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The purpose of this paper is to present a framework for identifying various inbound supply-risk factors and analyzing its indicators considering the contextual relationship between them. This study additionally proposes a framework for developing an overall inbound supply-risk score considering a real-life case of the electronics supply chain (ESC) in the Indian context.

In total, 32 risk indicators are identified by a systematic literature review approach and are validated by supply chain practitioners/experts and further categorized into six main risk factors. A hybrid multi-criteria decision-making-based DANP (DEMATEL and ANP) framework is employed to develop the overall inbound-supply-risk score (ISRS) and to prioritize the risk indicators. Indian ESC is chosen as a viable case study to demonstrate the effectiveness of the proposed framework.

The outcomes from the study reveal that the overall ISRS in the ESC is 36 percent and additionally forewarns critical inbound-supply-risk factors such as supplier performance, product, and buyer organization. Further, the study also identifies the most significant risk indicators such as price margin, investment, on-time delivery, order fulfillment and design changes for ESC.

Supply chain practitioners can adopt this framework as a useful inbound supply-risk assessment tool. Moreover, the hybrid framework will address subjectivity and interrelations among various factors through experts’ judgments. The results will assist the managers to have better insights on the critical risk factors and their complicated interrelationships and further strategize action plans to nullify the impact of incoming risks. This study mainly focused on risk identification and assessment of electronics inbound-supply-risk indicators in the Indian context. The framework can be used for other manufacturing and service industries, albeit the results derived are in the context of a developing country.

This paper provides an effective risk assessment framework for the supply chain practitioners/managers to develop a decision-support system for inbound-supply-risk quantification and prioritization of risk factors in the context of the ESC.

India has the largest public distribution system (PDS) in the world, working through over five million fair price shops (FPS) to distribute food grains among its beneficiaries at a subsidized rate. In this paper, the authors study the inventory system of Indian FPS. The system involves a distributor, who is solely responsible for the replenishment of the FPS. In a real-world scenario, the distributor is subjected to random supply and transportation disruptions. The purpose of this paper is to investigate and minimize the impacts of such disruptions.

In this paper, the authors adopt a simulation-based technique to explore the impacts of various traits of disruptions like frequency and duration on the FPS inventory system. A simulation model for the Indian FPS is developed and the impacts of disruptions are investigated by a case study.

The authors use a simulation-based optimization technique to suggest a simple managerial change that can lead to a minimization of inventory shortage up to 60 per cent and system cost up to 21 per cent over the existing practice.

The present study addresses the FPS inventory system of Indian PDS, which is by its nature unique and has not been considered by any other previous literature. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.

Product counterfeiting has been ubiquitously observed in various segments of the supply chain. The intrinsic values of brands create more opportunities for counterfeiting. The damaging reputation of such brands leaves them to deal with the fallouts of counterfeits. Hence, such companies address them mainly through legal action, price and quality strategy. However, consumer characteristics and the random distribution of counterfeit products to the consumer types affect the effectiveness of a counter strategy. This paper aims to generate insights on how to leverage digital technology to curb counterfeit entities with consideration of consumer characteristics and the random distribution of counterfeits to them.

The authors used game theory and vertical differentiation model to understand and encounter deceptive counterfeiting of brand products. The study understands the economic relationship between a brand product manufacturer and consumer types based on their awareness. Further, the authors have considered different cases in the model to gain useful insights.

The results reveal that when the consumers are proactive, informed and value-conscious brand product manufacturers take digital technology counterstrategy to earn the maximum revenue. Hence, this analysis highlights that the effectiveness of a counterstrategy critically depends on the consumer characteristics, whether they are proactive, informed or unaware.

The study outlines that brand product manufacturers must emphasize on the digital supply chain, product redesign and product tracking facility to empower informed and value-conscious and proactive consumers. Moreover, the government should take steps to create awareness among uninformed consumers via information campaigns.

This paper incorporates the role of consumers and brand product manufacturers to understand and address the deceptive counterfeiting issue.

