4 Steps for Calculating Project ROI (And How Predictive Simulation Helps)

Project Return on Investment (ROI) is a financial indicator used to evaluate and compare different investment options. It’s calculated as the ratio of the benefit yielded by an investment divided by its cost, and it comes with the notion of break-even point – the point when savings and/or gains fully compensate for costs.

Calculating ROI is a multi-faceted process. Advances in automation, AI and machine learning mean processes are becoming more complex – which means evaluating system performance involves an increasing number of variables. As a result, businesses generally know what their budget is and how much something will cost – but determining ROI presents unique challenges.

Let’s look at 4 steps involved in calculating ROI and presenting an accurate business case for capital projects.

1 – Understand the variables surrounding both CapEx and OpEx
Imagine you have a budget for a design-based project. When you optimize capital expenditure for that budget, you end up compromising. For example, rather than investing in top-of-the-line equipment, you may opt for cheaper machinery with slightly lower reliability. This has implications for operating expenditure because lower reliability leads to higher running and maintenance costs.

Therefore, understanding Total Cost of Ownership (TCO) is critical to ROI calculations. But when you’re looking at complex cyber-physical systems, the trade-offs between CapEx and OpEx are also complex (and TCO can be tricky to determine).

The first step, therefore, is to understand the different variables and trade-offs affecting TCO, so you can analyze them accurately.

2 – Determine potential project scenarios
Planning capital projects never involves a single cut-and-dried option. Instead, you have a variety of technologies and system designs that could achieve your goal. Your job is to determine the best one.

Therefore, in addition to understanding the different variables, you need to set out the range of possible scenarios. This could involve considering questions such as:

• “How quickly will we reach the break-even point, and what will the ROI look like after 3 years if we upgrade an existing process vs introducing a new one?”

• “What’s the implication of buying different equipment?”

• “Do ROI calculations justify a larger investment that’s delivered incrementally instead of a one-off a Capex?”

• “We’re ramping up factory production with automated systems – what’s the fastest way for us to reach our production target?”

3 – Build a flexible digital twin to test those scenarios
Whether you’re compiling a business case for a one-off upgrade, incremental investment or a large-scale transformation, predictive digital twins give you a holistic view of ROI above and beyond what you can achieve with static spreadsheet calculations. You can test scenarios by changing parameters, so you can experiment with everything from number of robots, machines and people to the design of internal logistics.

You then get a clear understanding of project Key Performance Indicators (KPIs), whether it’s the number of parts produced in a period, the amount of stock needed to run the system, the utilization rate of key equipment or the number of employees required to run the facility.

It also lets you test the logic and complex interactions behind different options. This helps you avoid unforeseen blockages that would cause the actual ROI to deviate from your prediction. For instance, even though production volumes may guarantee a good level of utilization for an expensive piece of equipment, its actual yield may be limited by operator skill availability, material handling constraints or even re-work rates.

4 – Deliver confidence in the digital twin’s prediction and support decision making
The final stage in achieving accurate ROI calculations is to provide confidence that the modeling from the digital twin is accurate.

This involves running scenarios using randomness, as well as repeating simulation executions with different random numbers. It also involves sensitivity analyses. You’re then able to say that the project will deliver as long as certain parameters are within a specified range. For example, the system will cope with demand at +50%. Or you’ll achieve profitability targets as long as raw material costs don’t increase by more than 10%.

Stakeholders then have complete transparency on what’s required to realize the business case and can choose the most robust design in terms of ROI.

Justify investments using predictive digital twins
As cyber-physical systems become increasingly complex, it becomes even harder to determine ROI using traditional methods. So it’s not surprising that a growing number of companies require analyses from predictive digital twins before proceeding with capital projects. And this approach is helping organizations make smarter investment decisions – and save millions. Here are 2 examples:

• Washington River Protection Solutions (WRPS) used digital twins to test $30 million of planned upgrade projects. Through the modeling, they discovered the investment would deliver negligible improvement unless a previously unidentified issue was addressed. Analyses from the digital twins then helped the team get rapid approval for $6 million of essential improvements that eliminated bottlenecks and helped the upgrades deliver double throughput.
• Nissan Motor Manufacturing UK used predictive digital twins to determine whether a facility needed to expand. The modeling showed that the existing facilities actually had the capacity to cope with projected demand increases, saving the company £25,000 in investment.

To learn more about how predictive digital twin can help you realize a greater return on investment, view our recent webinar: How to Maximise ROI Using Predictive Digital Twins

Are you maximizing your project ROI? Contact us today to discuss your challenges and opportunities, and how we can help you achieve better, more confident decision-making.