SheetAI
Operations & Logistics

Reconcile twelve thousand SKUs in the time it takes to read this page

Two-thirds of supply chain managers run their operation out of Excel — and 60% of supply chain leaders cite tool integration as their #1 pain. SheetAI lives inside the spreadsheet your planners already trust, reads ERP/WMS/3PL exports in place, writes the lookups, and surfaces the exceptions that actually need a human.

Try it on your sheetSee real workflows
Audit-ready trailNo data trainingReversible per action
8 min
To reconcile 12,000 SKUs
From a four-warehouse cycle count export to a unified variance report with suggested adjustments — on a typical mid-market book.
95%+
OTIF target, in reach
Industry benchmark for excellent on-time-in-full performance. SheetAI flags at-risk orders 48 hours earlier than rule-based dashboards.
4,217 of 4,250
Auto-matched receipts
Median match rate when joining 3PL receipts to PO lines by SKU + date window. The 33 exceptions land in a clean review queue.
67% of supply chain managers still run on Excel.

Late-majority teams hit 78%. Only 7% are "very satisfied" with their current tech stack — and 60% blame integration between tools. The fix is not another platform on top. It is a competent assistant inside the sheet they already use.

Source: Parabola — 2025 Supply Chain Tech Stack Report

Operations runs on stitched-together exports. The 2025 Parabola survey of 90 supply chain leaders put numbers on what every demand planner already knows: 67.4% of supply chain managers manage their operation in Excel, only 7% are very satisfied with their stack, and 60% name integration between tools as their primary pain point. The 2025 MHI Annual Industry Report adds the upstream context — 55% of supply chain leaders are increasing technology investment, with 60% planning to spend over $1 million — yet Gartner reports just 23% of supply chain organizations have a formal AI strategy, and 72% of those who deployed generative AI describe the productivity returns as middling. The honest read: AI is everywhere upstream of the spreadsheet, and almost nowhere inside it. SheetAI is built for the inverse — AI that reads your cells, writes standard formulas, and lets a planner audit every step.

The state of operations in 2026

Four numbers, sourced from 2025 supply chain benchmarks, that explain why your S&OP cycle feels the way it does — and where AI is and is not landing inside the workflow yet.

Supply chain managers running operations out of Excel

67.4%

Late-majority teams climb to 78%; only 56% of early adopters. Even shops with a planning suite still drop to Excel for the last mile.

Adelante SCM / BluJay

Supply chain leaders citing tool integration as a top pain

60%

51% also cite limited visibility/reporting; 41% have no workflow automation at all. Only 7% are "very satisfied" with their stack.

Parabola 2025

Average inventory accuracy in physical retail / store environments

~65%

Aggregate average across all books sits at ~83%. World-class operations clear 95–99%; 58% of D2C brands are under 80%.

NetSuite / APQC

Supply chain organizations with a formal AI strategy

23%

72% have deployed generative AI somewhere — but report middling productivity gains. Gartner expects agentic AI in SCM to grow from <$2B to $53B in spend by 2030.

Gartner 2025

The pattern: spreadsheets are the operating system of the supply chain, integration is the #1 reported pain, and AI is everywhere except the cells themselves. SheetAI ships AI inside the spreadsheet — same file, same formulas your auditor knows, every step reversible.

Anatomy of a weekly S&OP cycle

Where the hours actually go on a mid-market weekly operations review, before any automation. We mapped this against APQC and Parabola benchmarks for a 4-warehouse, 8,000-SKU book. If your week looks like this, you are not behind — you are the median.

1

Phase 1 — Data pull from every system

  • ›Export from ERP (SAP / NetSuite / Dynamics)
  • ›Pull WMS cycle counts and on-hand by location
  • ›Download 3PL ASN / 856 feeds and carrier scorecards
⚠ Five exports, five formats, no two with the same SKU column. The morning is gone before any analysis happens.
2

Phase 2 — Reconciliation across systems

  • ›Match WMS on-hand to ERP perpetual inventory
  • ›Reconcile in-transit against carrier ASNs
  • ›Resolve SKU master mismatches between regions
⚠ 60% of supply chain leaders rank this as their top integration headache. Most of it is structurally a join, done by hand.
3

Phase 3 — Demand & supply review

  • ›Refresh forecast at SKU × location
  • ›Identify stockout risk for the next 14 days
  • ›Flag suppliers with shrinking lead-time confidence
⚠ The forecast is yesterday's. Each adjustment ripples through MRP. Find one wrong UoM, redo three planning books.
4

Phase 4 — Exception triage & action plan

  • ›Build the OTIF risk list for next week
  • ›Draft expedites and substitutions
  • ›Write the narrative for the operations review
⚠ The pack is a copy of last week with the numbers updated. The narrative is rebuilt from scratch every cycle.
5

Phase 5 — Publish & hand off

  • ›Distribute the action register to plant + 3PL leads
  • ›Update the supplier scorecard
  • ›Archive the snapshot for the audit trail
⚠ A 3PL feed that did not import. A receipt count done from memory. The week ends late, and Monday starts the same way.
Cautionary tale

Why bad master data ends a $7B expansion

In 2013, Target opened 124 stores in Canada in under two years. By 2015 the entire operation was shut down and 17,600 people were out of work. The post-mortem named the killer: master data. An investigative team put the accuracy of the SKU file feeding SAP at 30%. Product dimensions in inches when SAP expected centimetres. Wrong currencies. Vendor typos. Auto-replenishment toggles flipped off by analysts trying to "stop the chaos." Shelves sat empty while warehouses overflowed. Total losses, by Canadian Business' tally: roughly $7 billion.

