Project

PRJ-0196

Industry

Machine Building & General Manufacturing

Application

SCADA & production data

Commissioned

Nov 10, 2023

Status

Supported

OEE measured on 14 machines and raised from 58.1 % to 67.0 % once downtime reasons were coded at the machine.

Project

PRJ-0196

Industry

Machine Building & General Manufacturing

Application

SCADA & production data

Commissioned

Nov 10, 2023

Status

Supported

OEE measured on 14 machines and raised from 58.1 % to 67.0 % once downtime reasons were coded at the machine.

Production data architecture across the OT/IT boundary

FIG. 1

SCADA & production data

PRJ-0196

01

Commissioning results

Measured outcome

01

Commissioning results

Measured outcome

OEE

58.1

→

67.0

%

14 machines · months 4–6 vs. first 4 weeks of data

OEE

58.1

→

67.0

%

14 machines · months 4–6 vs. first 4 weeks of data

Availability

72

→

79

%

Same period · performance 84 → 88 %, quality 96 → 96.4 %

Availability

72

→

79

%

Same period · performance 84 → 88 %, quality 96 → 96.4 %

Stops with a reason code

92

%

Stops longer than 2 min · months 4–6

Stops with a reason code

92

%

Stops longer than 2 min · months 4–6

Weekly reporting effort

6

→

0.5

h/week

Production engineering · spreadsheets replaced by dashboards

Weekly reporting effort

6

→

0.5

h/week

Production engineering · spreadsheets replaced by dashboards

02

Engineering record

Challenge, approach and architecture

02

Engineering record

Challenge, approach and architecture

Challenge

Context. A plant making machined and assembled components on 14 machines — CNC centres, presses and assembly stations — built between 1996 and 2021.

Existing process. OEE was estimated weekly from operator logs and ERP bookings. Engineers spent about six hours a week building reports, and the largest losses were argued about rather than measured.

Constraints.

  • Four controls generations, two PLC platforms and three machines without a usable controller interface.

  • A flat plant network shared with office IT.

  • No changes to machine safety or machine programs.

Challenge

Context. A plant making machined and assembled components on 14 machines — CNC centres, presses and assembly stations — built between 1996 and 2021.

Existing process. OEE was estimated weekly from operator logs and ERP bookings. Engineers spent about six hours a week building reports, and the largest losses were argued about rather than measured.

Constraints.

  • Four controls generations, two PLC platforms and three machines without a usable controller interface.

  • A flat plant network shared with office IT.

  • No changes to machine safety or machine programs.

Engineering approach

We segmented the network first, then connected machines through an edge gateway per area, and agreed a reason-code tree with operators before building any dashboard.

Scope. Network audit and segmentation with an IT/OT firewall; OPC UA connectivity for PLC-controlled machines; I/O modules for the three machines without an interface; Ignition SCADA with historian, OEE calculation and a downtime-coding screen at each machine; weekly reports generated automatically.

Engineering approach

We segmented the network first, then connected machines through an edge gateway per area, and agreed a reason-code tree with operators before building any dashboard.

Scope. Network audit and segmentation with an IT/OT firewall; OPC UA connectivity for PLC-controlled machines; I/O modules for the three machines without an interface; Ignition SCADA with historian, OEE calculation and a downtime-coding screen at each machine; weekly reports generated automatically.

System architecture

  • Network: separate OT VLAN with firewall to plant IT, documented addressing.

  • Edge: 3 gateways with local buffering, OPC UA to machines, MQTT to the server.

  • Server: Ignition with SQL historian, OEE model and reports.

  • Machine screens: 14 tablets for reason coding, maximum 2 taps per stop.

System architecture

  • Network: separate OT VLAN with firewall to plant IT, documented addressing.

  • Edge: 3 gateways with local buffering, OPC UA to machines, MQTT to the server.

  • Server: Ignition with SQL historian, OEE model and reports.

  • Machine screens: 14 tablets for reason coding, maximum 2 taps per stop.

Deployment & commissioning

Machines were connected in three groups over six weeks without production stops. The first four weeks of data served as the measured baseline; reason coding was introduced from week five, and improvement actions from month two.

Deployment & commissioning

Machines were connected in three groups over six weeks without production stops. The first four weeks of data served as the measured baseline; reason coding was introduced from week five, and improvement actions from month two.

  1. PLCMachines14, mixed generations
  2. EDGEGatewaysOPC UA, buffered
  3. MQTTTransportThrough OT firewall
  4. HISTHistorianSQL + OEE model
  5. DASHDashboardsShift and weekly
FIG. 3 · Signal path · Rev A

03

Specification & results

Specification, acceptance and results

03

Specification & results

Specification, acceptance and results

Machines

4 controls generations

14

Machines

4 controls generations

14

Gateways

Local buffering

3

Gateways

Local buffering

3

Protocols

OPC UA · MQTT

Protocols

OPC UA · MQTT

Reason codes

3-level tree

38

Reason codes

3-level tree

38

Data latency

Machine to dashboard

< 2 s

Data latency

Machine to dashboard

< 2 s

Parameter

Specified

Measured

Result

Machines connected

14

14

Pass

Machines connected

Specified

14

Measured

14

Result

Pass

Data latency

< 5 s

< 2 s

Pass

Data latency

Specified

< 5 s

Measured

< 2 s

Result

Pass

Gateway buffering

≥ 24 h

72 h

Pass

Gateway buffering

Specified

≥ 24 h

Measured

72 h

Result

Pass

Stops coded

≥ 90 %

92 %

Pass

Stops coded

Specified

≥ 90 %

Measured

92 %

Result

Pass

Results

Measure

Baseline

Months 4–6

Availability

72 %

79 %

Performance

84 %

88 %

Quality

96 %

96.4 %

OEE

58.1 %

67.0 %

OEE = availability × performance × quality: 0.72 × 0.84 × 0.96 = 58.1 %; 0.79 × 0.88 × 0.964 = 67.0 %. Most of the gain came from two causes the data made visible: waiting for material on four presses and unplanned tool changes on two CNC centres.

Results

Measure

Baseline

Months 4–6

Availability

72 %

79 %

Performance

84 %

88 %

Quality

96 %

96.4 %

OEE

58.1 %

67.0 %

OEE = availability × performance × quality: 0.72 × 0.84 × 0.96 = 58.1 %; 0.79 × 0.88 × 0.964 = 67.0 %. Most of the gain came from two causes the data made visible: waiting for material on four presses and unplanned tool changes on two CNC centres.

04

Drawings

Layout and detail drawings

Original technical drawings prepared for this record. Illustrative, not to scale.

04

Drawings

Layout and detail drawings

Original technical drawings prepared for this record. Illustrative, not to scale.

Production data architecture across the OT/IT boundary

FIG. 1 · Production data architecture

OEE breakdown, baseline vs. after

FIG. 2 · OEE breakdown, baseline vs. after

We stopped debating whose numbers were right. The meeting is now about the top three losses.

Plant Manager

Client · industrial components (anonymized)

PRJ-0196

05

Related

Solutions, platforms and resources

05

Related

Solutions, platforms and resources

A

Related solutions

B

Technology stack

C

Related resource

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