It's obvious: technological change is revolutionizing production. It is making production faster, more efficient and often more cost-effective. But today's customers expect more than that: they not only want a good product - i.e. a machine or system - but also a comprehensive service that can be "consumed". The answer to this is called 'X-as-a-Service', where the 'X' stands for a variety of services or solutions. The 'Equipment-as-a-Service' variant, EaaS for short, which goes far beyond previous leasing models, is increasingly in demand.

The principle involves a completely new remuneration model: the manufacturing company only pays for what it has actually produced with the machine or system. This is not primarily a question of running times, but of specific quantities. This means that the leased machine is paid for when it has actually generated added value for the manufacturing company.

Consequences in three dimensions

However, for this model to function optimally, consequences and implications must also be taken into account, which take place in several dimensions. The first of these dimensions is the seamless and smart monitoring of the respective machine or system. With the available (sensor) technology, this can be implemented without any further challenges as soon as a uniform industry standard is followed and does not usually pose any major problems for those involved.

The second dimension is different: it involves the development, implementation and realization of an adequate maintenance strategy. This is only possible if good asset management is in place. It must be possible to determine in real time whether all components of the machine are running faultlessly, whether any irregularities are registered - and what their potential effects could be. In short, it's all about predictive maintenance. With the help of machine learning and artificial intelligence (AI), potential malfunctions can be identified in advance and the appropriate measures implemented without causing operational interruptions or breakdowns.

In addition to this highly welcome effect, users benefit from many other advantages. For example, service technician deployments can be planned and carried out much more precisely and with fewer resources, which benefits both the environment and the company's finances. Predictive maintenance' therefore differs in key respects from the preventive maintenance practiced to date. This is tied to fixed intervals and cannot take into account whether a component actually needs to be replaced or repaired.

Predictive maintenance requires appropriate technical prerequisites, for example in the form of certain algorithms that can interpret incoming data in such a way that a potential problem can be identified. Ultimately, everyone benefits from this: the user, as they have fewer malfunctions to worry about, as well as the manufacturer and service provider, as they can save costs for unnecessary services and generate higher revenues if more is produced according to the EaaS principle. Last but not least, this also benefits the manufacturer's reputation as a producer of reliably running systems.

Cloud platform with industry expertise

This is where the third dimension comes into play: field service management. Operational planning must take into account factors such as "Who is on site?", "Which parts are needed?" or "Which route is the most efficient?". Business management aspects and the eco-footprint play a major role here. All three dimensions come together to form a completely new business model for EaaS providers: consisting of the three elements ERP/manufacturing, asset management/monitoring and field service management/provision.

How can all of this be brought under one technical umbrella? With an appropriate solution such as IFS Cloud. This offers a wide range of services - from reducing complexity, costs and risks to planning, managing and optimizing the availability of key assets, providing services and increasing workforce productivity. Such a solution combines comprehensive industry expertise with intelligent and autonomous functions in one product. This allows operational silos to be broken down and enterprise resource planning, human capital management, asset management, field service functions and much more to be combined.

This also applies to the three dimensions of production, monitoring and provision. Such a solution must cover all of them, because all of these areas are interrelated. Starting with the actual value creation, i.e. the design and construction of the machine, and not ending with monitoring. Monitoring data must also be fed back, analyzed and converted into recommendations for action. If all these steps are carried out via separate channels or systems, there is a risk of loss of know-how because data can be lost, loss of time because interfaces have to be passed through or data replication, as well as a lack of transparency because the information takes many convoluted paths.

The challenge of data consolidation

All-in-one solutions offer clear advantages here. But there are also hurdles to overcome, such as the challenge of how to get the machine monitoring data into the ERP. There are different machine protocols and an industry-wide standard does not yet exist. This means that vast amounts of data need to be processed and consolidated. The solution lies in cooperation with edge computing platform or IoT providers. These record the production data at the point of origin and consolidate it there. The (fault) relevant information is then forwarded to the ERP for pre-consolidation. The path continues via IoT data collection to the all-in-one solution, where the business process automation or workflow engine determines exactly what happens next.

With regard to the service aspect during ongoing operations and the MES (Manufacturing Execution System), it is all about connecting the planning level with the operational level. This is relevant if the user wants to control production with their machines. Communication with production machines must then be established from the ERP via a store floor workbench. This works via IoT sensors that automatically report back production orders and therefore quantities, as well as any faults. This automation also means a considerable reduction in the workload for employees. It also makes production more efficient and less prone to errors. This has been clearly evident over the past two years, during which the machines were down more frequently due to illness. Extensive automation brings significantly more safety in this respect.

Still room for improvement

The author: Thomas Knorr is Sales Director Installed Base & Channel, VP Alliances DACH at IFS.

© IFS

So what stands in the way of comprehensive modernization of production and a seamless EaaS offering? The necessary fundamental basis is not yet fully in place. Sensor technology already exists in many places, but not in all companies. There are also different sensor standards, which could stand in the way of potential users implementing a smart solution. Another aspect is the emotional readiness of customers. In many places, the attitude of "what I pay for should be mine" still prevails.

Those who focus on the future and embrace change will not only be better positioned to compete, but will also be better prepared for the inevitable medium-term transformation, which will no longer require a new or additional system. Compared to the past, the situation has been turned on its head: In the past, companies would first think about a new business model before thinking about a suitable ERP model. Today, companies are increasingly using the new functionalities to align their business models and processes.