Model Answer
0 min readIntroduction
Demand forecasting is a crucial element of supply chain management, enabling businesses to anticipate future product demand. Accurate forecasts minimize inventory costs, reduce stockouts, and improve customer satisfaction. In the food industry, particularly for perishable goods like cakes, precise forecasting is paramount due to limited shelf life and potential wastage. Traditional forecasting methods, such as moving averages and exponential smoothing, remain widely used despite the emergence of more sophisticated techniques. This response will analyze the provided demand data for cakes, applying these methods and evaluating their accuracy to recommend the optimal forecasting approach for the bakery.
Understanding the Problem
The bakery faces challenges in accurately forecasting cake demand, leading to both overproduction (resulting in wastage) and underproduction (leading to lost sales). The data provided represents weekly demand in dozens of cakes. The goal is to identify a forecasting method that minimizes the difference between predicted and actual demand.
Forecasting Methods
We will evaluate three common forecasting methods:
- Moving Average: Calculates the average demand over a specified period.
- Weighted Moving Average: Assigns different weights to past demand data, giving more importance to recent data.
- Exponential Smoothing: Uses a smoothing constant to weigh past demand data, with more recent data receiving higher weight.
Data Analysis and Calculations
The demand data for the past four weeks is as follows:
| Week | Demand (Dozens) |
|---|---|
| 1 | 10 |
| 2 | 12 |
| 3 | 15 |
| 4 | 13 |
1. Moving Average (2-Week)
Forecast for Week 5 = (Demand Week 3 + Demand Week 4) / 2 = (15 + 13) / 2 = 14
2. Weighted Moving Average (2-Week)
Let's assign weights of 0.6 to Week 4 and 0.4 to Week 3.
Forecast for Week 5 = (0.6 * Demand Week 4) + (0.4 * Demand Week 3) = (0.6 * 13) + (0.4 * 15) = 7.8 + 6 = 13.8
3. Exponential Smoothing
Let's assume a smoothing constant (α) of 0.5.
Forecast for Week 1 = Initial Forecast (Let's assume 11)
Forecast for Week 2 = α * Demand Week 1 + (1 - α) * Forecast Week 1 = 0.5 * 10 + 0.5 * 11 = 10.5
Forecast for Week 3 = α * Demand Week 2 + (1 - α) * Forecast Week 2 = 0.5 * 12 + 0.5 * 10.5 = 11.25
Forecast for Week 4 = α * Demand Week 3 + (1 - α) * Forecast Week 3 = 0.5 * 15 + 0.5 * 11.25 = 13.125
Forecast for Week 5 = α * Demand Week 4 + (1 - α) * Forecast Week 4 = 0.5 * 13 + 0.5 * 13.125 = 13.0625
Evaluating Forecast Accuracy – Mean Absolute Deviation (MAD)
MAD measures the average absolute difference between actual and forecasted values. Lower MAD indicates higher accuracy.
| Method | Forecast (Week 5) | Actual Demand (Week 5 - Assume 14 for calculation) | Absolute Deviation |
|---|---|---|---|
| Moving Average | 14 | 14 | 0 |
| Weighted Moving Average | 13.8 | 14 | 0.2 |
| Exponential Smoothing | 13.0625 | 14 | 0.9375 |
Based on this limited data and assuming actual demand for Week 5 is 14, the Moving Average method has the lowest MAD (0), followed by the Weighted Moving Average (0.2), and then Exponential Smoothing (0.9375). Therefore, the 2-week Moving Average appears to be the most accurate forecasting method for this bakery, given the available data.
Considerations and Limitations
This analysis is based on a small dataset. A longer historical data series would provide more reliable results. Furthermore, external factors like seasonality, promotions, and economic conditions can influence demand and should be considered in a more comprehensive forecasting model. The choice of smoothing constant (α) in exponential smoothing also impacts accuracy and requires optimization.
Conclusion
In conclusion, while all three forecasting methods offer viable options, the 2-week Moving Average demonstrates the highest accuracy based on the provided data, resulting in the lowest MAD. However, the bakery should continuously monitor forecast performance and adapt its approach as more data becomes available. Incorporating external factors and potentially exploring more advanced forecasting techniques like ARIMA models could further enhance accuracy and optimize production planning, minimizing waste and maximizing profitability.
Answer Length
This is a comprehensive model answer for learning purposes and may exceed the word limit. In the exam, always adhere to the prescribed word count.