The objective of this report is to present the issues related to successive equipment/machine failure within a Biorefinery industry in Angola. This work presents several complex problem-solving techniques that could be fully applied to the situation that enable the Planning and Control Maintenance (PC&M) department to interpret the information from large datasets from the machineries.
The combination of models and techniques allowed the team involved in the problem solving process to identify three preferred potential solutions related to equipment/machine failure. Through further analysis using the Plus/Minus/Interesting and Force Field Analysis it was possible to narrow these down to a single solution. The preferred solution was chosen by carrying out a comparative analysis and the weighted score indicated the suitability of the solution for the given scenario of the complex problem. Knowing that solution implementation is not readily accepted automatically, a PPA (Potential Problem Analysis) was presented in order to alleviate the risk of new problems occurring during the implementation of the chosen solution process. The implementation plan took into consideration the budget, the time and possible project cost escalation.
The recommendations that will be presented include stringent requirements for getting budget approval in order to expedite contractual agreements IoT (Internet of Things) providers, AI (Artificial Intelligence) companies and Outsource Expertise. It is suggested that if all of the requirements are met then the successive machine breakdown rate would achieve considerably low rates.
Table of Contents
1. Problem Structuring
2. Boundary Setting
3. Recognition
4. Interaction
5. Conclusion and Recommendations
Objectives and Core Topics
The objective of this report is to address the persistent issue of equipment and machinery failure within a biorefinery industry in Angola, specifically focusing on the challenges faced by the Planning and Control Maintenance (PC&M) department in interpreting large datasets. The research aims to develop a structured approach to problem-solving by identifying boundaries, engaging stakeholders, and applying analytical models to select and evaluate the most effective maintenance strategy.
- Application of brainstorming and root cause analysis techniques like Ishikawa and "Why" analysis.
- Stakeholder identification and mapping to ensure organizational alignment and consensus.
- Utilization of the Nominal Group Technique (NGT) for collaborative solution generation.
- Implementation of Morphological Analysis, PMI, and Force Field Analysis for systematic solution evaluation.
Auszug aus dem Buch
Problem Structuring
According to Czinkia and Hentschelb (2015) a problem is considered to exist whenever there is a gap between an initial and a desired situation or when a living creature has a goal but does not know how this goal is to be reached (Fischer et al., 2012). Problems are often categorized as simple, complex and complicated. Simple problems are referred for cases when there is a clear initial situation and a known way to achieve the desired solution (Czinkia and Hentschelb 2015), complex problems are referred for cases composed of many parts that interconnect in intricate ways and the domain has many features that are interdependent of the system/environment (Fischer et al., 2012), and complicated problems are referred for cases when there is a quantitative aspect that one could measure, count or weigh, i.e. if quantity becomes extreme then the problem will get complicated.
Within the biorefinery industry, a large amount of data from equipment and machineries is logged 24/7 to monitor the health state condition of the running equipment/machinery. The data logging process is carried out by many sensors such as accelerometers, thermocouples and operated computer vision reporting system. I am part of the PC&M (Planning, Control and Maintenance) department a subcontractor of the maintenance head division of Biofuel Inc. The department is responsible to operate and manage the amount of scattered data logged 24/7. Data analysis is often required to extract useful information that leads to important strategic decisions to be made about planning maintenance schedules to enhance business benefits such as improved performance, increased reliability and reduced cost (MathWorks, 2017).
Summary of Chapters
Problem Structuring: This chapter defines the theoretical framework of problem classification and describes the current operational challenges regarding data interpretation within the biorefinery maintenance department.
Boundary Setting: Focuses on the use of mental models, cognitive mapping, and Ishikawa diagrams to identify the parameters and causal factors of equipment failure.
Recognition: Explores the importance of stakeholder engagement and the process of identifying key internal and external personnel to achieve organizational consensus.
Interaction: Details the application of the Nominal Group Technique (NGT) to foster collaboration and manage differing viewpoints during the ideation phase.
Conclusion and Recommendations: Summarizes the effectiveness of the selected maintenance solutions and provides strategic recommendations for budget approval and future process improvements.
Keywords
Problem Solving, Maintenance Programs, Biorefinery, Data Management, Stakeholder Engagement, Morphological Analysis, Nominal Group Technique, Root Cause Analysis, Predictive Maintenance, Ishikawa Diagram, Process Efficiency, Machine Failure, Decision Making, Planning and Control Maintenance, Organizational Consensus
Frequently Asked Questions
What is the primary focus of this research?
The work focuses on addressing successive machinery failures within a biorefinery industry by applying structured problem-solving techniques to improve the interpretation of large machinery datasets.
What are the core thematic fields of the study?
The core themes include data management for predictive maintenance, personnel and stakeholder management, and the application of systematic creativity models in an industrial setting.
What is the primary goal of the author?
The primary goal is to move the PC&M department from a state of ineffective data interpretation to a structured, predictive maintenance model that reduces machinery downtime and operational costs.
Which scientific methods are employed throughout the paper?
The author utilizes a mix of qualitative and analytical tools, including Causal Mind Maps, Ishikawa Fishbone Diagrams, "Why" Analysis, Nominal Group Technique (NGT), Morphological Analysis, Plus/Minus/Interesting (PMI) evaluations, and Force Field Analysis (FFA).
What topics are discussed in the main body of the work?
The main body covers problem identification, stakeholder mapping, collaborative group work protocols, solution generation through combinatoric matrices, and multi-criteria evaluation of potential technical solutions.
Which keywords best characterize this work?
Key terms include Predictive Maintenance, Morphological Analysis, Stakeholder Engagement, Problem Structuring, and Data-Driven Decision Making.
How does the author manage stakeholder conflicts?
The author uses mapping techniques to visualize influence and interest, conducts open group discussions to dispel fears of job loss, and ensures transparent communication to build organizational consensus.
Why was the Morphological Analysis chosen for this specific project?
It was selected as a systematic way to synthesize the complex, multi-dimensional nature of the maintenance problem into manageable, evaluated solutions by creating a consistent attribute matrix.
How does the Potential Problem Analysis (PPA) contribute to the implementation?
The PPA is used to identify potential risks and obstacles during the implementation phase of the chosen solution, ensuring that necessary contingency plans are established to avoid project failure.
- Citar trabajo
- Antonio Paim (Autor), 2018, Applied problem solving for maintenance programs, Múnich, GRIN Verlag, https://www.grin.com/document/459876
