Development pace in. Production rate out. I lead engineering teams through the transition every hardware program dreads: turning one off builds into a line that ships on takt.
Aerospace rate production · precision machining (3/4/5-axis) · MES, machine health & manufacturing intelligence systems built from scratch · AS9100 configuration management.
Read the rate story Email me Download resume (PDF)The hardest transition in hardware isn't design; it's the day a qualified product has to ship every month. That's the work I do now.
At Votaw Engineering I'm leading the conversion of a job shop into rate production for GEM 63 solid rocket motor nozzle components and V-22 flight hardware; developing and implementing the manufacturing methods and processes, reorganizing floor flow for throughput, and setting the standard work that holds a sustained ship rate of 2 nozzle sets per month.
I serve as responsible manufacturing engineer on new launch vehicle structural hardware: owning the process plan, fixturing strategy, and VTL / 5-axis programming from ERB kickoff through first article for customers including Relativity Space, Lockheed Martin, Blue Origin, and Northrop Grumman.
Heroics don't scale; systems do. I built a custom MES from scratch (SQL Server / Python) that gives the floor live visibility, and I author the analytics and governance — delivery, yield, capacity, machine health that tell us where the constraint is moving before the schedule does.
I lead a five person team of manufacturing and design engineers plus IT setting team and individual goals, driving them to completion, and partnering with design engineering, quality, and production on build issues, NCR dispositions, and corrective action closure.
Most shops run on tribal knowledge and spreadsheets. I replace both with governed, validated systems the whole floor can see. These are real programs I authored shown at the methodology level, no customer data.
A complete operating framework I wrote for running a plant on data: four north-star questions (Are we executing? Are machines working? Are we making good parts? Are we efficient?), a full metric catalog with tiered sigma validation targets — 6σ for safety, contract, and traceability data down to 3σ for trend signals, five validation methods borrowed from gage R&R thinking, automated decision signals for hiring, redeployment, and capital purchases, and an 8-layer roadmap from clean data to predictive ML. Written so the program survives any individual including me.
A custom manufacturing execution system in SQL Server and Python: order status, station queues, and throughput on screens. Built to raise rate, not just report.
A weighted reliability / productivity / maintenance model with red gate logic, scored per machine across the full fleet and fed by daily loss driver data; setup, no-work, no-operator, down. One glance says which machine is bleeding hours, and why.
Python/SQL extraction of 60k+ purchase order lines — roughly $6M of penetrant, chemfilm, anodize, and etch spend bundled by qualified process to build the business case for bringing capability in house.
From a five person engineering team to a 200 person regulated operation — the job is the same: define the standard, remove ambiguity, keep promises to the schedule.
Leading a 5 person team (manufacturing/design engineers + IT) through a development to production transition on flight propulsion and launch vehicle hardware. Responsible engineer from ERB kickoff through first article; built the MES, machine-health system, and Manufacturing Intelligence Program; project manage contracted work from Relativity Space, Lockheed Martin, Blue Origin, and Northrop Grumman.
Managed engineering for precision machined aerospace structural components across the Relativity Space, Northrop, Blue Origin, Lockheed, ULA, Boeing, Honeywell, and Janicki supply chains. Led a 6 person team of machinists and engineers, ran the company's entire IT function, programmed 4/5-axis machining, designed fixtures and flight hardware, and owned customer technical relations with Northrop Grumman and Janicki.
Directed a ~200 person operation, across 3 sites in a regulated CLIA/FDA environment. Standardized procedures and operating metrics improved throughput 18% while holding full regulatory compliance — proof that discipline and rate are the same discipline.
Leadership only works if you can understand the work. Current program work first described at the hardware class level, no customer part data.
IN WORK — CURRENT PROGRAMS
| OP 10 | TURN — ROUGH & FINISH | VTL |
| OP 20 | CONTOUR FEATURES | 5-AXIS |
| OP 30 | DIMENSIONAL | CMM |
| OP 40 | NDT / PROCESSING | VENDOR |
| OP 50 | FINAL ACCEPT & SHIP | QC |
I own the manufacturing methods behind the ship rate; routings, fixturing, work instructions, and floor flow holding 2 nozzle sets per month plus the NCR and root cause loop when reality pushes back.
