Examining the role of logistics 4.0 enabled dynamic capabilities on firm performance

The potential of Logistics 4.0 technologies: a case study through business intelligence framing by applying the Delphi method

Introduction

The growing competitiveness and the importance of data availability for organizations have created a demand for intelligent information systems capable of analyzing data to support strategy and decision-making. Organizations are generating more and more data due to new technologies associated with Industry 4.0 and Logistics 4.0, making it essential to transform this data into relevant information to streamline decision-making processes. This paper examines the influence of these technologies on gaining a competitive advantage, specifically in a logistics company, which is scarce in the literature.

Methods

A case study was conducted in a Portuguese company using the Delphi method with 61 participants-employees who use the company's integrated BI tool daily. The participants were presented with a questionnaire via the online platform Welphi, requiring qualitative responses to various statements based on the literature review and the results of semi-structured meetings with the company.

Results

The study aimed to identify areas where employees believe more investment/ development is needed to optimize processes and improve the use of the BI tool in the future. The results indicate that BI is a crucial technology when aligned with a company's objectives and needs, highlighting the necessity of top management's involvement in optimizing the BI tool. Encouraging employees to use the BI tool emerged as a significant factor, underscoring the importance of leadership in innovative projects to achieve greater competitive advantage for the company.

Discussion

This study aims to understand the importance of Business Intelligence (BI) and how its functionalities should be adapted according to a company's strategy and objectives to optimize decision-making processes. Thereby, the discussion focused on the essential role of BI technologies in leveraging the company's competitive advantage.

The implications of smart logistics policy on corporate performance: Evidence from listed companies in China

Since the emergence of smart logistics as a vital paradigm, it has garnered significant interest from independent firms and governments worldwide, including China. This study aims to examine the relationship between Smart Logistics Policy (SLP) and firm performance both theoretically and empirically. Utilizing data from A-share companies listed on the Shanghai and Shenzhen stock exchanges between 2012 and 2017, this study analyzes the relationship between SLP and firm performance using Propensity Score Matching (PSM) and Difference-in-Differences (DID). The results indicate that SLP significantly enhances a firm's financial performance. Additionally, a heterogeneity test on financial performance reveals that the impact of SLP varies based on ownership and industrial sector. Unexpectedly, SLP has a negative impact on corporate social responsibility (CSR) performance. The heterogeneity test on CSR performance shows that the SLP effect on CSR exhibits no significant difference based on ownership. Furthermore, the impact of SLP on CSR is significantly greater for manufacturing firms compared to non-manufacturing firms. Consequently, this study offers theoretical support and empirical evidence regarding the effects of SLP on firm performance.

Multitask Multiobjective Genetic Programming for Automated Scheduling Heuristic Learning in Dynamic Flexible Job-Shop Scheduling

Evolutionary multitask multiobjective learning has been widely used for handling more than one multiobjective task simultaneously. However, it is rarely used in dynamic combinatorial optimization problems, which have valuable practical applications such as dynamic flexible job-shop scheduling (DFJSS) in manufacturing. Genetic programming (GP), as a popular hyperheuristic approach, has been used to learn scheduling heuristics for generating schedules for multitask single-objective DFJSS only. Searching in the heuristic space with GP is more difficult than in the solution space, since a small change on heuristics can lead to ineffective or even infeasible solutions. Multiobjective DFJSS is more challenging than single DFJSS, since a scheduling heuristic needs to cope with multiple objectives. To tackle this challenge, we first propose a multipopulation-based multitask multiobjective GP algorithm to preserve the quality of the learned scheduling heuristics for each task. Furthermore, we develop a multitask multiobjective GP algorithm with a task-oriented knowledge-sharing strategy to further improve the effectiveness of learning scheduling heuristics for DFJSS. The results show that the designed multipopulation-based GP algorithms, especially the one with the task-oriented knowledge-sharing strategy, can achieve good performance for all the examined tasks by maintaining the quality and diversity of individuals for corresponding tasks well. The learned Pareto fronts also show that the GP algorithm with task-oriented knowledge-sharing strategy can learn competitive scheduling heuristics for DFJSS on both of the objectives.

