Applications of Management Science: Volume 19

As the pressure to win and generate revenue and as the allegations of out-of-control spending continue to increase, there exists much interest in intercollegiate athletics. While researchers in the past have investigated specific issues related to athletics success, revenue generation, and graduation rates, no previous studies have attempted to evaluate these factors simultaneously. This chapter discusses the development of a data envelopment analysis (DEA) model aimed at measuring how efficient university athletic departments are in terms of the use of resources to achieve athletics success, generate revenue, and promote academic success and on-time graduation. Data from National Collegiate Athletic Association (NCAA) Division I Football Bowl Subdivision (FBS) universities are used to evaluate the relative efficiency of the institutions. The model identifies a series of “best-practice” universities which are used to calculate efficient target resource levels for inefficient institutions. The value of the proposed methodology to decision makers is discussed.

Real estate investment trusts (REITs) provide a mechanism through which investors can participate in the real estate market with liquidity and transparency. In this study, we benchmark the performance of 11 residential REITs for the period 2009–2013. The study tracks the performance of residential REITs through the economic crisis period. The data envelopment analysis (DEA) model uses well-performing units (efficiency of 1% or 100%) that are closest to the underperforming unit on the efficiency frontier as a “role model” (peer units) for the underperforming unit. In addition, the DEA model also calculates by how much a nonperforming unit should increase the output level or decrease the inputs level to be on the efficiency frontier (100%) (slack values). Thus, the DEA model identifies the underperforming units and the most feasible path to move to efficiency frontier. The DEA model identifies the peer units that are closely related to these units and calculates the value of the slack variables required to achieve the same efficiency level as their peers.

In this chapter, we evaluate the dollar amount spent on advertising relative to sales, profit margin, and growth rates to study the effectiveness of advertising in today’s retail environment, and whether it leads directly to higher sales and increased profits affording positive earnings for the investor. The study illustrates the use of data envelopment analysis (DEA) technique to benchmark 16 apparel firms to evaluate the effectiveness of their advertising dollars on the sales, profit margin, growth, return on assets (ROA), return on equity (ROE), and return on investment (ROI).

This chapter develops a productivity analysis of the US telecommunications industry using a data envelopment analysis (DEA) approach. The study concerns itself with eight telecommunications companies. Output variables used are market price, return on equity, and debt equity ratio. The input variables are sales to profit, return on equity, and debt ratio to capital.

This chapter studies the integration of quantitative and qualitative attributes of a particular issue in the strategic “designing” level of the reverse supply chain (RSC) process in a multicriteria decision-making environment. The study employs an analytical network process (ANP) to determine the performance indices of the collection centers derived through qualitative criteria from the remanufacturing facilities that are interested in buying used products. The evaluating criteria are comprised as a four-level hierarchy: the first level contains the objective of evaluating the collection centers, the second level involves the main evaluation criteria taken from the perspective of a remanufacturing facility, the third level contains the subcriteria under the main evaluation criteria, and the fourth level has the collection centers. ANP is presented herein as a matrix that comprises a list of all facets listed horizontally and vertically. This particular method is of value when key elements of a decision are difficult to quantify and contrast, and thus the identification of important facets and their incorporation into a linear physical programing (LPP) environment is of value. To determine the quality of end-of-life (EOL) products for transport from collection centers to remanufacturing facilities, a physical programming approach is adopted. Four criteria and their satisfaction are focused upon: (1) maximizing the total value of purchase; (2) minimizing the total cost of transportation; (3) minimizing the disposal cost; and (4) minimizing the purchase cost. A numerical example is considered in which three collection center locations are evaluated to identify the optimal collection center.

Two-dimensional warranty policies exist for certain consumer products, such as automobiles. Here, warranty is specified in terms of the time since the sale of the product as well as mileage incurred during that period. Thus, at the time of purchasing the product, the manufacturer may offer a warranty of three years or 30,000 miles, whichever occurs first. Failures in the product within this specified period of time or mileage will be covered by the manufacturer.

In this chapter, we consider the scenario of enterprise warranty programs, where customers are given the option of extending the original warranty. Thus, the buyer could be given an option to purchase a five year—50,000 mile warranty, whichever occurs first. Of course, the buyer will be expected to pay a premium to purchase this extended warranty. Such enterprise warranty programs are also found in other consumer durables, such as refrigerators, washers, dryers, and cooking ranges.

This chapter explores determination of the decision variables, such as product price, warranty time, and usage limit under the original conditions and further, for the enterprise warranty, that is, the extended warranty time and extended usage limit, as well as the premium to be charged to the buyer who selects the extended warranty. Mathematical models are developed based on maximizing the expected unit profit by selecting an enterprise warranty program. Additionally, some other objectives are also considered based on the proportional increase in the expected unit profit due to the increased market share attained through the offering of an enterprise warranty program. Some results are obtained through consideration of various goal values of the chosen objectives.

In this chapter, four bi-objective vehicle routing problems are considered. Weighted-sum approach optimization models are formulated with the use of mixed-integer programming. In presented optimization models, maximization of capacity of truck versus minimization of utilization of fuel, carbon emission, and production of noise are taken into account. The problems deal with real data for green logistics for routes crossing the Western Pyrenees in Navarre, Basque Country, and La Rioja, Spain.

Heterogeneous fleet of trucks is considered. Different types of trucks have not only different capacities, but also require different amounts of fuel for operations. Consequently, the amount of carbon emission and noise vary as well. Modern logistic companies planning delivery routes must consider the trade-off between the financial and environmental aspects of transportation. Efficiency of delivery routes is impacted by truck size and the possibility of dividing long delivery routes into smaller ones. The results of computational experiments modeled after real data from a Spanish food distribution company are reported. Computational results based on formulated optimization models show some balance between fleet size, truck types, and utilization of fuel, carbon emission, and production of noise. As a result, the company could consider a mixture of trucks sizes and divided routes for smaller trucks. Analyses of obtained results could help logistics managers lead the initiative in environmental conservation by saving fuel and consequently minimizing pollution. The computational experiments were performed using the AMPL programming language and the CPLEX solver.

During the 2004–2009 building boom, building materials in the United States were in short supply, in particular drywall. This shortage arose from the vast demand for repairing and rebuilding houses caused by several large hurricanes, namely, Katrina and Rita.

The situation is complex because there are many stakeholders involved: manufacturers, suppliers, contractors, insurance companies, and homeowners. The problem begins with the suppliers (apart from the natural disasters that exacerbated the issue). The first question that should be asked is Why import drywall from overseas to use in the United States? and What regulations were in place regarding the usage of drywall, and so on. The next item that needs to be looked at is the homeowners. This is a very bad situation for them as they must evacuate their homes. Some of them had to move out and rent an apartment. Some of them sold their houses for less than half of what they paid for them. The problem is What can they do about the defective drywall in their houses? Further, Will they get their money back? If they do, Who is going to pay for it? or Where are they going to stay?, and so on. Since there are an estimated 100,000 homes in more than 20 states that were effected in this situation, this chapter will focus on the homeowners who live in Virginia, as it is the residence of the chapter’s primary author. It is very important to understand the homeowners’ problems and also their options to overcome this problem. Various attempts have been made to solve the situation but the problem is still there. The problem not only involves homeowner compensation but also a need to prevent this situation from happening in the future.

During the past several decades, the decision-making process and the decision-makers’ role in it have changed dramatically. Because of this, the use of analytical tools, such as Excel, have become an essential component of most organizations. The analytical tools in Excel can provide today’s decision-maker with a competitive advantage. We will illustrate several powerful Excel tools that facilitate the decision support process.