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This study considers the fresh food city logistics that involves the last-mile distribution of commodities to the customer locations from the local distribution centres (LDCs) established by the e-commerce firms. In this scenario, the last-mile logistics is crucial for its speed of response and the effectiveness in distribution of packages to the target destinations. We propose a clustering-based routing heuristic (CRH) to manage the vehicle routing for the last-mile logistic operations of fresh food in e-commerce. CRH is a clustering algorithm that performs repetitive clustering of demand nodes until the nodes within each cluster become serviceable by a single vehicle. The computational complexity of the algorithm is reduced due to the downsizing of the network through clustering and, hence, produces an optimum feasible solution in less computational time. The algorithm performance was analysed using various operating scenarios and satisfactory results were obtained.

PurposeIn this research, the emphasis is multifold. First objective is to study differences amongst India's Make-in-India, Germany's Industry 4.0 and China's Made-in-China 2025 on a macro level. Second objective is to identify where does individual industry segment out of the five broad segments (prioritized by Make-in-India initiative) represented by ten firms in India stand in terms of adoption of Industry 4.0 technologies. Third objective is to identify key barriers for each of these five industry segments. Finally, socio-technical interventions are also proposed aimed at faster adoption of Industry 4.0 technologies.Design/methodology/approachA mixed methodological approach is followed to achieve the research objectives. First, for the macro-level comparison of three pertinent countries, extant research and industry literature have been relied upon. Thereafter, at a micro level, inputs from experts belonging to focal sectors are included in this study to ascertain the current level of readiness of adoption of Industry 4.0 technologies and the barriers to adoption. Finally, the authors argue for and propose some socio-technical interventions that are aimed at mitigation of barriers for adoption of Industry 4.0 technologies.FindingsIt has been ascertained that amongst the ten firms (two each from given focal sectors) considered in the study, the automotive and the software firm are perhaps best placed to adopt the Industry 4.0 technology, while the infrastructure project management firm is least ready for Industry 4.0 technologies. The common barriers to adoption of Industry 4.0 technologies, as elaborated by experts belonging to each of the ten firms, are also identified. These three commons barriers are resistance to change, unclear economic benefits and problems related to coordination and collaboration.Research limitations/implicationsThe study is one of first attempts to understand the nuances related to technology readiness across focal industries pertaining to the Make-in-India initiative and Industry 4.0. The study furthers the extant understanding of common and distinct barriers across industries. Employing the soft-systems methodology, the study advocates for a number of socio-technical interventions pertaining to establishment of e-skill ecosystem, community learning clusters and sector-focussed skill acquisition and augmentation. Since the study considers only two firms corresponding to each of the five focal sectors, including more firms across industries could have resulted in further validation of study as well.Practical implicationsContrasting the initiatives of the three countries results in identification of different thematic focus of the respective initiatives. While India's Make-in-India initiative has a strong social dimension, Germany's Industry 4.0 and Made-in-China 2025 have key objective related to integration of cyber-physical systems and to graduate to innovation-driven country, respectively. Further, analysis on the technology readiness for adoption of Industry 4.0 technologies based on the respective experts' assessment results in understanding of the underlying barriers.Social implicationsAdopting the soft-systems perspective linking nuances of stakeholders, socio-technical systems and socio-economic characteristics results in several propositions to further the social objectives of India's Make-in-India initiative. These propositions advocate for pathways in which extant strengths in terms of technology, people and existing socio-technical structures can be brought together to cater to the requirements related to employability and skill augmentation of new as well as existing workforce.Originality/valueExtant research literature is primarily focussed on certain specific topics within Industry 4.0 implementation and is mainly based on conceptual or theoretical basis. From a practitioners' perspective, only a few empirical papers could be found that too are typically focussed on single case studies resulting from pilot applications of Industry 4.0. However, such papers have not examined the broad implications of Industry 4.0 in terms of differences between key countries' manufacturing initiatives, readiness of key sectors, sectoral barriers and accompanying policy-level implications associated with implementation of Industry 4.0. Thus, the objective of this research is to abridge these research gaps.

