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A spurt in the usage of additive manufacturing (AM) is observed in industrial applications to produce final parts along with rapid prototyping and rapid tooling. Despite the potential benefits of on-demand and on-location production of customised or complex shape parts, widespread implementation of this disruptive production technology is not yet visible. The purpose of this paper is to examine the various barriers to implement AM in the Indian automotive sector and analyse interrelations among them.

Based on the extant literature and discussions with industry experts, ten major barriers are identified. The authors use a modified Fuzzy interpretive structural modelling (Fuzzy-ISM) method to derive strengths of relationships among these barriers, develop hierarchical levels, and thereafter group and rank these barriers.

ISM diagraph is developed to demonstrate how the barriers drive one another. Production technology capabilities and government support emerge as the most critical factors, with high driving power and medium dependence.

While identified barriers may be similar across the automotive industry, generalisation of results for interrelationships and ranks in other industries may be limited.

The findings may be useful to managers to develop suitable mitigation strategies, and take more informed decisions, with individual focus, level focus or cluster focus.

Findings clearly establish that the role of management and government is crucial in mitigating workers’ resistance to AM implementation.

This paper contributes to AM literature by the structured presentation of the barriers to implement AM in the Indian automotive sector. It also extends the Fuzzy-ISM method by presenting calculation of indirect relations using the appropriate max-product composition and in ranking the barriers.

The purpose of this paper is to identify various risk drivers which affect a food processing supply chain and to create a map of how those risk drivers propagate risks through the supply chain and impact important performance measures.

This study involves experts from food processing companies to elucidate the contextual relationships among the risk drivers and between risk drivers and performance measures. This is used to quantify the relationships and to determine the indirect and overall relationships applying Fuzzy Interpretive Structural Modeling.

Three categories of risk drivers which Indian food processing companies need to pay maximum attention to minimize risks are identified. These are supplier dependency and contracting, supplier variability, visibility and traceability and manufacturing disruptions. Analysis shows that collaborating with suppliers and logistics service providers, developing mutually beneficial contracts with them while ensuring that adequate technology investments are made can significantly mitigate risks and consequently improve margins and lead to revenue growth.

This study has been carried out with experts from large food processing companies in India, and hence, the results cannot be generalized across other types of food processing companies.

The proposed methodology can help understand the interrelationships between supply chain risks and between those risks and performance measures. Thus, it can help a food processing company to create business cases for specific supply chain risk mitigation projects.

This study is one of the earliest to create a comprehensive risk propagation map for food processing companies which helps in quantifying the impact the risk drivers have on each other and on performance measures.

This paper aims to address the conceptual and practical challenges in integrating triple bottom line (TBL) sustainability in the agriculture supply chain (ASC). It identifies the key enablers for each of the three dimensions of TBL sustainability, analyses their causal relationships as well as cross-dimensional interactions under each TBL dimension. Further, it develops a decision support framework (DSF) for the assessment of TBL sustainability practices and policies in ASC and validates it through a case study.

An interpretive structure modelling (ISM) methodology is deployed to establish the interrelationships among all TBL enablers and to identify the enablers with high driving power on sustainable ASC. Brainstorming by a group of experts was used to identify the relevant enables. Finally, a DSF was developed as a resultant of ISM.

The paper provides a set of enablers with high driving power that can significantly influence the sustainability practices and policies in ASC. The social enablers directly help to enhance the effect of economic enablers and collectively these enhance the effect of environmental enablers. If agriculture firms and supply chains design innovative policies and develop practices based on these enablers, they can achieve sustainable ASC. Consequently, the living standards of the people directly or indirectly associated with the agriculture firm or supply chain can be improved without compromising on economic performance.

The paper consolidates the fragmented knowledge of sustainable supply chain management in the agriculture sector and suggests a DSF to policymakers, managers and practitioners for assessing TBL sustainability practices and policies. The DSF has wide applicability in other sectors of production and operations management as these sectors also face the challenge of achieving TBL sustainability across their supply chain.

The DSF, developed in the paper, is a useful tool for practitioners to frame and analyse sustainability initiatives and policies for ASC. A firm or supply chain may achieve TBL sustainability if it succeeds in uplifting the social status of its stakeholders.

It is a first step towards addressing the practical challenge of integrating sustainability in the agriculture sector of emerging economies and provides a path to improve the livelihood of people in the agriculture sector. Stakeholder engagement with a focus on collaboration and awareness may lead to the desired social and environmental consequences. Potential adverse social effects also need to be considered.

This paper focusses on the so far rather neglected but essential aspect of integrating TBL sustainability in the agriculture sector of emerging economies. The hierarchal representation and classification of the TBL sustainability enablers of sustainability is a unique effort in the field of ASC. Development of DSF is one of the first attempts to create a mapping between various enablers of TBL sustainability. The novelty of the study lies in the sector-specific, holistic evaluation of TBL sustainability policy measures that may lead to improvements in practice.

Despite various consequences for different stakeholders in the mustard ecosystem, India prohibited blending in mustard oil to achieve self-reliance in edible oils and promote consumer health. This paper uncovers the implications of this policy on mustard production, consumption and prices.

