Business IntelligencePredictive Analytics

Small facilities have plenty of data for predictive analytics

By August 12, 2016 No Comments

There are many reasons companies drag their heels on predictive analytics, but most are due to false assumptions. VentureBeat listed the common excuses, which include technology shortages, reliance on data silos and unspecified goals for the practice. One of the most prevalent reasons for hesitancy is a lack of confidence.

It's not uncommon for smaller businesses to shoot down ideas used by major brands because they feel they can't compete with size and resources. Data, however, is plentiful to every organizations in this day and age. For example, a distribution center or inventory warehouse doesn't have to work with multiple locations and global consumers to make innovative business intelligence software worthwhile. Knowing where products currently sit and predicting demand are benefits that profit organizations of any size.

The small data warehouse
When looking at a data set like inventory levels, companies should be aware of a variety of factors. Warehouse workers have to see the details of customer orders, and managers must know how long it takes to replenish levels. Outside factors like weather, vehicle availability and distribution route conditions can slow transport or prevent supplies from reaching their destination when necessary.

Capturing real-time warehouse data helps companies forecast future trends. Capturing real-time warehouse data helps companies forecast future trends.

CMS Wire said small businesses that want to take advantage of predictive data models need to input integrated data into their information systems. Companies can create smart algorithms that create predictions for how consumer demand will change in certain seasons if they compare past trends to real-time customer data.

It's smart to begin implementation with a limited focus to test success. Companies should select two or three data sets to integrate and see how simple algorithms and forecasting models provide easy-to-utilize results. 

Finding the data
If a company thinks it doesn't have enough information to make predictive modeling tools worthwhile, the problem is probably in the data collection process. Each warehouse activity can provide a wealth of information. Every time a product is moved from a shelf, it creates a data point about consumer demand, employee performance and warehouse schedules. Businesses must find ways to capture the details of each action and make them visible in an inventory management system.

Some companies may want to invest in industry-specific devices and hi-tech sensors to get this data. However, there are still plenty of options for companies with limited resources. Camcode, the barcode label company, listed several apps businesses can utilize so employees can track inventory on consumer smartphones and other convenient options. 

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