Elizabeth J. Durango-Cohen

Supplier Commitment and Production Decisions Under a Forecast-Commitment Contract

Manufacturing firms in capital-intensive industries face inherent demand volatility for their products and the inability to change their capacity quickly. To cope with these challenges, manufacturers often enter into contracts with their customers that offer greater certainty of supply in return for more predictable orders. In this paper, we study a forecast-commitment contract in which the customer provides a forecast, the supplier makes a production commitment to the customer based on the forecast, and the customers minimum order quantity is a function of the forecast and committed quantities. We provide a complete analysis of the suppliers decisions when there is a single customer facing uncertain demand. We first show that the supplier has two dominant commitment strategies: committing to the forecast or committing to the production quantity. We then characterize the jointly optimal commitment and production strategy for the supplier and extend the results to consider a capacity constraint. We show that the proposed contract can moderate the suppliers motivation to underproduce, and due to the structure of the contract and the form of the suppliers optimal strategy, also limits the customers incentive to overforecast. We also provide results for a capacitated two-customer example, which show that the suppliers choice of production quantity for each customer is not necessarily nondecreasing in the total available capacity.

Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station

Funding pressures have forced many not-for-profit organizations to reduce their reliance on mass-marketing efforts, e.g., pledge drives, and increase the volume and sophistication of their direct marketing activities. The efficiency of direct marketing, however, is linked to an organization’s ability to target population segments effectively, which, in turn, has motivated the development of methodological approaches for market segmentation.

Outsourcing in Place: Should a Retailer Sell Its Store-Brand Factory?

ABSTRACTSeveral major grocery chains in the United States own factories that produce some of their store-brand products. Historically, these store-brand products have been the low-price, lower-quality alternatives to higher-priced national brands, but the quality and consumer acceptance of store brands have increased markedly in recent years. Although demand for store-brand products has grown, managing the associated factories can be costly for retailers, leading some to consider selling the factories to third parties.We study the impact of selling a retailer’s existing capacity-limited factory to a third party when a store-brand product competes with a similar national-brand product. We examine the equilibrium dynamics between two external suppliers and show how the outcome changes with respect to prices, capacity limitations, the distribution of profits, and the sequencing of pricing decisions. Among other things, we show that, surprisingly, the national brand’s equilibrium wholesale price may fall when...

A Bernoulli-Gaussian mixture model of donation likelihood and monetary value: An application to alumni segmentation in a university setting

Advances in computational power and enterprise technology, e.g., Customer Relationship Management (CRM) software and data warehouses, allow many businesses to collect a wealth of information on large numbers of consumers. This includes information on past purchasing behavior, demographic characteristics, as well as how consumers interact with the organization, e.g., in events, on the web. The ability to mine such data sets is crucial to an organizations ability to deliver better customer service, as well as manage its resource allocation decisions. To this end, we formulate a Bernoulli-Gaussian mixture model that jointly describes the likelihood and monetary value of repeat transactions. In addition to presenting the model, we derive an instance of the Expectation-Maximization Algorithm to estimate the associated parameters, and to segment the consumer population. We apply the model to an extensive dataset of donations received at a private, Ph.D.-granting university in the Midwestern United States. We use the model to assess the effect of individual traits on their contribution likelihood and monetary value, discuss insights stemming from the results, and how the model can be used to support resource allocation decisions. For example, we find that participation in alumni-oriented activities, i.e., reunions or travel programs, is associated with increased donation likelihood and value, and that fraternity/sorority membership magnifies this effect. The presence/characterization of unobserved, cross-sectional heterogeneity in the data set, i.e., unobserved/unexplained systematic differences among individuals, is, perhaps, our most important finding. Finally, we argue that the proposed segmentation approach is more appealing than alternatives appearing in the literature that consider donation likelihood and monetary value separately. Among them and as a benchmark, we compare the proposed model to a segmentation that builds on a multivariate Normal mixture model, and conclude that the Bernoulli-Gaussian mixture model provides a more coherent approach to generate segments.

Inventory control and LQG: Connections and extensions

This paper presents a review of two classical business approaches to inventory control. This is followed by a discussion of an engineering approach using Linear Quadratic Gaussian (LQG) control. The main result is to show that under certain conditions all three methods generate the same policy. Using the state-space model of the LQG formulation, we explore the multi-echelon system and model uncertainty cases.

A clusterwise linear regression model of alumni giving

We present a clusterwise regression model to analyse alumni contributions to a private, PhD-granting university in the Midwestern USA. The model provides a framework to simultaneously segment a population, and to explain the effect of various factors on the mean annual value of donations. We contribute a different approach to marketing studies in the university fundraising context, where segmentation is often based on intuitive, albeit possibly biased criteria. Instead, in clusterwise regression, individuals are assigned to segments with the objective of maximising the within-segment variation explained by a set of regression models. Our main finding is that individuals in different segments display systematic, but unobserved differences in their responses, i.e., the coefficients in the segment-level regression models exhibit differences in their magnitude, sign and level of significance. We discuss how characterising such differences can support tailored solicitation strategies.