MechNetIQ consulting for manufacturers
MechNetIQ helps manufacturers isolate controllable losses in downtime, diagnosis, recovery, scrap, and production support. We improve the workflow with supervised industrial AI only where it earns a measurable advantage, then validate the result against plant and financial metrics.
One production area, asset family, or recurring incident category. Fixed scope. Human-controlled by design.
Customer promise
MechNetIQ helps manufacturers identify a specific source of production loss, determine which intervention is worth pursuing, and validate the result before scaling.
We begin with the operating problem—not the technology. Downtime, slow fault recovery, scrap, rework, quality losses, bottlenecks, material-flow problems, maintenance inefficiency, and other measurable losses can be evaluated against actual operating data.
Where should you invest to recover the greatest amount of production value—and what evidence supports that decision?
The MechNetIQ method
Each step narrows uncertainty and keeps the scope tied to an operating metric the plant already understands.
Establish a measurable operating baseline across downtime, MTTR, throughput, first-pass yield, scrap and rework, cycle-time variation, schedule attainment, or bottleneck losses.
Translate the operating loss into economic terms where sufficient customer data exists.Analyze the process, equipment, data, workflow, maintenance, controls, networking, and human dependencies contributing to the loss.
Do not assume AI is the solution.Compare interventions using historical analysis, lightweight simulation, production modeling, or controlled experiments where appropriate.
Possible interventions include process, maintenance, controls, workflow, information, automation, AI-assisted diagnostics, integration, training, or escalation changes.Identify the smallest practical experiment capable of testing the recommended intervention.
Where human judgment or AI adoption affects the result, OT-Praxis can measure the proposed workflow before or alongside production deployment.Compare pilot performance against the agreed baseline and success criteria.
The question is not whether the technology worked; it is whether the production result improved enough to justify scaling.Initial engagement
A fixed-scope engagement that identifies one economically meaningful production loss, isolates the controllable portion, and defines a pilot with measurable acceptance criteria.
Capacity is not automatically revenue. Recovered time creates financial value only when the plant can use it; MechNetIQ does not double-count overlapping benefits.
Decision confidence
MechNetIQ separates measured facts from modeled assumptions. Recommendations communicate historical loss, addressable opportunity, expected improvement range, implementation cost, payback, confidence, uncertainty, assumptions, and the experiment that would reduce them.
No investment recommendation should depend on a model that cannot represent the operating problem.
Illustrative example — not a MechNetIQ customer result.
Validation principle
If the model is not sufficiently representative, we do not use it to justify the investment. We do not advertise universal prediction accuracy.
Powered by OT-Praxis
Some production improvements depend on whether people can recognize the problem, gather evidence, diagnose correctly, challenge unsupported AI recommendations, escalate, act within authority, recover safely, and verify restoration.
For qualifying engagements, OT-Praxis can reproduce that decision environment in the fictional Meridian environment and compare the current workflow with a proposed intervention. This adds evidence before deployment; it does not prove simulated behavior automatically transfers to production.
Human-controlled by default: read-oriented access, approved data sources, visible provenance, explicit approval, action logging, named owners, rollback procedures, and bounded pilots. No direct PLC writes, safety-system interaction, autonomous restart, recipe changes, or unapproved vendor access.
See OT-Praxis in Meridian →Decision-quality commitment
We don’t manufacture ROI cases to justify technology. If the evidence says an intervention isn’t worth your capital, we’ll tell you.
A $500,000 expected loss becomes $65,000 realistically addressable against a $140,000 implementation.
Illustrative example — not a MechNetIQ customer result.For qualifying diagnostics, the customer and MechNetIQ establish the required data, scope, baseline, validation criteria, responsibilities, and decision standard before work begins.
If the customer supplies those prerequisites and MechNetIQ cannot deliver the agreed decision-quality analysis, MechNetIQ will refund the applicable diagnostic professional fee, subject to the applicable Statement of Work.
A recommendation not to invest is a valid result.
Production results depend on implementation, mix, equipment, staffing, suppliers, schedules, and other conditions outside MechNetIQ’s control. Specific terms in the applicable Statement of Work govern each engagement.
Controlled improvement pilot
Use the smallest live experiment capable of testing a credible opportunity.
Illustrative example — not a MechNetIQ customer result. Objectives and guardrails are established in the applicable Statement of Work.
What is the loss?
Can we adequately represent the relevant historical behavior?
Which interventions appear economically promising?
Can the affected people and process achieve the intended change?
Does it work under real operating conditions?
What production value was actually recovered?
For qualified engagements with an established baseline and measurable outcome, MechNetIQ may structure a portion of its fees around agreed performance milestones.
Practitioner context
Devon X. Beck is an OT network and cybersecurity practitioner with more than a decade of experience across manufacturing technology, industrial security, network engineering, and infrastructure operations. He currently supports Ford Motor Company production environments through Sandalwood Engineering & Ergonomics, focusing on OT endpoint-security posture, policy and compliance recovery, application control, plant-floor asset reconciliation, vendor-system whitelisting, privileged access, and production-safe remediation. Previously, at General Motors, he worked across industrial cybersecurity, Splunk SIEM, CyberArk privileged access, IT/OT convergence, endpoint hardening, backup and recovery, penetration testing, and PLC and robotics security. Earlier, through Epitec at Ford Motor Company, he supported WAN planning, network segmentation, NetFlow capacity planning, FlexVPN, and routing modernization. He holds CISSP, CISM, CompTIA Security+, CompTIA SecAI+, and CCNA–CCNP credentials. That background informs MechNetIQ’s operating principle: production technology must be secure, supportable, measurable, and governed by the people accountable for plant outcomes.
Company names identify professional employment experience only and do not imply sponsorship, endorsement, or a consulting relationship with MechNetIQ or OT-Praxis.Questions
No. The engagement begins with an existing production loss. AI is recommended only when it offers a measurable advantage over process, documentation, network, ownership, or conventional engineering improvements.
The exact sources depend on the problem. Useful inputs may include downtime logs, CMMS work orders, MES records, alarm and event history, shift reports, quality data, vendor-support records, and relevant financial costs.
Not in the initial engagement. Early pilots are read-oriented, bounded, logged, and human-approved. Direct control, recipe changes, safety interaction, and autonomous restart are outside the initial scope.
The baseline, eligible events, exclusions, operating metric, and financial conversion are agreed before the pilot. The result is reviewed using customer operating data and the appropriate operations and finance owners.
A recurring incident or loss category that is economically meaningful, narrow enough to investigate, supported by some operating evidence, and owned by a reachable plant or engineering leader.
Next decision
Start with one line, cell, process, recurring failure, or operating constraint. We’ll determine what evidence is available, whether the opportunity can be credibly quantified, and what the smallest useful next step should be.
No production files or credentials are requested. Review the privacy notice before submitting.