Juan-Carlos Ferrer

OM Forum---Managing Customer Experiences: Perspectives on the Temporal Aspects of Service Encounters

Aservice encounter is an experience that extends over time. Therefore, its effective management must include the control of the timing of the delivery of each of the services elements and the enhancement of the customers experience between and during the delivery of the various elements. This paper provides a conceptual framework that links the duration of a service encounter to behaviors that have been shown to affect profitability. Analysis of the framework reveals a wide gap between the behavioral assumptions typically made in operations research (OR) and operations management (OM) models and the state of the art in the marketing and psychology literature. The central motivations behind this paper are (1) to help the OR and OM community bridge this gap by bringing to its attention recent findings from the behavioral literature that have implications for the design of queueing systems for service firms and (2) to identify opportunities for further research.

On pricing of multiple bundles of products and services

This paper considers the pricing decision faced by a seller of bundles composed of a service and an associated product offered to customers on a subscription basis using a two-part tariff scheme. An optimal pricing policy that maximizes the profit of a firm is obtained using a dynamic programming approach and it is found that, in the long run, there is an optimal number of customers associated to each bundle. Due to managerial purposes, two suboptimal fixed-price policies are derived and compared to the optimal pricing policy. The conditions under which it is profitable for the firm to expand its offer from one to two bundles is studied. Finally, it is concluded that introducing a fee for subscribed customers to deter the switching from one bundle to the other, increases the profitability of the firm.

Design and implementation of an optimization-based decision support system generator

Abstract Decision makers today need to be able to rapidly find good solutions to increasingly complex problems. Optimization-based decision support systems (OBDSSs) can help decision makers to meet this challenge. Building such systems, however, is expensive and time consuming. In this paper we present the design of an OBDSS generator that greatly automates the development process by integrating a model specification with a database, a solver, and a graphical user interface (GUI). The generator automatically creates the database for storing the instantiating data, the GUI, and the solver interface. With this generator, an OR/MS analyst can build an OBDSS faster and more efficiently than using traditional tools. The resulting OBDSSs can be used by decision makers with little knowledge of the underlying OR/MS technology. We also discuss our experience using the generator to develop production planning and truck dispatching OBDSSs for several firms in the context of a three-year R&D project.

Consumer price sensitivity in the retail industry: Latitude of acceptance with heterogeneous demand

Estimating the effect of price changes on demand is an essential task for retailers. This study proposes a methodology based on consumer utility for modeling the price thresholds phenomenon that allows for threshold asymmetry, incorporates consumer heterogeneity and uses weekly aggregated brand-level data. Unlike other studies based on consumer utility models, which generate results only for the price elasticity of market share, a methodology for estimating price elasticity of demand is also included. Data on fast-moving goods (detergents, toilet paper, soft drinks, meats, liquid juices and yogurts) supplied by a major retail chain are used to demonstrate the existence of price thresholds and their effects on price elasticity. In every case it was found that within the thresholds or latitude of acceptance, consumers are relatively less sensitive to price variations while beyond them a higher sensitivity was observed. In some cases a product brand was classified as inelastic within the latitude of acceptance and elastic outside of it.

Pricing and composition of bundles with constrained multinomial logit

In this paper, we propose an extension of the problem of bundling with multinomial logit, making an explicit inclusion of the consumers’ maximum willingness to pay (MWTP) by means of the constrained multinomial logit (CMNL). In the bundling problem, we determine the price and the composition of bundles offered for a single segment of consumers by a firm, which is competing with others in the market, and we compare this result to a base case in which the consumers’ MWTP is not considered. We assume these consumers as rational since they choose the bundle that maximise their utility and the bundle price is within their MWTP. The resulting model is a non-linear mixed integer programme which is solved in two steps: (i) pricing is the first step; the prices are numerically determined in a fixed point equations system and (ii) in the second step the composition of the bundle is determined by explicit enumeration. The results show that the price obtained is less than the one got in the case without CMNL (and bigger than the costs), and the composition of the offered bundle is different as well. It is possible to conclude that not considering the consumers’ MWTP in the context of the problem of bundling will imply an overestimation of the firm’s profit. We have analysed as well the results for a Chilean telecommunications company. These results show the importance of including the MWTP in the pricing and composition process.

Model for Optimization of Locations of Schools and Student Transportation in Rural Areas

Low population densities make it difficult to design efficient networks of schools in rural areas. The most common solution is to operate small schools with children in multiple grades in each classroom, even though this solution may not be the least expensive. School consolidation proposals are difficult to implement because communities may have emotional bonds with their local schools and the impacts on secondary services, such as student transportation, are difficult to evaluate. In rural areas, particularly in Brazil, reductions in the costs associated with school operations and student transportation could allow boards of education to allocate more resources to improve educational practices and teaching materials. This paper presents a methodology for optimizing the location of schools and student transportation in rural areas. This methodology is based on two mixed-integer programming models, which are used sequentially. The first one deals with the school location and sizing problem, and the second one deals with the school bus routing and shift programming problem. The methodology was applied with data from Barao de Grajaú, which is in the northeast region of Brazil, and provided savings in operating costs of 10% to 17%.

A mixed integer programming approach to multi-skilled workforce scheduling

The potential benefits of using human resources efficiently in the service sector constitute an incentive for decision makers in this industry to intelligently manage the work shifts of their employees, especially those dealing directly with customers. In the long term, they should attempt to find the right balance between employing as few labor resources as possible and keeping a high level of service. In the short run (e.g., 1 week), however, contracted staff levels cannot be adjusted, and management efforts thus focus on the efficient assignment of shifts and activities to each employee. This article proposes a mixed integer program model that solves the short-term multi-skilled workforce tour scheduling problem, enabling decision makers to simultaneously design workers’ shifts and days off, assign activities to shifts and assign those to employees so as to maximize and balance coverage of a firm’s demand for on-duty staff across multiple activities. Our model is simple enough to be solved with a commercial MIP solver calibrated by default without recurring to complex methodologies, such as extended reformulations and exact and/or heuristic column generation subroutines. A wide computational testing over 1000 randomly generated instances suggests that the model’s solution times are compatible with daily use and that multi-skilling is a significant source of labor flexibility to improve coverage of labor requirements, in particular when such requirements are negatively correlated and part-time workers are a scarce resource.

A proactive transfer policy for critical patient flow management

Hospital emergency departments are often overcrowded, resulting in long wait times and a public perception of poor attention. Delays in transferring patients needing further treatment increases emergency department congestion, has negative impacts on their health and may increase their mortality rates. A model built around a Markov decision process is proposed to improve the efficiency of patient flows between the emergency department and other hospital units. With each day divided into time periods, the formulation estimates bed demand for the next period as the basis for determining a proactive rather than reactive transfer decision policy. Due to the high dimensionality of the optimization problem involved, an approximate dynamic programming approach is used to derive an approximation of the optimal decision policy, which indicates that a certain number of beds should be kept free in the different units as a function of the next period demand estimate. Testing the model on two instances of different sizes demonstrates that the optimal number of patient transfers between units changes when the emergency patient arrival rate for transfer to other units changes at a single unit, but remains stable if the change is proportionally the same for all units. In a simulation using real data for a hospital in Chile, significant improvements are achieved by the model in key emergency department performance indicators such as patient wait times (reduction higher than 50%), patient capacity (21% increase) and queue abandonment (from 7% down to less than 1%).