Here’s a reality check that Fortune Global 500 companies learned the hard way: unplanned downtime cost them $1.5 trillion in 2021-22, as per IBM, up from $864 billion just three years ago. That’s an 11% slice of yearly turnover evaporating because equipment stopped when it shouldn’t have. And more often than not, the culprit isn’t the machinery itself. It’s the missing spare part that could have prevented or fixed the problem in minutes instead of weeks.

For industrial equipment manufacturers, Maintenance, Repair and Operations (MRO) cycles dictate when parts are consumed and how often assets fail or require servicing. They also tell where spares should be positioned across global networks.

Yet despite heavy investment in equipment hardware, many manufacturers struggle to align their spare parts planning with the very cycles that drive demand. This leads to either stockouts or excess inventory. And manufacturers are left with reactive firefighting instead of strategic foresight.

In this blog, we examine how MRO cycles affect spare parts planning and why traditional approaches fail. We will also explain how data-driven spare parts demand forecasting and modern catalog systems can transform planning and bring precision.

MRO Cycles Are Inherently Intermittent: So Spare Parts Demand Isn’t Predictable

Spare parts demand is fundamentally different from finished goods. Unlike finished products, spare parts are consumed irregularly. They depend on factors like:

• Planned maintenance intervals
• Unplanned failures
• Usage patterns tied to operating conditions
• Regulatory or compliance MRO events

There can be long periods of zero demand followed by sudden, urgent needs.

In aviation MRO, for example, up to 80% of spare parts demand is effectively unpredictable. There’s no known when or where until it happens. This erratic pattern is true across industrial equipment. Hydraulic pumps, gearboxes, compressors and other critical components don’t fail on fixed schedules.

Conventional forecasting (like simple historical averages) assumes regular demand. MRO demand breaks that assumption.

Planners must adopt probability-based and intermittent demand models to better reflect real usage patterns. Or they risk overstock and stockouts alike.

The Cost of Poor Demand Forecasting: Downtime Is More Expensive Than Inventory

A common question among OEM leaders: What’s the real cost of under- or over-planning spare parts inventory?

When unplanned maintenance strikes, downtime isn’t just inconvenient; it’s expensive. Industry analyses show that in manufacturing and heavy industries, unplanned downtime can cost thousands of dollars per minute in lost output, labor and expedited logistics.

An excavator sitting idle at a mine site can easily cost more than $200,000 per hour in lost revenue. Add to that the relationship damage with the customer and a competitor pouncing on the opportunity. For larger players, this can go much higher. 

Understanding MRO Cycles: The Three Demand Generators

Before we go into planning strategies, let’s understand how each type of MRO cycle alters spare parts consumption patterns.

1. Preventive Maintenance

Preventive maintenance operates on schedules. These interventions are based on time or usage triggers, like operating hours or production cycles.

This generates relatively predictable demand patterns, but frequency and part types may vary widely across assets.

While the timing is predictable, the actual consumption isn’t always linear. Sometimes inspections reveal parts are still serviceable. Other times, secondary components fail during scheduled maintenance, creating unexpected demand spikes.

2. Predictive Maintenance

Predictive maintenance is based on condition monitoring and data analytics. Modern IoT sensors and machine learning algorithms now predict failures before they occur.

According to industry practice, predictive maintenance helps transition from rigid schedules to condition-triggered events, which improves planning and reduces unplanned work orders. 

However, in reality, less than one-third of commercial fleets have implemented digital maintenance planning systems. That means the majority still operate reactively.

3. Reactive Maintenance

Reactive maintenance is triggered by unplanned failures. 

Equipment breaks. It’s inevitable. And often, this comes as a complete surprise to the manufacturer.

For example, a packaging equipment OEM analyzed 187,106 transaction lines across 28,344 materials and discovered that 85% of their parts exhibited sporadic demand patterns, making traditional time-series forecasting methods inadequate.

So, reactive maintenance yields the most erratic demand. Planners are required to build safety stock, risk pooling strategies and cross-location visibility to mitigate its impact.

Why this matters:

Each cycle requires a different forecasting and stocking strategy:

• Preventive demand leans on historical cycles.
• Predictive demand benefits from sensor, IoT and machine data.
• Reactive demand requires probabilistic and risk-based forecasting.

Forecasting Challenges: Why Traditional Spare Parts Planning Fails MRO Realities

Spare parts rarely have stable, smooth demand. Spikes occur unpredictably, even for identical assets. Further, demand varies with the equipment lifecycle and is heavily influenced by operating conditions.

Standard forecasting models (moving averages, linear regression) fail under such conditions. Instead, planners use intermittent demand models, like Croston’s method or probabilistic frameworks that account for zero-demand periods and sudden usage spikes. 

For example, a compressor seal may go years without replacement and suddenly require dozens over a short span due to operational stress. This is a pattern that simple trend models never capture.

This has made it important for leaders to adopt forecasting methods that accommodate zero-demand intervals and irregular spikes to avoid tying up excess capital in seldom-used parts.

