Modern retail companies are finding it difficult to maintain a steady Inventory level. When inventory levels run low, it means lost sales opportunities and disappointed shoppers. The opposite is equally true; too much inventory ties up liquidity and only increases the need for warehouse space and storage costs.
This problem only gets worse at the local store level, where demand can swing on a dime based on weather, local happenings, and community preference. If retailers do not predict these situations accurately, they will remain afflicted by the consequences of poor inventory management.
This is where an effective predictive demand planning software proves beneficial. By analyzing multiple factors using machine learning algorithms, this tool can provide businesses with accurate predictive insights. Thus, companies can master an inventory distribution strategy, which will minimize occurrences of stock-outs, prevent over-purchasing or excess inventory, and improve restocking across their outlets.
Why Legacy Demand Forecasting Solutions Fall Short
Conventional inventory demand forecasting solutions in retail tend to be quite basic, relying heavily on historical sales data, seasonal trends, and promotions. Despite being able to give relatively good estimates for stock quantity, the traditional method is unable to provide a true indication of stock quantities, as market conditions constantly change. Demand fluctuates based on weather changes, consumer needs, and other market conditions. As a result, the forecasts made using historical demand forecasting methods will not provide an accurate reflection of reality.
Traditional inventory demand forecasting solutions are too inflexible to accommodate real-time changes. Consequently, businesses risk having surpluses of undesirable items or shortages of highly sought-after products, both of which can negatively affect sales and customer contentment.
Additionally, managing stock across a diverse range of SKUs and locations introduces further complexity. Manual processes or outdated systems are unable to keep up with the rapid volume of decisions that retailers must now make.
How Machine Learning Enhances Predictive Demand Planning
Modern prediction tools utilizing machine learning are created to navigate the intricacies of conventional predictive demand planning software. It can help in analyzing extensive data from various sources to provide more precise and prompt insights.
Here’s how machine learning is changing the way companies predict retail inventory:
1. Ensuring Accuracy in Hyperlocal Demand Forecasting
The demand forecasting solution needs to evaluate various products across different locations to project demand accurately. This complexity might lead to mistakes in forecasts. Utilizing machine learning algorithms can assist in achieving precise hyperlocal demand predictions.
It helps to understand each store’s unique traits and produce accurate forecasts tailored for individual locations. For example, a certain product may sell quickly in one area while experiencing slower sales in another due to local conditions. Machine learning algorithms can extrapolate data from items sold at other stores and adjust demand based on finer, granular details. They help companies keep correct inventory levels at the right time and frequency.
2. Mitigating Supply Chain Disruptions
Supply chains continue to encounter risks, from lead times that fluctuate, orders that are not fulfilled, and delays, to less reliable suppliers. Retail tends to ignore disruptions in the chain, and consequently, comes up with reactive remedies instead of proactive ones.
Predictive demand planning software can implement machine learning algorithms to anticipate such disruptions. These systems learn to improve forecasts and when to restock, based upon factors such as lead times, fill rates, open orders, and safety stock levels.
Sometimes suppliers will, for some reason or another, produce much longer lead times than expected. Algorithms can change volume, and when orders are placed, even up to the time an order is shipped to a store, preventing stockouts in stores. This allows businesses to maintain efficient operations despite uncertainties within the supply chain.
3. Addressing Cannibalization Challenges
There are occasions when highly similar products aimed at the same customer segment within a store can lead to cannibalization. Sales promotions, newly launched seasonal items, or fresh products can shift consumer preferences, leading to unrealistic inventory demand forecasting.
Machine learning algorithms can assist in mitigating cannibalization effects. For instance, when a retailer plans a discount on a specific product, the machine learning algorithm will identify other comparable items and develop a suitable restocking schedule based on changes in demand.
4. Predictive Notifications for Risk Prevention
A key element of predictive demand planning software integrated with ML algorithms is the ability to anticipate problems before they escalate. By using machine learning algorithms, retailers can now obtain better estimates regarding when they will be placing orders and how much of each product should be ordered, which in turn helps prevent stockouts.
In addition to providing retailers with the means of predicting their stock levels and ordering with precision, the addition of machine learning also enables retailers to identify items that may soon expire. By identifying these items early on, retailers are better equipped to manage their inventory and avoid loss of profits due to spoilage.
5. Preventing Losses and Waste
When handling perishable items, retailers need to be mindful of the finite shelf life. Failure to take this into account could result in a retailer suffering substantial losses due to product spoilage. Therefore, utilizing machine learning will improve inventory ordering practices for both demand and vital supply-side aspects.
A number of factors are factored into machine learning algorithms to ensure proper product ordering. Retailers can now incorporate your machine learning algorithms into their inventory planning processes and ensure they are not overstocking items that are not likely to sell before their expiration dates.
Bottom Line
Machine learning has greatly improved the performance of inventory demand forecasting solutions, but it still presents an enormous opportunity to continue improving demand forecasting. Upcoming trends in machine learning for demand forecasting will likely include the integration of more intricate data sources, such as real-time social media sentiment, economic metrics, and even environmental factors like traffic flows or disturbances in global trade. This will enhance the ability of machine learning models to accurately anticipate shifts in demand.
Moreover, combining machine learning with other developing technologies can significantly enhance demand forecasting capabilities. These innovations encompass artificial intelligence, cloud solutions, and deep learning techniques. Retailers that embrace these advancements will sustain a competitive advantage, enabling them to increase efficiency, minimize waste, and enhance customer satisfaction.
