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Leg 03 · The edge

Discovery, Analytics & Intelligent Ordering

We measure the truth of your business. Then we rebuild ordering on that truth and automate it. Built by someone who has done the ordering himself for decades, and could also build the engine.

The value chain

Trusted data first. Then answers. Then automation.

Most retail analytics fails for one reason: it decorates numbers nobody verified. We work in the opposite order.

First

One trusted dataset

We mirror your store’s own till and ledger data into one dataset and test every stock claim against actual movement behaviour. Presence is proven by sales or counts, never by the number on the screen.

Then

Answers with reasons

Stock health, capital working versus capital dead, true rates of sale, your community’s buying rhythm. Every verdict carries its own reason in plain language. A number you cannot interrogate is a number you cannot trust.

Finally

Ordering, automated

Orders generated from demonstrated demand, shaped by payday and season, fitted to your budget, with every line able to explain itself. The discipline is public in the 8-Step Ordering Recipe.

The proof

The dark store story

A dark store is retail with no forgiveness. The customer orders against the ledger’s claim. If the ledger says three and the shelf holds none, the failure is measured inside the hour, on somebody’s phone, with a refund attached.

PG van der Westhuizen ran the Checkers Sixty60 dark store pilot in Cape Town, the first of its kind for the model, opened on Bree Street in the CBD. No walk-in customers. A full store existing purely to fulfil app orders. The model was scaled nationally after the pilot.

His attested record there: the highest-turnover Sixty60 store on the continent at the time, north of 1,000 deliveries picked per day, about 98% fulfilled and delivered within the hour, in arguably the fastest grocery delivery service on the planet. Shoprite’s own published group figures, 94% on time and 96.8% picked as placed, sit below the pilot’s record. The group supplied the model and the systems. The margin above the fleet average is the measure of what his management added: three decades of floor operations plus an analytics discipline applied hour by hour.

Every method in that discipline now works for independent stores, through SocialBrand.

See it

The demo dashboard

A fictional store, fully generated data, the real thinking. Watch a trading week move with payday, see phantom stock surfaced with its story, change a control on the order desk and watch the order rewrite itself line by line. Real clients see their own stores.

Open the live demo

What we never publish

Formulas, thresholds, configuration values and client data. The thinking is public in the Method Library. The mechanics are the moat, and your data stays yours: the platform runs on your own store’s data, for your eyes.

Made in store

How we cost what your store makes

The moment your store makes something instead of buying it, most retail systems go blind. A boerewors batch, a bread run, a deli tray. You buy the inputs, you sell the outputs, and in between the true cost and the true yield are usually a guess. We close that gap with a bill of materials, and there is more than one honest way to build it. Which one fits you depends on your setup and the data you can produce.

Model 1

The block test

A controlled, physical test. You take a known raw input, cut or produce it under proper conditions, and weigh every sellable output and every offcut. That gives you a yield table you can trust as a standard.

Best for: a store starting from scratch, or one that needs a benchmark to measure its people against.
Strength: the cleanest measure of what your product should deliver, independent of bad habits.
Limit: it describes perfect conditions. Real floors are not perfect, so it tells you the target, not the reality.

Model 2

Historical yield, from your own sales

Instead of testing, we read what your store has already done. Over a long enough period, what you bought in and what you sold out reveal the yield you are actually achieving, including the everyday waste and trim a block test excludes.

Best for: an established store with a reliable sales history and no appetite for stopping production to run tests.
Strength: it reflects your real world, not a laboratory.
Limit: it inherits whatever is wrong in your history, which is why the data health work comes first.

Model 3

Live yield, from the ledger

The advanced version. Every receipt and every sale is mirrored into a clean data layer, and the yield recalculates continuously against what is actually moving. Costs update as supplier prices move, and a drift in yield shows up as it happens rather than at year end.

Best for: a store or group with trustworthy transaction data and enough volume that a small yield drift is real money.
Strength: self-correcting. It turns yield from an annual argument into a daily number.
Limit: it demands a clean ledger. This is the model our own platform runs, and the destination we build clients toward.

Most stores start on model one or two and grow into model three. We scope which one fits you during the audit, because the honest answer depends on what your data can carry. The thinking behind all three is in the Method Library.

