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Lenovo Computes Supply Chain and Retail Success with DataRobot
Technology Category
- Analytics & Modeling - Machine Learning
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Predictive Maintenance
- Supply Chain Visibility
Services
- Data Science Services
The Challenge
Lenovo, a multinational technology company, was facing a challenge in balancing supply and demand for its products among Brazilian retailers. The company aimed to predict the sell-out volume, the number of units of a product that retailers sell to customers, but was constrained by resources. The team had started developing R code to predict sell-out volume, with a goal to have it updated weekly for their top ten retail customers. However, with only 2 people writing 1,500 lines of R code for one customer each week, reaching their target of predictions for ten customers each week was impossible. The team needed to either invest in more data scientists or find a tool that could automate all the modeling and forecasting steps.
About The Customer
Lenovo is one of the world’s largest technology companies, invoicing more than $45 billion of computers, laptops, and accessories globally each year. The Chinese multinational company considers Brazil to be one of its primary emerging markets, representing a great opportunity to establish itself among both customers and retailers as the South American market leader. Lenovo Tecnologia do Brasil Ltda. oversees sales and manufacturing operations in the region. The company has long known that accurately predicting sell-out volume would improve many aspects of the business, from identifying problems in the supply chain to making better marketing investments.
The Solution
Lenovo Brazil adopted DataRobot, an automated machine learning platform, to accelerate and improve the accuracy of their sell-out volume predictions. The team had identified 59 variables that could affect sell-out volume at retailers and used DataRobot to automate the model-building process. DataRobot quickly creates dozens of models using different algorithms, ranking them on a Leaderboard, and providing a quick summary of how accurate and predictive they are. The tool also allowed the team to easily interpret which variables were most predictive and transparently communicate the results of those models to business stakeholders. The use of DataRobot resulted in significant speed and efficiency gains, as well as dramatic accuracy improvements in their predictions.
Operational Impact
Quantitative Benefit
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