I n many services, the quality or value provided by the service increases with the time the service provider spends with the customer. However, longer service times also result in longer waits for customers. We term such services, in which the interaction between quality and speed is critical, as customer-intensive services. In a queueing framework, we parameterize the degree of customer intensity of the service. The service speed chosen by the service provider affects the quality of the service through its customer intensity. Customers queue for the service based on service quality, delay costs, and price. We study how a service provider facing such customers makes the optimal "quality-speed trade-off." Our results demonstrate that the customer intensity of the service is a critical driver of equilibrium price, service speed, demand, congestion in queues, and service provider revenues. Customer intensity leads to outcomes very different from those of traditional models of service rate competition. For instance, as the number of competing servers increases, the price increases, and the servers become slower.

In a manufacturing environment with volatile demand, inventory management can be coupled with dynamic capacity adjustments for handling the fluctuations more effectively. In this study, we consider the problem of integrated capacity and inventory management under non-stationary stochastic demand and capacity uncertainty. The capacity planning problem is investigated from the workforce planning perspective where the capacity can be temporarily increased by utilising contingent workers from an external labour supply agency. The contingent capacity received from the agency is subject to an uncertainty, but the supply of a certain number of workers can be guaranteed through contracts. There may also be uncertainty in the availability of the permanent and contracted workers due to factors such as absenteeism and fatigue. We formulate a dynamic programming model to make the optimal capacity decisions at a tactical level (permanent workforce size and contracted number of workers) as well as the operational level (number of workers to be requested from the external labour supply agency in each period), integrated with the optimal operational decision of how much to produce in each period. We analyse the characteristics of the optimal policies and we conduct an extensive numerical analysis that helps us provide several managerial insights. Corresponding author Abstract:In a manufacturing environment with volatile demand, inventory management can be coupled with dynamic capacity adjustments for handling the fluctuations more effectively. In this study, we consider the problem of integrated capacity and inventory management under non-stationary stochastic demand and flexible capacity uncertainty. The capacity planning problem is investigated from the workforce planning perspective where the capacity can be temporarily increased by utilizing contingent workers from an external labor supply agency. The contingent capacity received from the agency is subject to an uncertainty, but the supply of a certain number of workers can be guaranteed through contracts. We formulate a dynamic programming model to make the optimal capacity decisions at a tactical level (permanent workforce size and contracted number of workers) as well as the operational level (number of workers to be requested from the external labor supply agency in each period), integrated with the optimal operational decision of how much to produce in each period. We analyze the characteristics of the optimal policies and we conduct an extensive numerical analysis that helps us provide several managerial insights.

We study the optimal timing of adoption of a service innovation that a new entrant firm brings to a market populated by two incumbent firms. Our analysis is based on a model of competitive diffusion dynamics that extends the monopolistic Bass model to include customer churn processes, as well as a potential market expansion resulting from the introduction of the innovation. We obtain expressions for the time trajectories of the customer bases, i.e., the numbers of customers that use old and new service processes for the competing firms in a general setting, as well as sharper, closed-form characterizations for the setting with a stable market and homogeneous imitation process. In modeling competitive dynamics we consider settings where incumbents anticipate a potential failure of the innovation. We use the trajectories for the customer bases to model an optimal adoption response problem faced by one of the incumbent firms in the setting in which the adoption time for the other incumbent can be anticipated or is pre-announced, and analyze this problem in the absence of market expansion or intra-generational customer churn. Using the optimal response results, we provide the Nash equilibrium analysis of the adoption decisions by competing incumbent firms and derive sufficient conditions for the "now-now", "now-never" and "never-never" adoption equilibria. We use the trading volume data from the foreign exchange markets to estimate the parameters of the competitive diffusion dynamics for our model and to conduct a numerical investigation of the impact of the uncertainty associated with the success of the innovation on the incumbents' Nash equilibrium adoption times.