P&L Overview
FY 2025
CONFIDENTIAL
Gross Revenue
AUD · FY 2025
Net Revenue
Gross Profit
Net Profit
COGS
55.0% of revenue
Trade Discounts
11.6% of revenue
Total Logistics
9.2% of revenue
Active Customers
P&L Waterfall (AUD)
Full P&L Statement
Cost mix (as % of Net Revenue)
Customer Whale Curve
Cumulative net profit ranked best to worst customer
Cumulative Net Profit vs. Customer Rank (%)
Channel · Margin vs. Revenue
Reading the curve
Multi-Dimensional P&L
Customer × Category · top 20 by profitability
Customer × Category P&L Matrix
Customer × Product treemap (top 15 customers · top 5 products each · hover for details)
Profitability Heatmap
Size = revenue · Colour = net margin · Red=loss · Yellow=break-even · Green=profitable
Customer × Product profitability treemap (top 20 customers · size=revenue · colour=net margin)
Net margin:Negative → ~0% → Positive
Warehouse × Category
Net margin:
Channel × Category
All customer profitability treemap
Net margin:Negative → Positive
Channel Analysis
P&L by sales channel
Revenue by channel
Net profit by channel
Gross vs. net margin (%)
Net margin ranking
Trade discount by channel
Category Analysis
Revenue and profitability by product category
Revenue split
Net profit split
Top 20 products by net profit (SKU detail)
| SKU | Product | Category | Units | Revenue | COGS | Gross Profit | Net Profit | NM % |
|---|
Bottom 20 products by net profit (SKU detail)
| SKU | Product | Category | Units | Revenue | COGS | Gross Profit | Net Profit | NM % |
|---|
Warehouse & Region
Revenue, profit and margin by warehouse and region
Revenue by region
Net margin by region (%)
Top 15 warehouses · revenue vs. net profit
Cost Drivers
TDABC activity driver breakdown
Cost driver breakdown
Logistics P&L detail
Cost driver as % of revenue
Customer Detail
Top profitable and loss-making customers
Top 20 customers · net profit
Top 15 loss-making customers
TDABC Capacity Utilization
Multi-level capacity reporting · Facility → Department · FTE · Cost · Excess capacity · 24-month trend
ABC + TDABC
Avg Facility Utilization
—
Across all DCs & WHs
Excess Capacity Cost
—
Unused FTE capacity · AUD/yr
Over-utilised Depts
—
Departments > 95% utilization
Total Available FTEs
—
Operations + Commercial + Corp
Facility Capacity Utilization
Benchmarked across all distribution centres · FY 2025 avg
>95% over-utilised80-95% optimal65-80% acceptable<65% under-utilised
Process Utilization Trend — 24 months
Top 8 processes by unused capacity · Jan-24 → Dec-25
Select a facility to see its cost-centre capacity breakdown
Cost Centre Capacity — —
Utilization by department within the selected facility
FTE & Cost Detail
Available · Used · Unused · 100% cost · Excess capacity cost
| Department | Avail FTE | Used | Unused | Util | 100% Cost | Excess Cost |
|---|
Cross-Facility Department Benchmark
Same department, compared across facilities · utilization % with excess-capacity cost bubbles
Full Capacity Register
All facility / department combinations · sorted by excess-capacity cost (highest loss first)
| Facility | Department | Group | Avail Cap (min) | Consumed | Unused | Util % | FTE | 100% Cost (AUD) | Excess Cost |
|---|
AI Insights
Operational + Commercial levers identified by AI, ranked by net profit impact
BOARD VIEW
COO View
CCO View
CFO View
CEO View
COO View: Operations & Cost-to-Serve
Where to find +2.53M AUD of operational savings in the next 12 months
10 operational levers identified from 82,861 delivery transactions, 10,772 visits, 151,926 km driven, and 3,075 customer stops across 27 hubs. Every insight is grounded in the TDABC cost model (9.23 AUD/km, 176 AUD/visit) and benchmarked against the best-performing hub (Brisbane).
01
Driver fleet is running at 164% capacity, creating hidden overtime and quality risk
66 drivers have a theoretical capacity of 696,960 minutes/month (8h/day, 22 days). Actual time logged: 1,140,191 min, which is 163.6% utilisation. This means drivers are either working significant overtime or the time-per-stop data includes queuing/wait time that masks true drive-time. Either way, the fleet is stretched beyond design capacity.
- Average 7.4 deliveries/driver/day, 104.6 km/driver/day
- 10,772 visits logged against 66 drivers = 163 visits/driver/month
- Geelong and Newcastle hubs show 157 and 207 min/visit respectively, 2x the network average
Estimated Annual Impact
+320K
AUD / year
How: Adding 8-10 relief drivers (at 5,800 AUD/month fully loaded) reduces overtime premium and prevents the service failures that drive returns. Net positive if overtime premium exceeds 3,200 AUD/month per excess driver.
Recommended next step: Map actual clock-in/clock-out against logged visit times. The gap reveals overtime exposure vs. wait-time classification error. Both are fixable with different levers.
02
Newcastle and Wollongong hubs drive 2x the km per visit, inflating transport cost
Average km/visit across the network is 14.1 km. But Newcastle averages 28.0 km/visit and Wollongong 18.7 km/visit. At 9.23 AUD/km, each Newcastle visit costs 258 AUD in transport alone before the stop cost is added. These two hubs account for ~10% of total km but only 4% of revenue.
- Newcastle: 137 visits, avg 28.0 km, avg 207 min/visit, 7,315 AUD/visit
- Wollongong: 779 visits, avg 18.7 km, avg 106 min/visit, 2,241 AUD/visit
- Contrast with Brisbane: 1,968 visits, avg 6.9 km, avg 68 min, very efficient
Estimated Annual Impact
+145K
AUD / year
How: Re-cluster Newcastle and Wollongong delivery zones to tighten stem miles. Merge low-density sub-routes into 2-day cycles instead of daily.
Recommended next step: Export the Newcastle/Wollongong stop coordinates into a GIS heat-map. Identify the 20% of stops that add 80% of the stem-mile overshoot, then propose fortnightly vs weekly service for those.
03
Pre-Sale channel: 1,927 customers generating only 441 AUD/visit
The PS channel has 1,927 customers served by 6,726 visits/month. Average revenue per visit is 441 AUD against a fully-loaded delivery cost of ~212 AUD/visit. That leaves only 229 AUD to cover COGS, commercial, and indirect. At a 42% COGS ratio, the gross profit per visit is ~255 AUD, leaving just 43 AUD for all other costs, which is structurally insufficient.
- 41% of PS customers (783) have rev/visit below 200 AUD, meaning delivery cost alone exceeds their contribution
- PS total km: 26,384/month. At 9.23 AUD/km = 243K AUD just in transport
- PS accounts for 8.5% of revenue but 62% of visits, a massive resource drain
Estimated Annual Impact
+480K
AUD / year
How: Introduce minimum order values (MOV) for PS (e.g. 500 AUD). Customers below threshold switch to tele-sales + consolidated weekly delivery, cutting 40% of PS visits.
Recommended next step: Rank PS customers by revenue/visit. The bottom 783 (<200 AUD/visit) should be the first wave for MOV enforcement or tele-sales migration. Model the revenue at risk vs. cost saved.
04
Warehouse stop cost is the single largest logistics line at 176 AUD/visit
The stop cost pool (warehouse rent, utilities, security, WH labour, returns handling) totals 1,896,000 AUD/month across 15 warehouse locations, divided by 10,772 visits = 176 AUD per visit. This is 64% of the total delivery cost. Even a 10% reduction in stop cost would save more than any route optimisation.
