Sohoni, Milind
Sohoni, Milind
Professor
Deputy Dean - Faculty Development & Registrar's Office
Area Operations Management
Affiliation Indian School of Business
Email milind_sohoni@isb.edu
SSRN ID
385054
Personal Webpage https://sites.google.com/a/isb.edu/milind_g_sohoni/home
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Milind Sohoni is a Professor of Operations Management at the Indian School of Business (ISB), Hyderabad, India. He also serves as the Deputy Dean, Faculty Development. His other academic appointments include visiting positions at the Kellogg School of Management, Northwestern University, IL.
Professor Sohoni’s research interests span large-scale stochastic airline optimization, supply chain analytics, healthcare delivery and nonprofit operations management in resource constrained settings. His areas of expertise include large-scale computational optimization, stochastic network design, combinatorial optimization, empirical modeling, and game theoretic analysis. He has published several research articles in globally recognized, top-tier, academic journals. He works with non-profit, and private, organizations on research projects and has received major grants for his research work. He has also received excellence in research awards from academic and practitioner societies. He serves as an associate editor for leading academic journals in operations management.
Professor Sohoni teaches courses on supply chain management, decision models and optimization, revenue management and pricing, and decision making under uncertainty.
Professor Sohoni has several years of corporate experience in consulting and research. Prior to joining ISB, he was a research advisor with the R&D group at Delta Technology, a wholly owned subsidiary of Delta Air Lines, Atlanta, GA. Prior to that, he worked with the Advanced Decision Support Systems group at AT&T Bell Labs in Holmdel, New Jersey, and Ryder Dedicated Logistics, Miami, Florida.
Professor Sohoni holds a Ph.D. in Operations Research from the School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA.
Professor Sohoni’s research interests span large-scale stochastic airline optimization, supply chain analytics, healthcare delivery and nonprofit operations management in resource constrained settings. His areas of expertise include large-scale computational optimization, stochastic network design, combinatorial optimization, empirical modeling, and game theoretic analysis. He has published several research articles in globally recognized, top-tier, academic journals. He works with non-profit, and private, organizations on research projects and has received major grants for his research work. He has also received excellence in research awards from academic and practitioner societies. He serves as an associate editor for leading academic journals in operations management.
Professor Sohoni teaches courses on supply chain management, decision models and optimization, revenue management and pricing, and decision making under uncertainty.
Professor Sohoni has several years of corporate experience in consulting and research. Prior to joining ISB, he was a research advisor with the R&D group at Delta Technology, a wholly owned subsidiary of Delta Air Lines, Atlanta, GA. Prior to that, he worked with the Advanced Decision Support Systems group at AT&T Bell Labs in Holmdel, New Jersey, and Ryder Dedicated Logistics, Miami, Florida.
Professor Sohoni holds a Ph.D. in Operations Research from the School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA.
Ph D (Operations Research), School of Industrial and Systems Engineering, The Georgia Institute of Technology
MS (Manufacturing Systems Engineering), The University of Texas at Austin, Texas
B E (Mechanical Engineering), College of Engineering Pune
Published Papers
Bala, Ram.,Kunnumkal, Sumit M.,Sohoni, Milind G. (2016) "Evergreening and Operational Risk Under Price Competition", Naval Research Logisitcs, 63 (1)Read Abstract >Close >“Evergreening” is a strategy wherein an innovative pharmaceutical firm introduces an upgrade of its current product when the patent on this product expires. The upgrade is introduced with a new patent and is designed to counter competition from generic manufacturers that seek to imitate the firm's existing product. However, this process is fraught with uncertainty because the upgrade is subject to stringent guidelines and faces approval risk. Thus, an incumbent firm has to make an upfront production capacity investment without clarity on whether the upgrade will reach the market. This uncertainty may also affect the capacity investment of a competing manufacturer who introduces a generic version of the incumbent's existing product but whose market demand depends on the success or failure of the upgrade. We analyze a game where capacity investment occurs before uncertainty resolution and firms compete on prices thereafter. Capacity considerations that arise due to demand uncertainty introduce new factors into the evergreening decision. Equilibrium analysis reveals that the upgrade's estimated approval probability needs to exceed a threshold for the incumbent to invest in evergreening. This threshold for evergreening increases as the intensity of competition in the generic market increases. If evergreening is optimal, the incumbent's capacity investment is either decreasing or nonmonotonic with respect to low end market competition depending on whether the level of product improvement in the upgrade is low or high. If the entrant faces a capacity constraint, then the probability threshold for evergreening is higher than the case where the entrant is not capacity constrained. Finally, by incorporating the risk-return trade-off that the incumbent faces in terms of the level of product improvement versus the upgrade success probability, we can characterize policy for a regulator. We show that the introduction of capacity considerations may maximize market coverage and/or social surplus at incremental levels of product improvement in the upgrade. This is contrary to the prevalent view of regulators who seek to curtail evergreening involving incremental product improvement.
