On the matter of how 'small' things add up to produce the big, Julia Carney's immortal lines (from her poem, Little Things) are highly illustrative, which read as "Little drops of water, /Little grains of sand, /Make the mighty ocean/And the pleasant land. It does not matter whether you approach this proverb as a physical fact, or as a philosophical statement or for that matter, as the base for the ongoing phenomenon of Digital Twin-led transformation in businesses.
Gartner predicts that by 2021, half of large industrial companies will use Digital Twins, resulting in those organizations gaining a 10% improvement in effectiveness1. Since the advent of the concept, Digital Twin has unearthed new possibilities with advancements in the field of IoT/IIoT along with analytics, artificial intelligence/machine learning. It is now a tool for true digital transformation.
Adoption of Digital Twin
The complexity of ‘small’, for instance, a finger to that of a palm to that of a hand to that of an entire human body (Huge) traverses a journey that is pretty deterministic at the smallest level (finger), while it becomes more behavioural or pattern-based at the highest (human).
Typically, the biggest and most talked about use case of Digital Twin is Asset Performance Management and as such, the adoption is mostly confined to the entity level (finger). The technologies associated at that level are pretty deterministic: Predictive modelling around equipment’s performance and failure analysis is widely deployed. However, the biggest benefit of Digital Twin adoption is the knowledge maturity that comes along with it and how that transforms people (decision-making), process (optimization) and system (consolidation).
Let’s consider a scenario, where an air preheater prediction about potential failure enables a root causes analysis, which identifies a potential cause in debris from the latest soot blowing. The power generator evaluates the probable penalty options based on rate differences against the power purchase agreement signed. The loss in efficiency for a potential scenario of taking out the boiler to address the air preheater issue is directly linked to the economics of operation. Operating the boiler at a higher turndown ratio, might be the way to go till the next planned outage.
The sequence of events started with a prediction at an equipment level. This called for efficiency management of the boiler, which is driven by a thermodynamics principle. This further led to managing the economics of operation at the enterprise level, and ultimately provided the best-fit workaround to tackle the situation. This is the story of Digital Twin and not just the predictive model of the air preheater.
A holistic Digital Twin platform
Digital twin is nothing but a self-learning Enterprise Performance and Knowledge Management platform. The main attributes of this platform are Data Acquisition (to support multi-protocol collection of data, edge computing, smart devices etc.), Information Management (to support system modelling, algorithmic computations, federation etc.), and Basic & Cognitive Application services (to support procedural automation, machine learning, knowledge management etc.).
This Enterprise Performance and Knowledge Management platform will co-exist in the ecosystem with ERP / MES / PLM / SCM systems and work for improving the effectiveness at each level of the enterprise and its digital transformation journey.