ProblemsWe Do Not Know Which Products Make Money › Manufacturing

We Do Not Know Which Products Make Money
in Manufacturing

Every business has a line that everyone assumes is profitable, and it is usually the one being subsidised. This page works through it for manufacturers specifically — including an unedited excerpt from a real analysis of a manufacturer.

The short answer

Every business has a line that everyone assumes is profitable, and it is usually the one being subsidised. What makes this harder for manufacturers is structural: the $45M automation case depends on the very customer that causes the margin problem. Any credible answer therefore has to hold contribution per machine hour and capacity utilisation in the same view, which is exactly where most internal analysis stops because the two live in different systems.

Product-level profit is genuinely hard because most costs are shared, and the usual allocation — by revenue — quietly guarantees the answer. Allocating overhead in proportion to revenue makes high-revenue lines look expensive and low-revenue lines look efficient, which is precisely backwards when the low-revenue line consumes disproportionate attention.

A workable approach allocates only what is genuinely traceable and leaves the rest unallocated. You end up with contribution by line and one honest pool of shared cost, which is far more useful than a fully-absorbed number that nobody trusts.

The result is usually uncomfortable. In most portfolios a minority of lines carries the whole thing, and at least one long-standing line has been losing money for years with everyone assuming otherwise.

How to tell this is actually your problem

These three together are the signature. One on its own usually points somewhere else.

✓ Product profitability is quoted as a company-wide gross margin
✓ Nobody has discontinued anything in years
✓ Two people give different answers about the same product line

The move that usually makes it worse. Fully absorbing overhead into product lines, which produces a precise number built on an arbitrary rule and gets defended because it looks rigorous.

Who this is for — and who it is not

It is for you if you run or finance a manufacturer and product profitability is quoted as a company-wide gross margin. It is the situation where the numbers are available but nobody has put them in an order that produces a decision.

It is not for you if Percision is the wrong tool if you already know the answer and only need execution capacity, or if the business is pre-revenue — then the constraint is evidence about the market, not analysis of your own figures. Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library. Also wrong if you need facilitation, politics, or someone to sit with a lender or buyer. Those are human jobs.

Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library.

What this looks like when the analysis is actually run

Below is an excerpt from a real run of this analysis on a manufacturer. It is a sample profile rather than a customer, and it is unedited engine output — this is the format you get, on your own numbers.

The subject is Kessler Industrial Components, a sample company profile used for testing rather than a customer — $310M revenue, three plants.

Excerpt from a real Percision run · Competitive Positioning · sample company profile

The move. Automate Cedar Falls to lock in Customer A manifold volumes at 19% lower cost before Mexican alternates scale.

The leak it closes. Scrap rate reduced from 3.8% to 2.1%; 78-minute changeover reduced toward world-class 25 minutes

The assumption it rests on. Customer A does not activate dual-sourcing before automation payback (3.8 years) — the engine put the probability at 0.65.

What the run committed to
Investment required$45M total
Expected return24% IRR on $45M investment over 7-year Customer A programme life
Revenue, year 1$340M (no incremental revenue; cost protection only)
Revenue, year 2$351M (3% price-down offset by automation savings)
Revenue, year 3$362M (Customer A volume stability plus new Mexican OEM programmes)
Exit criteriaIf Customer A dual-source volume migration exceeds 25% by Month 18, cease further automation spend and redirect remaining capex to Querétaro expansion and aftermarket channel build-out.

This is one move out of a full analysis. Read a complete report — every page, no email required.

What the engine does with this question

This question routes to Matrix Strategy, one of 29 engagements the platform runs. For manufacturers it works through contribution per machine hour, capacity utilisation, customer concentration and scrap, then produces the sequence rather than a list of options — which move first, what it funds, and the observation that would say the sequence is wrong.

You watch the analysis get built before paying anything. Read a complete report here if you would rather see the depth first.

Questions people ask about this

Do I need activity-based costing?

Almost never for the decision at hand. Traceable costs plus an unallocated pool gets you the ranking, and the ranking is what you act on. Full ABC is a project that frequently outlives the decision that prompted it.

What about products that support others?

Say so explicitly and price the support. A loss-making line that genuinely pulls profitable revenue is a marketing cost with a name, which is a fine thing to be — as long as somebody decided it.

How often should this be redone?

Annually, and after any significant mix change. The ranking is more stable than the numbers, so the exercise gets cheaper each time.

Is this different in manufacturing than in other industries?

Materially, yes. The $45M automation case depends on the very customer that causes the margin problem — which changes both the diagnosis and the order of the fixes. The metrics that decide it here are contribution per machine hour, capacity utilisation, customer concentration, and an answer built on industry-general benchmarks will usually point at the wrong one first.

What data do I need before this analysis is worth running for a manufacturer?

Less than most people expect. Your last twelve months of revenue and cost split the way you already split it, plus whatever you hold on contribution per machine hour and capacity utilisation. The analysis is explicit about what it is assuming where your data stops, which is more useful than waiting for numbers you may never have.

When is Percision the wrong tool?

Percision is the wrong tool if you already know the answer and only need execution capacity, or if the business is pre-revenue — then the constraint is evidence about the market, not analysis of your own figures. Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library. Also wrong if you need facilitation, politics, or someone to sit with a lender or buyer. Those are human jobs.

Does Percision replace a lawyer, tax advisor, auditor, or AI implementation team?

Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library.

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