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Firms have been implementing lean manufacturing to improve their business performances. However, they have difficulties in the implementation due to the many barriers. In view of the lack of research and the importance in understanding them, the purpose of this paper is identify and evaluate the lean barriers with respect to their levels of importance in implementation.

As lean barriers are scattered in the literature and a variety of performance measures are used in practice, an extensive literature review is first carried out to identify the lean barriers and performance measures. A novel ranking technique – interpretive ranking process (IRP) – is adopted in the evaluation. In the IRP-based evaluation approach, a group discussion technique, where five Indian lean experts are involved, is applied to determine the most important lean barriers and performance measures. Several matrices are developed step by step for calculating the ranks of the selected lean barriers. Upon validating the ranks, an IRP-based lean barrier evaluation model is developed.

The IRP-based lean barrier evaluation model can help firms better understand lean barriers and their levels of importance in lean implementation. In the light of this model, to successfully implement lean, firms should provide sufficient management time and training to employees, develop a right culture, develop effective communication, carry out low-cost production, and obtain external funding.

The evaluation results provide the practitioners with a realistic framework to deal with many problems, especially those related to resource allocation, in lean implementation. Based on the framework, practitioners can prioritize lean barriers during implementation in accordance with performances targeted.

This is the first study that provides a comprehensive review of lean barriers available in the literature and evaluates them in accordance with performance measures. The combined use of literature review and experts in the evaluation approach justifies the value of the study.

Firms have been adopting lean manufacturing to improve their business performances. However, they are facing failures or less success in implementation, mainly due to lack of understanding in relating the lean practices (LPs) from the required performance measures perspective. In view of the lack of research and the importance of understanding them, the purpose of this paper is to prioritize LPs.

As LPs are scattered in the literature and a variety of performance measures are used, an extensive literature review is first carried out to identify the LPs and performance measures. The blend of interpretive structural modeling and interpretive ranking process interpretive tools is adopted in establishing the contextual relationship among LPs and then ranking them based on the performance measures. A three-dimensional priority matrix is proposed for better explanation of the results.

The proposed framework can help firms better understand LPs and their levels of importance in lean implementation.

The involvement of lean experts may produce some bias in evaluating the LPs.

The proposed framework can help practitioners to develop an industry-specific road-map for the result-oriented LP implementation. Based on the area of performance to be improved, practitioners can prioritize LPs for implementation.

This is the first study that provides a comprehensive review of LPs available in the literature and prioritizes them in accordance with performance with interpretive tools.

The purpose of this paper is to assess the implications of organizational knowledge, source of information and functional orientation, resource-based view of the manufacturing and global orientation, on manufacturing practices which include advanced manufacturing strategies.

An empirical study approach has been used to assess the implications of advanced manufacturing strategies on firm performance.

This paper provides a framework for managers to: assess competitive priorities of the industry; identify order winners for the industry; identify key decision areas or practices for improvements; and to assess the role of implications of organizational knowledge on the manufacturing practices.

The limitations are as follows: the issue of organizational knowledge and learning is assessed from manufacturing view point only; plants located all over India are considered for study; and considers plants employing different manufacturing systems and products.

Saturated with the conventional manufacturing technologies, a growing number of small- and medium-scale industries began to explore advanced manufacturing technologies (AMTs). Investment in AMTs remains a promising but potentially risky venture. This paper helps the small- and medium-scale industries to adopt viable AMTs and business performance strategies and then provides guidelines for enhancing their competitiveness.

This paper may help all the stakeholders of small- and medium-scale industry.

This paper is based on one of the few studies conducted to assess the implications of advanced manufacturing strategies on firm performance in Indian scenario.

In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).

This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.

This study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.

This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

The research article's primary purpose is to understand the advancements in urban logistics and allied fields over time along with a consideration of its enabling technologies.

An initial review is used to build a keyword vocabulary, combinations of which were then applied to the Scopus, ScienceDirect, Emerald Insights, the Web of Science (WOS), Elsevier, Taylor and Francis, Wiley, Inderscience, Springer, Google Scholar and IEEE Xplore for extracting academic publication collection. The first part includes bibliometric analysis; network analysis is done based on the finally selected 645 papers (only those articles include either of the keywords mentioned above in title, abstract, and keywords). The second part conducts a review of the existing literature review studies (only 21 literature review studies out of 645 articles). The last one discusses the advancement in the topics based on the selected research articles.

This research discussed the advancement of the urban logistics and allied field, key academic forums and key researchers. It is evident from the analysis that the research related to key emerging themes like implementing innovative concepts and sustainability; application of green technologies; data collection, visualization, monitoring and sharing; and automatic logistic systems are still in the nascent stage. However, these research areas gained momentum in the recent past.

Urban logistics are essential and play a crucial role for such rapidly growing cities to function. Despite playing a vital role, urban ecosystem logistics is often neglected in formal urban planning. Hence, as a response to customer and business demand, private entities regularly invest in new technologies and solutions. Since such investments are toward profits, various environmental, social and economic challenges arise.

This research investigates the advancements in urban logistics toward smart, sustainable reforms in developing enabling technologies and markets. The obtained research articles are subjected to bibliometric, descriptive, network and content analysis to present a rundown of advancements, relationships and trends in emerging research gaps.

The purpose of this paper is to analyse proposed cloud computing integration (CCI) and external integration (EI) effects on the relationship between the integration of supply chain and business performance of the organisation in the Indian context.

