Search results

In this chapter, we describe how time series analysis can often provide better insight than prior year data for predicting the total impact of an atypical event – including (1) taking into account other atypical events, (2) determining if the impact lasted greater than one season, and (3) adjusting for any performance/metric “rebounding” in subsequent seasons. We demonstrate using time series analysis to estimate the impact of the 9–11 terror attacks on the Hawaiian tourism industry. Terror attacks, in addition to the potential loss of life and property, can induce a post event fear factor that results in decreased revenue and profitability for businesses and their respective industries, insurers, and tax-receiving governments.

Assume that we generate forecasts from a model y=cx+d+. “c” and “d” are placement parameters estimated from observations on x and y, and is the residual.

If the residual is observed to be symmetric about the mode, it is usually assumed to be distributed by the Gaussian family of functions. If the residual is skew to the left of the mode, or to the right of the mode, it cannot be assumed to be normally distributed. A family of functions will then have to be found which will correctly represent the observed skew values for . The analyst has to search for a family on a case-by-case basis, trying one family of functions first, then another, till one is found which fits the observed non-symmetric -values correctly. This chapter aims to eliminate this time consuming estimation process. The chapter introduces a family of functions. The family is capable of taking any skew or symmetric locus by varying its placement parameters. The family will simplify the effort to correctly measure the densities of because the estimation problem is reduced to fitting only one function to the data if it is symmetric or skew.

This chapter shows how the forecasting and the planning functions in a supply chain can be organized so they will yield optimal forecasts for an entire supply chain. We achieve this result by replacing the process of generating forecasts with that of making optimal coordinated supply chain decisions. The ideal performance for a supply chain is to have the flows of materials perfectly synchronized with the demand rate for the finished product that the chain produces. When the equality is achieved, we have a pure “demand pull” supply chain. This ideal is difficult to achieve because forecasting and decision making in supply chains are typically decentralized and forecasting and planning uncoordinated. Creating a competitive advantage for the finished product requires achieving the ideal. The opposite, not achieving the ideal, leads to uncoordinated forecasts and decisions that trigger unintended buildup of inventories, lost sales and the bullwhip effects, slowness and high costs.

This chapter shows how (1) we can achieve the ideal synchronous supply chain flows by using temporal linear programs; (2) then, we guide each individual supply chain member company in developing his optimal operations plan to guide him in executing his part in the supply chain plan. The result from the two factors: the entire supply chain will achieve the ideal flow rates.

In this chapter, we analyze donor behavior based on the general segmentation bases. In particular, we study the behavior of the individual donor group's support for higher education. There has been very little research to date that discriminates the donor behavior of individual donors on the bases of their donation levels. The existing literature is limited to a general treatment of donor behavior using one of the available classical statistical discriminant techniques.

We investigate the individual donor behavior using both classical statistical techniques and a mathematical programming formulation. The study entails classifying individual donors based on their donation levels, a response variable. We use individuals’ income levels, savings, and age as predictor variables. For this study, we use the characteristics of a real dataset to simulate multiple datasets of donors and their characteristics. The results of a simulation experiment show that the weighted linear programming model consistently outperforms standard statistical approaches in attaining lower APparent Error Rates (APERs) for 100 replications in each of the three correlation cases.

Regression analysis is a commonly applied technique used to measure the relationship/predict/forecast of comparable units. A set of comparable units is some group of entities each performing somewhat the same set of activities. In this chapter, we will apply a modified version of our recently developed methodology to incorporate into the regression analysis a new variable that captures the unique weighting of each comparable unit. This new variable is the relative efficiency of each comparable unit that will be generated by a technique called data envelopment analysis (DEA). The results of applying this methodology with the DEA variable to a hospital labor data set will be presented.

This is a study of forecasting models that aggregate monthly times series into bimonthly and quarterly models using the 1,428 seasonal monthly series of the M3 competition of Makridakis and Hibon (2000). These aggregating models are used to answer the question of whether aggregation models of monthly time series significantly improve forecast accuracy. Through aggregation, the forecast mean absolute deviations (MADs) and mean absolute percent errors (MAPEs) were found to be statistically significantly lower at a 0.001 level of significance. In addition, the ratio of the forecast MAD to the best forecast model MAD was reduced from 1.066 to 1.0584. While those appear to be modest improvements, a reduction in the MAD affects a forecasting horizon of 18 months for 1,428 time series, thus the absolute deviations of 25,704 forecasts (i.e., 18*1,428 series) were reduced. Similar improvements were found for the symmetric MAPE.

This chapter will present a goal programming model which simultaneously generates forecasts for the aggregate level and for lower echelons in a multilevel forecasting context. Data from an actual service firm will be used to illustrate and test the proposed model against a standard forecast technique based on the bottom-up/top-down approach.