Journal of Manufacturing Technology Management最新文献

Purpose

Recent disruptions have sparked concern about building a resilient and sustainable manufacturing supply chain. While artificial intelligence (AI) strengthens resilience, research is needed to understand how cloud adoption can foster integration, collaboration, adaptation and sustainable manufacturing. Therefore, this study aimed to unleash the power of cloud adoption and AI in optimizing resilience and sustainable performance through collaboration and adaptive capabilities at manufacturing firms.

Design/methodology/approach

This research followed a deductive approach and employed a quantitative method with a survey technique to collect data from its target population. The study used stratified random sampling with a sample size of 1,279 participants working in diverse manufacturing industries across California, Texas and New York.

Findings

This research investigated how companies can make their manufacturing supply chains more resilient and sustainable. The findings revealed that integrating the manufacturing supply chains can foster collaboration and enhance adaptability, leading to better performance (hypotheses H1-H7, except H5). Additionally, utilizing artificial intelligence helps improve adaptability, further strengthening resilience and sustainability (H8-H11). Interestingly, the study found that internal integration alone does not significantly impact collaboration (H5). This suggests that external factors are more critical in fostering collaboration within the manufacturing supply chain during disruptions.

Originality/value

This study dives into the complex world of interconnected factors (formative constructs in higher order) influencing manufacturing supply chains. Using advanced modeling techniques, it highlights the powerful impact of cloud-based integration. Cloud-based integration and artificial intelligence unlock significant improvements for manufacturers and decision-makers by enabling information processes and dynamic capability theory.

Purpose

Despite the growing interest in enhancing mass customization capability (MCC), firms still have little knowledge of dealing with the superimposed challenges of increased market uncertainty and supply chain disruptions. Based on the dynamic capability view, this study focuses on the impacts of frequent sensing and reconfiguring processes on MCC and the mediating roles of proactive and reactive supply chain resilience (SCR).

Design/methodology/approach

We collected survey data from 277 manufacturing firms and conducted a structural equation model to test hypotheses.

Findings

The results reveal that although its direct effect on MCC is insignificant, sensing process improves MCC indirectly via reactive SCR. Our findings also show that reconfiguring process enhances MCC both directly and indirectly via reactive SCR.

Originality/value

This study provides theoretical and practical insights into how to combine dynamic capability and SCR to strengthen MCC.

Purpose

The objective of this article is to identify the relationships between the enablers in the implementation of Green Lean Six Sigma (GLSS) in the Mexican manufacturing industry (MMI).

Design/methodology/approach

To create the survey instrument, the authors did an extensive literature research, which they then applied in the MMI to find the relationships between enablers and their impact on the positive effects of implementing GLSS projects. Using exploratory and confirmatory factor analyses (EFA and CFA), the data were empirically and statistically corroborated. Furthermore, the authors validated the hypotheses that support the research using the structural equation modeling (SEM) approach in SPSS Amos.

Findings

The findings reveal that leadership has a positive impact on social and economic benefits (EcB), as well as an indirect impact on the environmental benefits (EB) of GLSS projects, with organizational involvement (OI) and performance measurement (PM) functioning as mediators.

Practical implications

This study represents an empirical reference for practitioners and researchers pursuing high-quality, low-cost, environmentally and socially sustainable products or processes through the implementation of GLSS projects in the manufacturing industry.

Originality/value

This study provides a statistically validated model using the SEM technique to represent the relationships between GLSS enablers in the MMI.

Purpose

Using the lens of the natural resource-based view (NRBV) theory, this study investigates the effect of green supply chain management (GSCM) practices such as green manufacturing (GM), eco-design (ED), green purchasing (GP) and investment recovery (IR) on the carbon-neutral supply chain (CNSC) performance of firms through the mediating influence of logistics eco-centricity (LE).

Design/methodology/approach

A conceptual framework that hypothesizes the relationship between GSCM practices, LE and the CNSC performance of firms is developed. Key GSCM practices are then identified using experts’ opinions. Furthermore, we collected responses from logistics companies to validate the conceptual framework using the partial least squares structural equation modeling (PLS-SEM) method.

Findings

Through this study, we found that GSCM practices significantly improve a firm's CNSC performance, and the relationships between GSCM practices and CNSC performance are positively mediated by LE.

Practical implications

The implications of the study suggest that logistics managers can benefit from the findings of this study to comprehend the impact of various GSCM techniques on LE and CNSC from the viewpoint of the NRBV paradigm.

Originality/value

This research provides valuable perspectives for managers and supply chain (SC) practitioners in their quest for sustainable and environmentally responsible SC operations through an extensive and novel analysis of the connection between GSCM practices, LE and CNSC performance.

Purpose

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

Design/methodology/approach

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Findings

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

Practical implications

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

Originality/value

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

Purpose

Despite increased interest in the Operator 4.0 concept, there has been scarce attention on practice-oriented frameworks that can assist firms in reaping the potential benefits of technologies within the I4.0 framework. The purpose of this paper is to provide an integrative framework on how to engage Operator 4.0 effectively.