This paper deploys system dynamics (SD) to model the mustard ecosystem. SD uses simulation modeling to comprehend the nonlinear behavior of complex systems over time utilizing causal-loop and stock-flow diagrams.

While the mustard price does not vary in the short run, it diverges toward a higher side in the long run due to the changed policy mandate. Surprisingly, due to the predominance of market prices, the policy administered minimum support price (MSP) was found to have a limited influence on mustard prices. Hence, the focus should be on supply augmentation through non-price-based measures like disseminating information to enhance the yield rate of seed production and promoting the adoption of efficient technologies with higher oil conversion efficiency.

The paper allows policymakers to quantitatively evaluate the effectiveness of policy interventions to mitigate the adverse impacts of policy mandate. It presents a reliable roadmap for policymakers to roll out effective policies.

The paper uncovers the system-level impact of policy on stakeholders and examines the effectiveness of MSP.

Government e-Procurement systems are generating tender-level process event data which are not being analyzed much. The purpose of this paper is to present a data visualization tool to benchmark the government tendering process. This tool collects, collates and presents e-Procurement process data in a meaningful way that enables comparisons and benchmarking leading to insights for process improvements and identification of the best practices. This tool is accessible on the website of South Asia’s first public procurement observatory (www.procurementobservatoryup.com) founded by the authors.

The authors started with an initial set of key performance indicators (KPIs) based on extant literature and existing practices. The authors got them verified by various relevant stakeholders through a series of formal and informal discussions. Some of them were dropped due to observed variations or their inability to offer insights to arrive at the final set of KPIs. In this paper, the authors use actual process-level data. Government of India’s National Informatics Center has implemented e-Procurement portals in various states and public enterprises (PEs) in India which provide tender-wise detailed e-tendering process data. The authors designed a web crawler that collects these data in a tabular format, which allows an easy analysis and comparison to measure and compare government tendering process performance for the last five years for the two large PEs. The authors also engaged in discussions with the procurement executives of the two PSEs to derive meaningful managerial insights from the results obtained.

Using the public procurement data visualization tool, the authors compare the procurement process of two of the largest Indian PEs, Coal India Limited and Indian Oil Corporation on the developed KPIs and draw insights. The results show significant difference in their procurement process performance due to different practices followed by them. Through interaction with the procurement managers of these two organizations, the authors identify few good procurement practices that can be applied to improve public procurement process.

This paper uses actual process-level data which can be used not just to identify improvement opportunities but also to gauge the impact of any process-level change. It presents a data visualization tool to generate insights from data generated by a procurement/tendering system. Such visualization and benchmarking can uncover better procurement practices and provide an impetus toward data-driven policy formulation. Apart from the two PEs as reported in this paper, this tool has also been applied on the public procurement data of eight Indian states.

The KPIs presented in this paper are aligned with the various dimensions of public procurement’s objectives. The visualization tool presented in this paper is based on the Open Contract Data Standard and has a universal application.

The use of technology and open data sharing as the enablers of benchmarking and process improvements help in establishing a dynamic competitive environment leading to financial savings, better services to citizens and proper use of taxpayers’ money.

This paper presents an original work carried out under the aegis of South Asia’s first procurement observatory at IIM Lucknow. The benchmarking tool presented in this paper uses open contract data standard and can be applied in most of the public procurement processes. This paper takes the discussions on e-Procurement to the next level, where the concern is no longer restricted to only adoption and assimilation issues, but also on how to make use of the data that these e-Procurement systems generate.

The purpose of this paper is to present issues and challenges faced during a firm’s facility relocation decision aimed at improving both cost and service performance in an innovative service context.

The reader is given background of the decision-making process behind single service facility relocation decision using a detailed case study. Key financial, operational and business data of the firm are collected, compiled and analysed. The solution methodology uses a combination of qualitative and quantitative analyses to choose the best among the three possible discrete location choices. For propriety reasons, some information has been disguised, and some data have been sanitized.

The factors that significantly influence relocation decision are proximity to high transaction customers, infrastructure and other input costs, customer service level requirements and extant regulations. Transportation has a direct impact on cost as well as service level. Most of the findings are in line with literature, but some of them differ too.

The approach is focused on a single case study of a pooling container firm in the Indian context and thereby limits the ability to generalize the findings. Nevertheless, this study may serve as a significant starting point for future research.

Firms can create a rational, efficient and even-handed approach for relocation of facilities applying a mix of qualitative and quantitative models judiciously. It provides managers better understanding and insights and actions needed for single service facility relocation.

This work is perhaps the first on facility relocation in emerging economies covering actual interventions and experiences. It gives new insights to a limited literature of relocating single service facility reflecting both theoretical imperatives and practitioner requirements.

The purpose of this paper is to develop a composite index (CI) to evaluate the sustainability performance of an aircraft manufacturing firm using Fuzzy Best Worst Multi Criteria (FBWM) decision-making approach. It identifies a wide range of sub-dimensions and their attributes to measure sustainability in a multi-echelon supply chain.

