How Predictive Analytics Is Being Used in Inventory Management

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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 Before retailers and manufacturers had access to predictive analytics platforms, they often used outdated methods like Excel spreadsheets to figure out what customers and suppliers wanted and when demand might spike.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.

Engaging People Through Suggestive Selling and Highlighting Bestselling Merchandise

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. The ABC approach works well when retailers are well aware of their strongest sellers. However, they don’t always have that knowledge.

Predictive analytics is propelling the practice of suggestive selling, and it could shed some light on the most-wanted items.

Companies like Spotify and Netflix regularly offer suggestions to people, encouraging them to get engrossed in content they’ll probably like based on their past interactions with those services. Amazon is perhaps the most famous company to do that in the e-commerce sector. As soon as a person adds an item to their shopping cart, they get suggestions for things to complement it.

Then, retailers can conquer a common challenge by identifying hidden opportunities to get people to buy more than they otherwise would. If a shopper just purchased a tent, it makes sense to suggest buying stakes and a mummy-style sleeping bag, too.

Such uses of predictive analytics cater to shoppers, plus give insights to retailers about which products sell best and why. Then, they can use the ABC method or other inventory control approaches in well-informed ways without guesswork, plus keep shoppers happy.

Depending on Analytics to Reduce Shrinkage

Both manufacturers and retailers deal with shrinkage, which happens when all expected or actual inventory doesn’t get sold due to problems like breakage during shipment or customers who steal things at stores.

Research shows data analytics could make shrinkage less problematic by picking up on risk factors people may not notice at all, or until it’s too late to be proactive.

A manufacturer might depend on predictive analytics to get insights on which supplies are likely to arrive at a plant in an unusable condition. Then, if patterns emerge, the company could authoritatively bring up concerns to its suppliers.

According to statistics, 65 percent of damaged cargo ends up that way due to getting incorrectly packed or secured in freight containers. Predictive analytics could generate an alert that a certain product a manufacturer receives is particularly prone to breakage, allowing that company to raise a complaint and cite data from an analytics platform to strengthen a case.

Also, although shoplifting is a substantial source of shrinkage for retailers, it’s among other factors that stimulate shrinkage. Employee theft may occur, and paperwork errors could also be to blame. By working with historical data, a predictive analytics platform may play a crucial role in inventory management by showing the prominent causes of retail shrinkage.

The reasons behind shrinkage are not identical for every manufacturer or retailer. But, predictive analytics provides the knowledge needed for those entities to act decisively to get to the bottom of shrinkage instead of resorting to trial and error to curb it.

A Customized Approach Works Best

Once companies start using predictive analytics for inventory management purposes, it’s ideal if they customize the outcomes of those tools as much as possible.

Then, those organizations can highlight ongoing or newly arisen obstacles and let data help them accomplish the desired results.

About the Author

Contributed by: Kayla Matthews, a technology writer and blogger covering big data topics for websites like Productivity Bytes, CloudTweaks, SandHill and VMblog.


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  1. Each business has their way of doing things, and the problems usually lie in those procedures. Such procedures don’t always take into account the critical factors that come into play when dealing with inventory.

    Cycle counting is a must for inventory control. Most of the excuses I hear are that it is difficult to monitor cycle counts of 10,000 SKU’s. And, while I agree that it has the potential to be difficult if not properly executed, there are strategies to make such an activity manageable.

  2. Hi.
    Thanks for hte article.
    Big data, produced by the supply chain if operated correctly can help considerably in sales, inventory, and operations planning. The inventory data point of sale data and production data real-time analytics can be used to identify and mitigate the risks of mismatches between supply and demand. Hence, appropriate actions can be taken. For example, by analyzing the link between production planning and weather forecasts, bakeries can foresee the demand for a specific product category.