Operation Risk Management of Planning and Piping Design in a Large Petrochemical Plant Project

Purpose – The purpose of this paper is to present practical and research lessons learned from analysis and the identification of failures which can occur. Failure mode and effects piping analysis (FMEPA) has been shown to be an effective way of improving piping design reliability. FMEPA is also employed for making sample control plans. Design/methodology/approach – To reduce project losses by using failure mode and effects piping analysis as a tool for analysis of the piping design department. The samples were selected from five projects. It was found that nine major points yielded a risk priority number (RPN) higher than 125. Findings – Results of RPN calculation concerning four topics revealed that the RPN value was reduced from 211 to 75, demonstrating a 64.4 percent improvement. Research limitations/implications – The study is limited to a planning and piping case study which considers RPN. Testing of the performance network regression model can be employed in companies, in which quality control has been implemented of solutions for failure prevention of piping design. Practical implications – This paper serves practitioners as a guideline and tool to understand and implement the FMEPA methodology. At this level, management sets the limits for determining measures. Management also decides whether a risk is acceptable or not. Management needs to clarify which risk priority number (RPN) represents the critical level above which requires risk reduction. Social implications – Conflicts and social unrest can cause costly delays to new projects and operations. Conflicts can also result in damage to a company’s reputation. This depends on the company’s responses to the conflict and the consequences or perceived consequences of its behavior and actions. Originality/value – This paper furnishes lessons learned for practitioners in various industrial sectors in preference to other methods of risk assessment and control activities.


Introduction
The "Operation Risk Management of Planning and Piping Design in a Large Petrochemical Plant Project" is in a state of uncertainty.Some possible event could have either a desirable or undesirable effect on maintainability and operability.This project concerns large chemical production at a petroleum plant project.It is based on a 3D piping design model that revolves around the following four steps: 1: Identifying key project risks in a timely manner.2: Assessing and analyzing the likelihood of risks crystallizing and the consequent cost/schedule impacts on the project.3. Developing appropriate strategies and actions to respond to risks.4. Monitoring and controlling risks and implementing action.
The piping problems were identified during fiscal years 2012 to 2014 during which time numerous projects were unable to be completed according to the requirements of the customers.The problems were mainly caused by internal processes.The problems focused sharply on piping design which was not directly related to actual site work.Many faults in piping design forced the company to reorder and rework.From the track record of problems during the period of this study, the project cost for reordered materials increased dramatically compared to original offer costs.Internal processes such as waiting for piping design between internal departments also delayed the overall process.Under current organization, each department is independently managed, leading to poor cooperation between departments.These issues led to poor quality of work and project delays caused overall lack of efficiency.Customer satisfaction and trust were damaged, threatening the company's chances of winning further projects.At the end of fiscal year 2014, on-going projects were valued at 32,200 million Thai baht.Meanwhile, backlogged projects were valued at 6,740 million Thai baht.
Loss of investment capital is the main factor that threatens any company.In most settings, 7 QC tools (Varsha et al., 2014) are applied and analyzed under statistical methods.For solving quality problems, the seven QC tools used are Pareto diagrams, cause and effect diagrams, histograms, control charts, scatter diagrams, graphs and check sheets.All of these are important tools that are widely used in the manufacturing field to monitor overall operations and to assure continuous process improvement.These tools are used to determine root causes and eliminate them in order to improve the manufacturing process.The modes of defects on a production line are investigated through direct observation and statistical tools.
The collected data is then used to make decisions on current problems with appropriate direction.Statistical tools are then employed for data collection using checklists.Data is then input into a Pareto diagram.From there, a team selects and arranges the problems according to their severity.They are all put into a cause and effect diagram, which shows the systematic relationship between a result, a symptom or an effect and its possible causes.It is an effective tool to systematically generate ideas about causes for problems and to present these in a structured form.This tool was devised by Dr Kaoru Ishikawa and is also known as an Ishikawa diagram.
On the other hand, several local industries have applied failure mode and effects analysis: FMEA is a systematic process intended for reliability analysis.It improves the operational performance of production cycles and reduces their risk level (Scipioni et al., 2002).FMEA was initially used in the industrial production of machinery, motor cars, mechanical and electronic components and electric motor control systems for vehicle  (Cassanelli et al., 2011).It has also been used in the pastry industry (Antikalamos and Kalamata, 2011) and food companies (Antonio et al., 2001).From failure analysis, the effects are classified in 3 groups.The first group of FMEA is used for analysis by the design team to evaluate potential failure trends, including mechanisms that can lead to failure.The second group of FMEA is needed to establish understanding of each activity in processes that poses risks.The third group of FMEA links these activities together to determine failure trends and employs analysis to control and reduce risks during the processes.Similar research having equivalent procedures started with rearranging the level of severe problems, then doing analysis with a fish-bone diagram, followed by analysis of failures and effects with FMEA.Finally, all analysis data was input to the control plan.Pasuk et al., (2009) studied waste reduction in the chromium plating process using FMEA and developing the quality of plating surface using six sigma.Their research reduced waste from the process by up to 70 percent.Jiwawongsawas et al., (2007) applied FMEA and AHP for process improvement in the ceramic coating industry as a major product faced serious quality problems.Prada and Kuptadsathien, (2007) performed analysis using FMEA for the fire protection coat production for all processes and calculated risk priority numbers (RPN) with a Pareto diagram.Next, they conducted a control plan showing that productivity increased up to 15.32 percent and waste in the process decreased by 11.15 percent.
Rittipakdee, (2011) studied ways to improve the painting process in the automobile industry.He used cause and effect diagrams to determine production problems and developed a relationship diagram together with a tree diagram, employing new 7 QC Tools to determine the problems.Thongpraiwa and Kuptadsathien, (2010) applied FMEA to improve the efficiency of the glass molding design and development processes.They found process RPN of 100 points or more.The major failures of mold design that needed immediate correction included 33 out of 65 topics.As a result of RPN correction, failure of mold testing was reduced from 2.7 times to 1 time for each molded product.Furthermore, production lead time was reduced on average from 75 days to 45 days, representing a 40 percent improvement.
A review of related literature reveals numerous ways to apply FMEA theory to real jobs of planning and piping design.It can be used to analyze and identify potential failures.FMEA has also been used to create a control plan for a sample company.

