Fortune 500 companies like Ford or Boeing already have an extremely mature engineering simulation department. From niche areas like combustion and acoustics to bread-and-butter topics like thermal management and aerodynamics, their engineers can support a vast array of products that are both in-production or are conceptual in nature. Eventually, these groups may look like those at Ferrari or the Oracle Red Bull Racing Team, in which no expense is spared to simulate and develop bleeding-edge products in their space. But how does a startup or small company achieve this end goal of having a world-class R&D department which can leverage simulation to complement physical testing?
Invest in the Tools
Let’s address the elephant in the room: simulation costs money. All good R&D does. A frequent concern I hear customers mention is the cost associated with simulation software even though they see the value it can bring. However, for analysis to be successful in your organization, your mindset needs to change from analysis being a cost to being an investment. Starting small in the simulation ecosystem, whether it’s purchasing Ansys Elastic Currency (AECs) or leasing a license of Ansys CFD Premium, will pay dividends. Even if you can reduce physical testing by 10%, or recurring warranty claims by 5%, those savings add up and compound into improved market-leading products. The path to growing simulation in an organization takes time, and even the smallest recurring investment in your talent through training and renewing simulation licenses will start your simulation organization on firm footing.
Choosing the Right Talent
The most important question you need to ask yourself is whether you and your company are committed to analysis for the long haul. If so, that means having the right engineers on your team to make it a success. This usually takes two paths: growing the talent internally or hiring the talent externally. For smaller companies, it is typically more feasible to have a few engineers moonlight as simulation engineers. This allows the company to (1.) determine if simulation provides the impactful ROI they seek and (2.) identify talent that can grow the department internally.
Where this can go awry is when you assume your half-time simulation engineer’s ROI will be exactly half or equal to a full-time simulation engineer’s ROI. A design engineer’s priority will always be launching products, not learning analysis, so the risk is that, when high-priority launches arise, their time investment in simulation will fall by the wayside. Learning analysis takes consistent time. You will be saving costs in terms of salary and associated benefits, but the time to fully utilize that simulation knowledge will take a few months compared to hiring a dedicated simulation engineer.
The flipside of the argument is hiring a simulation engineer. Their sole job is to do analysis, all day, every day. Most simulation engineers have years or even decades of experience that allows them to be deployed almost immediately in your organization. While they are more costly to hire, you get what you pay for. Not only are they experts in simulation, but through many years of supporting product development (potentially across various industries), they will bring fresh design insights that will lead to more robust product design. They also likely came from established simulation teams and, since analysis is very process-driven, they will be able to assist you in implementing best practices, industry-leading validation plans, and growth strategies.
To effectively stage growth in your simulation organization, utilize a two-pronged approach to building your team. To gain a firm footing when starting off, hiring an experienced simulation engineer will allow you to see meaningful impacts and ROIs in your product development organization in relatively little time. Their experience helps bridge the knowledge gap many companies have when starting to implement a simulation organization. As time and budget allow, expanding the team with at least one new(er) engineer (whether internal or external) will facilitate an effective knowledge transfer from your technical expert, building the team’s overall skills and increasing analysis throughput.
Additionally, having at least one understudy will act as a preliminary succession plan in case your technical expert changes roles or leaves the company. Given the recent spike in talent cost (in terms of salary, benefits, and turnover) across the job market, having this plan in place as soon as possible will prevent a tremendous amount of organizational pain down the road. Any group being taken over by a less-experienced team member who’s lived the role is always better than having to start over with no internal or external backfills immediately available.
Your engineers can only learn so much from internet searches, forums, and online video tutorials. Try utilizing some of the following avenues to expand your team’s skillset(s):
- Attending formal training offered by Ansys or your channel partner
- Attending simulation conferences
- Continuing formal education (advanced degrees, formal certifications, etc.)
- Subscribing to associations producing scholarly articles (i.e., Society of Automotive Engineers)
Even if your discretionary budget is small, channel partners are always hosting some form of free webinar throughout the year regarding new simulation tool capabilities that you can take advantage of. Seize all of these learning opportunities, big and small, because a simulation department cannot thrive in a vacuum.
Best Practices, Standard Work Procedures, Documentation
Practice makes permanent, and consistency breeds perfection. Analysis is an exacting science, and subtle deviations from workflows and best practices within a simulation team have potential to derail years of validation efforts. Eliminating tribal knowledge is another key to the team’s success in your R&D organization, and ensuring the whole team follows the same set of “instructions” for a given analysis ensures accurate, repeatable work. Coupled with a culture of continuous learning, your team will cultivate scientifically sound simulation workflows. When building an analysis organization from scratch, you need to leverage existing best practices (potentially from other companies, industry standards, etc.) to have a starting point for your own. As your team becomes more adept at using the tool and conducting validation studies, they can formulate their own set of practices that ensure the analyses are efficient, quick, and accurate. These practices then turn into standard work procedures.
Standard work procedures are the instruction manuals of running engineering simulations. Just as your physical test department has procedures to run tests, so should your simulation department. As your team takes on more types of simulations in different disciplines, it becomes more tedious to manually keep track of how previous simulations were done. This is especially risky when comparing future to current product, or even between design alternatives. Lastly, and most importantly, having standard work procedures allows your team to efficiently set up and execute simulations. This leads to quicker design guidance for your product development teams, reduced physical testing needs, and faster time-to-market.
Validate Your Work
Model validation closes the loop on all the hard work your team has done above. By comparing your simulation models to real physical test data, you can ensure you are making the correct assumptions and utilizing the best practices in your simulation organization. A common hang-up regarding model validation is that, from a business perspective, there isn’t much monetary value realized from it. However, it’s critical for keeping your analysis capability in top shape and allows them to keep giving the best design guidance possible to your product development teams. In many cases, having a validated simulation model can slash physical testing time (and cost) in a meaningful way that cascades benefit throughout the organization.
Less physical testing leads to product portfolio expansion and quicker time to market. These activities take time but starting them sooner will only compound your organization’s benefit later. Remember that validation doesn’t necessarily mean “the temperature I measured is exactly what I see in simulation”, but rather “the data between designs in reality trends well to my simulation’s predictions”. However, there are situations where a company wants to target the absolute gold standard of simulation validation and achieve razor-sharp correlation between simulation and reality. Be aware that this path has very low ROI unless your team is a massive simulation user and trying to almost completely displace physical testing with analysis.
Develop Your Strategic Roadmap
Where do you want your team to be in 5 years? How many engineers do you envision this team growing into? How will simulation be imbedded into your product development pipeline? Planning for where you want to go is just as important as starting the journey. Building relationships internally with your strategy team, finance department, and product development organizations will be critical to solidifying your simulation organization within your company’s day-to-day operations.
Each business unit will offer insights that will help shape and quantify your longer-term simulation department strategy, from budgeting to product pipeline volume. The key point to remember is that as your simulation organization’s success will cascade into other business units in the form of reduced product development spend, less rework, reduced prototype material spending, faster time-to-market, and a more robust product portfolio.
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