In a powerful demonstration of how Process Intelligence can amplify the impact of Artificial Intelligence (AI), Celonis has helped Smurfit Westrock, a global packaging leader, unlock substantial business value through AI-driven inventory optimization. Utilizing the Celonis Process Intelligence Platform and its AI Copilot, Smurfit Westrock is transforming spare parts management and expanding the scope of AI integration across its global operations. This collaboration underscores the strategic value of aligning AI with process intelligence, ensuring automation is applied where it matters most—reducing inefficiencies, cutting costs, and boosting operational performance.
Solving Complex Inventory Challenges with Process Intelligence
Smurfit Westrock manages a vast, complex inventory that includes hundreds of thousands of spare part SKUs. Recent acquisitions and geographically dispersed operations added layers of complexity, with challenges such as:
- Duplicate entries in master data
- Language inconsistencies across regional sites
- Semantic overlap (e.g., “lubricant” vs. “oil”)
These issues led to excessive inventory, procurement duplication, and escalated costs across maintenance and purchasing functions.
The Celonis Solution:
Using the Process Intelligence Graph and large language model (LLM)-driven analysis, Celonis enabled Smurfit Westrock to:
- Harmonize master data across business units
- Identify duplicate stock entries
- Avoid unnecessary purchase orders
- Surface idle inventory—some unused for over 8 years
This approach allowed the company to optimize stock levels, reduce redundant procurement, and redirect resources toward more strategic initiatives.
AI Copilot: Real-Time Decision-Making for Engineers
At the heart of the transformation is the Celonis AI Copilot, which acts as an intelligent assistant for plant engineers and maintenance teams.
Capabilities:
- Natural language interface: Engineers can search inventory using plain language, including technical terms or everyday descriptions.
- Smart recommendations: Suggests optimal parts based on availability, location, and historical usage.
- Proactive alerts: Flags duplicate orders and identifies cross-plant stock that can be repurposed.
In one standout case, during a plant breakdown in Europe, Smurfit Westrock faced a delayed supplier lead time for a replacement part. Using the Copilot, the team located the same part at a nearby plant and arranged a transfer within a day—a significant efficiency gain.
Driving Measurable Impact and Sustainability
The benefits of this AI-powered transformation are already visible:
- Faster resolution of equipment breakdowns
- Reduced procurement and warehousing costs
- Higher spare parts utilization
- Improved sustainability through resource reuse
According to Brian Dodson, Business Process Improvement Manager at Smurfit Westrock:
“With Celonis, we’re reaching new levels of operational efficiency, delivering more value to our teams, and contributing to sustainability goals—all while making maintenance smarter and faster.”
Strategic AI Roadmap for Global Optimization
Celonis is helping Smurfit Westrock build a strategic roadmap for AI deployment, ensuring initiatives are validated against:
- Process readiness
- Human intervention needs
- Measurable efficiency gains
By aligning AI applications with real business needs, Smurfit Westrock is scaling automation where it yields the greatest impact.
The company now plans to expand the use of Celonis AI beyond inventory management to include:
- Procurement
- Maintenance operations
- Production process optimization
These efforts will span operations across Europe, Latin America, and North America, marking a significant leap in enterprise-wide digital transformation.
The partnership between Celonis and Smurfit Westrock is a prime example of how Process Intelligence powers effective AI. By building a strong data foundation, leveraging real-time insights, and deploying intuitive AI assistants, Smurfit Westrock is achieving tangible operational excellence.
This collaboration not only optimizes inventory and procurement but sets a new standard for AI adoption in industrial enterprises—where process visibility and data harmonization are prerequisites for sustainable, scalable automation.