Today, manufacturing enterprises are adopting emerging Industry 4.0 technologies to create industrial intelligence-driven smart factories. This trend, in turn, is stimulating the advent of intelligent supply chains that can sync and support the rapid evolution of advanced industrial practices via supply chain digital transformation. Specifically, Artificial Intelligence (AI) and Machine Learning (ML) are emerging as vital breakthrough technologies that can help firms enhance profit margins, reduce supply chain costs, deliver excellent customer service, and make their supply chains intelligent. This paper identifies and analyzes 338 most influential research papers to scientifically examine the linkages among the AI-ML techniques and their applications in the SCM domain through bibliometric and network analysis, descriptive data analysis, and visual representation, thus furnishing a perspicacious knowledge base. The main contribution of this paper is to identify the unexplored potential and the contexts in which AI and ML can be used in managing and transforming supply chains digitally, including the aspects of intelligent and interpretative evolutions. Additionally, a fundamental contribution of this work is a comprehensive mind map that makes it possible to visualize, understand, and simulate the wide spectrum of findings from the bibliometric analyses. Finally, the study presents research gaps, implications, and future scope as a point of reference for researchers and practitioners.

The level of uncertainty, unpredictability and complexity is magnified in a food supply chain as compared to the conventional supply chains such as automobile and FMCG. This is mainly because of the short product shelf life and the need of high variety. This necessitates the food industry to adopt various quick response systems to achieve effective supply chain management. The situation becomes even more critical when dealing with humanitarian relief operations where time window is very short (usually 24 hours). One of the solutions which are adopted in modern food supply chains is to locate cross-docking centre (CDC). Cross-docking is used to reduce the turnaround time of the food products. The practical situation is complex as it caters to multiple customers. The number of suppliers, in such cases, plays a significant role. Selection of a right CDC is, therefore, a crucial task. It is a strategic decision and needs to be taken by considering the relationships between each stakeholder present in the supply chain. In this article, we present an approach to select one (or few) CDC/s facilities among n CDCs. The method is based on the relationship between each actor (actors are the stakeholders in the supply chain). The relationship is in terms of the physical flow of materials or information flow or another kind of flows or relationships that connects them to form a network. These network characteristics are required to find out key stakeholders. The present article proposes the application of social network analysis (SNA) to analyse the characteristics of the network, thus helping supply chain managers to locate strategic CDCs considering both qualitative and quantitative aspects. The proposed methodology can be easily extended to locate temporary warehouse site in the context of humanitarian relief operations.

PurposeThis study aims to devise generalized unconstrained optimization models for ascertaining the optimal level of product quality and production capacity level by modeling both product price and production cost as a function of product quality. Further, interrelations among investment for quality, product quality and production volume are considered. This study contributes toward the extant research, in that nuances related to price, production volume, and product quality are fused together such that two broad operational strategies of product quality optimization and production capacity optimization can be contrasted.Design/methodology/approachTo achieve the research objectives, the authors evolve unconstrained optimization models such that optimal product quality level and optimal production capacity level can be obtained employing the principles of differential calculus aimed at maximizing the manufacturer's profit. Specifically, nuances related to quality technology and efficiency, and quality loss cost has also been integrated in the integrated model. Thereafter, employing numerical analysis for a generalized product, the detailed workings of evolved models are demonstrated. The authors further carry out the sensitivity analysis to understand the impact of investment for quality onto the manufacturer's profit for both operational strategies.FindingsThe research demonstrates that the manufacturer would be better off adopting production capacity optimization strategy as an operational policy, as opposed to product quality optimization policy for the manufacturer's profit maximization. Further, considering the two operational strategies, the manufacturer does not obtain the highest possible theoretical profit when pertinent variables (product quality and production capacity) are set at highest possible theoretical level. This research discusses that in low-volume and high-margin products, it might be useful to adopt a product quality optimization strategy as a production capacity optimization strategy results in significantly high quality loss cost.Originality/valueThe findings of our study have a significant implication for industries such as steel-making, cement production, automotive industry wherein the conventional wisdom dictates that higher level of production capacity utilization always results in higher level of revenues. However, the authors deduce that beyond certain production capacity utilization, striving for higher utilization does not fetch additional profit. This work also adds to the extant research literature, in that it integrates the nuances of product quality, production volume and pricing in an integrative manner.

