How AI Cut a Tier 1 Supplier’s Spare Parts Inventory by 15% Without Risking Downtime

TL;DR

• A major global automotive Tier 1 supplier had accumulated ~$10M in MRO spare parts at a North American plant over two years, driven by legacy Min-Max policies and a blanket 100% availability rule — much of it slow-moving.
• LatentView replaced intuition-based stocking with a data-driven framework: cloud data ingestion, ABC-XYZ segmentation by value and demand variability, and Monte Carlo simulations to set safety stock and reorder points by part criticality.
• Differentiated service levels replaced the one-size-fits-all 100% availability policy, cutting overstock while keeping stockout probability low for critical parts.
• The engagement reduced inventory levels by 10–15% and improved demand forecasting accuracy by 28%.
• The next frontier is operational optimization — linking parts availability to machine health and downtime prediction, with agentic AI recommending or executing replenishment.

Data collection and centralization of data in the cloud is revolutionizing inventory control and optimization in the automotive and manufacturing industries. Research in advanced inventory optimization methods suggests companies can reduce inventory levels by 10-35% while maintaining service levels. 

A 2024 Gartner study found that AI-based inventory management solutions can lower holding costs by 20-30%. High-performing organizations are 2-4× as likely to use AI/ML to optimize key supply chain processes.

A closely associated process which leads to better inventory control is demand forecasting. Machine learning algorithms can improve forecasting accuracy by 20-50%, which has downstream effects on levels of inventory. These algorithms can continuously learn and improve forecast accuracies allowing manufacturers to respond to disruptions, optimize procurement, and fine-tune production schedules with higher confidence. According to a McKinsey report, companies implementing AI-driven demand forecasting have reduced errors by up to 50%.

Several Automotive OEMs and large Tier 1 suppliers have already implemented advanced ML techniques to optimize forecasts and inventory levels. BMW uses AI across its operations via a control tower that constantly observes supply chain events and enhances efficiency through real-time controls. BMW’s AI tools also use data from suppliers to help anticipate disruptions. Toyota applies AI in manufacturing plants to improve scheduling, inventory, and production control. Siemens employs machine learning algorithms to predict spare parts requirements based on equipment performance data, reducing unplanned downtime by 20% and optimizing inventory holding costs so that only necessary parts are stocked.

While the benefits of AI-driven inventory optimization are well documented, many Tier 1 suppliers are still in the early stages of translating these capabilities into measurable business outcomes. A recent engagement by LatentView with a major global automotive Tier 1 supplier illustrates how data-driven inventory optimization, demand forecasting, and simulation-based policy setting can reduce inventory costs while maintaining operational reliability. 

How LatentView helped a major automotive Tier 1 supplier optimize spare parts inventory 

LatentView was recently engaged by a major global Tier 1 supplier to optimize its spare parts inventory. One of its major North American plants maintained a large inventory of MRO (Maintenance, Repair and Operations) spare parts to support critical maintenance activities. A significant portion of this inventory was slow-moving and driven by legacy stocking practices. Current policies prioritize extremely high availability to avoid machine downtime but lack the data-driven calibration needed to reduce overstocking.

Approximately $10 million in inventory value had accumulated over a two-year period, driven by legacy Min-Max policies based largely on operational experience. Stockouts and their associated downtime impacts were not systematically tracked, part criticality was inconsistently defined, and inventory parameters in legacy systems were not decision-ready, limiting data-driven inventory decisions.

Building a Data-Driven Inventory Optimization Framework 

The primary objective of the project was to develop Min-Max recommendation policies across spare parts inventory that were able to meet the set service levels backed by Monte Carlo simulations, and to reduce overall inventory capital hold by 10-15%. Additionally, the objective was to introduce an algorithmic demand forecasting process and improve forecasting accuracy by at least 20%.

The proposed solution established a data-driven inventory optimization framework that replaced intuition-based stocking with automated, simulation-driven policy setting and proactive replenishment.

