Engineering & Automation
Engineeringand automation
Manufacturing engineering, digital transformation and automation under one engineering discipline: designing the process, capturing the data and supporting the decision.
- Area 01
- Manufacturing engineering
- Area 02
- Digital transformation
- Area 03
- Automation and integration
- Status
- Existing and developing services
01Three areas
Engineering that designs, connects and measures production
The status of each area is labelled openly: existing services are never mixed up with capability areas still being developed.
- 01
Manufacturing engineering and planning
Manufacturability review, process planning and operation sequence, workholding and fixture concepts, CAM strategy, tool lists and inspection planning.
Target capability area - 02
Digital transformation
ERP (production, stock, purchasing, sales, finance), CRM (pipeline and quotation tracking), data analysis and dashboards, AI-assisted decision systems, custom software and mobile business applications.
Existing service - 03
Automation and data integration
Shop-floor data-capture concepts, production tracking, lot and serial-number traceability, barcode and QR recording, ERP–quality–planning integration and reporting.
Target capability area
02Digital backbone
The data line from the shop floor to the decision
Data gains meaning layer by layer: what is measured is first recorded, then related, then analysed.
05
Decision
Planning and management
04
Analytics and AI
Dashboards, forecasting
03
ERP and MES layer
Work order, stock, quality
02
Data capture
Signal, barcode, manual entry
01
Shop floor and machines
Production and measurement points
Digitalisation is not buying software; it is designing which question will be answered with which data. That is why the process is mapped first, then data-capture points and integrations are decided.
Simple starts are valuable too: tracking work orders by barcode, recording downtime reasons by manual entry or a weekly report can lay the foundation of a broader integration.
03Production efficiency and digitalisation
From paper to measurable data
Efficiency cannot be improved without being measured. To measure, first define what is measured.
When work orders, shop records and quality measurements stay on paper, it is invisible how long a machine was stopped or why a part was scrapped. Digital records turn those questions from stories into numbers.
The indicators below are commonly used definitions for tracking production efficiency.
- OEE
- Overall Equipment Effectiveness: Availability × Performance × Quality.
- Scrap rate
- The share of non-conforming parts in the total produced.
- Cycle time
- The time taken to machine one part.
- On-time delivery
- The share of orders delivered on the committed date.
OEE calculator
Enter your own values to see the three factors that make up OEE and the result.
- Availability
- (Planned time − Downtime) ÷ Planned time
- 87.5%
- Performance
- (Ideal cycle × Total parts) ÷ Run time
- 71.4%
- Quality
- Good parts ÷ Total parts
- 95%
OEE
Availability × Performance × Quality
59.4%
Illustrative calculator — for educational purposes. Entered values are not stored.
04Way of working
How does an engineering engagement run?
The scope differs for every project; the method is the same.
01
Discovery
Goals, existing systems and constraints are listened to.
02
Process mapping
The current workflow and data flow are mapped together.
03
Solution design
Priorities are set; scope and integrations are designed.
04
Implementation
The solution is rolled out and tested in stages.
05
Training and handover
Users are trained; documentation is delivered.
06
Support
Improvement and support continue based on usage data.
05Digital services
Existing digital engineering services
The company's published service lines. This list contains no customer references.
- 01
ERP systems
Business software that brings production, stock, purchasing, sales and finance processes together in one place.
Existing service
- 02
CRM systems
Process management for the sales pipeline, customer communication and quotation tracking.
Existing service
- 03
Data analysis
Collecting and cleaning scattered data and presenting it to decision makers with dashboards.
Existing service
- 04
AI-assisted decision systems
Decision-support scenarios such as demand forecasting, anomaly detection and reporting support.
Existing service
- 05
Custom software
Scalable enterprise software architectures designed around the business's processes.
Existing service
- 06
Mobile business applications
Applications that let field teams carry out tasks, records and approvals from mobile devices.
Existing service
06FAQ
About engineering and digital transformation
Are the ERP, CRM and AI services still offered?
Yes. ERP, CRM, data analysis, AI-assisted decision systems, custom software and mobile business applications are the company's published service lines. Manufacturing is the new direction added to them.
How does a digital transformation project start?
By mapping the current processes and data flows together, followed by a prioritised solution design.
What infrastructure is needed to capture production data?
The scope depends on the machines and existing systems. A staged approach is followed, from simple methods such as barcodes and manual entry to machine-data integration.
What is AI used for?
In decision-support scenarios such as demand forecasting, anomaly detection and reporting support. Data quality is the precondition for these.
Related pages
Capability status
Eclipse Engineering is building its precision-manufacturing capabilities step by step. The manufacturing areas described on this page are target capability areas within the company's investment programme; equipment, capacity, material scope, tolerance class and lead time are confirmed separately for each project once the technical drawing has been reviewed.
Send Drawing