Process Plant Troubleshooting & Engineering Problem Solving

Process Plant Troubleshooting & Engineering Problem Solving

Course Category : Operations and Maintenance

A specialised programme for developing structured approaches to process plant troubleshooting, root problem analysis, engineering decision-making, and effective coordination across operations, maintenance, and design functions in accordance with the highest standards of quality and contemporary engineering.
Duration: 5 Days
Level: Advanced.

Starts On

25 - October - 2026

Ends On

29 - October - 2026

Location

Turkey - Istanbul

Language

English

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View Course Introductory PDF

Targeted Audience

Production and Operations Engineers
Maintenance Engineers
Process and Technical Engineers
Operations and Maintenance Supervisors
Technical Planners and Coordinators
Technologists and Technical Personnel involved in plant problem analysis
Personnel responsible for productivity and performance improvement initiatives
Employees involved in resolving operational and engineering problems

Targeted Skills

Structured Definition of Engineering and Operational Problems
Troubleshooting and Problem-Solving Terminology
Selection of Appropriate Analytical Tools and Techniques
Complex Relationship and Performance Variability Analysis
Decision-Making and Problem-Solving Frameworks
Human Factors and Organisational Culture Analysis
Structured Problem-Resolution Planning
Operations, Maintenance, and Design Integration
Continuous Performance Improvement

Expected Outcomes

Interpret operational and engineering problems using consistent terminology and structured methodologies.
Develop a systematic troubleshooting and problem-solving strategy.
Differentiate among analytical tools and techniques and select appropriate approaches for specific problems.
Analyse complex relationships, variability, and human factors affecting performance.
Evaluate the influence of organisational culture, group dynamics, and motivation on problem resolution.
Explain the relationship between operations, maintenance, and design functions across the asset lifecycle.
Formulate concise and accurate problem definitions to support engineering decisions.
Develop a structured framework for continuous improvement and problem resolution.
Empower key personnel leading plant troubleshooting and problem-solving initiatives.
Translate technical manuals and problem-solving tools into practical field application.

Training Topics Index

Defining the nature of problems and establishing common problem-solving terminology
Distinguishing asset-based problems from business process-based problems
Understanding the Six Big Losses and Seven Wastes in performance analysis
Examining performance levels and critical relationships affecting results
Introducing fundamental tools and techniques for structured problem solving

Decision-making frameworks for engineering problem resolution
Maturity indexing and its role in assessing problem-solving capability
Analysis of complex relationships and alignment between problem analysis and solutions
Criteria for selecting appropriate tools and techniques
Project selection approaches and improvement priorities

Influence of workplace culture on empowerment and problem resolution
Group behaviour dynamics and their effect on decisions
Internal and external motivation in performance improvement
Development of troubleshooting and problem-solving competencies
Organisational change management and effective transition

Strengthening cross-functional collaboration among technical functions
Role of maintenance strategy in troubleshooting and performance improvement
Functional contribution analysis and lifecycle considerations in design
Variability analysis and alignment of critical operational parameters
Strategy, planning, protocols, and continuous improvement

Developing a structured action plan for problem resolution
Configuration management principles and change control
Evaluation of commercial programmes related to improvement initiatives
Standard questioning techniques for concise problem definition
Data maturity stages and their contribution to informed decision-making