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The purpose of this paper is to examine and model the in-plant operational efficiency of tow trucks of an automobile manufacturing plant. Even though, tow trucks contribute toward the improvement of operational performance, little case-based evidence prevail in the literature. For this purpose, a case study has been conducted in an Indian automobile manufacturer to address the prevailing issues in material handling (MH).

Initially, this paper focuses on grouping of the sequence parts and finding the shortest path among the groups. To elucidate this, an analytical framework based on the distance and stuffing quantity is proposed. A fuzzy Dijkstra’s algorithm is used to solve the issues in grouping of the sequence parts and shortest path among the groups.

This study addressed the four aspects of MH: move cost, time, distance and material by integrating the function of grouping, finding the shortest path and communication with low cost devices. The result shows that logistics routes and activities should not be interrupted by any of the external factors. The availability of stock is a key performance variable to attain efficiency. In addition to this, effective communication between the truck operators and the production line managers is key performance indicator.

The paper helps the automobile practitioners on increasing the efficiency of tow truck by systemizing the routes. Logistics routes and activities should not be interrupted by any of the external factors. The availability of stock is a key performance variable to attain efficiency. In addition to this, effective communication between the truck operators and the line managers is key performance indicator.

Risk management in defence aircraft industry has considerable interest among academics and practitioners. The purpose of this paper is to develop interactions among risk factors dimensions (RFDs) and inspect the importance relationship among the performance measures in Indian aircraft industry and, finally, understand the effect of involvements provided by the managerial team on risk reduction process.

An extensive literature review was carried out to identify 26 risk parameters and 13 performance measure indices relevant for an aircraft industry. Survey method was used to obtain the importance of these parameters and measures. Further, these factors are grouped into five risk dimensions based on the brain storming session by the project managers. Initially, Risk factors for defense aircraft industry (RFDs) analyzed by Interpretative structural model (ISM) to know the contextual relationship among the RFDs and then applied Interpretive ranking process (IRP) to inspect the pre-eminence relationship among them. Finally, SD is applied to understand the effect of involvements provided by the managerial team on risk reduction process.

Government policy and legal RFDs has emerged as the key driving RFDs. In IRP modelling, technology RFD has emerged as more influential RFD which is the more relevant factor with respect to performance measure indices and this result is supported by detailed sensitivity analysis of system dynamic model.

The outcomes of this research can help project management team to identify the high severity risk factors which need immediate risk reduction/mitigation action.

Industry 4.0 (I4.0), the Fourth Industrial Revolution, integrates Big Data analytics, blockchain, cloud computing, digitisation and the Internet of Things to enhance supply chain (SC) activities and achieve sustainable growth through dynamic capabilities (DCs). This approach equips businesses with the necessary tools to optimise their operations and remain competitive in a dynamic business environment. The value proposition of a business encompasses a wide range of activities that add value at each stage. By leveraging DCs, a firm can achieve innovation, gain a competitive advantage and enhance its adaptability. Conversely, effective value chain management can amplify the influence of a firm's DCs on SC sustainability, by reducing waste, optimising resource utilisation and fostering strategic partnerships. This mutually beneficial connection takes the form of a dynamic interaction in which I4.0 technologies act as a catalyst to help organisations become more resilient, adaptive and responsive. The adoption of these technologies denotes a comprehensive approach to business shift, not merely technical integration. I4.0 has an impact on several organisational disciplines outside of manufacturing, from automation and efficiency advantages to quality enhancements. This chapter offers an extensive literature review to explore the level of SC sustainability that a business can achieve by combining its DCs and implementing strategic I4.0 adoption. The function of value chain management in moderating the effects of I4.0 and DCs on SC sustainability is also assessed. This study proposes a theoretical model that is grounded in the insights extracted from the literature review.