Devalkar, Sripad K.,Sohoni, Milind G.,Arora, Priyank. (Forthcoming) "Ex-post funding: How should a resource constrained non-profit organization allocate its funds?", Production and Operations ManagementRead Abstract >Close >We study the funds allocation problem for a resource-constrained non-profit organization (NPO) that implements social development projects for public good. In addition to raising funds from donors who contribute prior to project implementation (``traditional donors''), the NPO uses a novel approach, which we term as the ``ex-post funding'' approach, to also raise funds from donors who contribute based on the results delivered by the NPO (``ex-post donors''). In this approach, the NPO uses its initial funds to implement early phases of the project, creates ``results-certificates'' from the completed phases, and invites ex-post donors to purchase these certificates. The donations raised from selling the results-certificates are used to recover the NPO's own funds used in the project implementation. Operationalizing this approach is complicated when the project must incur a large fixed cost before any benefits are delivered by the project and the total benefit delivered is time sensitive. We show that for a given amount of initial funds available, there exists a threshold amount of funds that the NPO should raise from traditional donors before implementing the project phases so as to maximize the total expected benefit delivered. Through numerical studies, we analyze how the threshold of funds raised from traditional donors and the total benefit delivered vary with donor characteristics such as donor willingness to give and the proportion of donors who contribute prior to project implementation. Our numerical studies suggest that even with relatively small amount of initial funds, the NPO can deliver substantially higher benefit by using the ex-post funding approach when compared to using a traditional approach that requires the NPO to raise all the funds required upfront.
Deo, Sarang.,Sohoni, Milind G. (2015) "Optimal Decentralization of Early Infant Diagnosis of HIV in Resource-Limited Settings.", M&SOM, 17 (2), 191-207Read Abstract >Close >Unavailability of appropriate diagnostic capability is a major constraint in scaling up HIV early infant diagnosis (EID) programs in resource-limited countries. Due to the complexity of the existing diagnostic technology, most EID networks are highly centralized with a few laboratories serving a large number of health facilities. This leads to long diagnostic delays and consequent failure of patients to collect results in a timely manner. Several point-of-care (POC) devices that provide rapid diagnosis within the health facilities are being developed to mitigate these drawbacks of the centralized EID networks. We argue that the decision of which facilities should receive the POC device (the placement plan) is critical to maximizing their public health impact in the presence of tight budget constraints. To formalize this argument, we develop an operational queueing network submodel that quantifies the impact of POC placement decision on the diagnostic delay and link it to a patient behavior submodel that quantifies the impact of diagnostic delay on the likelihood of result collection. We embed these two submodels within an optimization model that maximizes the number of patients receiving results, which has the structure of a nonlinear, nonseparable knapsack problem and is not amenable to exact analysis. Hence, we adopt a two-pronged solution approach. First, we approximate the patient behavior submodel with a piecewise linear relationship between the average diagnostic delay at a health facility and the fraction of results collected at that facility. We also approximate the operational dynamics using extant results on queueing networks with batched service and superposition of arrival streams. In addition, we use auxiliary variables and constraints to linearize the approximate formulation and use it to derive an ``optimal'' placement solution. Second, we develop a computational model by combining a detailed discrete event simulation of the exact operational dynamics with a Monte Carlo simulation of the exact patient behavior. We calibrate the computational model with data from the EID program in an East African country and evaluate the impact of the optimal allocation described above and two thumb rules that have practical appeal. We find that the optimal allocation can result in up to 30% more patients collecting their results compared to the thumb rules. A thumb rule that allocates POC devices to highest volume health facilities performs well if the accuracy of the POC device is sufficiently high and if patients are not very sensitive to delay. In contrast, a thumb rule of allocating POC devices to minimize average diagnostic delay in the network performs well if patients are very sensitive to delay. Finally, we show that the effectiveness of POC devices is much higher than other conventional interventions such as increased laboratory capacity, reduced transportation delay, and more regularized transport that are aimed at improving the laboratory network operations.
