‘A Digital Twin is a virtual representation of a product, part, system, process or network that allows you to see how it will perform, sometimes even before it exists’.
Studies have shown that human computing power using more than 4 variables is only as accurate as chance, yet in many businesses today we leave complex, multivariable decisions to people. Normally because we struggled to replicate real world complexity in computer models.
However, with enhanced computing power the best Digital Twin models use stochastic modelling, which essentially allows performance of all elements of the model to perform based on probability, accurately predicting scenarios where specific events vary if actual performance. These Digital Twins will not only model scenarios but will determine the risk and probably of success based on replicating the future 100,000 times over (or however many you need to feel confident).
A tool this powerful can unlock significant benefit within your supply chain and can be used in many different forms, depending on the level of adoption.
In its most basic form a Digital Twin model can be used to improve decision making capability within a business to test scenarios, for example instantly identifying the optimum production sequence following a critical failure to protect customer service or cost.
Using actual performance data it will consider the full complexity of your factory or supply chain to determine the optimum solution to meet desired outcome. This could offer benefits in product costings, both present and future, not only accurately determining the cost of the new product, how it should be scheduled, but mapping the impact to other products, commonly missed.
Gone are capital expenditures or investments that don’t deliver, as you use your Digital Twin to see how changes in assets, suppliers, customers or sites affect the overall network before agreeing to these changes. This allows businesses to gain confidence in security of supply and true costs through ‘what if’ analysis.
To unlock the true capability of a Digital Twin, a business should integrate its power into their planning cycle. Using the twin to schedule and sequence production throughout the supply chain. A Digital Twin is designed to complement existing supply chain software, such as ERP and MES systems which are embedded in the infrastructure, and compliments both systems by creating a critical link between the two, more often than not occupied by lots of Excel sheets currently. When effectively employed, a Digital Twin can align and optimise multiple schedules inclusive of underlying variation, adding optimisation and risk analysis. It can offer real time allocation and rescheduling of shared resources across the entire value chain (e.g. labor, machines, tooling, jigs, forklifts, AGVs, etc.), rescheduling of a complex, multi facility value chains inclusive of multiple distribution networks all with real world variability and constraints, in mere minutes.
In essence a Digital Twin will exponentially increase responsiveness to a fluid operational environment keeping a business well ahead of its customers and competitors.
True, there has been linear simulation software available for over 30 years. The key difference today is the simplicity of the solutions available and their ability to replicate variability or ambiguity. What this essentially means, is you do not need to have a team of programmers, available software such as Simio uses simple object-based modelling which can be tailored at all levels of complexity to accurately represent the most complex supply chain.
The beauty of using object-based modelling, is once you have built a model, let’s say of a factory, this model can be imported into your network model as an object. Thus, adding complexity to an already built model will just build upon that complete, it does not require a whole new model to be created.
In essence, it is closer to the real world, maintainable and at an investment cost that is affordable space for the sector.
It is worth remembering the philosophy in the Tech world of startups, think big (what is your vision), start small (complete small trials to prove concept, do not get bogged down in trying to solve the world) and fail fast (identifying that something does not work can be just as valuable as the opposite).
In making the model, it is critical to understand the level of complexity needed for the first decisions you want to make, over time the sophistication and variables can be built upon.
At Pollen we have trained our own digital architects, but more importantly we developed a process to align processes and systems, building complexity with capability and ensuring we do not build a white elephant.
Written by Oliver North: Partner at Pollen Consulting Group (part of Argon & Co)