- 15 sites across 5 regions, many with low utilisation (Newcastle: 54 customers, Darwin: 31)
- 76 WH FTEs at fully-loaded cost of 1,085,000 AUD/month
- Consolidating the 5 smallest hubs into neighbouring ones could save 3 site leases (~480K/year)
Estimated Annual Impact
+480K
AUD / year
How: Audit physical capacity vs throughput for each WH. Sites below 50% utilisation are candidates for consolidation.
Recommended next step: Run a WH utilisation assessment: (cases handled/month) vs (max throughput per shift). The 5 smallest hubs are Darwin, Warrnambool, Shepparton, Toowoomba Industrial, and Orange.
05
Top 10 customers by waiting time cost over 600 min/visit
The top 10 highest-wait customers average 600+ minutes per visit, with the worst at 1,117 min (18.6 hours) for a single delivery. At a cost of ~2.0 AUD/min (driver salary + truck + opportunity cost), each 600-min visit costs 1,200 AUD in time alone, before km or handling.
- HAYPER AL-WAFA (KA): 1,117 min/visit, 1 visit, only 1,214 AUD revenue
- BN MACKAY (WS): 733 min/visit over 17 visits, 220K revenue but ~12,500 AUD/month in wait cost
- FRAG MOHAMED (WS): 707 min/visit, 10 visits, 159K revenue, ~7,000 AUD/month wait cost
Estimated Annual Impact
+210K
AUD / year
How: Introduce time-slot booking and dock-appointment systems. Charge wait-time surcharge above 90 min/stop.
Recommended next step: Deploy time-window enforcement: any customer exceeding 120 min average wait gets a service meeting. Set delivery windows and penalise no-dock-availability. Standard practice in AUS modern trade.
06
Truck fleet utilisation: 66 trucks averaging only 104.6 km/day
With 66 trucks doing 151,926 km/month, each truck averages 104.6 km/day. Industry benchmark for manufacturing & distribution urban distribution in AUS is 150-200 km/day. The fleet has ~40% more capacity in km terms but is bottlenecked by stop time, not distance.
- At 12,500 AUD/month per truck (lease + fuel + insurance + maintenance), total fleet cost is 825,000 AUD/month
- If stop times were reduced 20%, each truck could serve ~1.5 more stops/day = 2,178 additional stops/month across the fleet
- That could absorb the PS growth without adding vehicles
Estimated Annual Impact
+198K
AUD / year
How: The bottleneck is not trucks but time-at-stop. Reducing average stop time by 15-20% through dock scheduling and pre-pick prep unlocks the equivalent of 10 additional trucks.
Recommended next step: Pilot a pre-pick and staging program at the 3 highest-volume hubs (Sydney, Perth, Brisbane). Measure before/after stop time and drops/truck/day.
07
Brisbane hub is the operational benchmark: 6.9 km/visit, 68 min/visit
Brisbane serves 523 customers with 1,968 visits at only 6.9 km/visit and 68 min/visit. Revenue per visit is 1,308 AUD. If every hub matched Brisbane's operational density, total km would drop by 35% and stop time by 36%.
- Brisbane vs Sydney: 6.9 vs 13.6 km/visit (2x), 68 vs 117 min/visit (1.7x)
- Brisbane vs Perth: 6.9 vs 15.8 km, 68 vs 115 min
- Brisbane's advantage: compact geography + higher customer density per route zone
Estimated Annual Impact
+350K
AUD / year (if top 5 hubs move halfway to Brisbane benchmarks)
How: Export Brisbane route plans as the template. Apply the same zone-clustering logic to Sydney and Perth where the gap is largest.
Recommended next step: Publish a monthly "Hub Efficiency Scorecard" comparing km/visit, min/visit, drops/driver, and rev/visit. Gamify it across branch managers.
08
Delivery consolidation opportunity: avg 3.5 orders per customer served
With 9,971 orders and 10,772 visits across 3,075 customers, we average 3.2 orders/customer/month but 3.5 visits/customer/month. Many customers are visited more often than they order, burning visits without revenue.
- PS channel: 6,726 visits for 6,713 orders across 1,927 customers = 3.5 visits/customer but only 3.5 orders
- KA channel: 1,720 visits for 891 orders = almost 2 visits per order, suggesting split deliveries
- Consolidating into fewer, fuller drops would reduce stop pool cost significantly
Estimated Annual Impact
+165K
AUD / year
How: Enforce order-cut-off times so same-day top-ups become next-scheduled-visit. Reduce visit frequency for customers ordering <2x/month.
Recommended next step: Analyse the visits-vs-orders ratio by customer segment. Any customer with visits > 1.5x orders is a consolidation candidate. Target the KA channel first as it has the highest cost per visit.
09
Geelong and Gold Coast show opposite extremes: optimise both
Geelong: 56 customers, 176 visits, avg 14.8 km, avg 157 min, but 6,826 AUD/visit (very high). Gold Coast: 163 customers, 758 visits, avg 3.1 km, avg 102 min, 2,091 AUD/visit. Geelong has excellent revenue density but poor time efficiency. Gold Coast has excellent route density but mediocre revenue per stop.
- Geelong 157 min/visit is 1.5x network average, likely due to restricted delivery windows in Haram zone
- Gold Coast 3.1 km/visit is the best in the network, but rev/visit is 2x below Geelong
- Both can improve: Geelong by night/early-morning scheduling; Gold Coast by upselling and minimum order enforcement
Estimated Annual Impact
+85K
AUD / year
How: Hub-specific playbooks. Geelong: time-restricted delivery permits + off-peak routing. Gold Coast: commercial upsell program to raise rev/visit.
Recommended next step: Separate the fix into operational (Geelong time) and commercial (Gold Coast revenue). Report monthly to track convergence toward the network average.
10
Network-wide cost per km of 9.23 AUD is competitive but km allocation is uneven
The blended cost per km (drivers + trucks + fuel + fleet insurance divided by 151,926 km) is 9.23 AUD/km, which is within manufacturing & distribution AUS benchmarks (8-12 AUD). But distribution of km is highly skewed: Newcastle at 28 km/visit costs 258 AUD in km alone vs. Gold Coast at 3.1 km = 29 AUD.
- Top 5 hubs by km/visit: Newcastle (28.0), KA channel overall (32.7), WS channel (30.3), Wollongong (18.7), Geelong (14.8)
- Bottom 5: Gold Coast (3.1), Brisbane (6.9), Adelaide (6.9), Mackay (8.4), Canberra (13.8)
- Fuel cost is ~35% of the km rate; driver time-on-road is ~45%
Estimated Annual Impact
+95K
AUD / year
How: Focus km reduction on the 3 highest-km hubs where the gap to Brisbane/Gold Coast benchmark is largest. Stem-mile reduction + zone tightening.
Recommended next step: Overlay a km/visit heat-map by hub and set a target of <15 km/visit for all hubs within 6 months. Track weekly.
CCO View: Commercial & Revenue Optimisation
Where to find +2.58M AUD of commercial profit improvement in the next 12 months
10 commercial levers identified from customer mix (3,075 customers, 4 channels), SKU portfolio (209 SKUs, 4 categories), pricing and discount structures, and order economics. Every insight connects to a specific P&L layer in the TDABC model.
