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The purpose of this paper is to identify a classification scheme which represents the variation in business and supply chain performance of supply chains in Indian manufacturing industry. Classification is done by presenting an empirical taxonomy of clusters representing supply chains in Indian manufacturing industry based on variation in supply chain excellence index (SCEI) and business performance index (BPI).

The clustering of supply chains in Indian manufacturing industry is done by considering BPI and SCEI as clustering variables, which were found by using survey responses and results of a prior empirical study which was carried out in Indian manufacturing industry. The cluster analysis is performed by using Ward’s agglomerative hierarchical clustering followed by using K-means clustering algorithm to establish final set of clusters.

It was found that supply chains in Indian manufacturing industries can be clustered in four major clusters which are named as strategic, celebrity, capable and undeveloped cluster. The characteristics of these clusters reveal some major characteristics of supply chains in Indian manufacturing industry.

The research work presented in this paper takes a novel way to introduce the clusters of supply chains in Indian manufacturing industry. The researchers who are seeking patterns in large data sets of manufacturing companies of Indian industry will be benefitted by using the proposed clusters. While practitioners who are seeking to move their supply chain one step ahead will also reap the benefits of the paper by seeking the characteristics of particular cluster.

Product Lifecycle Management (PLM) systems have gained wide popularity for their role in manufacturing organizations for creating, managing and distributing product data. These systems are one of various enterprise systems which are required for smooth functioning and meeting the scaling up requirements organization. However, with introduction of cloud technology and other industry 4.0 initiatives, there has been focus on moving the on-premises IT application to the cloud. Such a move needs to be carried out by identifying and evaluating various challenges. This paper aims to discuss the aforementioned objective.

The challenges identified through literature review have also been confirmed to be present via interview, system observation and documentation review through case study-based validation in an automotive component manufacturing industry.

The article identifies needs and challenges of cloud PLM systems and presents a generic framework for developing an approach for cloud PLM journey for an organization. The article also provides an approach for resolving the different challenges to realizing the designed system.

The simplified generic framework has been presented for use by industry professionals and researchers for designing cloud PLM systems that would fulfill expectations of different levels of stakeholders.

The Product Lifecycle Management (PLM) system has found varieties of deployments in various domains of product-based industries. Current study aims to provide a framework for the adoption of PLM systems in manufacturing organizations to meet the actual requirements of industries.

First, a systematic review of extant literature was performed, and further, the case study approach is opted to study the process of New Product Development (NPD) in a manufacturing organization. Triangulation methodology was adopted wherein the interview results, actual observations, and authorized documentations were used to validate the result and provide conclusions.

A conceptual framework and implementation architecture for PLM is derived. The complete ecosystem for digital footprint is mapped for New Product Development (NPD) activities.

The study could be helpful for Techno-Functional Managers. For individuals with only functional/technical knowledge, additional training might be required to adopt the framework in actual practices.

The paper contributes to the existing literature by providing a framework and demonstrating the feasibility of implementation through the case study.

A supply chain (SC) involves many stakeholders, directly or indirectly, for satisfying consumers’ requirements. SC management is restricted by a lack of information sharing among stakeholders as parties of SC do not have direct communication and/or are not willing to share private and competitively sensitive information. In the SC, the bullwhip effect (BWE) is an undesirable phenomenon that aggravates the SC performance and increases the overall cost of SC. The main culprit of BWE is the lack of SC coordination among the parties, which results from wrong and lack of information sharing. Blockchain technology (BT) has the main characteristic of distributed shared ledger that makes all parties in the SC network able to access data. This paper aims to develop a BT model and implement it into the SC.

A blockchain is developed consisting four SC stakeholders and an integrated development environment has been used for coding in Python.

The analysis of the impact of the adoption of BT in SC shows the reduction in BWE.

In SC, BT can be considered as an effective tool to share the demand data among all SC partners. Sharing of such data will improve SC planning and reduce the BWE.

The research on supply chain risk management (SCRM) is visibly on the rise, although its literature still lacks the state of the art that critically analyzes its content. The SCRM literature seems to require studies that utilize risk typology, sources of risk, etc. for reviewing the topic. The purpose of this paper is to bridge the gap by synthesizing the information obtained from 343 articles across 85 journals. This study also presents a critical analysis of the content of SCRM in a structured manner to identify the directions for future research.

A systematic literature review (SLR) was devised and adopted, which involved the selection, classification, and evaluation of 343 research articles published over a period of 11 years (2004-2014). The content of extant SCRM literature was critically analyzed and synthesized from the perspective of the risk management process (RMP).

The analysis of extant literature shows that there is a marked rise in research in the SCRM area, especially after the year 2005. It was observed that not only risk but also different forms of uncertainties make supply chain (SC) operations difficult to manage. The SCRM actions yielded most benefits when their implementation was at chain or network level and managed strategically. The analysis also reveals that the manufacturing sector is most affected by risks and highly investigated by researchers.

A complete process for SCRM based on risk stratification, objectives of risk management, and RMP will be a guiding model for firms to manage risks. The research gaps identified and future directions provided here will encourage researchers and managers to devise new methods, tools, and techniques to address the risks in modern SC operations.

