Tüpraş boosts delivery reliability to 95% and cuts truck loading time 75% with AI-driven forecasting
A documented Demand Forecasting in Energy & Utilities deployment at Tüpraş, with source-attributed results and missing evidence labelled explicitly.
Evidence at a glance
- Evidence status:
- Automated evidence gate passed
- Deployment timeframe:
- Not reported by source
- Reported outcome metrics:
- 3 cited below
- Directory entry published:
The source-link check confirms reachability, not independent re-verification of every claim.
Source-reported figures — cited source: weforum.org
The Challenge
Following the launch of its Resid Upgrade Plant in 2014, Tüpraş İzmit faced rising crude oil type diversity, product complexity, and pressure from port-based sales that increased jetty congestion.
The Solution
The refinery launched a digital transformation integrating planning, inventory and logistics across the value chain, deploying AI-driven forecasting and optimization solutions to improve the entire supply chain.
Results
The site improved delivery reliability from 85% to 95%, shortened average truck loading times by 75%, increased forecasting labour productivity by 48%, and achieved significant reductions in CO2 emissions and water consumption.
Key Takeaways
- AI-driven forecasting dramatically improves delivery reliability in refining operations
- Integrated planning across inventory and logistics enables holistic supply chain gains
- 75% truck loading time reduction shows the power of digital logistics optimization
Explore Related
Details
- Industry
- Energy & Utilities
- Use Case
- Demand Forecasting
- AI Technology
- Predictive ML
- Company Size
- Enterprise
- Company
- Tüpraş
- Evidence status
- Automated evidence gate passed
- Deployment timeframe
- Not reported by source
- Directory entry published
Cited source
weforum.orgHave a similar implementation?
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