Problems › We Cannot Tell If the Strategy Is Working › Manufacturing
A strategy that cannot be wrong cannot be checked, and most written strategies are written so that they cannot be wrong. This page works through it for manufacturers specifically — including an unedited excerpt from a real analysis of a manufacturer.
A strategy that cannot be wrong cannot be checked, and most written strategies are written so that they cannot be wrong. The version of this question that applies to manufacturers is not the generic one. The $45M automation case depends on the very customer that causes the margin problem — so an answer that ignores contribution per machine hour will be confidently wrong. The analysis has to start from capacity utilisation and customer concentration rather than from revenue.
The usual reason a strategy cannot be evaluated is that it was never stated in a form that could fail. "Become the leading provider" produces no observation that would contradict it, so it survives indefinitely regardless of results.
A checkable strategy names the mechanism — this action produces this change in this number by this date — and the observation that would say the mechanism is not working. That second half is what converts a plan into something you can manage against.
The other frequent cause is lag. Strategies operate on horizons longer than reporting cycles, so the honest response is to identify leading indicators that move early and to state in advance what they should read.
These three together are the signature. One on its own usually points somewhere else.
✓ The strategy has no failure condition written anywhere
✓ Progress is reported as completed activity
✓ Reasonable people disagree about whether it is working and cannot resolve it with data
The move that usually makes it worse. Adding more reporting, which increases the volume of numbers without making the strategy falsifiable.
It is for you if you run or finance a manufacturer and the strategy has no failure condition written anywhere. 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.
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 · Quick Market Scan · sample company profile
The move. Monetise existing tooling and qualification stickiness by selling design-for-manufacturability services to the same OEMs that currently force 3% annual price-downs.
The leak it closes. Closes value leakage to OEMs via contractual price-downs; design authority creates new margin pool that subsidizes existing build-to-print programmes
The assumption it rests on. Customer A engineering manager will sign first paid DFM engagement within 6 months — the engine put the probability at 0.7.
| Investment required | $8-12M tooling CapEx + $4.5-6.0M annual engineering payroll (18-24 FTEs at $250K fully-loaded cost) |
| Expected return | 5.1× — $57-86M incremental EBITDA over 5 years / $12M maximum downside |
| Revenue, year 1 | $0.5-1.0M DFM service revenue |
| Revenue, year 2 | $3.5-5.0M DFM service revenue + $8-12M design-authority production revenue |
| Revenue, year 3 | $7-10M DFM service revenue + $35-50M design-authority production revenue |
| Exit criteria | Abandon this move if first paid DFM engagement is not signed by Month 9, OR if cumulative engineering hires fall below 12 FTEs by Month 18, OR if DFM-to-production conversion value falls below $4M by Month 24 |
This is one move out of a full analysis. Read a complete report — every page, no email required.
This question routes to Proprietary EFF Methodology, 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.
The mechanism it depends on, not the outcome it promises. Outcomes lag; mechanisms move early and tell you sooner whether the causal claim holds.
Decide before starting, and tie it to the mechanism's natural cycle. Deciding afterwards guarantees the timeline is chosen to fit whatever result arrived.
That is usually a sign the strategy was not specific enough to produce a clean test. Narrow it until one number would settle the argument.
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.
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.
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.
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