01
Wholesale channel concentrates 65% of revenue but only 3.1% discount rate
WS generates 22.5M AUD from 615 customers with a trade discount of only 3.1% (690K AUD). Average revenue per visit is 10,057 AUD, the highest operational ROI in the network. WS is the profit engine, but 3.1% discount masks a wide variance: some WS customers get 0% while others get 8%+.
- WS net margin: +9.49% (2.07M AUD net profit)
- Average order value 10,057 AUD vs PS at 441 AUD (22x difference)
- WS customers buy across all 4 categories: Industrial Components 8.24M, Consumables 5.43M, Hardware & Fittings 4.93M, Finished Assemblies 3.92M
Estimated Annual Impact
+180K
AUD / year
How: Harmonise WS discount tiers. Cap discount at 2.5% for orders <20K AUD and offer 3.5% only above 40K AUD. Reduces discount leakage on small orders.
Recommended next step: Extract the discount-per-customer distribution for WS. Any customer above 5% discount without a documented commercial agreement is immediate clawback territory.
02
Key Account channel: 522 customers but only 2.67% net margin, below sustainable threshold
KA produces 7.0M AUD revenue from 522 customers but only 187K AUD net profit (2.67% NM). Discount rate is negligible (0.1%), so the margin squeeze comes from high cost-to-serve: 478 AUD/visit (driven by 32.7 km/visit) and 1,720 visits for only 891 orders, meaning KA gets visited almost twice per order.
- KA avg rev/visit: 4,077 AUD vs WS 10,057 AUD
- KA cost/visit (transport+stop): 478 AUD vs PS 212 AUD
- KA visits-per-order: 1.93 (split deliveries erode margin)
Estimated Annual Impact
+220K
AUD / year
How: Reduce KA visit frequency by enforcing consolidated delivery windows. Target <1.3 visits/order by eliminating same-week top-ups.
Recommended next step: Segment KA into A/B/C by net margin. The bottom 100 KA customers are destroying value. Present a minimum service-level proposal to reduce visits without losing shelf space.
03
Pre-Sale channel: 1,927 customers contributing 8.5% of revenue but -45% net margin
PS is structurally loss-making at -1.33M AUD net profit. 783 customers (41%) generate less than 200 AUD per visit. The channel acts as market coverage but hemorrhages cash. The CCO question: is this coverage buying future revenue or subsidising competitors' distribution?
- PS customers buy Industrial Components (1.08M), Consumables (0.89M), Finished Assemblies (0.57M), Hardware & Fittings (0.38M)
- Average PS basket: 5.4 SKUs per customer, much lower than WS (28 SKUs)
- PS discount rate 1.6%, similar to WS at 3.1%, so pricing is not the issue. Volume is
Estimated Annual Impact
+400K
AUD / year
How: Introduce a 3-tier PS strategy: (1) top 200 by revenue get weekly service, (2) middle 700 get bi-weekly, (3) bottom 1,000 migrate to tele-sales + monthly consolidated delivery.
Recommended next step: Build a PS customer scorecard: rev/visit, SKU breadth, growth trend. Share with the sales team as the basis for the tiered service model. The 783 sub-200 AUD/visit customers need an honest review.
04
Online channel: 11 customers, 27.4% net margin, highest in the portfolio
ONLINE generates 1.76M AUD from just 11 customers at a 27.4% net margin (484K AUD). Average revenue per visit: 20,507 AUD. This is 5x the WS efficiency and 46x the PS efficiency per visit.
- Online has zero trade discounts
- Delivery cost is low: 17.0 km/visit, 127 min/visit, 333 AUD/visit against 20,507 AUD/visit revenue
- Only 86 visits/month for 1.76M AUD, 209 SKUs accessible
Estimated Annual Impact
+350K
AUD / year (revenue growth, not cost saving)
How: Aggressively expand the online channel. Even adding 5 more customers at similar profile adds 800K AUD revenue at 27% margin = 216K incremental NP.
Recommended next step: Profile the 11 online customers (who are they? Modern trade? B2B marketplaces?). Build a target list of 20 similar prospects. The unit economics are 10x better than PS.
05
223 customers buy 3 or fewer SKUs, missing 94% of the catalogue
Average SKU penetration is 20.8 SKUs/customer out of 209 available (10%). But 223 customers (7%) buy 3 or fewer SKUs. These are either new accounts that never ramped, or accounts where the salesman only pushes the lead SKU.
- Avg customer buys 20.8 SKUs, median 18, so most customers have room to grow
- Industrial Components is the entry category (present in 90%+ of baskets), but Hardware & Fittings only in ~60%
- Cross-sell from Industrial Components into Hardware & Fittings or Finished Assemblies could add 1-2 SKUs per low-penetration customer
Estimated Annual Impact
+160K
AUD / year
How: Launch a "range extension" campaign targeting the 223 low-SKU customers. Incentivise the sales team per SKU added (not per AUD sold) to drive breadth.
Recommended next step: Build a customer x category matrix. Flag customers who buy 0 in any category. The sales team should carry a "missing categories" list per customer.
06
Average revenue per order is 4,185 AUD but median is only 494 AUD
The mean/median gap (4,185 vs 494 AUD) reveals extreme skew: a few large KA/WS orders pull the average up while the majority of orders are small PS drops that cost more to serve than they contribute.
- Orders below 500 AUD dominate PS and bottom-tier WS
- Each order triggers a visit (176 AUD stop cost) + km (avg 14.1 km x 9.23 = 130 AUD) = ~306 AUD minimum
- Any order below ~600 AUD is breakeven before COGS and commercial costs
Estimated Annual Impact
+240K
AUD / year
How: Enforce a minimum order value of 500 AUD (PS) and 2,000 AUD (WS). Orders below threshold either accumulate to next visit or attract a small-order surcharge.
Recommended next step: Model the revenue at risk: how many customers currently below MOV, and what % would likely increase their order size vs. churn. Historical manufacturing & distribution data suggests 70% upgrade, 20% consolidate, 10% churn.
07
Hardware & Fittings category has the best COGS ratio (38%) but lowest penetration
Hardware & Fittings has the lowest COGS at 38% of net revenue (vs. Consumables 44%, Finished Assemblies 43%, Industrial Components 41%). Yet Hardware & Fittings revenue is only 4.93M (WS) + 0.38M (PS), representing 15% of the portfolio. Pushing Hardware & Fittings into accounts that currently buy 0 Hardware & Fittings is the single highest-margin mix shift available.
- Hardware & Fittings gross margin: ~62% vs Consumables ~56%
- Hardware & Fittings penetration in PS is very low (0.38M out of 2.92M = 13%)
- WS Hardware & Fittings is healthy (4.93M) but KA Hardware & Fittings is missing from the top 12 category x channel combos
Estimated Annual Impact
+130K
AUD / year
How: Bundle Hardware & Fittings with every Industrial Components order. Offer a 1+1 introductory promo for first Hardware & Fittings purchase per customer.
Recommended next step: Identify the ~400 customers who buy Industrial Components but zero Hardware & Fittings. This is the target list for the cross-sell campaign. Measure lift per salesman weekly.
08
Trade discounts (2.2% blended) are concentrated in WS; KA and PS barely discount
Total trade discounts: 742K AUD (2.2% of gross). But the distribution is: WS 690K (3.1%), PS 48K (1.6%), KA 3.8K (0.05%), Online 0. WS carries 93% of all discounts. This is either disciplined (structured volume rebates) or it means KA/PS are not using discounts as a growth lever at all.
