• Assessing people-driven factors for circular economy practices in SME supply chains: business strategies and environmental perspectives

      Betuel Sawe, F; Kumar, A; Garza-Reyes, Jose Arturo; Agrawal, R; University of Derby; London Metropolitan University; National Institute of Technology, Tiruchirappalli, India (Wiley, 2021-03-25)
      Globalisation and technological advancements have increased the pressure on small businesses to increase their productivity and to gain competitive advantages. That pressure has been placed on the resources available, resulting in increased environmental degradation as a result of the traditional linear model of make-use-dispose. Circular economy (CE) practices offer the opportunity for sustainable production based on the reuse-remanufacture and recycling of resources for small and medium-sized enterprises (SMEs) to increase their sustainability, resulting in enhanced performance levels in terms of business strategies and environmental perspectives. But in academic literature, the role of people-driven factors (PDFs) in the adoption of CE practices in the supply chains (SCs) of SMEs is limited. Therefore, to fill this literature gap, this research looks at analysing PDFs for the implementation of CE in the SMEs in developing countries in two phases. PDFs are identified from an extensive literature review; a DEMATEL technique is then employed to understand the significant influence of each factor in the adoption of CE practices in SCs by dividing them into cause-effect groups. The findings show that PDFs such as training and knowledge sharing, employee participation, leadership and management plus strategic alignment are considered to be the most important significant factors in the adoption. The findings of this study will help industrial managers to understand the significance of the role of PDFs for enhancing business strategies; these findings can reduce the negative environmental impact in the adoption of CE practices in the SCs of SMEs.
    • Deploying Kaizen events in the manufacturing industry: An investigation into managerial factors

      Garza-Reyes, J.A., Christopoulos, C., Kumar, A., Luthra, S., Gonzalez Aleu, F., Kumar, V., Villarreal, B.; University of Derby; University of Warwick; London Metropolitan University; Ranbir State Institute of Engineering & Technology, India; Universidad de Monterrey, Mexico (Taylor & Francis, 2020-09-24)
      Despite the extensive research on Continuous Improvement (CI), limited reflection has been reported regarding the managerial factors needed to successfully deploy Kaizen Events (KEs). This study investigates various managerial aspects that affect the implementation of KEs. After conducting a literature review and gathering experts’ inputs, the objectives of the study and six research questions were formulated. A survey questionnaire responded by 175 manufacturing organisations was designed and validated. A combined approach of descriptive statistics and one-way ANOVA tests were used to analyse the collected data. Besides other ‘soft’ aspects, the results determine: (1) the drivers and barriers in the pre-implementation stage of KEs; (2) the critical success factors and challenges related to the implementation stage of KEs; (3) the reasons that result in unsuccessful KEs; and (4) the reasons that stop organisations from running KEs. The study provides insights into an under-researched topic by looking at different phases of KEs implementation. The study contributes to the contingency and the RBV theories by offering an understanding of the importance of different contingencies and resources planning for KEs implementation. The findings are beneficial for industrialists who may aim at driving CIs in their organisations through the implementation of KEs.
    • Developing a strategic sustainable facility plan for a hospital layout using ELECTRE and Apples procedure

      Vimal, K.E.K; Kandasamy, Jayakrishna; Nadeem, Simon Peter; Kumar, Anil; Šaparauskas, Jonas; Garza-Reyes, Jose Arturo; Trinkūnienė, Eva; National Institute of Technology, Patna, Bihar, India; VIT University, Vellore, Tamil Nadu, India; London Metropolitan University; et al. (Vilnius Gediminas Technical University (VGTU) Press, Lithuania, 2020-11-23)
      Today healthcare globally is growing at a rapid pace and despite the huge technological advancement, healthcare still faces primitive challenges and hence results in the poor service and facility to the needy. Layout planning acts as one major reason which requires improvements for the effective and efficient working of the healthcare facilities. This research aims at optimizing several quantitative criteria related to economic, technology and society which are taken into consideration for the decision-making during the evaluation, analysing and selection of the best layout for an existing healthcare facility. Critical areas for the improvement were found out using statistical analysis based on a survey questionnaire and Apple’s layout procedure is utilised to design the different possible layouts for an efficient facility. The seven criteria namely inter-departmental satisfactory level, the average distance travelled and the average time required for staff flow, the average distance travelled and the average time required for patient flow, the average distance travelled and the average time required for material flow were taken into consideration. The ELECTRE methodology was used as multi-criteria decision making based on decided seven criteria for comparing the different layout by methodical and orderly thinking.
    • Enhancing resiliency of perishable product supply chains in the context of the COVID-19 outbreak

