Tag Archive for: utility vehicles

Blog Series: Failing to Optimize the Human-in-the-Loop at the Earliest Stages of Design is More Expensive than You Think – 3 of 3

For those who have just arrived, welcome. Post #1 can be found here and post #2 can be found here. For everyone else, thank you for following this series and for coming back for the third and final post.

The opening statement in this series was that most design processes, whether intentional or not, effectively prioritize product capability over usability. The considerable cost of failing to prioritize usability was then shown through client engagement examples from four different industries.

All examples presented in the first two posts would have benefitted significantly if design teams had the ability to evaluate the human-in-the-loop in ways that could inform and support product development decisions. That has been the promise of digital human modeling for decades. Yet, examples just like those presented continue and they are not only common but pervasive throughout most industries.

This final post focuses on why and, of course, provides a solution.

The Problem
Most commercially available digital human models are really just virtual mannequins. Like the mannequins found in department stores, virtual mannequin joints must be individually rotated into place until some recognizable human activity is achieved. Manipulating mannequin joints within a computer environment is tedious, non-intuitive, time-consuming, and subjective. It can also be quite frustrating, not only because non-human-looking results are a frequent option, but also because every design change requires the entire process to be repeated.

Companies that provide virtual mannequins have worked hard to mitigate this frustration by including the ability to leverage pre-recorded snapshots of human activity, primarily in the form of motion capture data. Use of motion capture data to drive virtual mannequin postures does circumvent the need to interactively manipulate their joints but that data is also expensive to acquire and time-consuming to process. In fact, recent estimates from one of our automotive clients indicated an internal motion capture budget of over $30,000 per subject, per motion capture study.

But the real problem with using pre-recorded data of any kind in design is that it’s inflexible. It cannot respond to change. It can only be used as acquired. Any design change that potentially affects human interactivity requires the acquisition of more data. This is great news for companies in motion capture-related businesses, but it’s a nightmare for design teams and their budgets and deadlines. Unfortunately, this contributes to an even bigger problem.

Because virtual mannequin joints must either be manually manipulated or driven by pre-recorded data, they can really only react to an existing design.  This means significant resources must first be expended to bring a design to a relatively high level of maturity before a virtual mannequin can be deployed. In other words, the use of a virtual mannequin requires a rather long list of traditional engineering efforts to be completed first. Consequently, at the point when human-centric evaluations can finally occur, any indicated need for change will be in direct conflict with all the resources already expended.

This is almost the same situation design teams were in before virtual mannequins existed; when product evaluations could only be accomplished through trial and error, physical prototypes, and focus groups. While the need for physical prototypes may be reduced, human-centric evaluations still occur too late to be effective.  What is most ironic is that the usability of your products by your customers—those who ultimately determine your product’s success in the market—is effectively being treated as if it is among the least important of your product’s design criteria.

Why are outcomes like those presented in this series so common?  Because traditional design processes do not allow human-in-the-loop evaluations to occur until late in a product’s development cycle when change is no longer a realistic option.

The Solution
To be clear, Santos® technologies offer significant advantages in these traditional workflows which appear to be pervasive throughout most industries. Santos® predictive models are fast, flexible, objective and of course, predictive. Because they’re predictive, they provide a fair amount of autonomy which makes them easier to use and easier to use correctly.

However, the real value of Santos® virtual human-in-the-loop solutions lies in the unique ability to predict human physical behavior and performance while taking into consideration the human-centric challenges we must all deal with every day in the physical world. These challenges include:

  • Simultaneously achieving multiple and competing task goals
  • Mitigating limitations in strength, flexibility, and fatigue
  • Optimizing grasp strategies
  • Ensuring we can see what we’re doing
  • Remaining in balance and avoiding collisions in spite of external forces that may be acting upon us
  • Trying not to get hurt

A truly predictive model makes trade-off analysis (the evaluation of what-if scenarios) possible. Trade-off analysis is why predictive models are created and why they are so valuable. A truly predictive human model can provide the task-focused trade-off analyses your teams need to optimize the human-in-the-loop at the earliest stages of design—where change is not only most effective but still an option.

Watch this video for one example of how this is done.

Like many of the companies we work with, your company has probably been in business for a very long time. Your teams probably have 100’s if not 1000’s of employee-years’ worth of experience using your existing design processes. And your revenues are likely in the millions if not billions of dollars per year.  By all objective measures, your company is exceptionally good at what it does.

