Inventory Reconciliation Automation

The Challenge

A major national grocery retailer operating large-scale direct-to-home fulfillment centers had a growing problem: two critical inventory systems that refused to agree.

One system managed the day-to-day operations of the fulfillment centers. The other tracked inventory, sales, and purchasing across the entire enterprise. These systems would routinely fall out of sync, creating a cascade of costly problems:

• Compliance failures — Inability to accurately report inventory levels for regulatory requirements
• Purchasing errors — Incorrect data on actual stock levels led to flawed replenishment decisions and inaccurate sales velocity calculations
• Daily manual reconciliation — Staff spent hours every day trying to manually identify and resolve discrepancies across thousands of SKUs
• Quarterly facility shutdowns — Nine multi-million-square-foot fulfillment centers had to be shut down once per quarter for 24-48 hours to perform manual inventory counts and balance updates

The direct costs were staggering. Each facility was processing roughly 5,000 orders per day. Shutting down nine of them quarterly — plus the labor costs of manual counting and daily reconciliation — was conservatively costing the business millions annually in lost revenue and wasted labor.

The Approach

We started by mapping the data flow between the two inventory systems to understand why discrepancies occurred and what a reliable reconciliation process would look like.

Discovery Findings

The investigation revealed several layers of complexity that would shape the solution:

1. Purchase order timing — Items in transit (under an active purchase order) couldn't be accurately counted by either system until officially received. These items had to be excluded from reconciliation.
2. Unit-of-measure mismatches — Products purchased in bulk units (cases, pallets) but sold individually created inherent counting challenges.
3. Scale of discrepancies — Each location had on the order of 10,000+ SKUs out of balance at any given time, making manual resolution impractical.
4. Data volume — The sheer volume of data required serious performance engineering to even display in a browser without crashing.

The Solution

We built a reconciliation dashboard that automated the entire process of comparing, analyzing, and correcting inventory discrepancies.

Data Integration Layer

The backend ran on a 24-hour cycle, pulling data from three separate sources:

• The fulfillment center inventory system
• The enterprise-wide inventory and sales system
• The purchase order system

It then cross-referenced every SKU to identify which items were out of balance, which were locked due to active purchase orders, and which could be safely corrected.

User-Facing Dashboard

The dashboard provided facility teams with:

• Actionable discrepancy lists — Clearly showing which items could be updated and which were locked, with enough context to make informed decisions
• Granular and bulk correction tools — Users could update individual SKUs or use a bulk reconciliation action for straightforward cases
• Item history tracking — The ability to view the reconciliation history of any SKU over time, turning the tool into a data forensics instrument
• Role-based access control — Integrated with the company's Azure Active Directory, with view-only, action, and admin permission levels across all nine facilities

Unexpected Value: Process Forensics

One of the most valuable outcomes was unplanned. By giving teams visibility into which SKUs were consistently out of balance, they could finally step back from fighting fires and start identifying the root causes — the 10% of items caught in broken processes that had been invisible while everyone was buried in day-to-day reconciliation.

The Results

After deployment across all nine fulfillment centers:

• Quarterly shutdowns eliminated — The entire practice of shutting down facilities for manual counting was retired
• Daily reconciliation automated — What previously consumed hours of manual labor per facility was reduced to a single button click per day
• Millions in recovered revenue — No more lost sales during 24-48 hour shutdown periods across nine high-volume facilities
• Process improvements surfaced — Teams discovered and fixed systemic inventory issues that had been hidden for years
• Compliance resolved — Accurate, auditable inventory reporting became the default rather than a quarterly scramble

Before and After

| Metric | Before | After | |--------|--------|-------| | Reconciliation method | Manual counting + spreadsheets | Automated daily sync | | Facility shutdowns | 4x per year, per facility | None | | Time to reconcile | 24-48 hours per shutdown | Minutes per day | | Discrepancy visibility | Reactive, quarterly | Real-time, daily | | Root cause identification | Nearly impossible | Built-in forensics |

Key Learnings

1. Disconnected systems are universal — Every industry has some version of this problem. Two or more systems that should agree but don't, with humans filling the gap through manual labor.

2. The real value is in the data you couldn't see before — The reconciliation tool was the stated goal, but the forensic capability to identify broken processes was arguably more valuable long-term.

3. Exclusion logic is as important as inclusion — Knowing what not to reconcile (items under purchase orders, unit-of-measure edge cases) was critical to building trust in the system.

4. Performance engineering matters at scale — With 10,000+ SKUs per location and multiple data sources, the UI had to be carefully engineered to remain responsive. Naive implementations would literally crash the browser.

5. Solve the daily pain first, then go deeper — Getting the shutdowns eliminated was the immediate win. The forensic capabilities emerged naturally once teams had bandwidth to think beyond survival mode.

What's Next

The approach used here — connecting disconnected systems, automating reconciliation, and surfacing root causes — is applicable to virtually any industry where multiple systems of record exist and humans are stuck in the middle trying to keep them in sync.

---

Dealing with disconnected systems and manual reconciliation? Let's talk about making that problem disappear.