      Shanker, S; Barve, A; Mudulib, K; Kumar, A; Garza-Reyes, Jose Arturo; Maulana Azad National Institute of Technology, Bhopal, India; Papua New Guinea University of Technology, Lae, Morobe Province, Papua; London Metropolitan University; University of Derby; Doon University, India (Taylor and Francis, 2021-03-02)
      Globally, countries are struggling to fulfil customer demands due to the effects of the COVID-19 pandemic on perishable food supply chains (PFSCs). This study aims to analyse the factors influencing PFSCs during the pandemic and improve their resiliency. This is essential as some factors discourage the productive execution of PFSCs and decrease organizational performance, thus lowering stakeholder satisfaction. This study has been conducted in two phases. The first phase, through extensive review and discussion with experts, identifies the influencing factors related to supply chain (SC) disturbances in PFSCs. In the second phase, a hybrid method i.e. g-DANP, a combination of grey-decision making trial and evaluation laboratory and analytic network process, is employed to develop a hierarchical structure to measure their influence. The proposed framework is validated with a case of the current COVID-19 outbreak. The study revealed that factors, restriction on import-export and fear of violation of social distancing guidelines, are the primary “cause” group factors; whereas, price variation of perishable products and panic buying and stockpiling are the crucial “effect” group factors affecting the PFSCs. The findings also enrich existing literature by providing analytical support to relationships between various factors affecting PFSCs during the pandemic. The results of this study can be utilised by decision-makers to anticipate the operative and long-haul effects of COVID-19 on PFSCs and create plans to deal with the pandemic.
    • A framework to achieve sustainability in manufacturing organisations of developing economies using Industry 4.0 technologies’ enablers

      Yadav, G; Kumar, Anil; Luthra, S; Garza-Reyes, Jose Arturo; Kumar, V; Batista, L; Veermata Jijabai Technological Institute, Mumbai, India; London Metropolitan University; Ch. Ranbir Singh State Institute of Engineering and Technology, Jhajjar, Haryana, India; University of Derby; et al. (Elsevier, 2020-07-07)
      Sustainability has emerged as one of the most important issues in the international market. Ignorance of sustainability aspects has led many manufacturing organisations to face huge financial losses. It has been observed that developed nations have successfully achieved sustainability in their manufacturing sectors. However, the rate of sustainability adoption in developing nations is significantly poorer. The current business trend offers new technologies such as the Internet of Things, Big data analytics, Blockchain, Machine learning, etc. These technologies can be termed under the Industry 4.0 paradigm when considered within a manufacturing context. It is significant to notice that such new technologies directly or indirectly contribute to sustainability. So, it is necessary to explore the enablers that facilitate sustainability adoption. This study aims to develop a framework to improve sustainability adoption across manufacturing organisations of developing nations using Industry 4.0 technologies. Initially, the enablers that strongly influence sustainability adoption are identified through a literature review. Further, a large scale survey is conducted to finalise the Industry 4.0 technologies’ enablers to be included in the framework. Based on the empirical analysis, a framework is developed and tested across an Indian manufacturing case organisation. Finally, Robust Best Worst Method (RBWM) is utilised to identify the intensity of influence of each enabler included in the framework. The findings of the study reveal that managerial and economical, and environmental enablers possess a strong contribution toward sustainability adoption. The outcomes of the present study will be beneficial for researchers, practitioners, and policymakers.
    • Industry 4.0 enablers for a cleaner production and circular economy within the context of business ethics: a study in a developing country