However, consider that your design teams Avoid the Cost and Uncertainty of Trial & Error in meeting:

  • Structural Performance Requirements through the use of Finite Element Analysis
  • Aerodynamic and Thermal Performance Requirements through the use of Computational Fluid Mechanics
  • Mechanical System Performance Requirements through the use of Multi-Body Dynamics

So, why continue to incur the cost and uncertainty in meeting human-in-the-loop requirements through trial and error? Those humans-in-the-loop are your customers. Their positive feedback is that next level of competitive advantage.

SantosHuman Inc.  When you need to get it right the first time.

Thank you for staying with the series, keep an eye on this space for new blog topics, and let us know what you think.  We’d love to hear from you.

– S

Blog Series: Failing to Optimize the Human-in-the-Loop at the Earliest Stages of Design is More Expensive than You Think – 2 of 3

Welcome to the second post in this blog series which continues with two additional examples on how traditional approaches to product design negatively affect your company’s bottom line and market share. The first post, which can be found here, provided examples from the Consumer Products and the Medical Device industries.  This post focuses on examples from the Industrial Lawn Care and the Powersports & Small Utility Vehicles industries.

Example #3: Industrial Lawn Care
A  manufacturer of industrial lawn care equipment had received enough complaints regarding the usability of one of its products to become concerned. As with most industries, poor customer feedback has a direct impact on market share so they needed to address this quickly. SantosHuman was contacted to help identify the operator-centric issues.

Small Woman on Lawn MowerOur predictive human models quickly indicated that smaller women would have to sit much further forward on the seat to effectively operate the primary controls than had been anticipated by the design. After demonstrating our initial findings, the client revealed that many of the complaints were actually from smaller women who said they could not keep the lawnmowers running. Prior to our demonstration, the manufacturer had assumed this was due to operator error. After seeing our presentation, they remembered that the vehicle seat assembly included a kill switch designed to turn the engine off if sufficient pressure is not applied while the vehicle is moving.

In this example, the product was already in production and receiving poor customer feedback. Focus groups could have evaluated fully functional physical prototypes prior to production to gain similar insight, but how would that have helped?

When evaluating human-in-the-loop issues for a mature design, there are basically two options available when a need for change is indicated that far downstream in the design process.  You can go back and ask for the budget to redesign the product based on the insight gained.  Or, you can go forward with the intention of leveraging that insight in some future iteration.  Regardless of which of those options are chosen, we begin to see why traditional design processes make it almost impossible for product usability insights to be incorporated effectively. But, we’ll dive deeper into that with the third and final post in this series.

Powersport VehicleExample #4: Powersports & Small Utility Vehicles
In this last example, a startup company outsourced two years’ worth of aesthetic and mechanical design towards the development of a new electric utility scooter.  After all design efforts were complete, the company also outsourced the creation of a production-ready prototype to present to their investors. We were told that while the prototype scooter “looked fantastic”, they soon realized it was an “ergonomic mess” and SantosHuman was asked to help identify the issues.  Over the course of a single weekend, our predictive human models provided a variety of operator-in-the-loop trade-off analyses that could be used to inform and support redesign efforts.

Again, while the ability to quickly and objectively evaluate mature product designs for human-centric issues has significant value, that is not the moral of the story. Rather, the examples presented in this and the previous post in this series are symptomatic of much larger problems which will be discussed in the final post on March 4th, 2019.

In the meantime, think back on the examples presented and imagine that these are your teams.  Imagine the cost of developing an electric utility scooter over two years. Imagine the cost of creating a production-ready physical prototype of a vehicle like that. And just in case you’re not aware of how expensive physical prototypes can be to create, consider that there is a rumor that the first working iPhone cost $1.5M.  With that in mind, imagine discovering that while your go-to-market product met the aesthetic and capability goals, it wasn’t fit for human use. Now imagine having to choose between entering a market where your first sale may be your last – or – asking your investors for additional funds for significant redesign efforts.  Does this seem like the career-enhancing moment that was likely envisioned when the project began two years earlier?  What would you have done differently?  Why do you think that would have helped?

Our experience with many companies, over many years, indicates the scenarios presented in these first two posts are not only common, they are pervasive throughout most industries.  Budget and deadline overruns, market rejection based on poor customer feedback, costly and time-consuming product redesigns, and amplified risk of injury liabilities are not the kinds of things you want to leave to chance.  So, tune in on March 4th, 2019 for the final post in this series to find out why traditional design processes are at odds with truly human-centric design and to discover a solution.

Until then, let us know what you think.  We’d love to hear from you.

– S