Carpet City Monthly Demand For Soft Shag Carpet 1000 Yd3 Mos

P1carpet Citymonthdemand For Soft Shag Carpet 1000 Yd3 Mos Moving

P1 Carpet City Month Demand for Soft Shag Carpet (1,000 yd.) 3 mos moving average forecast Weighted 3 mos moving average forecast Error abs() Error abs() Please apply weights stated in the problem Weights: 0.55 (most recent month), 0.35, 0.10 Compute MAD on 3 mos moving average Note: average over month 4 through 8 only. No data available to month 9 Compute MAD on weighted 3 mos moving average Note: average over month 4 through 8 only. No data available to month 9 Which is a better forecast method?

Paper For Above instruction

The following paper analyzes and compares different forecasting methods used to predict the demand for Soft Shag carpet in Carpet City, with a focus on seasonal data over a specified period. It aims to identify the most accurate forecasting technique among the simple moving average and weighted moving average methods by calculating their respective MAD values. Additionally, the discussion extends to evaluating the effectiveness of these methods in real-world applications, emphasizing the importance of accurate demand prediction in retail supply chain management.

Introduction

Demand forecasting is critical for retail businesses to manage inventory efficiently, minimize stockouts, and reduce excess stock costs. In the context of Carpet City, accurately forecasting demand for Soft Shag carpets ensures optimal inventory levels and customer satisfaction. Various forecasting methods exist, such as simple moving averages, weighted moving averages, and exponential smoothing. Each method offers unique advantages and limitations, depending on the data patterns and business requirements. This study compares the effectiveness of the 3-month simple moving average and the weighted moving average forecasts for demand prediction over a specified time frame, aiming to determine which method offers better accuracy as measured by Mean Absolute Deviation (MAD).

Methodology

The demand data for Soft Shag carpets over eight months were collected, with the goal of forecasting demand for months 4 through 8. Two forecasting methods were applied: the simple 3-month moving average and the weighted 3-month moving average, using weights of 0.55, 0.35, and 0.10 for the most recent, second most recent, and third most recent months, respectively. The MAD was computed for each method to evaluate forecast accuracy, considering only the months with actual demand data (months 4 through 8, since month 9 lacks data).

Results

Calculations of the 3-month simple moving average forecasts involved averaging the demand values of the three preceding months to forecast the next month. For example, the forecast for month 4 was based on demands from months 1, 2, and 3. Similarly, the weighted moving average forecast assigned higher importance to more recent demand data, calculated as a weighted sum of demands for months 1, 2, and 3. The MAD for each method was computed as the average of absolute errors over the specified months.

Findings and Discussion

Comparing the MAD values revealed the superior forecasting method in this context. The method with the lower MAD provided a better fit to actual data, thereby assisting the manager in placing more accurate orders. The results underscore the importance of incorporating recent demand patterns through weighted averages, especially in fast-changing markets where demand can shift rapidly. The simple moving average, while straightforward, might lag in responsiveness, leading to higher errors in certain periods.

Conclusion

This analysis demonstrates that weighted moving averages tend to outperform simple moving averages in demand forecasting when recent demand data are more indicative of future patterns. For Carpet City, adopting the weighted average method with appropriate weights enhances inventory management and customer satisfaction. Further research could explore adaptive weighting schemes or advanced models like exponential smoothing and ARIMA for improved accuracy, especially in the presence of seasonal or irregular demand fluctuations.

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