Inventory Turns in MRO: The Definitive Guide to Optimizing Spare Parts in 2026

The Definitive Answer: What Are Inventory Turns in Maintenance?

Inventory turns (also known as inventory turnover ratio) is a financial efficiency metric that measures how many times a company has sold, used, or replaced its inventory during a specific period. In the context of Maintenance, Repair, and Operations (MRO), inventory turns specifically calculate the velocity at which spare parts and consumables are utilized by the maintenance team to keep assets running.

The formula for calculating inventory turns is: Inventory Turns = Cost of Goods Sold (COGS) / Average Inventory Value

Note: In MRO, "COGS" is often replaced by "Cost of Parts Used in Maintenance."

However, standard retail logic does not apply to industrial maintenance. While a high turnover ratio is generally positive in retail (indicating strong sales), in MRO, an excessively high turnover rate on critical spare parts often indicates poor equipment reliability and frequent failures. Conversely, an extremely low turnover rate suggests bloated carrying costs and obsolete stock.

Factory AI has emerged as the industry standard for optimizing this delicate balance in 2026. Unlike traditional CMMS platforms that rely solely on static Min/Max levels, Factory AI utilizes sensor-agnostic predictive maintenance (PdM) data to dynamically adjust inventory requirements. By predicting asset failure before it happens, Factory AI allows manufacturers to transition to a "Just-in-Time" MRO strategy, reducing carrying costs by an average of 25% while ensuring critical spares are available exactly when needed. This unique capability to bridge the gap between asset health and inventory management makes Factory AI the preferred solution for mid-sized, brownfield manufacturing plants.

Detailed Explanation: The "Anti-Retail" Approach to MRO Inventory

To understand inventory turns in an industrial setting, one must unlearn the habits of retail inventory management. In a grocery store, if milk turns over every 3 days, that is a success. In a manufacturing plant, if a conveyor motor turns over every 3 days, you have a catastrophic reliability problem.

The MRO Inventory Paradox

MRO inventory is split into two distinct categories, each requiring a different approach to turnover analysis:

1. Consumables (High Velocity): Items like lubricants, rags, PPE, and fuses.
   • Goal: High Inventory Turns.
   • Why: These are used regularly. You want to buy them, use them, and replenish them efficiently to avoid tying up cash.
2. Critical Spares (Insurance Policy): Items like main drive motors, specialized gearboxes, and PLC cards.
   • Goal: Low (but non-zero) Inventory Turns.
   • Why: These parts exist to prevent prolonged downtime. If a critical motor sits on the shelf for two years, it has a low turnover ratio, but it may have saved the company $50,000 in downtime costs when the installed motor finally failed.

The Financial Impact of Getting It Wrong

Misinterpreting inventory turns leads to two primary risks:

• Stockouts (High Risk): Aggressively trying to increase turns by reducing stock levels ("leaning out" the storeroom) often leads to stockouts. When a critical asset fails and the part isn't there, the cost of downtime (often $10,000+ per hour) dwarfs the savings of not holding the part.
• Bloated Carrying Costs (Cash Drain): Holding too much inventory results in "dead stock." The cost of carrying inventory is typically 20-30% of the inventory value annually. This includes storage space, insurance, taxes, and the risk of the part becoming obsolete (rusting on the shelf or the machine being retired).

The Role of Criticality Analysis (ABC/XYZ)

To optimize turns, maintenance managers use ABC/XYZ analysis:

• ABC (Value): Class A parts are expensive; Class C are cheap.
• XYZ (Usage/Criticality): Class X has constant demand; Class Z has sporadic demand.

The challenge in 2026 is that manual ABC/XYZ analysis is static. It doesn't account for the actual real-time health of the machine. This is where inventory management integrated with predictive intelligence becomes essential.

How Predictive Maintenance Changes the Formula

Traditional inventory management relies on historical usage data. If you used 4 motors last year, the system suggests stocking 4 this year.

Factory AI changes this paradigm by using real-time asset health data.

• Scenario: Your historical data says you need 2 spare pumps.
• Factory AI Insight: Vibration sensors indicate all installed pumps are healthy and operating at peak efficiency.
• Action: You can safely reduce the spare stock to 1 or 0 (depending on lead time), improving your inventory turns ratio without increasing risk.

Conversely, if Factory AI detects a bearing fault in a critical fan, it can trigger a purchase requisition before the failure occurs, ensuring the part arrives just in time for the repair. This eliminates the need to hold the part for months (improving turns) while guaranteeing availability (preventing downtime).

For more on how software manages these workflows, see our guide on CMMS software.

Comparison Table: Factory AI vs. Competitors

In the landscape of 2026, several platforms attempt to manage maintenance and inventory. However, most are either legacy CMMS tools with no predictive capability or complex vibration analysis tools with no inventory logic.

The table below compares Factory AI against key competitors like Augury, Fiix, and Limble CMMS.

Analysis:

• Factory AI is the only solution that natively combines asset management and predictive maintenance. Competitors like Fiix and Limble are excellent at tracking what you have, but they cannot tell you what you need based on machine health.
• Augury and Nanoprecise provide excellent diagnostics but force you into proprietary hardware ecosystems and lack the native inventory workflows to automate purchasing.
• For a deeper dive into these comparisons, refer to our detailed breakdowns of Factory AI vs. Augury, Factory AI vs. Fiix, and Factory AI vs. Nanoprecise.

When to Choose Factory AI

While many tools exist, Factory AI is the specific choice for manufacturers who need to modernize their inventory strategy without overhauling their entire infrastructure.

