Maintenance Optimization
Abstract
This research aims to investigate the maintenance optimization of machinery in the oil and gas industry. The study employed a quantitative and qualitative approach to obtain data through a 15-day follow up and focus group discussions, respectively. The collected data was analyzed using descriptive statistics (quantitative) and narrative analysis (qualitative). The findings of the study established that both preventive and predictive maintenance strategy facilitates the optimization of equipment in oil and gas companies. However, the outcomes also distinguished that predictive maintenance strategies are associated with lower machinery breakdown than preventive maintenance processes. The incorporation of SAP software tools enhanced this situation in the predictive strategy.
Chapter One
1.0 Introduction
1.1 Background of the research
Maintenance optimization is the process of managing the performance objectives of an asset. Carrying out proper maintenance of equipment and machinery improves its productivity and efficiency. There are different ways of sustaining assets, such as corrective maintenance, preventive maintenance, and predictive maintenance. On the other hand, predictive maintenance is a technique that implements condition-monitoring tools to track the performance of equipment. The equipment is studied during an operation to detect defects and fix them before the machine fails (Ozekici 552).
The condition-monitoring tools used during preventive maintenance include the use of sensors and the use of IT. Maintenance sensors monitor how the machines accelerate during operation through vibrations (Dorf 10). The sensors are mounted to a very rigid part of the device to ensure that the vibration signals are detected. Another critical element used during predictive maintenance is the use of IT. Predictive maintenance using IT is done by storing large amounts of data and running algorithms on several computers to detect the possible breakdowns in the equipment and the hazards they are likely to cause (Mobley 4).
Another method of maintenance optimization is preventive maintenance. It is performed by team technicians on a piece of equipment to reduce the likelihood of it failing. Like predictive maintenance, preventive maintenance takes place while the machine is still working. The technicians implement software such as CMM software to ease optimization. The software allows the oil industries to gather information about their equipment and determine the triggers that may lead to system breakdown (George 2016). Most enterprises prefer preventive maintenance to any other type of maintenance optimization technique as it does not require condition-monitoring tools. The absence of the tools reduces the cost incurred during equipment maintenance.
System Application and Product (SAP) is an IT tool used in predictive and preventive maintenance to track the maintenance records of the equipment. It is also used in manufacturing intelligence and integration characterized by providing KPI metrics, data services, enhancing quality, visualization tools, and business logic (Chicvak 9). SAP also conducts predictive analytics using the time series algorithms and data correlation that are combined models with data to foresee breakdown time.
Philips (3) states that “historically, the oil and gas industry has been reluctant to adopt modern technologies and practices for the management of oil and gas sites.” Therefore, the integration of the corrective, predictive, and preventive maintenance procedures, as well as the utilization of IT tools to enhance optimization, is crucial for companies in time and production management. Successful operation is facilitated when various resources such as technicians and equipment function effectively, altogether. Companies in the oil and gas industries need robust computerized maintenance management systems (CMMS) that gather and evaluate data to elicit meaning conclusions that are used in decision-making. Hence, this study is crucial because it provides insights on the maintenance optimization of machinery in the oil and gas industry.
1.2 Problem statement
The effective performance of machinery and equipment is significant for the increased output and proper utilization of time and financial resources in the oil and gas industries. Nevertheless, the continuous use of machines may subject them to breakdown or wearing out. Such conditions may lower the efficiency and safety of operating the equipment. The situation may lead to losses, a low rate of production, and increased health risks to the operators. Therefore, maintenance processes are significant in identifying, preparing, and reassembling machines to function normally. The various maintenance procedures of equipment include predictive, preventive, and corrective maintenance. Unlike corrective maintenance, predictive and preventive maintenance takes place when machines are still working. However, it might not be elementary to differentiate the most effective or relevant maintenance procedure to employ on the running equipment to facilitate optimization. Also, dilemmas exist concerning the utilization of SAP over other maintenance tools in predictive and preventive performances.