Literature review
Similar research concerning equivalent procedures began with analyzing the severity of problems, then conducting analysis using a fish-bone diagram, followed by analysis of failures and effects with FMEA.Finally, all of the analysis data was input to a control plan.Jiwawongsawas et al., (2007) applied FMEA and an analytic hierarchy process (AHP) for process improvement at a ceramic coating industry facing major quality problems with some of its products.Prada and Kuptadsathien, (2007) analyzed FMEA for the production of fire protection coats for all processes and calculated the risk priority number (RPN) using a Pareto diagram.The control plan in that study revealed that productivity increased up to 15.32 percent and waste in process decreased by 11.15 percent.Rittipakdee, (2011) studied methods to improve the painting process for the automobile industry.He used cause and effect diagrams to determine production problems plus he created a relationship diagram and tree diagram to determine the major problems.Scipioni et al., (2002) applied FMEA to categorize risk evaluation as follows: slight risk (RPN<60), moderate risk (RPN<80), high risk (RPN<100) and crisis risk (RPN>100).
Review of related literature has enabled the researchers to apply FMEA theory in genuine planning and piping design.Scipioni et al., (2002) studied the ways in which FMEA can control and reduce waste from design processes which affect quality in the petrochemical industry.Klomjit and Kaewsaithom, (2010) studied ways to reduce downtime caused by machine breakdown during operation and to select preventative maintenance task categories based on reliability-centered maintenance (RCM) for machine components.The study began by identifying the critical machine or equipment that impacted paper production and then analyzing the root causes and failures analysis using FMEA.The next step was to simulate the failure patterns of component parts using statistical data to forecast reliability parameters.The final phase was selecting preventive maintenance tasks which met the reliability parameters of each failure mode.This study has shown that downtime decreased.Meanwhile, machine availability increased.Jang-Shyong et al., (2006) studied a probable failure analysis to determine the failure probabilities of piping segments, and a probable risk assessment model was employed to identify risks at a nuclear power plant.The multiplication of the piping failure probability and the consequences of that particular failure results in the risk contribution of the pipe.The degrees of risk for different piping segments can then be ranked and the results can be used as the basis for planning a risk-informed inspection program.Tavner et al., (2010) researched FMEA techniques to compare the prospective reliability of three versions of the geared R80 turbine with different drive train solutions.These solutions have been proposed to reduce the overall wind turbine failure rate and raise its reliability.The first solution incorporated a conventional LV doubly fed induction generator (DFIG) with partially-rated electrical converter and transformer.The second solution incorporated an innovative hydraulic converter coupled to an MV synchronous generator (SG) without a transformer.The third solution incorporated an innovative LV brushless doubly fed induction generator (GDFIG) with a partiallyrated electrical converter and transformer.Their research proposed modifications to the FMEA method to analyze and compare reliability.They applied that approach to three alternative designs in order to identify optimum solutions.

