Shell

Reduced inventory costs and improved supply chain efficiency.

Operations

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Energy & Utilities

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Classification

Project Overview

Shell developed a predictive model, hosted on Databricks, to optimize spare parts inventory across its global facilities.

Layman's Explanation

Shell uses AI to predict which spare parts they'll need and when at their various facilities around the world. This helps them make sure they have enough of the right parts on hand, but not too many, saving them a lot of money.

Analogy

Imagine an offshore oil rig that needs a very specific type of pump to keep operating. Getting that pump delivered can be a slow and expensive process. This AI acts like a very experienced logistics coordinator who understands the history of equipment failures on the rig and can anticipate what parts will be needed and when, making sure they're readily available, avoiding costly delays or unnecessary stockpiles.

Details

Shell's predictive model forecasts demand across 3,000 spare parts globally, improving inventory management and achieving significant cost savings.

Project Novelty

Predictive inventory management improves efficiency but is widely adopted.

Project Estimates

Timeline:

8 months

Cost:

$500,000

Headcount:

8

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