Human resource management (HRM) is struggling to cope with the increasingly volatile demand for skilled resources in the logistics and supply chain sector. Thus, this study discovers the possible integration of HRM and supply chain management (SCM) practices for improved supply chain performance. The purpose of this study is to explore the effect of intra HRM–SCM and joint HRM–SCM decisions on the performance of the supply chain.

An intra HRM–SCM and joint HRM–SCM model is developed following an empirical study. Survey data collected from 109 supply chain managers from Indian logistics firms are used to test the developed hypotheses. Structural equation modeling is used to analyze and validate the model.

The results suggest that supply chain performance is significantly influenced by joint HRM–SCM, compared to intra HRM–SCM practices, especially under volatile demand environments. Training and development, recruitment and selection, and performance management affect joint HRM–SCM significantly compared to the other three factors identified. Moreover, HRM and SCM show strong correlation and mutual support in identifying and fulfilling the demand of the logistics and supply chain sector.

With a growing trend toward globalization and digitalization, a joint HRM–SCM model will help businesses make robust and informed decisions for improved supply chain performance.

An empirical relationship between joint HRM–SCM, intra HRM–SCM, supply chain inhibitors and supply chain performance is established in this study. Although some part of this relationship may already exist, the study provides robust evidence to support this complex, collaborative relationship.

The purpose of this paper is to identify and analyze the interdependence of project complexity factors (PCFs) in metro rail projects using the Decision-Making Trial and Evaluation Laboratory (DEMATEL). The study provides qualitative and quantitative analysis of project complexities factors and their relationships. The results of the study facilitate effective project planning, proactive risk management and informed decision-making by stakeholders.

This study employs a case-based method for identifying PCFs and a DEMATEL method for analyzing the interdependence of complexity factors in metro rail projects. Initially, PCFs were identified through an extensive literature review. To validate and refine these factors, semi-structured interviews were conducted with thirty experienced professionals, each having 5–20 years of experience in roles such as project management, engineering, and planning. Further, elevated and underground metro rail projects were purposefully selected as cases, for identifying the similarities and differences in PCFs. A questionnaire survey was conducted with various technical experts in metro rail projects. These experts rated the impact of PCFs on a five-point Likert scale, for the evaluation of the interdependence of PCFs. The DEMATEL technique was used to analyze the interdependencies of the PCFs.

Metro rail projects are influenced by project complexity, which significantly impacts their performance. The analysis reveals that “design problems with existing structures,” “change in design or construction” and “land acquisition” are the key factors contributing to project complexity.

The study of project complexity in metro rail projects is limited because most of the studies have studies on examining complexity in mega projects. The existing literature lacks adequate attention in identifying project complexity and its effects on metro rail project performance. This research aims to bridge this gap by examining project complexity and interdependencies in metro rail projects.

The purpose of this research is to develop a project complexity index (PCI) model using the best and worst method (BWM) to quantitatively analyze the impact of project complexities on the performance of metro rail projects.

This study employed a two-phase research methodology. The first phase identifies complexities through a literature review and expert discussions and categorizes different types of complexities in metro rail projects. In the second phase, BWM, a robust multi-criteria decision-making (MCDM) technique, was used to prioritize key complexities, and a PCI model was developed. Further, the developed PCI was validated through case studies, and sensitivity analysis was performed to check the accuracy and applicability of the developed PCI model.

The analysis revealed that location complexity exerted the most substantial influence on project performance, followed by environmental, organizational, technological and contractual complexities. Sensitivity analysis revealed the varying impacts of complexity indices on the overall project complexity.

The study's findings offer a novel approach for measuring project complexity's impact on metro rail projects. This allows stakeholders to make informed decisions, allocate resources efficiently and plan strategically.

The existing studies on project complexity identification and quantification were limited to megaprojects other than metro rail projects. Efforts to quantitatively study and analyze the impact of project complexity on metro rail projects are left unattended. The developed PCI model and its validation contribute to the field by providing a definite method to measure and manage complexity in metro rail projects.