Asian logistics industry efficiency under low carbon environment: policy implications for sustainable development

Logistics is a crucial part of every business. The logistics sector not only contributes significantly to Asian economies but also has far-reaching effects on ecological and social concerns. Therefore, it is important to examine the factors that can affect the logistics performance of the country. Hence, the primary objective of the study is to estimate the impact of CO₂ emissions, ICT, and human capital on the logistics performance of the 20 Asian economies. In order to investigate the relationship between the variables, we have employed the OLS, 2SLS, GMM, and panel quantile regression. The estimates of CO₂ emissions and GHG emissions are significantly negative in 2SLS and GMM methods, implying that environmental pollution hurt logistic performance. The estimates of ICT and education are positively significant, suggesting that increased use of internet and higher education rate are crucial in improving logistics performance. In the panel quantile regression model, the estimates of CO₂, internet, and education are insignificant at most quantiles except at a few higher quantiles. Thus, governments should invest in the development of efficient logistics infrastructure to achieve sustainable development.

How to enhance supply chain resilience: a logistics approach

Purpose

Logistics capability is an important enabler of supply chain resilience (SCR). However, few studies have analyzed the underlying influence mechanism of logistics capability on SCR in extreme conditions, such as those of the COVID-19 pandemic. The purpose of this study is to increase understanding of the role of logistics capabilities in constituting a resilient supply chain.

Design/methodology/approach

Drawing upon the dynamic capability perspective and contingency theory, the proposed conceptual framework aims to demonstrate the relationship between a firm's logistics capabilities and SCR. Furthermore, the conceptual framework is illustrated by empirical evidence from a case study of a Chinese manufacturing company, which focuses on extracting practical lessons from the COVID-19 pandemic.

Findings

The findings suggest that digitalization, innovativeness, and modularization comprise potential mediating pathways for firm logistics capability to affect SCR and government policies, risk management culture, trust and cooperation moderate the effect positively. The potential associations are identified and elucidated by detecting the corresponding strategies and practices of a Chinese manufacturer that performed well amid the COVID-19 pandemic.

Practical implications

This study provides specific guidelines for logistics managers to enhance SCR during the COVID-19 pandemic. Seeing SCR as a dynamic capability, the framework is also instructive for manufacturers, supply chain members, and policymakers to achieve the sustained competitive advantage of supply chains.

Originality/value

The findings expand the understanding of enhancing SCR in a logistics approach. The empirical validation of propositions in the case study reveals a new vista for research on SCR.

Determinants of Remanufacturing Adoption for Circular Economy: A Causal Relationship Evaluation Framework

Organizations are transforming their linear models into circular models in order to become more sustainable. Remanufacturing is an essential element of the circular model; thus, there is an urgent need to adopt remanufacturing. It can offer organizations economic and environmental advantages and facilitate the transition to a circular economy (CE). Several aspects are crucial to the use of remanufacturing methods in order to transition to the CE. Therefore, in this study, we aimed to develop a framework for investigating the causal relationship among determinants of adopting remanufacturing processes for the circular economy. Through an integrated approach comprising a literature review and the Modified Delphi Method, we identified ten remanufacturing adoption determinants. The causal relationship among these determinants was established using the DEMATEL method. Furthermore, we classified these determinants into cause and effect groups. Five determinants, “consumer preferences”, “remanufacturing adoption framework”, “market opportunities”, “management commitment”, and “preferential tax policies”, belong to the cause group, and the remaining five belong to the effect group based on the effect score. To implement remanufacturing processes and transition to a circular economy, it is necessary to pay greater attention to these identified determinants, especially those that belong to the cause group. The outcomes of this study may aid management and policy makers in formulating strategies for effectively implementing remanufacturing methods within their organizations.

Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis

The Industry 4.0 (I4.0) concept paves the way for the circular economy (CE) as advanced digital technologies enable sustainability initiatives. Hence, I4.0-driven CE-oriented supply chains (SCs) have improved sustainable performance, flexibility and interoperability. In order to smoothly embrace circular practices in digitally enabled SCs, quantitative techniques have been identified as crucial. Therefore, the intersection of I4.0, CE, supply chain management (SCM) and quantitative techniques is an emerging research arena worthy of investigation. This article presents a bibliometric analysis to identify the established and evolving research clusters in the topological analysis by identifying collaboration patterns, interrelations and the studies that significantly dominate the intersection of the analysed fields. Further, this study investigates the current research trends and presents potential directions for future research. The bibliometric analysis highlights that additive manufacturing (AM), big data analytics (BDA) and the Internet of Things (IoT) are the most researched technologies within the intersection of CE and sustainable SCM. Evaluation of intellectual, conceptual and social structures revealed that I4.0-driven sustainable operations and manufacturing are emerging research fields. This study provides research directions to guide scholars in the further investigation of these four identified fields while exploring the potential quantitative methods and techniques that can be applied in I4.0-enabled SCs in the CE context.

