Every year LNS makes predictions around key trends in the Operational Excellence pillars. Our 2017 forecast focused on a new model emerging for asset...
Like manufacturing, asset performance management (APM) has evolved to leverage the digital technology associated with Cloud, the Industrial Internet of Things (IIoT), mobility, Big Data, and the associated analytics. This overall industrial shift has been referred to as both Industry 4.0 (or Industrie 4.0) and Smart Manufacturing. Accordingly, LNS set forth a vision of how APM has evolved in support of these highly digital, capable manufacturing assets which we call APM 4.0.
At the heart of the APM 4.0 concept is the Digital Twin. From LNS’s perspective, the definition of Digital Twin functionality must be in context of APM 4.0, and sets the stage for a discussion on how to drive Operational Excellence. Several areas of differentiation exist between assets that are production-related versus products designed for consumer use:
- Life of the asset/product
- Variability in configuration during the service life
- The complexity of the asset/product
- Service versus replacement decisions
Production Asset Life Measured in Years – A Twin Must Last as Long
Assets used in manufacturing or support of infrastructure and transportation have lifespans measured in years to decades. Many production plants have a useful life of 25 years or more while infrastructure assets such as a hydroelectric dam might have a service life of 100 years or more. Products targeted to the consumer market, even appliances and vehicles, typically have a life expectancy of five to ten years, with consumer electronics often replaced annually. The implication for the Digital Twin of these very different lifespans is significant. For physical assets, it means the twin must be accessible for the entire life span, and newer technology associated with Digital Twins must scale backward for compatibility.
Long Life Means Maintaining the Evolutionary Record
This long life means the historical record of the twin must be maintained to facilitate accounting and reporting for as long as that asset is in service. In certain industries, the actual configuration of an asset train must be part of the traceability and genealogy record of the products produced on that line. This means the twin must have a time synchronized history, and a product linked to history.
The data storage associated with this extended life and structurally complex twin all but demand Cloud storage. The ability to have the as-maintained bill-of-material (BOM) immediately accessible to both operations and maintenance staff means the twin must be in lock-step with the actual asset, not updated asynchronously. The U.S. military B-52 bomber is one example of this; it is often quipped that for these 50-60+ year-old aircraft the only original component still flying is the nameplate and everything else having been replaced over the lifespan of each airplane.
Production Asset Complexity is a Given and Singularity is Common
The smart products market is largely consumer-oriented, with many variations on each model. Products range from quite simple to moderately complex. In an industrial setting, production assets and the infrastructure to support them are just the opposite – they often exist as unique objects, and in the case of a large plant like a refinery or power station, each one can be incredibly complex. Additionally, the production complex frequently may have multiple moving elements such as a mine or rail system with assets that are mobile and repositionable. This means that as each asset is complex and unique, so must the twin be unique and sophisticated.
The implication is that the construction and maintenance of the twin must be automated and affordable since the cost can only be spread across that asset, not an entire production run. It also means that the performance data for the asset, in the form of simulation models, must be tuned to the specific asset and maintained in its as-operated state.
The Digital Twin Must Embrace Financial Models
Unlike most consumer smart products that are disposable or only marginally repairable, things like automobiles are an exception; production assets invariably are designed for long a long service life with repair processes part of the expectation of each asset’s history. This means the Digital Twin model of a production asset must incorporate adequate financial models to facilitate the simulation of alternative operation versus forecasted repair costs.
An example would be to simulate wear under different operating conditions and analyze the total cost to maintain in each scenario to facilitate the optimal operational decision. Ultimately, this extends to modeling within the twin; the service versus replacement cost scenarios and the disposal cost simulations. So, the APM 4.0 Digital Twin is going to need access to a very rich financial model as well as the operational performance and service life simulations.
The Production Asset Digital Twin is Community Property
In the consumer world, the Digital Twin often remains the property of the manufacturer. While they may share it with the consumer as part of the delivered product or as a service, the twin is vendor-centric, and the user has little need to use the twin except when the augmented reality (AR)/virtual reality (VR) experience is the product. This is very different in the production asset environment where dozens of parties may need to interact with the Digital Twin. Maintenance staff, from many different disciplines, operators, financial analysts, production engineers, and process designers need to communicate with the twin. This means the Digital Twin will be adding far more value, but it will also have to serve multiple masters.
Realistically, this next evolution of the Digital Twin for physical Smart Connected Assets is going to take several years to grow into full capability and for users to understand how to utilize twins best to improve their performance. Smart users will realize that by starting now. They must grow their skills and knowledge in parallel with the market as twins evolve to meet the complex needs. These users will be in a better position than those that delay and wait for a stable future. The reality is, that “stable future” will never come as technology keeps providing more tools and power to aid us in our pursuit of Operational Excellence.