There are so many times I look at a business crisis news story and think: ‘If only they had used predictive simulation!’ The organisations undoubtedly had crisis plans on paper but, at crunch time, couldn’t deal with volatility in their delicately balanced environment.
However, many delays and shortages that hit the headlines could be avoided if planners had more insight into demand, bottlenecks, resource constraints, transport issues, weather effects and more.
This blog looks at why organisations find themselves in this situation – and what could have happened if they’d used predictive simulation in their planning. I’ll start with why planning is so complex, look at the role of predictive simulation and then analyse a few recent headline crises. Let’s get started.
It’s hard to gain confidence that business processes and operations can cope with whatever volatility and uncertainty comes your way. After all, the pandemic, supply chain constraints, labour shortages and inflation mean there’s been lots of volatility and uncertainty out there.
Most businesses have a level of resilience planning but are simultaneously crossing their fingers and hoping for good luck. Because you can’t plan for everything, it doesn’t seem efficient to cover every eventuality. There are so many variables, within and outside of your control. And there are so many systems and processes, physical and digital.
It’s hard to make sense of all the combinations and dependencies.
Key reasons why it’s hard to get that confidence are:
As a result, most businesses get as far as they can with an analysis, settle for something incomplete and imperfect – and then rely on gut instinct and firefighting to minimise disruption as it comes.
When spreadsheets and standard BI tools can’t deal with the complexity, predictive simulation adds real value. It models assets and dynamic processes, allowing you to analyse ‘what-if’ scenarios involving complicated variables in a risk-free way.
For example, you can get answers to questions like:
You essentially gamify decision-making by running different scenarios.
With predictive simulation, you can analyse the supply chain, technology, personnel, operational patterns and maintenance requirements needed to ensure business resilience in any condition over the short, medium and long term.
Importantly, you can also understand the many knock-on effects of change so you’re not caught out – be it through unexpectedly high costs, bottlenecks, delays or safety issues.
So what does this look like in practice? You can read about a range of predictive simulation experiences here – from preventing contact centre bottlenecks to managing inventories and planning supply chains.
But going back to my forehead-slapping reaction to crisis news stories, here are a few recent headlines where predictive simulation could have made a difference.
It’s one of the world’s busiest maritime trade routes, and they recently announced they’re progressively reducing the number of available booking slots due to drought. The resulting traffic jams will majorly impact global logistics – exactly when Christmas demand comes in.
In dealing with this situation, there are so many variables at play. Should you put up with 10+-day shipping delays? Go for rail transport? Transship? And what are the cost and lead time implications of the different options? What are the knock-on effects on other operational processes and the rest of the supply chain?
This is ‘spreadsheet can’t cope’ territory. But by using predictive simulation to model your logistics and supply chains, you can run any number of ‘what-if’ scenarios to determine the best options based on shipping point, destination, cost, customer priority and more.
Even better, because the Panama Canal issue stems from an environmental factor, you can integrate climate risk intelligence into your modelling. That way, you can understand how water levels are likely to change and be prepared for a similar issue in future.
There were empty shelves and produce rationing after poor weather reduced harvest yields, the Ukraine war reduced imports and higher energy costs affected glasshouse crops. But supermarkets could have been more resilient if they’d used predictive simulation.
They could have modelled:
The Girl Scout cookie is a much-loved staple of American fundraising. But in spring 2023, the main baker wasn’t keeping pace with demand due to supply chain issues, labour shortages, mechanical issues and weather-related power outages. The inventory shortfall came as cookie-selling season entered its peak.
This is a great example of how multiple variables and interconnected, dynamic processes can create a ‘perfect storm’ situation. And that means it’s another great use case for predictive simulation.
By modelling cookie production and logistics processes, the baker would better understand bottlenecks. They could get quick answers to questions like: