Challenges in Demand Forecasting: Accuracy, Complexity, and Systematic Approach
Forecasting plays a crucial role in business planning, inventory management, and decision-making. However, it’s not without its challenges. Let’s delve into three key issues faced by forecasters:
1. Forecast Accuracy
The accuracy of a forecast heavily relies on the methods and data used. Traditional approaches, such as simple moving averages, may fall short when dealing with dynamic factors like promotions, seasonality, and trends. Here are some considerations:
- Short-Term vs. Long-Term: Short-term forecasts (e.g., 7 days) tend to be more accurate than longer-term ones (e.g., 10 days or beyond). As the forecasting horizon extends, uncertainty increases.
- Sophisticated Models: To enhance accuracy, consider using more sophisticated models. These might incorporate machine learning algorithms, historical patterns, and external variables.
2. Forecasting Complexity
Maintaining forecasts in Excel spreadsheets can be labor-intensive, especially for businesses with extensive product assortments and numerous clients. Here’s why it’s challenging:
- Manual Effort: Accounting for individual promotion conditions, holidays, and other events requires extensive manual calculations. This complexity grows exponentially as the number of products and clients increases.
- Dynamic Factors: Real-world scenarios involve ever-changing conditions. Excel-based approaches struggle to adapt swiftly to market shifts, leading to suboptimal results.
3. Lack of Systematic Approach
Effective forecasting demands a systematic approach that considers all relevant factors. Here’s what to keep in mind:
- Holistic View: Seasonality, promotions, client behavior, and market trends all impact sales. Ignoring any of these factors can lead to inaccurate forecasts.
- Granularity Matters: Aggregating forecasts at a high level (e.g., overall product category) may sacrifice accuracy. It’s essential to account for each item’s unique characteristics.
4. Solution: A New Approach to Forecasting
The solution to the identified forecasting challenges lies in transitioning from labor-intensive calculations to data-driven process management using machine learning (ML) algorithms.
How It Works
- Historical Data: Start by collecting historical data on sales, orders, and promotions spanning at least two years. Include additional variables like holidays, weekends, and weather conditions.
- Mathematical Model: Build a mathematical model that considers all relevant features. ML algorithms can handle complex patterns and adapt to changing conditions.
- Granularity: The model generates forecasts at a granular level—individual product categories, clients, or sales channels. This fine-grained approach captures nuances that aggregate forecasts miss.
- Frequency and Horizon: Choose the desired forecasting frequency (daily, weekly) and horizon (short-term or long-term). Remember that longer horizons may sacrifice accuracy.
Benefits of Cloud Deployment
- Cost Savings: Cloud computing allows paying only for the resources used, minimizing unnecessary expenses.
- Scalability: Easily scale resources as production grows without administrative overhead.
- Speed: Rapid calculations and processing due to cloud infrastructure.
- Security and Reliability: Cloud providers handle administration, ensuring data safety.
Implementing ML Forecasting
- Define Goals: Formulate clear goals and specify the forecasting scope (product categories, clients, etc.).
- Data Requirements: Create a specification for the required data (sales, orders, inventory, etc.) to build the ML model.
- Quality Metrics: Choose a quality metric (e.g., RMSE or MAPE) to assess the model’s success during testing.
- Deployment: If the model meets quality targets, deploy it for industrial use.