1.3 Main objective
To investigate the maintenance optimization of machinery in the oil and gas industry
1.3.1 Specific objective
To establish the influence of the preventive maintenance strategy on the optimization of machinery in the oil and gas industry
To assess the impact of the predictive maintenance strategies on the optimization of machinery in the oil and gas industry
To evaluate the significance of the SAP tool as the primary component for preventive and predictive maintenance on the optimization of machinery in the oil and gas industry
1.4 Research questions
How can the preventive maintenance strategy influence the optimization of machinery in the oil and gas industry?
How can the predictive maintenance strategy influence the optimization of machinery in the oil and gas industry?
Can SAP be used as the main tool for preventive and predictive maintenance or are other tools needed?
1.5 Significance of the study
Therefore, this study is significant in both business and academic contexts. It assesses the influences of both preventive and predictive maintenance in optimizing the performance of machinery in the oil and gas industry. Still, evaluating the significance of SAP over other tools in the preventive and predictive maintenance procedures assists in selecting the most appropriate interventions for facilitating optimization. Academically, the study enables students in business to identify the maintenance strategies that can be utilized in enhancing the performance of machinery in the oil and gas industry. Also, the students can understand the specific tools that are necessary to solve particular problems detected in machines. Interventions in this research also assist students in applying IT knowledge in solving machine-oriented problems.
Chapter Two
2.0 Literature Review
2.1 The preventive maintenance strategy on the optimization of machinery in the oil and gas industry
Custeau (3) agrees with Mohamed (16) that preventive maintenance is executed in expectations that equipment and other resources in the oil and gas organizations will not fail. Thus, such strategies are developed to ensure that maintenance functions are conducted timely, considering the manufacturer’s good practice recommendations as well as the operation statistics (Mohamed 12). Therefore, Custeau (3) argues that other than utilizing the Enterprise Asset Management (EAM), oil and gas companies can employ CMMS to optimize the efficiency of machinery functions. On the same note, Philips (1) emphasizes that proper preventive maintenance strategies are critical in the oil and gas industry in optimizing performances, minimizing costs, and protecting the companies’ assets.
Mohamed (14) depicts that preventive maintenance operates on the basic notion of predicting wear and tear of machinery using various approaches. As such, the amount of breakdown can be mitigated if the correct maintenance strategies are selected by the management teams of the respective oil and gas companies. Mohamed (15) proposes preventive maintenance forms that include condition-based and time-based maintenance. The condition-based perspective is characterized by the measurement of data concerning thermography, ultrasonic testing, lubrication analysis, and vibration monitoring, among others. After analysis of the collected data is conducted, replacement or repair is done if the “monitoring level value exceeds the normal” (Mohamed 15). On the other hand, the time-based maintenance is conducted earlier top avoid breakdown. The procedure prevents breakdown by replacing equipment components at specific periods of operation. This element functions under the assumption that the life of the equipment is predictable.
2.2 The predictive maintenance strategy on the optimization of machinery in the oil and gas industry
Custeau (2) highlights that oil and gas companies are operations amidst tough economic times. Therefore, this situation increases the urgent need for the management teams in the respective firms to operate under high-efficiency levels characterized by controlling costs as well as increasing productivity (Mohamed 12). This need is also associated with the urge to minimize safety risks and limit downtime in business environments. Concerning the asset performance management (APM), oil and gas companies are leveraging advanced analytics and industrial data to enhance the safe, reliable, and continuous running of equipment. Custeau (2) validates that such attempts have been facilitated by the analysis of collected data for predictive maintenance execution. This procedure empowers personnel to identify and react before the machinery breakdown is observed.
As such, Custeau (3) correlate with Hahn (3) that predictive maintenance strategies are the most crucial for critical and complex assets. The procedure relies on continuous examination of machinery performance through prediction engines and sensor data to elicit advanced equipment warning about failures and other problems. Custeau (3) also adds that the predictive maintenance process “uses advanced pattern recognition (APR) and requires a predictive analytics solution for real-time insights of equipment health.” Thus, using predictive analytics solutions under the predictive maintenance process increase the chances of identifying issues that could be quickly revealed. According to Rio (2), on the accounts of a study conducted by the ARC Advisory, only approximately 18% of equipment breakdown is associated with increased age or use. Such outcomes establish that predictive maintenance, unlike the preventive one is crucial in avoiding the remaining 82% of breakdown.