PurposeOver the years, the fruit and vegetable supply chain has encountered several challenges. From the harvesting stage until it reaches the consumer, a significant portion of fruits and vegetables gets wasted in the supply chain. As a result, the present study attempts to identify and analyze the growth barriers in the fresh produce supply chain (FPSC) in the Indian context.Design/methodology/approachAn integrated grey theory and DEMATEL based approach is used to analyze growth barriers in the FPSC. The growth barriers were analyzed and sorted based on their influence and importance relations.FindingsThe results emphasize that the most critical growth barriers in the FPSC that should be addressed to ensure food waste reduction are as follows: Lack of cold chain facilities (B2), lack of transportation or logistic facilities (B1), lack of collaboration and information sharing between supply chain partners (B3), lack of proper quality and safety protocols (B15), a lack of processing and packaging facilities (B14), and poor productivity and efficiency (B13). Results are also verified by conducting a sensitivity analysis.Practical implicationsThe results are highly useful for policymakers to exploit growth barriers within the FPSC that require more attention. The obtained results show that the managers and policymakers need to utilize more funds to develop the cold chain facilities and logistics facilities to develop the FPSC. By improving the cold chain facilities, it is possible to improve the quality of food, make the food safe for human consumption, reduce waste, and increase the efficiency and productivity of the supply chain. Also, this study may encourage policymakers and industrial managers to adopt the most influential SCM practices for food waste reduction.Originality/valueMany researchers have attempted to analyze the causes of food waste and growth barriers in the FPSC using various decision-making methods. Still, no attempts are made to explore the causal relations among various growth barriers in FPSC through the integrated Grey-DEMATEL technique. Also, we devise policy implications in the light of the new farm bills or the Indian agricultural acts of 2020. Lack of cold chain facilities (B2) was found to be the critical driving barrier in the FPSC, as it influences multiple barriers. Also, there is a dire need for cold chain facilities and transportation systems to enhance productivity and efficiency.

Purpose Recent years have witnessed a spike in Industry 4.0 initiatives among manufacturing organizations, particularly in the automotive sector. This acceleration aims to enhance competitiveness by addressing various aspects, from efficiency and workforce productivity to safety and insightful decision-making. However, merely adopting technological solutions in isolation may not suffice. Automotive companies need a holistic approach that integrates the antecedents of Industry 4.0 into their overall strategy. This study aims to identify and analyse key antecedents for Industry 4.0 adoption in the Indian automotive sector. Design/methodology/approach The study follows a structured six-stage methodology, which includes a systematic literature review, expert consultations and best–worst method (BWM) analysis. The research identifies, validates and systematically ranks 16 antecedents that are pivotal for Industry 4.0 adoption. Findings The study categorizes 16 antecedents into four dimensions: regulatory framework (RF), technology infrastructure (TI), operational optimization (OO) and performance dynamics (PD). The findings emphasize the significance of “Government policies to support smart factories”, “Support from top management”, “Financial performance” and “Technology readiness” as crucial antecedents for Industry 4.0 implementation in the Indian automotive sector. Research limitations/implications These findings provide valuable guidance for industry practitioners and policymakers in strategically planning the Industry 4.0 deployment in the automotive sector. Originality/value This study contributes to the limited body of research on the identification and analysis of key antecedents for Industry 4.0 adoption in the automotive sector, particularly in emerging economies such as India. By using the BWM, it offers a structured and efficient approach to determining the priority order of these antecedents.

