Seyed Iravani--Research

Supply chains are networks of interconnected businesses involved in the ultimate goal of providing goods or services to their end customers. Unfortunately, the optimal profit for the entire supply chain is not guaranteed even when each member of the supply chain optimizes their own profits. The combination of such self-interested behavior on the part of individual firms and the decentralization of decision making reduce the efficiency and profitability of the entire supply chain and, in the long run, also affect the profitability of each firm.
Supply chain coordination refers to a number of management practices that diminish the negative effects of any misalignment of incentives and of the decentralized decision making of the businesses involved in supply chains. Below are samples of some of my projects on Supply Chain Management.

Luxury retailers—such as Barneys New York, Saks Fifth Avenue, Neiman Marcus, and Nordstrom—frequently offer price markdowns on seasonal luxury products, e.g., high-fashion apparel, shoes, handbags, accessories. Because seasonal luxury products quickly become out of fashion, and consumers have little or no interest in them after a short selling season, after setting their initial inventory levels, retailers use intertemporal pricing and often adopt price markdowns to stimulate the demand for seasonal luxury products and thereby increase sales and profitability.

However, luxury and markdowns are, in many respects, contradictory concepts. Most customers purchasing luxury products consume conspicuously, and they do not only satisfy their material needs but also seek uniqueness, and they value a luxury product less as more people use it. Such consumers are labeled as snobs. If a luxury retailer increases its sales, that will have an adverse impact on snobs’ willingness to pay and therefore decrease the retailer’s profit (i.e., snob effect). Therefore, with exclusivity-seeking consumers, markdowns create difficult tradeoffs for retailers: On the one hand, markdowns stimulate demand and turn inventory into cash. On the other hand, markdowns reduce exclusivity and hence exclusivity-seeking consumers’ willingness to pay, and encourage affluent consumers to delay their purchases and wait for lower prices. In this project, we aimed to clarify this and address the following research questions. When should a luxury retailer adopt markdowns? What is the role of exclusivity-seeking consumer behavior on a luxury retailer’s markdown strategy? What is the impact of strategic consumer behavior when selling to exclusivity-seeking consumers?
We developed a game-theoretic model to analyze markdown and inventory rationing strategies for retailers selling to exclusivity-seeking and strategic consumers. Our analysis offers some useful insights into the above questions. (Joint work with Kenan Arifoglu and Sarang Deo)

Many factors influence the relationship between a manufacturer and a retailer in a supply chain. One such factor is a sales agent who functions as an independent entity that can substantially alter the dynamics within the supply chain. Sales agents are widely employed in the industry. According to Zoltners et al. (2001), nearly 12% of the total workforce in the United States is employed in full-time sales occupations. Furthermore, firms’ average expenditure on a sales force typically ranges between 10% and 40% of their sales revenues. In addition, an enhanced sales force management often can increase sales revenue by more than 10%. There are different types of sales agents depending on who hires them within a supply chain.

In this project, we focused on sales agents hired by upstream manufacturers. The U.S. Department of Labor has estimated that wholesale and manufacturing sales representatives held about two million jobs in the United States. These sales agents main task is to increase demand by improving the sales techniques of the retailer and/or marketing. We focused on two influential factors: (i) the sales agent’s efficiency and (ii) the accuracy of the available demand information at the downstream level. Our primary research question was how the expected profits of the manufacturer and the retailer depend on these two factors. For example, do the manufacturer and the retailer always benefit from a more efficient sales agent or more informed downstream parties? Using principle-agent models and game theoretical approach we developed new insights on several compensation contracts that improve a supply chain's performance. (Joint work with N. Ebrahimi and H. Shin)