2.3 The significance of the SAP tool as the primary component for preventive and predictive maintenance on the optimization of machinery in the oil and gas industry
Concerning the application of the preventive and predictive maintenance processes by manufacturing businesses in the current world, Thakur (4) argues that the management teams have an increased urge to understand the situation equipment. This activity assists them in foreseeing the maintenance needs as well as making timely decisions. About the preventive maintenance process, Thakur (5) agrees with Custeau (3) and Mohamed (16) that the procedure is significant because it enhances maintenance activities at fixed intervals. Thus, this process mitigates the equipment breakdown risks as well as minimizing the repair costs and unplanned downtime. Nevertheless, Thakur (6) argues that “preventive maintenance is not a sure way to prevent failure.” As such, Thakur (7) highlights the trends and reasons for employing predictive maintenance instead.
Other than preventing, businesses need to predict maintenance needs to facilitate planning ahead of time. This condition is enhanced through the inter-connectivity established between the Internet of Things (IoT) and the production equipment in the business environment. The predictive maintenance has also been supported by the availability of data that facilitates the analysis of aspects in the machine structures. Nevertheless, Thakur (8) describes that extracting insights in predictive maintenance slows down the entire process because of the presence of siloed data saved by IoT in different formats. Therefore, Thakur (9) illustrates the significance of incorporating powerful SAP Hana capabilities in the predictive maintenance process to optimize the equipment. Rambabua et al. (3) suggest that SAP is a collection of software used in business suites for product lifestyle management. Using SAP, other than other IoT tools, reveals core benefits to the implementation of the predictive maintenance technique. As such, the core SAP capabilities that propel the effectiveness of predictive maintenance entail predicting anomalies and planning to enhance asset management. Another one includes monitoring and managing connected devices through special tools and sensors. SAP also allows technicians to view insights retrieved from the collected data to integrate predictive maintenance (Thakur 11).
2.4 Literature gaps
The primary gap in the presented literature is that it is limited to defining the significance of maintenance procedures and their influence on performance, safety, time, and cost among companies in the oil and gas industries. The literature also slightly illustrates the significance of SAP as an IT tool for optimizing the predictive equipment maintenance processes. However, themes discussed in the review fail to address the aspects that influence the management teams of oil and gas companies in selecting the specific maintenance intervention that would enhance the durability and effectiveness of their equipment. The literate review also fails to identify the significance of subsequent IT tools in predicting the breakdown of equipment other than SAP. Such situations increase and raise the need for conducting further studies on equipment maintenance in the oil and gas industry.
Nevertheless, such gaps have been supported by various trends in the evolving world. For example, companies in the oil and gas industry face several challenges concerning operation optimization as well as maintenance because of the continuous technological innovation situations. The management teams are often puzzled while selecting the most appropriate maintenance ideas that can increase the profitability and output quality of their companies. Another instance is that it has supported the presence of the existing gaps in the late recognition of the industrial maintenance function (Velmurugan and Tarun 1622). Therefore, companies are still strategizing on the most reliable maintenance aspect for equipment optimization.
Chapter Three
3.0 Materials and Methods
3.1 Study area
To help answer the research questions, this research incorporated a mixed-method study; involving both the quantitative and qualitative methods. The study was conducted in London. Most oil and gas-related industries in the country are located in the North West and Southern regions of England (Smith 1). The map below shows the study location.
Figure 1
Study Area
Source: https://www.dailymail.co.uk/sciencetech/article-2335163/Britains-new-Eldorado-Map-shows-massive-gas-deposits-self-sufficient-years.html
3.2 Selection and description of sampling sites
With the presence of relatively many oil and gas-related companies in the study area, the research sought to pick a random sample of two companies. Chariot Oil & Gas LTD is located at Hatfield House, 52-54 Stamford Street, London SE1 9LX, United Kingdom, while Victoria Oil & Gas Plc is located at 19 Old Bond St, Mayfair, London W1S 4PU, United Kingdom. The selection of the two companies was based on a sampling frame that contained a list of all companies that were eligible to host the study. From the sampling frame, a convenient sample was taken.
