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This paper aims to develop supply chain strategies for the fashion retail supply chain (FRSC), likely to be disrupted by the current pandemic (COVID-19) under physical and online retail stores. The resilient retail supply chain design is proposed under budget allocation and merchandise capacity constraints.

This paper utilises the theory of constraint (ToC) and goal programming (GP) to address the COVID-19 impact on FRSC. The budgetary and capacity constraints are formulated with a constraint optimisation model and tested with six different priorities to deal with the physical and online stores. Next, all priorities are developed under different FRSC business scenarios. The ToC-GP-based optimisation model is validated with one of the Indian fashion retail supply chains.

The proposed optimisation model presents the optimal retailing strategies for selling fashion goods over physical and online platforms. The multiple scenarios are presented for developing trade-offs among different strategies to maximise the retailer's merchandise performance. This paper also highlighted the strategic movement from high merchandise density stores to low merchandise density stores. This implies a reduction of sales targets and aspiration levels of both online and physical fashion stores.

The proposed model is validated with one of the fashion retailers in India. Other nations or multiple fashion retailers might be considered for more generalisation of findings in the future.

This research helps fashion retail supply chain managers deal with consumer demand uncertainty over physical and online stores in pandemic times. Limitation: Other nations or multiple fashion retailers might be considered for more generalisation of findings in the future.

This is the first study that considered the impact of COVID-19 on the retail fashion supply chain. The effect of physical and online platforms is mainly discussed from consumer marketing perspectives, but an inventory and resilience perspective is missing in earlier studies. The role of merchandise planning is highlighted in this study.

The study aims to map the links between Industry 4.0 (I-4.0) technologies and circular economy (CE) for sustainable operations and their role to achieving the selected number of sustainable development goals (SDGs).

The study adopts a systematic literature review method to identify 76 primary studies that were published between January 2010 and December 2020. The authors synthesized the existing literature using Scopus database to investigate I-4.0 technologies and CE to select SDGs.

The findings of the study bridge the gap in the literature at the intersection between I-4.0 and sustainable operations in line with the regenerate, share, optimize, loop, virtualize and exchange (ReSOLVE) framework leading to CE practices. Further, the study also depicts the CE practices leading to the select SDGs (“SDG 6: Clean Water and Sanitation,” “SDG 7: Affordable and Clean Energy,” “SDG 9: Industry, Innovation and Infrastructure,” “SDG 12: Responsible Consumption and Production” and “SDG 13: Climate Action”). The study proposes a conceptual framework based on the linkages above, which can help organizations to realign their management practices, thereby achieving specific SDGs.

The originality of the study is substantiated by a unique I-4.0-sustainable operations-CE-SDGs (ISOCES) framework that integrates I-4.0 and CE for sustainable development. The framework is unique, as it is based on an in-depth and systematic review of the literature that maps the links between I-4.0, CE and sustainability.

This paper aims to create a new decision-making procedure that uses “Lot-by-Lot Acceptance Sampling Plan by Attributes” methodology in the production processes when any production interruption is observed in tobacco industry, which is a significant example of batch production.

Based on the fish bone diagram, the reasons of the production interruptions are categorized, then Lot-by-Lot Acceptance Sampling Plan by Attributes is studied to overcome the reasons of the production interruptions. Furthermore, managerial aspects of decision making are not ignored and hence, acceptance sampling models are determined by an Analytical Hierarchy Process (AHP) among the alternative acceptance sampling models.

A three-phased acceptance sampling model is generated for determination of the reasons of production interruptions. Hence, the necessary actions are provided according to the results of the proposed acceptance sampling model. Initially, 729 alternative acceptance sampling models are found and 38 of them are chosen by relaxation. Then, five acceptance sampling models are determined by AHP.

The current experience dependent decision mechanism is suggested to be replaced by the proposed acceptance sampling model which is based on both statistical and managerial decision-making procedure.

Acceptance sampling plans are considered as a decision-making procedure for various cases in production processes. However, to the best of our knowledge Lot-by-Lot Acceptance Sampling Plan by Attributes has not been considered as a decision-making procedure for batch production when any production interruption is investigated.

