This paper integrates the process oriented view of quality in manufacturing with the multi-attribute product positioning and customer preference models of marketing, within the context of traditional economic models of markets and competition. In manufacturing applications, “quality” is often defined as conformance to specifications or as meeting standards on the performance of the product. In the marketing and economics literature, “quality” typically refers to the performance level or “class” of the product. To capture both perspectives, a product is described by a vector of performance attributes, and the population of produced units is assumed to display a distribution on these attributes. The distribution perceived by customers may differ from the actual. The attribute levels (means) may be taken to define the class, or performance of the product. Quality in the sense of conformance is then conceptually identified with the absence of variation of the population. Consumer preferences are modelled by a cardinal utility function defined on the vector of attributes and price, and customers maximize expected utility. Product manufacturers are assumed to face a cost of producing a given population distribution. Under specific assumptions regarding costs and utility functions, models of monopoly, oligopoly and perfect competition are formulated. The model clarifies the distinction between product class (performance) and conformance quality, identifies the sources of quality improvement, and provides an economic framework relating issues like product positioning, process improvement, quality function deployment (QFD) and customer preference estimation. This framework is used to address issues such as quality costs and benefits and the economics of investments in quality.

This paper is the second of a two-paper study of fairness issues for decisions that affect the benefits received and the risks encountered by a population. The study examines fairness for individuals and for homogeneous groups within the population. It considers fairness both for population benefit-risk profiles and for probability distributions over profiles. Our study bases fairness on notions of envy among individuals and groups. The first paper focused on fairness in profiles and profile distributions when benefits and risks are not aggregated. The present paper uses individual preferences to assess fairness when benefits and risks are aggregated within groups or over the population. It concentrates on intergroup envy measures and fairness indices that account for ways that aggregated benefits and risks might be allocated to members of groups or to groups within the population.

We define a high volume factory to be a connected network of workstations, at which assigned workers process work-in-progress that flows at high rates through the workstations. A high rate usually implies that each worker processes many pieces per hour, enough so that work can be described as a deterministic hourly flow rate rather than, say, a stochastic number of discrete entities. Examples include mail processing and sorting, check processing, telephoned order processing, and inspecting and packaging of certain foods. Exogenous work may enter the factory at any workstation according to any time-of-day profile. Work-in-progress flows through the factory in discrete time according to Markovian routings. Workers, who in general are cross-trained, may work part time or full time shifts, may start work only at designated shift starting times, and may change job assignments at mid shift. In order to smooth the flow of work-in-progress through the service factory, work-in-progress may be temporarily inventoried (in buffers) at work stations. The objective is to schedule the workers (and correspondingly, the workflow) in a manner that minimizes labor costs subject to a variety of service-level, contractual and physical constraints. Motivated in part by analysis techniques of discrete time linear time-invariant (LTI) systems, an object-oriented linear programming (OOLP) model is developed. Using exogenous input work profiles typical of large U.S. mail processing facilities, illustrative computational results are included.

We show how genetic algorithms can be used to evolve strategies in oligopolistic markets characterized by asymmetric competition. The approach is illustrated using scanner tracking data of brand actions in a real market. An asymmetric market-share model and a category-volume model are combined to represent market response to the actions of brand managers. The actions available to each artificial brand manager are constrained to four typical marketing actions of each from the historical data. Each brand's strategies evolve through simulations of repeated interactions in a virtual market, using the estimated weekly profits of each brand as measures of its fitness for the genetic algorithm. The artificial agents bred in this environment outperform the historical actions of brand managers in the real market. The implications of these findings for the study of marketing strategy are discussed.

Multiple depot inventory systems with stock transfer are used by many companies especially when demand is high relative to storage capacity. The key issues in such systems are how many of each item to hold at each depot and what to do if there is a demand for an item at a depot that has none of that item in stock. This study was motivated by the inventory problem faced by a UK car part retailer that groups its depots into pairs. The company's policy for dealing with a demand at a depot that cannot be satisfied from local stock is to either transfer the item from the other depot in the group or to place an emergency order. The object of this paper is to characterise an optimal policy for this problem and to propose a method of calculating the parameters of such a policy.