Cost Reduction Program

This is not a benchmark exercise. Every initiative traces to a named vendor, a specific GL account, and an observed spending pattern in the ledger. The scan found an advertising contract whose monthly fee has escalated 14% in twenty months, a telecom relationship billing $135K against four overlapping expense accounts, a sole-source IT hardware arrangement, and a single $52,550 consulting invoice that tripled the August training expense.

Nine initiatives yield a $490,420 annual run-rate saving — $215K from cost of goods, $276K from operating expenses. Executed on plan, the program adds $1.21M of cumulative net income over three years and lifts net margin from roughly 18% to 21.5% by FY2029.

Every driver is drawn from a triangular distribution — growth (2–18% in year one, mode 12%), gross-margin drift skewed to the downside (−100 to +200bps), opex inflation (2–5.5%), savings capture (50–115% of plan), one-time costs (70–180% of plan) — and each trial carries a 20% probability of a material operational disruption event that cuts growth, damages margin, and delays capture.

1. Wave 1 · Quarter one — contracts & policy, zero operational risk. Telecom audit (I4), advertising reset (I5a), T&E policy (I6), services governance (I7). Combined run-rate $201,836 — 41% of the program with no supply-chain exposure. The FrisCo escalator alone justifies starting this month; every month of delay costs ~$210 in permanent run-rate.
2. Wave 2 · Quarters two–three — competitive tension. IT hardware reset (I3), marketing consolidation (I5b), facilities & supplies rebid (I8). Run-rate $74,005. These need vendor transitions but touch nothing customer-facing.
3. Wave 3 · Quarters three–six — sourcing, gated. Core supplier renegotiation (I1) and tail consolidation (I2), $214,579 — the largest tranche and the only one that can produce Scenario D. Define fill-rate and lead-time thresholds before the first negotiation; any breach pauses the initiative. Negotiate with the top four sequentially, never simultaneously, so the supply base is never destabilized all at once.
4. Protect revenue capacity while cutting. The tornado shows three points of growth are worth more than the entire savings program. No initiative should constrain the ability to sell, ship, or serve.
5. Hold a one-time contingency of ~$86K — fifty percent of the implementation budget. Its sensitivity swing is the smallest of any driver; cheap insurance.
6. Re-run this model after FY2026 close with actuals replacing the September–December estimate, and quarterly thereafter against captured-savings actuals by initiative.

• Davidson Leasing, $120,000 prepaid. A single August 2026 bill posted to Prepaid Expenses (1400), not yet amortizing into the P&L. When amortization begins it will add roughly $10K/month of expense that no forecast currently carries. Confirm the schedule and the underlying asset.
• COGS posts via monthly journals. Account 5310 ($9.7M since Jan 2025) is fed by 40 monthly journal entries, not item-level transactions — so vendor attribution for COGS must come from the purchase-order layer, as done here. Item-level margin analysis would require mapping those journals to receipts.
• Class attribution is asymmetric. Product classes are tagged on shipment COGS lines but largely absent from revenue lines (class-tagged revenue: $860K of $8.4M YTD). Until sales lines carry class consistently, per-category margin reporting is unreliable.
• Escalating recurring bills merit a watchlist. The FrisCo pattern (+$210/month) was visible only in the monthly series, not in any annual total. A simple recurring-vendor trend report would catch similar escalators in the future.

Pipeline

• Extract. Posting GL rows from transactionaccountingline joined to transaction and account, sign-corrected (−amount); vendor attribution from bills (VendBill headers) and purchase-order lines.
• Baseline. FY2026 estimate = Jan–Aug actuals + Sep–Dec 2025 actuals grown at the observed +19.1% YTD rate; COGS at the YTD 60.8% ratio; opex at Sep–Dec 2025 run-rate +3%.
• Identify. Vendor concentration, monthly trend anomalies, and account fragmentation scanned across COGS, IT, telecom, marketing, T&E, training, and facilities accounts.
• Project. Revenue-driver model: COGS savings scale with revenue, opex savings with inflation; capture ramps per scenario; one-time costs expensed in-year.
• Stress. Single-factor tornado on FY2028 net income; 10,000-trial Monte Carlo with triangular distributions and a Bernoulli disruption event.

