Automation, worked through.
These are not sales brochures, and they are not client stories. They are three scenarios we have modelled from South African industry cost benchmarks and the workflows our platform is built to run, so you can see the arithmetic rather than take our word for it.
- Scenarios
- 3, modelled
- Basis
- SA cost benchmarks
- Status
- Not client results
Read this first
Please read this first. The companies described below are illustrative composites, not Tapnet clients. Every figure is a modelled estimate built from published industry benchmarks and the platform's measured throughput, not a result measured at a customer. Your own numbers will depend on your operations, systems, and team, and we work them out with you on the assessment call.
The modelled savings do not deduct Tapnet’s fees. Set them against the setup and monthly fee of the tier you would need; we do that arithmetic with you on the diagnostic call.
Scenario 1: a 20-driver transport operator
Modelled profile: 20 drivers and 80–100 deliveries a day across Gauteng and North West.
The starting point: dispatch runs from a whiteboard. A coordinator arrives at 04:30 to plan routes by hand and phones each driver with their loads. PODs are paper slips that come back in a folder at the end of the day, sometimes two days later, and admin staff type them into Sage to raise invoices. Invoices go out 5–7 days after delivery, 3–5 PODs are lost a week, and the accounts team spends the first week of every month chasing proof of delivery before invoicing can start.
The modelled workflow: loads are assigned on screen and sent to the driver app, drivers capture the signature, photos and GPS position at handover, and the signed POD raises the invoice in Sage the same day.
Before
Whiteboard dispatch, paper PODs, invoices 5–7 days after delivery.
With Tapnet
On-screen dispatch, digital PODs, invoices the same day.
Scenario 1: modelled outcome at 90 days
Assumptions: 2 admin staff at R22,000 a month each, 1 dispatch coordinator at R25,000 a month, an average of 80 deliveries a day and 22 working days a month.
- Dispatch time per day
- Before3.5 hours
- After (modelled)25 minutes
- Change88% less
- POD processing time
- Before4 hours a day (admin)
- After (modelled)0 (automatic)
- ChangeRemoved
- Invoice turnaround
- Before5–7 days
- After (modelled)Same day
- Change5–7 days faster
- Days to payment (average)
- Before38 days
- After (modelled)19 days
- Change50% faster
- Lost or disputed PODs a month
- Before15–20
- After (modelled)0
- ChangeRemoved
- Disputes (GPS and photo proof)
- BeforeBaseline
- After (modelled)Modelled
- Change92% fewer
- Admin time (dispatch, POD, invoicing)
- BeforeBaseline
- After (modelled)Modelled
- Change65 hours a month saved
- Estimated monthly saving
- Before
- After (modelled)
- ChangeR48,000–R62,000
Saving built from admin time reallocated (R28,000), fewer disputes and credit notes (R8,000–R12,000) and the cash-flow value of faster invoicing (R12,000–R22,000 in interest or opportunity cost). Modelled, not measured at a customer.
Scenario 2: an FMCG distributor raising 450 invoices a month
Modelled profile: 450 invoices a month, 6 sales reps and 120+ retail outlets across Gauteng.
The starting point: a two-person accounts team, one of whom spends about 60% of their time on invoicing. Orders arrive from sales reps by email, WhatsApp and phone, and each invoice is created by hand in Sage, with prices checked against a spreadsheet that is mostly up to date. Wrong prices, duplicate invoices and invoices sent to the wrong contact follow, and month-end closes about five days late.
Average days to payment is 42 against 30-day credit terms. That 12-day gap shows up as cash-flow pressure and overdraft interest.
Before
Invoices typed into Sage by hand from email, WhatsApp and phone orders.
With Tapnet
Invoices raised automatically at current prices, with follow-ups on schedule.
