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Incorporating sustainable decisions with the retailer’s operational management has attracted enormous significance due to government regulations and customer demand for environmental consciousness. However, incorporating sustainable operations may interfere with the operational performance of the firm and, hence, retail companies need to examine the influence of these operations on overall supply chain efficiency. The present study develops a performance measurement model based on a two-stage network data envelopment analysis (DEA) technique for measuring the joint impact of sustainable operations and operational activities on the business performance of a retail company. A case study of an Indian electronic retail chain is presented to reveal the potentiality and suitability of the proposed models. The novelty of the paper lies in establishing DEA models for an Indian retail chain company and for providing an analytical understanding of the conditions under which the strategic decisions at the operational level successfully support the integration of sustainable operations into the SC management. The results show that the additional sustainable constraints lead to improved operational efficiency of some firms of the retail chain and result in improved business efficiency, while for other firms the integration of sustainable objectives decrease business efficiency. The significance of the study lies in providing efficient target conditions for inefficient retail stores to improve their performance. The findings of the study provide meaningful insights to Indian retailers venturing into sustainable retailing operations for enhancing the operational and business efficiency of the supply chain.

Six Sigma is a widely practiced, systematic, and structured methodology embedded with statistical methods and managerial philosophies for quality improvement by large firms along with small firms in the industrialized economies. Application of Six Sigma in Micro Small Medium Enterprises (MSMEs) in developing economies is limited due to several barriers these firms face. In academic literature, there is unavailability of sufficient evidence of successful implementation of the practice in these firms to encourage and provide a roadmap for its implementation. Thus, there is a need for studies that presents frameworks with illustrative case studies demonstrating the implementation of Six Sigma in MSMEs. In the present times, MSME firms in developing economies are facing competition from world leaders, and thus it has become essential for them to focus on the quality of their manufacturing products and at the same time being productive and efficient. Moreover, in the literature, it is observed that the potential of operations research (OR) methods is not sufficiently explored in Six Sigma studies. OR-based methods and tools should be integrated into Six Sigma implementation frameworks as these methods expand the application portfolio and tackle some of the barriers that limit the application of Six Sigma in small-scale firms. In this paper, a Six Sigma implementation framework for an MSME organization is developed incorporating Multi Criteria Decision Making (MCDM) tool Decision-Making Trial and Evaluation Laboratory, apart from other conventional tools like cause and effect analysis, benefit effort analysis, Pareto analysis, control charts, etc. The proposed framework is illustrated in detail with the help of a real-life case study of an MSME in India. The results obtained after the implementation of this framework signify that the incorporation of MCDM in Six Sigma lead to a significant improvement in the sigma level of the firm despite of unavailability of sufficient resources.

Product recovery, accompanied by cradle to cradle policies from the contemporary supply chain, becomes an essential element in meeting environmental compliance and waste management policies. Incorporation of reverse logistics into the traditional supply chains becomes a complementary factor for efficient product recovery. To begin with product recovery, consumers are encouraged to return their end-of-use/end-of-life products, and the steps of collecting and planning the movement of returned products are crucial decisions. The efficient planning of a cost effective recovery process in reverse logistics requires dealing with the uncertainty underlying in the quantity and quality of the returned products. In this paper, we propose establishing an initial collection point within a permissible radius of the customer zones to overcome some of the issues of uncertainty. The uncertainty in the quantity and quality of the returned products are modelled using fuzzy triangular numbers. To capture the real world conditions of the proposed problem, our model aims at maximizing the profit incurred in the recovery process in an uncertain environment. The model was solved with the help of fuzzy mathematical programming. The model is validated by a company case belonging to the manufacturing of electronic products. To increase the applicability of the product recovery process in the industry, we propose a recovery process for the planning horizon consisting of multi periods and multi products. The outcomes of the proposed model indicate that for the successful realisation of such network, customers need to be legally enforced to return their end of used products in the channels established for value recovery.

Efficient budget allocation between different communication channels is one of the fundamental activities of any media planner. In this paper, we attempt to develop a multi-objective integer linear programming model to determine the optimal schedule of advertisements for a set of multiple products of a firm in a segmented market (influenced by both mass and segment-specific media) over a planning horizon. The objectives are to maximize the gross impressions of advertisements and simultaneously minimize the advertising expenditure. To incorporate continuous changes that occur in the market, the total planning horizon is divided into shorter time periods and decisions for each of the subsequent time periods are taken, bearing in mind the changes that have occurred in the preceding time periods. Based on the gap that exists in the extant literature, we also jointly consider, in our model, the notions of (a) carry-over effect of gross impressions, (b) spectrum effect of mass media on segments, and (c) cross-product effect. The model is solved through a goal programming approach to achieve the best trade-off between the conflicting objectives. Further, the model could be adapted to provide solutions in a wide variety of real-life situations. This is substantiated via a numerical analysis for a firm that advertises products in the Indian market through mass and segment-specific media.