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

Clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables are presented. As opposed to standard approaches for graph clustering that assume known graph structures, the edge structure of the unknown graph is first estimated using sparse regression based approaches for sparse graph structure learning. Subsequently, graph clustering on the lower dimensional projections of the graph is performed based on Laplacian embeddings using a penalized k-means approach, motivated by Dirichlet process mixture models in Bayesian nonparametrics. In contrast to standard algorithmic approaches for known graphs, the proposed method allows estimation and inference for both graph structure learning and clustering. More importantly, the arguments for Laplacian embeddings as suitable projections for graph clustering are formalized by providing theoretical support for the consistency of the eigenspace of the estimated graph Laplacians. Fast computational algorithms are proposed to scale the method to large number of nodes. Extensive simulations are presented to compare the clustering performance with standard methods. The methods are applied to a novel pan-cancer proteomic data set, and protein networks and clusters are evaluated across multiple different cancer types.

World University root node

Computational Statistics & Data Analysis

Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer

The recent accelerated growth in the computing power has generated popularization of experimentation with dynamic computer models in various physical and engineering applications. Despite the extensive statistical research in computer experiments, most of the focus had been on the theoretical and algorithmic innovations for the design and analysis of computer models with scalar responses. In this article, we propose a computationally efficient statistical emulator for a large-scale dynamic computer simulator (i.e., simulator which gives time series outputs). The main idea is to first find a good local neighborhood for every input location, and then emulate the simulator output via a singular value decomposition (SVD) based Gaussian process (GP) model. We develop a new design criterion for sequentially finding this local neighborhood set of training points. Several test functions and a real-life application have been used to demonstrate the performance of the proposed approach over a naive method of choosing local neighborhood set using the Euclidean distance among design points. The supplementary material, which contains proof of the theoretical results, detailed algorithms, additional simulation results, and R codes, are available online.

Fulltext

Journal of Computational and Graphical Statistics

Local Gaussian Process Model for Large-Scale Dynamic Computer Experiments

Purpose Empowerment of women by social enterprises in emerging economies has received attention from researchers. This paper aims to study three social enterprises in India that aim to empower subsistence women entrepreneurs.Design/methodology/approach A comparative case analysis of three social enterprises is done to develop understanding of systems and processes adopted by them. Sources such as published journal and news articles, case studies, success stories and official websites are used for data collection.Findings The study found that all three organisations strive to increase the asset base of members by organizing disempowered subsistence producers lacking market power; extending services throughout the value chain, i.e. from raw material supply to marketing of products; ploughing back regular incomes and bonuses through mechanisms acceptable to all members; and empowering subsistence producers to gain market power and ability to choose. The interventions give market exposure and voice to the concerns of women producers. These organisations successfully leverage the strength of their relations with civil society and markets in sustaining the business. Women empowerment was found to be achieved through regular income, collective voice, ability to take decisions and creation of social value.Research limitations/implications This paper does not take into consideration enterprises promoted by government.Practical implications A conceptual framework is suggested in this paper along with insights for organisations interested in collectivising subsistence women for empowering them.Originality/value The conceptual framework provided in this paper enriches the understanding of social enterprises working with the objective of women empowerment.

Marketing

Strategic Management

Society and Business Review

Empowering subsistence women entrepreneurs in India: Insights from Lijjat, Mulukanoor and MPWPCL

Using big datasets from four diverse sources, of both suppliers and facilitators or recipients of digital payment platforms, we conduct a comprehensive analysis of the impact of unexpected demonetization in India on payment digitization. The drying-up of cash due to demonetization leads to a significant increase in the level of payment digitization and the rise applies to both traditional and non-traditional digital payment modes, including card and e-wallet. The usage pattern persists even during the re-monetization period when cash makes a comeback in the economy, indicating the success of demonetization in enabling new digitization technologies to make inroads into consumer payment habits. Heterogeneity tests reveals that regions that are underdeveloped economically, lag in financial inclusion and technology infrastructure and have a population with more conservative demographic characteristics experience a weaker digitization response.

SSRN

Demonetization and digitization

Social storage systems [1]–[3] are becoming increasingly popular compared to the existing data backup systems like local, centralized and P2P systems. An endogenously built symmetric social storage model and its aspects like the utility of each agent, bilateral stability, contentment, and efficiency have been extensively discussed in [4]. We include heterogeneity in this model by using the concept of Social Range Matrix from [5]. Now, each agent is concerned about its perceived utility, which is a linear combination of its utility as well as others utilities (depending upon whether the pair are friends, enemies or do not care about each other). We derive conditions when two agents may want to add or delete a link, and provide an algorithm that checks if a bilaterally stable network is possible or not. Finally, we take some special Social Range Matrices and prove that under certain conditions on network parameters, a bilaterally stable network is unique.