The effect of logistical immediacy on logistics service providers' (LSPs') business

Purpose

This study discusses the influence of logistical immediacy on logistics service providers' (LSPs’) business. Specifically, its role in the face of the emerging business scenario (e-commerce, disruptive technologies, and new models of logistical services) is examined.

Design/methodology/approach

As logistical immediacy is a nascent topic, this study utilizes a systematic literature review focusing on academic articles from the last five years related to logistical outsourcing to understand the changes imposed by logistical immediacy on LSPs.

Findings

The impact of transformations arising from an increasingly digital virtual world (DVW) on LSPs is contextualized. A theoretical view of the factors affecting LSPs' shift towards more immediate operations is presented, and how logistical immediacy impacts LSPs is discussed. Finally, a research agenda is presented as the study's main contribution.

Research limitations/implications

Due to the timeframe chosen, the restriction to a single database (Scopus), the specific search terms used related to LSPs, and limiting the search parameters to operations management, some relevant work may have been overlooked.

Practical implications

The article help LSPs' and contracting companies' managers to understand the influence of the immediacy expected in logistics operations. Possible logistics services trends and how they may impact companies are discussed.

Originality/value

This is one of the first articles in the area of operations and supply chains that addresses the issue of logistical immediacy and its impact on LSPs.

Blockchain announcements and stock value: a technology management perspective

Purpose

This study aims to explore the impact of blockchain announcements on enterprises' stock market value.

Design/methodology/approach

Based on resource-based theory, this study constructs a complete framework of the impact mechanism of blockchain announcements on the stock price of the announcing firm using the data of 143 blockchain announcements. An event study methodology is used in this research, and the market model, market-adjusted model and Carhart four-factor model are used to estimate stock abnormal returns after the blockchain announcement; and the cross-sectional regression model is used to test the influencing factors.

Findings

Blockchain announcements elicit a significantly positive market reaction on the release day. Compared to announcements not pertaining to technical innovation, blockchain technical innovation announcements exhibit a more positive market reaction towards the announcing companies. Strategic-level announcements exhibit a more positive market reaction than operational-level announcements. Enterprise characteristics, such as enterprise-scale and enterprise innovation ability, do not affect stock market reactions to blockchain announcements.

Practical implications

The findings reveal the economic value of conducting blockchain activities in the Chinese stock market. Findings of this study can help managers understand the value of implementing blockchain activities in a different market environment and guide them on how to improve the market value of their enterprises through the active implementation of blockchain activities.

Originality/value

To the best of the authors’ knowledge, this is the first event study to focus solely on the value of pure blockchain announcements in an emerging market. This study considers multiple resource and capability factors that would influence blockchain technology adoption, improve the current understanding of how blockchain announcements affect corporate stock prices and provide directions for future comparative studies of market reactions to blockchain announcements in different stock markets.

Data analytics capability and servitization: the moderated mediation role of bricolage and innovation orientation

Purpose

Despite the potential influence of data analytics capability on servitization, the understanding of the underlying mechanisms of this influence remains unclear. This study aims to explore how data analytics capability affects servitization by examining the mediation effect of bricolage and the conditional role of innovation orientation.

Design/methodology/approach

This study employs the moderated mediation method to examine the proposed research model with archival data and multiple-respondent surveys from 1,206 top managers of 402 manufacturing firms in the Yangtze River Delta area in China.

Findings

Bricolage partially mediates the positive relationship between data analytics capability and servitization, and innovation orientation positively moderates this effect.

Practical implications

Manufacturers can leverage bricolage to materialize data analytics capability for servitization. Manufacturers should also pursue an innovation orientation to fully glean the benefits of bricolage in transforming data analytics capability into servitization.

Originality/value

This study opens the black box of how data analytics capability affects servitization by revealing the underlying mechanism of bricolage and the boundary condition role of innovation orientation for this mechanism. It offers valuable insights for practitioners to leverage data analytics to improve servitization through developing bricolage and cultivating a culture of innovation orientation.

