AI Case Study
Shell saves over a million dollars annually by doing inventory analysis 32 times faster using machine learning
Shell improved inventory and supply chain planning using Databricks' cloud-native unified analytics platform which uses machine learning to map various scenarios based on built-in frameworks. This reduced inventory planning time from 48 hours to 45 min. They deployed these models in 50+ locations.
Industry
Energy
Oil And Gas
Project Overview
"To maintain production, Shell stocks over 3,000 different spare parts across their global facilities. It’s crucial the right parts are available at the right time to avoid outages, but equally important is not overstocking which can be cost-prohibitive. Their current processes and technology stack for maintaining inventory faced challenges.
Disjointed Inventory Distribution: Stocking practices are often driven by a combination of vendor recommendations, prior operational experience and “gut feeling”.
Limited DSS (Decision Support System) Data Availability: There has been limited focus directed towards incorporating historical data and doing advanced analysis to come up with decisions.
Lost Business Agility: This can lead to excessive or insufficient stock being held at Shell’s locations, like oil rigs which has significant business implications.
Databricks provides Shell with a cloud-native unified analytics platform that helps with improved inventory and supply chain management:
Databricks Runtime: The team to dramatically improved the performance of the simulations.
Interactive Workspace: The data science team is able to collaborate on the data and models via the interactive workspace.
Cluster Management: Significant reduction in total cost of ownership by moving to the Databricks cloud solution and gains in operational efficiency.
Automated Workflows: Using analytic workflow automation, Shell is easily able to build reliable and fast data pipelines that allow them to predictive when to purchase parts, how long to keep them, and where to place inventory items."
Reported Results
According to Databricks:
* Scalable predictive model deployed for more than 3,000 types of materials at 50+ locations.
* Inventory analysis and prediction time reduced to 45 minutes from 48 hours - a 32X performance gain
* Cost savings equivalent to millions of dollars per year
Technology
Function
Supply Chain
General Supply Chain
Background
"Shell is a recognized pioneer in oil and gas exploration and production technology and one of America’s leading oil and natural gas producers, gasoline and natural gas marketers and petrochemical manufacturers. Inventory planning was disjointed resulting in lack of agility at Shell's locations."
Benefits
Data