Out of stock situations can be frustrating for customers and costly for businesses. They can damage your reputation, reduce your sales, and increase your expenses. How can you prevent them with demand forecasting? In this article, we'll explain what demand forecasting is, why it's important, and how to use it effectively.
Demand forecasting is the process of estimating the future demand for your products or services based on historical data, market trends, and other factors. It helps you plan your inventory levels, production schedules, and purchasing decisions. Demand forecasting can be done at different levels of detail, such as by product category, SKU, or location.
I think it is important to distinguish between demand forecasting and demand planning. Demand forecasting in my experience is mostly driven by statistical modeling and other various quantitative means. Demand planning analyzes factors that a model would otherwise not know about such as limited time offerings, product transitions, new distribution centers opening, etc that will impact the forecast and thus, inventory. It is essential to have good demand planning with good demand forecasting in order to best manage the flow of material through the system.
Demand forecasting can help you avoid out of stock situations by ensuring that you have enough inventory to meet customer demand. It can also help you optimize your inventory turnover, reduce your holding costs, and avoid overstocking or obsolescence. Demand forecasting can also help you identify opportunities for growth, expansion, or innovation by analyzing customer preferences, seasonal patterns, and competitive dynamics.
There are various methods and tools for demand forecasting that depend on business goals, data availability, and complexity. Qualitative methods, such as expert opinions or market research, are useful for new products or niche markets. Quantitative methods, like mathematical models or machine learning, are useful for existing products or large markets. Hybrid methods, a combination of qualitative and quantitative methods, can improve the accuracy, reliability, or flexibility of forecasts. Spreadsheets are a simple and accessible tool that allow basic calculations and charts; software is a specialized tool that enables advanced functions such as automation; services are external providers that offer demand forecasting as a service. All of these tools can be used to meet different forecasting needs.
Demand forecasting is not an exact science and can be affected by many factors, such as data quality, human error, or external shocks. Therefore, it is essential to continuously monitor, evaluate, and improve your demand forecasting process. To do so, you should collect and analyze data from various sources like sales history, customer feedback, market research, or industry reports. Additionally, you should ensure that your data is accurate, complete, and consistent. Furthermore, you should choose and adjust methods and tools according to your business needs, data availability, and forecasting accuracy. Testing and comparing different scenarios, assumptions, and parameters can help with this. Finally, you should review and update forecasts regularly based on actual performance, feedback, or changes in the market. Tracking and measuring forecast errors, deviations or biases can also help with this.
One factor to add to the forecasting formula is “lost sales”. This will increase forecasting accuracy. One way to calculate them is counting the number of days out of stock by the daily forecast from the last period. Add this value to your current sales as a feed for your new forecast calculation. Also decide what model best fits for your data, weekly or monthly forecast. If it is a high volume item, weekly forecasting is the best way to go. Analyzing your data on a weekly basis and adjust accordingly will always help to the accuracy
Make sure to take your seasonality into account and plan for any very high or very low demand periods. Also, review your forecasts to ensure that there are not any large, unusual orders that you should account for in your demand model that might make it look like you will not have enough on hand in the next cycle.
Definitely the most important starting point is the quality of data. Planning our data modeling can build a solid path of becoming proactive. Human error and behavior can be the biggest challenges during this process. One factor that can be added to this data modeling is Safety Stock. Specially if you are struggling with human errors. Making sure to monitoring the progress because it can turn into overstocking. Once we are able to control this challenge it will take you to the next phase which is, negotiate with vendors the MOP (Minimal reordering point). I believe at the end it it will add more value to our data modeling and obtain better results.
Demand forecasting is a strategic process that can help you align your business objectives with customers, suppliers, and stakeholders. Therefore, it is essential to communicate your demand forecasting effectively. To do this, define and share your goals and assumptions for the forecasting, such as the purpose, scope, time frame, and criteria. Additionally, present and visualize your results and insights from the forecasting using clear language, formats, and charts. Finally, solicit and incorporate feedback and input from customers, suppliers, and stakeholders for the forecasting. Acknowledge any questions or issues that arise.
You need to have one voice within the company. If any suppliers are not sure of your needs or do not trust the numbers you provide, that almost inevitably leads to product that you need not being produced. Make sure the communication is standardized and that there is a feedback mechanism that insures the supplier understands exactly what you want, exactly when you want it, and exactly where you want it