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

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	Lenovo Computes Supply Chain and Retail Success with DataRobot DataRobot

Lenovo Computes Supply Chain and Retail Success with DataRobot

DataRobot

Technology Category	Analytics & Modeling - Machine Learning
Applicable Functions	Sales & Marketing Business Operation
Use Cases	Predictive Maintenance Supply Chain Visibility
Services	Data Science Services
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Applicable Functions

Sales & Marketing

Business Operation

Use Cases

Predictive Maintenance

Supply Chain Visibility

Services

Data Science Services

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 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.

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.

Impact #1	The number of DataRobot users within Lenovo Brazil has increased from two to 10 in just a few short months.
Impact #2	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.
Impact #3	Lenovo has surged to become the leader in volume share on notebook sales for the B2C segment in Brazil this year.

Impact #1

The number of DataRobot users within Lenovo Brazil has increased from two to 10 in just a few short months.

Impact #2

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.

Impact #3

Lenovo has surged to become the leader in volume share on notebook sales for the B2C segment in Brazil this year.

Benefit #1	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%.
Benefit #2	The accuracy of their early models has improved from 87.5% to over 90% today.

Benefit #1

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%.

Benefit #2

The accuracy of their early models has improved from 87.5% to over 90% today.

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Overview

Lenovo Computes Supply Chain and Retail Success with DataRobot

Operational Impact

Quantitative Benefit