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The demand for a product in the month of March turned out to be 20 units against an earlier made forecast of 20 units. The actual demand for April and May turned to be 25 and 26 units respectively. What will be the forecast for the month of June, using exponential smoothing method and taking smoothing constant α as 0.2?
- a)20 units
- b)22 units
- c)26 units
- d)28 units
Correct answer is option 'B'. Can you explain this answer?
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Verified Answer
The demand for a product in the month of March turned out to be 20 un...
α = 0.2, DMarch = 20 units DApril = 25 DMay = 26
FMar = 20 units FApril = 20 FMay = 21 FJun =?
FApril = α × DMar + (1 − α) FMar = 0.2 × 20 + 0.8 × 20 = 20
FMay = α × DApril + (1 − α) × FApril = 0.2 × 25 + 0.8 × 20 = 21
FJune = α × DMay + (1 − α) × FMay
FMay = 0.2 × 26 + 0.8 × 21 = 22 units
Most Upvoted Answer
The demand for a product in the month of March turned out to be 20 un...
Understanding Exponential Smoothing
Exponential smoothing is a forecasting technique that uses a smoothing constant (α) to weigh the most recent observations more heavily than older ones. The formula for the forecast for the next period (Ft+1) is:
Ft+1 = α * At + (1 - α) * Ft
Where:
- Ft = Forecast for the current period
- At = Actual demand for the current period
- α = Smoothing constant
Given Data
- March Forecast (F_March) = 20 units
- Actual Demand in March (A_March) = 20 units
- Actual Demand in April (A_April) = 25 units
- Actual Demand in May (A_May) = 26 units
- Smoothing constant (α) = 0.2
Step-by-Step Forecast Calculation
1. Forecast for April:
- F_April = (0.2 * A_March) + (0.8 * F_March)
- F_April = (0.2 * 20) + (0.8 * 20)
- F_April = 4 + 16 = 20 units
2. Forecast for May:
- F_May = (0.2 * A_April) + (0.8 * F_April)
- F_May = (0.2 * 25) + (0.8 * 20)
- F_May = 5 + 16 = 21 units
3. Forecast for June:
- F_June = (0.2 * A_May) + (0.8 * F_May)
- F_June = (0.2 * 26) + (0.8 * 21)
- F_June = 5.2 + 16.8 = 22 units
Conclusion
The forecast for June using the exponential smoothing method with α = 0.2 is 22 units. Thus, the correct answer is option B.
Exponential smoothing is a forecasting technique that uses a smoothing constant (α) to weigh the most recent observations more heavily than older ones. The formula for the forecast for the next period (Ft+1) is:
Ft+1 = α * At + (1 - α) * Ft
Where:
- Ft = Forecast for the current period
- At = Actual demand for the current period
- α = Smoothing constant
Given Data
- March Forecast (F_March) = 20 units
- Actual Demand in March (A_March) = 20 units
- Actual Demand in April (A_April) = 25 units
- Actual Demand in May (A_May) = 26 units
- Smoothing constant (α) = 0.2
Step-by-Step Forecast Calculation
1. Forecast for April:
- F_April = (0.2 * A_March) + (0.8 * F_March)
- F_April = (0.2 * 20) + (0.8 * 20)
- F_April = 4 + 16 = 20 units
2. Forecast for May:
- F_May = (0.2 * A_April) + (0.8 * F_April)
- F_May = (0.2 * 25) + (0.8 * 20)
- F_May = 5 + 16 = 21 units
3. Forecast for June:
- F_June = (0.2 * A_May) + (0.8 * F_May)
- F_June = (0.2 * 26) + (0.8 * 21)
- F_June = 5.2 + 16.8 = 22 units
Conclusion
The forecast for June using the exponential smoothing method with α = 0.2 is 22 units. Thus, the correct answer is option B.
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The demand for a product in the month of March turned out to be 20 units against an earlier made forecast of 20 units. The actual demand for April and May turned to be 25 and 26 units respectively. What will be the forecast for the month of June, using exponential smoothing method and taking smoothing constant α as 0.2?a) 20 unitsb) 22 unitsc) 26 unitsd) 28 unitsCorrect answer is option 'B'. Can you explain this answer?
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