The book Excel Forecasting for Dummies by Conrad Carlberg organizes sales data, charts a baseline and forecast trends with Mi






Excel Forecasting for Dummies’

The book ‘Excel Forecasting for Dummies’ by Conrad Carlberg organizes sales data, charts a baseline and forecast trends with Microsoft Excel workbook program. It assists the reader predict the future without a crystal ball or incense. The book helps the reader to predict trends and future sales based on something not quite like ethereal numbers. Data from the past can be used to predict sales forecast for the future. Excel has many useful tools that can be very helpful in predicting the future and this book shows the reader how to do so. The book explains why forecasting is a good idea and attempts to make sense of exponential smoothing. The only disadvantage is that the reader has to have a grasp of how to use excel. The book can be of no use to a person who does not know how to use the excel program. Carlberg (2005) makes the assumptions that the reader knows the basic of how to use excel. He also assumes that the reader has access to information on ones company sales history, that he or she does not have a phobia about numbers and that he or she has excel on his or her computer (Pg. 2).

The concepts from the book are applied in the pharmaceutical organization that I work for in many ways. Exponential smoothing is used to handle the company’s bad forecasts that happened earlier in order to improve the next forecast. The analysis pack that comes as a free gift when one buys Microsoft excel is used in the company to make forecasts for the managers. The data is put into charts so that the managers can see what is going on.

Pivot tables are also used to manage the data to prepare for a basic forecast. They allow the managers to get the company’s existing sales data together from lists so that they can make sensible forecasts. The managers cannot forecast without knowing what has already happened, they cannot also forecast sensibly without getting existing data together. For these reasons, pivot tables are very important in the company. Forecasting is also an important concept and the book makes the reader to believe in forecasting. The book urges the reader to look at the past in order to understand the future. The concepts are applied in the organization to ensure that the goals are met. For instance, they enable me to select and use the right forecasting method for the 14 branches. The corporate data is chosen and arranged in lists and then managed with pivot tables. The lists are then filtered and turned into charts that illustrate what is going on. To find existing relationship between data from the fourteen branches, I utilize the excel analysis two pack feature to generate forecasts automatically. These concepts have enabled me to gain more control over the company forecasting and to target various kinds of predictions. I have also learnt to predict sales and use moving averages. Currently a baseline has been set up where I chart and label. The company data is summarized with pivot tables, forecasts’ are made depending on regression. In ensuring that goals and corporate directives are made, correlation has played a major role and also smoothing has enabled the company to profit from its mistakes. The confidence in the ability to make sales predictions depends on these factors and they have led the company to be very successful. The parts of the book that will be valuable in years to come is part four which entails making advanced forecasts. These forecasts are a bit more complicated when it comes to math and the functions that are used. The advantage of these forecasts is that they shed more light on what is going on in the sales flow. The fourth part walks the reader through how to do forecasts using worksheet functions as opposed to the Analysis ToolPak. These forecasts enable managers to have more control over what is happening in the company. More control is normally better control if the managers are willing to take time to use it. Chapter 10 will also be valuable in years to come because it focuses on how to manage forecasts without relaying various tools. It is important to first learn to forecast using the tools. Learning how to forecast without relying on the tools should come later after one has become accustomed to the tools of forecasting. The fifth part of this would be a waste of time to read because the author focuses on the virtues and rants about its fault. In a rate of 1-10, 10 being the best, this book would be number eight. The book recognizes why forecasting is a good idea and makes sense of exponential smoothing. The reader should only have a basic understanding of using the Excel program. The book is efficient in assisting the manager to select and use the correct forecasting method for their business. It would also enable them to choose and arrange data in lists and then manage that data in pivot tables. The lists can also be filtered and turned into charts that show what is going on in the company. The manager can also find the relationships in the data especially from the 14 representatives. The book also offers a detailed analysis of how the Excel Analysis ToolPak can be used to generate forecasts automatically or venture into advanced forecasting using worksheet functions. By doing this a manager can acquire more control over their company forecasting and target particular kinds of predictions. He or she can then use moving averages and forecast seasonal sales. All these functions are important in forecasting sales and the guide gets one up and running easily and quickly. The book can install confidence in a sales manager’s ability leading to sales prediction, which soar right off the chart. This book rates highly because it does not only explain how to forecast but it also explains the advantages and disadvantages of the various methods of forecasting. The author also explains how forecasting errors can be reduced and provides a good insight into regression and other statistics involved in making and evaluating a forecast.

The book depicts a lot of statistical application tools that can be applied in a pharmaceutical organization. For instance, you can be is interested in determining the effect on sales revenue of certain pharmaceutical product after some promotional strategies. This might require some data on advertising. The amount spent on advertising these products can be collected. Besides, the amount of sale of the products as well can be collected. These data after being collected can be used to calculate if there is a relationship between the advertisement placed on the pharmaceutical products and sales. This relationship is usually called correlation coefficient in mathematical statistics.

The correlation coefficient usually ranges between negative one to positive one. A correlation of negative one means there is a strong inverse correlation, a correlation of zero means there is no correlation between the two variables; whereas a correlation of positive one means there is a strong direct relationship between the two variables in question and therefore we can make conclusion whether it is worth continuing with the advertisement on the products or not.

The second application tool that can be employed in a pharmaceutical firm is the ANOVA, which means ANalysis Of VAriance. There are essentially three distinct ANOVA tools. None is specifically useful for forecasting, but each of the tools can help you understand the set of data that you want to forecast using. The ANOVA tools help you differentiate among sections. For example, it can help you to determine whether a particular sample of your customers buy a particular drug compared to another different set of sample of the population.

The third statistical tool that is more paramount is descriptive Statistics. Just as the name suggests; descriptive Statistics tool is used to describe the set of data given. For example, we can use this tool to depict the average, the standard deviation of the data. The data can as well be described pictorially; by being represented in various forms of data presentation forms. The data can be represented graphically using frequency polygons, tabular representation, pie charts among others. These various types of pictorial representation of data helps one to have a mental picture of the nature of data and thus gives us a further insight and understanding of the data. In a pharmaceutical organization, this tool can be handy in visualizing sales of the pharmaceutical products as well as predicting future sales of the products.

In addition, the use of Moving Average can be employed in a pharmaceutical firm. A moving average, just as the name suggests, shows the average of results over a given period of time. The first one might be the average for, December, January, February, and March; the second would then be the average for January, February, March, and April; and may continue in that trend. This is a method of forecasting tends to center on the sign. That is it tells us what is what is actually taking place in the baseline and it can help a pharmaceutical organization to minimize the noise that is random fluctuations in the baseline having the data collected in the past.

Works cited

Carlberg, Conrad. Excel Sales Forecasting for Dummies. New Jersey: Wiley Publishing,


Harvey, Greg. Excel 2010 Workbook for Dummies. Workbook edition. For Dummies,


Hesse, Rick. Using Excel to Forecast: A Review of Two Recent How-To Books. SoftwareReview.