At the third stage, Visibility, things become truly interesting. All the data points are connected, creating an up-to-date digital model or 'digital shadow' of the plant. This is a move with a very elemental, radical effect, as it breaks down existing silos. It impacts the operational structure, even the corporate culture. Structural PLM (Product Lifecycle Management), ERP and MES data is combined with streaming data from PLMs and sensors to create a single source of truth.

In a fourth stage, Transparency, root cause analysis can be used to determine why certain events occur during the production process. Using data engineering, a foundation is also laid to add Predictive Capacity in the fifth stage. Predictive use cases such as predictive maintenance and predictive quality are extremely appealing to many companies. A lot of work (data) is still required to reach this point, however. In a final, ultimate stage, a company might achieve Adaptability, where, for example, a machine changes its own production sequence to avoid downtime, or submits work orders to the maintenance team. In short, the Acatech model reveals the viability of starting small and approaching things one step at a time.

The Data Is There for the Taking

The current generation of sensors and IIoT devices (Industrial Internet of Things) are fairly easy to connect to older machines and equipment, requiring limited effort and expense.

This means that data capture is easier than ever, and even smaller companies can now collect huge amounts of process data. The major benefit is the creation of real-time insight into production parameters, allowing them to address specific events or downtime much more quickly. As indicated in the model, a next step can be to add predictability through analytics and AI (Machine Learning), based on input such as that real-time data. For example, if a filter installation is fitted with a sensor, it can indicate when it needs to be replaced automatically. There's no need for visual inspections by maintenance staff, saving time and resources.

Such relatively small investments are negligible compared to the potential business gains. Process optimization, increased quality, more efficient energy management, less downtime (OEE), shorter cycles; the business benefits are clear, even for smaller or mid-tier companies.

Both large and small companies can benefit from a data-driven Smart Industry approach and a staged roadmap. The earlier you get started the better, even in these uncertain times. Does size matter? No, maturity does!

This blog is the second in a series in which we discuss the myths and facts surrounding digitization in the industrial sector. The first two myths have already been debunked, discover during the coming weeks which myths or facts remain.

Read also:

• Fact or myth: digitalization is not a must-have in industry