STATUS · AT RATE| OP 10 | ERB / PROCESS PLAN | MFG ENG |
| OP 20 | FIXTURE & PROGRAM | NX / CAM |
| OP 30 | LARGE-DIA MACHINING | VTL · 5-AX |
| OP 40 | IN-PROCESS VERIFY | PROBE · CMM |
| OP 50 | ACCEPTANCE DATA PKG | QC |
Responsible engineer on a first article, large diameter thin wall tank closure for a new launch vehicle — owning the process plan, fixturing strategy, and programming from engineering review board through acceptance.
STATUS · FIRST ARTICLE IN WORK| OP 10 | KICKOFF / READINESS | PROGRAM |
| OP 20 | PROCESS PLAN & MO | MFG ENG |
| OP 30 | TURNING | VTL |
| OP 40 | CONTOUR FEATURES | 5-AXIS |
| OP 50 | ADP / PACK & SHIP | QC |
Manufacturing engineering and NC programming for final machining of a large nozzle inner wall from program kickoff (design review, material readiness, risk register) through the acceptance data package.
STATUS · IN WORKPLAYBOOK LIBRARY — HOW I RUN PROGRAMS
My 90 day transition playbook: value stream map and takt vs demand math first; standard work and constraint relief second; a rate readiness plan staffed to takt third.
Demand → required starts after yield → station math that names the constraint and prices every lever: stations, touch time, test capacity, FPY.OP 50.
Inspection queues are the constraint OEE never shows. I model inspection as a work center, parallelize capacity, and gate WIP so accepted hardware not built hardware sets the rate.
Every metric earns its place against four north-star questions, carries one source of truth, and is validated to a sigma tier matched to the decision it drives — 6σ for safety and contract data, 3σ for trends.
Add capacity, replace aging, or buy capability; each with its own signal set and math: pre-buy OEE check, true annual cost of keeping, break even utilization.
Decision signals that separate headcount problems from distribution problems from backlog problems; sustained overtime %, utilization, No Staff hours by machine.
Datum strategy, dovetail then soft jaw fixturing, probing for automatic datum verification → repeatable tolerance control with fewer reclamps.
Engagement analysis, tuned step over and tilt, high-efficiency semi finish → ~22–30% cycle time reduction at full dimensional and cosmetic quality.
Engagement analysis, tuned step-over and tilt, high-efficiency semi-finish → ~22–30% cycle-time reduction at full quality.
Defined bend sequence, springback tooling, visual work instruction → operator-to-operator consistency, less hand rework.
Mixed laser + punch operations, tuned tabs and spacing → material waste down ~10–15%, faster forming and unloading.
Swappable detail plates on dowel locations, clamps clear of critical features → one fixture, many variants, minimal changeover.
Unified setup sheets, routings, and work instructions with controlled access → clearer docs, fewer errors, audit-ready by default.
Live program cards are described at the hardware class level only — no customer part numbers, drawings, or controlled data. Playbook entries summarize methods I run on real programs; hands-on entries use generic demo geometry.
Can the floor actually hit the manifest? This demo runs the same math I use on real transitions — takt vs. touch time, test capacity, first pass yield and names the constraint out loud.
Demonstration only — simplified on purpose. Real rate readiness work also carries learning curves, WIP policy, staffing to takt, and supplier lead times. The point is the decomposition: name the constraint, quantify the gap, then choose the cheapest lever; stations, touch time, test capacity, or yield.
If the floor is guessing, the process is already broken.
A boring, reliable process beats a hero setup only one person can run.
Plans are built around real setups, fixtures, tools, and inspection.
Escalations come with numbers. The MES exists so arguments end faster.
Machinists, brake operators, welders, and inspectors improve my process.
Staff to takt, set clear goals, protect the team from crisis driven overtime.
I'm in Southern California, working with teams that need development hardware turned into predictable production. Let's talk about your rate problem.