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The benefit of formalized forecasting is to better equip manufacturing when ordering raw materials, whether or not to increase or decrease production, add or delete shift labor, and plan inventory levels. It helps determine costs and profits due to increase cost of raw materials, tax increase or decrease, and petroleum increases which affects shipping costs and cost raw materials. Forecasting helps in the trending of seasonal operations whether or not to ramp up production to stock inventory or ramp down production due to changes in the industry. Forecasts help with long range plans or new product development and must be monitored often for accuracy.

For example: General Electric (GE)/Vital Signs Devices (VSD), is an anesthesia circuit manufacturer that build to order, mandates each sales representative to submit a weekly forecast funnel of potential product being sold < >85%. This forecast determines whether GE/VSD increase shift labor, purchase more or less raw material, assists in calculating costs and profits, as well as track trends in the industry. Because GE/VSD does not stock inventory of finished goods it is imperative that these forecasts be as accurate as possible.

Forecasting product number 1 the manager might use the technique for averaging forecast which generate forecasts that reflect recent values of time series (p. 81). Because week 7 was inconsistent due to flooding conditions, consider it an outlier which must be removed for consistency. To forecast the average take the sum of the last 5 weeks of orders and divide by 5. This would give the manager a weekly average forecast.

MA5 = last five weeks of orders

5

Week 15 = 82+85+87+92+96

5

Week 15 = 442

5

Week 15 87 units

16 88 units

17 89 units

18 89 units

Forecasting product number 2, the manager might take a more simple approach, the naïve forecast. This forecast is best with stable series that might be subject to seasonal variations, or with trends. Naïve forecast is a forecast for any period that equals the previous period's actual value (p. 79).Take the last two values 92 and 96, subtract 92 from 96, leaving 4; add the highest value 96, back into the equation. as mentioned this is a simple approach to forecasting however, one must monitor its accuracy weekly if not daily.

Week 15 = (96-92) + 96

Week 15 = 100

Week 16 = 104

Week 17 = 108

Week 18 = 112

Reference: Stevenson, W.J. (2009). Operations management (10th ed). New York : McGraw Hill/Irwin

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