2018 10th International Conference on Communication Software and Networks (ICCSN)

Data backup network formation with heterogeneous agents

Revenue management systems (RMS) and customer relationship management (CRM) systems are widely used in today's data enriched environments to improve a firm's profitability. Though these systems are used to improve business profitability, their approach and evolution are different, as a result of which these systems are being used separately. In case of RMS, the focus is on short term tactical revenue optimisation, whereas in case of CRM the focus is on developing long term customer relationships. This paper discusses some ways of enhancing performance of revenue management systems by using CRM data. Synergies and conflicts that may arise with the simultaneous use of these systems are discussed, and numerous applications that help in enhancing RMS performance are suggested.

International Journal of Business Information Systems

Enhancing revenue management systems performance with CRM systems data

PurposeThe purpose of this paper is to present the simulation model for manpower planning in electrical maintenance service facility and evaluates different scenarios to improve resource utilization while meeting the desired service level.Design/methodology/approachThe process systematically maps entire system of electrical fault rectification, identifies probability distributions of demand of electrical maintenance requests and its process times using historical data. The simulation software Arena was used to model the entire system and various possible improvements were evaluated to assess performance of maintenance service facility.FindingsThe simulation results obtained for the proposed changes in the system indicated the potential improvement in resource utilization while meeting the average waiting time expectations of customers.Practical implicationsThe proposed simulation model can help maintenance people to decide the optimum number of resources to meet the agreed performance level that is expected by various stakeholders.Originality/valueThe paper considers the computer simulation in modeling complex real-life system for understanding the resource requirement of electrical fault maintenance facility to improve resource utilization while meeting the desired service level.

Business Process Management Journal

Simulation modeling for manpower planning in electrical maintenance service facility

This paper proposes a new mathematical model for locating k-obnoxious facilities Here a vertex covering k-obnoxious facilities location problem on a plane has been modelled by combining it as a combination of three interacting criteria as follows: 1) maximise the minimum distances between any existing point and any variable point as well as between any pair of variable points; 2) maximise the number of existing points covered by various respective variable points; 3) minimise the overall importance of the various existing points associated to the various variable facility points. A nonlinear programming iterative algorithm for locating k-obnoxious facilities has been developed. The algorithm developed here is a generalised iterative algorithm. It has been proved that the optimal solution obtained by the algorithm is the non-dominated solution of the original multi-objective problem. Rectilinear distance norm has been considered as the distance measure as it is more appropriate to the various realistic situations.

International Journal of Operational Research

A mathematical model for locating k-obnoxious facilities on a plane

Revenue management (RM) systems help firms in enhancing their revenues by allocating capacity to those customers who value it the most. The revenue enhancement is largely affected by demand, competition, seasonality and other external factors. It is necessary to isolate the effect of such external factors while measuring the performance of RM systems. This paper discusses different ways of measuring performance of RM systems by reviewing the relevant literature, and it discusses the merits and demerits of the currently used performance metrics. It provides a framework for developing performance measurement metrics, and describes how these measures can be used to create a dashboard for monitoring performance of RM systems. The proposed framework considers the perspective of the RM decision makers and customers, and evaluates performance of the RM systems for the forecasting, inventory control and overbooking stages of revenue management.

International Journal of Business Excellence

Performance measurement of revenue management systems

Commonly used methods of evaluating the impact of marketing communications during political elections struggle to account for respondents&rsquo; exposures to these communications due to the problems associated with recall bias. In addition, they completely fail to account for the impact of mediated or earned communications, such as newspaper articles or television news, that are typically not within the control of the advertising party, nor are they effectively able to monitor consumers&rsquo; perceptual responses over time. This study based on a new data collection technique using cell-phone text messaging (called real-time experience tracking or RET) offers the potential to address these weaknesses. We propose an RET-based model of the impact of communications and apply it to a unique choice situation: voting behavior during the 2010 UK general election, which was dominated by three political parties. We develop a Bayesian zero-inflated dynamic multinomial choice model that enables the joint modeling of: the interplay and dynamics associated with the individual voter's choice intentions over time, actual vote, and the heterogeneity in the exposure to marketing communications over time. Results reveal the differential impact over time of paid and earned media, demonstrate a synergy between the two, and show the particular importance of exposure valence and not just frequency, contrary to the predominant practitioner emphasis on share-of-voice metrics. Results also suggest that while earned media have a reducing impact on voting intentions as the final choice approaches, their valence continues to influence the final vote: a difference between drivers of intentions and behavior that implies that exposure valence remains critically important close to the final brand choice. Supplementary materials for this article are available online.