Due to uncertainty in supply chains caused by the coronavirus disease 2019 (COVID-19), organizations are adjusting their supply chain design to address challenges faced during the pandemic. To safeguard their operations against disruption in order quantities, supply chain members have been looking for alternate suppliers. This paper considers a two-level supply chain consisting of a manufacturer and two suppliers of a certain type of components required for the production of a finished product. The primary supplier (supplier A) is unreliable, in the sense that the quantity delivered is usually less than the ordered quantity. The proportion of the ordered quantity delivered by supplier A is a random variable with a known probability distribution. The secondary supplier (supplier B) always delivers the order in its entirety at a higher cost and can respond instantaneously. In order for supplier B to respond instantaneously, the manufacturer is required to reserve a certain quantity at an additional cost. Once the quantity received from the main supplier is observed, the manufacturer may place an order not exceeding the reserved quantity.

A mathematical model describing the production/inventory situation of the supply chain is formulated. The model allows the determination of the manufacturer's optimal ordering policy.

An expression for the expected total cost per unit time function is derived. The optimal solution is determined by solving a system of nonlinear equations obtained by minimizing the expected total cost function.

The proposed model can be used by supply chain managers aiming at identifying various ways of handling the uncertainty in the flow of supplies across the chain.

This proposed model addresses a gap in the production/inventory literature.

Significant advances have been made in the field of healthcare service delivery across the world; however, health coverage particular for the poor and disadvantaged still remains a distant dream in developing world. In large developing countries like India, disparities in access to healthcare are pervasive. Despite recent progress in ensuring improved access to health care in past decade or so, disparities across gender, geography and socioeconomic status continue to persist. Fragmented and scattered health records and lack of integration are some of the primary causes leading to uneven healthcare service delivery. The devised framework is intended to address these challenges. The paper aims to discuss these issues.

In view of such challenges, in this research a Big Data and blockchain anchored integrative healthcare framework is proposed focusing upon providing timely and appropriate healthcare services to every citizen of the country. The framework uses unique identification number (UID) system as formalized and implemented by the Government of India for identification of the patients, their specific case histories and so forth.

The key characteristic of our proposed framework is that it provides easy access to secure, immutable and comprehensive medical records of patients across all treatment centers within the country. The model also ensures security and privacy of the medical records based upon the incorporation of biometric authentication by the patients for access of their records to healthcare providers.

A key component of our evolved framework is the Big Data analytics-based framework that seeks to provide structured health data to concerned stakeholders in healthcare services. The model entails all pertinent stakeholders starting from patients to healthcare service providers.

The purpose of the study is to understand the perception held by Micro Steel Enterprises in Punjab, India, towards the role played by FinTech in it. The quantitative study uses a structured questionnaire to collect primary responses. A total of 400 Micro Steel Enterprises have been approached from Punjab. Here, statistical analyses are used, including chi-square tests, Principal Component Analysis, Multiple Linear Regression and One-way ANOVA. The satisfaction levels across the three factors revealed a significant relationship with the financial aspect only. This clarifies the situation and reveals that using the FinTech services among the Micro Steel Enterprises is only a modification caused by the financial constraints that generate satisfactory levels in the respondents. However, based on the operational conditions of these enterprises is not the factor causing the satisfaction levels, neither are the banking processes. The advances in the course must be solely made based on the financial aspects of Micro Steel Enterprises. The two variables of the owner of these firms are the owner’s age and the firm’s ownership type. The banking aspect and the Micro Steel Enterprise are also found to have associations with them. To motivate the use of FinTech in the process, the owners can be motivated differently towards its usage. The study is a novel attempt to understand the Micro Steel Enterprises in Punjab and its perception towards the use of FinTech. Punjab is the hub of the Steel Industry in India.

Background: Insurance was discovered many centuries before Christ (BC). In the second and third millennia BC, Chinese and Babylonian traders traded risks. Insurance is now the backbone of the economy, but penetration is low in developing countries. Big data, internet of things (IoT), and InsurTech have recently ushered in the fourth industrial revolution in insurance.

Objective: This study examines the Indian challenges and solutions of using Big Data Analytics (BDA).

methodology: A SLR was used to extract themes/variables related to challenges and solutions in adopting BDA in the Indian insurance sector. Google Scholar was searched for relevant literature using keywords. Inclusion and exclusion criteria were used to filter the studies.

Findings: This study identified several barriers to BDA adoption in the Indian insurance industry. Policymakers could use the suggestions to improve insurance service delivery.

Practical implication: Insurers can understand the challenges, and accordingly, they can adopt the proposed solution in this study to enhance the insurance penetration in India.

This study presents the impact of Economic Policy Uncertainty (EPU)-induced Trade Supply Chain Vulnerability (TSCV) on the Small and Medium-Sized Enterprises (SMEs) in India by leveraging the World Bank Enterprise Survey data for 2014 and 2022. Applying econometric techniques, it examines firm size’ influence on productivity and trade participation, providing insights for enhancing SME resilience and trade participation amid uncertainty.

