IIM Indore seeks to be a contextually-relevant business school with world-class academic standards that develops socially-conscious managers, leaders and entrepreneurs. IIM Indore is committed to: Excellence in management education, research, and training, Use of contemporary participant-centric pedagogies and teaching methods, A presence in emerging segments of management education.

IIM Indore

Operation Management and Quantitative Techniques

Problem definition: Stockists are the entity in the supply chain who deliver to the millions of small shops that account for over 90% of retail sales in India. The stockist purchases product from the manufacturer, develops retailer interest in carrying the product, solves the logistics problem of delivery to retailers, and manages retail credit and money collection. In return, the stockist is provided assistance by the manufacturer and paid an associated margin that is a percent of the product’s retail price. We observed relatively low distribution margins offered to the stockists in India that was a surprise given infrastructure and financing challenges. We noted that the low margins are enabled by the efficiency in product delivery and demand stimulation induced by the stockist. In this paper, we focus on the analysis of screening contracts offered by manufacturers to stockists. Methodology: We use a principal–agent model structure and model the routing cost, inventory cost, and demand impact of the stockist’s capability. We then use a substitutes or complements relationship between manufacturer assistance and stockist impact to understand the optimal contract structure—i.e., level of assistance and associated retail margin. We describe the economics associated with the stockist, provide data regarding the margins and assistance provided by the manufacturer, and use the model to understand their use in developing an effective supply chain. Results: Our model uses three parameters to show that a manufacture can adjust the level of spatial retailer intensity and thus the impact of the stockist in generating demand, the fraction of high-performing stockists associated with the manufacturer and the nature of the relationship between assistance provided and its impact on the stockist’s performance (i.e., complements or substitutes relationship), to attain superior performance. Academic/practical relevance: We parameterize data from Indian firms in the consumer goods industry to understand the observed assistance provided by the manufacturer to the stockist, and associated margins. We then use our model to understand actions taken by a manufacturer in the Indian market. Managerial implications: Our analysis provides a framework for manufacturers to design screening contracts for stockists who play a vital role in managing the fragmented retail markets in developing countries.

World University root node

Manufacturing and Service Operations Management

Contract Design for the Stockist in Indian Distribution Networks

IIMA Working Papers

Benders decomposition for capacitated multi-period maximal covering location problem with server uncertainty

Missing of partial data is a problem that is prevalent in most of the datasets used for statistical analysis. In this study, we analyzed the missing values in ISBSG R1 2018 dataset and addressed the problem through imputation, a machine learning technique which can increase the availability of data. Additionally, we compare the performance of three imputation methods: Classification and Regression Trees (CART), Polynomial Regression (PR), Predictive Mean Matching (PMM), and Random Forest (RF) applied to ISBSG R1 2018 dataset available from International Standards Benchmarks Group. Through imputation, we were able to increase data availability by four times. We also evaluated the performance of these methods against the original dataset without imputation using an ensemble of Linear Regression, Gradient Boosting, Random Forest, and ANN. Imputation using CART can increase the availability of the overall dataset but only at the loss of some predictive capability of the model. However, CART remains the option of choice to extend the usability of the data by retaining rows that are otherwise removed from the dataset in traditional methods. In our experiments, this approach has been able to increase the usability of the original dataset to 63%, but with 2 to 3% decrease in its overall predictive performance. ©BEIESP.

Scopus

Evaluating imputation methods to improve data availability in a software estimation dataset

Computer simulators are nowadays widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modeling, and manufacturing. One fundamental issue in the study of computer simulators is known as experimental design, that is, how to select the input settings where the computer simulator is run and the corresponding response is collected. Extra care should be taken in the selection process because computer simulators can be computationally expensive to run. The selection shall acknowledge and achieve the goal of the analysis. This article focuses on the goal of producing more accurate prediction which is important for risk assessment and decision making. We propose two new methods of design approaches that sequentially select input settings to achieve this goal. The approaches make novel applications of simultaneous and sequential contour estimations. Numerical examples are employed to demonstrate the effectiveness of the proposed approaches.

Fulltext

ARXIV.ORG

Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator

In this paper, we study the formation of endogenous social storage cloud in a dynamic setting, where rational agents build their data backup connections strategically. We propose a degree-distance-based utility model, which is a combination of benefit and cost functions. The benefit function of an agent captures the expected benefit that the agent obtains by placing its data on others’ storage devices, given the prevailing data loss rate in the network. The cost function of an agent captures the cost that the agent incurs to maintain links in the network. With this utility function, we analyze what network is likely to evolve when agents themselves decide with whom they want to form links and with whom they do not. Further, we analyze which networks are pairwise stable and efficient. We show that for the proposed utility function, there always exists a pairwise stable network, which is also efficient. We show that all pairwise stable networks are efficient, and hence, the price of anarchy is the best that is possible. We also study the effect of link addition and deletion between a pair of agents on their, and others’, closeness and storage availability. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

