Leveraging Machine Learning Algorithms for Enhanced Demand Forecast Accuracy

Every day, customers' expectations for quick delivery and consistent product availability grow stronger. Retailers are experimenting with different strategies to meet these demands, with a focus on inventory cost management and ROI maximization. Demand forecasting is one of those critical strategies that can benefit all stakeholders in the supply chain, including suppliers, retailers, and customers.

However, maintaining accuracy is essential in demand forecasting. Failure to do so may result in a variety of consequences. These include overstocking, understocking, increased waste, higher costs, and supply chain disruptions.

The introduction of machine learning (ML) algorithms has transformed supply chain intelligence, allowing it to accurately predict demand and avoid problems.

1. Ensuring Accuracy in Hyperlocal Demand Forecasting

The retail planning software must analyze multiple products from various locations to predict demand. This complexity may result in forecast inaccuracies. Machine learning algorithms can help achieve hyperlocal precision in demand forecasting.

It can help to learn about each store’s specific characteristics to accurately create demand forecasts tailored to each store. These include past sales, population density in the area, time of the year, prevailing weather conditions, and customer behavior. For instance, one product may shift well in one region but may be slow-moving in another region because of regional factors.

Traditional methods of forecasting could not identify such fluctuations. However, ML algorithms can learn SKU-store demand and update the same using more localized data. The right inventory should be stocked at the right time and frequency to reduce the stockouts.

2. Addressing Cannibalization Challenges

There are situations when products that are very similar and are targeting the same market within the store can lead to product cannibalism. Sales promotions, new season arrivals, or new products in the market can alter customer needs and hence create unrealistic demand expectations.

Machine learning algorithms can help prevent cannibalization. It enables supply chain intelligence to analyze prior sales data, customer behavior, and promotional effects for making changes in the inventory. For instance, consider a retail store planning for a sale of a particular item in its inventory. The ML algorithm will recognize other similar products and then create an appropriate schedule for restocking products based on shifts in demand.

Retailers can confidently navigate the complexities of in-store competition and achieve optimal replenishment levels using ML algorithms.

3. Adaptive Algorithms to Mitigate Supply Chain Disruptions

Supply chains are still suffering from risks like changes in lead time, order delays, and the reliability of the suppliers. Retailers do not consider such disruptions and they are left to deal with it after it happens.

Supply chain intelligence platforms can use machine learning algorithms to forecast these types of disruptions. It continuously refines forecasts and replenishment plans based on lead times, fill rates, pending orders, and safety stock.

There are situations when lead times can suddenly increase due to supplier issues. ML algorithms can adjust order quantities and timing accordingly to prevent store stockouts. Similarly, it can adjust the safety stock buffer and adapt to changing fill rates. This enables businesses to maintain smooth operations even during supply chain uncertainties.

4. Predictive Alerts for Pre-empting Risks

One of the most important aspects of demand forecasting is the ability to detect problems before they occur. The use of machine learning enhances the utility of retail planning software, helping retailers to better control risks associated with inventory.

Since ML algorithms can identify the optimal quantities and time for placing orders, they can avoid store stockouts. In the same way, the model can adjust the safety stock buffer to accommodate changes in the fill rates. This makes it possible for businesses to operate as expected regardless of the existing supply chain disruption.

It also identifies products nearing their shelf life and helps to manage expiration risks. This allows retailers to prioritize sales through promotions or markdowns. These alerts allow businesses to take corrective action in advance, avoiding stockouts and reducing waste.

5. Reducing Shrinkage and Wastage

When dealing with perishable goods, the retailer must consider the limited shelf life. Ignoring this factor will result in shrinkage and wastage. Machine learning can optimize order plans for both demand and critical supply-side factors, providing a solution to this problem.

ML algorithms take into account a variety of factors to ensure that orders are placed accurately. These include lead times, minimum order quantities (MOQs), expiration dates, and shelf life. Retailers can replenish their inventory while avoiding situations in which they buy large quantities that may expire before being sold.

For example, the algorithm prevents the waste of products with short shelf lives. It recommends ordering these items in small quantities at regular intervals to avoid expiration.

Bottom Line

Machine learning has already improved the performance of supply chain intelligence platforms. However, the future still has a lot of potential. Future trends in ML for demand forecasting will most likely include the incorporation of more complex data sources such as real-time social media sentiment, economic indicators, and even environmental data such as traffic patterns or global trade disruption. This will improve the ability of ML models to predict demand shifts with greater accuracy.

Furthermore, integrating machine learning with other emerging technologies can improve demand forecasting. These technologies include artificial intelligence, cloud computing, and deep learning. Retailers who incorporate these developments will maintain a competitive edge and can increase efficiency, reduce waste, and improve customer satisfaction.