How Predictive Analytics Is Being Used in Inventory Management
The lack of effective inventory management practices can lead to upset customers, plunging profits and productivity losses. Fortunately, technology offers assistance in ways that weren’t possible in earlier eras.
When companies depend on predictive analytics platforms for inventory management, they can avoid pitfalls and succeed in demanding marketplaces.
Relying on Analytics to Forecast Appropriate Stock Levels
Numerous things, ranging from weather patterns to holidays, dictate the potential demand for items. Before retailers and manufacturers had access to predictive analytics platforms, they often used outdated methods like Excel spreadsheets to figure out what customers and suppliers wanted and when demand might spike.
But, that often meant those entities couldn’t meet needs and expectations as demand fluctuated. When organizations use advanced analytics in their operations, they can more accurately detect customer needs, thereby reducing unnecessary inventory and operating more efficiently during hectic periods.
Also, predictive analytics should reduce incidences of items on back order. Indeed, heightened demand can result in back orders, but so could poor foresight. Predictive analytics evaluates demand and improves planning, giving manufacturers more peace of mind and confidence when communicating with their customers.
Kroger reportedly uses predictive analytics to monitor customer and economic trends. Then, it leverages the information to make merchandising decisions.
Helping Manufacturers and Suppliers Keep the Right Parts on Hand
Manufacturers require the appropriate components to assemble the products a given facility. Plus, if those companies offer after-sales service, the manufacturers must determine which parts are most necessary for handling customers’ requests in that regard.
Manufacturing plans also must be aware of the potential for equipment breakdowns that could substantially hinder their operations if a plant doesn’t keep replacement parts readily accessible. If necessary, they must communicate changes to their suppliers about increased or reduced demand.
For all these reasons, predictive analytics is changing the supply chain and cutting down the likelihood of manufacturers having too many components or not enough.
In one case study involving one of the top parts suppliers for an airplane manufacturer, the performance of a supplier’s parts was a substantial predictor of fleet reliability.
After implementing a predictive analytics platform that calculated data, including diagnostic information from operating planes, the company saw expected cost savings of 1.8 to 3.8 million annually due to quicker issue resolution.
All retailers have products that are more popular than others. One of the best practices for inventory management is the ABC method. Products in the A group are the bestselling products, and those that need the tightest inventory control, whereas the items that are under the B umbrella don’t sell as rapidly and don’t bring in as many profits.
Finally, the things in the C group make up the smallest percentage of inventory and account for the least profits.
