How to maximise your ROI in predictive simulation

Let’s say you’re looking at ways to increase production capacity, boost throughput or cut costs. Maybe you’re expanding a facility, installing a new line or introducing automation. These decisions involve complex cyber-physical systems with interconnected processes. Predictive simulation helps you visualise those systems and processes, enabling you to experiment with changes and run ‘what-if’ scenarios in a risk-free way.

However, simply having software and building models will likely fall short when it comes to delivering the types of results that act as a multiplier within the business. These 4 tips will help you develop the skills, culture and processes needed to get the best outcomes and maximise your Return on Investment (ROI) in predictive simulation.

For example, your current priority may be to determine if you can increase throughput on a particular line. However, if you have a broader understanding of the organisation’s plans and challenges when scoping the model, you can build it to be re-configurable. That way, you can use that same investment in predictive simulation to support decision making for other locations, lines or products.

2. Organise for success

Here's a common scenario that leads to suboptimal ROI. The business invests in predictive simulation software and nominates someone to be the modeller. Let’s call him John, and he was chosen because he used similar software at university. But John has a day job, and when a team asks him to build a model, he has to fit it in with other priorities. When John finally has some free time between other projects, he quickly puts a model together. Soon after, John leaves the business or changes role. The organisation is left with an undocumented model, and there’s uncertainty about whether that model actually answers the team’s questions. Plus, the business now needs to train someone else on the software. In other words: there’s a very low return on investment.

There’s immense ROI to be had with predictive simulation (these impact stories give you a taste), but to generate that value, you need to organise for success. This involves providing the technology, training and processes that create the right environment. Importantly, it also involves making predictive simulation a key part of people’s role and objectives to ensure they’re invested in making it a success.

3. Understand your current capabilities and where, why and how you want those capabilities to develop

When we talk about having dedicated people with the right technology, training and processes, we don’t necessarily mean you need a team of in-house simulation experts. At Haskoning, we break organisational simulation capability into 4 levels:

• Level 1: Aware – The business is aware of what predictive simulation can achieve, but it’s more about enthusiastic dabbling than applying it to decision making
• Level 2: Project-led – Simulation is used for one-off projects, either with an internal modeller or external partner. However, there’s no consistency around processes or use
• Level 3: Capable – Frequent use of simulation, with analysts completing studies for decision makers
• Level 4: Expert – Simulation is baked into strategic and operational decision making. The organisation has a high-capacity team, and there’s a short time to answer. Re-configurable and scalable solutions are deployed

To maximise ROI in predictive simulation, consider your current capability level and where you want to be, with specific thought allocated to the ‘’why’ and ‘how’. This thinking will help shape your investment strategy. For example, you don’t need to aspire to Level 4 capabilities in-house. You could deploy an external partner model and internal ‘translators’ who coordinate between your partner and the business. These ‘translators’ own the simulation concept and are in charge of communicating the organisation’s challenges and requirements to the partner, who scopes and builds models to enable the results to you.

4. Implement governance and reporting processes

Whether you have expert modellers in-house or work with an external partner, your predictive simulation team shouldn’t just be left to get on with it. There should be established governance and reporting processes for continuous improvement and succession planning.

Effective governance ensures models are appropriately documented as a single version of the truth. It helps you codify knowledge within the business, unify understanding of processes across departments and mitigate the effects of people churn. Furthermore, it helps you measure and document successes, address issues and build value – which ensures you’re directing ongoing investment in the best possible way.

Are you ready to get started with or maximise your return on predictive simulation? Contact us today to discuss your challenges and opportunities – and how we can help you achieve better, more confident decision making.