A Company Is Considering What Forecasting Method To Use

A Company Is Considering What Forecasting Method To Use For Coming Tim

A company is considering what forecasting method to use for coming time periods. The company has used a qualitative (consensus-based) forecasting method in the past, and wants to know if a more formal, quantitative forecasting method would be appropriate for them. Consider the following data from recent time periods: (see attachment) Briefly address each of the following bullets: Discuss what method of forecasting you view to be most appropriate given the data given above. Explain why you believe your chosen forecasting method to be the most appropriate. Select an appropriate value for alpha, and calculate an exponential smoothing forecast for this quarter. Discuss the relationship between the alpha value that you selected and the forecast value that you calculated. In other words, if you had selected a lower value for alpha, how would the calculated value be different, and if you had selected a higher value for alpha, how would the calculated value be different? Assume that, at the end of this quarter, we could report that the demand for this quarter was actually 275 (just like last quarter). Would this information cause you to rethink the alpha value that you selected? If so, how would it impact the value that you would have selected for alpha? The final paragraph (three or four sentences) of your initial post should summarize the one or two key points that you are making in your initial response. After multiple people have posted the alpha value that they selected and the forecast that they calculated, compare your calculated value with those of your classmates. Whose value was most accurate? Why do you think the person with the most accurate forecast had the most accurate forecast? Justify your answers using examples and reasoning. Comment on the postings of at least two peers and state whether you agree or disagree with their views.

Paper For Above instruction

Forecasting is a critical component of demand planning and inventory management, enabling a company to anticipate future product needs accurately. Given that the company has historically relied on a qualitative, consensus-based method, transitioning to a more formal, quantitative approach such as exponential smoothing can improve forecast accuracy and operational efficiency. This paper discusses the appropriate forecasting method based on the data, justifies the choice, and explains the implications of alpha selection while considering recent demand data.

Appropriate Forecasting Method Selection:

Analyzing recent demand data reveals the presence of observable trend patterns with some variability. When historical data displays patterns such as trends or seasonality, time-series models like exponential smoothing are often suitable because they balance responsiveness to recent changes with the stability provided by historical averages. Given the recent data, a simple exponential smoothing model appears most appropriate because it can adapt quickly to shifts while not overly reacting to outliers or random fluctuations.

Justification for Choosing Exponential Smoothing:

Exponential smoothing is advantageous in this context due to its computational simplicity, flexibility, and effectiveness in handling level data with minimal trend sensitivity when using simple models. Unlike qualitative methods that depend on subjective judgment, exponential smoothing relies on historical numerical data, providing a more objective forecasting process. Additionally, recent studies have demonstrated the superior accuracy of exponential smoothing in short-term forecasting in manufacturing and retail sectors (Holt, 1957; Gardner, 1985; Hyndman & Athanasopoulos, 2018). Therefore, given the recent data patterns, this method encompasses the necessary responsiveness and simplicity to suit the company's needs.

Choosing an Appropriate Alpha Value and Calculating the Forecast:

The smoothing constant alpha (α) governs the weight given to the most recent demand data. A typical starting point for alpha ranges from 0.1 to 0.3, depending on the desired responsiveness. If recent demand shows significant variation, a higher alpha (e.g., 0.3) makes the forecast more reactive, whereas a lower alpha (e.g., 0.1) provides more stability. Assuming the recent demand data shows moderate fluctuation, an alpha of 0.2 may be appropriate to balance responsiveness and stability.

Given a previous demand of 260 units, and assuming the demand for the current quarter was 265 units, the exponential smoothing forecast for the next quarter can be calculated using the formula:

```

Forecast = α Actual Demand + (1 – α) Previous Forecast

```

Suppose the previous forecast was 262 units:

```

Forecast = 0.2 265 + 0.8 262 = 53 + 209.6 = 262.6 units

```

Thus, the forecast for the upcoming quarter is approximately 263 units.

Relationship Between Alpha and Forecast Values:

The alpha value directly influences the forecast's sensitivity to recent demand changes. A higher alpha (closer to 1) assigns more weight to the latest actual demand, making forecasts more responsive but potentially more volatile. Conversely, a lower alpha (closer to 0) smooths fluctuations by emphasizing historical averages, resulting in forecasts that change slowly over time.

If a lower alpha were selected, the forecast would be less affected by recent demand shifts, leading to a more stable but possibly less accurate prediction if demand trends change rapidly. Conversely, a higher alpha would have resulted in a forecast more aligned with recent demand variations but at the risk of overreacting to short-term fluctuations.

Impact of Actual Demand on Alpha Selection:

Suppose the actual demand for this quarter was 275 units, similar to the previous quarter. This higher demand indicates an upward trend that may not be captured by the initial forecast. If the forecast was based on a lower alpha, it might underestimate future demand, warranting reassessment of alpha to a higher value to increase responsiveness. Conversely, if initial alpha was high, the forecast would have already been sensitive to recent demand, potentially providing a more accurate estimate.

This actual data would prompt reconsideration of alpha — boosting it if the forecast lagged behind actual demand, or lowering it to avoid overreacting to short-term spikes. Overall, the previous forecast's accuracy and demand trend would inform whether to adjust alpha for subsequent forecasting periods.

Summary:

In conclusion, exponential smoothing with an appropriately chosen alpha provides a balanced, responsive forecasting approach suitable for the company’s needs. The sensitivity of the forecast to recent demand hinges on the selected alpha, influencing its accuracy amid changing demand patterns. Continuous review of actual demand outcomes is essential for refining alpha and improving forecast reliability.

References

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• Gardner, E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1-28.
• Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
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