Daily exception pack
ExcelA tab per exception type — PO Number, Vendor, Material, Pending Qty, Pending Days and Pending Value, supplier-wise.
Time & ROI
10,000 PO lines. Sixteen hours, or fifteen minutes.
Every step of the cycle, timed. Change the volume, the hourly rate and how often it runs to match your plant — the whole calculation follows.
Manual today
16 h
one full cycle
With automation
1 h 9 m
3.2 min machine + 66 min human review
Faster by
14×
92.8% of the time removed
Saved per day
14 h 51 m
back to the team
Each row is one step of the daily cycle. The bar is the time it takes today.
Manual todayAutomated
- 1SAP login, transaction, filters, execute20 min
- 2Export to Excel, open and format25 min
- 3Data cleanup — dedupe, blanks, date formats55 min
- 4PO confirmation check120 min
- 5ASN check against inbound delivery110 min
- 6Delivery date and late check90 min
- 7Plant mapping40 min
- 8Build pivots and filters70 min
- 9Review and decide what needs action150 min
- 10Supplier-wise grouping75 min
- 11Prepare follow-up emails165 min
- 12Send and log the follow-ups40 min
- 13Write a daily summary—
Axis is minutes, shared by both series. Automated steps are seconds, so they sit against the left edge.
| # | Step | Manual | What runs instead | Machine |
|---|---|---|---|---|
| 1 | SAP login, transaction, filters, execute | 20 min | Scheduled API fetch | 45 s |
| 2 | Export to Excel, open and format | 25 min | Load into SQL store | 20 s |
| 3 | Data cleanup — dedupe, blanks, date formats | 55 min | 8 validation checks | 15 s |
| 4 | PO confirmation check | 120 min | Rule: confirmation pending | 9 s |
| 5 | ASN check against inbound delivery | 110 min | Rule: ASN missing | 8 s |
| 6 | Delivery date and late check | 90 min | Rule: late / due / coming soon | 8 s |
| 7 | Plant mapping | 40 min | Rule: plant grouping | 5 s |
| 8 | Build pivots and filters | 70 min | Dashboard refresh | 10 s |
| 9 | Review and decide what needs action | 150 min | Priority scoring | 5 s |
| 10 | Supplier-wise grouping | 75 min | Supplier aggregation | 6 s |
| 11 | Prepare follow-up emails | 165 min | AI drafts one mail per supplier | 18 s |
| 12 | Send and log the follow-ups | 40 min | Send and write audit log | 30 s |
| 13 | Write a daily summary | not done | AI daily summary | 12 s |
| Total | 16 h | + 66 min human review | 191 s |
What that means in money
The same run, read through the value columns. This is the number a finance review asks for — not how many rows were flagged, but how much is sitting behind them.
Total PO value open
₹100.00 Cr
10,000 lines
Delivered value
₹78.00 Cr
goods receipted
Pending value
₹22.00 Cr
ordered, not yet received
Value in exceptions
₹2.15 Cr
215 lines need action
Pending Value × the lines the rule engine flagged.
- Confirmation pending₹1.00 Cr100 lines
- ASN missing₹50.0 L50 lines
- Delivery late₹40.0 L40 lines
- Data quality issues₹25.0 L25 lines
Driven by Pending Days. The 15+ bucket is where money gets stuck.
- 0–3 days72
- 4–7 days61
- 8–15 days49
- 15+ days33
33 lines are more than 15 days old — roughly ₹33.0 L that nobody has chased.
Per PO line
The same check, on one line, by hand and by machine.
- By hand
- 5.76 s
- By machine
- 19.1 ms
About 302× faster on pure processing, before any human review.
The only rows a person opens. Everything else already passed every rule.
- Confirmation pending100
- ASN missing50
- Delivery late40
- Data quality issues25
Over a year (250 cycles)
Manual effort now
4,000 h
spent on this one cycle
After automation
286 h
review and approval only
Hours returned
3,714 h
about 1.9 full-time people
Value of that time
₹18.6 L
at ₹500/hour
Assumptions behind these numbers
- Baseline is 10,000 PO lines taking 16 hours — two working days, matching the current process.
- Steps like logging into SAP and building a pivot are treated as fixed cost; the checking steps scale with volume.
- Machine timings assume API access and a single mid-range server. Screen automation on SAP GUI is slower for the fetch step — roughly 6 to 10 minutes.
- Human time left over is not a flat number — it is 66 min here, scaling as 12 s to triage each of the 215 flagged lines plus 30 s to approve each of the 45 supplier mails.
- Cost uses a fully loaded hourly rate and a cycle frequency you pick above. Both are inputs, not claims.
