-Period Moving Average Forecasting ✓ Solved

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The assignment involves conducting a comprehensive forecast analysis using different time series forecasting methods on a dataset provided by Elissa Torres. The dataset includes demand data across multiple periods and requires the application of various forecasting models such as the 2-period moving average, 3-period moving average, exponential smoothing, and trend-adjusted exponential smoothing. The goal is to analyze forecast accuracy through error metrics and to generate reliable demand forecasts for future periods.

Specifically, you are required to input past demand data into the appropriate forecasting models, calculate forecasts for upcoming periods, and evaluate forecast errors using metrics such as Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Bias. These calculations will help determine which forecasting method produces the most accurate predictions given the historical data.

Your analysis should include detailed calculations and explanations for each forecasting method applied, the error metrics computed, and insights into the selection of the best model based on the error analysis. Additionally, you should discuss the implications of your findings for demand planning and inventory management.

Paper For Above Instructions

Forecasting demand accurately is critical for effective inventory management and operational planning in supply chain management. Various time series forecasting methods exist, each with its strengths and limitations. This paper explores four common forecasting techniques applied to demand data: the 2-period moving average, 3-period moving average, exponential smoothing, and trend-adjusted exponential smoothing. Using hypothetical data provided by Elissa Torres, we will perform these analyses, calculate forecast errors, and interpret the results to identify the most suitable model.

Introduction to Time Series Forecasting

Time series forecasting involves analyzing historical data to predict future demand. The primary challenge is selecting a model that balances simplicity and predictive accuracy. The models under consideration include simple moving averages, exponential smoothing, and trend-adjusted exponential smoothing.

Application of 2-Period Moving Average

The 2-period moving average computes the forecast for the next period as the average demand of the most recent two periods. Mathematically:

Forecast for period t + 1 = (Demand in period t + Demand in period t-1) / 2

This method smooths short-term fluctuations and works well with data exhibiting no trend or seasonality. Using the provided data, forecasts are computed, with the error metrics indicating the model's accuracy.

Application of 3-Period Moving Average

Similar to the 2-period, the 3-period moving average considers the past three demand points:

Forecast for period t + 1 = (Demand in periods t, t-1, t-2) / 3

This approach offers more smoothing than the 2-period average, reducing the impact of anomalies but potentially lagging behind actual demand trends.

Exponential Smoothing

Exponential smoothing assigns exponentially decreasing weights to older observations. The formula is:

Forecast for the next period = α (latest demand) + (1 - α) (previous forecast)

where α is the smoothing constant between 0 and 1. A higher α responds more quickly to changes; a lower α results in smoother forecasts.

Trend-Adjusted Exponential Smoothing

This technique incorporates both the level and trend components:

Forecast = (Level) + (Trend)

with the level and trend updated each period:

• Level: α demand + (1 - α) (level estimate from previous period)
• Trend: β (new level estimate - previous level) + (1 - β) (previous trend)

This method is suitable when the data exhibits trend components, capturing both the level and trend in demand.

Analysis of Forecast Error Metrics

The accuracy of each model is evaluated using the following error metrics:

• Bias: Indicates systematic over- or under-forecasting.
• MAD (Mean Absolute Deviation): Average absolute forecast error, measuring overall accuracy.
• MSE (Mean Squared Error): Average squared forecast errors, penalizing larger errors more heavily.
• MAPE (Mean Absolute Percentage Error): Average percentage errors, useful for comparing forecast accuracy across different scales.

Calculations reveal that models like exponential smoothing and trend-adjusted smoothing tend to outperform simple moving averages when data exhibits trend components, as they adapt to changing demand patterns. However, for stable or cyclical demand, moving averages might suffice.

Implications for Demand Planning

The selection of the appropriate forecasting model significantly impacts inventory management, order quantities, and customer service levels. Models that incorporate trend and seasonality provide more reliable forecasts, reducing stockouts and excess inventory. Continuous evaluation of forecast errors allows managers to fine-tune model parameters such as α and β.

Conclusion

This analysis demonstrates the importance of choosing suitable forecasting methods based on historical data characteristics. While simple moving averages are easy to implement, more sophisticated models like exponential smoothing and trend-adjusted smoothing offer improved accuracy when trends are present. Integrating these insights into demand planning processes enhances supply chain efficiency and responsiveness.

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