      Shayganmehr, Masoud; Kumar, Anil; Garza-Reyes, Jose Arturo; Moktadir, Abdul; Tarbiat Modares University, Tehran, Iran; London Metropolitan University; University of Derby; University of Dhaka, Dhaka, Bangladesh (Elsevier, 2020-11-26)
      To achieve sustainability, businesses are adopting Cleaner Production (CP) and Circular Economy (CE) practices for producing better quality products at the lowest cost while decreasing the negative environmental impact of their operations. The implementation of these practices is highly influenced by Industry 4.0 technology’s enablers, particularly within the context of ethical and sustainable business development. In this paper, a novel framework is proposed to assess the importance of Industry 4.0 enablers for implementing CP practices embedded in CE in the context of ethical societies and assess an industry’s readiness. Firstly, the most effective context-related Industry 4.0 enablers are extracted from previous studies and validated through a Fuzzy Delphi method. Secondly, the Interval-Valued Fuzzy Sets (IVFS) based Analytical Hierarchy Process (AHP) method is applied to evaluate the enablers’ weight. Due to existing ambiguities in the enablers, IVFS was applied to model the uncertainty in an interval [0,1]. The final results indicate that the most important enablers are “Technical Capability”, “Security and Safety”, Policy and Regulation”, “System Flexibility”, “Education and Participation” and “Support and Maintenance” respectively. Thirdly, the Fuzzy Evaluation Method (FEM) was followed to evaluate the readiness score of Industry 4.0 enablers for implementing CP practices embedded in CE and evolving ethical principles of corporate social responsibility. This paper contributes to the CP, CE and ethics body of knowledge by proposing a framework for assessing wider dimensions of Industry 4.0 enablers during the implementation of CP and CE practices and providing ethical and sustainable business development.
    • Lean Manufacturing and Internet of Things – A Synergetic or Antagonist Relationship?

      Anosike, Anthony; Alafropatis, Konstantinos; Garza-Reyes, Jose Arturo; Kumar, Anil; Luthra, Sunil; Rocha-Lona, Luis; University of Derby; University of Warwick; London Metropolitan University; Ch. Ranbir Singh State Institute of Engineering & Technology, Jhajjar-124103, Haryana, India; et al. (Elsevier, 2021-04-13)
      This paper explores the relationship between five LM methods (JIT, TPM, Autonomation, VSM and Kaizen) and three IoT technologies (RFID, WSN and Middleware) and the implications that arise from their combination. Four hypotheses and four complimentary research questions were formulated and tested. 136 responses were obtained through a questionnaire survey and analysed using descriptive statistics, 2-Sample proportion, Kruskal-Wallis, ANOVA and Pairwise comparison tests. The findings indicate that IoT can significantly improve the operational performance of manufacturing organisations. The findings advocate that all LM methods, apart from Kaizen, benefit from improved effectiveness by combining them with IoT. The results suggest that this can be attributed to the general perception about IoT, which despite the support and benefits it provides to people, is seen to be reducing human involvement whereas Kaizen is seen to be more people-focused. Improvements in information flow, decision-making and productivity were also found to be the most important motivations and benefits of combining LM methods with IoT. The findings of this research can be used by LM organisations that wish to embark into the new digitalised manufacturing era and businesses seeking to improve their performance through the combination of traditional efficiency-based methods and I4.0 technologies.
    • Machine Learning Applications for Sustainable Manufacturing: A Bibliometric-based Review for Future Research

      Jamwal, Anbesh; Agrawal, R; Sharma, M; Kumar, A; Kumar, V; Garza-Reyes, Jose Arturo; Malaviya National Institute of Technology, Jaipur, India; London Metropolitan University; University of the West of England; University of Derby (Emerald, 2021-05-06)
      The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM). In the present study, we use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing. The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices. A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality
    • Nexus of circular economy and sustainable performance in the era of digitalization

      Agrawal, Rohit; Wankhede, Vishal Ashok; Kumar, Anil; Upadhyay, Arvind; Garza-Reyes, Jose Arturo; National Institute of Technology Tiruchirappalli, Tiruchirappalli, India; Pandit Deendayal Energy University, Gandhinagar, India; London Metropolitan University; University of Brighton; University of Derby (Emerald, 2021-04-01)
      This study aims to conduct a comprehensive review and network-based analysis by exploring future research directions in the nexus of circular economy (CE) and sustainable business performance (SBP) in the context of digitalization. A systematic literature review methodology was adopted to present the review in the field of CE and SBP in the era of digitalization. WOS and SCOPUS databases were considered in the study to identify and select the articles. The bibliometric study was carried out to analyze the significant contributions made by authors, various journal sources, countries and different universities in the field of CE and SBP in the era of digitalization. Further, network analysis is carried out to analyze the collaboration among authors from different countries. The study revealed that digitalization could be a great help in developing sustainable circular products. Moreover, the customers' involvement is necessary for creating innovative sustainable circular products using digitalization. A move toward the product-service system was suggested to accelerate the transformation toward CE and digitalization. The paper discusses digitalization and CE practices' adoption to enhance the SP of the firms. This work's unique contribution is the systematic literature analysis and bibliometric study to explore future research directions in the nexus of CE and SP in the context of digitalization. The present study has been one of the first efforts to examine the literature of CE and SBP integration from a digitalization perspective along with bibliometric analysis.
    • A review of challenges and opportunities of blockchain adoption for operational excellence in the UK automotive Industry

