What is demand forecasting and why is it important?
Demand forecasting is an essential component of the supply chain. It is the process that allows companies accurately to plan and predict the anticipated demand for products and shipments. Demand forecasting methods help organizations avoid inefficiencies, caused by a mismatch in demand and supply and prevent having an excessive amount of products in or out of stock/overstocking. It allows businesses to analyze the difference between forecast sales and actual sales, however, it is one of the most difficult business processes. So, what is the connection between AI and demand forecasting?
The problem with traditional forecasting methods
One of the most common problems in traditional (manual) forecasting is demand volatility. Those methods generally use past demand as an indicator of future demand.
However, according to Stich Labs research, 62% of the companies consider human error as the most common cause for inventory and fulfillment issues. And another study by Zebra, suggests that for 61% of warehouse organizations the biggest operational issue now, and within the next five years will be IT equipment.
As the industry is growing fast and becoming more complex, companies are trying to adopt other, much more efficient methods to improve demand forecasting, such as automation, which will radically change the workflow process and who it is done by.
How AI improves demand forecasting
The implementation of an AI solution depends on many variables, such as the quality and availability of data, the complexity of the processes, and the expectations from the forecasting solution.
AI is more reliable for forecasting demand because it takes into measure both internal and external variables that impact sales. It frees up employees from repetitive tasks while providing higher speed and precision. All of this creates a considerable level of automation in the logistics department.
Benefits of an AI-based solution:
- Improves accuracy in demand: According to the Tools group, AI-based forecasting can lower errors between 30% and 50% and increase accuracy up to 20%. The reduction is due to its focus on gathering and storing data constantly. The improvement in accuracy leads to a reduction of up to 65% in lost sales and warehouse inventory costs by up to 40% while increasing Return-on-Investment (ROI) at the same time.
- AI provides a real-time solution: AI is managing all types of data in real-time. This is an advantage because AI is making planning production and adjusting volume in unexpected situations easier.
- AI is using both real-time and historical data: With the help of AI, businesses can analyze a large amount of data, related to sales history, and customer data, and process other types of data at the same time. AI and ML-based algorithms increase the accuracy of demand forecasting by managing complex relationships in huge data sets that are hard to be processed for an employee.
- AI increases productivity and efficiency: As AI is taking care of time-consuming and repetitive tasks, employees can focus on completing more complex tasks that require human intervention. Accurate demand forecasting allows the reduction of inventory costs and data errors.
The future of warehousing:
AI is transforming the logistics industry and it is becoming more popular. Consequently, more and more companies will try to adapt it to their day-to-day operations. It is expected that by 2027, the market for AI in the supply chain will grow up to $12 billion. (all the research) 27% of warehouse companies plan to implement full automation, and 61% consider partial automation by 2024.