- The money view rests on one assumption — the average value of a PO line, set above. Delivered value is taken at 78% of the open order book. Swap both for your own figures and every rupee on this page follows.
Sanity check
How this compares with published figures
These are third-party numbers for the same job — chasing order confirmations, ASNs and late deliveries. We have listed them so you can check our arithmetic rather than take it on trust.
| Published figure | Source | Our model |
|---|---|---|
| 9 buyers, 8,500 POs a year, 18 h per buyer per week on follow-ups — 162 h/week, about $404k a year | Mid-market manufacturer case study | 16 h per cycle at 10,000 open lines. That company is smaller and spends more, so our figure is on the conservative side. |
| Expediting takes 20–30% of buyer bandwidth at manufacturers with 50+ active suppliers | Procurement automation research | Consistent with roughly 2 full-time equivalents on this cycle at 10,000 lines run daily. |
| Automation cuts expediting effort by 80–90%; teams reclaim 10–15 h a week | Vendor-reported, several studies | We model about 93% — above this band. See the note below. |
| SAP PO automation: manual touch time drops to 5–10 minutes for “exception review only” | SAP automation vendors | Same shape as our model — the leftover human time is exception review, and it scales with the exception count. |
| Procurement cycle times down 50–70%, manual effort down 60–80% | Broader PO automation studies | Lower band, because those cover PDF entry and approvals too. Our scope is narrower and more mechanical. |
| SAP’s own ME92F reminder only evaluates confirmation category AB | SAP knowledge base | Confirms the gap: no ASN check, no late-delivery check, no value. That is what the manual Excel pass exists to cover. |
| Median requisition-to-PO cycle time of 2.0 days across 1,250 companies | Cross-industry benchmark set | Not applicable — that measures raising a PO. This tool tracks POs that already exist. |
Where we sit: our 93% is at or above the top of every published band (60–80%, 80–90%). We think that is defensible because this workflow is narrower and almost entirely mechanical — no PDF entry, no approval routing, just rules over an extract. But it is the one number to challenge us on first.
Scope: neither column includes the actual supplier phone calls and negotiation. That work exists with or without the tool, so it sits outside both the 16 hours and the hour.
Sourcing: the percentage figures come from automation vendors describing their own products — indicative, not audited. The per-step minutes are our estimates for this workflow, anchored to the two-day cycle your team reported, not measured in your system. The honest version of this page is the one we rebuild after timing a real run on your data.
Reports
What lands in your inbox when the run finishes
Generated from the same run, so the dashboard, the email and the monthly pack can never disagree with each other.
Supplier follow-up log
Excel / PDFEvery mail drafted and sent, with the PO lines and value it covered, timestamped for audit.
Monthly supplier scorecard
PDFConfirmation %, ASN %, on-time %, late count, average confirmation lag and pending value per vendor.
Data quality report
ExcelRows that failed validation with the reason — blank vendor, invalid delivery date, duplicate PO line.
Value-at-risk summary
Excel / PDFTotal PO Value, Delivered Value and Pending Value rolled up by plant and by vendor.
Ageing analysis
ExcelPending Days bucketed 0-3, 4-7, 8-15 and 15+, with the value sitting in each bucket.
Analysis
Charts your BI tool gets, already modelled
The exception data is written to SQL in a shape a BI tool can read directly, so these views build in minutes rather than being re-derived from a spreadsheet.
Pending value by vendor
BarPending Value summed per Vendor — the money each supplier is holding up.
Vendor · Pending Value
Ageing buckets
Stacked barPending Days grouped 0-3, 4-7, 8-15, 15+, stacked by exception type.
Pending Days · PO Status
Exception trend
LineOpen exceptions per day — is the backlog shrinking or growing?
PO Date · PO Status
Ordered vs delivered
BarOrdered Qty against Delivered Qty per material, so short supply is obvious.
Material · Ordered Qty · Delivered Qty
Plant-wise load
BarException count and pending value per plant.
Plant · Pending Value
On-time delivery
LineOn-time goods receipt percentage by month, per vendor and overall.
Delivery Date · Delivered Qty
Priority mix
Stacked barCritical, high, medium and normal share of today's exception list.
PO Status · Pending Days · Total PO Value
Value realisation
BarDelivered Value against Total PO Value — how much of the order book has actually landed.
Total PO Value · Delivered Value
Every chart above is built from the same fourteen columns in the SAP extract — ten read straight from SAP, four calculated. See the full data model →
Run this calculation on your real numbers
Send us one day’s PO extract and we will time it end to end, on your data, in front of your team.