A two-stage, structural equation modelling (SEM) and artificial neural network (ANN) methodology are employed for the analysis, and for verifying the robustness of the developed model sensitivity analysis is performed.

The results of SEM revealed that out of 14 hypotheses, 12 hypotheses were supported. Furthermore output of SEM was used as input for the ANN model and the results highlighted that production flexibility is an essential factor for operational business performance (OBP) followed by customer integration, supplier integration, product quality, internal integration and on-time delivery (OD).

This study focussed on the emerging economies context and cannot be applied to all the countries, and there could be other derived variables from the real factors. This investigation is intended to guide various policy and decision makers of the case domain.

This study has introduced new factors such as CCI, EI and organisational business performance.

Companies have been implementing lean manufacturing to improve their business performances. However, many of them have difficulties in the implementation because of various barriers, thus encountering failures. This paper aims to prioritize and analyze the lean barriers for better understanding and interpretation for successful lean implementation.

Extensive literature review has been carried out to identify the lean barriers. Subsequently, total interpretive structural modeling (TISM) has been adopted where lean experts’ inputs have been sought to obtain the self-interaction and reachability matrix. Further, driving power and dependence of lean barriers have been derived, and TISM-based lean barrier model has been developed.

Insufficient management time, insufficient supervisory skills and insufficient senior management skills are the significant barriers with highest driving power and lowest dependence. With low driving power, cost- and funding-related barriers such as cost of the investment, internal funding and external funding are found to be less important barriers.

This model provides a more realistic approach to the problems faced by practitioners during lean implementation. Thus, it provides a roadmap to implement lean by focusing on reducing or eliminating important barriers.

The paper not only provides a TISM-based model of contextual relationships among lean barriers but also describes the validation of this model.

The advent of Internet of Things, cloud computing and advanced computing has endowed smart manufacturing environments with resilience, reconfigurability and intelligence, resulting in the emergence of novel capabilities. These capabilities have significantly reshaped the manufacturing ecosystem, enabling it to effectively navigate uncertainties. The purpose of this study is to assess the operational transformations resulting from the implementation of smart manufacturing, which distinguish it from conventional systems.

A list of qualitative and quantitative smart manufacturing performance metrics (SMPMs) are initially suggested and categorized into strategic, tactical and operational levels. The SMPMs resemble the capabilities of smart manufacturing systems to manage disruptions due to uncertainties. Then, industry and academia experts validate the SMPMs through the utilization of the Delphi method, enabling the ranking of the SMPMs.

The proposition of the SMPMs serves as a metric to assess the digital transformation capabilities of smart manufacturing systems. In addition, the ranking of the proposed SMPMs shows a degree of relevance of the measures in smart manufacturing deployment and managing the disruptions caused due to the COVID-19 pandemic

The findings benefit managers, consultants, policymakers and researchers in making appropriate decisions for deploying and operationalizing smart manufacturing systems by focusing on critical SMPMs.

The research provides a metric to assess the operational transformations during the deployment of smart manufacturing systems. Also, it states the role of the metric in managing the potential disruptions that can alter the performance of the business due to the COVID-19 pandemic.

This article sheds light on the missing links concerning the study of using integrated enabling technologies toward sustainable and circular agriculture supply chains by examining the available literature and proposing future research possibilities.

The relevant literature was researched through online databases such as Scopus, Web of Science, Academic Search Premier, Emerald, IEEE Xplore, Science Direct, World Scientific Net and Springer-Link Journals, covering a period from 1999 to 2020. A systematic literature review based on 75 papers analyzed the integration of the concepts of enabling technologies, sustainability, circular economy and supply chain performance in agriculture supply chains.

It was identified that enabling technologies and agriculture supply chains alone have been explored further than integrated enabling technologies, sustainability, circular economy, supply chain performance and agriculture supply chains. Enabling technologies and agriculture supply chains' main findings are: enabling technologies have been studied to improve food safety, food quality and traceability in agriculture supply chains. The main results regarding integrated enabling technologies, sustainability, circular economy, supply chain performance and agriculture supply chains are: Internet of Things and information communication technology play an important role in addressing food security, traceability and food quality, which help achieve sustainable development goals.

This review study provides 13 research questions to underpin future trends regarding integrated technologies' application in agriculture supply chains for circular and sustainable growth.

This study initially aims to identify the barriers to the big data analytics (BDA) initiative and further evaluates the barriers for knowing their interrelations and priority in improving the performance of manufacturing firms.

A total of 15 barriers to BDA adoption were identified through literature review and expert opinions. Data were collected from three types of industries: automotive, machine tools and electronics manufacturers in India. The grey-decision-making trial and evaluation laboratory (DEMATEL) method was employed to explore the cause–effect relationship amongst barriers. Further, the barrier's influences were outranked and cross-validated through analytic network process (ANP).

The results showed that “lack of data storage facility”, “lack of IT infrastructure”, “lack of organisational strategy” and “uncertain about benefits and long terms usage” were most common barriers to adopt BDA practices in all three industries.

The findings of the study can assist service providers, industrial managers and government organisations in understanding the barriers and subsequently evaluating interrelationships and ranks of barriers in the successful adoption of BDA in a manufacturing organisation context.

The paper is one of the initial efforts in evaluating the barriers to BDA in improving the performance of manufacturing firms in India.