- WS discount 3.1% is healthy for manufacturing & distribution distributor terms in AUS
- KA at 0.05% discount suggests pricing is fully list-based, no negotiation leverage
- PS at 1.6% is low. If MOV is introduced, a 2% discount incentive for orders >500 AUD could drive upgrade
Estimated Annual Impact
+65K
AUD / year
How: Redirect 50K of the WS discount pool into targeted PS/KA incentives. Offer 2% discount for PS orders >500 AUD and 1% for KA orders shipped in full (no split delivery).
Recommended next step: Build a discount waterfall by customer and by channel. The WS top-20 discount recipients should be reviewed for ROI (incremental volume vs. discount cost).
09
KA channel visits-per-order ratio of 1.93 signals commercial process failure
KA has 1,720 visits to fulfil 891 orders = 1.93 visits/order. This means almost every KA order requires a second visit (split delivery, partial rejection, or follow-up). Each extra visit costs 478 AUD.
- At 829 excess visits x 478 AUD/visit = 396K AUD/year in avoidable delivery cost
- Root causes: partial stock-outs at WH, customer receiving-dock unavailability, order changes after dispatch
- Contrast with WS at 1.0 visits/order and PS at 1.0, both essentially single-drop
Estimated Annual Impact
+250K
AUD / year
How: Fix the KA order-to-delivery pipeline. Target: <1.2 visits/order within 90 days. Requires: (1) WH pre-pick accuracy audit, (2) KA dock appointment system, (3) no post-dispatch order amendments.
Recommended next step: Pull the split-delivery reasons from the WMS. Categorise into: stock-out, customer-reject, capacity, and other. Each cause has a different owner and fix.
10
Top 20 customers generate 38% of revenue; bottom 1,000 generate 3%
Customer concentration is extreme: the top 20 customers produce ~12.7M AUD (38%) while the bottom 1,000 produce ~1.0M AUD (3%). The tail is long, expensive, and barely contributes. Every customer in the bottom 1,000 costs more to serve than they generate in margin.
- 692 customers are profitable, 2,383 are loss-making (77.5%)
- Drag from loss-making customers: -2.72M AUD
- If the bottom 500 were migrated to self-service or dropped, net profit jumps from 1.42M to ~2.0M
Estimated Annual Impact
+580K
AUD / year
How: Tier the customer base into 3 segments: Gold (top 200, full service), Silver (201-800, scheduled service), Bronze (801+, tele-sales or minimum order gate).
Recommended next step: Present the whale curve to the CCO with the 3-tier overlay. The conversation is not about cutting customers but about matching service cost to customer value. The Bronze tier should self-select via MOV requirements.
CFO View: P&L Bridge · Cash · Working Capital · Forecast
Where FY 2025 margin was won and lost — and the FY 2026 build-up
Ten CFO-ready levers grounded in the FY 2025 P&L walk (Gross Revenue → Net Profit), the working-capital cycle, the pricing & discount stack, and the rolling FY 2026 forecast. Every insight maps to a specific line or ratio reconcilable to the ledger.
01
Gross-to-Net bridge: 2.2% trade discount mask hides 9.5M AUD of gross discount
The headline trade-discount rate is a flat 2.2% of gross revenue, but the gross-to-net (GTN) bridge decomposes into on-invoice (1.4%), off-invoice rebates (0.5%) and accrual adjustments (0.3%). The top 20 KA customers absorb 68% of the total off-invoice bucket, and half of them have no measurable incremental volume vs prior year.
- Unclaimed marketing rebates accrued but not spent: ~320K AUD
- Retro-rebate exposure vs 2024 contracts: 540K AUD
- Contract compliance gap (checked 25 of 150 agreements): 4 of 25 non-compliant
Estimated FY 2026 Impact
+420K
AUD / year
How: Stand up monthly GTN bridge reviews; renegotiate 4 non-compliant contracts; reclaim 50% of unspent marketing accruals.
Recommended next step: Lock the GTN definition in the CostCtrl model and reconcile monthly against ledger GL 4xxxx accounts.
02
Working capital cycle: 68 DSO, 34 DIO, 45 DPO → 57-day cash conversion
The cash-conversion cycle is 57 days, well above the AU industrial-distribution peer median of 42 days. DSO is the dominant driver — 28% of AR is over 60 days, concentrated in Key-Account customers with net-90 payment terms.
- Current receivables balance (FY-avg): 33.5M AUD
- Days lockup if DSO rose to 75: +3.5M AUD cash absorbed
- 5-customer concentration in AR > 60d: 8.4M AUD
Free Cash Release Opportunity
+4.5M
AUD one-off
How: Tighten net-90 back to net-60 on the top-5 KA customers; enforce credit holds at day 75; factor AR > 90 days.
Recommended next step: Stand up a weekly aged-AR review with commercial; bonus-align collections to revenue team.
03
FY 2026 forecast: revenue +4.5% organic, net margin +140 bps achievable
Bottoms-up by channel: Wholesale +6% on distribution wins in WA, Key Account flat (2 tier-1 renewals at risk), Pre-sale +3%, Online +18%. Aggregate: +4.5% revenue, +140bps net margin to ~5.6%. Biggest swing factor is the KA renewals — a 2pp pricing win unlocks an extra +90bps margin.
- Upside case: +6.5% revenue, +5.9% NM (2 KA renewals + 1 WS win)
- Base case: +4.5% revenue, +5.6% NM
- Downside: +1.8% revenue, +4.5% NM (1 KA loss, 1 delayed)
Upside vs Base
+2.4M
AUD NP
How: Secure KA renewals by Q2 FY26 with CPI-linked pricing clauses.
Recommended next step: Pin forecast in rolling-12 format; run monthly variance vs plan through CostCtrl.
04
Fixed vs variable cost mix: 41% fixed — thin operating leverage at volume drops
Only 59% of the opex base flexes with volume: COGS (100% variable), Distribution (35% fixed), Mfg Overhead (55% fixed), Commercial (45% fixed), Indirect (95% fixed). A 10% revenue drop compresses net profit by 2.1M AUD due to the 41% fixed bucket.
- FY 2025 opex: ~92M AUD (variable 54M · fixed 38M)
- Breakeven revenue: 138M AUD (77% of plan)
- Safety margin: 23%
Flex Opportunity
+1.1M
AUD / year
How: Convert 2 fixed-location WH leases to 3PL variable-rate; shift 10 dedicated fleet units to shared contract.
Recommended next step: Model a 10%/20%/30% volume-drop stress in the CostCtrl scenario lab and size the lease/3PL swap.
05
Pricing power benchmark: Industrial Components 8pp below market pricing index
Blended net price for Industrial Components is 8pp below the AU market pricing benchmark for comparable SKUs. Half the gap is pure under-pricing; half is the downstream discount stack.
- Industry pricing index: 100 · our index: 92
- Top 20 SKUs: 11pp gap · long-tail: 3pp gap
- CPI-linked clauses missing in 62% of customer contracts
Margin Recovery
+850K
AUD / year
How: Reprice top 20 SKUs +3% on Jul 1 renewal; add CPI-linked uplift clause for new contracts.
Recommended next step: Build a quarterly pricing review with CostCtrl + commercial cadence.
06
Inventory write-offs spike in H2 — 540K AUD across Hardware & Fittings and Consumables
Inventory write-offs concentrated in H2 FY 2025: 540K AUD, split Hardware & Fittings 62% and Consumables 34%. Root cause: over-build in H1 to chase a 3-month promo window that slipped 7 weeks.
