This research aims at exploring barriers of adopting Industry 4.0 in manufacturing supply chains. Data were collected based on a review of extant literature on barriers Industry 4.0 adoption, individual interviews with a panel consisted of academic and industry experts. Following numerous previous studies, interpretive structural modeling (ISM) and matrix multiplication applied to classification (MICMAC) analysis were conducted to order 10 barriers based on their importance and impacts. The results excluded one barrier “cyber security challenges”, categorized another one as a dependent barrier “lack of digital strategy”, and eight barriers as linkage barriers “lack of infrastructure”, “personnel resistance to adopt new technologies”, “high investment requirements”, “data management and quality challenges”, “uncertainty of economic benefits”, “low maturity level of technology”, “lack of adequate skills”, and “job disruptions”. Henceforward, it was concluded that mitigating these eight barriers is very critical to ensure a successful adoption of Industry 4.0 technologies in supply chains. Further studies are required to categorize these eight barriers based on their importance and relationships.

The development of the shipbuilding industry is expected to meet the needs of the Indonesian Navy and the commercial vessels, and to support Indonesia's marine policy. The purpose of this study is to see the Shipbuilding industry Competitiveness, the influence of Technology Transfer to the Shipbuilding industry Competitiveness, and the influence of the industrial clusters on the Shipbuilding industry Competitiveness, as well as to analyze the institutional model of the Shipbuilding industry Competitiveness. This study uses the descriptive analysis, the Structural Equation Modeling (SEM) for the model causality testing, and the Interpretative Structural Modeling (ISM) for the institutional model of the competitiveness of the Shipbuilding Industry. This study uses the primary data, namely a survey of defense industry players, the national industry, the defense equipment users, the government institutions, the research institutes, and the universities that are determined purposively. ISM data are obtained from questionnaires and Forum Group Discussion (FGD) with 13 speakers representing academia, industry, and government. The results of the analysis of SEM state that the indicators on the industrial clusters, the competitiveness, and the technology transfer have a significant and real contribution to these variables. This research also shows that the industrial clusters and the technology transfer have a direct and significant effect on the competitiveness and the industrial clusters directly and significantly affect the technology transfer. However, the industrial clusters also have an indirect effect on competitiveness through the technology transfer to the shipbuilding industry. The results of the analysis of ISM conclude that the stakeholders involved have the greatest driving force, namely the Ministry of Defense and Ministry of State-Owned Enterprises, while the important factor affected by the stakeholders in strengthening the competitiveness of the shipbuilding industry is the Indonesian Navy Headquarters.

Manufacturing organizations are under the pressure to give quality products at least cost within the possible minimum delivery time, even during unpredictable economic conditions. Due to competition like improved customer service and cost reduction, organizations are looking for innovative ways for creating competitive advantage. One such way is the effective information sharing among all the members of supply chain. To understand information sharing, we have to know the benefits of information sharing within the supply chain. In this study, on the basis of the relevant available literature and the suggestions of an expert team composed of managerial and technical experts of the manufacturing organization and academicians, eleven information sharing benefits have been diagnosed. An ISM-based model has been formed to study the understanding of the information sharing benefits in adopting the right information sharing within a manufacturing industry. We propose the ISM model, and a MICMAC investigation is applied. Its practical significance is to make use of the decision makers’ knowledge to give a fundamental understanding of a complicated situation, followed by a course of actions for problem-solving.

The main objective of this paper is to identify and understand the relationship dynamics among the quality enabled factors affecting the scheduling stage of production system life cycle. For this purpose, Interpretative structural modelling (ISM) approach has been utilized for developing the relationships among the various factors of the scheduling system stage. Afterwards, MICMAC (Matriced Impacts Croises Multiplication Appliqueeaun Classement) analysis has been carried out in order to classify the factors into different categories and to disclose the direct and indirect effects of each factor on the scheduling system. It is an approach for refining the decision making in the scheduling stage of production system life cycle.

Lean Manufacturing is being adopted by many Indian industries based on its capability of improving manufacturing performance. Lean Manufacturing supports the concept of delivering the low cost and good quality product to customer by focusing on elimination of waste. Lean Manufacturing employs various elements which aspire to involve the employees to create a customer centric organization. This paper is an attempt to examine the significance of various lean manufacturing elements in Indian industries. Interpretive Structural Modeling (ISM) approach is used to examine their inter-relationship and ranking based on their driving power and dependence powers through development of diagraph and structural model in the Indian industry context.

The fast industrial development increases different types of risks for the industries. Many risk factors are inherent in the implementation of advanced manufacturing technologies (AMTs). Industries are developing methodologies for risk prevention and protection. The present research focuses to identify various risks that could influence the implementation of AMTs, and develop a framework to mitigate them. For this framework, interpretive structural modeling(ISM) has been used to depict the relationship and priority among the various risks. This research provides a path for managers and indicates the dominant risks on the basis of higher driving power. Also, this research classifies the relationship among various risks in AMTs implementation according to their driving power and dependence. The risks have been categorized into four categories as autonomous risks, linkage risks, dependent risks and independent risks. The proposed hierarchal model would help the management to effectively handle and develop strategies against the risks and hence new and latest technologies can be adopted with ease and effectiveness.

Reverse Logistics has become buzzword in today’s industrial word and big differentiating factor in quest of success for any organization word wide. Whole world is suffering from scarcity of resources and we have finite area available for the waste disposal. One of the sustainable solutions is to design product in such a way so that we can maximize its reuse through reverse logistics. Reverse logistics is fast becoming industrial design standard for majority of organizations worldwide. Reverse logistics has become a need of an hour. Some of the organization do it voluntarily and some because of the environmental rules and legislations. In order to achieve total customer satisfaction, one has to take care of product for its entire life cycle through activities of reverse logistics. In this research I tried to investigate the factors affecting performance of reverse logistics in relation to customer satisfaction. I have used interpretive structural modelling technique to understand the complex pattern of contextual relationship amongst the most important factors through diagraph, which explain performance of reverse logistics in relation to customer satisfaction. I have also used MICMAC analysis to classify these factors according to their driving power and dependency power. MICMAC analysis facilitate in identification of most important factors in explaining performance of reverse logistics in relation to customer satisfaction. Further this research may helpful in reverse logistics design process planning, driven by customer requirement.