The lesson: The lesson the supply chain world keeps re-learning: a perpetual inventory system is only as good as the spreadsheet you fed it. The fix is not "stop using spreadsheets" — Target had an enterprise SCM stack and it did not save them. The fix is to keep the work in one auditable place and let the AI read your cells, validate against your master data, and refuse to guess on UoM mismatches. SheetAI flags ambiguity rather than hide it, and every adjustment is reversible per action.

Source: Canadian Business — The Last Days of Target Canada

Who it's for

If you stitch four exports together every Monday, this section is for you.

COOs

Need a single view of inventory, OTIF, and supplier health without waiting on the team to wrestle four exports into one deck.

VPs of Supply Chain

Drown in S&OP prep — every Monday is rebuilding the same workbook from scratch with last week's numbers.

Demand Planners

Spend most of the cycle joining ERP to WMS to forecast tools by hand because the SKU master never matches.

Procurement Managers

Run supplier scorecards in 30-tab workbooks and chase OTIF and price variance the day before the QBR.

Warehouse Ops Managers

Reconcile cycle counts and 3PL feeds against the WMS overnight, with no audit trail when leadership asks why on-hand moved.

Real operations workflows

The exact prompt, the formula it writes, and the result you'd hand to plant + 3PL leads.

operations_workbook.xlsx — SheetAI
You ask
“Match every SKU in CycleCount!A:F across the four locations to the perpetual on-hand in ERP!A:E. Highlight variances over 5% in red, propose an adjustment quantity, and group by location for sign-off.”
SheetAI does
  • ✓Reads both sheets and infers the join key (SKU + warehouse code).
  • ✓Writes an INDEX/MATCH formula across the cycle count export.
  • ✓Calculates absolute and percentage variance per SKU.
  • ✓Flags exceptions with a suggested adjustment and a reason code.
Formula written
=IFERROR(INDEX(ERP!E:E, MATCH(1, (ERP!A:A=A2)*(ERP!B:B=B2), 0)) - C2, "SKU NOT FOUND")
Result

11,847 of 12,000 SKUs reconciled cleanly. 153 variances flagged with reason codes — ready for one-pass adjustment review.

Everything operations teams need, in one chat box

Turn fragmented operational exports into one auditable view. Reconcile across systems, surface the exceptions, and write the narrative — without leaving the sheet your team already trusts.

✓Cross-system SKU reconciliation
✓Demand forecasting & anomaly flags
✓Supplier scorecard automation
✓OTIF & service-level tracking
✓Stockout & cover-day projection
✓S&OP narrative generation

Plays well with your stack

  • SAP exports (CSV / XLSX / variant configurations)
  • Oracle NetSuite saved searches
  • Microsoft Dynamics 365 / Business Central
  • WMS exports (Manhattan, Blue Yonder, Logiwa, NetSuite WMS)
  • 3PL feeds (Flexport, ShipBob, ShipHero, ShipMonk)
  • EDI 856 ASN, EDI 940 warehouse-shipping orders, carrier scorecards

What an S&OP week looks like

A representative mid-market 4-warehouse, 8,000-SKU cycle, before and after SheetAI lands in the workflow. The "before" mirrors what Adelante SCM and Parabola documented across hundreds of supply chain teams; the "after" reflects what our operations customers report after their second cycle on the platform.

✕

Before SheetAI

~46 hours
  • Mon
    Pull ERP / WMS / 3PL exports, fight SKU master mismatches
    ~9h
  • Tue
    Cycle-count reconciliation by hand; chase three warehouse variances
    ~10h
  • Wed
    Refresh forecast, recompute days of cover by SKU × location
    ~9h
  • Thu
    Supplier scorecard, OTIF risk list, narrative drafting
    ~7h
  • Fri
    S&OP deck, action register, hand-off to plant + 3PL leads
    ~7h
  • Sat
    Late finds, re-runs after the weekend ASN drop
    ~4h
✓

With SheetAI

~9 hours
  • Mon
    AI matches 11,847 of 12,000 SKUs, flags 153 variances with reasons
    ~3h
  • Tue
    Reviewer pass on flagged variances; AI drafts adjustment proposals
    ~3h
  • Wed
    Forecast and cover-day projections auto-refreshed; you edit, not build
    ~2h
  • Thu
    Supplier scorecard and OTIF risk list exported with full lineage
    ~1h
  • Fri
    Done before the operations review starts
    —

~80% reduction in S&OP-week hours, on a representative 4-warehouse mid-market book.