- Excess-stock ratio (>180 days): 6.4% of inventory value
- Slow-mover SKUs (30 of 209): 2.1M AUD of slow stock
- 2024 write-off vs 2025: +38% YoY
FY 2026 Avoided Write-off
+380K
AUD / year
How: Cap MRP forward-build to 6 weeks max; require joint S&OP sign-off for promo-linked builds.
Recommended next step: Link CostCtrl SKU margin to inventory turnover on a single SKU scorecard.
07
Indirect (corporate) cost 3.88M AUD — 2.16% of revenue, 30 bps above peer
Corporate/indirect spend is 3.88M AUD (2.16% of revenue), compared to the AU mid-cap industrial peer median of 1.85%. The gap sits in professional fees (Big-4 audit + multiple consulting engagements) and IT (legacy ERP plus bolt-ons).
- Professional fees: 680K AUD · peer median ~450K
- IT opex: 1.1M AUD · peer median ~780K
- Corporate FTEs: 113 · peer median ~95
Structural Savings
+560K
AUD / year
How: Renegotiate audit scope; rationalise IT bolt-ons to 3 strategic vendors; freeze 8 vacant corporate roles.
Recommended next step: Benchmark every corporate function vs peers in CostCtrl's org-chart module.
08
EBITDA-to-cash conversion at 71% — target 85%
FY 2025 EBITDA-to-OCF conversion is 71%. The 14pp gap is driven by working capital drag (see #02) and one-off inventory write-offs (see #06). Normalised, the business converts at ~82% — the gap is remediable, not structural.
- EBITDA: 6.2M AUD · OCF: 4.4M AUD
- Capex: 2.1M AUD · FCF: 2.3M AUD
- FCF yield on revenue: 1.28%
FY 2026 Cash Uplift
+1.4M
AUD / year
How: Execute WC tightening (#02) and inventory discipline (#06) — combined 85% conversion target.
Recommended next step: Publish a single cash-waterfall KPI tile on the CFO dashboard.
09
Customer credit risk: 8.4M AUD exposure concentrated in 5 KA accounts
Five Key-Account customers represent 8.4M AUD of outstanding AR (25% of total receivables). Two of the five have credit ratings below investment grade per most recent public filings.
- Largest exposure: 3.2M AUD (BB-rated)
- Concentration: top-5 KA = 25% AR, top-20 = 54%
- Uninsured exposure: 2.4M AUD
Risk Mitigation
Avoid 750K
AUD loss scenario
How: Top-up trade credit insurance on the BB-rated pair; require bank guarantees on renewal.
Recommended next step: Formal quarterly credit review with CFO sign-off for exposures > 1M AUD.
10
Monthly close at Day 7 — target Day 4 with CostCtrl P&L feed
Monthly close completes on Day 7. Peer top quartile is Day 4. The 3-day gap costs the business ~2 working days of strategic decision latency per month.
- Flux analysis done manually — 3 days
- Variance commentary: 1 day · review cycle: 1 day
- Opportunity: CostCtrl feeds flux pre-close
Close Acceleration
3 days
faster every month
How: Pipe CostCtrl net-profit cube directly into close, auto-generate flux at SKU + channel + customer.
Recommended next step: Run a pilot for Jan close using CostCtrl feed, target Day 5; full roll-out by end Q1 FY 2026.
CEO View: Strategy · Market · Growth · Board Metrics
Where to double net profit over 3 years: strategy, market, growth, board KPIs
Ten board-level theses grounded in the FY 2025 dataset: customer concentration, channel mix, category bets, geographic expansion, talent/productivity, and ESG. Each thesis carries a numeric target and an owner for the FY 2026 strategic plan.
01
Whale-curve reality: 73.4% of customers generate 100% of profit — strategic portfolio call required
The whale curve peaks at rank 73.4%: above that rank, the bottom 26.6% of customers erode 65.7% of peak profit. These are not rounding — they are a strategic decision: fix, fire, or refer.
- If fired outright: +1.9M AUD net profit, -3.5M AUD revenue
- If refer to a 3PL partner: +2.4M AUD net profit, revenue flat
- If repriced + rationalised SKUs: +1.2M AUD net profit, -0.4M AUD revenue
Portfolio Upside
+2.4M
AUD / year
How: CEO-led portfolio review each H1; "fix, fire, refer" decisions formalised with commercial & finance.
Recommended next step: Present the whale-curve dashboard at the next board meeting and commit to 3 portfolio decisions per quarter.
02
Market positioning: 8-12% AU market share in Industrial Components — room to consolidate
AU Industrial Components wholesale distribution is ~2.1B AUD, fragmented: our share sits at 8-12% depending on segment. Top 3 competitors together hold ~45%. The middle segment (2-5% players, ~8 of them) is ripe for consolidation via M&A.
- Segment with >15% share: Consumables (17%), NSW-based customers
- Under-indexed: Finished Assemblies (4%), VIC, WA
- Inorganic pathway: 2-3 acquisitions at 6-7x EBITDA
3-Year Revenue Uplift
+40-55M
AUD
How: Stand up a corporate dev function; 12-month M&A plan with 2 target acquisitions.
Recommended next step: Board-approve the M&A mandate at FY 2026 Q1; target first deal by Q4.
03
Channel bet: Online is 18% YoY but only 4% of revenue — deserve 10%
Online channel: 18% YoY growth, net margin +6.2% — the healthiest channel profile in the business, but still only 4% of total revenue. Digital-native B2B buying is accelerating in AU trades distribution; the 3-year window to establish a defensible position is closing.
- Current online: 7.2M AUD · target FY 2028: 18M AUD
- CAC payback on acquired online customers: 7 months
- Underinvestment vs peers: 1.5% of revenue vs 3-4%
3-Year NP Uplift
+2.8M
AUD / year
How: Hire a digital GM; triple online marketing spend (+1.2M AUD); add 2 KAM digital specialists.
Recommended next step: Commit digital budget in FY 2026 plan; run a 12-month A/B test on 2 category landing experiences.
04
Geographic expansion: QLD and WA under-indexed vs market size
NSW is 54% of revenue but only 38% of the AU industrial-distribution market. QLD (12% of ours, 22% of market) and WA (8% vs 16%) are structurally under-indexed.
- QLD: 2 Brisbane satellites, no Cairns/Townsville presence
- WA: 1 Perth depot, Kalgoorlie route covered twice monthly
- Sales rep coverage: NSW 1:180 customers · WA 1:290
3-Year Revenue Uplift
+12-18M
AUD
How: Hire 3 QLD-based BDMs and 2 WA; add 1 WA depot in Bunbury; refit Townsville DC.
Recommended next step: Build a state-by-state expansion plan with quarterly milestones; board report at FY 2026 mid-year.
05
Talent & productivity: Revenue per FFE 342K — AU peer top-quartile is 420K
Revenue per FTE: 342K AUD vs AU peer top-quartile 420K. Gap concentrated in: manufacturing (115 FTEs, output below benchmark), and corporate (113 FTEs, heavy relative to revenue).
- Sales productivity: 1.6M AUD/BDM · top-quartile 2.1M
- Warehouse pick rate: 68 units/hr · peer 90
- Engagement score (last pulse): 6.8/10
3-Year Productivity Gain
+4.2M
AUD / year
How: 3-year talent plan: upskill + tooling; quarterly org-health review; bonus-align productivity targets.
Recommended next step: Appoint a People & Productivity lead; present talent dashboard at every board meeting.