The econometric techniques focus on export participation, along with variables such as total exports, firm size, productivity, and capital intensity. It addresses crucial factors such as the direct import of intermediate goods and foreign ownership. Utilizing the Cobb-Douglas production function, the study estimates Total Factor Productivity, mitigating endogeneity and multicollinearity through a two-stage process. Besides, the study uses a case study of North Indian SMEs engaged in manufacturing activities and their adoption of mitigation strategies to combat unprecedented EPU.

Results reveal that EPU-induced TSCV reduces exports, impacting employment and firm size. Increased productivity, driven by technological adoption, correlates with improved export performance. The study highlights the negative impact of TSCV on trade participation, particularly for smaller Indian firms. Moreover, SMEs implement cost-based, supplier-based, and inventory-based strategies more than technology-based and risk-based strategies.

Policy recommendations include promoting increased imports and inward foreign direct investment to enhance small firms’ trade integration during economic uncertainty. Tailored support for smaller firms, considering their limited capacity, is crucial. Encouraging small firms to engage in international trade and adopting diverse SC mitigation strategies associated with policy uncertainty are vital considerations.

This study explores the impact of EPU-induced TSCV on Indian SMEs’ trade dynamics, offering nuanced insights for policymakers to enhance SME resilience amid uncertainty. The econometric analysis unveils patterns in export behavior, productivity, and factors influencing trade participation during economic uncertainty.

This paper analyses the effect of circular economy practices on sustainable supply chain performance. The study explores the impact of mediating variables such as supply chain flexibility and capabilities and the moderating role of supply chain integration in the relationship between circular economy practices and sustainable supply chain performance in Indian manufacturing firms. The study builds on the stimulus-organism-response (S-O-R) model to conceptualise circular economy practices that influence supply chain capabilities, integration and flexibility, impacting sustainable supply chain performance.

This study adopted an online survey questionnaire distributed to managers of Indian manufacturing firms adopting circular economy practices. The data were analysed using SPSS Amos 25 and PROCESS macros.

The results suggest a positive impact of circular economy practices on sustainable supply chain performance in manufacturing firms. In addition, a supply chain manager's relationship with retailers is improved in the presence of supply chain capabilities and flexibility. Supply chain integration further strengthens this relationship as a moderating variable.

By examining the literature on circular economy practices and sustainable supply chain management, this study contributes to bridging the gap between supply chain capabilities, integration and flexibility using the S-O-R model. This study is possibly among the first to explore and provide empirical evidence on how circular economy practices in manufacturing firms can impact supply chain managers' experiences and thus help to improve environmental well-being. Both academics and business professionals might find these contributions interesting.

This study aims to present a study on the supply chain process of a Myanmar-based pharmaceutical company (named ABC Pvt. Ltd. in this study) that produces pharmaceutical products across Myanmar and aims of bringing quality medical products and best care for Myanmar people’s health. The study aims to identify the key supply chain challenges and manage the opportunities executed by this pharmaceutical company to improve the supply chain process during the COVID-19 outbreak.

This work used a case study and conducted semistructured interviews with the manager, senior managers and senior staff of the ABC Company to improve the supply chain process and develop a comprehensive structural relationship to rank them to streamline the uncertainties, real-time information and agility in a digital supply chain using grey relational analysis (GRA) method.

From the data analysis and results, “Impact of political factor,” “Delay in import process” and “Weak internet connection,” and “Weak knowledge of the use of digital platform,” “Poor information sharing in online by employees” and “Information flow from top management to operational level” have been identified as top and bottom three key challenges, respectively. “Inventory management,” “Selection of transport method” and “Operational cost”, and “Marketing and brand Innovation,” “Online delivery of products” and “E-commerce enablement (Launching applications, tracking system)” are identified as the top and bottom three managing the opportunities, respectively.

The findings of the study help to supply chain decision-makers of the company in their establishment of key challenges and opportunities during the COVID-19 era. As a leading company, it always tries to add value to its product through a supply chain system, effective management teams and working with skillful decision-making toward satisfying the demand on time and monitoring the supplier performance.

The novelty of this study is to identify the key supply chain challenges and opportunities by the GRA method to rank them, considering the case of Myanmar pharmaceutical manufacturing company as a case-based approach to measuring its performance during the COVID-19 outbreak era. This work will assist managers and practitioners help to the company to provide optimal services to its consumers on time in this critical situation.