Pillar
Manufacturing Systems & Data
Why this pillar exists
Most engineers sit on one side of this line
Manufacturing engineers usually understand the physical process and treat the ERP system as an administrative layer. ERP practitioners usually understand the transaction model and treat the physical process as a black box. The expensive failures in a transformation live precisely in the gap: a routing that cannot be executed as written, an inspection that never triggers, a goods movement that does not match how material physically moves.
My work sits on that line. The case study below is about validating a manufacturing process end to end — as a manufacturing engineer would, with acceptance criteria and objective evidence, rather than as a set of disconnected system tests.
What this pillar covers
- End-to-end manufacturing process validation across engineering, planning, procurement, production, quality and logistics
- Readiness governance — what has to be true before a site is allowed to go live
- Issue-closure discipline: ownership, corrective action, objective evidence, retest, sign-off
- Work-instruction and process-documentation control across multiple sites
- Readiness and defect-aging reporting built for decisions rather than status
- Manufacturing analytics — production performance, capability and supplier risk
Professional experience
Case study
End-to-End Manufacturing Process Validation for ERP Transformation
Flowserve · Manufacturing systems
Validated an engineer-to-order manufacturing process from customer order through engineering release, planning, procurement, production, quality and shipment — with an issue-closure gate that required objective evidence before sign-off.
Read the case study
AutoOps AI
Founder & product engineer · Manufacturing AI
A reasoning layer over fragmented manufacturing information — FMEA, CAPA, 5-Why, nonconformance tracking and quality reporting with SAP integration, built from 35 discovery interviews to a 20-user pilot.
Read the case study
In progress
Planned in this pillar
Production Layout Optimization Using Simulation
Tenneco · Industrial engineering — $1.3M projected savings
Manufacturing · Data
Reducing Operational Waiting with Prediction & Optimization
The Walt Disney Company · Operations analytics
Data · Manufacturing
Supplier Quality Analytics — Multi-Supplier SPC Dashboard
Independent study · Cp/Cpk, NCR aging, defect Pareto, supplier risk
Data · Quality
Designing a Readiness Framework for Multi-Site Transformation
Independent framework · Governance, cadence, decision thresholds
Programs · Systems
A note on analytics
Analysis methodology from this work is also implemented in Power BI and Python. The dashboards themselves are built on employer data and are not published; the planned supplier-analytics study uses a synthetic dataset so the method can be shown without the data.