Jain, Aditya.,Sohoni, Milind G. (2015) "Should Firms Conceal Information When Dealing With Common Suppliers", Naval Research Logistics, 62 (1), 1-15Read Abstract >Close >A firm making quantity decision under uncertainty loses profit if its private information is leaked to competitors. Outsourcing increases this risk as a third party supplier may leak information for its own benefit. The firm may choose to conceal information from the competitors by entering in a confidentiality agreement with the supplier. This, however, diminishes the firm’s ability to dampen competition by signaling a higher quantity commitment. We examine this trade-off in a stylized supply chain in which two firms, endowed with private demand information, order sequentially from a common supplier, and engage in differentiated quantity competition. In our model, the supplier can set different wholesale prices for firms, and the second-mover firm could be better informed. Contrary to what is expected, information concealment is not always beneficial to the first mover. We characterize conditions under which the first mover firm will not prefer concealing information. We show that this depends on the relative informativeness of the second mover and is moderated by competition intensity. We examine the supplier’s incentive in participating in information concealment, and develop a contract that enables it for wider set of parameter values. We extend our analysis to examine firms’ incentive to improve information.
Sohoni, Milind G.,Deshpande, Vinayak.,Arikan, Mazhar. (2012) "Building Reliable Air-Travel Infrastructure Using Empirical Data and Stochastic Models of Airline Networks", Operations Research, 61 (1), 45-64Read Abstract >Close >Flight delays have been a growing issue and they have reached an all-time high in recent years, with the airlines’ on-time performance at its worst level in 2007 since 1995. A recent report by the Joint Economic Committee of the US Congress (Schumer and Maloney 2008) has estimated that the total cost to the US economy due to flight delays was as much as $41 billion in 2007. The goal of this paper is to build stochastic models of airline networks and utilize publicly available data to answer the following policy questions: Which network based passenger-centric metrics could be used by the FAA to measure on-time performance and schedule robustness? Which are the bottleneck airports in the US air-travel infrastructure (i.e., airports that cause most delay propagation)? How would increasing airport capacity at these airports alleviate delay propagation? Which airlines have the least robust schedules? How could these schedules be made more robust? Which flight in an aircraft rotation is a bottleneck flight (and, hence, deserves managerial attention)? Flight delays are typically attributed to two factors: (i) The randomness in the intrinsic travel time for a scheduled flight (which is the travel time excluding propagated delays), and (ii) the propagation of this randomness through the air-travel network and infrastructure. We model both of these factors that cause travel delays. The contribution of this paper is two-fold. First, we develop stochastic models, using empirical data, to analyze the propagation of delays through air-transportation networks. Our stochastic models allow us to develop three important robustness measures for airline networks. Second, our analysis enables us to make policy recommendations regarding managing bottleneck resources in the air-travel infrastructure, which, if addressed, could lead to a significant improvement in air-travel reliability.
Sohoni, Milind G.,Chopra, Sunil.,Mohan, Usha.,Sendil, Nuri. (2011) "Threshold Incentives and Sales Variance", Production and Operations Management, 20 (4), 571 – 586Read Abstract >Close >In this article, we study threshold-based sales-force incentives and their impact on a dealer’s optimal effort. A phenomenon, observed in practice, is that the dealer exerts a large effort toward the end of the incentive period to boost sales and reach the threshold to make additional profits. In the literature, the resulting last-period sales spike is sometimes called the hockey stick phenomenon (HSP). In this article, we show that the manufacturer’s choice of the incentive parameters and the underlying demand uncertainty affect the dealer’s optimal effort choice. This results in the sales HSP over multiple time periods even when there is a cost associated with waiting. We then show that, by linking the threshold to a correlated market signal, the HSP can be regulated. We also characterize the variance of the total sales across all the periods and demonstrate conditions under the sales variance can be reduced.
Sohoni, Milind G.,Klabjan, Diego.,Lee, Yu-Ching. (2011) "Robust Airline Scheduling under Block Time Uncertainty", Transportation Science, 45 (4), 451-464Read Abstract >Close >Airline schedule development continues to remain one of the most challenging planning activity for any airline. An airline schedule comprises of a list of flights and specifies the origin, destination, scheduled departure, and arrival time of each flight in the airline's network. A critical component of the schedule development activity is the choice of flight block-times, which depend on several factors. Many airlines decide schedule block-times based on fixed percentiles of block-time distributions built from historical data, however, such techniques have not resulted in significantly improved on-time performance of the schedule during operations. Thus, from a passenger's perspective, the service level guarantee of an airline's network continues to be low. We first define two service level metrics for an airline schedule. The first one is similar to the on-time performance measure of the U.S. Department of Transportation and we define it as the flight service level. The second metric, called the network service level, is geared towards completion of passenger itineraries. We then develop a stochastic integer programming formulation that optimally perturbs a given schedule to maximize expected profit while ensuring the two service levels. We also develop a variant of this model that maximizes service levels while achieving desired network profitability. To solve these models we develop an efficient algorithm that guarantees optimality. Through extensive computational experiments, using real-world data, we demonstrate that our models and algorithms are efficient and achieve the desired trade-off between service level and profitability.