Original equipment manufacturers and other manufacturing companies rely on the delivery performance of their upstream suppliers to maintain a steady production process. However, supplier capacity uncertainty and delayed delivery often poses a major concern to manufacturers to carry out their production plan as per the desired schedules. The purpose of this paper is to develop a decision model that can improve the delivery performance of suppliers to minimise fluctuations in the supply quantity and the delivery time and thus maximising the performance of the supply chain.

The authors studied a single manufacturer – single supplier supply chain considering supplier uncertain capacity allocation and uncertain time of delivery. Mathematical models are developed to capture expected profit of manufacturer and supplier under this uncertain allocation and delivery behaviour of supplier. A reward–penalty mechanism is proposed to minimise delivery quantity and time of delivery fluctuations from the supplier. Further, an order-fulfilment heuristic based on delivery probability is developed to modify the order quantity which can maximise the probability of a successful deliveries from the supplier.

Analytical results reveal that the proposed reward–penalty mechanism improves the supplier delivery consistency. This consistent delivery performance helps the manufacturer to maintain a steady production schedule and high market share. Modified ordering schedule developed using proposed probability-based heuristic improves the success probability of delivery from the supplier.

Practitioners can benefit from the findings of this study to comprehend how contracts and ordering policy can improve the supplier delivery performance in a manufacturing supply chain.

This paper improves the supplier delivery performance considering both the uncertain capacity allocation and uncertain time of delivery.

The purpose of this research is to study and examine the influence of systematic supply risk management (SRM) on the buyer's firm performance, mainly focussing on the Indian electronics industry.

The study has framed a set of hypotheses on the risk management model. A thorough literature review and experts' opinion were considered in framing constructs and hypothesis for the model. We adopted self-administration questionnaires mainly focusing on the Indian electronics industry. The derived hypothesis is tested using partial least squares (PLS) method from 140 survey data pertaining to small, medium and large scale industries.

Study justify that constructs with high loadings for risk identification, risk assessment, risk reduction, and risk monitoring supports all hypothesized relation to better risk management. The model captures superior risk identification, risk control and risk monitoring for overall firm performance, but fails to justify with organization supply risk assessment process on overall firm performance.

The study mainly focused on SRM process on firm performance. Study mainly focused on single survey responses and expert's perceptions on SRM practices in Indian electronics industry.

Research empirically justifies the effects of SRM process on organization performance. Furthermore, effective SRM practices assist decision makers framing corrective strategies to mitigate risk occurrences and their negative impact.

This empirical work provides a deep understanding of SRM process on the Indian electronics industry and their perception towards firm performance. Moreover, this is one of the few empirical studies addressing SRM practices in the Indian electronics industry.

The Indian micro small and medium scale enterprises (MSMEs) are potential suppliers to the original equipment manufacturers (OEM). The implementation of sustainable supply chain practices (SSCPs) enhances the chances of developing a long-term partnership with these OEMs.

A hybrid framework of analytical network process (ANP) and fuzzy logic (FL) is developed for implementing SSCPs in Indian MSMEs. This model has been validated through a case study.

The study has identified several critical success factors (CSFs) for the implementation of SSCPs in MSMEs. This study has observed that Government regulation is the most important CSF for the implementation of SSCP in Indian MSMEs followed by management support and policy framework.

The article presents a mechanism, i.e. an adaptability test that enables the OEM decision-makers to assess the suitability of an MSME for a long-term partnership.

In the modern era of e-grocery, attended home delivery (AHD) has been identified as a crucial part of the last-mile delivery problem. This paper aims to deal with a real-life last-mile-delivery problem in the context of the Indian public distribution system (PDS). The authors identified two different environments for the said AHD problem and proposed two different approaches to address the issue under these problem settings.

In this study, the authors first consider the problem in a static environment and propose an iterated local search (ILS) integrated with an adaptive large neighborhood search (ALNS) meta-heuristic algorithm to obtain a routing solution. Thereafter, they extend the study in a dynamic environment where new delivery requests occur dynamically and propose a heuristic algorithm to solve the problem. For the dynamic case, multiple scenarios for the occurrence of delivery requests are considered to determine decisions regarding the opportunity to include a new request into the current solution.

