Peter A. Salzarulo

Locating specialized service capacity in a multi-hospital network

Multi-hospital systems have become very common in todays healthcare environment. However, there has been limited published research examining the opportunities and challenges of pooling specialized services to a subset of hospitals in the network. Therefore, this paper considers how hospital networks with multiple locations can leverage pooling benefits when deciding where to position specialized services, such as magnetic resonance imaging (MRI), transplants, or neonatal intensive care. Specifically, we develop an optimization model to determine how many and which of a hospital networks hospitals should be set up to deliver a specialized service. Importantly, this model takes into account both financial considerations and patient service levels. Computational results illustrate the value of optimally pooling resources across a subset of hospitals in the network versus two alternate approaches: (1) delivering the service at all locations and requiring each site to handle its own demand, or (2) locating the service at one hospital that handles all network demand.

The incremental value of central control in serial supply chains

We consider a two-echelon serial supply chain where a single manufacturer replenishes a single downstream customer who faces random, stationary and discrete demand. In this setting, we compare the performance of a traditional supply chain having no information sharing to one where the customer shares demand and inventory information with the manufacturer. We also consider the case of central control where the manufacturer has full control over replenishments. This full information sharing with control by the manufacturer captures what has been called a vendor-managed inventory scenario in the literature. In order to estimate the performance of these three supply chain scenarios, we utilise renewal theory to develop probability models for each. A computational analysis of the models determines the benefit which information sharing offers as well as the incremental benefit central control provides beyond that of information sharing alone, a value which has not typically been considered in the literature despite its importance. Results indicate central control offers a 4.0% improvement over the no-information sharing setting; however, information sharing alone accounts for some of that benefit, offering an average cost savings of 1.8% compared to the no-information sharing setting. We then conclude that central control offers only an additional 2.2% benefit over the information sharing setting.

Six Sigma sales and marketing: application to NCAA basketball

Purpose - Todays economic climate has fueled intense competition for entertainment dollars, including those spent on professional and intercollegiate sports. The purpose of this paper is to provide insight and demonstrate the use of the Six Sigma methodology as a way to improve event attendance in a sports marketing setting. Design/methodology/approach - The research utilizes the define-measure-analyze-improve-control (DMAIC) sequence to evaluate customer requirements and develop recommendations. Surveys, focus groups, and descriptive statistics comprise an important set of tools utilized to accomplish this aim. Findings - The paper concludes that Six Sigma can be readily applied to a sports marketing setting by explicitly demonstrating the steps employed to reverse a four-year decline in attendance for a collegiate mens basketball program. Originality/value - The paper is unique in detailing the use of Six Sigma, a traditional quality improvement methodology, in a sports marketing setting. It also highlights the unique elements associated with fully implementing a Six Sigma project in such a seasonal setting as an athletic enterprise.

Simultaneous use of customer, product and inventory information in dynamic product promotion

Currently, retail data are both accessible and plentiful while the retail space has become increasingly competitive. When combined with technology like mobile computing and low cost analytic techniques, data can now be leveraged by companies to dynamically offer individualised promotions in real time. This paper considers the relative value of three retail information elements which can be used by retailers to dynamically identify a subset of product offerings to promote to their customers. The retail information elements considered are: (a) product markup, (b) customer preference estimates gleaned from purchase history and (c) retailer inventory positions. The importance of each element is evaluated singularly and in combination as is their effect on promotion success, inventory costs and average markup. Computational results show that, on average, dynamic promotion policies incorporating all retail information elements can increase expected profit by 14.5% over policies that consider only customer preference and by 8.4–9.1% over policies that consider only product margin or inventory. Results demonstrate that customer preference information alone does little to improve performance but provides substantial synergistic benefits when combined with either inventory or markup information elements. The most information intensive dynamic promotion policy is then extended to include price as a decision variable.