Corn or Soybean: Dynamic Farmland Allocation under Uncertainty

Informations Systems and Operations Management

Speaker: Onur BOYABATLI
Assistant Professor , Singapore Management University

5 June 2015 - HEC Paris - Jouy en Josas Campus - Building S - Room 227 - From 10:30 am to 12:00 pm

This paper studies the farmland allocation decision of a farmer between two crops in a multi-period framework. In each growing period, the farmer chooses the allocation in the presence of revenue uncertainty, and crop rotation benefits across periods, i.e. revenue is stochastically larger when a crop is planted in a rotated land (where the other crop was planted in the previous period). We identify two strategies, monoculture, i.e. fully allocate the farmland to one of the crops, and rotate, i.e. plant each crop in the rotated farmland, which characterize the optimal allocation decision in each period. Our analysis provides rules of thumb for the impact of revenue uncertainty: The farmer benefits from a lower revenue correlation between the two crops. Interestingly, the farmer benefits from a higher revenue volatility only when this volatility is sufficiently high; otherwise, a lower revenue volatility increases the profitability. We propose a heuristic allocation policy which we characterize in closed form. Using a calibration based on a representative farmer planting corn and soybean in Iowa, we show that the proposed policy is near-optimal, and significantly outperforms the commonly used heuristic allocation policies in practice (such as the myopic policy, always-rotate policy and monoculture policy).

Measuring the effectiveness of mobile marketing

Informations Systems and Operations Management

Speaker: Anindya Ghose
Professor of Information, Operations and Management Sciences and Professor of Marketing , New York University's Leonard N. Stern School of Business

3 June 2015 - HEC - Jouy-en-Josas Campus - Building V - Council Room - From 11:00 am to 12:00 pm

The explosive growth of smartphone and location-based services (LBS) has contributed to the rise of mobile advertising. In this talk, we will present results from multiple studies in Europe and Asia that are designed to measure the effectiveness of mobile marketing promotions. In the first randomized field experiment, using data from a location-based app for smartphones, we measure the effectiveness of mobile coupons. The aim is to analyze the causal impact of geographical distance between a user and retail store, the display rank, and coupon discounts on consumers’ response to mobile coupons. In a second large scale field study where we exploit a quasi-natural experiment we examine the role of contextual crowdedness on the redemption rates of mobile coupons. We find that people become increasingly engaged with their mobile devices as trains get more crowded, and in turn become more likely to respond to targeted mobile messages. The study results were consistent across peak and off-peak times, and on weekdays and weekends. The change in behavior can be accounted for by the phenomenon of “mobile immersion”: to psychologically cope with the loss of personal space in crowded trains and to avoid accidental gazes, commuters can escape into their personal mobile space. In turn, they become more involved with targeted mobile messages they receive, and, consequently, are more likely to make a purchase in crowded trains. These studies causally show that mobile advertisements based on real-time static geographical location and contextual information can significantly increase consumers’ likelihood of redeeming a geo-targeted mobile coupon. However, beyond the location and contextual information, the overall mobile trajectory of each individual consumer can provide even richer information about consumer preferences. In the third study, we propose a new mobile advertising strategy that leverages full information on consumers’ offline moving trajectories. To examine the effectiveness of this new mobile trajectory-based advertising strategy, we designed a large-scale randomized field experiment in one of the largest shopping malls in the world. We find that mobile trajectory-based advertising can lead to highest redemption probability, fastest redemption behavior, and highest transaction amount from customers at the focal advertising store as well as in the shopping mall. Our studies help firms better understand the question of which kinds of mobile advertising are most effective and how machine learning techniques can be combined with statistical models and field experiments to offer the right product to the right audience at the right time on the right channel.

