How can you adjust your demand forecasting model to keep up with market changes?

1 Identify the sources of uncertainty

The first step to adjust your demand forecasting model is to identify the sources of uncertainty that affect your demand. These can be internal or external factors, such as seasonality, trends, promotions, competitors, customer behavior, regulations, weather, or events. You need to understand how these factors influence your demand patterns, how likely they are to occur, and how much they vary. You can use historical data, market research, expert opinions, or scenario analysis to assess the impact of these factors on your demand.

2 Choose the appropriate forecasting method

The next step is to choose the appropriate forecasting method for your demand. There are many methods available, such as time series analysis, regression analysis, exponential smoothing, moving averages, or machine learning. The best method depends on the characteristics of your demand, such as the level of uncertainty, variability, seasonality, and trend. You also need to consider the data availability, quality, and frequency, as well as the computational complexity and accuracy of the method. You can compare different methods using error metrics, such as mean absolute error (MAE), mean squared error (MSE), or mean absolute percentage error (MAPE).

3 Update your forecasting model regularly

The third step is to update your forecasting model regularly with the latest data and information. This will help you capture the changes in the market conditions and adjust your demand forecasts accordingly. You need to decide how often you will update your model, depending on the frequency of your data, the volatility of your demand, and the lead time of your operations. You also need to monitor the performance of your model and evaluate its accuracy and reliability. You can use feedback loops, error analysis, or validation techniques to improve your model over time.

4 Incorporate risk and uncertainty into your forecasts

The fourth step is to incorporate risk and uncertainty into your forecasts. This will help you account for the possible variations and scenarios that may affect your demand. You can use probabilistic or stochastic methods, such as Monte Carlo simulation, bootstrapping, or Bayesian inference, to generate a range of possible outcomes and their probabilities. You can also use sensitivity analysis, confidence intervals, or error bands to measure the uncertainty and variability of your forecasts. This will help you communicate your forecasts more effectively and support your decision making.

5 Adjust your inventory and production plans accordingly

The final step is to adjust your inventory and production plans accordingly based on your demand forecasts. You need to balance the trade-off between having enough stock to meet the demand and minimizing the holding and ordering costs. You also need to optimize your production capacity and scheduling to match the demand and reduce the waste and idle time. You can use inventory models, such as economic order quantity (EOQ), reorder point (ROP), or safety stock, to determine the optimal inventory levels and reorder policies. You can also use production models, such as linear programming, network analysis, or simulation, to optimize your production plans and resource allocation.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?