Games 2019

Formation of stable and efficient social storage cloud

MSC Codes 60G55, 60G51The Poisson process of order i is a weighted sum of independent Poisson processes and is used to model the flow of clients in different services. In the paper below we study some extensions of this process, for different forms of the weights and also with the time-changed versions, with Bern?stein subordinator playing the role of time. We focus on the analysis of hitting times of these processes obtaining sometimes explicit distributions. Since all the processes examined display a similar structure with multiple upward jumps sometimes they can skip all states with positive probability even on infinitely long time span.

arXiv

Superposition of time-changed poisson processes and their hitting times

This paper presents the salient aspects of the performance analysis of two mechanisms for shop floor control namely, order review/release (ORR) and dispatching rules in an assembly job shop system in a multi-objective environment. A simulation model of an assembly job shop is developed for experimentation with eight dispatching rules and four ORR mechanisms from the literature. An ORR policy based on the concept of backward scheduling and interval release is developed and incorporated in the simulation model. Grey relational analysis is used for ranking the ORR policy-dispatching rule combinations. Multivariate and univariate hypotheses are developed. The results are analysed using ANOVA and MANOVA. The analysis of results reveals that integrating dispatching rules with ORR policies provides better system performance than using dispatching rules alone. The proposed ORR policy namely, backward scheduling with interval release policy provides better results than other ORR polices.

International Journal of Industrial and Systems Engineering

Scheduling an assembly job shop production system with multiple objectives: a simulation study

Cloud computing has become a rapid growing industry that fundamentally expanded the realm of possibilities organizations can accomplish with technology. Moreover to the entire cost minimization, there exist several deliberated goals in deploying cloud systems in the telecom sector. Telecom cloud adoption can create and deliver business value by achieving the present business most proficiently and successfully, generating novel business strategy to provide the present services or generating completely novel businesses. This paper presents a Benefits, Costs, Opportunities and Risks (BOCR) decision model for cloud strategy selection in the telecom sector which helps business leaders to cautiously evaluate their institutions to estimate the level of cloud adoption most closely match, and explains the methodology of how the organizations balance the cloud to generate business value which assures long-term development and benefits. The research reveals that even though there are many decision support model for cloud computing exist, the proposed model reflect the distinctiveness of telecom service providers by considering their distinctive assets and capabilities. Our study contributes to cloud computing business research by providing a holistic decision model for telecom which can guide on the selection of different cloud computing strategies. © BEIESP.

A BOCR decision model for telecom cloud strategy selection

Software Project Managers are often faced with the dilemma of estimating the cost and duration of new projects and enhancement requests. The software industry is relatively new, and surveys conclude that software size and effort estimation is a challenge and needs research solutions. In the last three decades, research has come up with a substantial amount of analysis and study resulting in a variety of software estimation models. Recently, with the onset of Machine Learning and AI related technologies, numerous models have been evidenced that provides better technical accuracy to the problem of software estimation. At the same time, research still evidences that Estimation based on Expert Judgment remains the most widely used methodology. While estimating smaller units of work as in an enhancement or maintenance project is less error-prone, previous research does not report enough accuracy. The objective of this research is to bring a structure to the EJ methodology and bring in consistency and accuracy to an otherwise human-centered and intuition led approach. We employed a lean and straightforward work break down (WBS) to each of the enhancement requests that were put up to a support team for estimation purposes. We evidence that our approach of employing this work-breakdown can bring in two-fold improvement to the estimation process while using expert judgment. This approach also ensures a stable estimation process as evidenced through experiments using Statistical Process Control techniques. We applied our methodology to one of the projects in an IT organization and collected data from two years of operations. Comparing our results with other previous studies, we were able to reduce the error to 25% of the value of this metric, while more than doubling the accuracy of predictionat PRED(25). © BEIESP.

An experiment to improve expert judgment software estimation through work breakdown structure

The goal of this research is to study jointly physician prescription decisions and patient drug request behaviours. We have adopted a binary logit model and a multinomial logit model to study patient drug request data with excessive zero requests and a multinomial logit model to capture physician prescription decisions. These models are further joined by a flexible non‐parametric multivariate distribution for their random effects. We also adopt an analytically consistent expression for interaction effects in our non‐linear and joint modelling framework. We apply our model to a unique physician panel data set from the erectile dysfunction category. Our key empirical findings include that the triggering of drug requests by direct‐to‐consumer advertising (DTCA) is complex with category level DTCA reducing patients’ probabilities of making drug requests and drug‐specific DTCA driving drug requests for the drug advertised, patients’ characteristics may play a role in both the influence of DTCA on drug requests and the influence of patients’ requests on physicians’ prescription decisions, patients’ drug requests have a positive effect on physicians’ prescription decisions and patients can be consistent with physicians in choosing a drug based on their diagnosis levels and some unobserved factors, and there are significant correlations between physician level random effects that drive both patients’ drug requests and physicians’ prescription decisions, which validate the joint modelling approach.