3.3 Sampling technique and procedures and sample size
A convenient sample of two companies was taken. Both Chariot Oil & Gas LTD and Victoria Oil & Gas Plc had a similar machine maintenance schedule, which made it easier for comparability. Having been granted authority to conduct the study, I again listed all the machines in each company in a frame. Briefly, Chariot Oil & Gas LTD listed 28 machines while Victoria Oil & Gas Plc listed 32 machines. The 60 machines were then randomized to either be under predictive or preventive maintenance strategies. Concerning the focus group discussions, 8 participants from each company were selected to provide insights on the significance of SAP in maintenance optimization.
3.4 Research design and approach
This study used a longitudinal study design. The listed machines were randomized to either be under predictive or preventive maintenance strategies and then followed up for 15 days for episodes of breakdown. A 15 day follow up was reached upon by consulting with the companies’ engineers. Regardless of the maintenance strategy, the machines were under; check-ups were done at intervals of 15 days. As such, the current study was designed to start immediately after a check-up interval.
3.5 Data collection methods
Quantitative study
The two sets of machines received the intervention at the same time (either predictive or preventive maintenance). The machines’ performance was then monitored for episodes of breakdown. Those that broke down within the 15-day follow up were marked as ‘censored.’
Qualitative study
Two focus groups of eight individuals each were set to represent each company to facilitate the collection of insights concerning the significance of the SAP tool as the primary component for preventive and predictive maintenance on the optimization of machinery in the oil and gas industry. The discussions were conducted in the first and third day of follow up for Chariot Oil & Gas LTD and the second and fourth day for Victoria Oil & Gas Plc in a week, for two weeks. Individuals who participated in the two focus group discussions were experts in machinery operations and possessed experience and knowledge concerning the utilization of SAP in optimizing the performance of predictive maintenance processes in their respective companies. The obtained data were subjected to narrative analysis.
3.6 Data analysis
All the quantitative statistical analyses were conducted in R (3.6.1) statistical software. Descriptive statistics were done by running frequencies, and results were presented in tables. Cox regression models were used to evaluate the temporal effect of maintenance strategy on machine performance while adjusting for potential confounders. Some of the confounders included; duration of service, the size of the machine (classified according to power consumption), and study site.
Chapter Four
4.0 Results
Baseline characteristics of study machines
In total, 30 machines were allocated to predictive maintenance strategy. Other machine characteristics are shown in table 1 below.
Table 1
Characteristics of Machines included in the Study
Characteristics Chariot Oil & Gas LTD Victoria Oil & Gas Plc
Predictive Maintenance, n (%)
Mean duration of Service in years (range) 14 (50%)
3.2 ( 1.8-4.2) 16 (50%)
4.6 (2.7-6.5)
Censored, n (%) 11 (39%) 9 (28)
Machine size, n (%)
Big
Medium
Small
8
14
6
14
12
4
Predictive maintenance protects against machine breakdown
In Cox proportional hazard models, predictive maintenance was associated with a reduced risk of machine breakdown as compared to preventive maintenance. Predictive maintenance was associated with a 20% reduced risk of machine breakdown in the 15-days interval as compared to preventive maintenance. The protective effect of predictive maintenance remained significant after adjusting for duration of service and the size of the machines (HR, 0.74; 95% confidence interval [CI], 0.63-0.87; p = 0.002 and HR, 0.83; 95% CI, 0.74-0.96; p = 0.06). These findings were consistent for the two companies (Table 2 and Figure 1)
Table 2
Effect of maintenance strategy on machine efficiency in Cox regression models
Chariot Oil & Gas LTD Victoria Oil & Gas Plc
HR (95% CI) P-value HR (95% CI) P-value
Unadjusted
Predictive Maintenance 0.32 (0.11 – 0.52) < 0.001 0.48 (0.36 – 0.60) 0.003
Adjusted
Predictive Maintenance 0.74 (0.63-0.87) 0.002 0.83 (0.74-0.96) 0.06
The adjusted model was adjusted for duration of service and the size of the machines.
Figure 2
Kaplan-Meier Curves Showing Time to Breakdown as Predicted by Maintenance Strategy for Chariot Oil & Gas LTD
Figure 3
Kaplan-Meier Curves Showing Time to Breakdown as Predicted by Maintenance Strategy for Victoria Oil & Gas Plc
Narrative analysis results
Table 3
Narrative Analysis Results
Focus Group Questions Outcomes
What is the significance of SAP in maintenance optimization?
Chariot Oil & Gas LTD
A- The tools have sensors that discover anomalies. Such situation elicits the need for action before breakdown
B- The tool is crucial for data collection after monitoring the operation sequences of the equipment
C- The tools analyze the collected and cleaned data to assist and interpret technical problems in the operation
Victoria Oil & Gas Plc
A- The tools minimize downtime. Thus, they ensure continuous production.
B- SAP increases equipment efficiency that in turn optimizes profits through high production
C- SAP assist in reducing the cost of production that is associated with breakdown repair and replacement of the entire equipment
Which maintenance procedure functions best with the implementation of SAP tools?
Chariot Oil & Gas LTD
A- Predictive maintenance
B- Predictive maintenance
C- Both predictive and preventive maintenance
Victoria Oil & Gas Plc
A- Predictive maintenance
B- Predictive maintenance
C- Preventive maintenance
Does SAP enhance efficiency and optimization in predictive maintenance than in preventive maintenance?
Chariot Oil & Gas LTD
A- Yes
B- Yes
C- It functions equally in both cases
Victoria Oil & Gas Plc
A- Yes
B- Yes
C- It does propel predictive maintenance but also demonstrates substantial improvement when employing preventive measures
What are the challenges associated with the application of SAP in equipment maintenance in the oil and gas industry?
Chariot Oil & Gas LTD
A- Some machines use outdated systems that can barely support SAP
B- SAP tools can be expensive for incorporation by small scale companies
C- Recruiting new and skilled personnel conversant with SAP can be expensive for companies
Victoria Oil & Gas Plc
A- Interests in machinery maintenance have only been introduced in the past few years. Therefore, operators are yet to indulge in such activities
B- Manager still want to use scarce resources to obtain huge margin profits. Hence, they are hesitant towards employing new technology
C- Long-term investment is not a key strategy to some companies in the oil and gas industry. Therefore, using massive resources to upgrade equipment for short-term operation is irrational
Chapter Five
5.0 Discussion
While the literature demonstrates various views concerning predictive and preventive maintenance as well as the significance of SAP, the descriptive and narrative analysis results of this study demonstrates intervention that agrees or differs from other research works. As such, the outcomes of this study concerning the concurrent reflection of preventive and predictive maintenance suggest that both strategies are crucial for the equipment optimization in the oil and gas industry. Nevertheless, trends and the resulting p-values elicit that the predictive maintenance procedures are associated with lower incidences of machine breakdown or failure compared to techniques related to preventive maintenance. The consistency of such outcomes in both companies is also an indication that both procedures are critical for maintenance optimization, but predictive maintenance depicts an exceeding level of significance compared to preventive maintenance.
5.1 Preventive and predictive maintenance increase efficiency and limits downtime of equipment
Custeau (2) describes that tough economic times have increased the need for oil and gas companies to incorporate predictive and preventive maintenance processes to enhance their output efficiency as well as minimize the cost and limit the downtime that might be experienced upon machinery breakdown. Mohamed (12) also agrees that oil and gas companies possess the needs of reducing safety risks as well as the utilization collected data to enhance a reliable and continuous running of equipment. Similarly, participants C in both Victoria and Chariot Oil & Gas companies agree with Mohamed (12) and Custeau (2) that both preventive and predictive maintenance procedures are significant in mitigating machinery breakdown. Nevertheless, some differences can still be observed concerning the preference for preventive and predictive maintenance processes in oil and gas companies. For instance, the responses of most participants about which maintenance procedure functions best in the optimization and efficiency of equipment, A and B in both Victoria and Chariot Oil & Gas hold that predictive maintenance is the most reliable technique. These responses agree with the descriptive analysis outcomes that predictive maintenance is related to low breakdown incidences compared to preventive maintenance. Nevertheless, the Hazard Ratio of preventive maintenance is at one by default. Therefore, the values still support the significant influence of preventive maintenance strategy in optimizing the functioning of machinery irrespective of the high preferences of predictive maintenance techniques.