MRO Cycles Are Local Plus Global: Planning Must Reflect Geography and Network Structure

Industrial OEMs often serve global fleets: multiple plants, regions and maintenance depots. Spare parts consumed in one region may not transfer easily to another due to customs, lead times, tariff differences, or regulatory constraints.

Legacy planning systems often treat all locations the same. This leads to:

• Overstock in low-demand zones
• Stockouts in high-usage hubs
• Excess logistics costs

Advanced planning must incorporate multi-location demand forecasting, balancing regional usage patterns with central stock pools.

Aligning MRO Cycles with Intelligent Spare Parts Planning

Modern forecasting goes beyond spreadsheets. Data platforms that integrate operational usage, work orders, MRO cycles and equipment telemetry can forecast demand more accurately. Overall, this requires treating MRO cycles and spare parts planning as an integrated system, not separate silos.

The benefits are numerous:

• Reduced safety stock levels
• Improved parts availability
• Lower emergency procurement costs
• Higher first-time fix rates

According to IBM, aligning MRO cycles with spare parts planning has been shown to cut parts-driven downtime by 50%, reduce inventory costs by 40%, lower maintenance budgets by 35% and raise service levels by 25%.

Practical Steps for MRO-Driven Spare Parts Planning

Here’s a pragmatic framework industrial equipment manufacturers can adopt:

1. Map MRO Cycles to Parts Demand

Classify parts by maintenance type: preventive, predictive and reactive. Also, classify them by criticality (failure impact). Then, forecast separately for each pattern. One model won’t fit all.

2. Use Probabilistic Forecasting

Intermittent demand and irregular patterns require advanced forecasting (Croston’s, intermittent models) rather than simple averages. Apply predictive analytics to reactive maintenance, where failures correlate with equipment age, usage intensity, or environmental conditions.

3. Integrate Real-Time Work Orders

Tie planned jobs and upcoming work orders into forecast inputs, not just historical usage.

4. Align Inventory with Network Logistics

Consider latency, duty, tariffs and inter-location transfers in your safety stock calculations.

5. Adjust Inventory by Equipment Lifecycle Stage

Continuously rebalance inventory as fleets move from early life to aging and obsolescence. Increase safety stock for critical parts as failure risk rises. Make last-time-buy decisions for legacy equipment.

6. Measure KPIs Regularly

Track forecast accuracy, stockout rates, lead times and service levels for continuous improvement.

Also Read: 13 Strategies for Spare Parts Inventory Management

The Technology Stack: Enabling MRO-Integrated Spare Parts Planning

This is where theory meets execution. Without the right systems, MRO-driven planning doesn’t scale.

Modern AI-powered electronic parts catalog software acts as the backbone of MRO-driven spare parts planning. It connects installed base data, maintenance history and schedules, inventory positions and supplier lead times in one place.

Intellinet Systems’ Intelli Catalog is built for this. When a technician identifies a part from an exploded diagram, they immediately see where it’s stocked and how long it will take to replenish. They also get real-time insights on supersession/substitution for obsolete parts.

Its Intelli Forecast feature adds AI-driven demand forecasting by combining historical usage with location and environmental context to reduce both stockouts and overstocking.

Overall, they address two core challenges at the heart of MRO-influenced planning: forecast accuracy and catalog data integrity. So, identification, planning and ordering happen in a single workflow.

The result: organizations that implement Intelli Catalog report 40% reduction in incorrect part orders.

Conclusion: MRO Cycles Must Drive Spare Parts Planning

For industrial equipment manufacturers, spare parts planning fails when forecasting ignores the rhythm of maintenance itself. MRO cycles (preventive, predictive and reactive) define when and how parts are consumed. Forecasting that doesn’t reflect these cycles will either burden capital with excess stock or erode service levels with shortages.

By adopting advanced forecasting logic and enriched catalog data, OEMs can improve service uptime and reduce capital tied up in inventory by anticipating replacement needs rather than reacting to failures.

Ready to connect your MRO cycles with intelligent spare parts planning? Schedule a demo.

Frequently Asked Questions (FAQs)

1. How do OEMs align spare parts planning with mixed MRO strategies across regions?

Answer: Most global OEMs run different MRO strategies by market. Mature markets lean predictive. Regulated ones stay preventive. Price-sensitive regions still react. Effective spare parts planning requires segmented policies by region, not a single global stocking rule.

2. Which spare parts should not be forecasted using historical demand?

Answer: Anything touched by engineering changes, warranty spikes, overhauls, or lifecycle shifts. History lies in the moment the product changes. These parts need BOM intelligence, failure patterns and maintenance logic, not demand history alone.

3. How do equipment lifecycle stages affect spare parts stocking decisions?

Answer: Early lifecycle equipment has low-volume, high-uncertainty spare parts requirements. Mid-lifecycle settles down and becomes predictable. End-of-life demand drops, but availability becomes mission-critical. If you ignore lifecycle context, then you’ll either drown in excess stock or miss parts when downtime suddenly matters most.