Modern software solutions that reduce maintenance and service costs.

2BM software develop SAP-based Predictive Maintenance solutions, which use AI from IBM to help companies optimise maintenance, benefiting recruitment, budgets and the climate. Artificial intelligence will now also help with the optimisation of the crucial points in the Copenhagen Metro.

Machine learning and other areas within artificial intelligence (AI) are impacting more lives and more areas of our lives, as well as society as a whole.

We can use AI for purely entertainment purposes, like creating fun new pictures of the Mona Lise just by talking to a computer. But we can also use AI to predict when points in the metro are likely to fail or when they should be replaced before they fail in order to avoid expensive stoppages. This means AI is of enormous benefit for a lot of people.

“It’s certainly a good thing if the Copenhagen Metro can run without stoppages when 40,000 people need to return home from not one, but four Ed Sheeran concerts,” said Martin Pock. He is the CEO at 2BM Software, which provides SAP and cloud-based mobile software solutions for reducing maintenance and costs for companies.

Explosive growth

The company is experiencing explosive growth with a new type of software, which utilises the potential in combining the Internet of Things (IoT), artificial intelligence from IBM and an intelligent ERP system like SAP.

2BM Software provides its customers with a solution called ‘Mobile Predictive Maintenance’ – a data-based “extra intelligence level” for the maintenance of machines, buildings and infrastructure, which is an area that has seen explosive growth in recent years. According to global market research firm Allied Market Research, the global market for predictive maintenance is expected to grow from USD 2.8 billion in 2018 to USD 23 billion in 2026.

“Predictive Maintenance can prevent expensive production downtime and extend the lifetime of production equipment by analysing data that the equipment generates via different sensors and meters. It creates a better decision basis based on the state of the equipment for when maintenance of industrial equipment or production machinery or points in a metro needs to be carried out, which is of vital importance for citizens and society,” said Martin Pock.

High level of uptime across service periods

Pock’s choice of the replacement of points in a metro as an example of a function where artificial intelligence and computer power can make a difference to a company’s operations and maintenance is quite deliberate. One of 2BM Software’s latest business cases is the company Metro Service, which operates and maintains the Copenhagen Metro. 2BM Software has already developed a solution for Nordic Sugar, where the focus is on the maintenance of the sugar producer’s most expensive and most important plant equipment that can lead to downtime. Now it concerns the monitoring and analysis of the many points that help traffic to flow smoothly in the underground Copenhagen Metro.

“The Metro already performs with a high level of uptime across service periods – in fact it has one of the best in the world. By training the software to see the connection in the data from various sensors, the aim is to optimise maintenance and prevent costly and disruptive stoppages on the points by being able to predict and respond to estimated stoppages 10-20 days before they will happen.”

No more excessive maintenance

Thanks to the power of AI, companies can leave behind the maintenance plans many of them currently use today, which are based on preventative maintenance, and which can be both expensive and undesirable for the environment.

“AI can prevent you replacing something that has nothing wrong with it, something you are replacing just because a maintenance plan states you should replace it. It is the same thing as taking your car for a service because it says in the service logbook it is time to service the car – but in reality there is nothing wrong with your car. In terms of labour and the purchasing of components, that kind of maintenance strategy is costly, both financially and environmentally. Many companies have a strong assumption that they have excessive maintenance, which can be avoided using AI because they can see earlier on that the component can remain in operation longer and does not need to be replaced. It is a paradigm shift in the maintenance of large factories, machines and infrastructure, and in the long term, it can be used everywhere where preventative maintenance is used on a large scale.”

Recruitment challenges

The optimisation ability of AI-based software for maintenance will thus become an important element in companies’ work to reduce their CO2 emissions as much as possible, just as AI will contribute to making machines and equipment safer for employees by predicting breakdowns before they happen.

But there are several more positive prospects with predictive maintenance, both for companies and society in general, where problems with recruitment are already a challenge faced by many industries.

“Even though we live in a time when we know that we live longer and shall work longer, it is becoming increasingly difficult to get people to do the hard work. Not many young people apply for jobs as maintenance technicians today. Companies have difficulty finding replacements for the generation that came after World War II, and therefore they focus a great deal on automation and the possibilities of using AI to limit the tasks that require human eyes and hands to execute.”

Think big – but start small

According to Martin Pock, one of the signs that the market for predictive maintenance is still immature, is that the new solutions are often specifically aimed at a particular company’s challenges in a relatively limited area.

“You can advantageously start small, but still think big. Better to do a small, quick project for a limited scope than a major risky business case with a wide roll-out of new technology and methods, which many companies do not have any experience with. Often the data that is already in the company has not been used before. Implementing predictive maintenance is a little bit more uncertain than purchasing a robot lawnmower! Therefore, many large and complex companies prefer carrying out a smaller proof-of-concept on a specific machine, which we then develop in collaboration with their innovative maintenance department and IT. Better to introduce predictive maintenance that way, where the business, users and IT employees work together so that everyone can see the usefulness and value of a solution that works and which can actually predict breakdowns before they happen.”

ERP from SAP and AI from IBM

2BM Software collaborates regularly with IBM, which provides the latest AI technology for the solutions. The result is a unique combination of ERP from SAP and AI from one of the world’s biggest providers.

“It is reassuring for customers to know that our AI comes from IBM, while at the same time, with more than 20 years of experience as SAP experts, we know the customers’ ERP systems inside out. At the end of the day, this is also where maintenance costs will be managed.”

A suite of solutions

2BM Software is a subsidiary of 2BM, one of Denmark’s leading SAP consultancy companies. 2BM is a SAP Platinum Partner – the highest level of partner.

Mobile Predictive Maintenance is part of 2BM Software’s existing suite of software solutions. Mobile Warehouse and Mobile Work Order help maintenance workers gain an overview of and insight into the maintenance tasks for machinery and equipment, regardless of which mobile device they use as they move among the machinery and equipment. Mobile Work Order is designed for companies that use SAP PM (Plant Maintenance), which covers 90 % of the world’s largest manufacturing companies.

Article from Berlingske, August 31st 2022

Martin Pock

Martin Pock

CEO, 2BM Software


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