Raw Materials, Yields & Waste: Making the Production Line Tell the Truth
Between the raw store and the finished shelf, margin disappears through yields, waste, and unmeasured issues — the batch discipline that makes a Kenyan processor's production line tell the truth.
Ask a miller what their extraction rate was last week and you learn everything about the business. The ones who answer with a number — "78.2%, down half a point, we are checking the conditioning" — run production as a measured system. The ones who answer "good" run it as a hope. The difference is not equipment or scale; it is whether raw materials are issued against recipes and outputs are weighed against inputs, batch by batch.
The batch loop
- Plan: a production order states what will be made and the recipe computes expected material consumption.
- Issue: the store releases materials against the order — weighed, recorded, and nothing more. Open-door stores where production "takes what it needs" cannot be measured.
- Produce: the run happens; downtime, rework, and waste get recorded as they occur, with reasons.
- Receive: finished output is weighed/counted into finished-goods stock; by-products (bran, cake, offcuts) are received as their own items.
- Reconcile: input vs recipe (consumption variance) and output vs input (yield) — two numbers, every batch, reviewed weekly.
Reading the two numbers
| Signal | Likely cause | First move |
|---|---|---|
| Consumption above recipe, yield normal | Over-issuing, spillage, or store leakage | Tighten issuing; weigh at the store door, not the mixer |
| Consumption normal, yield falling | Process problem — moisture, settings, raw quality | Check the input batch quality records; inspect the line |
| Both drifting together | Recipe no longer matches reality | Re-baseline the recipe with a supervised test run |
| Numbers perfect, always | Records are being written backwards from output | Weigh independently; separate the recorder from the operator |
Waste is a transaction
Damaged output, floor sweepings, burnt batches, trimmings — every kilo of waste is recorded with a reason, or it becomes the line's all-purpose explanation. Processors who start recording waste typically discover it was covering for something else: a scale that reads heavy, a recipe that drifted, or product leaving through the side gate.
Raw material quality is a yield input
Agri inputs vary: moisture in maize, fat content in milk, fiber in cane. A batch that yields poorly often traces to a delivery that was accepted generously — which is why receiving records (weight, moisture, grade, supplier) belong in the same system as production. Over time, yield-by-supplier is one of the most valuable reports a processor owns: it converts "we like this supplier" into "this supplier's maize yields 2% better, which is worth KES 1.80 per kilo more than we pay them". Quality holds at receiving enforce the standard; the yield data justifies it.
Committed stock: the promise ledger
A confirmed order for 500 cartons next week is a claim on raw materials today. Systems that show only physical stock let sales promise the same materials twice — production then chooses which customer to disappoint. Available-to-promise = physical − committed − quality-held, and it is the number sales should see. The costing side of the same chain — what those materials truly cost — is the landed cost discipline; together they make the manufacturing margin honest.
What AWRA OpsHub does today
- Landed cost on receipts — freight, duty, clearing and transport captured per purchase order, allocated across the lines by value or by quantity, written onto the batch and carried into the item's weighted average cost.
- Batch, lot and serial tracking — expiry date, supplier and originating purchase order recorded on every batch, with serial numbers where you need them.
- Stock adjustments with mandatory reason codes, attachments and full history — the record a waste transaction would be written as today.
- Quality holds that are enforced, not advisory — quarantined, inspection-pending, damaged, expired and returned stock is excluded from issuing and selling by the allocation query itself. Hold, release and dispose each record the quantity, the reason, a note and the person who did it.
- Supplier performance — on-time delivery rate, quality score and pricing trend per vendor, which is the yield-by-supplier question answered from the buying side rather than the production side.
More we can add to your workspace
- A bill of materials. A BOM or recipe with a version and an effective date — the entity itself, which the rest of this article hangs on.
- A production order and an issue-against-recipe. Materials leave the store as a stock adjustment or a transfer, not as consumption measured against an expected quantity.
- A yield and a consumption variance. Expected-versus-actual is the entire point of a recipe, and there is nothing here to compute it from.
- A by-product receipt, so bran, cake and offcuts are received as their own items off a run. That starts with the run entity.
