OPTIMIZE & IMPROVE
Factories, enterprises, logistics networks, energy systems, buildings, robotics and service organisations do not have the same processes — but they share the same challenge: turn reliable evidence into the right action at the right time.
Benefits are planned against capacity, resources and process complexity.
Before a Analysed we define the available data, operational capacity, resource constraints and complexity of the target process. From this we build a measurable improvement plan with a baseline, target range, implementation effort and verification period.
Illustrative comparison plot
Indexed example only: current operation = 100. Lower is better for cost, energy, CO₂, errors and downtime. The target bars show an example planning envelope, not a guaranteed result.
Analysed planning scenarios
| Scenario | Typical condition | Planning effect |
|---|---|---|
| Low-access | Few sources, limited engineering time | Observe + identify opportunities |
| Standard | Usable history, stable process, moderate integration | Measure + rank + validate actions |
| Advanced | Good data coverage, cross-system access, active operator support | Cross-domain orchestration + controlled execution |
The exact KPI set is selected per plant: energy/unit, peak demand, CO₂/unit, OEE, downtime, scrap/rework, maintenance cost, throughput, labour hours, decision delay or other agreed indicators.
One optimisation core. Different domain models.
POLx°IMAT OOEP does not force every organisation into one factory-shaped or office-shaped model. A universal core handles evidence, state, uncertainty, optimisation, governance and execution while domain packs define the entities, processes, KPIs, constraints and actions that matter in each environment.
Optimisation adapts to the organisation.
The core remains stable. The domain pack changes what is observed, what is optimised, which constraints apply and which actions are available.
Industrial / Factory
Optimise interacting production, quality, energy, maintenance, material, labour and risk constraints.
Enterprise / Office / Service
Optimise sales flow, decisions, document movement, finance, workloads, waiting time and lost opportunities.
Logistics & Warehouse
Balance routing, fleet utilisation, warehouse state, delivery time, energy, cost and service constraints.
Energy & Utilities
Optimise generation, demand, storage, tariff exposure, carbon intensity, reliability and maintenance.
Buildings & Facilities
Coordinate HVAC, occupancy, comfort, maintenance, energy use, schedules and operating cost.
Robotics & Automation
Optimise task allocation, fleet state, exceptions, maintenance context and supervised human-machine execution.
Healthcare Operations
Operational optimisation can coordinate resources, scheduling, logistics and service flow while clinical decisions remain under appropriate professional authority.
Construction & Projects
Coordinate schedules, subcontractors, materials, cost, milestones, documents, risk and site constraints.
Custom Domain
Where a process has measurable state, constraints, actions and outcomes, a new domain pack can be mapped onto the same OOEP core.
Connect the plant. Improve the whole operation.
A proposed industrial Analysed for an engineering partner such as Bajorath: connect available production and energy evidence, understand cross-system dependencies, and evaluate improvements before changing operations. The diagram shows the intended architecture, not an existing customer installation.
MES / ERP / Orders
Energy meters / tariffs
Quality / maintenance
Warehouse / people
HIS Intelligence → HMCDIOE
HIS/EGU → NEXT ACT → HCMOL
VERIFY / LEARN
Production / energy scheduling
Maintenance prioritisation
Quality / exception routing
Human-approved actions
Identify idle consumption, inefficient operating windows, peak-load exposure and opportunities to coordinate production with energy demand.
Connect energy and process evidence to emissions accounting and evaluate lower-carbon operating alternatives without inventing savings.
Compare energy, labour, maintenance, material and delay costs so local savings do not create larger costs elsewhere.
Detect missing, conflicting or outdated information; improve handovers, exception routing and verification of completed actions.
Prioritise maintenance and process interventions using available evidence, dependencies and expected operational impact.
Give engineers and operators a traceable NEXT ACT with evidence, uncertainty, constraints, responsible owner and expected outcome.
Build only the optimisation stack you need.
Start with evidence and observation, then add process intelligence, optimisation, orchestration and controlled execution as the customer environment becomes validated.
Register systems, APIs, files, machines, sensors and external sources.
Preserve source, identity, time, provenance, quality and raw references.
Normalize schema, units, IDs and business/process meaning across systems.
Create current cross-system state for cases, processes, resources and risks.
Understand process state, dependencies, deviations, unknowns and opportunity.
Re-rank what matters now as deadlines, capacity, risk and events change.
Evaluate interacting objectives and candidate actions under constraints.
Govern evidence sufficiency, uncertainty, reliable horizon and authority.
Route tasks to people, software or permitted machine actions with approval.
Coordinate dependencies across departments, sites, machines and vendors.
Compare predicted and realised outcomes and update the next cycle.
Human supervision for runtime, questions, incidents, approvals and audit.
Same core — different optimisation objective.
| Domain | Primary State | Typical Objective | Key Constraints | Execution Target |
|---|---|---|---|---|
| Factory | Machines, orders, quality, energy, maintenance | Throughput + quality − energy − downtime − waste − risk | Safety, process envelope, capacity, quality | Engineer, MES, operator, permitted OT |
| Enterprise | Customers, deals, people, documents, finance | Revenue + conversion + productivity − waiting − cost − risk | Authority, compliance, capacity, evidence | Employee, CRM, DMS, finance workflow |
| Logistics | Fleet, routes, inventory, orders, capacity | Service + utilisation − delay − distance − cost − risk | Delivery windows, capacity, traffic, regulation | Dispatcher, TMS, WMS, fleet |
| Energy | Generation, load, storage, tariff, carbon | Reliability + value − cost − carbon − imbalance | Grid limits, equipment, reserve, tariff | Energy operator, scheduler, controller |
| Buildings | Occupancy, HVAC, comfort, energy, faults | Comfort + availability − energy − cost − failure risk | Comfort bands, schedules, equipment limits | Facility team, BMS, maintenance |
| Robotics | Robot state, tasks, battery, location, exceptions | Task completion + utilisation − idle − risk − energy | Safety, workspace, battery, permissions | Fleet manager, robot, human supervisor |
Optimisation is continuous, not a one-time recommendation.
Every relevant event can update the state, change dependencies, alter priorities and create a new admissible NEXT ACT set. Independent branches can continue while blocked branches wait for evidence or approval.
Start small. Validate. Expand.
Analysed / Reference Case
- One site, process, line or enterprise workflow
- Read-only evidence collection
- Baseline and process discovery
- Shadow recommendations
- Human-approved NEXT ACT
- Measured before / after verification
Scale / Enterprise Runtime
- Multiple departments, sites and systems
- Shared control plane + tenant isolation
- Cross-domain priorities and dependencies
- Operator control and incident handling
- Reusable client / industry configuration packs
- Controlled expansion of execution authority