Product recalls result from a lack of quality assurance in the manufacturing and/or design processes of one or many supply chain partners and can affect a large number of products manufactured over extended periods of time. For example, in 2002, Ford reported that 76% of the company’s quality problems stem from its first tier suppliers. In addition to preventive initiatives, it is also becoming a common practice among manufacturers to present suppliers with quality cost sharing agreements to ensure accountability of quality problems and to create incentives for process improvement. In this project, we address the optimal design of a recall cost sharing contract when both the supplier’s and the manufacturer’s quality improvement efforts are subject to moral hazard and when the manufacturer has uncertainty regarding the quality of the supplier’s process. Using insights from supermodular game theory and mechanism design theory, we characterize the levels of effort the manufacturer and the supplier would exert in equilibrium to improve their component failure rate when their effort choices are subject to moral hazard. For the case in which the information about the quality of the supplier’s product is not revealed to the manufacturer (i.e., the case of information asymmetry), we develop a menu of contracts that can be used to mitigate the impact of information asymmetry. We show that the menu of contracts not only significantly decreases the manufacturer’s cost due to information asymmetry, but also improves product quality. (Joint work with G. Chao and C. Savaskan)

In 2004, a consumer research study in the auto industry reported initial product quality as the second most important factor affecting consumers’ purchasing decisions after product price. The large number of product recalls and lawsuits in the auto industry demonstrate how undetected quality problems and related production delays can lead to a huge profit loss and degrade a company’s brand equity. For instance, in 2007, Ford’s concerns about a design related quality problem in cruise control switches resulted in a recall of 3.6 million vehicles manufactured between 1992 and 2004, increasing the total number of vehicles recalled for the same quality problem to 9.6 million (Associated Press 2007).

The fundamental problem in any supply chain system is efficiently matching supply with demand. Because supply and demand are uncertain, one strategy to better match supply with demand is to design more flexible supply chains. Two major operational flexibilities in supply chains are (i) logistics flexibility (the ability to ship products to different locations) and (ii) process flexibility (the ability to produce different types of products). With these types of flexibility, the production or logistics capacities of different members of supply chain can be shifted from one product type to another, allowing firms to enhance their ability to adjust to fluctuations in either the supply of materials or demand for products. Consequently, in designing a supply chain, the main questions are: which factory should be made flexible to produce which set of parts, subassemblies or products, and to which supply chain members these parts must be shipped?

The goal is to minimize the total production and logistics cost. In this project, we consider these two types of flexibility—logistics flexibility and process flexibility—and examine how demand, production, and supply variability at a single stage impact the best stage in the supply chain for each type of flexibility. Under some assumptions we show that both types of flexibility are most effective when positioned directly at the source of variability. However, although expected profit increases as logistics flexibility is positioned closer to the source of variability (i.e., downstream for demand variability and upstream for supply variability), locating process flexibility anywhere except at the stage with variability leads to the same decrease in expected profit. (Joint work with W.J. Hopp and W. Lu Xu).

The easy use of modern technologies have significantly influenced store customers' behavior and led them towards online shopping instead. Subsequently, online sales have been rapidly increasing (around 15% per year according to Zaroban (2016)), and this has forced traditional retailers to change their business models and develop their e-commerce capabilities alongside their brick-and-mortar stores in order to survive in today's markets. Omni-channel management refers to the perfect integration of a retailer's sales channels with the purpose of improving efficiency and flexibility. An omni-channel retailer has to take into account various decisions at the same time, including determining the best prices in all sales channels and whether they should be the same or different, so that the highest possible profit is achieved by the retailer.

Recent observations show that retailers sometimes set different prices in their different sales channels for the same item, with online prices usually set lower than in-store prices. When the online price is lower than the in-store price, it is fairly likely that a price-aware customer at store (a strategic customer) will request the online price. Therefore, another major decision is whether the retailer should offer the in-store item at its online price (i.e., self-price matching). Data shows that although many retailers set different prices in different channels, not all of them are committed to self-matching. For instance, Target, Best Buy, and Home Depot offer self-matching, while Macy's and Urban Outfitters do not. Also, some retailers match the price of their competition to attract customers (competitive-price matching). This project uses optimization methodologies and game theory to investigate the following questions: (i) How do factors such as market size, customer mix, and customers' price-sensitivity levels affect pricing strategies under self-matching and competitive-price matching scenarios? (ii) What are the conditions (e.g., demand size, price elasticity of demand, customers' price-awareness, customers' channel preferences, and inventory levels) under which self-matching and competitive-price matching generate relatively higher revenues? In other words, when should a retailer offer to match its online price or its competition price? (Joint work with E. Sangari and I. Duenyas)