Mümin Kurtuluş

A Multiordering Newsvendor Model with Dynamic Forecast Evolution

We consider a newsvendor who dynamically updates her forecast of the market demand over a finite planning horizon. The forecast evolves according to the martingale model of forecast evolution (MMFE). The newsvendor can place multiple orders with increasing ordering cost over time to satisfy demand that realizes at the end of the planning horizon. In this context, we explore the trade-off between improving demand forecast and increasing ordering cost. We show that the optimal ordering policy is a state-dependent base-stock policy and analytically characterize that the base-stock level depends on the information state in a linear (log-linear) fashion for additive (multiplicative) MMFE. We also study a benchmark model where the newsvendor is restricted to order only once. By comparing the multiordering and single-ordering models, we quantify the impact of the multiordering strategy on the newsvendors expected profit and risk exposure.

The Value of Collaborative Forecasting in Supply Chains

Motivated by the mixed evidence concerning the adoption level and value of collaborative forecasting (CF) implementations in retail supply chains, in this paper, we explore the conditions under which CF offers the highest potential. We consider a two-stage supply chain with a single supplier selling its product to consumers through a single retailer. We assume that both the supplier and the retailer can improve the quality of their demand forecasts by making costly forecasting investments to gather and analyze information. First, we consider a noncollaborative model where the supplier and the retailer can invest in forecasting but do not share forecast information. Next, we examine a collaborative forecasting model where the supplier and the retailer combine their information to form a single shared demand forecast. We investigate the value of CF by comparing each partys profits in these scenarios under three contractual forms that are widely used in practice (two variations of the simple wholesale price contract as well as the buyback contract). We show that for a given set of parameters, CF may be Pareto improving for none to all three of the contractual structures, and that the Pareto regions under all three contractual structures can be expressed with a unifying expression that admits an intuitive interpretation. We observe that these regions are limited and explain how they are shaped by the contractual structure, power balance, and relative forecasting capability of the parties. To determine the specific value of collaborative forecasting as a function of different factors, we carry out a numerical analysis and observe the following. First, under noncoordinating contracts, improved information as a result of CF has the added benefit of countering the adverse effects of double marginalization in addition to reducing the cost of supply--demand mismatch. Second, one may expect the value of CF to increase with bargaining power, however this does not hold in general: The value of CF for the newsvendor first increases and then decreases in his bargaining power. Finally, whereas one may expect CF to be more valuable under coordinating contracts, rather than a simple wholesale price contract that is prone to double marginalization, the magnitude of the gain from CF is in many cases higher in the absence of quantity coordination.

Retail Assortment Planning Under Category Captainship

Retail assortment planning can have a tremendous impact on a retailers bottom-line performance. Over the past years, retailers have increasingly relied on their leading manufacturers for recommendations regarding the assortment to be offered to the consumers in a particular category, a practice often referred to as category captainship. Our research investigates the consequences of using category captains for assortment selection decisions. We develop a game-theoretic model where multiple manufacturers sell their products to consumers through a single retailer. We compare a model where the retailer selects the assortment in the category with a model where the retailer relies on a category captain for assortment decisions in return for a target category profit. We show that category captainship can, in some circumstances, benefit not only the retailer and the category captain, but also the noncaptain manufacturers. Our results have implications regarding the implementation of category captainship practices.

Category Captainship: Who Wins Who Loses?

Retailers in the consumer goods industry often rely on a leading manufacturer for category management, a form of manufacturer-retailer collaboration referred to as category captainship. There are reported success stories about category captainship, but also a growing debate about its potential for anti-competitive practices by category captains. Motivated by conficting viewpoints, the goal of our research is to deepen our understanding of the consequences of such collaboration initiatives between the retailer and only one of its manufacturers.

Category Captainship: Outsourcing Retail Category Management

Retailers in the consumer goods industry often rely on a leading manufacturer for category management, a form of manufacturer-retailer collaboration referred to as category captainship. There are reported success stories about category captainship, but also a growing debate about its potential for anti-competitive practices by category captains. Motivated by conflicting viewpoints, the goal of our research is to deepen our understanding of the consequences of such collaboration initiatives between the retailer and only one of its manufacturers. To this end, we develop a game theoretic model of two competing manufacturers selling through one retailer that captures the basic tradeoffs of using category captains for category management. We consider two scenarios that are in line with traditional retail category management and category captainship. In the first scenario, the retailer is responsible for managing the category and determines retail prices and assortment. In the second scenario, we assume that the retailer delegates part or all retail category management decisions to one of the manufacturers in return for a target category profit, and implements its recommendations. We compare these two scenarios to investigate the impact of the transition on all stakeholders in the supply chain. We conclude with design recommendations on the scope and structure of category captainship.

Investing in Forecast Collaboration

Motivated by the mixed results of collaborative forecasting initiatives in the fast-moving consumer goods sector, this paper investigates the conditions that favor the establishment of collaborative forecasting between a supplier and a retailer. We consider a two-stage supply chain where a single supplier sells to a single retailer who faces the newsvendor problem. Both the supplier and the retailer have forecasting capabilities and both can exert costly effort to improve the quality of their local demand forecasts. We assume that the supplier and the retailer exert effort independently and then pool their local forecasts to form a single shared demand forecast. We interpret the existence and stability of an equilibrium in which both parties invest in improving the forecast quality as collaborative forecasting being sustainable. We characterize conditions under which collaborative forecasting is sustainable. Our results have implications concerning the appropriateness of investing in collaboration technology to extract and use information from both parties in a supply chain.

Category Captainship Practices in the Retail Industry

This chapter explores the consequences of a recent trend in consumer goods retailing wherein a retailer cedes partial control of category management decisions such as assortment and/or pricing to one of the category’s leading manufacturers (usually referred to as the captain). This process is formally referred to as Category Captainship and has been used extensively in retailing for the past decade. There are reported success stories about category captainship, but also a growing debate about its potential for anti-competitive practices by category captains. The goal of this chapter is to provide an overview of the existing research on category captainship, and identify research directions that would improve our understanding of its impact.

How collaborative forecasting can reduce forecast accuracy

Abstract In this paper, we provide an analytical perspective on the link between supply chain collaboration and forecast accuracy, showing that collaborative forecasting can lead to less accurate demand forecasts over a wide range of cost and demand parameters. The result is explained by the decision maker’s relative preference for investing in forecasting vs. order quantities to manage demand uncertainty.

Collaborative Forecasting in Retail Supply Chains

Collaborative forecasting (CF) is a supply chain information sharing initiative in which two or more members in a supply chain (e.g., a retailer and a manufacturer) share their demand forecasts to form a single shared demand forecast, which is used as the basis for production and replenishment decisions. This chapter provides an overview of the existing academic research on CF practices in retail supply chains. In particular, this chapter focuses on highlighting the models used to capture the essence of CF practices and summarizes the key findings and insights about the value of CF.

The Forecasting Newsvendor: Benefits of Improved Demand Forecasts When Acquisition Costs are Increasing

We investigate the supply strategy of a firm that faces uncertain seasonal demand. While additional demand information becomes available in the form of signals throughout a finite planning horizon, it also becomes more expensive to acquire the product. In this setting, we explore the tradeoff between increasing acquisition cost and benefits of improved demand forecast. First, we show that the optimal replenishment policy is a state-dependent base-stock policy, where the state represents a statistic of the observed signals. Then, we characterize the base stock policy and show that the optimal base stock level is a linear function of the last updated mean demand. We also discuss the impact of fixed ordering and information acquisition costs on the firms supply strategy. Finally, using a special case of our main model, we characterize the conditions under which a responsive, frequent ordering strategy with information updating is valuable.