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The purpose of this paper is to develop a model for the production planning decision of a dairy plant in a multi-product setting under supply disruption risk and demand uncertainty while determining the optimal product-mix and material planning requirement.

A mixed-integer nonlinear programming model is proposed to determine the optimal product-mix that maximizes the expected profit of a dairy. The data are collected through visits to the dairy site, conducting brainstorming sessions with the plant manager and marketing head at the corporate office. Disruption data are collected from the India Meteorological Department, Odisha.

From the analysis, it is recommended that the dairy should not produce curd during the planning period. Moreover, turnover from toned, double toned and baby food is maximum than that of the curd and these products are produced in the planning period. The expected profit increases from its present value when an optimal product-mix is followed. Sensitivity analysis is performed to analyze the effect of demand uncertainty, supply disruption and production quota. The expected profit decreases as the supply failure probability increases.

The model is implemented in a dairy plant under Orissa State Cooperative Milk Producers Federation, Odisha, India. The proposed methodology has not been validated, theoretically. The concerned dairy is based on the Indian context, but the authors believe that the study is highly relevant to other dairies as well.

This study provides a methodology for dairy plant managers to plan production effectively under supply disruption risk with demand uncertainty. It also suggests material requirement planning at different factories of the dairy plant.

This paper develops a mathematical model for the production planning decision of a dairy plant that determines the optimal product-mix, which maximizes the expected profit of a dairy under disruption risk and demand uncertainty (in the Indian context).

The study aims to develop an integrated quantitative approach and suggest a framework to assess the impact of a technological intervention on the internal process dimension of the vaccine supply chain (VSC) system for multiple administered regions.

An evaluation index system is developed by selecting suitable performance indicators (PIs) that define the objectives of a VSC. Then multicriteria decision-making (MCDM) methods are applied to obtain pre and post-intervention relative ranks for the regions and performance scores of the objectives. A bilateral data envelopment analysis (DEA) compares significant efficiency differences between improvement and deterioration groups.

This study demonstrates that technological intervention improves the internal process dimension of a VSC for the regions under consideration. The empirical study delivers two groups of regions showing improvement or deterioration in relative performance ranking due to the technological intervention. However, the efficiency-based bilateral comparison may reveal an insignificant difference between the two groups.

Decision-makers associated with VSC will find the suggested model helpful in assessing the impact of technological intervention. They can easily identify specific objectives of VSC's internal process dimension, whether a particular region has observed an improvement or deterioration in its relative performance and maximize the outcome by focusing on the areas of concern for a specific region.

This study is the first to provide a quantitative approach that empirically determines relative performance improvement or deterioration of different regions for a set of identified VSC objectives in the context of the Indian states.

Rapid innovation and developments in personal electronic technology have encouraged users to change users' devices more frequently than ever, which has resulted in creating a massive increase in the amount of electronic waste. The study focuses on identifying the barriers to closed-loop supply chain (CLSC) in the electronic industry.

A framework for analyzing the relationships among CLSC adoption barriers is designed. The authors adopted the decision-making trial and evaluation laboratory (DEMATEL) technique to determine the critical barriers of electronic CLSC from the opinion of experts in the field.

The outcome from the analysis suggests that cost barriers, financial barrier, process barriers and supplier-side barriers are the main causal factors that prevent the adoption and implementation of e-waste CLSC. The causal relationship indicates that financial barrier is the most influential factor, while phycological barrier is the most flexible barrier to the adoption of e-waste CLSC.

This study is restricted to CLSC adoption barriers in the electronic industry by evaluating 36 sub-barriers grouped into 8 main dimensions related to different members of the supply chain.

Closed-loop adoption barriers have been proposed to understand the crucial barriers to implementation of CLSC in the electronic industry. The cause-and-effect relationship indicates the critical factors to be improved to increase adoption of e-waste CLSC, helping managers and regulatory bodies to mitigate the problem areas.

This study contributes to the literature on CLSC by adopting a multi-criteria decision-making (MCDM) technique which captures the critical barriers of e-waste CLSC adoption in Indian scenario.

The paper proposes multi‐sourcing models for optimal order allocation in newsvendor setting under supply disruption with stochastic demand where suppliers are capacity constrained.

Mathematical models are constructed to describe the stochastic single period two echelon supply chain. We first investigate the uncapacitated suppliers’ problem. Then capacity constraint is included in the model to study the effect on sourcing decision. A numerical example and its solution are included to illustrate the solution procedure. We find the solution using traditional optimization approach, genetic algorithm and simulation optimization approach.

The models capture the impact of disruption risk on optimal sourcing decision. When demand is highly uncertain the order should be place with the lowest cost suppliers in case of uncapacitated problem; whereas, it is to be appropriately split among a set of low‐cost suppliers in case of capacitated problem. Simulation optimization found to be the best solution approach for such problem.

The model is applicable for a single period short‐life cycle product.

The models can be utilized for any number of suppliers. The numerical study illustrates the impact of probability of disruption, its consequences and cost of purchase on sourcing decisions.