Conference Agenda

Firms such as Walmart, Fabindia and Asket have heavily invested in their supply chains to gain competitive advantage. These investments take the form of technological advancements that help the supplier firm create a more stable supply environment. To gain a deeper understanding of firm behavior and motivations, we explore this relationship from two perspectives namely a market-facing retailer exploring the idea of supplier improvement as a means for mitigating supply risk, and her supplier making a choice of whether to enter this contract.

We propose an investment that the retailer can make to stabilize her supply by improving the suppliers’ capabilities. The buyer, or the retailer, approaches this as an investment problem, wherein she makes investment decisions to improve the quality of supply. We explore this question through mathematical constructs in a newsvendor retailer setting. Through modelling the choice of investment as a decision variable, we establish the benefits for a retailer to invest in her supplier and discuss various market scenarios where they may be beneficial. We simultaneously model the retailers’ optimal order quantity to maximize the expected profit.

Our paper establishes a choice matrix which can be used by procurement managers and makes an important finding for cases of firms facing highly constrained or niche product supply. We find that in such cases where switching costs are high, she is still better off investing in the existing supplier, compared to if she does not make this investment.

Social Media Site Selection for Product promotion in Online Business-

A Hesitant Fuzzy Linguistic Approach.

DevaHarshini Bhavisetty (devaharshinibhavisetty@gmail.com)

Bindusree Velugula (velugulabindusree@gmail.com)

School of Management

Indian Institute of Technology, Mandi, India

The proposed work introduces a new procedure based on Hesitant Fuzzy Sets, that enables a company to identify social media sites to promote its products that will have the maximum effect on the consumers. As the impact of social sites on the buyers is difficult to assess in single numeric or linguistic terms, the proposed work measures the influence of the buyers towards social sites, in Hesitant Fuzzy Linguistic Values (HFLVs). The linguistic term set with respect to buyers visit to the social sites are taken as: {Rare, Infrequent, Occasional, Regular, Frequent, Very Frequent}. Instagram, Facebook, WhatsApp, Telegram, YouTube, TikTok and Snapchat are the social sites considered in our work. The entropy, representing the uncertainties of buyers’ impressions are suitably derived in the paper, after converting the linguistic terms in HFLVs into their numeric counterparts. The reverse of entropy is taken as the levels of self-confidence of the buyers in the social media sites. The proposed work picks the social media sites that provides the maximum buyers reach after accounting buyers’ level of confidence and their frequency of visit. The methodology works under a budget constraints and modelled as a Binary Integer Programming problem.

A pest population grows in a habitat according to a simple deterministic immigration-birth-death process. The rate of the cost caused by the pests is increasing with respect to the population size. The pest population may be controlled by some action that introduces total catastrophes. The length of time until the occurrence of a catastrophe is exponentially distributed. We restrict our attention to the class P of control-limit policies that take controlling action if and only if the pest population size exceeds a critical number. The control-limit policy which minimizes the expected long-run average cost per unit time within the class P is found. The optimal control-limit policy is compared with the optimal control-limit policy that we obtain in the case in which the pest population grows according to the simple stochastic immigration-birth-death process. Numerical results are provided to illustrate the theoretical results.