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The aim of this paper is to analyze international and national research and development (R&D) programs and roadmaps for the manufacturing sector, presenting how agile and lean manufacturing models are addressed in these programs.

In this review, several manufacturing research and development programs and roadmaps – national programs from the USA and Canada, and international programs from the European Union and from one international organization – are reviewed.

The major finding of this review is that the main concerns in agile manufacturing, as highlighted in these programs, are networks, supply chain and product/service customization, and lean manufacturing's inclination towards achieving better cost efficiency. Although the lean manufacturing approach has been considered in many past and present programs, analysis of the most recent programs shows a greater priority is given to the agile manufacturing approach. The path towards sustainable manufacturing is delineated by pro‐active attitude and action towards customers.

The study analyzes two national R&D programs from the USA, one international program from the European Union, three international roadmaps from the European Union, one business plan from Canada and one international roadmap from the global organization Intelligent Manufacturing Systems.

The findings of this paper are intended to help managers, researchers and practitioners from the manufacturing sector to enhance their understanding and define suitable strategy for their organizations' sustainability and identify suitable manufacturing path with respect to agile and lean philosophies. This study could also help academics in defining course curricula for students more coherent with the R&D policies and/or requirements towards sustainable manufacturing with respect to agile and lean philosophies.

There are reviews comparing agile and lean manufacturing paradigms, but there are no reviews about how the two manufacturing concepts are addressed in manufacturing R&D programs and roadmaps.

This editorial aims to introduce the theme of the special issue: “Lean vs agile from an organizational sustainability, complexity and learning perspective”.

The methodology of the editorial is that of a survey. In the first part it presents the relevance of the theme and in the second part it presents the papers included in the special issue, including their themes, findings and novel contributions.

The individual findings by the papers present significant new contributions in a deeper insight of the “lean” and “agile” philosophies, or approaches in, and to, organizations. It could be noticed that the controversies of the issue “lean vs. agile” still remain. However, it could be said that an eventual further investigation in the phenomenology of “lean” and “agile” will be more informed after consideration of the results presented in this special issue.

Further investigation should be undertaken on a more abstract “level” of the theories of “lean” and “agile” and their mutual relationship, such as theories about the internal processes of “lean”/“agile” users, general “lean”/“agile” theories, epistemology of “lean”/“agile”, and ontology of “lean”/“agile”, and relationship with learning organization and chaordic organization.

Readers, both theoreticians and practitioners, will find in this editorial a “guide” to the issues of their interest concerning the valuable explanations, ideas and tools, presented in the special issue, for both concrete applications in enterprises and organizations, and for further research and development of learning, complex and sustainable organizations, and towards new ideas and insights generation.

This editorial presents an analysis of the special issue on “lean vs agile”, contributing to the higher levels of the theories of “lean” and “agile” and their mutual relationship, namely to the theories about the internal processes of “lean”/“agile” users, general “lean”/“agile” theories, and epistemology of “lean”/“agile”.

This paper aims to conduct a systematic literature review of the research in the field of Artificial Intelligence (AI) and Big Data Analytics (BDA) in Supply Chain Risk Management (SCRM). Finally, future research directions in this field have been suggested.

The papers were searched using a set of keywords in the SCOPUS database. These papers were filtered using the Title abstract keywords principle. Further, more papers were found using the forward-backward referencing method. The finalized papers were then classified into eight categories.

The previous papers in AI and BDA in SCRM were studied. These papers emphasized various modelling and application techniques for AI and BDA in making the supply chain (SC) more resilient. It was found that more research has been done into conceptual modelling rather than real-life applications. It was seen that the use of AI-based techniques and structural equation modelling was prominent.

AI and BDA help build the risk profile, which will guide the decision-makers and risk managers make their decisions quickly and more effectively, reducing the risks on the SC and making it resilient. Other than this, they can predict the risks in disasters, epidemics and any further disruption. They also help select the suppliers and location of the various elements of the SC to reduce the lead times.

The paper suggests various future research directions that fellow researchers can explore. None of the previous research examined the role of BDA and AI in SCRM.

The purpose of this paper is to identify the critical factors influencing the cloud computing adoption (CCA) in the manufacturing micro, small and medium enterprises (MSMEs) by employing a decision-making trial and evaluation laboratory (DEMATEL) methodology.

Through literature review and expert opinions, 30 significant factors were identified, and then a DEMATEL approach was applied for exploring the cause–effect relationship between the factors.

The results of study highlighted that five factors, namely, “hardware scalability and standardisation”, “cost (subscription fees, maintenance cost and implementation cost (CS1)”, “innovation”, “installation and up gradation (CS28)”, and “quality of service” were the most significant factors influencing the CCA in the case sector.

The DEMATEL model was developed by considering expert inputs, and these inputs could be biased which can influence the reliability of the model. This study guides the organisational managers, cloud service providers and governmental organisations in formulating the new policies/strategies or modifying the existing ones for the effective CCA in the case sector.

For the first time. interdependency between the critical factors influencing CCA was discussed by employing the DEMATEL approach in the Indian manufacturing MSMEs context.

The purpose of this paper is to review and analyze the perishable inventory models along various dimensions such as its evolution, scope, demand, shelf life, replenishment policy, modeling techniques and research gaps.

In total, 418 relevant and scholarly articles of various researchers and practitioners during 1990-2016 were reviewed. They were critically analyzed along author profile, nature of perishability, research contributions of different countries, publication along time, research methodologies adopted, etc. to draw fruitful conclusions. The future research for perishable inventory modeling was also discussed and suggested.

