Modelling of Determinants of Logistics 4.0 Adoption: Insights from Developing Countries

With the emergence of industry 4.0, several elements of the supply chain are transforming through the adoption of smart technologies such as blockchain, the internet of things and cyber physical systems. Logistics is considered one of the important elements of supply chain management and its digital transformation is crucial to the success of industry 4.0. In this circumstance, the existing logistics system needs to be upgraded with industry 4.0 technologies and emerge as logistics 4.0. However, the adoption/transformation of logistics 4.0 is dependent on several determinants that need to be explored. Therefore, this study has the prime objective of investigating the determinants of logistics 4.0 adoption in the context of a developing country, specifically, India. Initially, ten determinants of logistics 4.0 are established after a survey of the relevant literature and the input of industry experts. Further, a four-level structural model is developed among these determinants using the Interpretive Structural Modelling (ISM) approach. In addition, a fuzzy Matrix of Cross-Impact Multiplications Applied to Classification (MICMAC) analysis is also conducted for the categorization of these determinants as per their driving and dependence power. The findings show that top management supports, information technology infrastructure and financial investment are the most significant determinants towards logistics 4.0 adoption. This study facilitates the supply chain partners to focus on these high-level determinants for the effective adoption of logistics 4.0. Moreover, the findings lead to a more in-depth insight into the determinants that influence logistics 4.0 and their significance in logistics 4.0 adoption in emerging economiesinfo:eu-repo/semantics/publishedVersio

A general outline of a sustainable supply chain 4.0

[EN] This article presents a literature review to identify the current knowledge of supply chains 4.0 from the sustainability perspective. Reviewed papers were classified in terms of objectives, results, and sustainability approaches. Additionally, a critical discussion with the main results and recommendations for further research was carried out. Manufacturing supply chains have been contemplated but agri-food supply chains and chains related to diversified cropping systems have been also considered. In this way, 54 articles were identified and revised, and were classified according to the three main aspects of sustainability: economic, social, and environmental. The classification of articles indicated that more attention has been paid to the environmental aspect in the industry 4.0 (I4.0) context in the literature, while the social aspect has been paid less attention. Finally, reference frameworks were identified, along with the I4.0 models, algorithms, heuristics, metaheuristics, and technologies, which have enabled sustainability in supply chains.This research was supported by the European Commission Horizon 2020 project entitled 'Crop diversification and low-input farming cross Europe: From practitioners' engagement and ecosystems services to increased revenues and value chain organisation' (Diverfarming), grant agreement 728003; and the Spanish Ministry of Science, Innovation, and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)' (RTI2018-101344-B-I00).Cañas, H.; Mula, J.; Campuzano-Bolarín, F. (2020). A general outline of a sustainable supply chain 4.0. Sustainability. 12(19):1-17. https://doi.org/10.3390/su121979781171219Design Principles for Industrie 4.0 Scenarios https://ieeexplore.ieee.org/document/7427673Liao, Y., Deschamps, F., Loures, E. de F. R., & Ramos, L. F. P. 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Procedia Manufacturing, 21, 438-445. doi:10.1016/j.promfg.2018.02.142Müller, J. M., Kiel, D., & Voigt, K.-I. (2018). What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability. Sustainability, 10(1), 247. doi:10.3390/su10010247Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408-425. doi:10.1016/j.psep.2018.05.009Hidayatno, A., Destyanto, A. R., & Hulu, C. A. (2019). Industry 4.0 Technology Implementation Impact to Industrial Sustainable Energy in Indonesia: A Model Conceptualization. Energy Procedia, 156, 227-233. doi:10.1016/j.egypro.2018.11.133Sustainable Value Stream Mapping and Technologies of Industry 4.0 in Manufacturing Process Reconfiguration: A Case Study in an Apparel Company https://ieeexplore.ieee.org/document/8476750Kumar, R., Singh, S. P., & Lamba, K. (2018). Sustainable robust layout using Big Data approach: A key towards industry 4.0. Journal of Cleaner Production, 204, 643-659. doi:10.1016/j.jclepro.2018.08.327Wiśniewska-Sałek, A. (2018). Sustainable Development in Accordance With the Concept of Industry 4.0 on the Example of the Furniture Industry. MATEC Web of Conferences, 183, 04005. doi:10.1051/matecconf/201818304005Müller, J. M., & Voigt, K.-I. (2018). Sustainable Industrial Value Creation in SMEs: A Comparison between Industry 4.0 and Made in China 2025. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(5), 659-670. doi:10.1007/s40684-018-0056-zTsai, W.-H., & Lu, Y.-H. (2018). A Framework of Production Planning and Control with Carbon Tax under Industry 4.0. Sustainability, 10(9), 3221. doi:10.3390/su10093221Birkel, H., Veile, J., Müller, J., Hartmann, E., & Voigt, K.-I. (2019). Development of a Risk Framework for Industry 4.0 in the Context of Sustainability for Established Manufacturers. Sustainability, 11(2), 384. doi:10.3390/su11020384Roda-Sanchez, L., Garrido-Hidalgo, C., Hortelano, D., Olivares, T., & Ruiz, M. C. (2018). OperaBLE: An IoT-Based Wearable to Improve Efficiency and Smart Worker Care Services in Industry 4.0. Journal of Sensors, 2018, 1-12. doi:10.1155/2018/6272793Ardanza, A., Moreno, A., Segura, Á., de la Cruz, M., & Aguinaga, D. (2019). Sustainable and flexible industrial human machine interfaces to support adaptable applications in the Industry 4.0 paradigm. International Journal of Production Research, 57(12), 4045-4059. doi:10.1080/00207543.2019.1572932Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). 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Process Safety and Environmental Protection, 118, 254-267. doi:10.1016/j.psep.2018.06.026Chaim, O., Muschard, B., Cazarini, E., & Rozenfeld, H. (2018). Insertion of sustainability performance indicators in an industry 4.0 virtual learning environment. Procedia Manufacturing, 21, 446-453. doi:10.1016/j.promfg.2018.02.143Smart Factories in Industry 4.0: A Review of the Concept and of Energy Management Approached in Production Based on the Internet of Things Paradigm https://ieeexplore.ieee.org/document/7058728Bonilla, S., Silva, H., Terra da Silva, M., Franco Gonçalves, R., & Sacomano, J. (2018). Industry 4.0 and Sustainability Implications: A Scenario-Based Analysis of the Impacts and Challenges. Sustainability, 10(10), 3740. doi:10.3390/su10103740De Sousa Jabbour, A. B. L., Jabbour, C. J. C., Foropon, C., & Godinho Filho, M. (2018). When titans meet – Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. 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Success Factors for the Adoption of Green Lean Six Sigma in Healthcare facility: An ISM-MICMAC Study