SELECT TO_CHAR(t.trandate,'YYYY') AS fy, a.accttype, ROUND(SUM(-tal.amount),2) AS amount
FROM transactionaccountingline tal
JOIN transaction t ON tal.transaction = t.id
JOIN account a ON tal.account = a.id
WHERE t.posting = 'T'
  AND a.accttype IN ('Income','OthIncome','COGS','Expense','OthExpense')
  AND t.trandate >= TO_DATE('2023-01-01','YYYY-MM-DD')
GROUP BY TO_CHAR(t.trandate,'YYYY'), a.accttype
ORDER BY TO_CHAR(t.trandate,'YYYY'), a.accttype

SELECT a.accttype, a.acctnumber, a.fullname, ROUND(SUM(-tal.amount),2) AS amount
FROM transactionaccountingline tal
JOIN transaction t ON tal.transaction = t.id
JOIN account a ON tal.account = a.id
WHERE t.posting = 'T'
  AND a.accttype IN ('Income','OthIncome','COGS','Expense','OthExpense')
  AND t.trandate >= TO_DATE('2025-01-01','YYYY-MM-DD')
  AND t.trandate <  TO_DATE('2026-01-01','YYYY-MM-DD')
GROUP BY a.accttype, a.acctnumber, a.id, a.fullname
ORDER BY a.accttype, SUM(-tal.amount)

SELECT TO_CHAR(t.trandate,'YYYY-MM') AS month, a.accttype, ROUND(SUM(-tal.amount),2) AS amount
FROM transactionaccountingline tal
JOIN transaction t ON tal.transaction = t.id
JOIN account a ON tal.account = a.id
WHERE t.posting = 'T'
  AND a.accttype IN ('Income','COGS','Expense')
  AND t.trandate >= TO_DATE('2025-01-01','YYYY-MM-DD')
GROUP BY TO_CHAR(t.trandate,'YYYY-MM'), a.accttype
ORDER BY TO_CHAR(t.trandate,'YYYY-MM'), a.accttype

SELECT v.entityid AS vendor, COUNT(t.id) AS bills,
  ROUND(SUM(t.foreigntotal),2) AS total
FROM transaction t
JOIN vendor v ON t.entity = v.id
WHERE t.type = 'VendBill' AND t.posting = 'T'
  AND t.trandate >= TO_DATE('2025-01-01','YYYY-MM-DD')
GROUP BY v.entityid
ORDER BY SUM(t.foreigntotal) DESC
FETCH FIRST 25 ROWS ONLY

SELECT v.entityid AS vendor, COUNT(DISTINCT t.id) AS pos,
  ROUND(SUM(ABS(tl.netamount)),2) AS po_value
FROM transaction t
JOIN vendor v ON t.entity = v.id
JOIN transactionline tl ON tl.transaction = t.id
  AND tl.mainline = 'F' AND tl.taxline = 'F'
WHERE t.type = 'PurchOrd'
  AND t.trandate >= TO_DATE('2025-01-01','YYYY-MM-DD')
GROUP BY v.entityid
ORDER BY SUM(ABS(tl.netamount)) DESC

SELECT a.acctnumber, a.fullname, v.entityid AS vendor,
  ROUND(SUM(tal.amount),2) AS spend
FROM transactionaccountingline tal
JOIN transaction t ON tal.transaction = t.id
JOIN account a ON tal.account = a.id
JOIN vendor v ON t.entity = v.id
WHERE t.posting = 'T'
  AND t.trandate >= TO_DATE('2025-01-01','YYYY-MM-DD')
  AND a.acctnumber IN ('6060','6260','6655','6671','6672','6673','6674', ...)
GROUP BY a.acctnumber, a.fullname, v.entityid
ORDER BY a.acctnumber, SUM(tal.amount) DESC

SELECT t.tranid, t.trandate, BUILTIN.DF(t.entity) AS entity, tal.amount, t.memo
FROM transactionaccountingline tal
JOIN transaction t ON tal.transaction = t.id
JOIN account a ON tal.account = a.id
WHERE t.posting = 'T' AND a.acctnumber = '6260'
  AND t.trandate >= TO_DATE('2026-08-01','YYYY-MM-DD')
ORDER BY tal.amount DESC