Pillar

Manufacturing Operations & Quality

Work on physical operations: reducing scrap and defects, raising utilization, shortening changeover, and qualifying capital equipment before it is allowed to make product.

Try the engineering lab

Put an engineering decision to the test.

Explore layout, work design, quality, manufacturing ML and production flow in one fictional factory. All demonstrations are synthetic and separate from professional work.

Explore the factory

Industrial & Manufacturing Engineer

Lead the team. Improve the process. Hold the gain.

At Signify, I led a nine-person team and the improvement agenda: scrap reduction, utilization, changeover, control plans and equipment acceptance.

Reported process results: 15% less scrap, 20% higher gross utilization and 18% shorter changeover time. A separate value-stream mapping program captured $180K; the utilization work carried $250K+ in projected annual benefit.

Earlier layout simulation and Lean Six Sigma work at Tenneco identified $1.3M in projected savings. The full layout case study is not yet published.

Process performance / Signify

15%less scrap

Before · normalized100

After85

01
Verify data
02
Isolate cause
03
Control drift

Summary of the published case. Index illustrates the reported reduction; it is not a production dataset. Employer records are not reproduced.

Professional experience

Case studies

Both projects below were performed in a professional role. Process details are abstracted — the method is described, not the employer's drawings, records or proprietary parameters.

Professional

High-Volume Manufacturing Scrap Reduction

Signify · Manufacturing / Quality

Structured root-cause analysis and statistical process control on high-volume production lines, cutting scrap 15% and holding it through control plans rather than operator vigilance.

SPCRCAPFMEAControl plans

Read the case study

Professional

Production Equipment Run-Off & Acceptance

Signify · Equipment / Quality

Translated an equipment specification into measurable acceptance criteria, ran vendor run-off, dispositioned a punch list, and held buy-off until process capability was demonstrated.

Equipment acceptanceRun-offCapabilityPunch list

Read the case study

How the work is structured

Improvement is a sequence, not an intervention

The pattern below is what I actually run. Most of the value sits in the last two steps, which are also the two most often skipped: an improvement that is not written into a control plan and a data feed does not survive the engineer who made it.

Quantify the loss
Scrap, downtime, changeover or utilization — in units and dollars, from the system of record.
Segment before theorizing
By line, shift, tool, material lot and part number. Segmentation usually eliminates most candidate causes for free.
Verify the measurement system
If gauge or data capture cannot resolve the effect, nothing downstream is trustworthy.
Establish the true root cause
Fishbone to narrow, 5-Why to deepen, then a designed check against process data.
Change the process, not the vigilance
Tooling, parameter windows, fixturing, standard work — something that holds without supervision.
Prove it statistically
Control charts and capability before and after, over enough production to be meaningful.
Write it into the system
PFMEA, control plan, work instruction, inspection criteria and the reporting that flags drift.

Equipment acceptance is where manufacturing engineering and quality engineering meet. If you are reading this from an equipment or infrastructure perspective, the run-off and acceptance case study is the closest professional analogue to a factory acceptance test, and the liquid cooling skid study extends the same discipline to mission-critical cooling equipment.