DataRobot > Case Studies > Lenovo Computes Supply Chain and Retail Success with DataRobot

Lenovo Computes Supply Chain and Retail Success with DataRobot

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Customer Company Size
Large Corporate
Region
  • America
Country
  • Brazil
Product
  • DataRobot
Tech Stack
  • R
  • Machine Learning
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Productivity Improvements
  • Revenue Growth
Technology Category
  • Analytics & Modeling - Machine Learning
Applicable Functions
  • Sales & Marketing
  • Business Operation
Use Cases
  • Predictive Maintenance
  • Supply Chain Visibility
Services
  • Data Science Services
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 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.
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
  • The number of DataRobot users within Lenovo Brazil has increased from two to 10 in just a few short months.
  • Lenovo Brazil now has a web simulator linked directly to DataRobot. With this simulator, more than twenty stakeholders — from sales, marketing, and demand planning areas — can tweak and change variables related to sell out and DataRobot will provide updated predictions in real-time based on these new inputs.
  • Lenovo has surged to become the leader in volume share on notebook sales for the B2C segment in Brazil this year.
Quantitative Benefit
  • Before using DataRobot, model creation took 4 weeks and productionalizing models took 2 days with an accuracy of predictions less than 80%. After using DataRobot, model creation took 3 days, productionalizing models took 5 minutes with an accuracy of predictions of 87.5%.
  • The accuracy of their early models has improved from 87.5% to over 90% today.

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