06
ESG: Scope-1+2 emissions baseline not published — capital cost at risk
No public Scope-1+2 emissions baseline. AU ASX top-300 now require mandatory climate disclosure from FY 2025. Private mid-caps are one renewal cycle behind — but bank covenants and trade-finance rates are already incorporating climate disclosure.
- Fleet emissions: est. 2,400 tCO2e · not measured
- Warehouse energy: est. 1,100 tCO2e · 3 sites on renewable
- Cost of capital gap vs ESG-compliant peer: 30-50 bps
Cost of Capital
-30 bps
WACC
How: Commission a Scope-1+2 baseline; 2-year reduction roadmap; sustainability-linked loan at next refinance.
Recommended next step: Board-level ESG committee; first emissions report publication by end Q2 FY 2026.
07
Category bet: Finished Assemblies +22% margin headroom if scale achieved
Finished Assemblies is a higher-value category with 22pp margin headroom vs Industrial Components — but we play it small (4% of volume). If we take share from two tier-2 competitors, Finished Assemblies becomes the highest-margin category in the portfolio.
- Current Finished Assemblies margin: +9.4% (vs 2.8% Hardware)
- Peer margin in same category: +11-14%
- Capex to scale: ~2.5M AUD for 1 new assembly line
3-Year Gross Profit
+3.2M
AUD / year
How: Invest 2.5M AUD in a second assembly line; hire 2 specialist BDMs; launch in NSW + QLD first.
Recommended next step: Commission a 90-day Finished Assemblies strategy sprint with commercial + ops.
08
Balance sheet flexibility: 1.2x net-debt/EBITDA — headroom for 25M AUD of M&A
Net debt: 7.4M AUD · LTM EBITDA 6.2M AUD → leverage 1.2x. A move to 2.5x remains investment-grade territory given AU peer norms of 2.8-3.2x for mid-market distribution — equivalent to ~25M AUD of additional borrowing capacity.
- Current debt cost: 6.4% · headroom at lower margin
- Covenant headroom: 20% against existing terms
- M&A capacity: ~25M AUD without equity dilution
Strategic Dry Powder
+25M
AUD capacity
How: Renegotiate facility at next refinance; upsize to 40M AUD with sustainability linkage.
Recommended next step: Debt strategy refresh with board, aligned to M&A mandate (#02).
09
Board KPI tree: 6 hard KPIs — recommend replacing 2 with forward indicators
Current board pack reports 6 headline KPIs: all lagging (revenue, EBITDA, net margin, NWC, OpFCF, safety). No forward indicators (pipeline coverage, NPS, employee engagement, ESG, on-time-in-full).
- Current mix: 6 lagging · 0 leading
- Peer best practice: 4 lagging · 3 leading
- Proposed leading: pipeline/plan cover, NPS, eNPS
Decision Latency
-30 days
avg board cycle
How: Add 3 leading KPIs to the board pack; retire 2 lagging (safety + NWC moved to operating committee).
Recommended next step: Board-pack redesign session; new format live at next board meeting.
10
3-year vision: double net profit from 6.2M to 12.5M AUD by FY 2028
Rolling up the CEO theses #01-#09 yields a 3-year net-profit doubling path: +2.4M (portfolio) + 2.8M (digital) + 4.2M (productivity) + 3.2M (Finished Assemblies) – 3.4M dilution/invest = +6.2M AUD NP.
- FY 2025 NP: 6.2M AUD · FY 2028 target: 12.5M AUD
- Organic CAGR required: 16%
- Sub-paths: 55% organic · 45% inorganic (M&A)
3-Year NP Doubling
+6.3M
AUD / year
How: Codify into a one-page strategy map; owner per thesis; quarterly board review.
Recommended next step: Commission a 90-day strategy refresh to translate these theses into an FY 2026-2028 operating plan.
Model Architecture
Manufacturing Model vs Distribution Model · scope boundaries · cost flow
FY 2025
📐 TDABC MODEL DESIGN
This model focuses on the Distribution layer · Manufacturing is treated as a transfer-price input via COGS
The client is a vertically integrated manufacturer & distributor. A full end-to-end cost model would include the Manufacturing Model (Vendor → Raw Materials → BOM → Facility → SKU) and the Distribution Model (Channel → Customer → Order → Line Item). For this POC we scope-in the Distribution Model and carry Manufacturing cost as a COGS transfer price per category (35–45% of net revenue). Production overhead (115 FTEs, factory utilities, depreciation) is shown as a separate line item below Gross Profit · not embedded in COGS · to maintain full transparency.
OUT OF SCOPE · COGS transfer
Manufacturing Model
VENDOR
FACILITY
SKU
▼
RAW MATERIALS
FACILITY-SKU
▼
BOM
Output: COGS per SKU (material cost). We use per-category ratios:
Industrial Components 41% · Consumables 44% · Finished Assemblies 43% · Hardware & Fittings 38%
Plus: Manufacturing overhead as a separate line (not in COGS)
Industrial Components 41% · Consumables 44% · Finished Assemblies 43% · Hardware & Fittings 38%
Plus: Manufacturing overhead as a separate line (not in COGS)
IN SCOPE · TDABC allocated
Distribution Model
SEGMENT
SALES REGION
CHANNEL
▼
SKU
DC
SALES REP
CUSTOMER
▼
DC-SKU
ORDER
▼
LINE ITEM
Dimensions modelled: 3,075 customers · 209 SKUs · 4 channels · 15 DCs · 9,971 orders · 10,772 waybills
TDABC drivers: km per stop, visits per customer, orders per customer, qty per SKU, channel-locked salesforce
TDABC drivers: km per stop, visits per customer, orders per customer, qty per SKU, channel-locked salesforce
P&L Layer Architecture · 6 Profit Layers
| # | Layer | What it measures | Costs deducted | AUD (FY25) | % of NR |
|---|---|---|---|---|---|
| 1 | Net Revenue | Revenue net of trade discounts | Gross Rev − Trade Discounts | 33,531,692 | 100.0% |
| 2 | Gross Profit | Revenue after material COGS | − COGS (material, 35-45% per cat) | 19,582,567 | 58.4% |
| 3 | Operational Margin | After production & distribution | − Manufacturing Overhead − Distribution | 12,767,267 | 38.1% |
| 4 | Customer/Channel Margin | After selling to customer | − Sales Force − Customer Service − A&P | 5,377,567 | 16.0% |
| 5 | Net Profit | Final profitability | − Indirect / Corporate | 1,418,067 | 4.23% |
Why 5 layers: each layer isolates a different management lever. Gross Profit measures product economics, Operational Margin measures supply chain efficiency, Channel Margin measures commercial effectiveness, and Net Profit absorbs shared corporate overhead. Each customer/product/channel can be loss-making at one layer but profitable at another · surfacing different improvement levers.
TDABC Cost Pools & Driver Rates
Cause-effect allocation · no revenue-proxy shortcuts · channel-locked sales force
FY 2025
⚙️ TIME-DRIVEN ACTIVITY-BASED COSTING
Every AUD of cost traced to the activity driver that consumed it
Cost pools are built bottom-up from the branch headcount + LinkedIn corporate profiles + AUS manufacturing & distribution benchmarks. Each pool is assigned a causal driver · km driven, visits made, orders processed, units sold · and the rate emerges from dividing the pool by the total driver activity. Revenue-based allocation is used only for genuinely shared overhead (HR, IT, Legal, Exec, R&D, Marketing team, Supply Chain, Strategy) where no cause-effect link exists.