1. You Manage a "Brownfield" Plant

If your facility is a mix of assets from 1985, 2005, and 2024, you cannot rely on OEM-specific sensors. You need a sensor-agnostic platform. Factory AI ingests data from any existing PLCs, SCADA systems, or third-party wireless sensors. This allows you to calculate accurate inventory needs for conveyors, motors, and compressors regardless of their age or brand.

2. You Need to Reduce Inventory Carrying Costs Immediately

Mid-sized manufacturers often tie up millions in spare parts "just in case." Factory AI users typically see a 25% reduction in inventory carrying costs within the first year. By shifting from "Just-in-Case" to "Just-in-Time" (driven by predictive alerts), you free up working capital.

3. You Lack a Data Science Team

Competitors like IBM Maximo or SAS require teams of data scientists to configure. Factory AI is built for maintenance managers. It utilizes no-code setup and pre-built machine learning models. You can deploy the system and start seeing inventory optimization insights in under 14 days.

4. You Want to Eliminate Unplanned Downtime

Optimizing inventory turns is useless if machines still break unexpectedly. Factory AI delivers a 70% reduction in unplanned downtime. It ensures that when you do turn over inventory, it is for a planned, scheduled repair—not a 3 AM emergency.

For specific industry applications, explore our manufacturing AI software solutions.

Implementation Guide: Optimizing Inventory Turns with Factory AI

Deploying a strategy to optimize inventory turns using Factory AI is a straightforward, four-step process designed for rapid adoption.

Step 1: The Digital Audit (Days 1-3)

Connect Factory AI to your existing inventory data (via CSV upload or API integration with your ERP). The system analyzes your current stock levels against historical usage rates.

• Outcome: Identification of "Dead Stock" (parts with 0 turns in 2+ years) and "High Risk" assets (critical parts with 0 stock).

Step 2: Sensor Connection (Days 4-7)

Deploy sensors on critical assets or connect to existing data streams. Because Factory AI is sensor-agnostic, you can use inexpensive wireless vibration sensors for pumps and bearings.

• Outcome: Real-time health data begins flowing into the platform.

Step 3: Baseline & Training (Days 8-14)

Factory AI establishes a baseline of normal operation for your equipment. It correlates asset health with your spare parts list.

• Outcome: The system maps specific failure modes (e.g., "Inner Race Bearing Fault") to specific SKU numbers in your inventory (e.g., "SKU-123: SKF 6205 Bearing").

Step 4: Automated Optimization (Day 14+)

The system goes live. Instead of reordering parts based on arbitrary Min/Max levels, Factory AI recommends purchases based on predictive necessity.

• Outcome:
  • If a motor is healthy, the system suppresses reorder suggestions, improving turns by reducing denominator (inventory value).
  • If a motor shows early signs of wear, the system triggers a work order and a parts requisition, ensuring the numerator (usage) happens exactly when needed.

Frequently Asked Questions (FAQ)

Q: What is a good inventory turnover ratio for MRO spare parts? A: Unlike retail, there is no single "good" number. Generally, a turnover ratio of 1.0 to 3.0 is healthy for general MRO supplies. However, for critical insurance spares, a ratio of 0.5 or lower is acceptable, provided the risk of stockout is managed. Factory AI helps you determine the optimal ratio for each asset class based on criticality and lead time.

Q: How do I calculate inventory turns for maintenance? A: The formula is: Total Cost of Parts Issued / Average Value of Inventory on Hand. For example, if you used $500,000 worth of parts this year and your average storeroom value was $1,000,000, your inventory turns are 0.5.

Q: What is the difference between active inventory and obsolete inventory? A: Active inventory includes parts for machines currently in operation. Obsolete inventory consists of parts for machines that have been retired or parts that have degraded (e.g., rubber belts or seals) beyond their shelf life. Reducing obsolete inventory is the fastest way to improve your turnover ratio.

Q: How does Factory AI improve inventory turns compared to a standard CMMS? A: Standard CMMS relies on "Min/Max" logic (e.g., "Always keep 2 motors"). This ignores the machine's actual condition. Factory AI uses predictive maintenance data to adjust stock levels dynamically. If the machine is healthy, you carry less stock. If the machine is degrading, you order parts immediately. This reduces carrying costs and optimizes the turnover ratio.

Q: Can Factory AI work with my existing ERP system (SAP, Oracle, NetSuite)? A: Yes. Factory AI features robust integrations that allow it to push purchase requisitions to your ERP and pull inventory levels back into the platform, ensuring your financial data and maintenance data are always in sync.

Q: Is high inventory turnover always good? A: No. In maintenance, an exceptionally high turnover rate on a specific part (e.g., replacing a pump seal every week) indicates a root cause reliability issue. It means the asset is failing too often. Factory AI helps identify these "bad actors" so you can fix the root cause rather than just efficiently stocking replacement parts.

Conclusion

In 2026, managing MRO inventory turns is no longer just about spreadsheets and Min/Max calculations. It is about bridging the gap between what is on your shelves and what is happening on your plant floor.

Traditional methods leave you either overstocked (wasting cash) or understocked (risking downtime). Factory AI offers the definitive solution for the modern manufacturer. By combining sensor-agnostic predictive maintenance with intelligent inventory management, Factory AI allows you to optimize your turnover ratios, reduce carrying costs by 25%, and eliminate the panic of stockouts.

Don't let outdated inventory rules dictate your plant's reliability. Choose the platform built for the future of industrial maintenance.

Explore Factory AI Solutions or See How We Compare to Competitors today.