Green Lean Six Sigma (GLSS) is a sustainable development approach that leads to improved patient care with improved safety and quality of service to patients. The aim of this study is the identification, study, modeling, and analysis of GLSS success factors for the Indian healthcare facility. Interpretive structural modeling (ISM) and Impact Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) analyses have been used to understand the hierarchical structure among the GLSS success factors. This enabled the development of dependency relationships between success factors, in particular, which factors support the development of other factors. Specifically, this study found that the success factors ‘commitment of management’ and ‘financial availability’ are the most critical to GLSS implementation success, as they support the development of all other success factors. Meanwhile ‘embedding sustainable measures at each stage of the service’; ‘the capability and effectiveness of real-time data collection; and ‘feedback and corrective actions’ most directly support the GLSS implementation in the healthcare facility, and serve as the final indicators of implementation progress. This research work is the first of its kind that deals with the identification and analysis of the prominent factors that foster the inclusive implementation of GLSS within the healthcare facility. The major implication of the present research work lies in suggesting a direction for practitioners to execute the GLSS approach through a systematic understanding of classification and structural relationships among different enablers. The study also facilitates healthcare managers to explore different Green Lean wastes in hospitals and challenges to sustainability pursuits in healthcare that assist in an organization’s efforts towards sustainable development

Barriers to adoption of industry 4.0 and sustainability: a case study with SMEs

The concepts of sustainable supply chains and Industry 4.0 are progressively getting attention in different domains. Companies have started developing and implementing these practices in their business models. However, several challenges influence the adoption of sustainability and Industry 4.0 (I4.0) in small and medium-sized enterprises (SMEs). This study aims (i) to identify the adoption barriers of sustainability and I4.0 and (ii) establish the interrelationship among these barriers for SMEs. An extensive literature search supported by interviews with supply chain practitioners from three SMEs identified 12 critical barriers to adoption. The barriers are then ranked using “Interpretive Structural Modeling.” The results suggest that the “lack of resources” and the “lack of employee’s competence/experts” are the most influencing barriers. Changing government regulations on the allocation of capital and financial incentives for SMEs to encourage training and skills development programs could promote sustainable supply chains and practices. The study also reflects short-, medium- and long- term planning strategies for supply chain practitioners for adoption of sustainability and I4.0 in SMEs