- A committed-versus-available. The `reserved` and `allocated` statuses exist as labels, but nothing sets them — a confirmed order does not ring-fence the raw materials behind it.
- A weighbridge or moisture capture at receiving. Verified weight, moisture and grade are not fields — they would be notes, and supplier payment is not computed from them.
This is a discipline every processor should run, and the five steps are the right five. Four of them — plan, issue, produce, reconcile — need the production entity in the middle column. What we genuinely carry today is the receiving and store side: batches, landed costs, holds and adjustments with reasons. If you are choosing a system on yield control, price that build before you commit.
Anything above that you need, we can build for you
Everything listed above as something we can add describes what ships in the standard product today — it is a starting point, not a limit on what AWRA OpsHub can do for your organisation. Kenya's eTIMS integration and its maintained payroll engine are both in the product because clients needed them and commissioned them; neither appeared by itself, and the same door is open for whatever you just read about. One qualification so this is worth what it claims: a small number of things on this blog we deliberately leave to a specialist rather than build — a statutory ledger we will not sign our name to, a rule that would decide a tax question for you, a clinical or member-funds record that belongs in a regulated system — and where that is true the post says so in those words. Everything else is a scope, a timeline and a price.
The operational work, which is what most commissions actually are
An extra approval stage in a chain that does not match the standard one, a custom field set on employees or assets that only your sector needs, an expiry that has to block an order rather than send an email, a report your board asks for in a shape nothing produces, or a scanner or weighbridge feeding the goods-in door. These are the commissions we are asked for most often and the smallest ones we quote — and unlike a revenue-authority pipeline, none of them waits on a regulator.
The module-shaped additions, which are the ones readers ask for most often
A price list with real discount authority, a customer-facing quotation that expires, a bill of materials or recipe costing, a staff advance that is issued, acquitted and chased, a member or unit ledger, a matching rule that holds a payment. Each of these is a build rather than a setting, and each has been quoted before — a bigger piece of work than a custom field, with a written spec and a date instead of a roadmap slide.
The report, document or pack nothing currently produces
The board pack in the shape your board actually asks for, a donor or funder layout, an invoice or receipt template carrying what your regulator or your customer expects, a dataset the report builder cannot reach yet. Usually the fastest thing on this list to deliver, because the data is already in the system.
Systems, rails and hardware you already run
The accounting package, CRM, online store, core banking or custom database you intend to keep — connected through our API so a fact is entered once and appears everywhere it is needed. Plus the physical edge: a scanner, a scale, a weighbridge or a till peripheral feeding the door it belongs to.
How it works: you describe the requirement, we return a written scope, timeline and cost, and once agreed it is built into your environment and maintained as part of the product. Nothing here waits on a regulator or a published specification, which is why operational builds are the ones we quote fastest. Tell us the requirement that would otherwise rule us out — that is a better first conversation than a demo.
Tell us what your operation needsMake the line tell the truth
Batch receiving, true landed costs, enforced quality holds and adjustments with reasons — the store side of the batch loop. Production runs, recipes and [yield variance](/glossary/yield-variance) are not built; see the note above.
See the store sideFrequently asked questions
Our production is continuous, not batch-based. Does the loop still apply?
Yes — define a batch as a shift or a day and run the same reconciliation on that window. Continuous processes actually benefit more from the weekly trend line, because drift compounds quietly when there is no natural batch boundary to force a count.
How accurate do recipes need to be before we start?
Start with your best current estimate and let the variance reports correct it: three weeks of real consumption data produces a better recipe than three months of pre-launch measurement. The discipline of issuing against something matters more than the initial precision of the something.
Where does rework go in the numbers?
Rework re-enters as an input to a new run, recorded as such — its materials were already consumed once, so hiding it inflates yield and flatters the week that produced the defect. Visible rework is annoying; invisible rework is a margin lie.
What is a realistic yield improvement from doing this?
Sector-dependent, but the pattern is consistent: the first quarter of measurement typically recovers 2–5% — not because anything was optimized, but because measurement itself closes the deniability that leakage and drift lived in. Optimization comes after, on top.
