From Jordan to the factory floor

I’m an industrial engineer from Jordan who has always been interested in how complex systems move — production lines, supply chains, enterprise transformations, football teams and organizations. The through-line is not a single job title. It is a preference for problems where the answer is hidden in how a system actually behaves rather than in how it is described.

I studied Industrial Engineering at Iowa State University with a minor in Data Science, which is the reason I tend to reach for a dataset before an opinion. Early on, that combination sent me in two directions at once: continuous improvement work at Tenneco, where Lean Six Sigma analysis and discrete-event simulation of Ford production-line layouts produced $1.3M in projected savings, and operations analytics at Disney, where Python predictive and optimization models cut port wait times 25% and redesigned material and transportation flow for an 82% cost reduction. A summer building SQL and ETL pipelines at Epic Systems taught me how much of an operations problem is really a data-architecture problem.

At Signify I moved from analysis to ownership. As a manufacturing and industrial engineering manager I led a nine-person team, cut scrap 15% through structured root-cause analysis and SPC, raised gross utilization 20%, and reduced changeover time 18%. Just as formative was the equipment side of that role: defining acceptance criteria for new production equipment, running vendor run-off, dispositioning a punch list, and withholding buy-off until build conformance, cycle time and process capability were actually demonstrated. That is where I learned that the decision to accept or reject equipment is the most consequential quality decision most engineers get to make, and that it has to rest on evidence rather than schedule pressure.

I now lead manufacturing-process readiness for an SAP S/4HANA deployment at Flowserve across two industrial pump plants supporting more than $200M in operations. The work is quality work wearing different clothes: translate a manufacturing process into validation scenarios from engineering release through BOM and routing, planning, procurement, receiving, production, inspection, warehousing and shipment; then hold the line that no critical defect is closed without an owner, a corrective action, objective evidence, a retest and a site sign-off. Industrial pumps also gave me something specific: familiarity with rotating equipment, P&IDs, build-to-print verification and the kinds of defects that only appear under test.

Alongside that I build AutoOps AI, a platform for manufacturing quality workflows — FMEA, CAPA, 5-Why analysis, nonconformance tracking and quality reporting over plant data. Thirty-five discovery interviews and a twenty-user pilot made one thing obvious: manufacturing quality depends as much on information architecture as on analytical technique. A perfectly good root-cause method fails when the evidence needed to run it is spread across an ERP system, a spreadsheet, a PDF and someone’s shift notes.

What I’m most interested in now is manufacturing environments where small upstream quality failures create disproportionately large downstream consequences — capital equipment, industrial systems, and mission-critical infrastructure. That is a different risk profile from consumer manufacturing, and it rewards exactly the discipline I care about: define requirements precisely, set measurable acceptance criteria, inspect risk upstream, use data to detect deterioration before failure, and require objective evidence before closing anything.