The Future Plant: Current Challenges In Asset Performance

Alexandra Glombik

Part 1 of a three-part series

When Horst started his work as a machine technician at a manufacturing plant 20 years ago, asset management looked very different than it looks today. Having climbed up the career ladder to become an asset manager, Horst has created a modern maintenance environment that tackles many of the major problems German manufacturing companies are concerned with.

Horst no longer has to do a daily tour through the plant to note downed-machine issues or check on maintenance-due dates. Instead, Horst uses asset management software that provides him a constant overview of all assets, right from his desk. Every asset is digitally represented by its digital twin and can permanently be monitored via a visual display.

By continuously collecting relevant data, designated devices automatically enrich an asset’s digital twin with information about its current performance and condition. Data analytics algorithms can use this information to generate a set of relevant KPIs throughout each asset’s entire lifecycle.

For Horst, it is crucial to always be prepared for any possible machine breakdown. Therefore, he is especially interested in knowing an asset’s mean time to failure (MTTF) or mean time between failures (MTBF), as well as the frequency of these incidents.

Knowing particular failures, and how often they typically occur with certain assets, helps Horst classify machine problems to common failure modes and get an understanding of when a failure is likely to happen. It also supports him in grouping his assets into certain risk categories depending on how often, how severe, and how detectable failures are occurring with an asset. This entire process is called Failure Mode Analytics – an important analysis for strategic asset management that is strongly enabled by the ability to monitor each asset’s performance.

Two other important KPIs are relevant once a predicted failure occurs: mean time to repair (MTTR) and mean downtime. As the main measures of machine availability, these KPIs are supposed to be relatively low to enable a maximum level of production continuity.

Following the principles of lean management, Horst is constantly engaged in putting appropriate measures in place to reduce the time a machine is down for repair. In this context, respective breakdown costs also play a meaningful role in managing asset performance.

Last, but not least, an asset’s comprehensive performance can be evaluated in the Overall Equipment Effectiveness KPI. This KPI indicates the percentage of time in which an asset is producing only good parts (quality) as fast as possible (performance) with no stop time (availability). Combining the aspects of quality, performance, and availability makes this measure a very powerful tool for Horst in assessing his assets and in gaining data-based knowledge about his overall plant productivity.

The variety of different KPIs makes it possible to have continual, real-time insight into all assets and their performance. For Horst, who always needs to have a profound overview of his assets’ current state, this really makes life easier. More importantly, the asset performance software equips him with a reliable base for decision-making.

While in the past, most decisions were made based on gut feeling, today the digital twin and its KPIs serve as the source for making machine diagnoses and determining asset maintenance routines. Also, standardized KPIs allow comparisons between several groups of assets or across different plants. This makes processes more transparent and more reliable, therefore helping Horst achieve the best possible asset operation.

By enabling technologies for the smart factory, companies are achieving Mission Unstoppable: making facilities management a transparent, manageable process.


Alexandra Glombik

About Alexandra Glombik

Alexandra Glombik is a working student at SAP. As part of her job, she supports the Business Transformation CxO Advisory Office in several research projects regarding future market opportunities. In her Master thesis at Technical University of Munich, she deals with the SAP S/4HANA Cloud ecosystem.