The application of Industry 4.0 technologies in sustainable logistics: a systematic literature review (2012-2020) to explore future research opportunities

Nowadays, the market competition becomes increasingly fierce due to diversified customer needs, stringent environmental requirements, and global competitors. One of the most important factors for companies to not only survive but also thrive in today's competitive market is their logistics performance. This paper aims, through a systematic literature analysis of 115 papers from 2012 to 2020, at presenting quantitative insights and comprehensive overviews of the current and future research landscapes of sustainable logistics in the Industry 4.0 era. The results show that Industry 4.0 technologies provide opportunities for improving the economic efficiency, environmental performance, and social impact of logistics sectors. However, several challenges arise with this technological transformation, i.e., trade-offs among different sustainability indicators, unclear benefits, lifecycle environmental impact, inequity issues, and technology maturity. Thus, to better tackle the current research gaps, future suggestions are given to focus on the balance among different sustainability indicators through the entire lifecycle, human-centric technological transformation, system integration and digital twin, semi-autonomous transportation solutions, smart reverse logistics, and so forth.

Industry 4.0 implementation and Triple Bottom Line sustainability: An empirical study on small and medium manufacturing firms

Background

The current level of industrialization has generated many challenges worldwide, including ecological hazards, climate change, and the overuse of non-renewable natural resources, thereby creating an increasing demand for achieving the goal of the Triple Bottom Line (TBL). In this regard, Industry 4.0 can be used as a crunch point to contribute to the production process that can help achieve sustainable development.

Purpose

While the Malaysian government proposed the “Industry4ward” approach to enhance technological adoption, there is scarce empirical evidence in the literature that validates SMEs for Industry 4.0. Using Dynamic Capability View (DCV), this study proposes a framework that includes core determinants like top management commitment, supply chain integration, and IT infrastructure, that can significantly influence Industry 4.0 implementation toward achieving TBL sustainability.

Design/methodology/approach

Employing simple random sampling, the study adopted a quantitative approach based on 199 useable respondent's feedback collected through a survey questionnaire of 900 employees from Malaysian SMEs. The statistical analysis was performed using Structural Equation Modeling (Partial Least Square, SmartPLS 3.3.2).

Findings

The results show that top management and IT infrastructure significantly impact Industry 4.0 implementation and sustainability. In contrast, the analysis also demonstrates that supply chain integration is insignificant to Industry 4.0 implementation in SMEs. The findings also indicate that the relationship between the determinants of Industry 4.0 and TBL sustainability can be mediated by the “effective implementation” of Industry 4.0.

Recommendations

The study highlights the practical consequences of the role and use of the determinants in Industry 4.0 implementation. Its findings help managers and policy-makers to optimize value creation to achieve sustainable development goals.

Limitations and future research

Focusing only on Malaysian manufacturing SMEs may restrict the generalization of the study; thus, a benchmarking analysis from other industrial settings is encouraged. The questionnaire-based survey is a further limitation of the study.

Technology and leadership styles: a review of trends between 2003 and 2021

Purpose

Leadership style denotes the behavioural pattern of a leader, which bears on employee's attitude, perception about organization, manager and job satisfaction. The existence of different styles has presented leadership from diverse perspectives related to individuals' personality and behaviour. The main objective of this article is to explore the association between leadership styles and technology, major themes in this area and what can be the future research directions of this work.

Design/methodology/approach

Leadership style denotes the behavioural pattern of leader, which bears on employee's attitude, perception about organization, manager and job satisfaction. The existence of different styles has presented leadership from diverse perspectives related to individuals' personality and behaviour. The present article aims to review significant work by eminent researchers towards technology and leadership styles in the form trends, annual scientific production; popular affiliations and sources, a three-field plot of countries, scholars and themes, most cited references, trending keywords, thematic analysis of leadership styles and technology research by taking insights from situational leadership theory.

Findings

The findings indicate connections between various keywords and provide interesting themes like transformational leadership style is connected to knowledge management, transactional leadership, empowering leadership, psychological capital and e-leadership. Similarly, leadership is connected to leadership development, gender stereotypes, emotional exhaustion, innovative leadership and organizational performance.

Originality/value

This review analysis of leadership styles and technology is in itself a novice contribution and first of its nature. The identified themes are presenting good knowledge and food for thought for future researches.

Supply chain resilience in mindful humanitarian aid organizations: the role of big data analytics

Purpose

The purpose of this paper is to understand the nomological network of associations between collective mindfulness and big data analytics in fostering resilient humanitarian relief supply chains.

