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The purpose of this paper is to examine the propagation of benchmarking concepts among Indian manufacturing companies after liberalization of Indian economy.

To determine the propagation of benchmarking concepts in the Indian manufacturing sector, first, a postal survey was conducted on 500 Indian manufacturing companies. Only 97 companies participated in the survey. Subsequently, interviews with 20 managers of six manufacturing companies were undertaken.

Research showed that benchmarking in Indian manufacturing sector is still in the nascent stage, but there is a positive attitude towards adoption of benchmarking concepts. The industry sectors, which typically show above average levels of benchmarking activity, are motor vehicle, electric and electronic industries. By contrast, it is the process sectors, which record the lowest incidences of benchmarking. Identification of suitable benchmarking partner was considered to be the most important problem among Indian manufacturing companies.

A limitation of research is the number of respondents, which precludes the generalization of findings.

The study can help managers of manufacturing companies understand the problems and inhibitors to successful benchmarking.

The paper provides an attempt to find out propagation of benchmarking concepts in Indian context. The paper should be of interest to those researching or managing in the Indian manufacturing sector. This is one of the first studies on dissemination of benchmarking in developing countries and should encourage further research in these countries.

This paper aims to present existence comprehensive analysis of state of implementation of benchmarking concepts in Indian automotive companies.

The research is carried out through a mixed method of research approach comprising of a survey of 300 auto companies in India. Out of 300, 48 valid responses together with three additional case studies were used in the data analysis. Inclusion of case studies was aspired to get deeper insight into the issues pertaining to adoption of best practices, and subsequently the implementation of benchmarking activities.

Benchmarking has been unanimously accepted as an effective performance and productivity improvement tool by Indian auto companies. However, Indian automobile manufacturers still see benchmarking as a tool to compare product attributes, quality attributes, operations, and processes. Moreover, it has been perceived as being less applicable at strategic level. Results also show that benchmarking is in its primary stage in the Indian automotive industry, and it still needs much more commitment from top management for its proliferation. Lesser significance is given to competitor benchmarking due to the fear of losing competitive advantage, and the problem of confidentiality. Reasons identified in this study for not using benchmarking include “lack of human resources” as most important, followed by “financial constraints”, and “lack of internal expertise”.

Research results should be generalized and reproduced with a larger sample size. Owing to the scarce application of benchmarking in small and medium enterprises (SMEs), separate study should be carried out to find ways to encourage benchmarking implementation in Indian auto component manufacturing SMEs.

The paper provides insight into the extent of implementation of benchmarking concepts in Indian automobile industry. This study is the first attempt to understand propagation of benchmarking concepts, exclusively among Indian auto companies.

The purpose of this study is to develop a chicken product that could supply calcium, vitamin E and vitamin C together with high sensory acceptability. The present study was envisaged to develop low-fat chicken patties fortified with calcium, vitamin E and vitamin C without any adverse effects on sensory attributes.

Three different levels of calcium lactate as a source of calcium viz. 1.5, 1.75 and 2.0 per cent, α-tocopherol acetate for vitamin E at 0.019, 0.023 and 0.029 per cent and ascorbic acid for vitamin C at 0.09, 0.12 and 0.15 per cent in low-fat chicken meat patties were tried and the optimum level was standardized based on physico-chemical, proximate and sensory parameters.

The calcium lactate at 1.75 per cent, α-tocopherol acetate at 0.029 per cent and ascorbic acid at 0.15 per cent were found to be optimum on the basis of proximate, physico-chemical and sensory parameters. The textural attributes of the standardized product was comparable to that of the control. The a*, b* and Chroma values for the low-fat chicken patties fortified with calcium, α-tocopherol and ascorbic acid were significantly higher (p < 0.01) than that of the control. The calcium and ascorbic acid concentration of the standardized product was significantly higher (p < 0.01) than that of the control.

The levels in the fortified product were found to be suitable to achieve a 20 per cent RDA of calcium and almost a complete RDA for vitamin C. The research findings demonstrated the development of a single-designer chicken product rich in calcium, vitamin C and vitamin E.

