Barriers to adoption of industry 4.0 and sustainability: a case study with SMEs

The concepts of sustainable supply chains and Industry 4.0 are progressively getting attention in different domains. Companies have started developing and implementing these practices in their business models. However, several challenges influence the adoption of sustainability and Industry 4.0 (I4.0) in small and medium-sized enterprises (SMEs). This study aims (i) to identify the adoption barriers of sustainability and I4.0 and (ii) establish the interrelationship among these barriers for SMEs. An extensive literature search supported by interviews with supply chain practitioners from three SMEs identified 12 critical barriers to adoption. The barriers are then ranked using “Interpretive Structural Modeling.” The results suggest that the “lack of resources” and the “lack of employee’s competence/experts” are the most influencing barriers. Changing government regulations on the allocation of capital and financial incentives for SMEs to encourage training and skills development programs could promote sustainable supply chains and practices. The study also reflects short-, medium- and long- term planning strategies for supply chain practitioners for adoption of sustainability and I4.0 in SMEs

Antecedents for blockchain technology-enabled sustainable agriculture supply chain

Blockchain can solve the problems that the agriculture supply chain (ASC) is facing to achieve sustainable growth. In a nation like India, blockchain application in the supply chain is still new; therefore, supply chain players need a better understanding and awareness of blockchain through valuable insights. This article aims to study the mediating role of blockchain technology adoption (BLCT) for sustainable supply chain performance (SSCP). This study investigates the influence of numerous factors such as green and lean practices, supply chain integration, supply chain risk, performance expectancy, top management support, cost, internal and external environmental conditions, regulatory support, and innovation capability on BLCT adoption. A sample of 316 respondents from Indian ASC industries was collected, and structural equation modeling (SEM) was used. This study's outcomes show that green and lean practices, supply chain integration, supply chain risks, internal and external conditions, regulatory support, innovation capability, and cost positively influence BLCT adoption. Moreover, BLCT positively influences sustainable agriculture supply chain performance. This article is valuable for policymakers, managers, service providers, researchers, and academicians to understand the role of factors in influencing BLCT and BLCT's role in improving sustainable supply chain performance (SSCP)

A hybrid decision support system for analyzing challenges of the agricultural supply chain

Agricultural supply chain management includes all the events involved in moving products of the agricultural sector from the field to the customer, and is a crucial aspect ensuring the rich contribution of the agricultural sector to the economic growth of the nation. The purpose of this paper is to add value to the present knowledge base by ascertaining the challenges of the agricultural supply chain in India on the basis of a thorough literature survey and the Delphi technique. Following this, the decision-making trial and evaluation laboratory approach was used to model the identified challenges, explore the cause–effect interrelationship, and to develop the systematic hierarchical structures of challenges through an interpretive structural modeling methodology. The implementation of the approach in the Indian context led to the inference that two factors, namely limited integration among the national agricultural markets, and limited agricultural market infrastructure were the most important ones. The integrated model obtained as an output of this study intends to guide the agricultural policy- and decision-makers to improve the performance of the agricultural supply chain in India. Also, some essential recommendations have been given to improve the efficiency of the agricultural supply chain management

Big data analytics ::implementation challenges in Indian manufacturing supply chains

Big Data Analytics (BDA) has attracted significant attention from both academicians and practitioners alike as it provides several ways to improve strategic, tactical and operational capabilities to eventually create a positive impact on the economic performance of organizations. In the present study, twelve significant barriers against BDA implementation are identified and assessed in the context of Indian manufacturing Supply Chains (SC). These barriers are modeled using an integrated two-stage approach, consisting of Interpretive Structural Modeling (ISM) in the first stage and Decision-Making Trial and Evaluation Laboratory (DEMATEL) in the second stage. The approach developed provides the interrelationships between the identified constructs and their intensities. Moreover, Fuzzy MICMAC technique is applied to analyze the high impact (i.e., high driving power) barriers. Results show that four constructs, namely lack of top management support, lack of financial support, lack of skills, and lack of techniques or procedures, are the most significant barriers. This study aids policy-makers in conceptualizing the mutual interaction of the barriers for developing policies and strategies to improve the penetration of BDA in manufacturing SC

The role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries

Purpose : 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. Design/methodology/approach : 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. Findings : 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. Research limitations/implications : 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. Originality/value : 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