Demand forecasting is a very challenging aspect of supply chain management for manufacturers, especially considering the number of factors that can affect it. Natural disasters, product shortages, delays, and other disruptive events can impact the supply chain. However, businesses with complex supply chains can use manufacturing analytics to improve their demand forecasting capabilities.
How Analytics Can Improve Demand Forecasting
Make More Accurate Predictions
Analytics is a critical manufacturing component, driving better demand forecasting and reducing overhead costs. Predictive analytics can help manufacturing organizations analyze historical data, current market trends, and external market factors to accurately forecast future sales.
Using analytics for forecasting reduces manual effort and offers better short-term forecasts, allowing businesses to be more flexible. However, it requires more data upfront than passive demand forecasting, in which companies use historical data to make predictions. The maintenance needs for the technology can be slightly more complex than traditional prediction methods.
Analyze Historical Data to Discover Trends
Past purchase information can help you identify your best-selling products and the demographics they appeal to the most. By integrating your SCM application with your analytics platform, you can automatically pull sales data into your analytics platform and deliver valuable insights. You can also ask your customers for sales forecasts to better estimate their likely future orders. You might have to offer small perks or incentives to get this information.
Keep Up with Competition
If a competitor launches a discount or promotional offer for one of your products, you may not be able to react quickly or match the price, which can impact your business. However, you can adjust your sales forecast to account for the lost sales. On the other hand, you should also monitor the competitor’s business for data breaches or other disruptive events that can cause significant delays or loss of trust for customers. They may turn to your business instead, resulting in a sudden spike in demand for your products.
Keep Up with Inventory Elasticity
If the sales of your products change substantially when the price does, it means your inventory has some elasticity. Imagine a scenario where you forecast high product sales but don’t sell nearly as much as you predicted at the full price due to changed market conditions. Manufacturing analytics can quickly alert you to the difference in actual sales vs. forecasts and recommend reducing the price, so you can sell the excess inventory and still turn a profit instead of discarding it. Manufacturers can also stock up on raw materials because they are less likely to waste. When the pandemic struck, many manufacturers had to pivot their offerings to better serve market demand. For example, several alcohol manufacturers diverted their production lines to make hand sanitizers and other alcohol-based cleaning supplies instead of alcoholic beverages. This flexibility allowed them to keep getting revenues without wasting the raw materials they already had in stock.
Reduce Operating Costs
With better demand forecasting, manufacturers can lower their operating costs by reducing inventory levels and storage space. Additionally, when companies can make accurate sales forecasts, they can place orders for large quantities of inventory in one go, driving economies of scale. Manufacturing analytics also helps organizations reduce error-prone, manual efforts and optimize demand forecasting.
