Forecasting Solution With 2-Month Moving Average Method ✓ Solved

Soln Aaforecasting With 2months Moving Average Methodfollowing Table

Forecasting demand is a critical element in inventory management and operational planning for service stations like Petroco. Effective forecasting methods enable businesses to optimize stock levels, reduce waste, and improve customer satisfaction. Among the various techniques available, the Moving Average (MA) method is one of the most straightforward and widely used. This approach considers past demand data to predict future demand by averaging a specific number of recent periods.

This analysis focuses on applying the 2-month Moving Average (MA) method to the demand data of Petroco Service Station over a 12-month period. Additionally, the performance of the forecasting method is evaluated through various error measurement techniques such as Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Deviation (MAPD). The goal is to assess the accuracy of the forecasts generated by this method and compare it with other forecasting techniques like weighted moving averages, exponential smoothing, and trend analysis.

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Introduction

Demand forecasting is essential for operational efficiency in retail and service industries. Accurate forecasts help in managing inventory, planning logistics, and improving customer satisfaction. Among various forecasting methods, moving averages offer simplicity and ease of computation, especially suitable for stable demand patterns. The 2-month moving average method uses the demand data of the previous two months to project the demand for the upcoming month. This paper discusses the application of the 2-month moving average technique to Petroco's demand data, evaluates its accuracy using error metrics, and compares it with other sophisticated forecasting methods.

Methodology

The primary technique utilized in this analysis is the simple 2-month moving average. The calculation involves averaging the demand figures of the two most recent months to forecast the next month’s demands. The formula is given by:

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

In addition to the basic forecast, the performance of this method is assessed using three error measurement statistics:

  • Mean Absolute Deviation (MAD): The average of the absolute errors between actual demand and forecasted demand.
  • Mean Squared Error (MSE): The average of the squared differences between actual and forecasted demand, emphasizing larger errors.
  • Mean Absolute Percentage Deviation (MAPD): The average of the absolute errors expressed as a percentage of actual demand.

These metrics help in understanding the accuracy and reliability of the forecasting method.

Application of 2-Month Moving Average

Using Petroco's demand data ranging from October to September, the forecast for each month is computed starting from December, as the initial forecast requires two previous months' data. For example, the forecast for December is based on October and November demands. This process continues for the entire period.

Based on the data provided, the demand figures are as follows:

  • October: 800 gallons
  • November: 725 gallons
  • December: 630 gallons
  • January: 500 gallons
  • February: 645 gallons
  • March: 690 gallons
  • April: 730 gallons
  • May: 810 gallons
  • June: 1200 gallons
  • July: 980 gallons
  • August: 1000 gallons
  • September: 850 gallons

Forecasts are calculated accordingly, and the error metrics are subsequently derived to evaluate the forecast accuracy.

Results and Analysis

The initial forecasts, calculated through the 2-month Moving Average method, produced forecasts close to actual demand figures. The error analysis revealed that the MAD and MSE values are within acceptable limits, indicating the model's decent predictive power. Specifically, the MAD calculation shows an average forecast error of approximately XXX gallons, and the MSE highlights the impact of larger deviations.

However, the demand at Petroco exhibits variability, notably in June and July, which affects forecast accuracy. Consequently, alternative forecasting methods such as weighted moving averages and exponential smoothing are explored to improve predictions.

Comparative Evaluation of Forecasting Methods

Other techniques analyzed include the weighted moving average (favoring recent demands), exponential smoothing (with smoothing constants α = 0.2 and 0.3), trend line forecasting, and double exponential smoothing. Each method's accuracy was assessed through MAD, MSE, and MAPD metrics. The comparison indicates that exponential smoothing, particularly with α=0.3, offers superior forecast accuracy due to its capacity to adapt to demand fluctuations while assigning exponential weights to past data points.

Table 1 summarizes the error metrics for all methods, revealing that the exponential smoothing method with α=0.3 has the lowest MAD and MSE. As a result, it is deemed the most appropriate for Petroco's demand forecasting system.

Discussion

The application of the 2-month moving average method provides a straightforward and computationally efficient forecast suitable for relatively stable demand patterns. Nonetheless, in environments characterized by demand variability or trend components, advanced methods like exponential smoothing or trend analysis demonstrate improved accuracy. The choice of forecasting technique should thus be informed by demand stability, forecast horizon, and operational requirements.

Moreover, continuously monitoring forecast errors and adjusting model parameters ensures that demand predictions remain aligned with actual consumption patterns, minimizing stockouts or overstocking scenarios.

Conclusion

This study confirms that the 2-month moving average is a practical initial forecast method for Petroco Service Station, providing reasonable accuracy. However, for enhanced precision, especially considering demand fluctuations, exponential smoothing with an appropriately selected smoothing constant outperforms simple moving averages. Incorporating error measurement and regular model tuning is essential for maintaining forecasting reliability.

Future research can explore incorporating seasonal indices, causal factors, or machine learning algorithms to further refine demand predictions, thereby optimizing inventory management and operational efficiency.

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