The purpose of this study is to measure the supply chain competitiveness of the e-commerce industry in Indonesia.

The study used a multi-criteria decision-making model based on the analytic hierarchy process. Four main criteria are used to measure the supply chain competitiveness, i.e. cost, differentiation, sustainability and infrastructure.

The findings of this study show that cost is the most important criterion with a degree of importance of 33.19%, followed by infrastructure of 29.40%, differentiation of 27.96% and sustainability of 9.45%. It shows that the internally controlled strategy contributes about 70% of supply chain competitiveness. The internal infrastructure criterion that consists of software and hardware contributes 65.92% to the whole infrastructure criterion. The internal infrastructure then contributes 19.38% to supply chain competitiveness. Therefore, the internally controlled strategies and internal infrastructure contribute up to 90.08% to the supply chain competitiveness of e-commerce in Indonesia. This result implies that to attain the supply chain competitiveness, the company must carry out strategies focusing on the performance such as cost, differentiation, sustainability as well as on the internal infrastructure such as software and hardware.

In this paper, the authors limited their study to the business to business (B2B) and business to consumer (B2C) players because these two platforms have been experiencing a very rapid growth. While e-commerce business can take many platforms besides B2B and B2C, the future research should include other platform such as consumer to consumer as well. Because the focus in this study is more the information and material flows, it will be of great interest if the future research covers the platform of mobile payment as well that guarantee the ease of cashflows within supply chains. Also, with the occurrence of the Covid-19 pandemic when this paper was written, in the near future, it is then of great interest to incorporate the pandemic context into the proposed model used in this study. The further study should analyze long-term changes happened as the result of pandemic such as behavioral changes of online shopping from customer side or shift in e-commerce supply chain infrastructure and inventory practice.

With this study, it is expected that it can be determined which criteria contribute the most to the supply chain competitiveness of the e-commerce industry in Indonesia that will be useful for industry player.

E-commerce development in Indonesia is still facing serious challenges. The multi-criteria decision making approach used in this research lays a foundation of how supply chain competitiveness is determined based on the judgment of experts coming from major companies within the supply chain.

Supplier selection (SS) is one of the prime competencies in a sourcing decision. Taking into account the key role played by suppliers in facilitating the implementation of green supply chain management (GSCM), it is somewhat surprising that very little research attention has been imparted to the development of a strategic sourcing model for GSCM. This research aims to develop a strategic sourcing framework in which supplier organizations are prioritized and ranked based on their GSCM performance. Accordingly, the benchmark organization is identified and its strategy is explored for GSCM performance improvement.

The research develops an innovative GSCM performance evaluation framework using six parameters, namely, investment in corporate social responsibility, investment in research and development, utilization of renewable energy, total energy consumption, total carbon-di-oxide emissions and total waste generation. An integrated multicriteria decision-making (MCDM) approach is proposed in which the entropy method calculates criteria weights. The Complex Proportional Assessment (COPRAS) and the Grey relational analysis (GRA) methods are used to rank supplier organizations based on their performance scores. A real-world case of green supplier selection (GSS) is considered in which five leading India-based automobile manufacturing organizations (Supplier 1, Supplier 2, Supplier 3, Supplier 4 and Supplier 5) are selected. Surveys with industry experts at the strategic, tactical, and operational levels are carried out to collect relevant data.

The results reveal that total carbon dioxide emission is the most influential parameter, as it gains the highest weight. On the contrary, investment in research and development, and total waste generation have no significant impact on GSCM performance. Results show that Supplier 5 secures the top rank. Hence, it is the benchmark organization.

The proposed methodology offers an easy and comprehensive approach to sourcing decisions in the field of GSCM. The entropy weight-based COPRAS and GRA methods offer an error-free channel of decision-making and can be proficiently used to outrank various industrial sectors based on their GSCM performances. This research is specific to the automobile manufacturing supply chain. Therefore, research outcomes may vary across supply chains with distinct characteristics.

