Digitalization

The rapid pace of engineering Digitalization is increasing the number of systems that contribute to the creation of production-related information. At the same time, data volumes are constantly growing and becoming more and more heterogeneous. On top of that, data volumes keep growing and becoming increasingly heterogeneous. Today, data is created in a wide variety of systems: MCAD, electrical CAD and electronic CAD tools along with any product-related digital information in Office documents, ERP, CRM and support systems.

Managing this data is a complex challenge because it needs to be looked at along the product’s entire lifecycle. As business departments are moving towards integrative Collaboration, it is also crucial that they go beyond working with their own data alone. The manual transfer of information from one system to another, however, is highly error prone.

In this context, the interpretation of the term Digitalization often falls short.

1.1 Disruptive Innovation

Being as innovative as a startup with ongoing operations in full swing: Many established SMBs in technical industries still struggle to strike the right balance here. The ability to fine-tune technologies and propel them to worldwide leadership is a hallmark of German engineering. The things we are seeing in the context of Digitalization go beyond mere technological advancement. It is a revolution that has left no shortage of established companies that were steamrolled by a previously completely unknown competitor.

Disruptive innovation is not so much driven by technical differentiation as by the underlying business model. For SMBs in the engineering and other sectors, this is the one characteristic of Digitalization that makes it so challenging for them. After all, established companies can’t just kick everything they built to the curb and dedicate all of their resources to new ideas without a care in the world. They rather have to take a two-pronged approach to back innovation with economic certainty by continuing with the old model while making the transition.

But how can you disrupt yourself while going full speed ahead? How can you dynamically explore new directions with the enthusiasm of a Silicon Valley startup without being weighed down by existing commitments?
The two most important factors in designing new business models are timing and management. It’s not the early bird who gets the worm, but the one who is ideally positioned when the early majority of customers comes around, that is, the group of buyers that generate demand for the new product or the new service.

Disuptive Innovation - Early Majority

 

1.2 PLM and Industry 4.0

A December 2014 survey by Staufen Academy showed that the future path of products and the organization in development, manufacturing, and maintenance will be largely determined by the technologies collectively described as “Industry 4.0”. About one third of all engineering companies have given little thought to Industry 4.0, particularly with respect to the correlation between PLM and Industry 4.0. Another 33% are beginning to explore it and looking at application scenarios. The remaining one-third consider themselves on the brink of the fourth industrial revolution.

From a PDM and PLM perspective, the control processes in Industry 4.0-driven manufacturing need to be based on end-to-end product data management.

“The ability to efficiently and effectively manage this digital product model along every step of the way – from development to sales, from manufacturing and commissioning to customer experience and provision of product-related services – has come to be known as product lifecycle management or PLM since the beginning of the new millennium. And increasingly, PLM is going beyond mechanical geometric data models to include the logics behind electrics and electronics and the programs of the embedded software.

Being able to manage product data in this way is the most basic prerequisite for modern, smart, connected products to work and successfully compete in a globalized market. It is the most basic prerequisite for a smarter approach to organizing networked manufacturing. It is the most basic prerequisite for Industry 4.0.” (Hechenberger Thesen / Sendler Circle)

So, what exactly is a digital business process?

Many companies have processes in place that only appear to be digital. The hallmark of a truly digital business process, however, is the fact that operable information is available for further processing.

Nowadays, you have to look very closely to distinguish truly digital business processes from the fake ones. Eliminating paper alone does not mean that the information is now digital. In reality, it has merely been “electronified”. The scanned version of an invoice that a supplier emails to its customer is a typical example for this. The invoice information it contains is far from digitally operable. But without truly digital information, there is no genuinely digital business process.

In case of an invoice this means using optical character recognition to extract the written text from the image, capture the header and position data, and, ideally, compare it to the corresponding purchase order in the ERP system. If the invoice values correspond to those in the order, a workflow is triggered to forward the invoice data to accounts payable – a classic case of touchless processing without human intervention and a prime example of a truly digital process. Such a process is characterized by the availability of digitally operable information that is automatically processed and by systems that are interconnected for this very purpose.

What is true for invoices on the business side can also be applied to design and development practices. An engineering change request for a product created in the PLM system in PDF format is not an operable piece of information. To become just that the instruction it contains needs to be available separately and automatically be put into context with the corresponding part. The fact that the individual change items are listed in the request alone does not constitute a complete digital thread. And it is not just the information about the change itself that needs to be available in digital form and connected to the change documentation, but also the resulting task.