The purpose of this study was to develop functional emu meat nuggets incorporated with finger millet flour (FMF) with high fibre content and improved oxidative stability.

FMF was incorporated at 4, 6 and 8 per cent levels with replacement of emu meat in nuggets formulation and on the basis of various physico-chemical, instrumental colour and sensory parameters, 6 per cent FMF was selected as optimum. The functional emu meat nuggets incorporated with optimum level of FMF as well as control were aerobically packaged in low density polyethylene (LDPE) bags and stored for 21 days at refrigeration (4 ± 1°C) and evaluated for oxidative stability, microbiological quality and sensory attributes at regular interval of 7 days.

The fat content showed significant (p < 0.05) decrease, whereas the crude fibre content increased significantly (p < 0.05) with the increasing levels of incorporation. The sensory scores for all the attributes in 6 per cent FMF-incorporated product were higher than control. During entire storage studies, the thiobarbituric acid reactive substances (TBARS), free fatty acid and peroxide value followed an increasing trend for control as well as treatment product; however, treatment showed a significantly (p < 0.05) lower value than control throughout the storage period. Standard plate count increased significantly (p < 0.05) for control and treatment product, but the counts were lower than the prescribed limits even on 21st day of storage.

The developed products will have functional value by increasing the calcium and dietary fibre content by utilizing the minor cereals. This will be highly beneficial to both the agriculture and meat industry.

The research findings demonstrated the use of FMF in the development of calcium- and fibre-enriched emu meat nuggets with improved oxidative stability.

This research aims to outline the key factors responsible for industry 4.0 (I4.0) application in industries and establish a factor stratification model.

This article identifies the factor pool responsible for I4.0 from the extant literature. It aims to identify the set of key factors for the I4.0 application in the manufacturing industry and validate, classify factor pool using appropriate statistical tools, for example, factor analysis, principal component analysis and item analysis.

This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries from the factor pool. This study would shed light on critical factors and subfactors for implementing I4.0 in manufacturing industries. Strategy, leadership and culture are found key elements of transformation in the journey of I4.0. Additionally, design and development in the digital twin, virtual testing and simulations were also important factors to consider by manufacturing firms.

The proposed I4.0 factor stratification model will act as a starting point while designing strategy, adopting readiness index for I4.0 and creating a roadmap for I4.0 application in manufacturing. The I4.0 factors identified and validated in this paper will act as a guide for policymakers, researchers, academicians and practitioners working on the implementation of Industry 4.0. This work establishes a solid groundwork for developing an I4.0 maturity model for manufacturing industries.

The existing I4.0 literature is critically examined for creating a factor pool that further presented to experts to ensure sufficient rigor and comprehensiveness, particularly checking the relevance of subfactors for the manufacturing sector. This work is an attempt to identify and validate major I4.0 factors that can impact its mass adoption that is further empirically tested for factor stratification.

The paper aims to identify key human and operational focused barriers to the implementation of Total Quality Management (TQM). It develops a comprehensive structural relationship between various barriers to successfully implement TQM for sustainability in Indian organizations.

With the help of expert opinions and extant literature review, we identified the case of TQM failure companies and barriers to implement TQM effectively. Interpretive Structural Modeling (ISM) and fuzzy MICMAC techniques are employed to develop a structural model and the identified barriers are categorized based on their dependence and driving power in the various categories.

From the intensive case analysis, we identify fourteen barriers that constrain the successful implementation of TQM. The findings also provide a hierarchy of barriers in which the absence of top management involvement and ineffective leadership are the human barriers having the highest dependence.

The critical inputs show the implementation of TQM in the firms being more proactive and well prepared in the selected five companies. The study's emphasis on barriers will help organizations in implementing TQM for better sustainability in an organizational context.

In the successful implementation of TQM, barriers need to be identified because failure has often eliminated the organizations from the market. Thus, TQM is the source of strength to achieve higher productivity, profitability, and sustainable business performance. The barriers must be identified to improve organizational performance to contribute to sustainable development.

“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.

As rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).

Results obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.

The result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.