key takeaways
If you only read 30 seconds of this article:
- Demand planning fails for MRO spares: A bearing that fails twice in five years has no history. The formula returns zero. You order zero. The bearing fails. The line stops for three weeks.
- 50–60% of MRO inventory is excess or obsolete—industry studies confirm this. The other 10–15% is at stockout risk. Standard spreadsheets miss both simultaneously.
- Optimize criticality, not demand history. Stocking decisions should use failure mode, lead time, and failure impact, not sales velocity. Most ERPs force the wrong model.
- ROI in weeks, not years. Best practices work on your data as-is across SAP, Maximo, and Oracle at the same time—no data warehouse or cleanse required (based on Verusen customer results).
Why Most Plants Are Overstocked and Understocked at the Same Time
The paradox is real: most asset-intensive manufacturers carry excess inventory on parts that rarely fail while simultaneously facing stockouts on the critical spares that stop production lines. Industry estimates suggest the average manufacturer carries 20–30% excess MRO inventory and simultaneously faces stockout risk on 10–15% of critical parts — consistent with Verusen's experience across hundreds of implementations. This contradiction isn't caused by bad buyers. It's caused by misaligned incentives between the teams responsible for inventory.
Procurement teams are measured on cost control. Operations teams are measured on uptime. When a bearing fails unexpectedly and a production line stops, operations needs that part now—cost doesn't matter. Procurement, meanwhile, is trained to consolidate orders, negotiate volume discounts, and reduce SKU sprawl. The result: procurement stocks high-volume, low-cost parts to look efficient. Operations hoards critical spares to avoid the $260,000-per-hour cost of unplanned downtime for an industrial manufacturer (Aberdeen Strategy & Research). Neither team wins. The plant ends up with both excess inventory and stockout risk.
How misaligned data makes the problem worse
The second layer of the problem is visibility. MRO inventory data lives in different systems—ERP, EAM, legacy spreadsheets, supplier catalogs—each with its own naming conventions, unit-of-measure codes, and hierarchies. A bearing might be called a "rolling element bearing" in SAP, a "ball bearing" in Maximo, and "bearing PN 6205" in a three-ring binder. No one has a single view of how much you carry, where it is, or when it last moved.
Without that visibility, procurement and operations can't align on stocking levels. Procurement sees "we ordered 12 units last year, we should order 12 again." Operations sees "we ran out three times, we need more buffer." No one sees that 8 of those 12 units are still on the shelf from the year before, or that the part is obsolete because you replaced that equipment in a 2021 acquisition and never updated the master data.
Why traditional inventory methods fail for spare parts
Standard safety stock formulas require demand history. For a bearing that fails twice in five years, there is no history. The formula returns zero. So the plant orders zero. Then the bearing fails and the line stops for three weeks. The next year, Operations overorders to prevent a repeat. Procurement negotiates the order down. The cycle repeats.
The fix is to move from cost-driven procurement and reactive operations toward how to approach MRO inventory optimization as a unified function. That means starting with alignment: procurement and operations define stocking policy together based on criticality and failure risk, not cost alone. It means consolidating your data so both teams see the same inventory picture. And it means treating MRO optimization as a strategic initiative with C-suite sponsorship—not a one-time audit project.
Secure C-Suite Commitment: Position MRO as a Value Driver, Not a Cost Center
Without executive endorsement, MRO optimization efforts collapse within years—usually when leadership changes or budget pressures spike. The reason: MRO must shift from a cost-control mandate owned by procurement to a strategic asset owned by operations and the C-suite together. When that ownership is unclear or fragmented, reversions to old behaviors happen fast.
The core problem is misaligned incentives. Procurement is measured on cost reduction; operations is measured on uptime. Procurement wants lower inventory; operations wants higher inventory to avoid stockouts. Without executive clarity on how MRO serves both goals simultaneously, these teams optimize against each other instead of for the business.
Reframe MRO From Cost Center to Value Driver
Position MRO as a lever for downtime reduction and working capital recovery, not as overhead to cut. Unplanned downtime costs the world's 500 largest companies about $1.4 trillion a year—roughly 11% of annual revenue, up from $864 billion in 2019–2020 (Siemens, True Cost of Downtime, 2024). MRO inventory optimization directly attacks this cost. When your plant has the right parts in stock at the right locations, you reduce unplanned downtime. When you eliminate excess and slow-moving inventory, you unlock working capital—based on Verusen customer results, an average of $20M per customer.
