Niyojan
niyojan - planning, deliberate arrangement
Multi-plant production planning at enterprise scale.
Niyojan plans production across dozens of plants at once, balancing demand, capacity, and constraints that no hand-built schedule can hold together. It optimizes across 60+ facilities, honors real limits like materials and capacity, and replans as conditions change – recovering the capacity and margin that manual planning leaves behind.
Workflow
Problem
Planning production across dozens of plants by hand leaves capacity and margin on the table.
Outcome
Optimized scheduling across 60+ facilities, balancing demand, capacity, and constraints.
01
Model the network
Demand, capacity, and constraints across all plants are captured.
02
Optimize multi-plant
Schedules are solved across 60+ facilities together.
03
Honor constraints
Capacity, materials, and demand limits are respected.
04
Replan
Schedules adapt as conditions change.
Modules
Multi-Plant Optimization
Schedule across 60+ facilities together.
Constraint Solving
Honor capacity, materials, and demand.
Replanning
Adapt schedules as conditions change.
Representative use cases
Global 60+ Plant Capacity Balancing & Workload Re- Allocation
A multinational industrial equipment manufacturer operating 60+ production facilities across 4 continents, producing custom hydraulic pumps, valves, and heavy gearboxes.
Dynamic Resequencing Following Unplanned Primary Line Failure
A high-volume automotive stamping and welding plant producing body panels for 3 concurrent vehicle assembly platforms.
Setup Grouping & Changeover Minimization in Contract Electronics
A high-mix Contract Electronics Manufacturer (CEM) operating 12 Surface Mount Technology (SMT) lines producing PCBA boards for automotive and industrial IoT clients.
Order SLA Protection & Overtime Minimization During Demand Spikes
A commercial HVAC equipment manufacturer producing custom rooftop air handling units with high seasonal demand surges during pre-summer months.
Planning a New Product Introduction Into an Already-Loaded Network
A manufacturer must introduce a new product family into a plant network already running near capacity, without breaking existing customer commitments.
Evidence
What Wayam can show for this entry
Curated solution design with an owner and a review date. No performance or production claim.
Allowed claims at this tier: Possible workflow, typical stack, prerequisites.
- Evidence tier
- T4 · Reference pattern
- Integration status
- Typical enterprise system
- Metric status
- Scenario only
- Evidence owner
- Not yet attached
- Validated
- Not yet attached
- Valid until
- —
- Content version
- 2026.09
Pending evidence attachment
This entry represents an enterprise architectural reference pattern. Customer benchmarks, run replays, and metric verifications are established during technical discovery.
Limitations
- Headline metrics are representative until a customer result is attached.
Representative metrics · Reference · scenario only
60+
facilities planned
These describe the intended outcome of the design. They are not measured customer results until a validated case is attached above.
Architecture & controls
Composed across the operating loop
Ingest
Connect the systems of record and read the signals the work already produces.
Covered by the platform
Reason
Ground context, score options against policy and the stated goal, draft the next step.
3 agent roles
Act
Execute an approved step in the system of record and keep the evidence.
7 agent roles
Govern
Set policy, gate consequential actions on a named approver, and audit what ran.
Covered by the platform
Pack Architecture & Agent Composition Graph
10 Composed Agents- AGT-0157
Production Schedule Optimization Agent
Manufacturing Operations
- AGT-0201
Computer Vision Quality Inspection Agent
Manufacturing Operations
- AGT-0337
Plant Digital Twin Agent
Manufacturing Operations
- AGT-0222
Incident Triage Agent
IT Operations
- AGT-0223
Root Cause Analysis Agent
IT Operations
- AGT-0224
Change Risk Assessment Agent
IT Operations
- AGT-0425
Predictive Maintenance Agent
Asset Management
- AGT-0428
Maintenance Work Order Optimization Agent
Asset Management
Control gates
- A named approver on every consequential write-back
- Evidence and audit trail kept with every run
Capability atoms
- constraint-solving
- system-connectors
- forecasting
- scenario-simulation
- workflow-orchestration
Typical enterprise systems
Typical stack for solution design; compatibility is validated during discovery.
- SAP Ariba
- Coupa
- Snowflake
- Dun and Bradstreet API
- Cognex VisionPro
- Siemens MES MCP
- NVIDIA Jetson
- SAP S/4HANA
Designed for
manufacturing, automotive
Business case
Model a Niyojan scenario with your own baseline
Three scenarios from the numbers you enter. Capacity released is time; it becomes a saving only when roles or costs are actually removed or avoided.
Improvement is seeded at the midpoint of this entry's directional band (20–40%). Source status: scenario only. Edit it to your own estimate.
| Scenario | Improvement | Capacity released | Cashable savings | Net annual | Payback |
|---|---|---|---|---|---|
| Conservative | 11% | — | — | — | — |
| Expected | 21% | — | — | — | — |
| Upside | 29% | — | — | — | — |
Enter an annual volume and a baseline cost to see figures.
Illustrative planning scenario, not a guarantee. Results depend on process baseline, adoption, data quality, integration scope, controls, and deployment costs.
Pilot this
From solution design to a bounded pilot
Step 1 · 3–4 weeks
Discovery Sprint
Validate the workflow, data, controls, baseline and business case before anything is built.
Gate: Blueprint and pilot plan signed by the business owner, technical owner and Wayam.
Step 2 · 6–10 weeks
Bounded Pilot
Prove quality and value on agreed data against agreed acceptance tests.
Gate: Acceptance thresholds met on the evaluation set; go/no-go decision recorded.
Agrani · evidence pending7 Agrani candidates in this solution: Computer Vision Quality Inspection Agent, Incident Triage Agent, Change Risk Assessment Agent, Predictive Maintenance Agent, Maintenance Work Order Optimization Agent, Production Schedule Optimization Agent, SOP Compliance Verification AgentPairs well with