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Sales Forecasting “All planning begins with a forecast.”

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SAP R/3 Production Planning Process

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Sales Forecasting at IDES Mfg. IDES manufactures a desktop computer, the “Eman”. The computer is made at the IDES plant in Flagstaff. It is the beginning of January 2003 and different groups of IDES management are engaged in planning for the next month, the next year, and the next five years. Since all planning begins with a forecast, IDES needs to get busy creating forecasts.

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Planning Activities at IDES For the 1 st quarter of 2003, IDES needs to make plans to schedule production. This requires determining the labor and materials needs to meet sales demand. For the entire year 2003, IDES needs to determine if any additions to capacity will be required to meet sales demand. Present capacity per quarter = 312,000 regular time and overtime. IDES needs to know if the present Flagstaff plant will be able to produce enough “Eman” computers to meet sales demand over the next five years.

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IDES Historical Sales Data: Annual Sales YearUnit Sales , , , ,321,000 Quarterly Sales Qtr Qtr Qtr Qtr Total

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The time frame for the sales forecast will determine the appropriate forecasting “technique”. In many situations, short-run sales forecasts are made using the technique, “same period last ____”. Other short-run sales forecasting techniques are “moving average”, “weighted moving average”, and “exponential smoothing”. “Trend Projection” can be used to make both short- run and intermediate-run sales forecasts. This techniques uses the historical pattern of % growth or a linear trend line. In “Trend Projection”, the assumption is made that sales depend on the passage of time. Sales follow a “trend” (increase, decrease, remain the same) as time passes.

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Calculating the % Growth in Sales Growth Rate YearUnit SalesYear over Year ,000Base , % , % ,321, % 2003 ? Ave Annual Growth Rate = 46.5%

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2003 Sales Forecast using % Growth Use Actual Sales for 2002 and multiple this by the appropriate % growth: Sales Forecast 2003 = Actual Sales 2002 * = 1,321,000 * = 1,935,265

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Calculating a “Trend” Forecast using Simple Linear Regression Arrange the Annual Sales Data in SLR Format: Year (x) Sales (y) (x)2 xy Sum x-bar = 2.5 y-bar = 816

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Using SLR (trend projection) to create the “Long-Term” Sales Forecast: Use the formulas on page 94 to calculate “b” (the slope) and “a” (the intercept) of the SLR “trend” line. b = 9651 – 4(2.5)(816) = – 4(2.5)(2.5) a = 816 – 298.2(2.5) = 70.5

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Calculate the 5-Year Sales Forecast Y t = a + bx t = (x t ) Substitute the next 5 values for “x” into the above equation and solve: Year (x t )Unit Sales (000) (y t )

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Calculate the 2003 Sales Forecast by Quarter: Find the “Seasonal Index (SI)” from the historical data (2001 and 2002) Unit Sales (000) Quarter Ave. SI Sum

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What does the “Long-Term” Sales Forecasts tell us? Forecast Present Capacity Year SLR Reg Time O/T Shortage IDES Manufacturing will have to subcontract for 64,000 units during 2003 and begin to add capacity during 2003 to meet the expected demand during the next five years.

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Another method for making the “Intermediate-Term” Sales Forecast: Calculating a “Seasonally Adjusted Trend” Sales Forecast: 1. From the historical data, find the “Seasonal Index” (SI) for each quarter of the year. 2. Using the “trend line” calculate the sales for 2003 (entire year). 3. Multiply the appropriate SI by the sales forecast for the year to obtain the “Seasonally Adjusted” sales forecast for each quarter.

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Seasonal Index = Ave for Qtr/Ave for Year Total Ave. Seasonal Index Qtr Qtr Qtr Qtr Total Total sales for both years = = 2222 The average annual sales for the two years = 1111 To calculate the SI: Qtr.1 = (Ave Sales Qtr 1)/(Ave Annual Sales) =0.1962

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Seasonally Adjusted Forecast for 2003 Multiply the SI for each quarter by the forecast for the entire year: Qtr 1: (0.1962)(1562) = Qtr 2: (0.2421)(1562) = Qtr 3: (0.2367)(1562) = Qtr 4: (0.3258)(1562) =

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Forecast for Qtr 1 of 2003: Use the forecast for Qtr 1 we just calculated. It incorporates both the trend (growth in this case) and the seasonal influences that are evident in the historical sales data. Qtr 1 Forecast =

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What does the Qtr 1 Forecast tell us? Production capacity at regular time exceeds the sales forecast. Regular time capacity = 312 Forecast = IDES does not need to use overtime production or subcontracting to meet the needs for this quarter. But Qtr 2 sales are expected to be and this does exceed regular time capacity. What should IDES do?

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