Scenario 2: modelled outcome at 90 days
- Average days to payment
- Before42 days
- After (modelled)24 days
- Invoice accuracy
- BeforeAbout 82%
- After (modelled)96%
- Admin time (invoicing and follow-up)
- BeforeBaseline
- After (modelled)48 hours a month saved
- Late month-end closes
- Before5 days late on average
- After (modelled)None
Modelled, not measured at a customer.
Scenario 2: where the monthly saving comes from
Assumptions: 1 accounts person spending 60% of their time on invoicing (salary R26,000 a month), 450 invoices a month and an overdraft facility at prime + 2%.
- Admin time (invoicing)
- Before (monthly)R15,600 (60% of salary)
- After (monthly, modelled)R3,900 (15% oversight)
- SavingR11,700
- Credit notes (pricing errors)
- Before (monthly)R18,000 average
- After (monthly, modelled)R2,200
- SavingR15,800
- Overdraft interest (late payments)
- Before (monthly)R14,500
- After (monthly, modelled)R4,800
- SavingR9,700
- Month-end overtime
- Before (monthly)R4,200
- After (monthly, modelled)R0
- SavingR4,200
- Total estimated saving
- Before (monthly)
- After (monthly, modelled)
- SavingR41,400 a month
Modelled, not measured at a customer.
Scenario 3: a two-branch distributor with 3,200 SKUs
Modelled profile: 3,200 SKUs across 2 branches, 5 warehouse staff and 80–120 orders picked a day.
The starting point: stock accuracy sits around 78%, so nobody trusts the system and the warehouse manager keeps his own notebook. Each quarter the warehouse shuts for two days for a full count and finds R120,000–R200,000 in discrepancies. The annual stock write-off is R180,000.
Picking runs off printed lists. A picker finds an empty bay, walks to the office to check whether the stock moved, then walks to where it actually is, fifteen or twenty times a day, while orders that should have shipped that morning wait.
Before
Printed pick lists, a notebook nobody else can read, quarterly shutdown counts.
With Tapnet
Stock tracked as it moves, so pickers go straight to the right bay.
Scenario 3: modelled outcome at 6 months
- Stock accuracy
- Before78%
- After (modelled)97.2%
- Pick error rate
- Before5.1%
- After (modelled)0.3%
- Annual write-off
- BeforeR180,000
- After (modelled)R22,000
- Shutdown stock counts
- Before4 a year
- After (modelled)None
Modelled, not measured at a customer.
Scenario 3: where the annual saving comes from
Assumptions: 5 warehouse staff at an average of R16,000 a month, 3,200 SKUs across 2 branches and a two-day shutdown each quarter for a physical count.
- Stock write-off and shrinkage
- Before (annual)R180,000
- After (annual, modelled)R22,000
- Annual savingR158,000
- Quarterly shutdowns (lost revenue)
- Before (annual)R96,000 (4 × 2 days)
- After (annual, modelled)R0
- Annual savingR96,000
- Pick errors (re-picks, returns, credits)
- Before (annual)R72,000
- After (annual, modelled)R4,300
- Annual savingR67,700
- Picker time spent searching for stock
- Before (annual)R38,400
- After (annual, modelled)R3,200
- Annual savingR35,200
- Total estimated saving
- Before (annual)
- After (annual, modelled)
- Annual savingR356,900 a year
Modelled, not measured at a customer. Tapnet’s setup and monthly fees are not deducted.
Related
- Core platformOne platform for South African logistics: dispatch, offline driver POD and automated invoicing, connected to Sage, Xero, WhatsApp Business and Cartrack.
- Driver POD appTapnet’s driver app captures signatures, photos and GPS-stamped proof of delivery on Android, even with no signal; iOS is coming soon. R200 per driver a month on Starter.
Put your own numbers through it
Every business is different. The numbers above are modelled estimates built from South African industry cost benchmarks, not results measured at a customer. The only way to know what automation would do for your specific operation is to put your own numbers through it. No sales pitch. Just an honest look at your processes and where the biggest wins are.