From Reactive to Predictive: Agentic AI Service Parts Planning

Service parts planning has always been one of the toughest challenges I’ve faced. Unlike traditional supply chains where demand follows somewhat predictable patterns but is increasingly impacted by disruptions, service parts planning operates in a just-in-case (JIC) model, requiring parts to be available whenever and wherever failures occur.

Too much inventory means high carrying costs. Too little means missed service level agreements (SLAs), unhappy customers, and lost revenue. Balancing that has always been a struggle, and I know firsthand how frustrating it can be to make critical supply chain decisions with imperfect information.  

That’s why AI-driven solutions like ketteQ are game-changers—finally offering a way to plan service parts from end-to-end in a way traditional methods never could.

AI: From Reactive to Predictive Service Parts Planning

AI is transforming how we forecast demand, optimize inventory, and manage replenishment. Instead of relying on static parameters and past averages, AI continuously learns from real-time data, adapts to changing conditions, and runs multiple parallel simulations to determine the best outcomes.

Honestly, I can’t help but think, “Where was this technology when I needed it?”

“Where was this technology when I needed it?”

Carrier’s AI-Powered Transformation with ketteQ

Carrier, a $20 billion global leader in HVAC and refrigeration, has one of the most complex service part operations in the industry. Managing tens of thousands of SKUs across a global network of technicians and repair centers requires precision, speed, and adaptability.

My friend Gary Bobb, former VP Global Service and Aftermarket at Carrier, summed it up best:

“ketteQ is giving us the ability to optimize our service parts planning globally in ways that were previously impossible. AI-driven forecasting and inventory optimization allow us to improve parts availability, reduce excess inventory, and deliver better service while lowering costs.”

And that’s exactly what AI-powered planning should do—create better outcomes for both businesses and customers.

Why AI is Essential for Just-in-Case Planning

Most supply chains are built on just-in-time (JIT) principles—ensuring inventory arrives precisely when needed, minimizing waste and storage costs.

But service parts planning is different. While traditional supply chain demand is somewhat predictable, service parts demand is highly unpredictable, driven by failures, breakdowns, and urgent service needs.

This is why service parts planning must take a JIC approach—strategically stocking parts without overstocking. AI bridges this gap by balancing risk and cost, ensuring parts are available without unnecessary waste.

How AI Solves the Biggest Challenges in Service Parts Planning

1. Hyper-Accurate Demand Forecasting
   AI analyzes failure patterns, weather data, IoT sensor inputs, and historical trends to predict when and where parts will be needed—improving service levels while reducing excess stock.

2. Adaptive Inventory Optimization
   AI dynamically adjusts stocking levels based on real-time demand, lead times, and supply constraints, ensuring the right parts are in the right place at the right time.

3. Real-Time Supply Chain Coordination
   When disruptions occur—like supplier delays—AI instantly identifies alternative sources or redistributes inventory, keeping operations running smoothly.

4. AI-Driven Prescriptive Analytics
   Instead of manually reviewing spreadsheets, planners get clear, data-backed recommendations for adjusting safety stock, expediting orders, or reallocating inventory.

Why More Companies Are Choosing ketteQ

As a member of ketteQ’s Executive Advisory Board, I’ve seen more and more companies making the switch from systems that were not specifically designed to fully tap into the power of AI, ML, and cloud computing to ketteQ.

Manufacturers, high-tech firms, and consumer goods companies are adopting ketteQ because of its faster implementation, seamless Salesforce integration, and AI-driven optimization.

The Future: From Planning to Autonomous Execution

Looking ahead, an agentic AI powered solution like ketteQ won’t just assist planners—they will automatically execute decisions within set parameters.

Imagine AI systems that forecast demand, adjust inventory levels, issue supplier orders, and redistribute stock—without human intervention. That’s the evolution of service parts planning, which is being led by ketteQ. Companies investing in AI now will set the standard while others scramble to catch up.

The Future is Now

Earlier, I asked myself, “Where was this technology when I needed it?” Fortunately, today’s service parts leaders won’t have to ask that question because the technology is available right now.

The challenges in service parts planning haven’t changed, but the solutions certainly have. AI-powered adaptive planning is already transforming how companies anticipate demand, optimize inventory, and deliver the right parts at the right time. That’s why a growing number of organizations are turning to ketteQ—because waiting is no longer necessary, and neither are the struggles of the past.

If I were leading a supply chain organization today, I wouldn’t hesitate. I’d put this technology to work before my competitors do.

Learn more about the transformational power of AI in service parts planning by reading ketteQ’s customer success stories.