Late catches cost more
Defects that escape inspection can become scrap before anyone spots the pattern.
ACCOUNT RESEARCH FOR MACHINE-VISION INSPECTION SYSTEMS SELLERS
Scrap Less. Measure the Part You Can Actually Save.
Reviewed buyer-fit research for machine-vision sellers: start with defects a line can avoid, then test the true cost per scrapped unit—not its selling price.
The accounts. The business case. The next move.
- 01Avoidable defects
- 02True unit cost
- 03Inspection-fit evidence
- 04Evidence → account report
THE PAIN / NOT ANOTHER GENERIC LIST
When defects slip through, scrap adds up
Before selling time becomes sunk time, test the obstacles.
Selling price is the wrong yardstick
A scrapped part’s revenue is not the same as its avoidable material and processing cost.
One model won’t fit every line
Detection depends on the defect, imaging setup and representative training data; false positives matter too.
WHAT ALMA HUNTER DELIVERS
Count the scrap you can actually avoid
- 01
Start with a real baseline
Count avoidable scrap units by defect type from the buyer’s own production records.
- 02
Use the cost of the lost unit
Estimate unrecovered material and processing cost per unit, excluding selling price and double-counted costs.
- 03
Prove fit on the line
Check representative images, false rejects and integration needs before treating a pilot as scalable.
ALMA HUNTER / ACCOUNT REPORT
01Business fitEvidence + unknowns
02Buyer ROIFull cost ↔ realized value
03Technical fitEvidence + unknowns
04IntentEvidence + unknowns
05TimingEvidence + unknowns
06Decision teamEvidence + unknowns
BANTBudget · Authority · Need · Timing
BUDGET UNKNOWNHOLD / VALIDATE
Illustrative qualification, not a researched account. Unknowns stay unknown.
THE KPIs / TRACK, DO NOT PROMISE
Better evidence.
Clearer sales decisions.
Metrics to monitor—not guaranteed lifts, meetings or sales.
Avoidable-defect baseline
Units per year by defect type, grounded in actual scrap records and screened for avoidability.
Unit-cost evidence coverage
Share of baseline scrap units with a documented, buyer-validated avoidable cost per unit.
Pilot disposition agreement
Agreement between system flags and human-reviewed outcomes on a representative, labeled image sample.
THE BUYER’S RETURN FROM YOUR SOLUTION
If the numbers don’t work,
the pitch won’t fix them.
This models your buyer’s return from your solution—not your revenue from buying leads.
YOUR BUYER’S ROI / FICTIONAL INPUTS
YEAR ONE / USD / MODEL, NOT A FORECAST
-58.0%Modeled buyer ROI
- Net year-one benefit
- -$58,000
- Year-one break-even units
- 14,285.7
- Simple upfront payback
- 37.8 months
Below year-one break-even. Revisit the case—not the arithmetic.
Illustrative, editable assumptions—not validated results, guarantees or Alma service pricing.
Make the value real.
Illustrative fictional USD inputs only—not a quote, price benchmark or promised outcome. Gross benefit estimates buyer savings as annual avoidable scrap units × validated avoidable cost per unit × realized share. Do not use selling price; exclude costs already recovered through salvage or counted elsewhere. The realized share is a savings assumption, not the probability the project succeeds.
See formulas, evidence needed and research context
Gross benefit = potential units × economic unit value × realized share.
Full year-one cost = full upfront deployment + annual operating cost. Net = benefit − full cost. Year-one ROI = net ÷ full cost × 100. Each cost is subtracted once.
Simple upfront payback = upfront cost ÷ (annual benefit − annual operating cost) × 12 months, only if annual net benefit is positive. It assumes steady annual realization, not a discounted cash-flow forecast. Year-one break-even units = full year-one cost ÷ realized unit value.
Validate avoidable units from the buyer’s scrap and defect records, and unit cost from finance/operations records for unrecovered material and processing cost. The buyer is deploying the seller’s solution; year-one cost is upfront plus annual ongoing cost, counted once. NIST research demonstrates visual defect detection in a defined casting/weld X-ray dataset, not universal line performance; test representative production conditions and false rejects.
These sources explain the economic mechanism and measurement—not Alma Hunter performance:
- Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning — Technical study proposing CNN-based detection and segmentation of casting defects in X-ray images; reports dataset-specific results, notes a false positive, and describes training as complex and computationally expensive. Supports the inspection mechanism and the need to validate on representative data, not any particular system’s outcome.
- ASQ: Cost of Quality (COQ) — Defines scrap as defective product or material that cannot be repaired, used or sold, and distinguishes scrap from rework and other internal failure costs. Supports separating scrap units and their costs rather than treating all quality costs as interchangeable.
- NIST MEP: The Rise of Artificial Intelligence (AI) in U.S. Manufacturing — Describes computer vision and image-recognition cameras and algorithms for inspection and defect detection, and lists implementation barriers including data quality, initial costs, skills, cybersecurity and legacy-system integration. Supports the use case and qualification questions, not market size or campaign results.
FROM THE FOUNDER / NOT A CUSTOMER TESTIMONIAL
Built from the seller’s side.
“Finding companies was never the hard part.”
Scott Hawkyard · Founder, Alma Hunter
A buyer needs a supported business case—not another profile match.
THE NEXT MOVE
Find the buyers.
Bring the case.
Tell us what you sell and who buys it. We agree scope, price and delivery before payment. An inquiry does not start research.
Find my buyersInquiries, support or privacy: sales@almahunter.com
ALMA HUNTER / YOUR ACCOUNT REPORT
Buyer value.Evidence.Your next move.
Accounts reviewed against your brief.
Unknowns left visible.