Singfat Chu

Efficiency, effectiveness and productivity of marketing in services

Abstract How can a service firm right-size marketing expenses and yet strive to maximize revenue? This paper represents the first attempt to model these efficiency and effectiveness issues using a 49-unit Asia–Pacific hotel chain as illustration. We employ a triangular DEA model with total expenses (controlling for number of rooms) as the raw input, marketing expenses as intermediate output/input and revenues from room rentals and F&B as final outputs. We infer that efficiency tails off when more than 12% of the budget is expended on marketing activities. In terms of effectiveness, we find that all the units rated as relatively inefficient can accrue increasing returns to scale in revenues from marketing activities. By contrast, in the productivity stage linking the raw inputs to the revenues, we observe mostly decreasing returns to scale. Our results highlight the crucial role of marketing in service organizations.

Retail productivity and scale economies at the firm level: a DEA approach

We investigate the concept of productivity in retailing. In the literature, there are unresolved issues concerning the construct of retail output and measurement methodologies. In accordance with recently developed theory, we define the output of the retail firm as a set of explicitly priced market goods accompanied by distribution services that are implicitly priced. We operationalise these services, and measure productivity of a chain of grocery stores using data envelopment analysis. We also find evidence of increasing returns to scale. Managerial implications of the findings are provided.

Pricing the C's of Diamond Stones

Many statistical problems can be satisfactorily resolved within the framework of linear regression. Business students, for example, employ linear regression to uncover interesting insights in the fields of Finance, Marketing, and Human Resources, among others. The purpose of this paper is to demonstrate how several concepts arising in a typical discussion of multiple linear regression can be motivated through the development of a pricing model for diamond stones. Specifically, we use data pertaining to 308 stones listed in an advertisement to construct a model, which educates us on the relative pricing of caratage and the different grades of clarity and colour.

Auctioning rights to vehicle ownership: Singapore's experience with sealed-bid tenders

The monthly auction of rights to vehicle ownership in Singapore will switch from a sealed to an open bid format in 2Q2001. Allocation and pricing will however remain contingent on the lowest successful tender. This paper uncovers patterns in sealed-bidding over a period of 70 months. The findings serve as a platform for a discussion on the likely changes in bidding behaviour under an open auction format.

A Robust Optimization Model for Managing Elective Admission in a Public Hospital

The admission of emergency patients in a hospital is unscheduled, urgent, and takes priority over elective patients, who are usually scheduled several days in advance. Hospital beds are a critical resource, and the management of elective admissions by enforcing quotas could reduce incidents of shortfall. We propose a distributionally robust optimization approach for managing elective admissions to determine these quotas. Based on an ambiguous set of probability distributions, we propose an optimized budget of variation approach that maximizes the level of uncertainty the admission system can withstand without violating the expected bed shortfall constraint. We solve the robust optimization model by deriving a second order conic problem (SOCP) equivalent of the model. The proposed model is tested in simulations based on real hospital admission data, and we report favorable results for adopting the robust optimization models.

Some Initiatives in a Business Forecasting Course

The paper reports some initiatives to freshen up the typical undergraduate business forecasting course. These include (1) students doing research and presentations on contemporary tools and industry practices such as neural networks and collaborative forecasting (2) insertion of Logistic Regression in the curriculum (3) productive use of applets available on the Internet to convey abstract concepts underlying ARIMA models and (4) showcasing forecasting tools in timely or familiar applications. These initiatives align with the best practices framed across the “Making Statistics More Effective in Schools of Business” (MSMESB) conferences. Course experiences and student feedback are also discussed.