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Demand seasonality in the U.S. Imported Beer industry is common. The financial cycles of the past decade brought some extreme fluctuations to industry demand, which was trending upward. This research extends previous work in this area by comparing seasonal forecasting models for two time periods: 1999–2007 and 1999–2012. The previous study (Kros & Keller, 2010) examined the 1999–2007 time frame while this study extends their model using the new data. Models are developed within Excel and include a simple yearly model, a semi-annual model, a quarterly model, and a monthly model. The results of the models are compared and a discussion of each model’s efficacy is provided. While, the models did do a good job forecasting U.S. Import Beer sales from 1999 to 2007 the economic downturn starting in 2007 was deleterious to some models continued efficacy. When the data from the downturn is accounted for it is concluded that the seasonal models presented are doing an overall good job of forecasting U.S. Import Beer Sales and assisting managers in shorter time frame forecasting.

This chapter presents an Excel-based regression analysis to forecast seasonal demand for U.S. Imported Beer sales data. The following seasonal regression models are presented and interpreted including a simple yearly model, a quarterly model, a semi-annual model, and a monthly model. The results of the models are compared and a discussion of each model's efficacy is provided. The yearly model does the best at forecasting U.S. Import Beer sales. However, the yearly does not provide a window into shorter-term (i.e., monthly) forecasting periods and subsequent peaks and valleys in demand. Although the monthly seasonal regression model does not explain as much variance in the data as the yearly model it fits the actual data very well. The monthly model is considered a good forecasting model based on the significance of the regression statistics and low mean absolute percentage error. Therefore, it can be concluded that the monthly seasonal model presented is doing an overall good job of forecasting U.S. Import Beer Sales and assisting managers in shorter time frame forecasting.

The relationship between electricity demand and weather in the United States has been studied as of late due to increased demand, de-regulation, and new pricing models. The influence of weather or seasonality in energy consumption, particularly electricity demand, has been widely researched. A significant scientific interest in the seasonality of energy consumption has led to an important number of papers exploring the role of weather variability and change on energy consumption. Most of these papers model demand as a function of seasonal climate factors.

The goal of this research is a broad examination of monthly residential electricity demand for a region of the mid-Atlantic using Excel and step-wise regression. This is achieved by using a sequence of models built in Excel in which different patterns are gradually introduced in the estimations. Data over a seven-year period is utilized. A backward elimination step-wise regression analysis is employed to determine which independent variables best model the data. Initial independent variables included high monthly temperature, low monthly temperature, time, year, month, seasonal quarter, and introduction of a “green” tax credit for solar and wind energy.

Models for forecasting the electricity demand and the predictive power of these models is assessed. The work is organized as follows: Data description and the methodology, trend and the seasonality of electricity usage in the mid-Atlantic region, the predictive power and seasonality of the models, and main conclusions drawn from the study.

Examines the way in which the spreadsheet has developed into a sophisticated modelling tool. This has been achieved by considering various releases of Lotus 1‐2‐3 and the latest version of Microsoft Excel. Refers to specific spreadsheet facilities, but does not provide the mechanical details of invoking the various commands, in order that the ideas described can be transferred to other spreadsheets offering similar facilities. Explores the development of the spreadsheet on two fronts; first with reference to its functional and programmable capability, and second by considering the advancement of its “what if” facilities.

Undertakes a comparative study of the statistical capability of three spreadsheets which are commonly used in the business sector. The spreadsheets considered are Lotus 1‐2‐3, Microsoft Excel and Quattro Pro. Considers five areas of statistical analysis regularly used by business decision makers (rather than specialist personnel). In order to obtain an objective measure of the statistical provision of each spreadsheet, comparison has also been made with dedicated statistical software regularly used by business decision makers, namely MINITAB. By making this comparison, argues that the spreadsheet is not only a tool for analysis, but also for presentation. Moreover, considers that two spreadsheets in particular, namely Excel and Quattro Pro, offer a user‐friendly statistical provision which should be sufficient for most business decision makers.

Maintaining efficiency in despatching goods from a depot to geographically dispersed customers may require management at the depot to adjust its delivery routes daily if these customers’ demands fluctuate from day to day. One type of adjustment is to give drivers daily “skip lists” instructing them not to visit customers who have indicated that they do not need delivery on the day in question. This adjustment, which is appropriately referred to as semi‐variable routes, increases the depot’s outbound logistics productivity by eliminating some unnecessary travelling. Using extensive experimental data, develops a regression model that efficiently and accurately estimates this productivity increase, and illustrates how spreadsheets can be used as a decision support medium for using the model in pedagogical and applied settings. Confirms the model’s validity by standard model validation tests as well as by comparison with an existing model from previous research.

Reviews the literature on capacity planning at strategic, tactical and operational levels but points out that, in practice, many enterprise resource planning systems make unrealistic assumptions for production planning; and the advanced production software packages which can deal with uncertainty are both complex and expensive. Uses a theoretical company to demonstrate how a normal Excel spreadsheet can be used in conjunction with a common add‐on package (@RISK) to improve analysis and run Monte Carlo simulations as a basis for decision making. Compares the results produced with standard spreadsheet analysis and discusses the additional financial and operational insights they provide into the implications of different capacity levels under conditions of uncertainty. Warns that the validity of the simulation depends on the quality of the data and model; and that human judgement is still required to actually make a decision.

Considers alternative types of computer models designed to assist decision makers on questions of site location. Relates to a larger scale research project which aims to examine the necessary requirements for computers to provide practical decision support. It involves comparing a wide range of modelling techniques, with a view to establishing their relative strengths and the conditions under which each would be the most appropriate choice. Discusses the range of modelling techniques available and provides a more detailed review of one particular model which combines a “conventional” approach, based on algebraic equations, with an expert systems approach.

Decision support systems (DSS) is the mathematical‐model building tool of business environments. Artificial intelligence (AI) is the tool that will hopefully help computers make better business decisions by allowing the computer to learn from its experiences. The combination of DSS and AI takes us out of the theoretical by integrating the analytical tools into the management decision process, thereby making the decisions practical and useful. Discusses the integration of DSS and AI into the business decision‐making process.