Fail fast, fail often. It’s a mantra that took root in Silicon Valley’s start-up culture at the turn of the millennium and has been widely misunderstood ever since. It’s a design philosophy which believes we learn more from failure than from success, and hence we should embrace failure as an iterative part of any design process. There’s a very important caveat to all this – to learn from our mistakes, we need to survive the outcome of the experiment. Experience quickly teaches us that the real world is an expensive, and potentially dangerous, place to prototype something, – especially when that something is change to your own business.
The trouble with complex operational systems is the interactions between their moving parts. A change in one part of the system could have an unintended, and disproportionate, detrimental impact somewhere else. For example, if you’re running a subway station and notice there’s always a crush at the station entrance during rush hour, you might decide to install more escalators to speed up passenger flow. Unfortunately, unless you also schedule more trains, you’ll just move the rush hour crush down to the platform, packing more and more people into a confined space close to the live rails. The station entrance might be clear, but you’ve made the situation worse, not better.
That’s a very simple example. Now imagine the number of moving parts in your own organization. Think about the number of hand-offs between processes and departments. Think about the number of variables, both internal and external, and the number of potential combinations, all of which have real world impacts – some good, others not so much.
What if there was a risk-free way to understand what to change and how? Luckily there is – digital simulations. Initially architects and engineers used them to design everything from bridges to planes, Formula One cars to rocket ships. Then they were used to model complex systems like supply chains and passenger flows in airports. Now they’re being used to model business operations. A simulation of your end-to-end business processes enables you to see those complex interactions. It enables you to look at your operation holistically, ask more strategic questions, and get better answers.
If you’re contemplating an operational or digital transformation, the advantages of simulating the changes first, rather than hoping they work the real world, are overwhelming. For example, you can:
We believe using simulations to model different scenarios increases the speed at which you can transform and reduces the risk of failure. There’s an additional up-side too. Even after you successfully complete your initial transformation, your simulation will continue to deliver value because it’s an enabler for continuous improvement, an enduring asset you can use again and again to evolve and maintain your competitive advantage as your market changes.
The usefulness of a simulation depends entirely on how accurate a representation it is. To explore the gaps, disconnects and potential alternative states, your simulation needs to be as close to its real-world counterpart as possible. It needs to be built based on real-world metrics, rather than assumptions – and it shouldn’t require an army to maintain and run it. Our platform helps our customers design successful digital transformations, so we understand the capabilities a good simulation needs – and we’re going to share that with you in the next edition.