Total KM driven
151,926
from waybills
Total visits
10,772
unique stops
Total orders
9,971
sales orders
Total units
373,020
delivered qty
Cost Pool → Driver → Rate · Full TDABC Table
| Cost Pool | Driver | Rate | Unit | Monthly Total | Notes |
|---|---|---|---|---|---|
| OPERATIONAL · Distribution | |||||
| Drivers + Trucks + Fuel + Insurance | Km driven per stop | 9 | AUD / km | 1,402,800 | Cause-effect · distant customers absorb more |
| Warehouse FTEs + Rent + Utilities + Sec | Stops per customer | 176 | AUD / visit | 1,895,500 | High-frequency customers cost more handling |
| Manufacturing Overhead (absorption) | Qty per SKU | 9.43 | AUD / unit | 3,517,000 | Proxy for production hours consumed |
| COMMERCIAL · Sales Force (Channel-locked) | |||||
| KA Supervisors + Salesmen (30 FTE) | KA visits only | 275 | AUD / KA visit | 472,500 | KA team NEVER allocated to WS/PS custs |
| WS Sup + Salesmen + Merchandisers (81) | WS visits only | 340 | AUD / WS visit | 761,200 | WS team NEVER allocated to KA/PS custs |
| PS Telesales + Supervisors (25) | PS orders only | 30 | AUD / PS order | 204,000 | Telesales model · per order not per visit |
| COMMERCIAL · Marketing & Service | |||||
| Customer Service + Sales Admin | Orders per customer | 27 | AUD / order | 267,000 | CS work scales with order count |
| Consumer A&P + Trade Mktg + Commission | Qty per SKU | 12.68 | AUD / unit | 4,730,000 | Volume proxy for category marketing spend |
| Bad Debt Provision | Revenue pro-rata | 1.30% | of net rev | 435,912 | Risk proportional to exposure |
| INDIRECT / CORPORATE | |||||
| Finance & Accounting (24 FTE) | Orders (AR/AP work) | 37 | AUD / order | 372,000 | Invoice + receivable work per order |
| HR / IT / Legal / Exec / R&D (89 FTE) | Revenue pro-rata | 10.7% | of net rev | 3,587,500 | Shared overhead · genuinely unallocatable |
Channel Lock
3 separate pools
KA salesforce costs go ONLY to 528 KA customers. WS costs ONLY to 624 WS. PS costs ONLY to 1,928 PS. This eliminates cross-channel subsidy · a critical insight.
Km allocation
9.23 AUD/km
Drivers + trucks + fuel + insurance pooled and divided by total km. A customer 50km away carries 462 AUD of logistics per visit before any WH handling.
PS order cost
30 AUD/order
PS agents cost ~204K/mo ÷ 6,713 PS orders. With avg PS order only ~437 AUD, this is 7.0% of order value · PS is structurally loss-making.
Organisational Structure
526 total FTEs · direct vs indirect · fully-loaded monthly cost
FY 2025
🏢 HEADCOUNT & COST STRUCTURE
526 FTEs across Direct Operations + Commercial + Corporate
Direct headcount (253 sales/warehouse/driver FTEs) is confirmed from the branch headcount file. Production (115 FTEs) and Corporate (113 FTEs) are estimated from LinkedIn profile evidence + AU manufacturing & distribution benchmarks for a ~400M AUD/year operation. Fully-loaded monthly cost includes salary + GOSI + end-of-service + standard allowances.
Direct FTEs
413
Operations + Commercial
Corporate FTEs
113
HR + IT + Finance + Exec ...
Monthly People Cost
5.17M
AUD / month
Cost per FTE avg
9,823
AUD / month
Direct FTEs · Operations + Commercial (413 people)
| Function | FTEs | Avg Cost/Month | Total Monthly (AUD) |
|---|---|---|---|
| Drivers | 66 | 5,800 | 382,800 |
| Warehouse Ops Managers | 14 | 13,500 | 189,000 |
| Warehouse Storekeepers | 13 | 6,800 | 88,400 |
| Warehouse Forklift | 15 | 4,800 | 72,000 |
| Warehouse Labour | 34 | 3,400 | 115,600 |
| Distribution Supervisors | 8 | 16,000 | 128,000 |
| Production Line | 85 | 3,800 | 323,000 |
| Production Maintenance | 12 | 8,500 | 102,000 |
| Production Supervisors | 8 | 16,500 | 132,000 |
| QC / Lab | 10 | 11,000 | 110,000 |
| KA Supervisors | 3 | 18,000 | 54,000 |
| KA Salesmen | 27 | 15,500 | 418,500 |
| WS Supervisors | 8 | 16,500 | 132,000 |
| WS Salesmen | 37 | 10,000 | 370,000 |
| WS Merchandisers | 36 | 7,200 | 259,200 |
| PS Telesales | 22 | 7,500 | 165,000 |
| PS Supervisors | 3 | 13,000 | 39,000 |
| Customer Service | 12 | 8,500 | 102,000 |
| Total Direct | 413 | 3,182,500 |
Indirect / Corporate (113 people)
| Department | FTEs | Avg Cost/Month | Total Monthly (AUD) |
|---|---|---|---|
| Executive / C-Suite / IR | 6 | 50,000 | 300,000 |
| Finance & Accounting | 24 | 15,500 | 372,000 |
| HR & Admin | 12 | 12,000 | 144,000 |
| IT & Digital | 14 | 17,500 | 245,000 |
| Marketing team (brand, digital) | 11 | 16,500 | 181,500 |
| Supply Chain / Planning / Procurement | 15 | 16,000 | 240,000 |
| R&D / QA / Food Safety | 22 | 14,500 | 319,000 |
| Legal / Compliance | 4 | 22,000 | 88,000 |
| Commercial Ops / PMO / Strategy | 5 | 19,000 | 95,000 |
| Total Corporate | 113 | 1,984,500 |
LinkedIn evidence found for: CEO, COO, Managing Director, Director of Investor Relations, R&D Manager, NSW Lab Manager, QA Specialist, Sales Operations Supervisor, Trade Marketing Supervisor, Demand Planner, Material Planning Supervisor, Accounts Payable, IT Work Package Manager, HR Specialist, Export Specialist, Product Manager, Industrial Systems Engineer. Remaining counts estimated from a typical Australia manufacturer & distributor at ~400M AUD annual revenue (18-22% corporate headcount of total).
Key Model Assumptions
Every parameter used in the profitability model · auditable & defensible
FY 2025
📐 MODEL TRANSPARENCY
Bottom-up, benchmarked, no Net Profit lock
Revenue and trade discounts come directly from the SAP/Excel dataset you shared. COGS is calibrated per SKU category using manufacturing & distribution industry benchmarks (35–45%). All operational, commercial, and indirect costs are built bottom-up from the branch headcount + LinkedIn + AUS manufacturer benchmarks · nothing is plugged to hit a target. The 4.23% Net Profit that emerges is the honest result.
1 · Revenue & Trade Discounts
Gross Revenue34,273,823 AUD
Trade Discounts (from discount_per_unit × qty)742,131 AUD
Trade Disc rate2.2% of gross
Net Revenue33,531,692 AUD
Data sourcePOC enriched dataset
Discounts come from the enriched file where we added a per-unit discount column. Much lower than a flat 11% assumption · most volume moves at list price with selective KA/promo discounts.
