We consider a contract manufacturer who procures multiple components from independent suppliers to produce an assemble-to-order customized product for a client. The unit price of the product depends on the manufacturer's delivery lead time. We explore how the manufacturer can use a vendor-managed consignment inventory (VMCI) scheme to manage the underlying risk and coordinate independent suppliers' decisions on the production quantities of their components under demand uncertainty. We formulate the problem as a Stackelberg game played by the manufacturer against her component suppliers to determine her pricing policy for suppliers' consignment inventories. We further develop an efficient algorithm for finding the manufacturer's optimal pricing scheme. Our results provide useful insights for managing components in these types of assemble-to-order environments and for understanding how component production cost and procurement lead times affect individual firms' performance in decentralized assembly channels.

The measurement of customer lifetime value is important because it is used as a metric in evaluating decisions in the context of customer relationship management. For a firm, it is important to form some expectations as to the lifetime value of each customer at the time a customer starts doing business with the firm, and at each purchase by the customer. In this paper, we use a hierarchical Bayes approach to estimate the lifetime value of each customer at each purchase occasion by jointly modeling the purchase timing, purchase amount, and risk of defection from the firm for each customer. The data come from a membership-based direct marketing company where the times of each customer joining the membership and terminating it are known once these events happen. In addition, there is an uncertain relationship between customer lifetime and purchase behavior. Therefore, longer customer lifetime does not necessarily imply higher customer lifetime value. We compare the performance of our model with other models on a separate validation data set. The models compared are the extended NBD–Pareto model, the recency, frequency, and monetary value model, two models nested in our proposed model, and a heuristic model that takes the average customer lifetime, the average interpurchase time, and the average dollar purchase amount observed in our estimation sample and uses them to predict the present value of future customer revenues at each purchase occasion in our hold-out sample. The results show that our model performs better than all the other models compared both at predicting customer lifetime value and in targeting valuable customers. The results also show that longer interpurchase times are associated with larger purchase amounts and a greater risk of leaving the firm. Both male and female customers seem to have similar interpurchase time intervals and risk of leaving; however, female customers spend less compared with male customers.

Loyalty programs are very common in practice. Many researchers have worked at understanding the impact of loyalty programs on market competition and the mechanism behind it. Interestingly, almost all of the studies have explored a symmetric equilibrium where both of the competing firms offer a loyalty program. To our knowledge, the extant literature has not investigated in-depth whether asymmetric equilibrium can exist where only one firm chooses to offer a loyalty program and the other firm chooses to compete via lowering prices. Such a question is important because some markets do support such asymmetric equilibriums with respect to loyalty programs. Also, the existence of asymmetric equilibrium shows that a loyalty program need not be profitable for some firms. In this paper, we use a game-theoretic framework to investigate specific types of customer loyalty programs that provide benefit to loyal customers in the form of discount over market prices. The model considers consumer switching and includes two types of consumer heterogeneity. The first type of heterogeneity concerns the differences between customers with respect to their liking for loyalty programs, and the second type concerns the differences among the loyalty program members with respect to their ability to collect enough loyalty points to redeem loyalty rewards. By analyzing a duopoly market, we find that both symmetric equilibrium (i.e., where both competing firms offer the loyalty program) and asymmetric equilibrium (i.e., where one firm alone offers the loyalty program) can be sustained. The paper explores conditions for the existence of these two equilibriums.

Entrepreneurs play a fundamental role in bringing new technologies to market. Because technologies are often configurable to serve a variety of different markets, it is possible for entrepreneurs to identify multiple market opportunities prior to the first market entry of their emerging firms, and if they elect to do so, to therefore have a choice of which market to enter first. The empirical results presented in this paper offer three new insights regarding this important early-stage choice in new firm creation. First, they reveal that serial entrepreneurs have learned through prior start-up experience to generate a “choice set” of alternative market opportunities before deciding which one to pursue in their new firm creation. Second, the analysis indicates that entrepreneurs who identify a “choice set” of market opportunities prior to first entry derive performance benefits by doing so. Third, the positive relationship between the number of market opportunities identified prior to first entry and new firm performance is nonlinear and subject to decreasing marginal return. The research literature has yet to acknowledge the notion of multiple opportunity identification prior to entry, and the related idea of selecting the most favorable market opportunity for the creation of a new technology firm.

This paper is an update of a paper that five of us published in 1992. The areas of multiple criteria decision making (MCDM) and multiattribute utility theory (MAUT) continue to be active areas of management science research and application. This paper extends the history of these areas and discusses topics we believe to be important for the future of these fields.