Companies looking to establish a digital business process in the PLM ecosystem need to do more than just email a task and include the parts it affects as an attachment. The key is to use a task file in the PLM system to assign the task and to have documents stored in a single location. With this in place, the change process defined in the PLM software will control all product data and documents associated with the change, accompanied by the task file.

These two scenarios demonstrate what a truly digital process should look like. A company cannot truly realize end-to-end Digitalization unless it makes all of its information digitally available and ensures that it is instantly available to other systems. This means that information is capable of being leveraged in its digital for without human interaction and triggering actions and downstream processes. This is known as digital impact management. Digitalization in engineering cannot be done properly without the appropriate platform. A Product Data Backbone serves as such a single source of information.

To achieve this, companies need to start by laying the technical foundation in their IT. The following three areas are crucial to Digitalization: the ERP system (with SCM, business intelligence, and maintenance) to connect production, accounting, sales, and service, the Office systems including intranet, portal, and CRM system, and the PLM software for product creation and management – the Product Data Backbone.

1.3 Systems Engineering

The development of ever more sophisticated mechatronics systems relies on the close Collaboration of experts from all disciplines involved. When companies begin to design a new product or make fundamental changes to existing ones, they often can’t and don’t yet know exactly what functionality will be implemented with what technology. The important task at this point is to very accurately describe the functionality of a product. This allows everyone involved in development, manufacturing, and marketing to figure out what to do. This is the primary responsibility of systems engineering.

Systems engineering is the first stage of product lifecycle management. It also accompanies every subsequent stage of the PLM process. Consequently, systems engineering and product lifecycle management need to go hand in hand. Systems-oriented design that is detached from the way it is implemented by means of mechanics, electrics, or software will become immensely important in the future and is a cornerstone of any Industry 4.0 strategy.

Systems Engineering und PLM

Today, products need to be more strongly defined by the underlying business model. This means bridging the gap between development and the respective business departments and business model. This bridge is going to be built by systems engineering, which needs to serve as the overriding concept. Because in the future, the share of software and electronics components in these products will only go up and with it the market pressure of offering these product components. This shift in value proposition share will become the driver of systems engineering methods. Leveraging them will result in shorter product cycles and make the entire development process much more dynamic at every step of the product’s lifecycle.

1.4 Digital Information Twin

The structure of machinery and equipment is growing increasingly complex. As Digitalization and Industry 4.0 are intensifying, the share of electronic and software components is ever increasing while the share of mechanical components is going down. By merging the product information on all equipment components in a product and document lifecycle management system and by doing so throughout its entire lifecycle, companies create a digital information twin of the equipment that was delivered to the customer.

Computer-based models of objects that allow for virtual simulations known as digital twins have been garnering more and more attention lately. In the engineering industry, this concept is closely tied to Digitalization and Industry 4.0. By mirroring a process, a product, or a service, the digital twin connects the real world to the virtual world. Sensors installed in a real object will transmit their data to the digital twin, which then processes and analyzes them. Systems monitoring allows companies to anticipate potential issues and avoid problems before they occur.

In day-to-day operations, however, these types of complete digital twins are still in their early stages due to the technical challenges they present. Currently, it is mostly individual parts of a piece of equipment that are being monitored, for example to enable predictive maintenance.

The Lifecycle File as one variant of the digital twin

A “digital information twin”, however, is undoubtedly easier to implement than a complete digital twin. Really what this is, is a Lifecycle File of a product or plant. It mirrors the technical structure of all elements in the plant and consolidates any pertinent information that is relevant to product creation and product management into a central location. The Lifecycle File aggregates all product data and documents across the lifecycle of the plant, bringing together the information on a particular product in a customer-related or project-specific manner to create a digital information twin of the delivered equipment.

Being able to assess recurring malfunctions of a piece of equipment with respect to the quality of the product, for example, requires a structured and consistent documentation of how every last piece of the customer’s machinery looks like. What pump and motor were installed? What changes has this motor already undergone? What software version is powering the drive control? Where is the accompanying description stored? Having this exhaustive digital documentation readily available at the click of a button makes it much easier to assess the conclusions drawn from malfunctions. What’s more, manufacturers can automatically create their documentation, match their products to initial specifications, or analyze the impact of engineering change requests.