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  • By Darren Travers
  • In Blog
  • Posted 04/10/2021

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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 digital twins help you visualize 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 maximize your Return on Investment (ROI) in predictive digital twins.

1. Start with the question, not the process

There is something of an art and science to building a predictive digital twin, and the way you build it determines the quality of result you get – in terms of output and ability to experiment with ‘what-if’ scenarios. Simply opening software tools and trying to model a process will only get you so far. Instead, before touching the tools, start with a scoping exercise to explore the business questions that need answering over the short and medium-term and in context with the business’ longer-term strategic aims.

Here’s an example. Don’t start with: “We’re looking at some options for the Product A/B process. Can you model it for us?”

Here’s a better starting point: “Over the next 3 years, we anticipate a 30% increase in volume of Product A and a 15% decrease for Product B. We need to understand what the current capabilities of the process are – and whether it’s capable of handling the shift in demand given the mix of work content in each product. Our future manufacturing strategy focuses on automation where it makes sense, so the business needs to understand the impact of labor availability and skills levels vs equipment investment so we can achieve this with a capacity buffer of 10%. Can you set up a scoping exercise that will help us detail the different inputs, levers and outputs we need to experiment with to meet this requirement?” 

The second approach helps you maximize ROI for 2 reasons. First, you end up with a predictive digital twin designed specifically to answer critical questions and achieve business goals. Secondly, that broader strategic understanding means you can build models to be reusable. 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 organization’s plans and challenges when scoping the model, you can build it to be re-configurable. That way, you can use that same digital twin investment to support decision-making for other locations, lines or products.

2. Organize 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 modeler (let's call him John). John 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 his role. The organization 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 on predictive digital twins (these case studies give you a taste), but to generate that value, you need to organize for success. This involves providing the technology, training and processes that create the right environment. Importantly, it also involves making digital twins 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 Lanner, we break organizational simulation capability into 4 levels:

2926-4_Steps_Infographic_resized_png.png 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 modeler or external partner. However, there’s no consistency around processes or use.

Level 3: Capable – There is frequent use of simulation, with analysts completing studies for decision-makers.

Level 4: Expert – Simulation is baked into strategic and operational decision-making. The organization has a high-capacity team, and there’s a short time to answer. Re-configurable and scalable solutions are deployed.



                       


To maximize ROI in predictive digital twins, 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 organization’s challenges and requirements to the partner, who scope and build models to enable the results to you.

4. Implement governance and reporting processes

Whether you have expert modelers in-house or work with an external partner, your predictive digital twin 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 maximize your return on predictive digital twin technology? Contact us today to discuss your challenges and opportunities, and how we can help you achieve better, more confident decision-making. And don't forget to register now for our upcoming webinar: How to Maximize ROI Using Predictive Digital Twins. 


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