Show the board a single metric: downtime risk and working capital tied to MRO performance. Make MRO part of operational strategy, not a procurement project. Once MRO becomes a strategic function with executive ownership, procurement and operations stop competing and start collaborating on the same target.
Build Governance Structures That Outlast Leadership Changes
Create a cross-functional MRO steering committee with clear roles: operations owns criticality and uptime targets; procurement owns cost and supplier performance; finance owns working capital goals. Assign a single executive sponsor—typically a VP of Operations or VP of Supply Chain—with P&L visibility into MRO performance.
- Define MRO policy in writing—what parts are critical, what safety-stock rules apply, when to order and when to reduce. Policy prevents drift when priorities shift.
- Review MRO performance quarterly alongside operational metrics: uptime, stockout incidents, and working capital. If MRO is invisible on the executive dashboard, it becomes invisible in the budget.
- Codify the decision authority. When a plant manager wants to order extra inventory for safety, who decides: the plant, procurement, or a data-driven algorithm. Clarity prevents reversions.
- Lock in the MRO governance model in writing as part of your ERP or EAM governance, not as a one-time initiative. MRO decisions must flow through the same change-control gates as any system-of-record change.
Organizations that treat MRO as a permanent governance function—not a project—maintain gains through M&A, leadership transitions, and budget cycles. Those that treat it as a one-time cleanse or cost-reduction initiative see the problems return within 2–3 years.
Address the Root Causes, Not the Symptoms
Isolated efforts—a data cleanse, a new supplier agreement, a single-site pilot—fail because they don't fix the structural misalignment. Procurement and operations remain in conflict. Data remains fragmented across multiple ERPs. Priorities shift when the sponsor moves. The real fix is executive clarity: MRO is a strategic function with measurable uptime and working-capital targets, owned by operations, and governed at the enterprise level.
Consolidate Inventory Data Across Multiple ERPs, EAMs, and Sites Without Waiting for a Data Cleanse
You don't need perfect data first. Aggregate as-is from SAP, Maximo, legacy systems, and post-M&A silos simultaneously and begin analysis immediately. Most manufacturers waste months extracting data manually, reconciling spreadsheets across departments, and waiting for a data cleanse that never quite finishes. By then, the inventory problem has shifted, priorities have changed, and the version of truth you built is already obsolete.
Manual extraction and spreadsheet manipulation create multiple versions of the truth across teams. Procurement sees one inventory number. Operations sees another. Finance sees a third. No one can agree on whether a critical part is actually in stock or flagged for reorder. Each department makes decisions based on different data, and those decisions conflict—driving both excess inventory and stockout risk simultaneously.
Real-time visibility across 100+ sites and multiple ERPs works differently. Instead of waiting for IT to extract and cleanse, you connect to your existing systems as they are today—data quality issues and all—and the platform begins optimizing immediately. A Fortune 500 CPP manufacturer with 41 sites and multiple SAP instances identified $63M in MRO inventory savings and verified $60M, reducing material review time from over 20 minutes to 4 minutes per review. That speed came from aggregating scattered data across all sites at once, not from cleaning it first.
Post-merger environments are where this matters most. After an acquisition, you inherit conflicting part numbers, duplicate suppliers, overlapping stocking strategies, and no single source of record. Rather than freeze operations while data cleansing teams reconcile naming conventions and build a unified catalog, you can connect all legacy systems in parallel and see the full inventory picture across old and new sites in weeks. A global industrial and petrochemical manufacturer operating 27 sites with three different ERPs identified $69M in MRO inventory savings during evaluation by aggregating data from all systems simultaneously—without waiting for integration.
The practical outcome: your procurement and operations teams stop working from conflicting spreadsheets. Stocking policies align across sites because everyone sees the same data. And you unlock $20M average working capital per customer based on Verusen customer results—not after a year-long data project, but in weeks.
Classify Parts by Criticality, Not Just Demand History
Criticality-driven classification separates parts that stop production lines from those that don't, enabling targeted stocking policies that demand-history formulas cannot support. A bearing that fails once every five years has no demand history — standard safety-stock formulas return zero, so the plant orders zero. Then the bearing fails and the line stops for three weeks. The solution is a spare parts criticality analysis that ranks parts by their impact on production, not by how often they've moved.
This is fundamentally different from demand-planning categories. Demand planning sorts parts by sales volume (A, B, C items in an ABC analysis for finished goods). MRO spares don't sell — they fail. A pump seal may sit for two years, then fail and cost you 15 hours of downtime. In asset-intensive operations, unplanned downtime can cost as much as $260,000 per hour (Aberdeen Strategy & Research). The part's criticality is its consequence, not its velocity.
Build a Three-Tier Criticality Framework
Tier 1: Production-Critical. Parts whose failure stops the line or creates immediate safety risk. These require higher safety stock and faster replenishment, regardless of demand history. A coupling on a primary motor qualifies; so does a seal on a pressure vessel.
Tier 2: High-Impact. Parts whose failure degrades output or requires significant rework but doesn't stop production immediately. These warrant moderate safety stock and defined lead-time buffers. A worn conveyor roller falls here — you can limp along, but efficiency drops and secondary failures follow.
Tier 3: Low-Impact. Parts whose failure has minimal consequence or long detection-to-failure windows. These can run leaner inventory and standard just-in-time replenishment. A non-critical bolt or standard fastener belongs in this tier.
Once you've classified, you set stocking policies per tier: Tier 1 might carry 6 months of stock for long-lead items; Tier 3 might carry 1 month. This replaces the one-size-fits-all safety-stock formula that fails for slow-moving critical spares. A Fortune 500 CPG manufacturer reduced material review time from over 20 minutes to 4 minutes by applying this framework across 41 sites, identifying $63M in MRO inventory savings and verifying $60M (based on Verusen customer results). The same clarity enables procurement and operations to align: operations specifies criticality; procurement builds sourcing strategy around it.
| Why This Matters Now. Most plants carrying inventory recognize excess stock on non-critical parts and simultaneous stockout risk on critical ones — industry estimates suggest 20–30% excess and 10–15% critical-part shortages, consistent with Verusen's experience across hundreds of implementations. Criticality-driven classification is the first step to fixing both at once. |
Break the Procurement-Operations Conflict: Unified Incentives and Decision Authority
Procurement and operations optimize for different metrics, and that misalignment is why you end up with both excess slow-moving inventory and stockout risk at the same time. Procurement minimizes cost per unit. Operations minimizes downtime risk. Neither is wrong—but when they report to different leaders and compete for budget, the plant loses.
The result: procurement negotiates volume discounts on parts that rarely fail, so the plant carries months of inventory in those categories. Operations, fearing a stockout on critical equipment, requests safety stock that procurement flags as excessive and refuses to fund. So the critical part sits on backorder while the slow-moving part ties up working capital. Both teams are optimizing themselves into failure.
A Fortune 500 CPG manufacturer with 41 sites faced exactly this problem. Material reviews took over 20 minutes per SKU because procurement and operations could not agree on what belonged in stock. Once they centralized MRO decisioning under a single governance team with shared KPIs—working capital reduction and uptime improvement, not procurement savings alone—review time fell to 4 minutes. The unified team had one goal: optimize inventory for both cost and availability.
Move decisioning authority out of competing silos
Consolidate MRO inventory decisions into a single authority—a dedicated MRO governance team that owns both procurement strategy and uptime risk. This team sets stocking policies and criticality rankings; procurement and operations execute, not negotiate. Reporting to a single leader removes the incentive to optimize one metric at the expense of the other.
Align incentives on working capital reduction and uptime improvement. If you reward procurement on cost savings alone, they will minimize SKUs and order sizes, and you will have stockouts. If you reward operations on inventory on hand, they will overstock and you will have excess. Measure both. Pay for both. Georgia Pacific, managing ~$1B in MRO across 110 US sites and 4 ERP systems, collapsed decisioning from hundreds of people to a dedicated team of 7—and flagged 2,900 materials at stockout risk that would have been invisible under siloed review.
Without unified authority, this conflict resurfaces every acquisition, reorganization, and budget cycle. With it, your MRO function becomes a recognized strategic asset rather than a cost center caught between two departments.
Move Beyond One-Size-Fits-All Safety Stock: Dynamic Policies by Part Behavior
A blanket safety-stock percentage—say, 20% across the board—treats a critical bearing and a cleaning supply as inventory twins. They're not. One fails twice in five years and stops a production line for weeks; the other moves predictably and costs $15. The same stocking rule destroys working capital on one and leaves you exposed on the other.
The problem runs deeper than math. Standard safety stock formulas require historical demand data. For parts that fail infrequently or haven't been ordered in years, that history doesn't exist—or the formula returns zero. So the plant carries nothing. Then the part fails, the line stops, and you're three weeks into expedited shipping and overtime.
Dynamic stocking policies sort parts into three archetypes based on failure behavior and consequence, not cost alone. Fast-moving, non-critical items use min/max rules—reorder when stock hits a floor, cap it at a ceiling. Bulk supplies and non-urgent consumables shift to just-in-time ordering, cutting excess inventory without risk. Production-critical spares get strategic reserves sized to the uptime cost of a stockout, not a percentage.
The shift from one-size-fits-all to behavior-based policies isn't manual work. A Fortune 500 CPG manufacturer with 41 sites updated 800+ stocking policies in a single cycle using AI-driven optimization, reducing material review time from over 20 minutes to 4 minutes and identifying $63M in inventory savings while verifying $60M—the highest verified-to-identified ratio in the dataset, based on Verusen customer results. The alternative—spreadsheet-by-spreadsheet hand audits—would have taken months and delivered less precision.
The result: you reduce excess inventory where it bleeds capital while protecting availability where it matters most. Industry estimates suggest the average asset-intensive manufacturer carries 20–30% excess MRO inventory and simultaneously faces stockout risk on 10–15% of critical parts—consistent with Verusen's experience across hundreds of implementations. Dynamic policies close both gaps at once.
Track ROI Through Working Capital, Uptime, and Material Review Time—Not Just Unit Costs
Stop measuring MRO success by cost-per-unit. That metric hides the real value: dollars unlocked from inventory, hours of uptime reclaimed, and time freed from manual review. Most plants optimize for the wrong thing—squeezing supplier discounts while sitting on millions in excess stock and simultaneously facing stockout risk on critical parts.
The three metrics that matter are working capital reduction, uptime improvement, and material review efficiency. Each one moves a different business needle, and each one is measurable within weeks of implementation.
1. Working Capital Unlocked
Every dollar of excess inventory is capital locked in a shelf. Verusen customers unlock an average of $20M in working capital per site, based on Verusen customer results. That's not a projection—it's cash returned to operations within the first few months.
This happens because the platform identifies which of your 41M+ unique MRO materials are true excess (slow-moving, redundant, or beyond criticality) versus which ones genuinely prevent downtime. A Fortune 500 CPG manufacturer identified $63M and verified $60M across 41 sites—the highest identification-to-verification ratio we've seen. That's the difference between guessing and knowing.
2. Uptime Improvement
Unplanned downtime costs the world's 500 largest companies about $1.4 trillion a year—roughly 11% of annual revenue, up from $864 billion in 2019–2020, according to Siemens' True Cost of Downtime (2024). For a single asset-intensive manufacturer, that translates to hundreds of thousands of dollars per incident. Verusen customers see an average of 2.8% improvement in uptime, based on Verusen customer results—which means fewer production halts tied directly to missing spare parts.
This improvement comes from fixing stockout risk on critical parts. A Georgia Pacific operation across 110 US sites flagged 2,900 materials at stockout risk during the evaluation—parts that would have caused downtime but were sitting in excess elsewhere in the fleet. That visibility alone changes the conversation with operations teams.
3. Material Review Time
Your procurement and maintenance teams spend hours each week manually reviewing stocking levels, digging through spreadsheets, cross-checking multiple systems, and debating whether to order more or cut stock. Verusen customers reduce material review time by 60%, based on Verusen customer results. A Fortune 500 CPG manufacturer cut that review from over 20 minutes per material to 4 minutes—across thousands of SKUs.
That's not process improvement theater. A Georgia Pacific implementation recovered 6,600 hours across the team in the first year, and centralized inventory decisioning from hundreds of people arguing over levels to a team of 7 making data-backed calls. When your team spends 60% less time on inventory reviews, they spend more time on strategy.
| Why cost-per-unit misleads you. A 5% discount on a part you're already overstocked on looks like a win in procurement metrics. It's a loss in working capital. Measure what moves your business: cash freed, downtime prevented, and time reclaimed. |
Embed AI-Native Optimization Into Your Existing Systems—No Rip-and-Replace, No Years of Implementation
You don't need to rip out your ERP, hire consultants for a year-long data cleanse, or wait for a multi-year implementation cycle. The fastest path to MRO optimization is to connect your existing systems—SAP, Oracle, Maximo, or any combination—and let AI work with your data as-is. Purpose-built MRO optimization platforms are designed to work across heterogeneous data without requiring you to fix it first, returning measurable value in weeks rather than years.
Most legacy approaches treat MRO as a project: hire a third party, spend months cleansing data, configure a demand-planning module not designed for spare parts, then wait for results that arrive too late to matter. That model fails because it misses the real problem. Your data is messy not because you're sloppy—it's messy because you've merged companies, switched suppliers, renamed parts across regions, and your teams use different naming conventions. Fixing all of that before you optimize is a tax that delays every dollar of value.
When you embed AI-native optimization into your existing tech stack, you skip the cleanse. The platform ingests your ERP, EAM, and P2P data in their current state, applies machine learning to identify excess inventory, stockout risk, and slow-moving materials across all your sites simultaneously, and surfaces actionable recommendations within the first weeks. Based on Verusen customer results, this approach unlocks an average of $20M in working capital per organization and reduces material review time by 60%.
The second advantage is alignment. When procurement and operations teams have fought over inventory levels—procurement pushing down costs, operations pushing up safety stock—it's because they lack shared visibility into what you actually need. A centralized AI platform that speaks to all your systems answers the question for both: which parts are truly critical, which are excess, and what the optimal stocking level is given your failure history and lead times. That shared ground eliminates the political argument.
Third, this approach scales across M&A. When you acquire a company with a different ERP, different naming conventions, and different inventory practices, you don't integrate the systems—you integrate the data into your optimization layer. One manufacturer with 27 sites and three separate ERPs identified $69M in optimization opportunities during evaluation; another with 110 US sites across four ERP instances identified $55M and centralized decision-making from hundreds of people to a team of seven. Neither required a system migration.
The path forward: demand a platform that works with your data now, connects to your systems in weeks, and delivers ROI from day one. Anything else is a cost center masquerading as a solution.
Frequently asked questions
Organize MRO inventory by criticality — parts that stop production lines get priority stocking and faster access, while slow-moving parts are stored separately or consigned. The average asset-intensive manufacturer carries 20–30% excess inventory while facing stockout risk on 10–15% of critical parts (industry estimates suggest this — consistent with Verusen's experience across hundreds of implementations), so criticality-based organization forces you to stock what matters. Use ABC analysis tied to failure mode, not purchase frequency. Centralize stocking policies across all sites to eliminate duplicative inventory and eliminate the practice of each plant ordering independently.
Safety stock for MRO parts requires failure-rate data, not demand history — standard safety stock formulas fail when a bearing fails twice in five years because there is no demand pattern to forecast. For truly critical parts, calculate safety stock as (time to receive + time to diagnose + buffer for production impact) multiplied by failure rate. If a replacement takes three weeks to receive and the part fails once every 18 months, you need at least one unit on hand. AI-driven optimization can model criticality across your entire portfolio simultaneously, which manual calculation cannot do at scale across thousands of parts.
Your existing ERP or EAM system can track MRO supplies if you use it correctly — the issue is that most plants try to apply demand-planning logic built for finished goods to spare parts that fail unpredictably. Purpose-built MRO inventory optimization platforms connect to your current ERP, EAM, or P2P system without requiring a data cleanse first and analyze failure patterns, lead times, and criticality simultaneously. The advantage: you get optimization in weeks, not years, and you keep your system of record intact. Avoid rip-and-replace: overlay an optimization layer on your existing infrastructure.
The contradiction is false — you reduce costs by eliminating the wrong inventory (overstock on parts that rarely fail) while increasing stock of critical parts that stop lines. Based on Verusen customer results, manufacturers unlock an average of $20M in working capital by reallocating inventory from excess to essential. A Fortune 500 CPP manufacturer identified $63M and verified $60M in MRO savings across 41 sites while reducing material review time from over 20 minutes to 4 minutes. The optimization identifies which dollars are trapped in slow-moving stock and which parts create actual downtime risk — then you reallocate.
Reorder point depends on lead time, failure rate, and consequences of stockout — not on annual usage volume. For a part with a three-week lead time and a 30% chance of failure in that window, your reorder point should trigger when stock falls to one unit; for a part with a one-week lead time and a 5% chance of failure, zero may be acceptable if you can expedite. The calculation requires modeling failure probability over lead time, which spreadsheets handle poorly across thousands of parts. AI platforms ingest your ERP data as-is and optimize reorder points across your portfolio simultaneously, accounting for site-specific lead times, supplier reliability, and failure history.
PN
- Paul Noble
- Founder & CEO, Verusen
Paul founded Verusen to bring AI-native systems of record to industrial materials. He has spent 15+ years working alongside F&B, oil & gas, and manufacturing operators on the MRO data problem.