Problems › We Cannot Tell If the Strategy Is Working › Manufacturing

We Cannot Tell If the Strategy Is Working
in Manufacturing

A strategy that cannot be wrong cannot be checked, and most written strategies in manufacturing 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 short answer

The usual reason a strategy cannot be evaluated is that it was never stated in a form that could fail. Statements such as raising contribution per machine hour across the three plants produce no observation that would contradict them when the $45M automation case rests on the customer that already drives the margin problem, so the plan survives regardless of results on capacity utilisation or scrap rate.

A checkable strategy names the mechanism — this changeover reduction or takt adjustment produces this shift in contribution per machine hour by this date — and the observation that would say the mechanism is not working, such as the concentrated customer failing to lift volume enough to cover the added capacity.

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, such as scrap rate or capacity utilisation, and to state in advance what those indicators should read before the next plant review.

How to tell this is actually your problem

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

✓ No target for contribution per machine hour is written that would halt or reverse the automation spend if the key customer does not increase orders.
✓ Weekly reports list completed changeovers or takt improvements without showing whether those moves moved contribution per machine hour or reduced customer concentration.
✓ The CEO and plant director reach different conclusions on whether capacity utilisation is rising because each uses a different customer-volume assumption with no shared data to settle it.

The move that usually makes it worse. Adding more reports on scrap rate and utilisation, which increases the volume of numbers without tying any of them to a failure condition on the automation case.

Who this is for — and who it is not

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.

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 · 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.

What the run committed to
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.

What the engine does with this question

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.

Questions people ask about this

What should I measure to know if a strategy is working?

The mechanism it depends on, not the outcome it promises. Outcomes lag; mechanisms move early and tell you sooner whether the causal claim holds.

How long before I judge a strategy?

Decide before starting, and tie it to the mechanism's natural cycle. Deciding afterwards guarantees the timeline is chosen to fit whatever result arrived.

What if the numbers are ambiguous?

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.

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.