Also in this leg

Human-led automation

Automation that answers to a person, not the other way around. Every automation we build has a human owner, a reason it exists and a point where it hands back to someone who can overrule it. A machine that cannot be questioned is not an improvement on a clerk who can.

AI reception

An AI receptionist answering calls and messages after hours, taking orders and bookings your store would otherwise miss. Anything it cannot answer goes to a person by name, not into a queue.

Website voice agents

A voice agent on your website answering the questions customers actually ask: hours, stock, specials, directions. It says it does not know rather than guessing, because a wrong answer costs more than no answer.

Collections support

Automated, polite and persistent follow-up on outstanding accounts, run under the client operations discipline of co-founder Lizeka Mgodeli. The tone stays yours and a person decides when to stop.

Why we insist on the human in the loop

Retail automation fails in one of two ways. It runs on numbers nobody checked, so it repeats a mistake faster than a person ever could. Or it becomes a black box, and the team stops arguing with it long before it stops being wrong. We build against both. Every automated verdict carries its reason in plain language, so the person reading it can disagree, and the person who owns the process stays accountable for the outcome. That is the Story Test, and it is why we will not automate a store’s ordering before its ledger has earned the trust.

Straight answers

Questions owners ask us

The stock, ordering and data questions that come up most often, answered without the vendor language.

Why does my stock system say I have stock when the shelf is empty?

Because a stock number on a screen is a claim, not a fact. Receipts land on one product code while sales drain a twin. Production consumes ingredients the ledger never releases. Barcodes get recycled. The arithmetic stays consistent while the truth walks away. Under the Presence Law a line counts as present only if it sold recently or was counted recently. Everything else goes to a count, not to an order.

What is phantom stock?

Phantom stock is stock your system says you have and your shelf does not. It costs more than it looks, because a line that claims stock never reorders itself, so the gap never shows up anywhere as a lost sale. The shelf sits empty while the screen reads healthy. You find it by testing every stock claim against actual movement behaviour, then proving the doubtful ones with a physical count.

How do I calculate true rate of sale?

Not by dividing units sold by days elapsed. That method counts the days the shelf stood empty as days of weak demand, so a line that ran out looks slow and gets ordered thinner still. True rate of sale corrects for the days the product was not there to sell, and it resolves pack families so a case and its singles read as one product. It must also respect the pay cycle, because a payday week and a mid-month week are not comparable.

Should I automate my store’s ordering?

Yes, once your data has earned it. Automation applied to a ledger you cannot trust only makes wrong orders faster. Fix stock integrity first, then automate. When we do, every proposed line has to explain itself in plain language before it ships, so you can overrule it with a reason rather than a feeling. The discipline is published as the 8-Step Ordering Recipe.

How do I order for payday in a rural store?

Order for the week your town is about to have, not for an average week that never happens. Money arrives on knowable days: salaries near month-end, pensions and grants early in the month, with season and the school calendar on top. Every line follows that calendar differently. Staples sell flat and want steady depth. Payday proteins spike and must be built into the window on the deliveries before it.

What is a dark store?

A dark store is a retail store with no walk-in customers. It exists purely to pick and fulfil online orders. It is retail with no forgiveness, because the customer buys against the ledger’s claim, so a phantom stock record becomes a failed order inside the hour with a refund attached. PG van der Westhuizen ran the Checkers Sixty60 dark store pilot in Cape Town, the first of its kind for that model.

How do I cost what my store makes in-house?

You need a bill of materials, and there is more than one honest way to build one. A block test measures yield under controlled conditions and gives you a target. Historical yield reads what your store has already achieved, everyday waste and trim included. Live yield recalculates from the ledger as receipts and sales move, so drift surfaces in days rather than at year end. Which one fits depends on what your data can carry. See the three models.

What is GMROI and does it matter?

GMROI is gross margin return on inventory investment. It asks how much gross profit each rand tied up in stock returns over a period. It matters because stock value on its own tells you nothing. Two stores can hold the same rand of stock and run opposite businesses, one turning it steadily and one aging half of it in a stockroom. We judge stock by what it does rather than what it costs, and call that capital velocity.

Find out what your numbers are hiding

The Store Health Audit tests your data against your own tills. Findings in rand, two to three weeks, fixed fee.

Book the auditSee the demo first