PurposeE-commerce refers to the facilitation and delivery of goods and services to the customers employing an electronic arrangement. For an e-commerce firm, the customer service level provided by its suppliers can make or break the firm. The purpose of this research is to help e-commerce enterprises in addressing the vast challenge of complex supplier selection and evaluation process that must be performed vigilantly.Design/methodology/approachThe present study utilizes a three-pronged approach that integrates supplier management practices with the operational business practices of an e-commerce enterprise. In the first step, key performance factors for e-commerce capable suppliers are identified through an expert opinion and existing supplier management literature. Further, Data Envelopment Analysis (DEA) is employed to obtain the efficiency score for each supplier that enables their ranking on various performance parameters. Lastly, the suppliers are classified into different categories based on their performance and efficiency.FindingsUnder the proposed classification scheme, top five suppliers, i.e. supplier 1, 7, 9, 11 and 17 are categorized as HE (High Performance and Efficient). It is suggested that e-commerce enterprises must build long-term relationship with the identified top performing suppliers. The study also provides real insights into supplier's performance on a number of objective criteria. Further, the present study enhances the overall performance and productivity of an e-commerce firm by achieving input cost minimization and output quality maximization, simultaneously.Research limitations/implicationsThe results are valid for e-commerce enterprises in general. However, the present DEA model can be further evolved when applied in case of any particular e-commerce enterprise depending upon the internal capabilities of that firm. The nuances related to a firm's own supply capability development can be further explored by practitioners and researchers.Practical implicationsThe proposed approach is expected to motivate decision-makers to consider using more sophisticated approached like DEA in supplier evaluation processes. Also, as a benchmarking technique, the proposed supplier classification approach is expected to be highly useful for practitioners in real-life settings.Originality/valueThe novel contribution of this study includes the supplier evaluation, ranking and classification for e-commerce enterprises based on the real-life data. The insights would help the practitioners to formulate novel strategies for appropriately investing in supplier relationships.

With the enormous growth in the public and private vehicles fleet, traffic congestion is increasing at a very high rate. To deal with this, an intelligent mechanism is required.Therefore, this work proposes a novel Neuro-fuzzy based intelligent traffic light control system, which accounts for vehicle heterogeneity by dynamically generating traffic light phase duration considering the real-time heterogeneous traffic load. For this purpose, the proposed model establishes peer-to-peer connections among neighboring traffic light junctions to fetch the respective real-time traffic conditions and congestion. A fuzzy membership function is utilized to generate an intelligent traffic light phase duration. Further, to obtain an effective fuzzy membership function input value considering real-time heterogeneous traffic scenarios, an adaptive neural network is utilized. The proposed system adopts three execution modes: Congestion Mode (CM), Priority Mode (PM), and Fair Mode (FM). It automatically activates and switches to the best mode based on the live traffic conditions. The performance of the proposed model is evaluated via a realistic simulation on the Gwalior city map of India using an open-source simulator known as Simulation of Urban Mobility (SUMO). The results evident the effectiveness of the proposed model over the existing state-of-the-art approaches.

PurposeOptimization of resources related to man, money, manpower and those related to organization is critical in context of after-sales supply chains. Many times, organizational objectives in terms of resource optimization and providing superior customer experience might be conflicting, however.Design/methodology/approachOne such instance is when customers expect near 100% service level in which case the organizational costs to meet such high service level goes up significantly. To this end, in this research a novel bi-objective optimization model has been evolved for a typical after-sales service supply chain network constituted of the manufacturer, the retailer and the customer. The first objective function pertains to maximization of the manufacturer's and the retailer's profit. The second objective function is related to the minimization of tardiness of order fulfilment (by the retailer) for the customer.FindingsEmploying a small problem instance, the authors generate a number of findings related to service level and information asymmetry. In particular, the authors observe that achieving best possible manufacturer-retailer profit and at the same time 100% service level is a mathematical impossibility. Furthermore, reducing information asymmetry between the customer and the retailer (as opposed to reducing information asymmetry between the retailer and the manufacturer) actually yields higher profits for the manufacturer-retailer pair.Originality/valueThis research describes the mathematical structure of a three-tier after-sales supply chain wherein information quality and service level requirements are key constraints. Furthermore, the study evolves the bi-objective optimization model as a formulation that can drive the operational decisions of manufacturers and retailers who are part of such after-sales service supply chains.