Methodology
FMEA, which originated in 1950, is a form of reliability analysis technology used for the prevention of accidents.It was first used in the primary operation system in the Grumman Aircraft Corporation to analyze relevant processes, detect potential failure modes and effects, take corrective action to eliminate potential failures and bring about continuous improvement.Included is the important concept and skill of the risk classification/assessment method.
FMEA is a reliable technology for preventing defects and improving product safety and quality.The main function of FMEA is to point out a design or system failure mode, explore the impact of the failure on the system, give qualitative or quantitative assessments, take necessary corrective measures and then implement preventive policies.

Studying design and drawing process
The process of design and drawing comprises a variety of steps.It begins with project data as shown in Figure 2. The FMEPA technique does not account for technical specifications, design and drawing.After the design and drawing are complete, the isometric process, plus the piping and instrument diagram (P&ID) are matched with the vendor's drawings together with information from other departments.Then, the data is rechecked and calculated.If the data is not correct, the process goes into a loop until it passes the qualifications before it is handed over to the construction department.3.2 Fault data from design and drawing

Design and drawing before improvement
This research collected data between the years 2012 and 2014.It was found that the percentage of losses over the project value tended to increase continuously as demonstrated in Table 1 and Figure 3. Faults were classified into four types of problems.Each group includes internal details with a description of the type of loss as shown in Table 1.

Cause and effect of faults and waste analysis using fish-bone diagram
From the design and drawing process through the project handover to the end customer comprises 8 internal processes.Group brainstorming among several departments was conducted to analyze the effects of faults.The quality tool used for this analysis was a cause and effect diagram as shown in Figure 3.

Failure analysis using FMEA technique
Failure analysis is very important to determine cause and effects in the manufacturing process.It is used to solve problems systematically.It helps prevent losses before they occur.FMEA technique also enhances systematic problem solving skills.It is used by a project team to rearrange processes and prevent the high probability of loss on projects.FMEA consists of the methods explained below.
3.4.1 Pipe layout, material selection, pipe loading design and risk analysis are considered for selection and design.Brainstorming raises issues for design properties.Requirements for internal work and design must consider maximum usage; design must meet customer requirements and must aim for maximum safety.From brainstorming to analyzing the trends of failures due to piping design, nine types of failures were categorized.Failures were mainly caused by poor design which did not comply with the customer's specifications.Some designs contributed to poor efficiency.Some designs failed due to material selection.Moreover, some design work caused parts damage during actual use.   3 shows guidelines for fault control.This data is used for calculations in FMEA by arranging the risk priority number (RPN).RPN refers to results that will cause harm to the project.A higher RPN relates to a higher degree of risk.The calculation of RPN is shown in equation 1 (American Society for Quality (ASQ), 2005) as follows: Where S is Severity, O is Occurrence, D is Detection constraints: S, O and D, are integers ranging from 1-10 No adverse effects on product/process quality can be derived.The failure consequences are wholly insignificant.

