In this unpredictable global market, we all wish we had a crystal ball to give us a window into the future. Fortunately, Supply Chain Planning offers us the next best solution: digital twins.
These virtual replicas of real-life products and processes run simulations and test the resilience of supply networks by harnessing Big Data. A digital twin contains a 1-to-1 match of every element of the living, breathing supply chain so that we can monitor, control, and plan for a rotating cast of scenarios in real-time. Such copies bridge the gap between the virtual and physical realms, capturing loads of data that can help companies stay one step ahead of a volatile market.
A digital twin is like a parallel universe of its physical counterpart. That means all operational details are mirrored: supplies, transactions, third-party relationships, assets. The breadth and depth of the supply chain are captured in full. Using such a model, it’s possible to stress test your network by manipulating an endless pool of variables.
The result: proactive – rather than reactive – supply chain planning. Responding to the market’s inevitable whims, rather than reacting to them.
When we consider SCP, the level of granularity of a model is important. That’s because higher resolution digital twins are well suited for the near-term, but the further ahead we gaze, the more abstract our modeling becomes. Digital twins are built for three distinct types of planning — operational, strategic, and tactical — each designed for a different time frame and resolution.
Imagine a production plan with determined dates and quantities. An operational digital twin gives us the tools to identify the most efficient way to bring that plan to life with the assets available. This is the most granular of the models, offering a deterministic simulation. We know all of the input data and parameters. Because of its high resolution, operational planning is often focused on the short-term horizon. A digital twin of this sort is very useful for, say, optimizing the schedule of a production department for next week.
Operational planning is used for discrete-event simulations. These models can simulate the movement of individual materials through a complex system. That might mean a truck leaving the dock with its cargo at a set time, or a manufacturing batch processed on a particular machine at a given hour.
If we need to broaden our horizon a little further, we’ll have to employ a different sort of digital twin…
This is what we refer to as what-if scenario planning. Digital twins built for the strategic time frame look months, quarters, or years into the future. The mindset is probabilistic rather than deterministic. Numbers, dates, and many other critical pieces of data are unknown or uncertain. Still, we can inform ourselves about long-range decisions by running simulations from all angles well in advance. It just involves rolling up our sleeves and getting a little more comfortable with ambiguity.
Grey areas often inspire unease. Most managers prefer one clear answer, an undeniable nugget of truth. But the reality is, we don’t always have the data we need — and that doesn’t mean we should forego planning. On the contrary, long-range SCP is fundamental, and murky waters come with the territory. With a strategic time frame, It’s entirely possible to run a series of simulations on the supply network even (and especially) in the absence of specific data.
Toss out the notion of optimization when working within the strategic time frame. This type of SCP involves stress testing your digital twin to learn how it might react under various sets of circumstances. What often rises to the surface is a solution that fares best under all conditions, even if it doesn’t fall squarely within the “optimal” category.
Something of a grey zone, a tactical planning horizon may look out a few weeks or a few years. We’re parked somewhere between the operational and the strategic, halfway between deterministic and probabilistic.
A digital twin built for tactical planning attempts to predict discrete events at a high resolution far into the future. The difficulty with this model is that any number of variables may alter our data on that timeline, so the clarity it appears to offer is a mere illusion. While a clear cut answer may be appealing to managers, the reality is, tactical planning may not deliver the security it suggests.
Still, on a conceptual level it’s important to be aware of the tactical time frame and the rationale behind employing a digital twin at this level of granularity.
Planning isn’t the only purpose of a digital twin — we can also use this tool to test solutions to problems. And sometimes, it’s necessary to create a digital twin that’s entirely geared towards one specific problem.
Case in point: a manager might want to test how a set of engineering changes might impact the output of a single production line. Another might want to answer the question, “How can I reconfigure my manufacturing footprint around the globe to make it the most cost- and tax-efficient?”
These questions can be answered with the power of a digital twin.
We’ve only scratched the surface of how this revolutionary technology can optimize and simulate a supply chain’s processes. Ready to dive in deeper? The best way to do so is asking for a free consultation with us.