An SLR and risk-based content classification of SCRM literature were performed. To identify, locate, select, and analyze the SCRM literature, a structured and systematic process was adopted with some very rarely used methods such as two levels of search keywords, and strings were formulated to locate the most relevant articles in major academic databases.

The purpose of this paper is to assist a manufacturing firm in designing the closed-loop supply chain network under risks that are affecting its supply quality and logistics operations. The modeling approach adopted aims at the embedding supply chain risks in a closed-loop supply chain (CLSC) network design process and suggests optimal supply chain configuration and risk mitigation strategies.

The method proposes a closed-loop supply chain network and identifies the network parameter and variables required for closing the loop. Mixed-integer-linear-programming-based mathematical modeling approach is used to formulate the research problem. The solutions and test results are obtained from CPLEX solver.

The outcomes of the proposed model were demonstrated through a case study conducted in an Indian hospital furniture manufacturing firm. The modern supply chain is mapped to make it closed loop, and potential risks in its supply chain are identified. The supply chain network of the firm is redesigned through embedding risk in the modeling process. It was found that companies can be in great profit if they follow closed-loop practices and simultaneously keep a check on risks as well. The cost of making the supply chain risk averse was found to be insignificant.

Although the study was conducted in a practical case situation, the obtained results are not indiscriminate to the other circumstances. However, the approach followed and proposed methodology can be applied to many industries once a firm decides to redesign its supply chain for closing its loop or model under risks.

By using the identified CLSC parameters and applying the proposed network design methodology, a firm can design/redesign their supply chain network to counter the risk and accordingly come up with planned mitigation strategies to achieve a certain degree of robustness.

The purpose of this paper is to present a methodology to analyze the risks present in perishable food supply chain and to determine the most effective risk mitigation strategies. It is achieved by understanding the dynamics between various risks in perishable food supply chain and modeling them using interpretive structural modeling (ISM).

Four categories and 17 types of risk are established from literature and conducting brainstorming sessions with managers/engineers in Indian dairy firms. A methodology is proposed using ISM, risk priority number and risk mitigation number to prioritize risk mitigation strategy decisions for the dairy industry.

For a perishable food supply chain, risk positioned at lower levels (levels 1 or 2) in the hierarchy should be targeted first, while formulating mitigation strategies. To investigate further, risk- enabling factors which are identified for an Indian dairy firm for these levels 1 and 2 risks and mitigation strategy prioritization show that supplier side risks are more dominant followed by market risks and process risks.

This proposed methodology has not been statistically validated or empirically tested, and factors taken are in the Indian context, but the authors believe that the study is highly relevant to other markets as well because the ISM-based analysis is for generic perishable food supply chain environment.

This study provides a useful approach to managers/decision makers to identify, analyze and prioritize risk in the supply chain. It also provides insights into the mutual relationships of supply chain risks which would help them to focus on the effective risk mitigation strategies formulation. The study provides the insights to benchmark and risk management in the dairy industry environment with priority considerations.

This paper provides an integrated approach to identifying, quantify, analyze, evaluate and mitigate the risks of perishable food (in the dairy environment) in the Indian context.

Adoption of blockchain technology (BT) leads to shrinking supply chains, produces a better quality product, saves product development time and increases the customized offering to the customer. Since it is a new and growing area of research, researchers and practitioners are not entirely aware of challenges in the adoption of BT in supply chain (SC). Therefore, the objective of this research is to provide an overview of BT in SC and modeling of challenges in the adoption of BT in SC.

Interpretive structural modeling (ISM) is used to model the challenges of adoption of BT in SC. Challenges are identified by the literature review and verified by expert's opinions.

This study presents some key challenges, i.e. to develop trust within SC management system, obey the governance rules and regulations of institution while implementation of BT in SC, provide transparency of data, obtain tamperproof data to avoid fraud, improve coordination and information sharing among the SC partners, choose a profit-earning product SC for adoption of BT, adopt societal changes and train the participants to strengthen behavioral intention towards adoption of BT in SC. Additionally, interrelationships among the challenges have been identified. This work discusses potential implications based on challenges and provides a platform for the adoption of BT in SC.

The interrelations of the challenges will provide valuable understandings for developing BT solutions in SC to the practitioners.

Lean manufacturing (LM) concepts have been widely adopted in diverse industrial sectors. However, no literature review focusing on case studies describing LM implementation is available. Case studies represent the actual implementation and provide secondary data for further analysis. This study aims to review the same to understand the pathways of LM implementation. In addition, it aims to analyse other related review questions, such as how implementing LM impacts manufacturing capabilities and the maturity level of manufacturing organisations that implemented LM, to name a few.

A literature review of case studies that discuss the implementation of LM during the last decade (from 2010 to 2020) is carried out. These studies were synthesised, and content analyses were performed to reveal critical insights.

The implementation pattern of LM significantly varies across manufacturing organisations. The findings show simultaneous improvement in manufacturing capabilities. Towards the end of the last decade, organisations implemented LM with radio frequency identification, e-kanban, simulation, etc.

Reviewing the case studies documenting LM implementation to comprehend the various nuances is a novel attempt. Furthermore, potential future research directions are identified for advancing the research in the domain of LM.