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

https://arxiv.org/abs/2102.

Horseshoe shrinkage methods for Bayesian fusion estimation

Purpose: The purpose of this paper is to investigate the complex interrelationships among the key constructs, supply chain orientation (SCO), supply chain integration (SCI) and supply chain performance (SCP) in Indian manufacturing industries. These relationships have been studied using the relational view (RV) and the knowledge-based view (KBV) theoretical perspectives. Design/methodology/approach: The conceptual model was derived from the existing body of knowledge in the supply chain domain. The study is based on a sample size of 122 data collected via face-to-face meetings with the Indian manufacturers using well-established scales. The covariance-based structural equation modeling was used to test the proposed hypotheses. Findings: In Indian manufacturing and supply chains, SCO has a positive relationship with SCI and SCP. Moreover, the direct impact of SCO on SCP diminishes when SCI is used as a mediating variable. This study also observes positive impact of: i) SCO on SCP, ii) SCI on SCP and iii) discovery of mediating role of SCI on SCP under the theoretical lenses of RV and KBV. Research limitations/implications: Cross-sectional survey of manufacturing firms of one country (using one response per firm) calls for validation covering other parts of the world and demands a longitudinal survey. This research will trigger more scholarly, practice and policy debate among researchers studying Indian and emerging economies context. Practical implications: The notion of a holistic view of the SC with a focus on improving the customer value can enhance strategic partnerships among the SC partners (i.e. SCI) and overall SCP. Firms should make efforts to include SCI in SC designs to successfully transform SCO into SCP. Originality/value: The originality of the research lies in studying the complex interrelationships among key concepts of SC in a unique Indian manufacturing context. The Indian supply chains operate in a set of unique characteristics, which have been detailed out in this paper. This paper not only establishes the mediating role of SCI for overall SCP in emerging economies but also enhances the scholarly knowledge in the SC domain. Most studies report SCO as a single-order construct, measured by scales comprising of only few items. The second-order SCO measures in this study bring credibility to the findings. Additionally, it contributes to both academicians and practitioners alike in the context of an integrated SC in emerging economies. © 2021, Emerald Publishing Limited.

World University root node

Journal of Knowledge Management

Linking supply chain integration to supply chain orientation and performance – a knowledge integration perspective from Indian manufacturing industries

Modeling the dynamics of COVID-19 pandemic spread is a challenging and relevant problem. Established models for the epidemic spread such as compartmental epidemiological models e.g. Susceptible-Infected-Recovered (SIR) models and its variants, have been discussed extensively in the literature and utilized to forecast the growth of the pandemic across different hot-spots in the world. The standard formulations of SIR models rely upon summary-level data, which may not be able to fully capture the complete dynamics of the pandemic growth. Since the disease spreads from carriers to susceptible individuals via some form of contact, it inherently relies upon a network of individuals for its growth, with edges established via direct interaction, such as shared physical proximity. Using individual-level COVID-19 data from the early days (January 30 to April 15, 2020) of the pandemic in India and under a network-based SIR model framework, we performed state-specific forecasting under multiple scenarios characterized by the basic reproduction number of COVID-19 across 34 Indian states and union territories. We validated our short-term projections using observed case counts and the long-term projections using national sero-survey findings. Based on healthcare availability data, we also performed projections to assess the burdens on the infrastructure along the spectrum of the pandemic growth. We have developed an interactive dashboard summarizing our results. Our predictions successfully identified the initial hot-spots of India such as Maharashtra and Delhi and those that emerged later, such as Madhya Pradesh and Kerala. These models have the potential to inform appropriate policies for isolation and mitigation strategies to contain the pandemic, through a phased approach by appropriate resource prioritization and allocation.Competing Interest StatementThe authors have declared no competing interest.Funding StatementS.B. is supported by DST INSPIRE Faculty Award Grant No. 04/2015/002165 and also by IIM Indore Young Faculty Research Chair Award grant. S.M. was partially supported by Precision Health at the University of Michigan. V.B. was supported by NIH grants R01-CA160736 and P30 CA 46592 and NSF DMS grant 1463233 and start-up funds from the University of Michigan School of Public Health. Author DeclarationsI confirm all relevant ethical guidelines have been followed and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:NAAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesIndividual-level, location-informed COVID-19 data for Indian states and union territories were obtained from the Kaggle database available at \urlhttps://www.kaggle.com/sudalairajkumar/covid19-in-india. Data on state-wide daily infections is available from \urlhttps://api.covid19india.org/. https://www.kaggle.com/sudalairajkumar/covid19-in-india https://api.covid19india.org/ https://bayesrx.shinyapps.io/COV-N/

medRxiv

Network-based Modeling of COVID-19 Dynamics: Early Pandemic Spread in India

Contract renewal is critical to maintaining a company’s recurring revenue source. Therefore, there is a significant emphasis on setting up an efficient process for renewal. In this study, a machine learning technique was followed to improve contract renewal rates. In addition to this, key factors affecting renewal rates were also studied in detail. The solution presented in this study used an unsupervised machine learning technique to segment high-risk resellers with relatively lower probability of renewal, which was further actioned upon by a proactive contact strategy soliciting a contract renewal. This solution was tested and monitored for a period of three quarters. It resulted in an incremental improvement in the renewal rate for the company. As part of the implementation, a user interface application was also developed, which enabled the sales specialist to list and contact high-risk (or underperformer) resellers quarter-on-quarter.