The basic propositions of this research are based on a real-world case. Hence, the research findings are practically feasible. The less significant parameters identified in this study would enable managers to impart more attention to vulnerable areas for improvement. This research may help policymakers identify the influential parameters for effective GSCM implementation. As this research considers all aspects of sustainability, the strategies of the benchmark supplier have a direct impact on organizations' overall sustainability. The study would enable practitioners to make various strategies for GSCM performance improvement and to develop a cleaner production system.

The originality of this research lies in the consideration of both economic, social, environmental and operational aspects of sustainability for assessing the GSCM performance of supplier organizations. Quantitative criteria are considered so that vagueness can be removed from the decision. The use of an integrated grey-based approach for developing a strategic sourcing model is another unique feature of this study.

The prime objective of this paper is to design a green supply chain management (GSCM) framework to evaluate the performance of environmental-conscious suppliers using multi-criteria decision-making (MCDM) approach.

The literature survey reveals critical factors for implementing GSCM, adopted methodologies and the result obtained by several researchers. Data have been collected by conducting surveys and interviews with strategic-level personnel of five esteemed organizations in automobile manufacturing sectors. A GSCM framework is developed in which a mathematical tool entropy–the technique for order of preference by similarity to ideal solution (TOPSIS) has been used to analyze the six parameters of automobile manufacturing unit. Initially, entropy is used to find the weights of each of the parameters that influence the decision matrix of the TOPSIS method. Secondly, the proposed GSCM framework ranks the supplier. Finally, sensitivity analysis of the model satisfies the GSCM framework and benchmarked the supplier.

The result shows that “Total CO₂ emission” has an influential role for GSCM sustainability, and hence, firms should put more effort to reduce emissions to improve overall performance. Again, the parameters like investment in R&D and total waste generation may be ignored in the selection process. The result reveals the benchmarked supplier and its strategies for effective sourcing, which would have an indirect effect on organizations' overall sustainability.

This research entirely focuses on sustainability within supply chain considering economic, social and environmental paradigms. The mathematical modeling of the proposed work considers many influential parameters and provides an easy and comprehensive decision-making technique.

The methods may be adopted by the industries for sustainable supply chain management. This study benchmarks the supplier organizations and explores the adopted policies by benchmarked organizations. Other organizations should follow the policies followed by benchmarked organization for enhancing environmental, social and economic performance. Organizations striving for sustainable development can adopt this framework for evaluation of supplier performance and benchmark with better accuracy.

The design of the GSCM framework explores both the qualitative and quantitative data based on environmental, social and economic parameters simultaneously in the evaluation of environmentally conscious suppliers. The research also investigates the constraints of the system to implement the GSCM in automobile manufacturing unit. Additionally, the sensitivity analysis justifies the benchmarked supplier and the adopted strategies to be followed by other manufacturing unit.

The purpose of this paper is to empirically examine the relationships between low carbon supply chain practices and their relationships with environmental sustainability (ES) and the economic performances (EP) of firms. The study also includes an examination of the low carbon supply chain practices that are utilized by Indian manufacturing firms.

Through a questionnaire-based survey, the data received from 83 Indian manufacturing firms was analyzed using a variance-based structural equation modeling technique to test the proposed hypotheses.

The study indicates that carbon governance is a strategic imperative for the adoption of low carbon supply chain practices. Similarly, low carbon product and process design (LCPPD), manufacturing and logistics lead to improved ES. In addition, low carbon purchasing is positively related to the adoption of LCPPD, manufacturing and logistics. No significant relationship was found between the adoption of low carbon supply chain practices and the EP of a firm.

The findings of this study may assist manufacturing managers in prioritizing operational practices for the reduction of emissions.

This study provides two major contributions to green supply chain management. First, it provides comprehensive empirical evidence on low carbon supply chain practices that are being followed by Indian manufacturing firms. Second, this study also empirically validated a structural model of low carbon supply chain practices.