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Dr. Jigar Patel


Assistant Professor | Information Management & Business Analytics | Feliciano School of Business

Email: patelji@mail.montclair.edu

Telephone: 9736553513

Office: Feliciano School of Business, 482

Biography

Dr. Jigar Patel has his undergraduate training in Mathematics and Computer science. After undergraduate, he entered in the PhD program of Operations Management at the Stern School of Business at the NYU. I obtained my PhD in September 2014, after which he joined the Information Management and Business Analytics department in the Business School at the MSU

Areas of Expertise

Operations Management, Optimization, Game theory, Business analytics

Education

  • Ph D, Operations Management, 2014, Leonard Stern School of Business at NYU
  • Masters of Philosophy (MPhil), Operations Management, 2012, Leonard Stern School of Business at NYU
  • Bachelor of Science with Honors, Mathematics and Computer Science, 2008, Chennai Mathematical Institute

Refereed Publications

  • Lobel I., Patel J., Vulcano G., Zhang J., (2016). Optimizing Product Launches in the Presence of Strategic Consumers. Catonsville, MD. Management Science.62 (6). (pp. 1778-1799).

Awards and Honors

  • Doctoral Fellowship, NYU Stern (2008)
    Research Fellowship to pursue Doctoral studies in Operations management at the Stern School of NYU
  • Selected for fellowship in Chennai Mathematical Institute, (2005)
    Fellowship for undergraduate studies in Mathematics and Computer Science

Research

  • A major factor in managing innovation is the role of competition. A firm is always under the pressure to outperform a competitor's product, the pressure is intensified due to shortened product life cycles and uncertain nature of technology evolution.
    A firm's innovation strategy depends upon how the competitor is innovating its products; if the competitor is aggressive in its investment in R\&D capabilities, the firm would also feel compelled to innovate efficiently to retain market share and get a comparable product in the market in a timely manner. Often the investment decisions in R\&D and the resulting technological breakthrough for a product are not easily accessible until the finished product is launched in the market. So firms need to strategize their innovation such that they remain competitive in the market; and they have to make decisions not knowing the competitor's strategy fully. We can see this kind of competitive market environment among many firms in the technology sector. For example, Intel and AMD competing for microprocessors, Apple and Microsoft in the personal computer software domain, various automobile manufacturers competing for producing better models over time, firms competing for launching better smartphones.


    We look at two competing firms that manage their product innovation to capture market share. The firms cannot observe each others' actions. The technology level of a product is a positive integer, and the technology process is modeled as a Poisson process whose intensity is the effort level of each firm; there is a proportional cost associated with the effort. Each firm competes for market share. The market share of each firm is proportional to the difference between the technology level of the two competing products. A higher technology level difference implies a higher market share for the firm that has the better product. Firms make their effort decisions based on the state of the existing technology difference between products, in other words, we look at the Markov perfect equilibria of the game played between the two firms.


    We analyse the types of equilibria that exist under symmetric and asymmetric cost structures. We find that multiple equilibria exist for the game depending upon the parameters. Some equilibria enable firms to maintain a constant market share over time which allows both firms to co-exist in the market. In the case where one firm has a cost advantage over the other firm, the former would not only capture a large market share eventually, but it is possible that the firm can compete even if the initial state favors the other firm. This has interesting implications in terms of market entry; for example imagine a firm that would like to enter a new market segment which is dominated by a monopolist. If the firm has the advantage of efficient innovation, then it can enter the market initially with a product that is not as advanced as the monopolist's product, and eventually can capture a significant market share by successively launching better products over time. We also analyze how the firms' strategies are affected by the sensitivity of the market share to the innovation differential. If the market share is very sensitive to the innovation differential, firms tend to innovate aggressively in the states where the market share varies significantly. While as the market share sensitivity tapers down, the firms do not have high incentives to innovate.
  • The luxury clothing industry constitutes a significant share of the retail industry. These high-end retailers face the problems of deciding the right amount of inventory of the prod- ucts they carry. Although clothing is not traditionally thought of as perishable, the fashion products are seasonal and therefore have a limited time window to sell. To add to the com- plexity, these products have high margins but also high uncertainty of success in the mar- ket place. Current literature on marketing and operations management indicates that the consumers of these high-end fashion products are quite strategic. Many of them prefer to delay purchases during the season so that they can purchase the products at a discounted rate. In this study, we explore the pricing strategy of a retailer (firm) when they have limited inventory to sell to these strategic consumers. We have results that characterize the optimal revenue of the retailer given the consumers’ purchasing strategies. Specifically, we look at the gaming between the consumers and the firm with asymmetric information and characterize the equilibrium pricing strategies.
  • There has been substantial research on location selection for successful hospitality and foodservice ventures such as hotels, full-menu gourmet restaurants, and quick service restaurants in the field of hospitality management. Most of the work use some rules of thumbs for multicriteria decision making that lacks solid empirical foundation for the underlying demand structure of the service discipline of interest. The location of a restaurant is a critical factor to its success as its likelihood of patronage is directly related to the characteristics of the facility and distance from the customers. Therefore, exerting significant effect on market share and profitability of the restaurant. This is a joint work with Dr. Anish Parikh in the Hospitality Management department of SBUS. We have started working toward a restaurant location selection model that provides data driven econometric foundations for the selection. Our model aims to provide demand estimation for a prospective facilities based on many relevant parameters such as demography, locale, cuisine interest, income levels, and age-group among others. A preliminary non-technical draft is attached in the binder.
  • This working paper aims toward the Business Analytics research that looks at the recommender systems heuristics. Most of the heuristics that propose the solutions want to analyze a large set of distributions for the purpose of finding few good recommendations. The set of possible recommendations is generally a huge set of probability distributions, the challenge lies in finding few solutions (subset of probability distributions) in a very efficient manner. The state of the art heuristics in the fields can provably give 'bad' solutions in many cases and extremely inefficient in some other cases. We have initial results pertaining to the topic that looks at the specific class of distributions and provides guarantees showing small set of possible best and efficient solutions in the time bound manner.
  • This is a joint work with Dr. Misra and Dr. Ravinder from the IMBA department of SBUS.
    Many large-scale retail businesses carry a vast amount of stock, usually storing units (SKUs) in their retail space. Each item has a different demand distribution, and thus the optimal inventory management policy of a single item would differ from other items in terms of order placement time and the number of units needed per order. Optimal individual or- dering has been unrealistic because there are thousands of units to manage and because storage costs are typically outweighed by significant savings in transportation costs by combining many orders from the same vendor. The joint optimization of inventory for multiple products is known to be a difficult problem in the operations literature. There- fore, there are many policies in practice that try to accomplish the inventory management using different heuristics based on ABC analysis or some categorization rule. We intend to study and compare different categorization rules through simulation and address the questions that arise naturally pertaining to the issue, such as the optimal number of cate- gories, robustness of optimality, and multicriteria categorization.
  • This work with Dr. Anish Parikh explores the efficiency of Meals on Wheels programs in the country. This article is submitted in the Journal of Aging & Social Policy. This work of interdisciplinary nature. We look at the food service supply chain of the Meals on Wheels program and explore the maximum room for improvement in terms of cost savings. There has been substantial work done on the humanitarian logistic side of the supply chain, but food costs analysis is often ignored. We find that the food costs of many Meals on Wheels programs are very high when benchmarked with other similar agencies’ food operational costs.