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Predictive Maintenance on Cargo Ship Engines
Based on a ranked list provided by IoT-Analytics, and on the companies' revenue, the top companies that offer predictive maintenance on cargo ship engines are Schneider, ABB, GE, and SKF.
SCHNEIDER
- Estimated revenue: $28 billion
- They claim to be one step ahead of the game with their "condition-based maintenance for ships." This means that "technical parameters of the equipment are measured and combined with environmental parameters relating to system usage."
- According to the company's website, "the benefits of CBM for ship operators are multiple. First it will improve the overall system reliability and significantly reduce downtime. It will also make it possible to adjust and optimize the maintenance plan frequency based on actual figures. Therefore, it leads to significant cost reductions."
ABB
- Estimated revenue: $27.7 billion
- They promise "up to 70% reduction of on-call service engineers and up to 50% reduction in maintenance through extended and predictive monitoring."
- They have three service levels for the marine industry. Their prediction service includes Onshore Prediction Analytics to IOC, Onboard Edge Analytics, Fleet Portal, Data Transfer, Critical Trip notification, Connectivity & System Monitoring, Incident Report, and 24/7 Care—Remote Assistant.
GE
- Estimated revenue: $27.3 billion
- GE offers "fleet data analytics and predictive maintenance through advanced digital platforms."
- They also offer customized services for a wide array of industries, including the marine industry.
- GE advertises that their solutions can "drive up energy efficiency and better return on assets."
SKF
- Estimated revenue: $9.7 billion
- SKF offers monitoring and predictive maintenance solutions for all auxiliary equipment: motors, pumps, fans, compressors, purifiers, electric motors, generators, turbines rotary dryers, transmissions, centrifuges, and gearboxes.
- Their purpose is to "maximize the reliability and availability" of all auxiliary equipment.
- SKF representatives believe that their solutions can "extend the time between repairs, eliminate machine problems on time, allow spare part optimization, track ship and leet condition and reduce maintenance costs. The system furthermore facilitates ease of use and communication, crew involvement and team work."
RESEARCH STRATEGY:
We started by searching for predictive maintenance market leaders that offer predictive maintenance to start-up companies, and that focus on predictive maintenance solutions for cargo ship two-stroke engines. We explored market report collections from reputable financial research databases, as well as business publications. Then, we closely investigated tech publications. We especially focused on those tech publications that concentrate on analytics, and the Internet of Things, such as IoT-Analytics. Furthermore, we looked at maritime news magazines. However, despite our thorough research, it appears that our topic has yet to be explored by leading industry experts or market researchers. We came across recent scientific articles that look into predictive maintenance for cargo ship two-stroke engines. However, while they discussed the topic in depth, there was no mention of the main market players in the sector.
Therefore, we abandoned this strategy and continued our project by expanding the scope of our research. Thus, we started looking for leading companies that offer predictive maintenance solutions for cargo ship two-stroke engines. As a result, we were able to identify cargo ship two-stroke engine manufacturers that also offer predictive maintenance for their own products. Such manufacturers include MAN Diesel SE, and Winterthur Gas & Diesel Ltd. We were also able to identify companies that specialize in creating smart technology solutions that offer predictive maintenance for cargo ship two-stroke engines, such as Wärtsilä. Furthermore, we found data analytics companies that channel their efforts into creating predictive energy solutions for cargo ship components, including two-stroke engines; Qvartz Analytics is an example of this. Parker Kittiwake offers predictive maintenance systems designed for various industries. They advertise predictive maintenance solutions for cargo ship two-stroke engines. However, despite our efforts, we were not able to determine whether these companies are some of the biggest competitors in the sector. Although these are public companies and their annual revenue is publicly available, we were not able to find any market reports, statistics, or estimations to help us determine the cargo ship predictive maintenance market size.
However, we were able to find several market reports that include data regarding the predictive maintenance market size as well as the names of some of the key competitors in the market. Unfortunately, each report mentioned different key competitors. Although some of the competitors were mentioned by several reports, not all of those competitors offered predictive maintenance for cargo ship engines. We continued our research by looking for predictive maintenance key competitors and we came across an IoT-Analytics report that included a top 20 predictive maintenance company ranking. However, this top 20 list was not created based on sales or revenue. It was created based on "Google searches in conjunction with 'predictive maintenance'," media mentions in the context of "predictive maintenance," and the number of employees that tag themselves with "predictive maintenance" on social media. It is important to note that not all of these companies offer predictive maintenance for cargo ship engines. Therefore, we investigated each of these companies' offerings in order to find out which ones design predictive maintenance solutions for cargo ship engines. Regarding revenue, after investigating the available financial statements, we concluded that the data regarding the revenue generated from the companies' predictive maintenance solutions sales, and even more so from the predictive maintenance solutions designed for cargo ship engines, was not publicly available. Based on the ranking provided by IoT-Analytics. Therefore, based on our efforts to find out which companies offered predictive maintenance on cargo ship engines, the top companies in the sector are GE, SKF, ABB, and Schneider.