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The accuracy of forecasts has a critical impact on an organization. A new, practical, and meaningful forecast performance measure, percentage forecasting error (PFE), was introduced by the authors in an earlier publication. In this chapter, we examined the accuracy of the PFE under several different scenarios and found the results to indicate that PFE offers forecasters an accurate and practical alternative to assess forecast accuracy.

Producing good forecast is a vital aspect of a business. The accuracy of these forecasts could have a critical impact on the organization. We introduce a new, practical, and meaningful forecast performance measure called percentage forecast error (PFE). The results of comparing and evaluating this new measure to traditional forecasting performance measures under several different simulation scenarios are presented in this chapter.

In many educational and professional environments, diversely talented teams are created to solve problems requiring different skill sets. In the educational setting teams may be used to conduct a learning project; in a work setting teams may be used to develop a new product. Teams are usually constructed from “players” in different functional departments. Because the “best” player in each department can’t be on all teams, constructing teams so that all teams function optimally is a challenging and often arbitrary process. This chapter describes a multiple criteria model for team selection that balances skill sets among the groups and varies the composition of the teams from period to period. The results of applying this team selection model to a cohort-structured Executive MBA (EMBA) program and to team selection in a Fortune 100 corporation are presented. The results of this project suggest that an implantation of a quantitative method, such as our Model III, markedly improves team performance and achieves that improvement in a timelier manner.

A common problem that many universities face, especially with their specialized programs, is coordinating faculty availability and class offerings. The schedule is usually developed using paper and pencil after numerous iterations. As a result, the objectives of the program, such as course integration, length of course, and student workload, are most likely compromised in lieu of faculty availability. This chapter describes a multiple objective approach to this class assignment problem that considers the program’s objectives and faculty preferences. The results of applying this class assignment model to an Executive MBA (EMBA) program are presented.