Case Study: MMS from Asset Onboarding to Downtime Closure

Context

In many factories, maintenance data lives in separate places: asset lists in one tool, service notes in chats, and downtime records in spreadsheets.

That structure creates hidden delays and weak accountability.

Objective

Build one connected MMS flow from asset creation to maintenance closure, with both web and mobile usability for supervisors and mechanics.

End-to-End Workflow We Implemented

1. Asset foundation

• Asset onboarding from UI or API
• Unique asset coding with QR/barcode
• Location mapping by plant and line
• Responsibility assignment by department/user
• Warranty and AMC metadata capture

2. Service operations

• Preventive and ad-hoc service requests
• Mechanic utilization visibility (busy/free)
• Technician assignment and reassignment
• Checklist-based execution
• Evidence upload through images and notes

3. Downtime control

• Supervisor-initiated breakdown request via mobile
• Root-cause capture at service level
• In-progress status tracking across roles
• "Unable to fix" path for escalation
• Request closure flow with approve/reject governance

4. Spare parts and inventory tie-in

• Spare request from service execution screen
• Part code, quantity, and reason logging
• Bulk spare upload and inventory review support
• Service continuity linked with parts issuance

Design Choices That Improved Operations

• Mobile-first execution for mechanics to reduce delayed logging
• QR-based lookup to avoid wrong-asset servicing
• Explicit state transitions to avoid closure ambiguity
• Evidence-backed approvals for better maintenance quality

Resource Pooling Logic We Recently Added

To improve breakdown response time and avoid uneven mechanic load, we introduced a section-based resource pooling model.

How it works

• Each mechanic logs into a specific unit section at shift start.
• New breakdown tickets enter that section's assignment queue.
• The assignment engine ranks available mechanics by workload weight.
• Weight is derived from completed ticket count in the active window.
• The mechanic with lower completed-ticket count is assigned first.

Why this improved performance

• Balances workload instead of repeatedly assigning the same fast responder.
• Reduces mechanic idle pockets in busy sections.
• Keeps assignment decisions deterministic and auditable.

Node.js event-loop style execution

We designed assignment processing like an event loop:

1. Ticket-raised event enters queue.
2. Availability and weight snapshot is fetched.
3. Lowest-weight mechanic is selected.
4. Assignment event is emitted and state is updated.
5. If assignment fails or times out, ticket is re-queued safely.

This event-driven pattern made assignment throughput predictable during peak breakdown bursts.

Outcomes

• Faster movement from complaint to assigned action
• Better traceability of who did what and when
• Improved confidence in downtime closure decisions
• Cleaner maintenance history for audits and planning

Leadership Insight

The biggest value came from workflow integrity, not only ticket creation. When service, downtime, and closure share the same operational timeline, managers can act with confidence instead of assumptions.

Executive Snapshot

Maintenance quality improves when asset identity, service execution, and closure governance live in one system timeline.

Response Visibility

ImprovedMechanic assignment and status were transparent across roles.

Closure Confidence

HigherEvidence-backed approvals reduced ambiguous shutdowns.

Audit Readiness

StrongerAsset-linked service history became structured and traceable.