How can demand forecasting reduce the environmental impact of your supply chain?

1 What is demand forecasting?

Demand forecasting is the process of estimating the future demand for your products or services based on historical data, market trends, customer behavior, and other factors. It helps you anticipate how much you need to produce, order, store, and ship to meet customer expectations and avoid overstocking or understocking. Demand forecasting can be done at different levels of granularity, such as by product category, SKU, region, channel, or customer segment. It can also be done for different time horizons, such as short-term, medium-term, or long-term.

2 Why is demand forecasting important for sustainability?

Demand forecasting is essential for sustainability, as it can help reduce the environmental impact of your supply chain. Accurate forecasting can lower inventory levels and storage costs, optimize production schedules and capacity utilization, improve distribution planning and routing, and enhance customer satisfaction and loyalty. These measures can lead to lower energy consumption, greenhouse gas emissions, material and resource usage, waste generation, fuel consumption, transportation emissions, sales and retention rates, as well as customer acquisition costs.

3 How to do demand forecasting for your supply chain?

Demand forecasting is not a one-size-fits-all solution, as it depends on your industry, product type, customer profile, and business objectives. However, some general steps to consider when doing demand forecasting for your supply chain include collecting and analyzing historical data on sales, inventory, production, distribution, and customer behavior. Various methods and tools such as time series analysis, regression analysis, machine learning, or software applications can be used to identify patterns and trends in the data. Additionally, you should monitor external factors that can affect your demand such as market conditions, competitor actions, consumer preferences, seasonality, weather, events or regulations. Sources like industry reports, surveys, social media or news can be used to gather relevant information and insights. Generating and evaluating demand forecasts for different scenarios and time horizons is also important. Quantitative and qualitative techniques such as statistical models, expert opinions or market research can be used for this purpose. Metrics like mean absolute error, mean absolute percentage error or root mean square error can measure the accuracy and reliability of your forecasts. Furthermore, it is important to update and revise your demand forecasts regularly based on new data or changes in the environment. Exponential smoothing, moving averages or adaptive learning are some methods that can help adjust your forecasts to reflect the latest reality.

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?