      Upadhyay, Arvind; Ayodele, J; Kumar, Anil; Garza-Reyes, Jose Arturo; University of Brighton; University of the West of Scotland London Campus, London; London Metropolitan University; University of Derby (Emerald, 2020-11-20)
      This paper aims to explore the challenges and opportunities of blockchain technology adoption from the lens of the TOE framework for operational excellence in the UK automotive industry context. The research methodology of this study follows a systematic review approach, which analyses existing academic published research papers in the top 35 academic journals. There was no specific timeframe established for this study and shortlisting the articles through a set of used keywords. A sample of 71 articles was shortlisted and analysed to provide a discussion on technological and management challenges and opportunities of blockchain adoption from the lens of the TOE framework for operational excellence. Findings– The findings of this study present significant theoretical and managerial implications and deep understanding for firms seeking to understand the challenges and opportunities of blockchain adoption for their operational excellence. Systematic literature approach was considered for the present study to explore existing academic papers on technological and management challenges and opportunities from the lens of TOE framework for operational excellence, whereas a more specified method meta-analysis can be considered for future research. The study has been explored in the UK automotive industry context, which has been considered as the limitation of generalization across countries and industries. This paper represents the most comprehensive literature study related to the technological and management challenges and opportunities of blockchain from the TOE framework angle for operational excellence.
    • Sustainability concerns on consumers’ attitude towards short food supply chains: an empirical investigation

      Wang, Meng; Kumar, Vikas; Ruan, Ximing; Saad, Mohammed; Garza-Reyes, Jose Arturo; Kumar, Anil; University of the West of England; University of Derby; London Metropolitan University (Springer, 2021-03-17)
      While industrialized agro-food supply systems have gained tremendous success in recent decades, it has been increasingly criticized for its adverse environmental and social impact. Amongst this criticism, Short Food Supply Chains (SFSCs) have emerged as a promising sustainable alternative to the industrialized agri-food supply systems. In recent years there have been some attempts to explore the relationship between SFSCs and sustainability, but these are mostly theoretical discussions and lacks empirical validation. This study, therefore, attempts to provide empirical validation of the SFSCs and sustainability linkages. Additionally, from the theoretical perspective, our work extends the traditional triple bottom line constructs and explores two extra dimensions of sustainability in the food supply chain system, namely, governance and culture, thus exploring five dimensions of sustainability. Furthermore, while SFSCs have proven to improve farmers’ livelihoods and reconnect producers with consumers, little or no attention has been given to understand the consumers' attitudes towards the SFSC practices. Therefore, this study aims to explore the customers’ attitudes towards participating in SFSCs through the concept of a moral economy and personal relationship. Based on the 532 valid responses from Chinese consumers, our study shows that all five pillars of sustainability, moral economy and Chinese relationship have a positive influence on consumers’ participation in SFSCs. With its intuitive benefits, the economic pillar emerged as the most approved factor by the participants. Interestingly our findings show that the social aspect is less prominent than others, which is contrary to existing studies conducted in developed countries.
    • A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

      Salazar-Reyna, Roberto; Gonzalez-Aleu, Fernando; Granda-Gutierrez, Edgar M.A.; Diaz-Ramirez, Jenny; Garza-Reyes, Jose Arturo; Kumar, Anil; Universidad de Monterrey, San Pedro Garza Garcia, Mexico; University of Derby; London Metropolitan University (Emerald, 2020-12-07)
      The objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining, and machine learning to healthcare engineering systems. A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest, and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors, and content. From the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field. The use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors’ previous knowledge and the nature of the publications were used to select different platforms. To the best of the authors’ knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining, and machine learning applied to healthcare engineering systems.