By computational experiments, the authors show that the proposed approach performs significantly well for large size problem instances. They demonstrate the differences and advantages of the dynamic problem setting through a set of different scenarios. Also, they present a comparative analysis to show the benefits of adopting the algorithm in dynamic routing scenarios.

Future research may extend the scope of this study by incorporating stochastic delivery failure probabilities and customer behavior affecting the delivery response. Also, the present study does not take inventory policies at the depot into consideration. It will be of interest to see how the system performs under the uncertainty of supply from the depot. Despite the limitations, the authors believe that this study provides food for thought and encouragements for practitioners.

This study shows the benefits of adopting an AHD problem in a dynamic setting in terms of customer service as compared to a traditional static environment. The authors clearly demonstrate the differences and advantages of the dynamic problem setting through a set of different scenario analysis.

This paper investigates a real-life AHD problem faced by the Department of Food, Supply and Consumer Affairs, India. The findings of this study will be of particular interest to the policy-makers to build a more robust PDS in India.

The study is unique and highly relevant for real-world applications and can help build a more robust AHD system. Also, the proposed solution approaches to aid the problem in both static and dynamic routing scenarios will be of particular interest to practitioners.

Logistics sector is recognized as one of the core enablers of the economic development of a nation. However, inefficiency in logistics operations impedes the achievement of intended targets by increasing the cost of doing business. Also, it is difficult to improve the efficiency of a country’s logistics operations without a metric for evaluating and understanding logistics capabilities and efficiency. Therefore, the present study has developed In-country Logistics Performance Index (ILP Index) to propose a benchmarking tool to measure the in-country logistics competitiveness, particularly in the setting of emerging economies, i.e. India.

This study has developed a unified index using principal component analysis and quintile approach. In addition, the proposed index relies on several dimensions that are developed and illustrated using quantitative secondary panel data.

The findings of this study reveal that the quality of infrastructure, economy, and telecommunications are the three most important dimensions that may significantly support the growth of the transportation and logistics sector. The results reveal that Gujarat, Tamil Nadu, and Maharashtra are the top performers whereas, Bihar, Jharkhand, and Jammu and Kashmir scores the least due to the insufficient logistics infrastructure as compared to other Indian states.

Given the extensive focus on international-level logistics index (like World Bank’s LPI) in the existing literature, this study intends to develop in-country logistics index to evaluate the logistics capabilities at the regional and state level. In addition, unlike prior studies, this study utilizes quantitative secondary data to eliminate cognitive and opinion bias. Moreover, this benchmarking tool would assist decision-makers in idealizing standard practices toward sustainable logistics operations. Additionally, the ILP index could serve the international investors in crucial decision-making, as it provides valuable insights into a country’s logistics readiness, influencing their investment choices and trade preferences. Finally, the proposed approach is adaptable to measuring the overall performance of any other industry/economy.

This paper aims to focus on studying the batch desorption of adsorbed crystal violet (CV) from date stones (Phoenix dactylifera), untreated (UDS) and treated using NaOH (TDS).

The process variables such as different desorbing agents, volume and concentration of the desorbing agent, contact time, dye concentration before adsorption and temperature affecting CV desorption from CV-loaded untreated date stones (CV@UDS) and treated adsorbent (CV@TDS) were optimized. The UDS and TDS were regenerated using 0.6 m HCl as eluent.

The HCl solution was an excellent eluent for the CV desorption from CV@UDS (96.45%) and CV@TDS (98.11%). The second-order model and the Langmuir model well exemplified experimental data with maximum desorption capacities were 63.29 mg g⁻¹ for the CV@UDS and 243.90 mg g⁻¹ for the CV@TDS. The calculated thermodynamic showed that the CV desorption was spontaneous, endothermic and physical. Good regeneration and reusability of UDS and TDS for the CV removal for four consecutive adsorption–desorption cycles.

This study provided a good example of reusing UDS and TDS with NaOH for fast removal of a toxic organic pollutant, CV from the wastewaters.

The use of UDS and TDS with NaOH for the first time for desorption study and their reusability to removing CV from their aqueous solutions.