Smart Manufacturing / MES

Manufacturing Execution Dashboard

Shift Performance, Machine Utilisation and Quality in One Pane

Manufacturing Execution Dashboard
Smart Manufacturing
Industry
12 months
Duration
5 → 9
TRL
4
Disciplines

Background

A plastics manufacturer running 34 injection moulding machines across two shifts planned production in spreadsheets and reconciled output the following morning from paper shift sheets. Supervisors could tell you what had happened yesterday but not what was happening now, so recovering a slipping order meant discovering the slip a day late.

The problem

What made it hard.

The machine fleet spanned four vendors and two decades — some exposed OPC UA, others offered only dry contacts for cycle and alarm. Operators had rejected a previous MES rollout because data entry competed with running the machine. And plant-floor connectivity dropped often enough that a cloud-only application would have been unusable during exactly the shifts that mattered.

Approach

What we built.

We normalised the fleet behind a single machine model: OPC UA where available, and a small retrofit counter module reading cycle and alarm contacts everywhere else, so every machine reports the same events regardless of age. A plant-local service holds the authoritative production record and syncs to cloud when the link is up, which keeps the floor running through outages. The operator terminal was deliberately reduced to four touch targets — job start, job end, scrap with reason code, downtime with reason code — because anything more elaborate is what killed the previous attempt. Planning, OEE, shift reporting and quality tracking build on that event stream rather than on separate data entry.

Outcome

What it measured.

61% → 74%
OEE across 34 machines in eight months
34
machines unified across four vendors and two decades
45 min/day
of supervisor reporting eliminated

The plant gained live order status against plan for the first time, and supervisors began intervening on slipping orders within the shift instead of the next morning. Measured OEE rose from 61% to 74% over eight months, driven mostly by downtime reasons finally being visible and attributable. Shift reporting that had taken a supervisor 45 minutes a day became automatic.

Our role

OT integration and retrofit modules; platform architecture; operator UX; OEE and quality modelling; rollout and training.

Technologies

ReactTypeScriptNode.jsPostgreSQLTimescaleDBOPC UADockerKeycloak

Gallery

Inside the build.

OEE breakdown by machine and shift with availability, performance and quality bands.

Figure 1 — OEE breakdown by machine and shift with availability, performance and quality bands..

The four-target operator terminal mounted beside a machine.

Figure 2 — The four-target operator terminal mounted beside a machine..

Retrofit counter module wired into an older machine's alarm contacts.

Figure 3 — Retrofit counter module wired into an older machine's alarm contacts..

Order-versus-plan Gantt with a slipping job flagged mid-shift.

Figure 4 — Order-versus-plan Gantt with a slipping job flagged mid-shift..

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