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Nexus iMES

AI-Powered Software for Manufacturing Optimization.

Nexus iMES is the intelligence layer that orchestrates manufacturing operations. From ERP work orders to shop-floor execution, Nexus connects data, models constraints, optimizes schedules, and learns from reality.

What Nexus does

  • Hit promise dates with resilient, constraint-aware schedules, not spreadsheets.
  • Increase throughput by aligning machines, people, and materials around priorities.
  • Reduce firefighting with real-time execution signals and exception-driven workflows.

Four core capabilities

Four integrated capabilities that transform manufacturing operations from reactive to predictive.

Capability What it does
Trusted Scheduling and Rescheduling Publish schedules you can trust, and adapt them in minutes when reality changes
Shop-Floor Execution and Visibility (iMES) Close the loop between plan and reality with operator updates and real-time status
Master Data and Constraint Management Make constraints explicit, editable, and validated so schedules stay correct as operations evolve
KPIs, Bottlenecks and Operational Insights Turn schedule and execution data into action: bottlenecks, root causes, KPI movement

How Nexus works

Five steps to transform your manufacturing operations.

  1. Connect data from ERP, MES, spreadsheets, and shop-floor signals.
  2. Model constraints (routings, resources, skills, tooling, dependencies) and validate completeness.
  3. Optimize schedules against objectives (OTD, throughput, cost trade-offs) and publish plans.
  4. Execute and monitor with operator and machine updates and exception alerts.
  5. Learn and improve by updating cycle times and constraints from actual performance, and iterating.

Integrations

Nexus connects to your existing systems while maintaining enterprise-grade security.

Layer Supported systems
ERP Microsoft Dynamics 365, SAP, NetSuite, Infor, Epicor (via APIs, data exports, direct database)
MES Plex and other MES platforms via API-based integration and reconciliation patterns
OT OPC UA, MTConnect, and edge gateways for machine-level signals where available

The connector-first approach: Nexus ingests work orders, routings, inventory readiness, and execution updates through APIs or scheduled exports. Built-in validation discrepancy checks highlight missing operations, mismatched statuses, and relationship gaps before they break schedules.

Security and compliance

Role-based access control, granular permissions by user role and department, TLS encryption in transit, encryption at rest for all data, complete audit logs of schedule and master-data changes, and flexible deployment (cloud, hybrid, or on-premises) to align with IT policies. See Security and Compliance.

Implementation timeline

Structured deployment with clear milestones and fast time to value.

Weeks Milestone
0 to 4 Data readiness + trusted schedule MVP: core routings and constraints with the first schedule publish
4 to 8 Execution loop: operator updates, exception alerts, and adherence tracking
8 to 12 Expansion: additional lines and sites, deeper constraint modeling, KPI cadence, continuous improvement

Time to first value: 30 days to a first trusted scheduling loop, assuming minimum viable data is available. Faster if routings and resources are clean; slower if master data relationships must be rebuilt.

What you need to provide

  • Work order feed including IDs, due dates, quantities, and priorities
  • Routings and operations with standard times, or historical production data to learn them
  • Machine list with capabilities, and operator list with shifts and skills (as available)
  • Inventory and material readiness signals, even a simple state machine to start
  • Operational rules such as outsourcing steps, changeover logic, and dispatching preferences

Proven results

KPI Outcome
Capacity increase ~29 percent observed in a discrete manufacturing context
Cycle time reduction 20 to 30 percent typical range once constraints and execution loop are in place
Fulfillment improvement 10 to 15 percent uplift
Planner efficiency Fewer manual rebuilds and faster reaction to disruptions through constraint-aware rescheduling