01
Which production decisions are being optimized?
Define the objective, constraints, planning horizon and operational levers available to the decision maker.
AI, automation and optimization
Production Optimization
Connect plant constraints, demand, energy, maintenance and capacity in a decision system that can optimize production at scale.
Production optimization requires more than a solver. The operating model must combine real-time and historical data, business constraints, model governance, computing resources and the decision process used by engineers and operations leaders.
We design and scale optimization environments that integrate industrial systems, predictive inputs and mathematical models so production decisions can respond to changing demand, energy cost, maintenance schedules and capacity constraints.
NoèmeX perspectiveMake the system intelligible first: reveal the structure, constraints and variables that determine where change can produce the intended outcome.
Related leadership priorities
AI priorities depend on who owns the decision, risk and operating outcome.
Use these pathways to connect AI, automation and optimization work to the executives responsible for governance, foundations and business performance.
Questions we address
The decisions that shape the production optimization work
We start with the business process, decision, information and operating controls around the use case before selecting models, platforms or automation technologies.
02
Which real-time and historical inputs are required?
Map SCADA, historian, maintenance, demand, energy, inventory and other sources that influence the optimization problem.
03
How should algorithms and computing resources be shared?
Design a platform that supports scale, concurrency, deployment and resource governance across sites or regions.
04
How will engineers trust and use the result?
Provide transparency, scenario comparison, human review and operating controls around algorithm recommendations.
What we deliver
A production-minded capability, not an isolated experiment
The exact scope depends on the organization and use case, but the work typically produces a combination of these outputs.
01
Optimization problem formulation
Objectives, constraints, decision variables, scenarios and business rules documented in operational terms.
02
Industrial data architecture
Integrated real-time and historical data pipelines feeding the optimization environment.
03
Optimization platform design
Scalable compute, solver, deployment and access architecture for plant and regional use.
04
Algorithm deployment framework
A governed method for engineers to deploy, test, reuse and evolve optimization algorithms.
05
Usage and cost analytics
Visibility into solver, infrastructure and platform-resource utilization for capacity and cost decisions.
06
Governance and operating model
Ownership, prioritization, feature governance, support and continuous-improvement practices.
Selected work
Evidence and operating context from enterprise transformation work
This service is directly grounded in a documented multinational production-optimization platform.
Solver and infrastructure cost reduction70%+Europe, Middle East and APAC · Chemical manufacturing
Centralizing production optimization across regions and plants
Optimization algorithms were running locally on employee laptops while regions purchased redundant solvers and worked with fragmented industrial data. NoèmeX built a centralized platform, integrated SCADA and enterprise historian sources, enabled shared computing resources and established governance for algorithm deployment and platform evolution, reducing solver and infrastructure costs by more than 70%.
Read the related caseRelated services
Continue into the adjacent capability.
AI, automation and optimization often intersect with data, analytics, process and governance work. These pages keep each capability focused while making those relationships explicit.
How we work
Use the engagement model that fits the problem.
Work can begin as focused advisory and continue into implementation, embedded support, managed operations or run-and-maintenance depending on the maturity and criticality of the capability.
Advisory
Frame the optimization problem, value case, constraints and target operating model before implementation.
Read more →Project delivery
Build the data pipelines, optimization platform, interfaces and deployment framework.
Read more →Run & maintenance
Support production continuity, solver resources, platform performance and ongoing enhancements.
Read more →Your Contact Persons
Connect directly with the people responsible for this work. Explore their experience and contact options.
Zahra Fathisalout
Founder · Eng., CSP, CMLAI
Corporate governance · ASC-C.Dir. candidate
Works across strategy, enterprise transformation, data, AI and governance, bringing operational engineering experience to complex transformation decisions.
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Brendan Young
B.Com, Director of Operations
Connects transformation design with operating discipline, drawing on logistics, fulfillment and high-throughput operational leadership.
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Production Optimization
Start with the business problem, the workflow and the decision that needs to improve.
Share the production objective, operational constraints, data sources, algorithms already in use and how decisions are made today. Our teams can determine the architecture and operating model needed to scale optimization safely.