and in the future.

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 purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.

Lean distributed manufacturing (LDM) is being considered as an enabler of achieving sustainability and resilience in manufacturing and supply chain operations. The purpose of this paper is to enhance the understanding of how LDM characteristics affect the resilience of manufacturing companies by drawing upon the experience of food manufacturing companies operating in the UK.

The paper develops a conceptual model to analyse the impact of LDM on the operational resilience of food manufacturing companies. A triangulation research methodology (secondary data analysis, field observations and structured interviews) is used in this study. In a first step, LDM enablers and resilience elements are identified from literature. In a second step, empirical evidence is collected from six food sub-sectors aimed at identifying LDM enablers being practised in companies.

The analysis reveals that LDM enablers can improve the resilience capabilities of manufacturing companies at different stages of resilience action cycle, whereas the application status of different LDM enablers varies in food manufacturing companies. The findings include the development of a conceptual model (based on literature) and a relationship matrix between LDM enablers and resilience elements.

The developed relationship matrix is helpful for food manufacturing companies to assess their resilience capability in terms of LDM characteristics and then formulate action plans to incorporate relevant LDM enablers to enhance operational resilience.

Based on the literature review, no studies exist that investigate the effects of LDM on factory’s resilience, despite many research studies suggesting distributed manufacturing as an enabler of sustainability and resilience.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

The existing literature expresses a strong need to develop tools that support the manufacturing reshoring decision-making process. This paper aims to examine the suitability of analytical hierarchy process (AHP)-based tools for initial screening of manufacturing reshoring decisions.

Two AHP-based tools for the initial screening of manufacturing reshoring decisions are developed. The first tool is based on traditional AHP, while the second is based on fuzzy-AHP. Six high-level and holistic reshoring criteria based on competitive priorities were identified through a literature review. Next, a panel of experts from a Swedish manufacturing company was involved in the overall comparison of the criteria. Based on this comparison, priority weights of the criteria were obtained through a pairwise analysis. Subsequently, the priority weights were used in a weighted-sum manner to evaluate 20 reshoring scenarios. Afterwards, the outputs from the traditional AHP and fuzzy-AHP tools were compared to the opinions of the experts. Finally, a sensitivity analysis was performed to evaluate the stability of the developed decision support tools.

The research demonstrates that AHP-based support tools are suitable for the initial screening of manufacturing reshoring decisions. With regard to the presented set of criteria and reshoring scenarios, both traditional AHP and fuzzy-AHP are shown to be consistent with the experts' decisions. Moreover, fuzzy-AHP is shown to be marginally more reliable than traditional AHP. According to the sensitivity analysis, the order of importance of the six criteria is stable for high values of weights of cost and quality criteria.

The limitation of the developed AHP-based tools is that they currently only include a limited number of high-level decision criteria. Therefore, future research should focus on adding low-level criteria to the tools using a multi-level architecture. The current research contributes to the body of literature on the manufacturing reshoring decision-making process by addressing decision-making issues in general and by demonstrating the suitability of two decision support tools applied to the manufacturing reshoring field in particular.

This research provides practitioners with two decision support tools for the initial screening of manufacturing reshoring decisions, which will help managers optimize their time and resources on the most promising reshoring alternatives. Given the complex nature of reshoring decisions, the results from the fuzzy-AHP are shown to be slightly closer to those of the experts than traditional AHP for initial screening of manufacturing relocation decisions.

This paper describes two decision support tools that can be applied for the initial screening of manufacturing reshoring decisions while considering six high-level and holistic criteria. Both support tools are applied to evaluate 20 identical manufacturing reshoring scenarios, allowing a comparison of their output. The sensitivity analysis demonstrates the relative importance of the reshoring criteria.

Industry 4.0 technologies are promising to increase manufacturing companies' performance through the new knowledge that such digital technologies allow to create and manage within the firm boundaries and through customer interactions. Despite the great attention on the Industry 4.0 adoption paths, little is known about the relationships with previous waves of digital technologies, namely, information and communication technologies (ICTs), and how different groups of both types of technologies link to knowledge and its related performances.

The study employed a quantitative research design using a survey method. Submitting the questionnaire to entrepreneurs, chief operation officers or managers in charge of the operational and technological processes of Italian manufacturing firms, 206 respondents stated that their firm has adopted at least one of the seven Industry 4.0 technologies investigated.

The findings of the study highlight the positive relationship between ICT and Industry 4.0 technologies in terms of both intensity and groups of technologies (Web-based, Management and Manufacturing ICT; Operation, Customization and Data-processing 4.0), and how technologies affect knowledge-related performances in terms of products and processes, job-learning, product-related services and customer involvement.

This study is one of the first attempts to link groups of ICT to groups of Industry 4.0 technologies and to explore the effects in terms of knowledge-related performances as a measure of technology use. The study shows strong path dependency among ICT, Industry 4.0 and knowledge performance, enriching the literature on technological innovation and knowledge management.

Globalization and the importance of emerging markets have increased the pressure of high-cost manufacturing locations to sustain operations. However, there are still some countries in which manufacturing is prospering despite high costs (like Germany, Sweden and Switzerland). This study examines seven competitive priorities through 24 different capabilities, using a case survey of four manufacturing companies located in Sweden. This study aims to develop a contemporary understanding from vital priorities and capabilities.

A case survey was conducted in four different-sized manufacturing companies in Sweden during the autumn of 2018. In total, the survey attracted 89 responses. Respondents were mainly middle managers and other management team members.

In general, companies assess the importance of manufacturing capabilities higher than performance and improvement. The authors’ analysis shows that quality priority through product and process capabilities is ranked highest in terms of importance, performance and improvement. In addition, delivery capability shows a similarity with quality. At the other end, being lowest ranked are typically different flexibility and advertising capabilities. This study demonstrates with correlation analysis that most often capabilities have a positive correlation in terms of their importance, performance and improvement needs. Some capabilities show potential correlations across importance, performance and improvement.

This research is limited to one high-cost environment and to four companies within that environment. Further research should examine the impact of the pandemic era on manufacturing priorities and capabilities.

In general, case surveys have relatively rarely been used in management studies. This research offers an alternative and deeper perspective from high-cost country manufacturing, as the responses are from numerous persons in management positions.

This study aims to investigate the manufacturing reshoring decision-making content from an Eclectic Paradigm perspective.

Data were collected through a six-step systematic literature review on factors influencing manufacturing reshoring decision-making. The review is based on 100 peer-reviewed journal papers discussing reshoring decision-making contents published from 2009 to 2022.

In total, 80 decision factors were extracted and then categorized into resource-seeking (8%), market-seeking (11%), efficiency-seeking (41%) and strategic asset-seeking (16%) advantages. Additionally, 24% of these were identified as hybrid, which means that they were classified into multiple categories. Some decision factors were further identified as reshoring influencing factors (i.e. drivers, enablers and barriers).

Scholars need to consider what other theories can be used or developed to identify and evaluate the decision factors (determinants) of manufacturing reshoring as well as how currently adopted theory can be further advanced to create clearer and comprehensive theoretical frameworks.

This research underscores the importance of developing clearer and more comprehensive theoretical frameworks. For practitioners, understanding the multifaceted nature of decision factors could enhance strategic decision-making regarding reshoring initiatives.

To the best of the authors’ knowledge, this is the first study to investigate the value and practicality of the Eclectic Paradigm in categorizing factors in manufacturing reshoring decision-making content and presents in-depth theoretical classifications. In addition, it bridges the gap between decision factors and influencing factors in the decision-making content research realm.