Design/methodology/approach

The authors conceptualize a research model grounded in literature and test the hypotheses using survey data collected from informants at humanitarian aid organizations in Africa and Europe.

Findings

The findings demonstrate that organizational mindfulness is key to enabling resilient humanitarian relief supply chains, as opposed to just big data analytics.

Originality/value

This is the first study to examine organizational mindfulness and big data analytics in the context of humanitarian relief supply chains.

The role of 3S in big data quality: a perspective on operational performance indicators using an integrated approach

Purpose

This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.

Design/methodology/approach

A fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.

Findings

The effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.

Research limitations/implications

The outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.

Originality/value

Big data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.

Towards a Conceptual Development of Industry 4.0, Servitisation, and Circular Economy: A Systematic Literature Review

Industry 4.0 (I4.0) technologies have been highlighted in recent literature as enablers of servitisation. Simultaneously, businesses are advised to implement a circular economy (CE) to bring new opportunities. However, it is pertinent to mention that little attention has been given to assess the role of I4.0 in adopting the CE and servitisation in a fully integrated manner. This research fills this gap by developing a conceptual framework through a systematic literature review of 139 studies investigating the relationship between the I4.0, CE, and servitisation. This study identifies the impact of these variables on a firm’s operational and financial performance (revenue stream, growth, and profitability). Our research findings advocate that adopting I4.0 technologies to the business and manufacturing model enables sustainability, energy and resource efficiency while enhancing performance and offering innovative products through smart services. Thus, firms must systematically adopt I4.0 technologies to support a CE model that creates value through servitisation. This study identifies the research gaps that are unexplored for practitioners and future researchers while providing insight into the role of I4.0 in implementing CE in the servitisation business model.

Leveraging big data analytics capabilities in making reverse logistics decisions and improving remanufacturing performance

Purpose

The study investigated the effect of big data analytics capabilities (BDACs) on reverse logistics (strategic and tactical) decisions and finally on remanufacturing performance.

Design/methodology/approach

The primary data were collected using a structured questionnaire and an online survey sent to South African manufacturing companies. The data were analysed using partial least squares based structural equation modelling (PLS–SEM) based WarpPLS 6.0 software.

Findings

The results indicate that data generation capabilities (DGCs) have a strong association with strategic reverse logistics decisions (SRLDs). Data integration and management capabilities (DIMCs) show a positive relationship with tactical reverse logistics decisions (TRLDs). Advanced analytics capabilities (AACs), data visualisation capabilities (DVCs) and data-driven culture (DDC) show a positive association with both SRLDs and TRLDs. SRLDs and TRLDs were found to have a positive link with remanufacturing performance.

Practical implications

The theoretical guided results can help managers to understand the value of big data analytics (BDA) in making better quality judgement of reverse logistics and enhance remanufacturing processes for achieving sustainability.

Originality/value

This research explored the relationship between BDA, reverse logistics decisions and remanufacturing performance. The study was practice oriented, and according to the authors’ knowledge, it is the first study to be conducted in the South African context.

Air quality management using genetic algorithm based heuristic fuzzy time series model

Purpose

The purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc.

Design/methodology/approach

In this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules.

Findings

The proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models.

Practical implications

The management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place.

Originality/value

The proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals.

The impact of Logistics 4.0 on corporate sustainability: a performance assessment of automated warehouse operations

Purpose

The purpose of this study is to gain a better understanding of the impacts of Logistics 4.0 initiatives (focusing on automated warehousing systems) on the economic, environmental and social dimensions of firms' sustainability performance. To achieve this objective, a new framework for the assessment of sustainable warehousing in the 4.0 era is developed.

Design/methodology/approach

The framework, developed via the item-objective congruence index, Q-sort method and interviews with experts, is employed to assess performance changes through management interviews in two warehousing companies after the implementation of automation technologies.

Findings

Most aspects of both companies' sustainability performance are considerably improved (e.g. productivity, accuracy, air emission, worker safety and supply chain visibility); however, the outcome for some criteria might be worsened or improved depending on each company's solutions and strategies (e.g. increasing electricity bills, maintenance costs and job losses).

Practical implications

The findings provide insight into the effective implementation of warehousing technologies. The proposed framework is also a valid and reliable instrument for sustainability assessment for warehousing operators, which companies can utilise for self-assessment.

