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Forecasting business needs


Forecasting business needs
Our forecasting team recently completed the project to forecast next year’s business needs, and specifically to arrive at a production schedule for you. To arrive at our forecast it was necessary to consider several variables and how they impact our sales. We took into account the overall industry sales, our market share, our growth rate, and seasonal variations in sales.
We anticipate sales this year to reach $2. 4 billion dollars. This is based on our 6 percent market share of a $40 billion industry. The advertising budget has been forecast based on sales due to its high correlation coefficient with sales (. 96). Historically, at Blues Inc., changes in sales are closely reflected in changes in the advertising budget. Based on the $2. 4 million figure, we forecast an advertising budget of $162 million.
We have also forecast the overall market size based on its recent performance. Currently the market size is approximately $39 billion. To eliminate the effects of temporary changes in market conditions, we used a two-year weighted moving average, with the most recent year having greater significance. This gives us an indication of the trend, and helps to even out any spikes or sudden changes.
You have also requested a production schedule, by quarter, for the next year. We have based the overall production level based on our 6 percent market share of a $39 billion industry. This is approximately 777 million units. Accounting for the industry growth rate, for Blue inc, this will mean 50 million units for the year. Because there has been a considerable fluctuation in sales during the fourth quarter of previous years, we used a centered moving average to arrive at our results to compensate for the seasonal changes. We utilized the past 6 years of data. This gave us sufficient information to arrive at an answer, without using data that is outdated and may skew the results. The results have been included in a separate report.
Arriving at the results of this forecast has required my team to consider past results to predict our future performance. When deciding which method to use, it was important to review the existing data to see which method would yield the best results. The chosen method needs to account for a high correlation coefficient of independent variables, the number of periods that affect the next period, the statistical impact of the most recent period, and compensate for any seasonal or temporary fluctuations.

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