Supply Chain Management (SCM) is a complex subject, which is an important determinant of success or failure of any manufacturing enterprise. It is absolutely essential for executives of manufacturing enterprises to be thoroughly aware about all the essential components of supply chain management and understand the impact that it might exert on the overall efficiency of the organisation. This knowledge will enable them to focus on those variables which add value to organisations and considering the significance of SCM, especially in Indian context, the researcher has made a sincere attempt to find a solution to the research question of 'What is the impact of important components of SCM on the performance of the supply chain per se and also on the organisational performance in Indian context?' Based on the review of literature relating to supply chain management components, competitiveness and organisational performance of manufacturing enterprises, a conceptual model was framed and the resulting hypotheses were empirically tested. Data was collected from the executives of these manufacturing enterprises by administering a well structured questionnaire, using personal interview method and tested the proposed research hypotheses. The findings from the structural equation model results support all the 10 formulated hypotheses that there is a significant positive relationship with the supply chain management and organisational performance.

PurposeThis paper is a literature review on use of artificial intelligence (AI) among agricultural value chain (AVC) actors, and it brings out gaps in research in this area and provides directions for future research.Design/methodology/approachThe authors systematically collected literature from several databases covering 25 years (1994–2020). They classified literature based on AVC actors present in different stages of AVC. The literature was analysed using Nvivo 12 (qualitative software) for descriptive and content analysis.FindingsFifty percent of the reviewed studies were empirical, and 35% were conceptual. The review showed that AI adoption in AVC could increase agriculture income, enhance competitiveness and reduce cost. Among the AVC stages, AI research related to agricultural processing and consumer sector was very low compared to input, production and quality testing. Most AVC actors widely used deep learning algorithm of artificial neural networks in various aspects such as water resource management, yield prediction, price/demand forecasting, energy efficiency, optimalization of fertilizer/pesticide usage, crop planning, personalized advisement and predicting consumer behaviour.Research limitations/implicationsThe authors have considered only AI in the AVC, AI use in any other sector and not related to value chain actors were not included in the study.Originality/valueEarlier studies focussed on AI use in specific areas and actors in the AVC such as inputs, farming, processing, distribution and so on. There were no studies focussed on the entire AVC and the use of AI. This review has filled that literature gap.

In this globalized scenario, the overall performance of the manufacturing industries is the backbone of the development of the countries’ economies. In this research, the authors’ main objective of the study is to segment the manufacturing industries by using the triangular interval-valued fuzzy TOPSIS Method and find out the factors determining its performance. The researchers have collected the data from 350 manufacturing industries located in Puducherry, India. They applied a Simple Random sampling method by using a structured questionnaire from manufacturing industries. To analyze the data, the researchers used software packages like Excel, SPSS and LISREL 8.72. The researchers applied Confirmatory Factor Analysis, Triangular Interval-Valued Fuzzy TOPSIS Method, Chi square and Correspondent Analysis to conclude the result. Based on the factors loadings of the items, the contribution made by the items in respect of Performance may be ranked as Sales growth, Market share, Profit margin and Return on investment. With the help of Triangular Interval-Valued Fuzzy TOPSIS Method researchers segmented the manufacturing industries into three groups and by using the Chi square analysis the researchers found that the five demographics characteristics like Number of years in Business (Company), Scale of industry, Kind of manufacturing, Number of employees and location of the production plant of the respondents and these are significantly associated with segmenting the manufacturing industries and determine the performance of manufacturing industries.

Many business organizations attempt several efforts to managing their supply chain. Such effort to manage the supply chain paves the way for effective and positive impact on various practices of supplier selection and that enhances the organizational performance. The foremost initiative of this research paper is to empirically probe the various aspects and variables that have been already addressed in the previous literatures related to supplier selection, supply effort management and organizational performance. Further, this research delves to develop a measurement framework and pragmatically prove the framework through the measurement model. Preliminarily a factor structure for various constructs is made and the initial validity is determined from practicing managers and academicians. This research employs survey method and the data is collected from 358 supply chain professionals in India from manufacturing organisation. A measurement model is developed and proved with various tests of reliability and validity. The paper provides various constructs related to supplier selection, supply effort management and business performance. Finally the scale or instrument was developed.

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