Originality/value

This paper contributes to establishing a body of literature that explores the previously unclarified effects of Logistics 4.0 on firms' sustainability performance. The proposed framework, which captures critical concerns of corporate sustainability and technological adaptation, is also the first of its kind for warehouse performance assessment.

Evaluation of machine tool substitute under data-driven quality management system: a hybrid decision-making approach

Purpose

Today, industrial revolutions demands advanced technologies, means, mediums, tactics and so forth for optimizing their operating behavior and opportunities. It is probed that the effectual results can be seized into system by not only developing advance means and technologies, but also capably adapting these developed technologies, their user interface and their utilization at optimum levels. Today, industrial resources need perfect synchronization and optimization for getting elevated results. Accordingly, present study is furnished with the purpose to expose quality-driven insights to march toward excellence by optimizing existing resources by the industrial organizations. The present study evaluates quality attributes of mechanical machineries for seizing performance opportunities and maintaining competitiveness via synchronizing and reconfiguring firm's resources under quality management system.

Design/methodology/approach

In the present study, Kano’s integrated approach is implemented for supporting decision rational concerning industrial assets. The integrative Kano–analytic hierarchy process (AHP) approach is used to reflect the relative importance of quality attributes. Kano and AHP tactics are integrated to define global relative weight and their computational medium is adapted along with ratio analysis, reference point theory and TOPSIS technique for understanding robust decision. The study described an interesting idea for underpinning quality attributes for benchmarking system substitutes. A machine tool selection case is discussed to disclose the significant aspect of decision-making and its virtual qualities.

Findings

The decision executives can realize massive benefits by streaming quality data, advanced information, technological advancements, optimum analysis and by identifying quality measures and disruptions for gaining performance deeds. The study determined quality measures for benchmarking machine tool substitute for industrial applications. Momentous machine alternatives are evaluated by means of technical structure, dominance theory and comparative analysis for supporting decision-making of industrial assets based on optimization and synchronization.

Research limitations/implications

The study linked financial, managerial and production resources under sole platform to present a technical structure that may assist in improving the performance of the manufacturing firms. The study provides a decision support mechanism to assist in reviewing the momentous resources to imitate a higher level of productive strength toward the manufacturing firms. The study endeavors its importance toward optimizing resources, which is an evident requirement in industries as the same not only saves money, escalates production, improves profit margins and so forth, but also gratifies the consumption of scarce natural resources.

Originality/value

The study stressed that advance information can be sought from system characteristics in the form of quality measures and attributes, which can be molded for gaining elevated outcomes from existing system characteristics. The same demands decision supports tools and frameworks to utilize data-driven information for benchmarking operations and supply chain activities. The study portrayed an approach for ease of utilizing data-driven information by the decision-makers for demonstrating superior outcomes. The study originally conceptualized multi-attributes appraisement framework associated with subjective cum objective quality measures to evaluate the most significant machine tool choice amongst preferred alternatives.

Application of industry 4.0 technologies in SMEs for ethical and sustainable operations: Analysis of challenges

In the era of Industry 4.0 and circular economy, small and medium enterprises (SMEs) are under huge pressure to make their manufacturing operations ethical and sustainable. Business with ethical and sustainable operations has become the need of the day in the present environment of Industry 4.0 and circular economy. It has been observed that the application of Industry 4.0 technologies may help in achieving the goal of ethical and sustainable operations. Although a lot of research has been done in context to larger enterprises, limited research is available on the application of Industry 4.0 technologies in SMEs for ethical and sustainable operations. The espousal of Industry 4.0 technologies is a challenging task for SMEs due to various operational and financial constraints. The problem is more acute, specifically in context to developing countries like India. Keeping in mind the role of technologies in ethical business and circular economy, we have identified fifteen challenges, impacting the application of Industry 4.0 technologies in SMEs. A questionnaire was designed for collecting the response from industry and academic experts. On the collected data, the DEMATEL approach has been applied to check the degree of influence and interrelationship among challenges. It has also helped in the categorization of factors as cause and effect. Sensitivity analysis is also performed to validate the results obtained from the DEMATEL approach. Authors have observed that lack of motivation from partners and customers on the application of I4.0 technologies is the leading challenge. Fear of failure of I4.0 technologies is the main effect group challenge. The findings of the study will help SMEs in formulating strategies for implementing Industry 4.0 technologies for ethical and sustainable business processes.