There are plethora of perishable inventory studies with divergent objectives and scope. Besides demand and perishable rate in perishable inventory models, other factors such as price discount, allow shortage or not, inflation, time value of money and so on were found to be combined to make it more realistic. The modeling of inventory systems with two or more perishable items is limited. The multi-echelon inventory with centralized decision and information sharing is acquiring lot of importance because of supply chain integration in the competitive market.

Only peer-reviewed journals and conference papers were analyzed, whereas the manuals, reports, white papers and blood-related articles were excluded. Clustering of literature revealed that future studies should focus on stochastic modeling.

Stress had been laid to identify future research gaps that will help in developing realistic models. The present work will form a guideline to choose the appropriate methodology(s) and mathematical technique(s) in different situations with perishable inventory.

The current review analyzed 419 research papers available in the literature on perishable inventory modeling to summarize its current status and identify its potential future directions. Also the future research gaps were uncovered. This systemic review is strongly felt to fill the gap in the perishable inventory literature and help in formulating effective strategies to design of an effective and efficient inventory management system for perishable items.

The case is written on the basis of published sources only.

Doodlage, a start-up incorporated in 2012 by Kriti Tula, Paras Arora and Vaibhav Kapoor, used discarded waste to create sustainable fashion products. It had a first-mover advantage in recycled fashion goods in the first 10 years of its existence. The company contributed to sustainable fashion by providing an alternative to fast fashion production, creating enormous clothing waste and environmental degradation. In the first quarter of 2022, it saved and reused 15,000 m of fabric waste. From 2018 to 2021, the company grew 150% annually, targeting the right customers and regions to expand its business. It ensured that postproduction industrial waste and postconsumption garments were used to produce clothes. It also confirmed that the waste generated in its fabric screening process was used to create stationery items and other valuable accessories.

However, the sustainable fashion model that gave the company a competitive advantage became obsolete in 2022 due to increasing competition in the industry as various players using unique ideas entered the market. The company is encountering operational and logistical challenges that are affecting its performance. The demand for its products was also subdued due to high prices of upcycled and recycled clothes and less consumer spending post-COVID pandemic. The competitors of Doodlage offered multiple products produced using environmentally friendly farming and manufacturing techniques, attracting sustainable purchasers. What should be the new portfolio of products for the company to explore future growth opportunities? Considering their vast price, can consumers be encouraged to buy upcycled clothes? How should the company ride the winds of change in the industry?

The instructor should initiate the class discussion by asking questions such as how frequently do you shop for clothes? Do you care about the fabric of your apparel? After you discard your clothes, do you think about where these goods finally end up? Data on the amount of total waste generated in the fashion industry should be communicated to students to connect it with the importance of the concept of circular economy. Post this, the instructor should introduce the business model of Doodlage to bring the discussion into the context of the fashion industry before going ahead to discuss the company’s dilemma.

In the digitalization era, supply chain processes and activities have changed entirely, and smart technology impacts each sustainable supply chain movement. The warehouse and distribution of various organizations have started adopting smart technologies globally. However, the adoption of smart technologies in the Indian warehousing industry is minimal. The study aims to identify the implementation barriers of smart technology in the Indian warehouse to achieve sustainability.

This study employs an integrated Delphi-ISM-ANP research approach. The study uses the Delphi approach to finalize the barriers identified from the detailed literature review and expert opinion. The finalized 17 barriers are modeled using interpretive structural modeling (ISM) to get the contextual relationship. The ISM method's output and analysis using the analytical network process (ANP) illustrate priorities.

The study's findings showed that the lack of government support, lack of vision and mission and the lack of skilled manpower are the most significant barriers restricting the organization from implementing smart and sustainable supply chain practices in the warehouse.

This study would help the practitioners enable the sustainable warehousing system or convert the existing warehouse into a smart and sustainable warehouse by developing an appropriate strategy. This study would also help reduce the impact of different barriers that would strengthen the chance of technology adoption in the warehouses.

The literature related to adopting smart and sustainable practices in the warehouse is scarce. Modeling of adoption barrier for smart and sustainable warehouse using an integrated research approach is the uniqueness of this study that have added value in the existing scientific knowledge.

Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.

A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.

Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.

This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.

For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

After the COVID-19 pandemic, companies from a variety of sectors began repurposing their product development and manufacturing activities. To be successful, repurposing requires a framework that illustrates Agile New Product Development (ANPD) and Industry 4.0 practices. The current study aims to focus on developing a framework that managers and decision-makers can use to successfully adopt ANPD-Industry 4.0 practices and decision-making activities.

Initially, a literature review is conducted to identify practices related to ANPD and Industry 4.0. Similarly, performance metrics are identified through a review of the literature. To compute the weights of the shortlisted practices, the Pythagorean fuzzy Analytical Hierarchy Process is used and the Pythagorean fuzzy Combined Compromise Solution (PFCoCoSo) method is used to rank the shortlisted performance metrics.

According to the findings, ANPD practices (ADP) are the most prominent among shortlisted practices. Following that are Technology Adoption Practices, Organizational Management Practices (OMP), Human Resource Management Practices and System Integration Practices. Customer requirement analysis, for example, is an ADP practice that has a significant impact on the successful repurposing of product development activities.

The identified practices can make a significant contribution during repurposing product development activities. Practices that promote sustainable product development, as well as the use of advanced technologies, will be beneficial in improving organizational performance. Managers can evaluate performance using performance metrics that have been prioritized.

After the COVID-19 pandemic, this could be the first of its kind to develop an RPD framework.

With big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).

The factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM).

A total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM.

This research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model.

This research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project.

The result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society.

This research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions.