The Real Cost of Inventory Inaccuracy — And How Structured Order Workflows Fix It

When a customer places an order for an item your system says is in stock, only to discover during picking that it’s actually not there, the immediate cost is obvious: a cancelled sale, a disappointed customer, and time wasted. But this single event—replicated across thousands of transactions—represents one of the most significant hidden drains on business profitability.

Inventory inaccuracy isn’t just an operational inconvenience. It’s a silent profit killer that compounds with every transaction, every transfer, and every order. For Australian businesses managing complex inventory across multiple locations and sales channels, the cost is measured not just in lost sales, but in wasted labour, excess stock, poor purchasing decisions, and eroded customer trust.

The surprising truth? The root cause isn’t theft, damage, or careless counting. It’s unstructured order processing that treats inventory changes as simple deductions rather than carefully managed state transitions. And the fix isn’t more frequent stocktakes or better warehouse discipline—it’s implementing the same rigorous controls for inventory that accountants have used for centuries with financial records.

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The Scale of the Problem

The numbers are staggering. According to research by the IHL Group, inventory distortion—encompassing shrinkage, out-of-stocks, and overstock—cost the global retail industry $1.77 trillion in 2023. This isn’t a retail-specific problem; it affects manufacturers, distributors, and service businesses with parts inventory equally.

The Auburn University RFID Lab’s longitudinal studies consistently show that average retail inventory accuracy hovers around 63%. Think about that for a moment: in a typical retail operation, more than one-third of inventory records are incorrect at any given time. While some industries perform better, many Australian SMBs face similar challenges.

For a business managing 10,000 SKUs with even a seemingly modest 5% error rate, that means 500 SKUs with incorrect counts at any moment. Some will show stock that doesn’t exist (leading to overselling), others will show zero stock when product is actually available (leading to lost sales), and still others will show incorrect quantities that cascade into poor purchasing decisions.

Research from ECR (Efficient Consumer Response) indicates that Australian FMCG businesses typically experience stockout rates between 5-10%, with each stockout representing both a lost immediate sale and potential long-term customer loss. A study by the Australian Retailers Association found that 43% of customers who experience a stockout will purchase from a competitor instead of waiting or choosing a substitute.

But the truly hidden cost is harder to measure: the labour time spent investigating discrepancies, expediting emergency orders to cover oversells, handling customer complaints, and conducting reconciliation activities. For many businesses, this represents hundreds of hours per month of staff time—time that could be spent on value-adding activities instead of firefighting inventory problems.

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Why Inventory Becomes Inaccurate

Understanding why inventory becomes inaccurate requires looking beyond the obvious culprits of theft and damage. While these contribute to inventory shrinkage, they’re rarely the primary source of the record inaccuracies that plague day-to-day operations.

Order Processing Without Reservation

In many inventory systems, when an order is created, the system immediately deducts stock. Simple enough. But what happens when that order is cancelled? In theory, the stock should be added back. In practice, this often fails:

• The cancellation happens in one system (e.g., Shopify) but doesn’t sync properly to the inventory system
• The cancellation is partial (customer reduces quantity) and the adjustment gets lost
• Staff manually cancel the order without triggering the stock reversal
• The order sits in “pending” status indefinitely, neither fulfilled nor properly cancelled

Each failure is a small discrepancy. But across hundreds of orders per week, these small discrepancies compound into significant inventory errors.

No Distinction Between Reserved and Committed

Most basic inventory systems track a single number: “quantity on hand.” But this creates a fundamental problem. Consider this scenario:

• System shows 10 units on hand
• Customer A orders 6 units (now 4 remaining)
• Customer B orders 5 units (now -1 remaining)
• Customer A’s order is cancelled (now 5 remaining)

If Customer B’s order was placed when the system showed 4 units, should it have been accepted? The system has no way to distinguish between stock that’s physically present and stock that’s available to promise. This leads to a cascade of overselling, followed by manual intervention to “fix” the numbers, which introduces more errors.

Manual Adjustments Mask Root Causes

During periodic stocktakes, staff discover discrepancies and adjust the system to match physical counts. This makes the numbers correct at that moment, but it doesn’t address why the discrepancy occurred. Without understanding the root cause:

• The same process errors will recreate the same discrepancies
• Patterns of shrinkage or process failure remain invisible
• Staff lose confidence in the system and start working around it

The stocktake becomes a band-aid, not a cure.

Transfer Gaps and Atomic Operations

When stock moves between locations, there’s an inherent risk of creating temporary discrepancies:

• Stock is deducted from Location A
• During transit, it belongs to neither location
• Stock is added to Location B

If these steps aren’t executed as an atomic transaction (all succeed or all fail together), system crashes, network issues, or human error can result in stock disappearing from one location without arriving at the other. The infamous “ghost inventory” problem.

Return Processing Delays

Customer returns introduce another source of inaccuracy. Returned stock often sits in a returns queue awaiting inspection and processing. During this time:

• The stock isn’t reflected in available counts
• Staff see low stock levels and order more
• The returned stock is eventually processed, but by then, excess stock has been ordered
• The cycle perpetuates

Multi-Channel Desynchronisation

For businesses selling across multiple channels (retail, online, wholesale), each channel may have its own view of inventory. If stock is sold on one channel but the update doesn’t propagate to other channels quickly enough:

• The same unit can be sold twice
• Staff manually adjust inventory to compensate
• Trust in the central system erodes, leading to channel managers keeping “buffer” stock or safety margins, reducing overall efficiency

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The Root Cause: Unstructured Order Processing

Here’s the insight that transforms how we think about inventory accuracy: Most inventory inaccuracy isn’t caused by theft or damage—it’s caused by order processing that doesn’t maintain accurate state transitions.

Every order is an inventory transaction. When a customer orders a product, stock moves from “available” to “allocated.” When that order is fulfilled, stock moves from “allocated” to “shipped.” When it’s delivered, it moves from “shipped” to “completed.” If the customer cancels, it moves back to “available.” If they return it, it moves to “returns processing” before returning to “available.”

Each of these state transitions is a potential point of failure. If the transitions aren’t structured—if there’s no clear definition of what each state means and what triggers movement between states—then each transaction introduces a small potential for error.

Consider the accounting analogy. Accountants use double-entry bookkeeping for a reason: every debit has a corresponding credit, creating a self-balancing system. You can’t accidentally create or destroy money in a double-entry system; the books must balance. Any discrepancy is immediately visible.

Inventory needs the same discipline. But most inventory systems use single-entry bookkeeping: they simply increment or decrement a counter. There’s no corresponding entry that validates the transaction. There’s no inherent balance check. Errors accumulate silently.

Over hundreds of orders per week, small errors compound. A 0.5% error rate per transaction might seem negligible, but across 1,000 transactions, that’s five errors. Across 52,000 transactions annually, that’s 260 inventory discrepancies—each requiring investigation and correction.

The solution isn’t to eliminate errors (impossible) but to structure the process so that errors are caught immediately, can be corrected before they compound, and leave a clear audit trail for investigation.

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How Structured Workflows Maintain Accuracy

A structured order workflow divides the journey from order to delivery into distinct, deliberate phases. Each phase serves a specific purpose in maintaining inventory accuracy.

Phase 1: Reservation Maintains “Available to Promise”

When a customer places an order, the first step is reservation. This is a temporary allocation that:

• Immediately reduces “available to promise” quantity
• Prevents the same stock from being sold twice
• Can be cleanly reversed if the order is cancelled
• Doesn’t yet commit to a permanent inventory change

The system now tracks three separate quantities:

Quantity Type	Definition	Example (10 units on hand, 6 reserved)
On Hand	Physical stock in the location	10 units
Reserved	Allocated to orders not yet committed	6 units
Available	Available for new orders	4 units

This prevents the double-booking problem that plagues single-number systems. When Customer B tries to order 5 units, the system correctly shows only 4 available and prevents overselling.

Cancellations during the reservation phase cleanly release stock back to available. Because no permanent inventory change has occurred, there’s no complex reversal process. The reservation simply expires or is explicitly released.

This is the inventory equivalent of placing a hold on a hotel room: the room is no longer available to others, but you haven’t checked in yet, and you can cancel without penalty.

Phase 2: Commitment Creates the Permanent Record

The commit step is the single moment when inventory counts permanently change. This is the critical control point for accuracy.

Because commitment is a deliberate action (not automatic), it can be:

• Reviewed: Staff can verify that the order is legitimate, the stock is genuinely available, and all details are correct
• Approved: For high-value or unusual transactions, approval workflows can be enforced
• Audited: Every commit is logged with timestamp, user, order reference, and quantities

The commit operation is atomic: for complex operations involving multiple items or locations, either all items commit successfully or none do. This prevents partial commits that leave inventory in an inconsistent state.

Consider a transfer between warehouses:

1. Deduct 100 units from Warehouse A
2. Add 100 units to Warehouse B

In an atomic transaction, if step 2 fails (network issue, system crash, validation error), step 1 is automatically rolled back. The stock remains at Warehouse A. No ghost inventory is created.

Every commit creates a permanent, traceable record: who authorised the change, when it occurred, which order it relates to, what quantities were involved, and what the inventory levels were before and after. This audit trail enables:

• Investigation of discrepancies: “Why does this SKU show 15 units when the stocktake found 12? Let’s review all commits since the last count.”
• Pattern analysis: “We’re consistently short on this SKU. Are we experiencing shrinkage, or is there a receiving error?”
• Compliance: For regulated industries, full traceability of inventory movements is often a legal requirement

Phase 3: Fulfilment Tracks Physical Execution

The fulfilment phase bridges the gap between system records and physical reality. This is where warehouse tasks confirm that actual stock movements match the system’s committed quantities.

Warehouse tasks for picking, packing, and shipping serve as validation checkpoints:

• Picking: Staff physically locate and collect the items. If an item isn’t where the system says it should be, a discrepancy is immediately flagged.
• Short picks: When less stock is found than the system indicates, the short pick is recorded as an exception, triggering investigation.
• Damages: Items found to be damaged during picking are recorded separately, providing visibility into damage rates and patterns.
• Packing validation: Barcode scanning during packing confirms that the right items in the right quantities are being shipped.

Completion of warehouse tasks validates that physical reality matches the system state. When a picking task is marked complete, it’s a confirmation that:

• The items were where the system said they’d be
• The quantities matched system records
• The items were in acceptable condition
• They’ve been physically moved to the next stage (packing, shipping, etc.)

Any discrepancies between committed quantities and fulfilled quantities are immediately visible and recorded as exceptions. This prevents the silent drift between system and reality that characterises unstructured processes.

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The Compounding Effect of Accuracy

Inventory accuracy isn’t just about getting the numbers right—it’s a foundational element that affects nearly every aspect of business operations. When accuracy improves, the benefits compound across multiple domains.

Confident Selling Eliminates Overselling

When sales staff, e-commerce platforms, and customer service teams can trust inventory numbers, they can sell confidently. They don’t need to maintain artificial safety buffers or tell customers “let me check if we really have that” before confirming an order.

Fewer oversells means fewer customer complaints, fewer emergency expedite orders, and less time spent on damage control. Each avoided oversell preserves customer trust and retention.

Research consistently shows that customers who experience overselling are significantly less likely to return. A study by Narvar found that 69% of consumers would be less likely to shop with a retailer again after a negative delivery experience, including receiving incorrect items or quantities.

Efficient Warehouse Operations

When warehouse staff can trust that the system accurately reflects physical stock locations and quantities, they spend less time searching and investigating. Pick accuracy improves, pick time decreases, and throughput increases.

A distribution centre operating with 95% inventory accuracy will have dramatically lower pick times than one operating at 70% accuracy. The difference isn’t just 25 percentage points—it’s the elimination of the extended searches, supervisor interventions, and location verifications that occur with each inaccurate record.

Industry benchmarks suggest that improving inventory accuracy from 70% to 95% can reduce pick time per order by 15-25%, translating to significant labour savings in high-volume operations.

Better Purchasing Decisions

Purchasing decisions depend on accurate data. When inventory records are unreliable, purchasing managers compensate by:

• Ordering earlier than necessary (increasing carrying costs)
• Ordering more than necessary (increasing overstock risk)
• Conducting manual checks before each order (wasting time)

Accurate inventory enables data-driven purchasing: automated reorder points, economic order quantity calculations, and demand forecasting all depend on trustworthy historical data.

A study by the Aberdeen Group found that companies with high inventory accuracy (>95%) carried 20% less safety stock than those with lower accuracy, while maintaining higher service levels. This directly improves cash flow and reduces warehouse space requirements.

Improved Cash Flow

Inventory ties up capital. Excess inventory—whether from over-ordering due to inaccurate counts or dead stock from poor visibility—represents cash that could be deployed elsewhere in the business.

When inventory accuracy improves:

• Less capital is tied up in safety stock
• Faster inventory turnover releases cash more quickly
• Reduced write-offs from obsolete or excess stock
• Better supplier terms from more predictable, optimised ordering patterns

For a business carrying $500,000 in inventory, a 10% reduction in excess stock frees up $50,000 in working capital. At a 10% cost of capital, that’s $5,000 annually—before considering the warehouse space savings and reduced handling costs.

Higher Order Fulfilment Rates

Perhaps the most direct benefit: accurate inventory means orders can be fulfilled as promised. Various supply chain studies have found that businesses improving inventory accuracy by 10% typically see 5-8% improvement in order fulfilment rates.

Higher fulfilment rates drive:

• Increased customer satisfaction and retention
• Reduced costs for handling returns and reshipments
• Improved supplier relationships (for B2B businesses)
• Better reviews and word-of-mouth referrals

The compounding effect becomes clear: accurate inventory → confident selling → fewer oversells → fewer complaints → higher retention → more repeat business → more efficient operations → lower costs → better margins → more capital for growth.

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Measuring Inventory Accuracy

You can’t improve what you don’t measure. Establishing clear metrics for inventory accuracy creates accountability and provides objective data for evaluating process improvements.

Inventory Record Accuracy (IRA)

IRA is the percentage of items where the system count matches the physical count. This is typically measured during cycle counts or stocktakes.

Formula:

IRA = (Number of SKUs with matching counts / Total SKUs counted) × 100

Example: In a cycle count of 200 SKUs, 185 had matching counts.

IRA = (185 / 200) × 100 = 92.5%

Target benchmark: World-class operations achieve IRA >98%. Good performance is >95%. Below 90% indicates significant process problems.

Key insight: IRA treats all discrepancies equally. A 1-unit variance on a high-value item counts the same as a 1-unit variance on a low-value item. Some organisations weight IRA by inventory value to reflect the business impact more accurately.

Mean Absolute Percentage Error (MAPE)

MAPE measures the average deviation between system and physical counts as a percentage of the actual quantity. Unlike IRA, MAPE captures the magnitude of errors.

Formula:

MAPE = (Σ |Actual - System| / Actual) / n × 100

Example:

SKU	System Count	Actual Count	Absolute Error	Percentage Error
A	100	95	5	5.26%
B	50	52	2	3.85%
C	200	200	0	0%
D	75	80	5	6.25%

MAPE = (5.26 + 3.85 + 0 + 6.25) / 4 = 3.84%

Target benchmark: MAPE <3% is good performance. MAPE >5% indicates systematic issues.

Key insight: MAPE is more sensitive to large discrepancies than IRA. An operation can have high IRA but high MAPE if most items are accurate but a few have large errors.

Cycle Count Variance

Cycle count variance tracks the discrepancies found during regular cycle counts, providing ongoing visibility into accuracy trends rather than relying on annual stocktakes.

Best-practice cycle counting programmes:

• Count high-value items (A items) monthly
• Count medium-value items (B items) quarterly
• Count low-value items (C items) annually
• Count problem items (frequent discrepancies) more frequently

Variance tracking shows whether accuracy is improving or degrading over time and highlights problem SKUs, locations, or product categories.

Order Accuracy Rate

Order accuracy measures the percentage of orders fulfilled correctly—right items, right quantities, undamaged condition.

Formula:

Order Accuracy = (Orders fulfilled correctly / Total orders) × 100

This metric captures the customer-facing impact of inventory accuracy. An order can be inaccurate due to inventory discrepancies (wrong stock count), picking errors (wrong item picked), or packing errors (incorrect quantity shipped).

Target benchmark: World-class operations achieve >99% order accuracy. Below 95% indicates significant problems affecting customer satisfaction.

Key insight: Order accuracy is the ultimate validation. You can have perfect inventory records, but if orders aren’t fulfilled correctly, the process has still failed.

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Practical Steps to Improve Accuracy

Improving inventory accuracy requires both process changes and cultural shifts. Here’s a practical roadmap.

Step 1: Implement a Structured Order Workflow

Replace the simple “order → deduct stock → ship” process with a three-phase workflow:

1. Reserve: Temporary allocation, prevents double-selling, reversible
2. Commit: Permanent inventory change, creates audit trail, atomic transaction
3. Fulfil: Physical execution, validates system accuracy, records exceptions

This is the foundational change that enables all subsequent improvements. Without structured state transitions, other accuracy measures are band-aids on a broken process.

Step 2: Track Reserved, Committed, and Available Separately

Modify your inventory data model to distinguish between:

• On Hand: Physical stock in the location
• Reserved: Allocated to orders not yet committed
• Committed: Inventory change recorded, order ready for fulfilment
• Available: On hand minus reserved minus committed

This visibility enables accurate “available to promise” calculations and prevents overselling.

Implementation note: Most inventory systems track only “on hand.” Adding these fields may require customisation or migration to a system that supports them natively.

Step 3: Use Atomic Transactions for Multi-Location Operations

Ensure that transfers, multi-location picks, and other complex operations execute atomically: all steps succeed or all fail together.

Example: Transfer of 100 units from Warehouse A to Warehouse B:

BEGIN TRANSACTION
  Deduct 100 units from Warehouse A
  Add 100 units to Warehouse B
  Record transfer in audit log
COMMIT TRANSACTION

If any step fails, the entire transaction rolls back. No ghost inventory.

Implementation note: This often requires database-level transaction support. Ensure your inventory system uses proper transaction handling, not sequential updates.

Step 4: Implement Cycle Counting

Replace annual stocktakes with continuous cycle counting:

• ABC classification: Classify inventory by value (A = high, B = medium, C = low)
• Count frequency: A items monthly, B items quarterly, C items annually
• Random sampling: Include random SKUs in each cycle to avoid predictable patterns
• Investigation protocol: When discrepancies are found, investigate root cause before adjusting

Benefits over annual stocktakes:

• Continuous visibility into accuracy trends
• Less disruptive to operations
• Errors caught and corrected more quickly
• Patterns become visible (e.g., “SKUs in Location 3B consistently have errors”)

Step 5: Audit the Commit Trail

Every inventory change should be traceable to a specific event: an order, a transfer, a return, an adjustment. Implement regular audits:

• Daily: Review high-value commits (threshold based on business size)
• Weekly: Sample commits for completeness of audit trail
• Monthly: Analyse patterns (which staff, which SKUs, which locations have most adjustments)
• Quarterly: Full audit of adjustment transactions (these should be rare)

Red flags:

• Frequent manual adjustments to the same SKUs
• Adjustments without documented reasons
• Large quantity changes without supporting documentation
• Adjustments by unauthorised staff

Step 6: Handle Exceptions Through Structured Processes

Define and document how to handle common exceptions:

Short picks (less stock found than system indicates):

1. Record short pick quantity on picking task
2. Automatically create investigation task for inventory manager
3. Adjust committed quantity to reflect actual picked quantity
4. Notify customer service if order must be partially cancelled

Damaged items (stock present but unusable):

1. Record damage on picking task with reason code
2. Move damaged items to quarantine location
3. Adjust inventory to reflect damaged stock separately
4. Trigger quality control investigation if damage rate exceeds threshold

Returns:

1. Receive returned items into quarantine location
2. Inspect and record condition
3. Return to available stock or write off
4. Track return reasons for pattern analysis

Cycle count discrepancies:

1. Perform recount to confirm discrepancy
2. Investigate cause before adjusting system
3. Document reason in adjustment record
4. Escalate if discrepancy exceeds tolerance (e.g., >5% variance or high-value item)

By handling exceptions consistently, you create data that reveals systematic problems rather than hiding them behind ad-hoc fixes.

Step 7: Create a Culture of Accuracy

Process changes alone won’t succeed without cultural buy-in:

• Train staff on why accuracy matters and how their actions affect it
• Empower warehouse staff to flag discrepancies without fear of blame
• Celebrate improvements: Track and share accuracy metrics, recognise teams that improve
• Investigate, don’t punish: When errors occur, focus on understanding root cause, not assigning blame
• Lead from the top: Ensure management treats inventory accuracy as a strategic priority, not just a warehouse concern

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The Cost of NOT Fixing It

The business case for improving inventory accuracy becomes clear when you calculate the full cost of inaccuracy.

Consider a typical Australian SMB with $5 million in annual revenue, 2,000 active SKUs, and 5% inventory inaccuracy (IRA = 95%, which seems reasonable but is actually problematic).

Lost Sales from Stockouts

At 5% inaccuracy, approximately 100 SKUs have incorrect counts at any time. Assume half (50 SKUs) show zero stock when product is actually available.

• Average SKU value: $50
• Average quarterly sales per SKU: 20 units
• Lost sales: 50 SKUs × 20 units × $50 = $50,000 per quarter
• Annual lost sales: $200,000

This doesn’t account for the long-term customer loss from stockouts, which compounds the cost.

Cost of Overselling

The other 50 inaccurate SKUs likely show stock that doesn’t exist, leading to overselling.

• Average cost to resolve an oversell (expedite shipping, customer service time, goodwill gestures): $75
• Frequency: 5 oversells per week
• Annual oversell costs: $19,500

Labour for Discrepancy Investigation

Each discrepancy requires investigation: reviewing transactions, checking locations, discussing with staff.

• Average investigation time: 30 minutes
• Warehouse manager hourly rate: $45
• Discrepancies investigated per week: 15
• Annual labour cost: 15 × 0.5 hours × $45 × 52 weeks = $17,550

Excess Inventory Carrying Costs

Inaccurate data leads to over-ordering. Assume the business carries 15% excess safety stock due to lack of confidence in inventory data.

• Average inventory value: $500,000
• Excess stock: $75,000
• Annual carrying cost (storage, insurance, cost of capital at 20%): $15,000

Total Annual Cost

Cost Category	Annual Cost
Lost sales from stockouts	$200,000
Overselling resolution	$19,500
Labour for investigation	$17,550
Excess inventory carrying	$15,000
Total	$252,050

That’s 5% of annual revenue, directly attributable to a seemingly modest 5% inventory inaccuracy rate.

For context, a comprehensive inventory management system with structured order workflows typically costs $10,000-$50,000 to implement (depending on business size and complexity), with ongoing costs of $5,000-$15,000 annually. The ROI is typically achieved within 3-6 months.

Even partial improvements deliver substantial returns. Improving from 95% to 98% accuracy (reducing inaccurate SKUs from 100 to 40) would:

• Reduce lost sales by 60% ($120,000 saved)
• Reduce overselling by 60% ($11,700 saved)
• Reduce investigation labour by 60% ($10,530 saved)
• Reduce excess inventory by 40% ($6,000 saved)
• Total annual savings: $148,230

The cost of NOT fixing inventory inaccuracy exceeds the cost of most inventory management solutions by an order of magnitude.

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Moving Forward

Inventory accuracy isn’t a nice-to-have. It’s a fundamental requirement for profitable, scalable operations. The businesses that treat it as a strategic priority—implementing structured processes, measuring rigorously, and creating a culture of accuracy—consistently outperform those that treat it as a warehouse problem to be solved with more frequent stocktakes.

The path forward is clear:

1. Recognise that order processing is the root cause, not warehouse discipline
2. Implement structured workflows with separate reserve, commit, and fulfil phases
3. Track the right metrics: available to promise, not just on hand
4. Create audit trails for every inventory change
5. Handle exceptions systematically, not ad hoc
6. Measure continuously through cycle counting, not annual stocktakes
7. Treat accuracy as a strategic priority, not an operational detail

The compounding benefits—confident selling, efficient operations, better purchasing, improved cash flow, higher customer satisfaction—transform inventory from a cost centre into a competitive advantage.

EQUOS9’s three-step order workflow (Finalize, Commit, Fulfil) is specifically designed to maintain inventory accuracy through structured state transitions. Every inventory change is auditable, reversible before commitment, and traceable to a specific order. Combined with comprehensive inventory management that tracks on-hand, reserved, and available quantities separately, it provides the foundation for maintaining the >95% accuracy that world-class operations demand.

The question isn’t whether your business can afford to implement these processes. It’s whether you can afford not to.