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How to apply prescriptive analytics using your Digital Twin for the highest possible service levels at the lowest possible cost
This is part one of a two part series on Digital Twins in the Supply Chain. Read part two: Solving Supply Chain Problems with Digital Twins here.
My experience with digital twins goes back to my engineering days at both GE and GM. Whether it was jet engines or my more complex nuclear missiles guidance systems, I needed to create digital twins to test variables around heat transfer along with validating that the manufactured items actually met the complex design geometries. In this sense my digital twins were digital replicas of manufactured components to be used for analysis and prediction. We also applied prediction within our digital twin representations for the six-axis robots we utilized to manufacture the guidance systems in order to schedule maintenance prior to losing tolerance when manufacturing our gimbals.
In this sense my digital twin was related to a process and the predictive models designed to improve that process. At One Network Enterprises we have now applied similar digital twin techniques to supply chain network planning, operations, and execution.“Digital Twin and Digitization have become duffel bag terms. So what is a digital twin? Well it's not a separate system from your planning and execution platform...” -Joe Bellini Click To Tweet
Today, digital twin and digitization in general have become what I call “duffel bag terms”. You can define it many different ways, and it all loosely fits in the duffel bag. A similar situation exists around AI as a duffel bag term.
The reason I created my original digital twins was to generate a useful result that I couldn’t get with the actual units unless I was willing to shoot them into space or sometimes even blow them up under stress.
The benefits of these digital twins were twofold: first finding design issues that would lead to failures so I could correct the designs before any failures occurred; and second, evaluating design changes related to troublesome variables/ measures or for actual failures themselves. Much of my work was done with CAD/CAM/CAE/CIM along with heat transfer differential analysis.
“Digital twins are virtual replicas of a physical product, process, or system”Michael Monteith (ThoughtWire)
Digital Twins Solve Supply Chain Network Problems
I have found that extending digital twin problem-solving techniques to a supply chain network requires that we first must model the entire end-to-end supply chain network to form the foundation for our analysis.
Since the opportunities or problems that will be exposed by our analysis could manifest themselves over strategic, tactical, or operational time frames, the foundation should be seamless across time horizons. It should offer services, algorithms, and analysis that run across the network representation in real time, whether we are solving problems predicted to happen in six months, or during a delivery scheduled for later this afternoon.
The implication here is that the correct foundation for a digital twin is an execution platform whose item level modeling and representation can scale in detail from execution all the way forward into longer term sales and operations planning. This digital twin is not a simulation.
A Digital Twin is Not a Separate System
Planning software vendors often try to provide digital twin capabilities by using simulation techniques, or even try to import operating data from the ERP system using some kind of rapid response, but this method falls far short in its ability to generate high value results.
If a vendor starts talking about the right level of resolution based on differing forecast horizons, that means the foundation/platform cannot scale properly to solve the problem.
Some vendors even talk about using approximations for longer term time horizons which is nothing but an average and will return average results.
The real problem is that those architectures are antiquated and not really designed for digital twin type problem solving. Their problem-solving algorithms, if modeled in detail, would run seemingly forever due to their architectural limitations. The in-memory approach we took back in the early 90’s has been surpassed in performance, scalability, and reliability by newer real-time supply network platforms.“Digital twins have to be extracted from real-time networks if they are to be accurate and effective.” -Joe Bellini Click To Tweet
An extension of these limitations leads some folks to talk about “concurrent planning”. The fact that there is any demand in the market for concurrent planning means that somehow users have been convinced they need two different software representations or applications for operational and tactical planning and that the two should be interoperable. This is ridiculous.
That’s because modern supply chain platforms can certainly scale detail from operational to tactical to strategic. Changes in time, or temporal changes, are nothing but state changes related to something like an order. The order may start life 2 years out as part of Sales & Operations Planning (S&OP), and then change state to a forecast order at 12 months, then a planned order at 6 months, then a committed order at 1 month, then a shipped order at 1 week, then an in-transit order at 4 hours, and then a received order at time zero, then an authorized to pay invoice order time minus 1 day.
On a real-time supply chain network, these are just state changes across a seamless platform where all levels of detail are available in all time horizons. When planning, operating, and executing in this type of platform environment, the entire network becomes more resilient, more responsive, and the likelihood of the plan being executed without major problems is much higher.
“There is nothing better than having a digital twin capability that is an extension of your operating platform. You can evaluate choices based on the top algorithms in the market and then make those choices actionable in real time.”Joe Bellini
Your Supply Chain Network Platform Should Be Your Digital Twin Environment
An end-to-end real-time supply network platform foundation enables the ability to test out new supply chain policies, network resiliency, the feasibility of strategic or tactical plans, activate alternate parts or suppliers, modify modes of transportation, or even add additional shifts at a plant. In this sense it is the platform itself that also serves to enable the digital twin.
You don’t need to configure and support a separate reference model. Your digital twin is a sandbox extension of your supply network platform that applies different statistical, machine learning, and AI algorithms along with various workflow options to solve different types of problems. The planning & operations platform and the digital twin are one and the same system.
As we have discussed, problem-solving analytics are embedded as part of the network platform, thus obviating the need to add an additional, separate supply chain analytics platform. For example, in the health care sector One Network is offering a resilient platform from hospitals upstream through distributors and into suppliers and manufacturers. The platform is designed to predict potential problems related to resiliency to activate alternative plans or actions during times of disruption or to react to inventory disruptions in real time as they occur.“Vast supply chain networks provide rich opportunities to exploit digital twins across tactical and strategic time horizons.” -Joe Bellini Click To Tweet
The potential for improvement in complex, dense, hospital and healthcare networks is tremendous, given all the touch points including hospitals, clinics, 3rd party providers, distributors, pharma manufacturers, CMS, HHS and the FDA. The number of shipments, cross docks, forward stocking locations, and distribution centers across multiple modes of transportation originating from hundreds of countries certainly provides an opportunity for tactical and operational improvement.
If you’re interested in this topic I recommend you join me for a discussion of digital twins in the supply chain.
In part 2 of this article we look at predicting, prescribing and solving supply chain problems with digital twins.