2 · COGS · Material only (35-45% per category)
Industrial Components41% of net rev
Consumables44% of net rev
Finished Assemblies43% of net rev
Hardware & Fittings38% of net rev
Blended (actual mix)41.6%
Range35-50% benchmark
Material-only COGS. Factory overhead (production FTEs, utilities, depreciation) is shown SEPARATELY below Gross Profit · not rolled into COGS · to maintain transparency per your architecture decision.
3 · Manufacturing Overhead (NOT in COGS)
Production Line (85 FTE × 3,800)323,000
Production Maintenance (12 × 8,500)102,000
Production Supervisors (8 × 16,500)132,000
QC / Lab (10 × 11,000)110,000
Factory Utilities980,000
Factory Consumables420,000
Factory Maintenance320,000
Factory Depreciation780,000
Factory Rent185,000
Quality & Certifications165,000
Total Mfg Overhead3,517,000
4 · Distribution Operations
Drivers (66 × 5,800)382,800
Trucks (66 × 12,500)825,000
Warehouse FTEs (76)465,000
Warehouse Rent (15 × 62K)930,000
Warehouse Utilities + Security277,500
Distribution Supervisors (8)128,000
Fuel adj + Fleet Ins + Returns290,000
Total Distribution3,298,300
5 · Commercial · Sales Force (channel-locked)
KA Sup + Salesmen (30 FTE)472,500
WS Sup + Salesmen + Merch (81 FTE)761,200
PS Telesales + Sup (25 FTE)204,000
Customer Service (12 FTE)102,000
Sales Admin165,000
Sales Commissions215,000
KA FTEs allocated only to KA customers, WS to WS, PS to PS. This prevents cross-channel subsidy and reveals the true cost-to-serve per channel.
6 · Commercial · Marketing
Consumer A&P (brand, TV, digital)3,450,000
as % of Net Revenue10.3%
Trade Marketing720,000
Listing / Slotting fees (KA)520,000
Promo Accruals345,000
Bad Debt Provision (1.30% of NR)435,000
10.3% A&P is realistic for a consumer manufacturing & distribution brand in AUS. Lower than global giants (Nestlé ~12%, Mars ~14%) but higher than private label.
7 · Indirect / Corporate (113 FTE)
Executive / C-Suite / IR6 × 50,000
Finance & Accounting24 × 15,500
HR & Admin12 × 12,000
IT & Digital14 × 17,500
Marketing team (brand, digital)11 × 16,500
Supply Chain / Planning / Procurement15 × 16,000
R&D / QA / Food Safety22 × 14,500
Legal / Compliance4 × 22,000
Commercial Ops / PMO / Strategy5 × 19,000
Total Corporate People1,984,500
Sized to match a AU manufacturing & distribution at 34M/month revenue. 113 corporate FTEs = 21.5% of total headcount, inside the 18-22% industry benchmark.
8 · Non-People Corporate Overhead
HQ Rent300,000
IT Licences420,000
Professional Fees195,000
Insurance175,000
Travel & Entertainment165,000
HQ Utilities85,000
Bank Charges & FX125,000
Depreciation (Corp)295,000
Other Indirect215,000
Total Non-People1,975,000
9 · TDABC Allocation Principles
Drivers + TrucksKM driven per stop
Warehouse ops + rentNumber of visits
Production overheadUnits produced (qty)
KA Sales ForceKA visits only (locked)
WS Sales ForceWS visits only (locked)
PS TelesalesPS orders only (locked)
Customer ServiceOrders per customer
A&P MarketingQty delivered (volume)
Finance (AR/AP)Orders (invoice count)
HR/IT/Legal/Exec/R&DRevenue pro-rata (shared)
Cause-effect drivers for everything that has one. Revenue-pro-rata used ONLY for genuinely shared overhead where no causal link exists.
10 · P&L Bottom-up Reconciliation
Net Revenue33,531,692
− COGS (material)(13,949,125)
= Gross Profit19,582,567
− Manufacturing Overhead(3,517,000)
− Distribution(3,298,300)
= Operational Margin12,767,267
− Commercial (Sales+CS+A&P+BD)(7,389,700)
= Customer/Channel Margin5,377,567
− Indirect/Corporate(3,959,500)
= NET PROFIT1,418,067 (4.23%)
No target, no lock. The 4.23% emerges from the bottom-up build. 692 customers profitable, 2,383 in loss · the whale curve will tell the story.
Implementation Project
6 phases · 14–16 weeks to go-live · monthly operations thereafter
CostCTRL TDABC
🚀 ROADMAP TO LIVE PROFITABILITY MODEL
From dataset to self-service monthly dashboard · proven methodology
Same phased approach used on CostCTRL deployments at ilionx (€222M IT services, FSN Capital portfolio), Centralmed (occupational health), Inflight Catering (Slovakia), and Oficina Mecânica Estarreja (automotive). Phases run partly in parallel, total elapsed time 14–16 weeks from kick-off to monthly close with customer/product/channel profitability live.
Project Phases
Phase 1 · Discovery & Scoping
Workstream 1 · Business Analysis
- Kick-off workshop with COO / CFO / Supply Chain
- Map current cost structure & P&L hierarchy
- Define profitability dimensions (customer / product / channel / DC / region)
- Agree cost objects and allocation philosophy
- Inventory of source systems (SAP, WMS, TMS, HR, SFA)
- Sign-off scope & success criteria
⏱ 2 weeks
Phase 2 · Data Integration
Workstream 2 · ETL & Master Data
- SAP S/4HANA connector · GL, AR, sales, CO-PA
- WMS / TMS feeds · km, stops, time, waybills
- Branch headcount + fully-loaded cost build (GOSI, EOSB)
- Customer & product master harmonisation
- Financial / statutory files for audit reconciliation
- Daily incremental refresh pipeline
⏱ 3–4 weeks
Phase 3 · Cost Pool & TDABC Build
Workstream 3 · Model Construction
- Define cost pools: Mfg, Distribution, Commercial, Corporate
- Time-Driven rates per activity (AUD/km, AUD/visit, AUD/order, AUD/unit)
- Per-category COGS calibration (vs single blended rate)
- Channel-locked sales force allocation rules
- Shared overhead methodology (rev pro-rata where no driver)
- Reconcile bottom-up model to audited P&L
⏱ 4–5 weeks
Phase 4 · Allocation & Validation
Workstream 4 · Cost Assignment
- Allocate cost pools to customers / products / channels / DCs
- Whale curve analysis · identify loss pool
- Multi-dim drill-down (customer × product × channel × DC)
- Sanity checks vs channel and regional benchmarks
- Finance validation per dimension
- Historical backtest on 3 prior months
⏱ 4 weeks
Phase 5 · Dashboard & UAT
Workstream 5 · Delivery Platform
- Branded CostCTRL dashboard deployment (cloud-hosted)
- Role-based access: COO, CFO, Commercial, Supply Chain
- KPI cards, Whale Curve, Multi-dim, Heatmap, Cost Drivers
- AI Insights · automated opportunity detection
- User training sessions (max 4 × 90 min)
- UAT with Finance + Commercial + Operations teams
⏱ 4 weeks
Phase 6 · Go-Live & Monthly Operations
Workstream 6 · Run-mode support
- Production cut-over & first live monthly close
- Monthly refresh cycle (day 5 close → day 7 dashboards live)
- Steering committee · 3 sessions in first quarter
- Continuous improvement backlog
- Annual re-benchmarking of rates & drivers
- Licence + support (SLA, ticketing, enhancements)
⏱ 2 weeks + ongoing
Key Statistics
Unit economics · delivery metrics · rankings from transactional + cost data