Assessment of Critical Barriers to Industry 4.0 Adoption in Manufacturing Industries of Bangladesh

Purpose: Regardless of the potential benefits of Industry 4.0, firms are still experiencing difficulties in properly integrating new technologies into their business. This study focuses on addressing critical barriers to implementing Industry 4.0 in the manufacturing industries of Bangladesh with a view to exploring readiness. The paradigm of adopting new technologies is yet to be unlocked in Bangladesh, which serves as this study's motivating force. Approach: In order to accomplish the research objectives, "Interpretive Structural Modelling (ISM)" analysis is used, which identified and outlined the most significant barriers to adopting Industry 4.0. This was also combined with MICMAC analysis, which categorized the barriers based on their driving and dependent power. To evaluate the industries' preparedness, "cross-tabulation and frequency analysis" is performed using SPSS. Key findings: According to the findings, “Lack of technical knowledge about industry 4.0” has been identified as the most crucial barrier. Limitations of the study: To address the firm’s preparedness, we looked at responses from management personnel. Nevertheless, if we could do this on a larger scale, a better scenario of Bangladesh's manufacturing industries toward Industry 4.0 might be shown. Practical implication: The study's findings will be conceptually linked to relevant aspects of I4.0, particularly for strategic planning, decision-making, future opportunities, and formulating policy-related guidelines. Originality: While several studies concentrate on particular sectors adopting Industry 4.0, no study has been conducted on manufacturing industries focusing on the industry's preparedness, which makes this study distinctive

Interactions among Inter-organizational Measures for Green Supply Chain Management

Collaboration among supply chain partners is essential to enhance environmental performance during the life cycle of a product. Inter-organizational measures for green supply chain management tend to show diverse patterns because of various requirements that emerge in a complex supply chain. However, this diversity hampers the comprehensive understanding and systematic adoption of these measures. Therefore, this paper classifies various inter-organizational measures for green supply chain management into several collaboration patterns and analyzes their structural relations through an interpretive structural modeling. The results reveal the collaboration patterns that have higher driving power and dependency than other patterns and, thus, require further attentions

The impact of Industry 4.0 implementation on supply chains

Purpose The study aims to analyse the impact of Industry 4.0 implementation on supply chains and develop an implementation framework by considering potential drivers and barriers for the Industry 4.0 paradigm. Design/methodology/approach A critical literature review is performed to explore the key drivers and barriers for Industry 4.0 implementation under four business dimensions: strategic, organisational, technological and legal and ethical. A system dynamics model is later developed to understand the impact of Industry 4.0 implementation on supply chain parameters, by including both the identified driving forces and barriers for this technological transformation. The results of the simulation model are utilised to develop a conceptual model for a successful implementation and acceleration of Industry 4.0 in supply chains. Findings Industry 4.0 is predicted to bring new challenges and opportunities for future supply chains. The study discussed several implementation challenges and proposed a framework for an effective adaption and transition of the Industry 4.0 concept into supply chains. Research limitations/implications The results of the simulation model are utilised to develop a conceptual model for a successful implementation and acceleration of Industry 4.0 in supply chains. Practical implications The study is expected to benefit supply chain managers in understanding the challenges for implementing Industry 4.0 in their network. Originality/value Simulation analysis provides examination of Industry 4.0 adoption in terms of its impact on supply chain performance and allows incorporation of both the drivers and barriers of this technological transformation into the analysis. Besides providing an empirical basis for this relationship, a new conceptual framework is proposed for Industry 4.0 implementation in supply chains

Critical factors of digital supply chains for organizational performance improvement

Technological advancement is redefining supply chains (SCs) processes and soon traditional ways of managing SCs will no more be feasible and effective. Due to recent advancement in technology, digitalization has become an emerging topic among decision-makers and researchers. To cope-up with this emerging trend in customer behavior and remain competitive, organizations must move from their traditional ways of managing their SCs to digital supply chains (DSCs) for improved organizational performance. Therefore, the purpose of this article is in two folds: First, to identify critical factors of DSCs that are essential for transitioning traditional SCs to DSCs to improve organizational performance. Second, interpretive structural modeling is used to establish the relationship among critical factors and (matriced’ impacts croise´s multiplication applique´e a´un classement used to identify the driving and dependency power of the critical factors. Thus, this article identified fifteen DSC critical factors and established their direct and indirect effect on DSCs. The results show that “SC resilience”, and “proactive prevention” have the highest dependency power factors whilst “integration” and “advanced operational models” have the highest driving power factors. This article can help SC managers and decision-makers to understand the critical factors essential in adopting DSCs for improving organizational performance