Sohoni, Milind G.,Seshadri, Sridhar.,Jain, Aditya. (2011) "A Differential Pricing Mechanism for Information Sharing Under Competition", Production and Operations Management, 20 (2), 235-252Read Abstract >Close >In this paper we consider a two-echelon supply chain in which downstream retailers engage in Cournot competition. Each retailer is endowed with private information about the uncertain demand intercept. The manufacturer is the Stackelberg leader who sets the contract terms with the retailers, and benefits from retailers sharing their private information. Under Cournot competition we show that the manufacturer is unable achieve full and truthful information sharing in equilibrium using a single wholesale price. We then derive a wholesale pricing scheme by relaxing the single wholesale pricing constraint. The proposed differential pricing mechanism rewards (penalizes) a retailer for providing optimistic (pessimistic) information by allowing the manufacturer to price discriminate among retailers based on their shared information. We show that under such a pricing scheme a retailer is better o® by sharing information in all circumstances, i.e., truthful information sharing is a dominant strategy for all retailers. Furthermore, the manufacturer's profit, as well as the supply chain surplus, increase when all the retailers share information. Finally, we argue that the unique pure strategy Nash equilibrium is characterized by all retailers revealing their information under the proposed differential pricing mechanism
Sohoni, Milind G.,Sunil Chopra, Achal Bassamboo, Usha Mohan, Nuri Sendil. (2010) "Threshold Incentives over Multiple Time Periods and the Sales Hockey Stick", Naval Research Logistics, 57 (6), 503-518Read Abstract >Close >In this article, we study threshold-based sales-force incentives and their impact on a dealer’s optimal effort. A phenomenon, observed in practice, is that the dealer exerts a large effort toward the end of the incentive period to boost sales and reach the threshold to make additional profits. In the literature, the resulting last-period sales spike is sometimes called the hockey stick phenomenon (HSP). In this article, we show that the manufacturer’s choice of the incentive parameters and the underlying demand uncertainty affect the dealer’s optimal effort choice. This results in the sales HSP over multiple time periods even when there is a cost associated with waiting. We then show that, by linking the threshold to a correlated market signal, the HSP can be regulated. We also characterize the variance of the total sales across all the periods and demonstrate conditions under the sales variance can be reduced.
Xu, Jiefeng.,Sohoni, Milind G.,Lim, Alvin. (2008) "Solving the hierarchical data selection problem arising in Airline Revenue Management", International Journal of Revenue Management, 2 (1), 63-77Read Abstract >Close >In this paper, we describe an important optimisation problem arising in airline revenue management systems. The problem is to select the maximum number of average fare data while keeping the selected data in a non-increasing hierarchical order. We first formulate the problem mathematically using 0–1 binary integer programming, and then further derive a stronger formulation using clique cuts. Moreover, an extension of the problem is studied where the relative importance of each data point can be derived from passenger count information. We develop an efficient dynamic programming-based algorithm to solve the problem optimally. The preliminary computational results using real airline data show that our approach can solve the problem efficiently and save significantly much information that are previously discarded.
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Courses Taught
Logistics and Supply Chain Management
Decision Making under Uncertainty
Decision Models & Optimization
Optimization
Operations Management
Optimization - A
Operations 2 - Optimization
Operations Management 1 - Simulation
Revenue Management and Network Optimisation
Introduction to Management Science
Sohoni, Milind (2011), Decentralization of Resource Constrained Networks, Pharma News Weekly, December 31, 2011
Sohoni, Milind (2011), Using Operations Management to Improve Healthcare Access, ISBInsight, October 31, 2011
Sohoni, Milind (2008), Are Private Airports in India Ready to Take Off?, India Knowledge @ Wharton, April 30, 2008
Sohoni, Milind (2007), Information, Incentives, and Supply Chain Contracts, ISB Insight, December 31, 2007