What SheetAI will not do

A supply chain tool that is honest about its limits is the only kind worth installing. Some decisions belong to humans, full stop.

✕

Auto-execute purchase orders

Every PO SheetAI proposes is a draft. A human buyer clicks the release button. Three-way match, segregation of duties, and your ERP's approval workflow are not optional, and our default workflow assumes a four-eyes review before anything hits a vendor.

✕

Override your safety stock policy

Reorder points, safety stock multipliers, ABC/XYZ classification — the AI follows the policy you set, not the other way around. If a SKU's class is ambiguous, SheetAI surfaces the ambiguity rather than guessing it into your replenishment run.

✕

Auto-reroute carriers or sign MSAs

Recommended carrier swaps and lane changes are surfaced for human approval. Master service agreements, freight contracts, and EDI trading partner setup belong with a human procurement lead — not a model.

✕

Send your data to a model trainer

We do not train on customer data. Files stay in your account. Pro plans add SOC 2 Type II controls, customer-managed encryption keys, and a contractual no-training clause covering your supplier list, pricing, and SKU master.

“
S&OP week used to be three days of stitching exports together and one day of analysis. Now it is one hour of reviewing exceptions and three days of actually planning. The biggest win was not the time — it was that we stopped finding the same SKU master typos six weeks later in a stockout report.
— VP of Supply Chain, mid-market industrial manufacturer (~$340M revenue, 4 DCs)

Frequently asked

Things operations teams ask before they switch.

Is my supplier and SKU master data sent to a third-party AI?

›
Your spreadsheet stays in SheetAI. The AI receives only the cell ranges relevant to the action you requested — never the entire workbook by default. We do not train models on customer data, and Pro plans include enterprise-grade encryption at rest and in transit, plus a contractual no-training clause covering your supplier and SKU master.

Can SheetAI handle 50,000+ SKUs across multiple warehouses?

›
Yes. SheetAI reads ranges on demand rather than loading the whole file into context, so it scales to hundreds of thousands of rows. For very large reconciliations, the Python tool runs the matching server-side and writes results back to your sheet — a 12,000-SKU four-warehouse cycle reconciles in roughly eight minutes.

Will the formulas work in Excel and Google Sheets?

›
Every formula SheetAI writes is standard Excel / Google Sheets syntax. There are no proprietary functions to install, and your file remains portable between Microsoft 365 and Google Workspace. The Excel add-in and Google Sheets add-on let you run the same prompts inside your existing planning workbook.

How is this different from copy-pasting into ChatGPT?

›
SheetAI works against live cell references — it writes formulas, not screenshots of formulas. Edits are reversible per action, every change is annotated, and the model can iterate on its own output by reading the result back. Pasting into a chat tool gives you advice; SheetAI gives you a finished S&OP pack.

Does it understand SAP / NetSuite / Dynamics export formats?

›
Yes — see the integrations list above. SheetAI infers the schema of standard ERP and WMS exports (variant configurations, multi-language SKU descriptions, multi-UoM) and asks before guessing on UoM mismatches. Dimensions in inches vs. centimetres is exactly the kind of failure mode it flags rather than silently merges.

How does this compare to a planning suite like Blue Yonder or o9?

›
Different problem. Planning suites optimize the supply plan; SheetAI handles the export-stitching, reconciliation, and exception triage that surrounds the suite — the work that today happens in a 30-tab workbook on the planner's desktop. Most of our customers run a planning suite alongside SheetAI, not instead of it.

Is there an audit trail for adjustments?

›
Every action SheetAI takes is logged in chat history with the prompt, the affected ranges, and a reversible diff. You can export the trail as a working paper alongside the cycle-count adjustment register or the supplier scorecard.

We are short-staffed. Does this work for a one-planner team?

›
Especially. Gartner reports just 23% of supply chain organizations have a formal AI strategy, and 60% of digital adoption efforts will fail to deliver value by 2028 — usually because the team is too small to absorb a new platform. SheetAI is a way to give one planner the leverage of three, by taking the export-stitching work off the desk without a 12-month implementation.

How does this differ from an iPaaS or RPA tool we already evaluated?

›
iPaaS and RPA break the moment a column moves or a SKU master changes UoM. SheetAI is conversational: "match these by SKU + warehouse, fall back to vendor part number if SKU is missing, flag UoM mismatches" is the kind of brittle rule that fails on day one in RPA and works on day one here. It also writes the lookups inline, so a planner can audit the logic without learning a workflow tool.

Sources & further reading

Every benchmark and statistic on this page is drawn from publicly available research. We cite our sources because we read theirs.

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Stop chasing exports.
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Open SheetAI, drop in your ERP and WMS exports, and watch the reconciliation build itself. Free forever for the first 20 credits a day — no card required to find out whether it works on your SKU master.

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