Mohammad Al-Araidah
محمد العرايضةOperations Playmaker
Industrial & Manufacturing Engineer
I design, improve, validate, and digitize manufacturing systems — combining engineering, quality, data, and automation.
Manufacturing EngineeringQuality EngineeringIndustrial EquipmentSAP S/4HANAData & AnalyticsProcess Validation
Selected impact
$1.9M+
Documented and projected impact
Savings and cost avoidance across manufacturing, transportation, layout simulation and quality-improvement work.
$200M+
Operations supported
Business-side SAP S/4HANA transformation and manufacturing-process readiness across two industrial pump plants.
20%
Gross utilization improvement
$250K+ projected annual impact from time studies, line balancing and standard work, with changeover reduced 18%.
15%
Manufacturing scrap reduction
Root-cause analysis and SPC on high-volume production lines; a parallel value-stream program captured another $180K.
The work spans shop-floor leadership, equipment acceptance, process validation, operational excellence, manufacturing data and product engineering. Other results include a nine-person team led, 25% lower cruise-port wait time, 82% lower shuttle-routing cost, 500K+ healthcare records processed, and a 20-user manufacturing pilot for AutoOps AI.
Featured engineering work
One project from each part of the work
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.
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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.
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Supplier Qualification & FAT Strategy for a 1 MW Liquid Cooling Skid
Independent study · Data center cooling
Supplier qualification scoring, manufacturing readiness review, inspection and test plan, FAT protocol and shipment release for a hypothetical liquid-cooling skid — with every decision gate and its evidence stated.
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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
Method
How I approach manufacturing systems
- Understand
- Measure
- Analyze
- Improve
- Validate
- Control
- Scale
- 01Understand
- Map the process as it actually runs, not as it is documented. Most problems described as behavioural turn out to be structural.
- 02Measure
- Establish whether the measurement system can even detect the effect being discussed. Bad data produces confident wrong answers.
- 03Analyze
- Separate variation from shift, and common cause from special cause, before assigning a cause to anything.
- 04Improve
- Change the process, the equipment or the standard — not the level of attention required from the operator.
- 05Validate
- Translate requirements into measurable acceptance criteria and require objective evidence against them.
- 06Control
- Put the gain into a control plan, a work instruction and a data feed, so the improvement survives the person who made it.
- 07Scale
- Move the method into systems — ERP, analytics, documentation — so it applies across sites rather than one line.
Current technical focus