Introduction
The programme “Developing Smart Factory Control Systems Using Artificial Intelligence and Machine Learning” develops professional practice in Engineering. It connects the subject with workplace challenges by examining existing evidence and practices and choosing methods appropriate to the organization and its available resources.
The learning progresses from electrical systems, control, and operational efficiency to project planning and implementation control, with practical work on production improvement, workflow, and waste reduction, training design and measurement of learning transfer, engineering design, modeling, and simulation. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Read a training electrical system and identify inspection, protection, and control requirements in an application related to “Developing Smart Factory Control Systems Using Artificial Intelligence and Machine Learning”.
- Analyze a production flow and identify an intervention that improves quality, time, and resource utilization.
- Design a training intervention that begins with a performance gap and ends with evidence of application.
- Build an engineering model and assess its assumptions, limitations, and verification results.
- Prepare a project plan connecting scope, time, resources, and risks.
Learning areas
1. Electrical Systems, Control, and Operational Efficiency
- Read a training single-line or control diagram, identifying supply, loads, protection and measurement devices, and system boundaries.
- Analyze load, power quality, and efficiency requirements and fault indicators, checking data accuracy and conformity with equipment specifications.
- Identify the roles of protection, earthing, isolation, coordination, and control, referring settings and execution to specialists and approved technical references.
- Design an inspection and testing checklist covering criteria, tools, responsibilities, safety procedures, and permits without instructions for intervention on an energized circuit.
- Prepare a technical report comparing readings with approved limits and presenting possible causes, proposed actions, and approval requirements before execution. The application examines a case related to “Developing Smart Factory Control Systems Using Artificial Intelligence and Machine Learning”.
2. Production Improvement, Workflow, and Waste Reduction
- Map a product or service flow from order to delivery, collecting data on cycle time, waiting, work in progress, and defects.
- Identify the constraint, bottlenecks, sources of waste, and rework, distinguishing improvement at one workstation from improvement in overall flow performance.
- Compare work balancing, planning, pull, and workplace organization alternatives, considering changes in demand, skills, and safety constraints.
- Design a small improvement pilot with an acceptance criterion and baseline, and define measures to maintain quality and prevent defects from passing between stages.
- Monitor productivity, quality, delivery compliance, and resources, documenting the improved work method and responsibilities for sustaining results.
3. Training Design and Measurement of Learning Transfer
- Analyze performance gaps at organizational, job, and individual levels, and verify that training addresses a cause related to knowledge or skill.
- Write observable learning objectives, define entry requirements, and link each objective to a training activity and assessment evidence.
- Build a sequence of content, activities, and practical cases, balancing explanation, experimentation, and feedback within the available time.
- Select suitable learning methods, face-to-face delivery, and digital learning, and ensure accessibility, material quality, and delivery instructions.
- Design pre- and post-learning assessments and a follow-up plan with the line manager to measure application and identify additional support needs.
4. Engineering Design, Modeling, and Simulation
- Translate needs into requirements, design limits, and load or operating cases, documenting criteria and references relevant to the case.
- Prepare an engineering model showing dimensions, materials, relationships, and constraints, and review consistency between drawings, assumptions, and inputs.
- Select a simulation method suited to the problem, define boundary conditions, and examine the sensitivity of results to input quality and model settings.
- Compare outputs with a reference calculation or available test data, distinguishing calculation accuracy from the validity of the model's representation of reality.
- Prepare a design report explaining results, limitations, risks, and required modifications, specifying matters requiring specialist engineering review and approval.
5. Project Planning and Implementation Control
- Define the project rationale, expected results, stakeholders, and success criteria, and document the scope of work and exclusions.
- Break deliverables into work packages and related activities, and estimate durations, resources, and activity dependencies.
- Prepare a schedule and cost baseline, identify delivery and review milestones, and clarify approval and escalation responsibilities.
- Analyze implementation risks, define responses and warning indicators, and control change requests and their effects on time and cost.
- Build a progress report covering variances, decisions, and lessons learned, and define procedures for accepting deliverables and closing the project.
Practical outputs
- A functional diagram, checklist, and preliminary analysis of electrical system performance.
- A value stream map, bottleneck analysis, and production improvement plan.
- A needs analysis, concise learning package, and application evaluation plan.
- Design specifications, a simulation model, and a verification and review report.
- A project charter, implementation schedule, and change and risk tracking register.
Who should attend
- Engineers, technicians, supervisors and operations staff whose work relates to the programme subject.
- Managers and teams working in engineering, according to their practical responsibilities.
- Professionals responsible for applying the outputs of “Developing Smart Factory Control Systems Using Artificial Intelligence and Machine Learning” within their organizations.
Prerequisites
- Working familiarity with the programme field and the ability to discuss a professional case or challenge.
- Confirm the suitability of your experience and any specialist prerequisites with the Union before participation.
Conclusion
The programme “Developing Smart Factory Control Systems Using Artificial Intelligence and Machine Learning” brings together concepts, case review and a practical application. Participants prepare steps that can be adapted to their responsibilities and organizational resources, with an indicator to follow application and review results after returning to work.
Participation arrangements and any special requests are specified in the confirmation issued by the Union.