Case Study: Clover Technologies Improves Forecast Accuracy with AI & Machine Learning

Overview

Clover Technologies is a worldwide company focused on recapturing value for customers throughout the imaging, wireless and telecom industries. The company’s three divisions employ 19,000 people located in over 60 locations across 18 countries. Clover’s IT team has partnered with Concurrency on several projects, including an AI and machine learning project that applies cutting-edge techniques to enable Clover to significantly improve accuracy of its business forecasts—and vastly decrease the time and effort required to generate them.

This specific project relates to Clover’s largest business—imaging—in which the company is a market leader in print and copy cartridges. Just in the U.S., Clover has seven distribution centers at which it packages and ships products to its customers, which include large office supply and technology retailers.

To manage operations at its distribution centers, Clover generates a monthly product sales forecast. The data output is critical to Clover’s business operations, because these figures drive everything from manufacturing to packaging and supply chain decisions. Prior to this project, Clover’s forecasting team used a combination of ERP tools and Excel—and significant manual effort—to set up the data for computational analysis.

Then, the actual computer processing time to generate the forecast was over 18 hours—and often had to be re-run due to failures. Finally, more days of analysis would be required in Excel to prepare raw outputs for business use. Given Clover’s outstanding historical data availability, its business situation was ideal for applying machine learning and predictive analytics.

Solution

Concurrency’s Data Analysis team, including the firm’s Data Scientist, worked with Clover’s IT leaders and forecasting team, as well as the Microsoft data science team, to completely revolutionize the firm’s approach to monthly sales forecasting. The project involved preparing historical data for analysis, training computer models to produce high-quality forecasts, and ultimately going live with a new approach to forecasting that cut computational time from 18 hours to 10 minutes—with more accurate results.

Concurrency built the machine-learning-based modeling to be an automated process that does not require manual effort. (The specific technology involved includes Microsoft’s flexible machine learning platform, which gives organizations flexibility to run it either in SQL Server or as an Azure cloud service.)

The results indicate a 44% increase in demand forecast accuracy, which will positively affect inventory positions and supply-chain directives. Furthermore, the machine learning forecasts run in only about 10 minutes, compared to over 18 hours for the prior approach.

This project yielded two major gains through operational intelligence. Importantly, the cost of selling a broad diversity of products was identified based on up-to-date information describing product sales and procurements, which resulted in a decision to begin reducing the number of unique products offered to customers by consolidating similar products into a single brand or identity. Furthermore, we enabled real-time extraction of inventory characteristics including the substantial cost of an inflated inventory position relative to an “optimal” inventory and procurement policy.

Results

Having proven out the new approach to forecasting, the next step is to deploy the machine learning approach to a production environment and run it in parallel to the current, more manual approach for a period. After further evaluation at this “live” stage, Clover will then cut over to the machine learning model.

As forecasting is improved, Clover anticipates significant business benefits, including:

• Less re-boxing
• Lower backorder levels
• Decrease in brand substitutions
• Increase in customer satisfaction
• Decrease in overtime for manufacturing and packaging
• Decrease in production line costs to build more product
• Decrease in supply chain costs

Furthermore, Clover can apply the lessons and approach of this first machine learning project in other aspects of the business to continue to improve operations. For example, a key business focus is increasing the velocity of order fulfillment, such as by optimizing the physical location of inventory within distribution centers—and among distribution centers—to maximize efficiency in order preparation and delivery. With some 20,000 individual products and tens of millions of dollars quarterly in shipping costs, Clover recognizes important opportunities for further efficiency gains to be derived from powerful insights provided by machine learning.

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An intelligent supply chain awaits and Concurrency is ready to help you make it happen! Contact us today and discover how AI and machine learning can drive accuracy and efficiency for your organization.