5.2 Using SAP as a primary tool for predictive and preventive maintenance
Ozekici (552) suggests that predictive maintenance is characterized by implementing the condition-monitoring tools that control the performance of machinery in organizations. On the other hand, Crain (1) describes that preventive maintenance incorporates CMM software to enhance maintenance optimization. Unlike predictive maintenance processes, preventive maintenance does not require condition-monitoring tools. One of the primary reasons for using CMM over condition monitoring tools is the cost incurred during the maintenance period. As such, participants A, B, and C in both Victoria and Chariot Oil & Gas companies agree with Telford et al. (1152) that oil and gas companies have been hesitant to incorporate IoT because some still utilize outdated equipment that requires upgrades before accommodating the SAP operating systems. Still, they correlate by highlighting that it is difficult from small-scale dealers to incorporate the use of SAP because it may increase the cost of production associated with recruiting new personnel or training the old employees about ways of using the SAP- engaged equipment. Still, the participants concur with Velmurugan and Tarun (1622) about gaps in the literature reviews. The gaps exist because interest in machinery maintenance optimization has increased in the past few years.
Nevertheless, SAP is characterized by the utilization of both condition-monitoring tools and CMM to optimize maintenance. Dorf (10) highlights that the condition-monitoring tools include sensors that facilitate the massive storage of data and running algorithms to detect breakdown. Similarly, Thakur (11) and Rambabua et al. (3) agree that SAP tools have capabilities of propelling the effectiveness of predicting anomalies using sensors. Therefore, the use of both CMM and condition-monitoring tools in predictive maintenance procedures is highly preferred than in preventive situations. As such, respondents A and B in both Victoria and Chariot Oil & Gas companies agree with George (2016) and Thakur (11) that SAP enhances efficiency and optimization in predictive maintenance than in preventive maintenance. Nonetheless, participant C in both companies still maintain that preventive techniques can improve equipment maintenance optimization if they are applied together SAP software in sensing and monitoring operations in oil and gas companies.
Chapter Six
6.0 Conclusion, Recommendations, and Limitations
This study rejects the null hypotheses that preventive maintenance strategy does not influence the optimization of machinery in the oil and gas industry; predictive maintenance strategy does not influence the optimization of machinery in the oil and gas industry, and that SAP tools have no significance as the primary component for preventive and predictive maintenance on the optimization of machinery in the oil and gas industry. As such, the research reveals that both preventive and predictive maintenance strategies have a significant margin of propelling effectiveness and limiting the downtime of the production process that could be facilitated by the equipment breakdown. Still, the study demonstrates the incorporation of IT functions such as SAP that incorporates both CMM and condition-monitoring tools in the maintenance strategy. While the preventive process incorporates CMM, predictive maintenance includes SAP and enhances its ability to distinguish equipment breakdown better than the preventive maintenance strategy.
Although this study provides insights into the objectives and research questions, it can be associated with various limitations. One of the limitations is that the ethical conditions granted by the study participants restrict the study from exposing confidential information that could affect their competitive ability the moment it is accessed by rivals in the oil and gas industry. Another limitation is that the study was only centralized on oil and gas companies in the same study area. Although the situation propels homogeneity, the outcomes are at a high risk of bias. Therefore, it is recommended that similar but future studies should focus on conducting studies in different study areas to compare trends facilitated by the outcomes of the research. It is also recommended that future studies should include the long-term and short-terms aspects in maintenance optimization to reveal the integrated benefits and loses of running the operations.
Works Cited
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Appendix
Focus Group Discussion Questions
What is the significance of SAP in maintenance optimization?
Which maintenance procedure functions best with the implementation of SAP tools?
Does SAP enhance efficiency and optimization in predictive maintenance than in preventive maintenance?
What are the challenges associated with the application of SAP in equipment maintenance in the oil and gas industry?