The first step in the solution was to ingest historical and ongoing spare parts consumption, procurement, and supplier data into a cloud platform from source ERP systems. This included full historical batch loads along with daily/weekly incremental refreshes, supported by robust data quality checks.

Then, part-level segmentation based on the consumption behavior and criticality was applied to the data. ABC-XYZ segmentation was applied to active spare parts over the past two years, based on value contribution and demand variability respectively, categorizing them into segments such as fast-moving, critical and others to enable targeted inventory strategies. Segment-specific safety stock levels, along with minimum and maximum inventory thresholds, were determined using statistical simulation techniques, supported by automated reorder point optimization to ensure operational reliability. 

Policy changes were also recommended by segment to reduce inventory levels. The previous policy was based on a very conservative 100% availability for all spare parts. However, through the use of Monte Carlo simulations applied to each segment, more relaxed policies for spare part availability were created, which provided a low probability of stockouts. The model incorporated daily inventory and lead-time data, applied differentiated service levels based on part criticality, and included outlier treatment methods to optimize overall inventory costs.

A demand forecasting model was also developed to better predict the need for spare parts, which would reduce inventory.

A consolidated and interactive dashboard was also created that provided stakeholders with a comprehensive view of inventory performance and Min-Max policy recommendations. Additionally, Excel-based result workbooks included an operational feedback section to support continuous improvement and future policy refinement.

The impact was a significant reduction of 10-15% in inventory levels due to segmentation and new policy levels for inventory by segment. Additionally, the project improved demand forecasting accuracy by 28%. 

Path Ahead For the Industry

The next phase of the journey is moving from inventory optimization to operational optimization. By linking spare parts availability with machine health and downtime prediction, manufacturers can better quantify the business impact of inventory decisions. Agentic AI can further extend this capability by automatically recommending, or even executing, replenishment decisions based on inventory levels, supplier lead times, cost, and quality considerations. 

The direction of the industry is becoming increasingly clear. Ninety-one percent of supply chain respondents plan to use AI or GenAI for demand forecasting over the next two years. Eighty-five percent intend to use AI for inventory management, while 76% see potential for autonomous AI agents to handle tasks such as reordering and shipment rerouting.

While many automotive OEMs have already implemented AI-driven inventory control systems, many Tier 1 suppliers remain in the early stages of adoption. The potential for savings is significant as companies implement such solutions and use the data they are gathering to make decisions that drive better returns on investment.

FAQ

01. How does AI reduce spare parts inventory without increasing downtime risk? 

By replacing blanket availability rules with differentiated service levels. Parts are segmented by value and demand variability, then Monte Carlo simulations calibrate safety stock and reorder points for each segment — so critical parts stay protected while slow-movers are right-sized. In this engagement, that approach cut inventory 10–15% while maintaining set service levels.

02. What is ABC-XYZ segmentation and why does it matter for MRO inventory? 

ABC-XYZ classifies parts on two axes: value contribution (ABC) and demand variability (XYZ). This lets teams apply targeted stocking strategies — tight control on high-value, predictable parts and looser policies on low-value, erratic ones — instead of managing thousands of SKUs with a single rule.

03. How accurate is AI-driven demand forecasting for spare parts? 

It depends on data quality and demand patterns, but the gains are substantial. Industry research points to 20–50% improvements in forecast accuracy; in this LatentView engagement, accuracy improved by 28%.

04. Why are Monte Carlo simulations used to set inventory policy? 

Spare parts demand and supplier lead times are variable, so a single deterministic reorder point either overstocks or risks stockouts. Monte Carlo simulations model that variability across thousands of scenarios, producing Min-Max policies that hit a target service level at the lowest viable inventory.

05. Why are Tier 1 suppliers behind OEMs on AI-driven inventory? 

Many OEMs (BMW, Toyota, Siemens) have already deployed AI across supply chain operations. Tier 1 suppliers often still rely on legacy Min-Max policies set from operational experience, with stockouts untracked and inventory parameters that aren’t decision-ready — leaving significant savings on the table.