Low
No adverse effects on product/process quality are likely to be derived.The failure consequences are insignificant.

Low
An applicable product can be expected.The master batch record is fulfilled, although some deviations in the process exist.

Low
An applicable product can be expected.The master batch record is fulfilled, although considerable deviations in the process exist.

Medium
The use of the product is limited; process is stable.

Medium
The use of the product is limited; slight deviations in the process exist.

Medium
The use of the product is limited; process is unstable.

High
The product has to be rejected; High The product has to be rejected; Process change has to be considered.

High
The product has to be rejected; Process must be changed.

Risk Priority Number Calculation (RPN)
Results from RPN calculation reveal that the highest RPN value was 280 points and the lowest value was 32 points as shown in Table 5.This table shows the RPNs for the piping layout process.

Process selection for analyzing control plan with Pareto diagram
When RPN numbers are rearranged using a Pareto diagram, the data is distributed and grouped to reveal the stability of data by frequency distribution count.Important data will have a low number or a few vital points.In contrast, less important data will yield a high number or many trivial points.Data analysis revealed that the major processes can be classified into nine crucial processes as demonstrated in Table 5.  RPNs were arranged from low to high as shown in Figure 5.In this study, RPNs higher than 125 points were selected for improvement.This included four out of nine problems.
The important issues comprised pipe layout in two problems, material selection in one problem and risk of use in one problem.Based on Table 5, the action team discussed the problems and solved them by referencing other project databases.The problems were solved as demonstrated in Table 6.
Results were discussed to resolve failures.Topics with RPNs higher than 125 points are summarized in Table 6.From RPN point re-calculation of four major types of failure, it was found that RPN points were reduced from 211 to 75 after improvement, representing a 64.4 percent reduction in RPN.The results reveal that the average percentage of the cost due to design error decreased from 0.31 to 0.08 percent, achieving the goals that were set.Using a reduced percentage of losses, costs were reduced to 74.2 percent (percentages comes from 100-(0.08/.31)*100).

Trend of Failure or Mechanism Solution
The cost of reorder materials (in millions USD) The cost of Rework (in millions USD) The noise impact of fluid inside the pipe.
New pipeline planned in accordance with the type of liquid.0.17  The most commonly used measure for profitability is the ratio of revenue and cost.Productivity represents the ability of the organization to utilize its resources for generating outputs.Then, performance measures (in terms of a ratio) that relate to the two performance criteria are developed.The following Table 10 demonstrates some of the performance measures and their respective results from the data that has been collected.The next step involves the use of the performance network concept.This concept represents an attempt to cluster different performance measures into one group.This cluster is based on the cause-and-effect relationships among the performance measures.Given the establishment of the PNs on profitability and productivity, the next step is to test the significance (in terms of the reliability and the goodness of the equations) of the interrelationships among different measures (which have been clustered).Usually, the Significance-F Value is less than 0.05. Figure 6 demonstrates this step in details for the PNs on both profitability and productivity respectively.The most commonly used measure for profitability is the ratio of revenue and cost.Productivity represents the ability of the organization to utilize its resources for generating outputs.Then, performance measures (in terms of a ratio) that relate to the two performance criteria are developed.The following Table 10 demonstrates some of the performance measures and their respective results from the data that has been collected.The next step involves the use of the performance network concept.This concept represents an attempt to cluster different performance measures into one group.This cluster is based on the cause-and-effect relationships among the performance measures.Given the establishment of the PNs on profitability and productivity, the next step is to test the significance (in terms of the reliability and the goodness of the equations) of the interrelationships among different measures (which have been clustered).Usually, the Significance-F Value is less than 0.05. Figure 6 demonstrates this step in details for the PNs on both profitability and productivity respectively.Failure mode and effects piping analysis (FMEPA) of late project transfer to customers significantly reduced problems.During the study of FMEPA on five ongoing projects, nine crucial failure topics were identified.Four topics had RPNs higher than 125.A committee then researched ways to determine solutions to the problems.The RPNs were reduced from an average score of 211 points to 75 points, representing a 64.4 percent reduction of problems.The research revealed that coefficient X 5 yielded the highest value.This indicates that the Number of Pipe Lines-to-Rework cost ratio (X 5 ) affects the Rework cost / Total cost (Y).Therefore, managers should consider the weight value for optimization.
From equation Y, the value of the high secondary coefficient was 1.55.This indicates the Labor Cost-to-Total Cost ratio (X 7 ).Therefore, the piping design should improve the Number of Pipe Lines-to-Rework cost ratio.

