If you want to break free from the sales-draining effects of stockouts and overstocks, it’s time to embrace proactive demand forecasting.
Out-of-stock products lose revenue to competition, and sitting on a heap of unused inventory incurs storage costs and production overhead. ML-powered predictive analytics can help you stay ahead of shifting consumer demands and manage your inventory effectively.
Predicting consumer demand
The best-in-class supply chain planners use various techniques to forecast sales, including statistical forecasting and collaborative market intelligence. They also incorporate data from external sources such as infectious disease trends, government data, weather, etc. These insights help businesses improve their starting point for demand forecasts and give teams a faster way to prioritize the most value-add work.
This helps reduce the number of times a company has to order too many products, which incurs unnecessary storage costs. This includes equipment maintenance, warehouse utilities, and the stock’s expiration chance. Additionally, the extra inventory can take up space that could be used for other purposes, like attracting new customers and building brand awareness.
Proper demand planning solutions can help to keep more products in stock and better meet customer demands. After all, a business that fails to meet the needs of its customers will find it hard to retain them. A well-planned supply chain can prevent this by accurately anticipating consumer demand based on consumption data from the point of sale. This can also be achieved by reducing the time spent on manual data entry and analysis while improving the accuracy of the forecasts. This allows teams to spend more time on strategic projects.
Optimizing information
The best approach to predicting consumer demand involves collaboration between all departments involved in supply chain management. This includes sales, marketing, operations, and finance. The goal is to create an efficient data analysis process highlighting risks and growth opportunities. To improve forecasting accuracy, businesses should also consider tracking metrics like inventory levels, order fulfillment lead times, and cost of goods sold.
The key is to use a statistical model that will provide an accurate picture of consumer demand. These models can be implemented through software and should incorporate consolidated information that has been streamlined from different departments to avoid redundancies and errors. In addition, they will consider external factors impacting sales, such as weather patterns and economic conditions.
Machine learning algorithms can enhance the accuracy of forecasts by identifying trends and recognizing patterns that may be overlooked. In addition, they can integrate information from various sources, including infectious disease patterns, government statistics, and even weather changes, to predict demand in real-time. This is known as “demand sensing” and can reduce downstream latency while meeting customer expectations. These capabilities can help reduce costs, improve production lead times, and enable businesses to adapt quickly to changing market conditions. The Covid-19 pandemic has shown that this is more important than ever before.
DeepCast – overcoming versatility in demand
Getting accurate consumer demand forecasts is critical to supply chain efficiency. However, identifying the right mix of products, stores, and channels can take time and effort. Especially when the unexpected happens. During the COVID-19 pandemic, for example, consumers demanded in-demand products like toilet paper, hand sanitizer, and grocery staples – sending companies scrambling to keep up with rapid shifts in demand.
Optimize inventory distribution with automated, statistically driven forecasting models based on consumer demand data and predictive analytics. Reduce transportation costs and improve customer satisfaction by predicting the optimal quantity to move, store, and ship each product.
Proactive demand planning solutions that prioritize forecast accuracy and use advanced AI technology to predict even the most minor trends and fluctuations. DeepCast uses as little as a few weeks’ worths of sales to generate more accurate predictions than any other method – even under the most volatile conditions.
Become an agile supply chain superhero with a more innovative, flexible consumer demand management strategy powered by analytics and AI. Learn how SAS helped Levi Strauss streamline the SKU mix, cluster trade areas, increase shipments to key accounts, and boost e-commerce fulfillment — all while reducing shipping costs and improving inventory turnover. Download our case study to find out how we can help you significantly impact your supply chain operations.