Combining Data Analytics and Optimization to Connect Global Patterns with Local Constraints: An Application in Assortment Planning and Growth Projections

Informations Systems and Operations Management

Speaker: Sudip Bhattacharjee, Ph.D
Associate Professor , University of Connecticut

29 May 2015 - HEC - Jouy en Josas Campus - Building V - Council Room - From 2:30 pm to 4:00 pm

Firms with multiple branches frequently set growth targets on an ad hoc basis, and disregard branch specific constraints or advantages. This leads to skewed management-set incentives and results. We use a nation-wide plastics wholesaler with over $500 million revenue to show how data mining helps to find global knowledge from branch sales patterns. We develop metrics and algorithms to overcome well-known data mining problems, which also help to compare branch performance and provide differentiated growth projections. Finally we optimize using the global rules from data mining and local market conditions of each branch. We identify stores that are at the top of their “game”, and those that can improve revenue up to 100%. The methodology can also determine locations to open new branches. Our solution offers important insights for supply chain designers and merchandising managers on product portfolio selection, including complements vs. substitutes and product bundling.

Transmission of Risk in a Supply Chain: The Case of the Refinery Industry

Informations Systems and Operations Management

Speaker: Hamed Ghoddusi
Professeur Assistant en Finance , Howe School of Technology Management, Stevens Institute of Technology

19 May 2015 - HEC Paris - Jouy en Josas Campus - Building S - Room 126 - From 10:30 am to 12:00 pm

We develop an equilibrium model of price dynamics and the transmission of shocks in a supply chain and determine the equilibrium price process for the input, the output, and the spread between input and output prices. We then present a set of stylized facts about crack spreads and calibrate our model for the case of crude oil and refined products. As we show, the relative volatility of oil and gasoline, as well as their correlation depends on the volatility and correlations of supply and demand shocks, elasticities, the convexity of the production function, and the competitiveness of the refinery market. Our research has implications for understanding the volatility of energy markets, investment incentives and optimal regulation of supply chains, and optimal risk management policies.

Hamed Ghoddusi is an Assistant Professor of Finance at the Howe School of Technology Management, Stevens Institute of Technology. Before joining Stevens he was a postdoctoral associate at MIT's Engineering Systems Division (ESD). He has received his Ph.D. from the Vienna Graduate School of Finance (VGSF) and holds degrees in Economics, Management Science, and Industrial Engineering from the Institute for Advanced Studies (Vienna) and Sharif University of Technology (Tehran). Hamed has been a visiting scholar/consultant at Oxford Institute for Energy Studies (OIES), International Institute for Applied Systems Analysis (IIASA), UT Austin, UC Berkeley, UNDP, and UNIDO. His research interests include Energy and Resource Economics, Society-Centered Financial Innovation, Macro-Finance, and Risk Management. He has done research on biofuels, natural gas, crude oil, refinery, electricity, and energy project financing markets.

The Role of Contract Expirations in Service Parts Management

Informations Systems and Operations Management

Speaker: Cerag Pince
Assistant Professor of Operations and Supply Chain Management , Kühne Logistics University, Hamburg

28 April 2015 - HEC Paris - Jouy en Josas Campus - Building S - Room 127 - From 10:30 am to 12:00 pm

The majority of after-sales service providers manage their service parts inventory by focusing on the availability of service parts. This approach, combined with automatic replenishment systems, leads to reactive inventory control policies where base stock levels are adjusted only after a service contract expires. Consequently, service providers often face excess stock of critical service parts that are difficult to dispose due to their specificity. In this paper, we address this problem by developing inventory control policies taking into account contract expirations. Our key idea is to reduce the base stock level of the one-for-one policy before obsolescence (a full or partial drop in demand rate) occurs and let demand take away excess stock. We refer to this policy as the single-adjustment policy. We benchmark the single-adjustment policy with the multiple-adjustment policy (allowing multiple base stock adjustments) formulated as a dynamic program and verify that for a wide range of instances the single-adjustment policy is an effective heuristic for the multiple-adjustment policy. We also compare the single-adjustment policy with the world-dependent base stock policy offered by Song and Zipkin (1993) and identify the parameter combinations where both policies yield similar costs. We consider two special cases of the single-adjustment policy where the base stock level is kept fixed or the base stock adjustment is postponed to the contract expiration time. We find that the initial demand rate, contract expiration time, and size of the drop in demand rate are the three key parameters driving the choice between the single-adjustment policy and its special cases.