No terms have captured the hype in manufacturing more than Industry 4.0 (or Industrie 4.0) and smart manufacturing. The supporting technology of the Industrial Internet of Things (IIoT) has promoted the concept of digital transformation, promising to shift manufacturing to the same degree that Uber has altered the for-hire ride business or Airbnb has altered the travel lodging industry.
Business leaders are piling on the Industry 4.0 wagon for fear that failure to employ new technology will render them uncompetitive (at best) or irrelevant (at worst). Market forecasts portend technology investment over the next five to 10 years that means every business will have some degree of new smart manufacturing capability. Even those organizations that invest only minimally will still see a significant change in their value chain, whether they are at the beginning, the middle, or the end of it.
Getting return on investment will prove to be a challenge to some, while others will see immediate benefits by adopting advances such as additive manufacturing (3D printing); having the ability to sell value-added services such as machine performance insight; or even selling capacity instead of products, much like GE sells engine thrust rather than jet engines.
All companies, however, will face a distinct challenge that remains as an issue in any process change initiative: sustaining improvements. For organizations embracing Industry 4.0, that challenge will prove especially vexing if they continue with their current maintenance practices. The reality is that the vast majority of manufacturers and asset-intensive industries still are both immature and unsophisticated in their maintenance practices.
There is a lot of confusion as to what maintenance practice maturity is; many confuse sophistication with maturity. The original capability maturity model (CMM) from Carnegie Mellon University views maturity as more of a cultural rating than a technological approach. Too many maintenance practitioners equate things like reliability-centered maintenance (RCM) or predictive maintenance (PdM) with maturity. A mature maintenance culture certainly employs these tools, but it does so only as appropriate for critical assets, and it employs them across the enterprise in a systematic way. Understanding this is the first step to adequately support the next generation of smart connected assets that are beginning to populate the plant floor.
To gain the most from these smart assets, businesses will need to do more than just catch up to best practices today. They will need to make as big a leap in their maintenance approach as they are in their manufacturing capabilities. Understanding the makeup of an asset performance management (APM) 4.0 solution is critical. LNS Research categorizes providers of APM 4.0 capabilities as either platform providers or ecosystem partners.
APM 4.0 platform providers are coming from the automation community as well as the enterprise software supplier community; these are companies that provide both a significant footprint of functionality from the APM landscape and the infrastructure that enables:
- Rapid IIoT integration
- The ability to leverage big data and analytics for prescriptive maintenance (RxM)
- Support for AR/VR technology to fully enable the digital twin
- Support for mashup solutions
- Embedded best practices and enforcement of these
APM 4.0 ecosystem partners fall into one of two subgroups. The first constitutes the traditional maintenance and reliability-centered/condition-based maintenance (RCM/CBM) tools, other predictive analytics elements, and financial modeling tools.
The second consists of those companies that provide critical infrastructure elements or the beginnings of a platform but that currently fall short to such a degree that substantial other capabilities must be grafted onto the solution to meet APM 4.0 functionality requirements.
The digital twin is the ultimate mashup of capabilities that sets APM 4.0 apart from today’s best-in-class APM solutions. In the context of APM 4.0, the digital twin is more than just the static virtual/digital engineering representation of an asset or plant. It is the ability to have a dynamic model that is constantly in sync with the real physical asset and is capable of parallel what-if scenario analysis to evaluate the impact of potential operational and maintenance cases, all with the goal of providing prescriptive advice that notes the financial implications of the options produced. This starts with a rich engineering model of the asset, but it also requires enough information from the asset to fully model its behavior while it is in use.
APM 4.0 is not as much a specific product functional set as it is an evolving aspirational objective that companies should work toward if they want to be able to sustain the benefits they hope to gain from embracing Industry 4.0. No vendor today can deliver a full APM 4.0 solution any more than a single vendor today can deliver a fully capable smart manufacturing plant. APM 4.0, like Industry 4.0, is something businesses need to strive for if they are to thrive in today’s uncertain but dynamic economic environment.