Journal of Business Analytics

An integrated approach to renew software contract using machine learning

The health system is intricate due to its dynamic nature and critical service requirements. The involvement of multiple layers of health service providers quadrupled this complexity and results in a complicated operating environment. Simulation is often considered an apt technique to model and study complex systems in the literature. The popularity of simulation in the healthcare domain had only accelerated with time and resulted in a large number of articles intended to solve myriad healthcare problems. This article analyzes healthcare simulation literature of the past decade (2007--2016) that addresses operations management issues in various healthcare service delivery levels and categorizes the literature accordingly. In the next step, we attempt to assimilate the entire literature to capture specific health issues addressed, operations management concepts applied, and simulation methods used, and identify major research gaps. Finally, we develop the research agenda from dividing these gaps into the contextual, conceptual, and methodological genre that is consistent with the previous state-of-the-art literature reviews in operations management. Furthermore, this article demonstrates other minute aspects such as “sources of funding” and “tools used for the research” to maintain coherence with the previous reviews in the healthcare simulation. The objective of this work is twofold: to connect the knowledge continuum to the present, and to provide potential research directions for future academicians.

ACM Transactions on Modeling and Computer Simulation

Application of Simulation in Healthcare Service Operations: A Review and Research Agenda

We examine the implications of vertical augmentation – delegation and strategic decentralization – by involving an intermediary in a supply network of competing firms with limited supply capacity offering multiple product brands. We identify (i) the conditions for the competing firms to characterize their single- versus multi-product incentives, and (ii) the conditions for network sustainability. We develop a game-theoretic model to analyze equilibrium outcomes for competing firms in a decentralized network where the firms respond to horizontal and vertical competition in a multi-product setting. In examining the role of an intermediary in the network, we provide insights into the network setting from the perspectives of (i) consumer valuation for the firms’ products, (ii) size disparity between the network firms, (iii) the intermediary’s efforts in marketing and brand-building, and (iv) the network firms’ mandate for the intermediary. We show that vertical augmentation can be a viable alternative to manage the competing firms’ multi-product incentives, provided a suitable mandate is given to the intermediary. We highlight the importance of the marketing agent in managing the network firms’ multi-product incentives favorably when the firms’ supply capacities are quite disparate, and the consumer valuation for the shared product of the network is relatively high. Our work explores an alternate business model that is relevant for empowering small and micro-entrepreneurs in developing countries.

European Journal of Operational Research

Multi-product supply networks: Implications of intermediaries

Doctoral Colloquium on Management and Development (DOCMAD) 2020

Essays on Order Fulfilment and Inventory Management in Omnichannel Retailing

Multi-modal transportation takes advantage of multiple transport modes and can be an effective way to ease the negative environmental effects of freight transportation. This paper addresses a multi-modal distribution network design problem with the aim of balancing the trade-off between economic and environmental benefits. The distribution network that we consider includes both transportation with trucks and ships. We propose a new mixed integer programming (MIP) model which decides the optimal design of the system, i.e. how many ships of each type to use, their corresponding routes and sailing speeds. It also suggests an optimal cargo flow through the maritime and road-based network. We show how the MIP model can be used to provide decision support through a case study from the distribution of automobiles in India, which has a vast coastline that can be used for maritime transportation. Environmental emissions from automobile transportation in India have been rapidly increasing in recent years with the fast economic development, and it has become a major contributor to regional air pollution and road congestion. One possible way to ease these negative environmental consequences is to consider a modal shift where some of the automobile transportation from the production facilities located close to the coastline is replaced by roll-on roll-off (RoRo) ships. Our study shows that multi-modal distribution is both environmentally friendly and economically beneficial, especially if more industrial players can collaborate to create economies of scale. © 2020 The Authors

Fulltext

Transportation Research Part E: Logistics and Transportation Review

Design of a sustainable maritime multi-modal distribution network – Case study from automotive logistics

This paper empirically examines the mediating role of shopping satisfaction between electronic logistics service quality (e-LSQ) and repurchase intention. Further, this paper investigates the moderating role of gender, payment options, and returns or replacement experience on the link between e-LSQ and shopping satisfaction (and repurchase intention). Empirical data comprising 640 Indian online shoppers are analyzed with covariance-based structural equation modeling. The findings indicated that the condition of the shipment is the most crucial e-LSQ dimension and its linkage with shopping satisfaction varies across payment options, gender, and returning experience. The poor condition of the shipment might have triggered the returns in e-tailing in the Indian context. The findings will help e-tail managers design a robust logistics network to retain and win despondent customers.

Journal of Retailing and Consumer Services

Electronic logistics service quality and repurchase intention in e-tailing: Catalytic role of shopping satisfaction, payment options, gender and returning experience

In this article we consider the analysis of progressively censored competing risks data obtained from a simple step‐stress experiment. It is assumed that there are only two competing causes of failures at each stress level and the lifetime distribution of each one of them is one parameter exponential distribution. Based on the cumulative exposure model assumption, the conditional maximum likelihood estimators (MLEs) of the unknown parameters can be obtained in explicit forms. Confidence intervals of the unknown parameters based on the exact distributions of the conditional MLEs and percentile bootstrap method, are constructed. Further we obtain Bayes estimates and the associated credible intervals based on a very flexible Beta‐gamma prior on the unknown parameters. A simulation experiment has been performed to observe the performances of the different estimators.

Statistica Neerlandica

<a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/stan.12226">Analysis of progressive Type‐II censoring in presence of competing risk data under step stress modeling</a>