FBWM, a hybrid multi-criteria decision making method, relies on multiple sub-dimensions and attributes for assessment of sustainability. Sub-dimensions and attributes are identified from a detailed literature review and inputs from 17 experts. The weights are evaluated using best worst method (BWM). Quantitative measurements are very difficult for attributes like living conditions, noise emission, etc., so the performance of attributes are assessed using Fuzzy logic. The proposed methodology is validated with the case study of a single unit of an Indian aircraft manufacturing firm.

Economic concerns emerge as the most influential dimension of sustainability in the aerospace sector. The results reveal that the firm considered in the case study is “Very Sustainable.” Firm’s performance ratings are excellent in three out of 79 attributes considered for the study. Further, 24 weaker (least performing) attributes are identified with the help of fuzzy performance index. These require managerial action for improvements.

The present study is based on inputs from a small group of managers in a single firm in India. It can be extended to a large group of executives in other firms like Boeing, Airbus, etc. for testing the validity of the proposed methodology and generalization of the findings.

Managers, consultants and audit agencies can use the proposed CI developed in this paper for evaluating the sustainability performance of a firm. It assists managers to identify weaker attributes for which they may plan and prioritize their activities for improvements.

To the best of the authors’ knowledge, this is the first paper to measure sustainability in an aircraft manufacturing firm. The paper proposes a novel framework based on fuzzy BWM, for assessing sustainability performance.

The study aims to analyze the intellectual structure in sustainable procurement (SP) research to identify the knowledge research clusters and provide potential avenues for future research.

The study conducted a bibliometric analysis to analyze the intellectual structure in the area of SP. Overall, 1,294 articles were selected from the Scopus database published between 2000 and 2022. The analysis was conducted using bibliometric R package, Biblioshiny and VOSviewer software. Further, content analysis of research clusters was carried out to set the future research agenda.

The study identifies four major knowledge research clusters of SP, namely, (1) green supply chain practices, (2) socially responsible purchasing, (3) environmental purchasing and (4) public procurement and policy. The study suggests a few research directions in the SP field. Moreover, the future research directions are aligned with specific organizational theories applicable in the area of SP research.

The study is dependent on the Scopus database for the source of research publications on SP. Future studies may consider other research database sources.

Identifying knowledge research clusters of SP research is of paramount importance for developing policies in the near future. These policy initiatives pave the way for the adoption of SP practices in the business. The findings indicate the issues managers encounter while implementing SP in organizations.

The study offers valuable insights concerning parameters such as significant publication outlets, influential countries concerning the number of publications, impactful authors, title keywords and identifying major knowledge research clusters of SP to suggest future research directions. Further, the present study highlights emerging areas that require further research, including process governance, supplier diversity, innovation, the role of emerging technologies and the application of organizational theories in SP.

The purpose of this paper is to use a conceptual model from literature for designing value recovery (VR) networks for three categories of post‐consumer product returns.

A bi‐level optimization model is developed to determine the disposition decision for refrigerators, washing machines and passenger cars in the Indian context using data for product returns from literature. Using standard off‐the‐shelf software, the break‐even values of returns are calculated for setting up various VR facilities under different scenarios to maximize profits for a ten‐year time‐horizon.

The VR activities are profitable for all the three categories of products beyond a certain minimum quantity of returns. Experimentation across the three product categories shows that presently remanufacturing is not a viable economic proposition in the Indian context. Further, the VR network design suggested by this approach is volume flexible.

A “push” system where the volumes and grades of returns drive the VR decisions. Optimization has been carried out for three product categories and not brands or OEMs. No free choice of facility locations.

The insights and learning under different scenarios may be utilized as inputs for decision‐making and for designing various incentive plans.

This work is a first step towards VR network design in the Indian context. Various tools from the methodological perspective are used and provide detailed network design from the topological perspective.

The purpose of this paper is to present a framework to manage product returns for reverse logistics by focusing on estimation of returns for select categories of products in the Indian context.

The paper develops a conceptual model and thereafter an integrated modeling framework borrowing from existing literature and industry practices. It utilizes product ownership data, average life cycle of products, past sales, forecasted demand and likely impact of environmental policy measures for estimating return flows. Informal interviews with 84 stakeholders are carried out to estimate significant parameters. Software packages, decomposition methods and heuristics are utilized for solution.

The integrated framework helps in estimating returns for select categories of products and thereafter taking simultaneous decisions on their disposition, location and capacity of facilities and flows of returned products for a given time horizon under various strategic, operational and customer service‐related constraints.

A “push” system where the volumes of returns drive the decisions. Estimations and optimization have been carried out for select product categories and not brands or original equipment manufacturers (OEMs). No free choice of facility locations.

The insights and learning under different scenarios may be utilized as inputs for decision making by various stakeholders such as OEMs and their consortia, local remanufacturers and third party service providers.

At methodological level, our framework combines descriptive modeling with optimization technique, while at topological level; it provides detailed solutions for network configuration and design.