Conclusion
This research shows the importance of applying operation risk management analysis and identifying potential failures by improving piping design reliability.Due to the difficulty of each piping design pattern, managers should increase the knowledge of technical staff and improve procedures before starting work.Therefore, employees can increase the number of pipes to make more quality in the model.Overall, working hours can be reduced.

Recommendations
The RPN cannot be used to measure the effectiveness of corrective actions.Further, the three risk factors (S, O and D) are difficult to precisely evaluate.There is a need to split risk factors to reduce their vagueness and add other risk factors in the determination of risk priority of failure modes.FMEA innovation can become a more powerful tool for safety and reliable analysis of systems, processes, designs and services in an organization when risk factors and risk priority methods are appropriate for the specific risk evaluation problems.

Contribution
Financial benefits are also derived from the design improvements that FMEA is expected to facilitate, including reduced warranty costs and increased sales through enhanced customer satisfaction.Conflicts and social unrest can cause costly delays to new projects and operations.Conflicts can also result in damage to a company's reputation.This depends on the company's responses to conflicts and the consequences or perceived consequences of its behavior and actions.Using FMEA to identify the risk factors related to those sustainability metrics and integrating them into QFD to formulate the best sustainability strategy of service operation is still relatively scarce in the literature.

Management Implications
Management determines measures and then decides whether a risk is acceptable or not.Management needs to clarify which RPNs represent a critical level above which risk reducing measures need to be implemented as shown in Figure 8.
Figure 1: Research Methodology detection methods in the current situation employ 3D simulation.These methods are used to determine failures and test the most suitable design.The design is then transferred to CAESAR II program (Pipe stress analysis) for design and calculation of mechanical support as shown in Figure 4. 3.4.4Process control during the current situation is employed to control possible failures.Table

Figure 5 :
Figure 5: Risk priority number for each issue Percent of the cost compared to the value of the project.(overall 17.6 million US dollar) 0.08 Figure 6: Piping Detail Design Network Performance Measurements for 21 weeks

Figure 6
Figure 6 Piping Detail Design Network Performance Measurements for 21 weeks

4. 2
The equations model was obtained from the multiple regression equation of the Rework cost / Total cost (Y) ratio.

Table 1 :
Unplanned costs due to design and drawing faults from 2012 to 2014 Table 5 summarizes the processes that led to failures.
3.4.2.Potential failure mode is a normal specification in the sub-processes.If a subprocess does not comply with original specifications, it raises the question, "what will each department do to resolve the failure?"Potential failure mode is shown in Table5.Pobrane z czasopisma International Journal of Synergy and Research http://ijsr.journals.umcs.pl

Table 2 :
Severity (S) of a Failure Pobrane z czasopisma International Journal of Synergy and Research http:

Table 6 :
RPNs Evaluation and final improvement resultsA summary of the problems causing rework due to design errors are summarized in Table7.Conclusion costs from design errors in Table12are based on the project valued at 17.8 million USD for the study.