Design/methodology/approach

Following meta-synthesis logic and integrative conceptual analysis, this study synthesizes and integrates experimental case studies on Operator 4.0 solutions. The analysis is conducted on 24 cases retrieved from peer-reviewed articles.

Findings

The analysis revealed several factors that determine the type and extent of benefits that can be realized within a human-cyber-physical system. However, enhancing these capabilities is not straightforward, as several factors can inhibit the realization of these capabilities. Firstly, the benefits vary depending on the type of Operator 4.0. Secondly, the interoperability of the introduced I4.0 solution with the existing systems is crucial. Thirdly, the adaptability of the solution to serve multiple purposes is crucial, as it tends to increase technology acceptance.

Research limitations/implications

The studies reviewed in this research were based on experimental cases, with limited implementation of the solutions they proposed. As such, their implications when implemented on a larger scale are tentative.

Originality/value

The present study has synthesized existing experimental cases and developed an integrative framework to guide the implementation of Operator 4.0. By gathering primary insights from these experiments, we have clarified when and how Operator 4.0 and the required interactions can lead to successful implementation.

Purpose

Exploring the Lean and Green relationship goes back to the beginnings of Lean manufacturing. Most cases established that companies implementing Lean have Green results. However, there are Lean practices with a higher impact on Green, but others with less impact. Therefore, this paper presents research that explores the relationship between Lean and Green in manufacturing companies and aims to determine whether Lean practices have a higher association with Green aspects.

Design/methodology/approach

A survey was conducted amongst manufacturing firms to determine their Lean Index (LI). The internally related elements of the Lean construct determined each firm’s LI, whilst Cronbach alpha determined internal LI consistency. The survey also identified firms developing six Green aspects: International Organisation for Standardisation (ISO) 14001, ISO 50001, general Green aspects and the specific aspects of materials, energy and water. An individual sample t-test shows different LI levels of association for each Green aspect. Binomial logistic regression shows the LI element association for each Green aspect.

Findings

LI is higher at firms reporting the inclusion of Green aspects. More than half of LI components have a statistically relevant association with the six Green aspects. In general, Ishikawa diagrams had the highest association with Green aspects whilst the lowest was seen in workers as improvement initiators. By grouping the LI elements into their categories, the Lean practices related to controlling processes have a higher association, whilst the involvement of employees has the lowest.

Research limitations/implications

Further research found in this paper identifies the possibilities for investigating the specificities of each Lean tool to develop Green aspects in companies.

Practical implications

Practitioners learn that Lean and Green are not separate issues in business. This article provides evidence that Lean practices in place at companies are already associated with Green aspects, so integration may already be happening.

Originality/value

This paper provides specifics on the relationship between each Lean practice and developing Green aspects. Thus, this paper specifies the Lean practices that contribute most to Green efficiency to support the joint development of both themes.

Purpose

With social media usage growing rapidly in business sustainability in manufacturing sectors, the means through which manufacturing firms can incorporate it into the pursuit of environmental sustainability are largely under-researched by academics. Drawing upon the dynamic capability view, this study examines the indirect relationship between social media usage and sustainable innovation in manufacturing sectors.

Design/methodology/approach

The study was based on analyzing data collected through a questionnaire survey on Chinese manufacturing firms. Partial least squares structural equation modeling was applied to test the hypotheses.

Findings

The results show that social media usage has a positive effect on sustainable innovation in the manufacturing sectors. Firms' sustainable dynamic capability mediates the relationship between social media usage and sustainable innovation. In addition, the effects of social media usage on firms' sustainable dynamic capability are stronger when stakeholder pressure is stronger and top management team commitment is higher.

Originality/value

This study highlights social media application as an important, yet underexplored, factor in sustainable innovation of manufacturing firms. The findings also untangle the mechanism of sustainable dynamic capability by which social media usage improves sustainable innovation for manufacturing firms.

Purpose

Manufacturers face various challenges and risks during their digital servitization (DS), due to the complexity caused by introducing breakthrough technologies, increasingly complex product-service solutions and new stakeholders in the business network. The process necessitates the implementation of various changes that usually happen over a long period of time. Using complexity management as a theoretical lens, this paper delves into manufacturers’ DS journeys and explores how manufacturers manage the associated complexities.

Design/methodology/approach

This paper investigates the DS journey of two manufacturers in a longitudinal case study from 2014 to 2021.

Findings

Three main complexity management actions during the DS journey were identified: shaping the digital service system, shaping the organization and shaping the network. Tied to different types of complexities, these actions demonstrate how manufacturers navigate their journey. The findings also reveal different complexity management approaches used at the different stages of this journey.

Originality/value

This paper offers a comprehensive framework for understanding complexity management in the DS journey, including the types of complexities, complexity management actions and complexity management approaches and their rationale. This paper shows that different requirements are created during emerge, consolidate and evolve stages of the DS journey. Manufacturers need a dynamic approach that considers changes in complexities and actions over time.

Purpose

A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.

Design/methodology/approach

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.

Findings

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.

Research limitations/implications

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.

Practical implications

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.

Originality/value

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.