Posts Tagged "industry insights"

“To specialize or not to specialize, that is the question.”

The question of specializing vs. generalizing has arisen in so many aspects: biology, health, higher education, and of course, software.  When one has to decide between the two ends of the spectrum, the benefits and risks must be weighed.

muskrat_eating_plantAs environments have changed over time, animals have had to make a decision: change or perish. Certain species adapted their biology to survive on plants – herbivores – others, meat 0 carnivores.  When in their preferred environments with ample resources, each can thrive.  However, if conditions in those environments change so that those resources are not as bountiful, they may die out. Then comes the omnivore, whose adaptation has enabled them to survive on either type of resource. With this wider capability of survival, there comes a cost of efficiency. The further you move up through the food chain, the less efficient the transfer of energy becomes.  Plants produce energy, only 10% of which an herbivore derives, and the carnivore that feeds on the herbivore only gets 10% of that 10%; i.e. 1% of the original energy.

Three hundred trout are needed to support one man for a year.
The trout, in turn, must consume 90,000 frogs, that must consume 27 million grasshoppers that live off of 1,000 tons of grass.
— G. Tyler Miller, Jr., American Chemist (1971)

doctor-1149149_640When it comes to deciding on a course of action for a given health problem, people have the option to go to their family doctor, a.k.a. general practitioner, or a specialist. There are “…reams of papers reporting that specialists have the edge when it comes to current knowledge in their area of expertise” (Turner and Laine, “Differences Between Generalists and Specialists“)., whereas the generalist, even if knowledgeable in the field, may lag behind the specialist and prescribe out-of-date – but still generally beneficial – treatments.  This begs the question, what value do we place on the level of expertise?  If you have a life-threatening condition, then a specialist would make sense; however, you wouldn’t see a cardiologist if your heart races after a walk up a flight of stairs – your family doctor could diagnose that you need some more exercise.

graduation-907565_640When it comes to higher education, this choice of specializing or not also exists: to have deep knowledge and experience in few areas, or a shallower understanding in a broad range of applications. Does the computer science major choose to specialize in artificial intelligence or networking? Or none at all? How about the music major?  Specialize in classical or German Polka? When making these decisions, goals should be decided upon first. What is it that drives the person? High salary in a booming market (hint: chances are that’s not German Polka)? Or is the goal pursuing a passion, perhaps at the cost of potential income? Or is it the ability to be valuable to many different types of employers in order to change as the markets do? It’s been shown that specialists may not always command a higher price tag; some employers value candidates that demonstrate they can thrive in a variety of pursuits.

Whether you’re looking to take advantage of specialized design products (for instance, sheet metal or wire harnesses), or gaining the value inherent in a general suite of tools present in a connected PLM platform that can do project management, CAPA, and Bill of Materials management, we have the means. A “Digital Engineering” benchmark can help you decide if specialized tools are right for your company. Likewise, our PLM Analytics benchmark can help you choose the right PLM system or sub-system to implement.

Specialize, or generalize? Which way are you headed and why?

417px-the_tortoise_and_the_hare_-_project_gutenberg_etext_19994In a race, the quickest runner can never overtake the slowest, since the pursuer must first reach the point whence the pursued started, so that the slower must always hold a lead.

— Aristotle, Physics VI:9, 239b15

This paradox, as first developed by Zeno, and later retold by Aristotle, shows us that mathematical theory can be disproved by taking the hypothesis to an absurd conclusion.  To look at it another way, consider this joke:

A mathematician and scientist are trapped in a burning room.

The mathematician says “We’re doomed! First we have to cover half the distance between where we are and the door, then half the distance that remains, then half of that distance, and so on. The series is infinite.  There’ll always be some finite distance between us and the door.”

The engineer starts to run and says “Well, I figure I can get close enough for all practical purposes.”

The principle here, as it relates to simulation like FEA, is that every incremental step taken in the simulation process gets us closer to our ultimate goal of understanding the exact behavior of the model under a given set of circumstances. However, there is a limit at which we have diminishing returns and a physical prototype must be built. This evolution of simulating our designs has saved a lot of money for manufacturers who, in the past, would have had to build numerous, iterative physical prototypes. This evolution of FEA reminds me of…

2000px-mori_uncanny_valley-svgThe uncanny valley is the idea that as a human representation (robot, wax figures, animations, 3D models, etc.) increases in human likeness, the more affinity people will have towards the representation. That is, however, until a certain point.  Once this threshold is crossed, our affinity for it drops off to the point of revulsion, as in the case of zombies, or the “intermediate human-likeness” prosthetic hands.  However, as the realism continues to increase, the affinity will, in turn, start to rise.

Personally, I find this fascinating – that a trend moving through time can abruptly change direction, and then, for some strange reason, the trend reverts to its original direction. Why does this happen? There are myriad speculations as to why in the Wikipedia page that I’ll encourage the reader to peruse at leisure.

elmer-pump-heatequationBut to tie this back to FEA, think of the beginning of the Uncanny Valley curve as the start of computer assisted design simulation. The horizontal axis is time, vertical axis is accuracy.  I posit that over time, as simulating software has improved, the accuracy of our simulations has also increased. As time has gone on, the ease of use has also improved, allowing non-doctorate holders to utilize simulation as part of their design process.

And this is where we see the uncanny valley; as good as the software is, there comes a point, if you use specialized, intricate, or non-standard analysis, where the accuracy of the software falters. This tells us that there will still be needs for those PhDs, and once they get on the design and start using the software, we see the accuracy go up exponentially.

If you need help getting to the door, or navigating the valley, talk to us about our Simulation benchmark process. Leave a comment or click to contact us.

 

So you’re an executive at a manufacturing company. You make things that are useful to your customers and you return profits to ever-demanding shareholders. You have probably heard of PLM before; perhaps your staff have mentioned the acronym. But how badly do you need it?

Here are 10 indicators that you definitely need PLM:

  1. Your engineering organization is often late meeting customer deadlines. This results from poorly executed projects, inefficient processes and lack of clear deliverables. All of these problems can be addressed by a PLM system supporting the engineering organization.
  2. Warranty costs are creeping up. One of the largest contributors to poor product quality is sloppy design and incomplete engineering definition. Installing appropriate PLM technology to support design activities results in a better specification been communicated to manufacturing.
  3. Factory scrap rates are above industry standards. For example, scrap and rework is often traced back to a wrong drawing, an incorrect dimension or a poorly specified component. Complete and accurate product design is supported by a robust PLM system.
  4. R&D costs as a percentage of revenue are excessive. Engineering and design activity is bloated with too much headcount and overhead. Yet they are late with deliverables. PLM means efficiency in R&D.
  5. The organization struggles with coordination. It appears as if manufacturing and engineering are always at odds with both departments blaming one another for mistakes. PLM can offer objective data to resolve these issues.
  6. There is no accountability in the organization. It is difficult to diagnose where mistakes were made and who is responsible. People are always blaming other departments. A PLM system can provide objective data that allows the root cause to be addressed.
  7. Expedited freight costs are bleeding away your profits. Excessive expedited freight costs are common in companies that are late with deliveries and have to ship under duress to avoid customer penalties. Better upstream engineering supported by PLM can improve this problem.
  8. Your competitors always beat you to market with new products. Is innovation management and new product introduction a problem for your organization? A better PLM system can make dramatic differences in this area.
  9. Customers complain that they do not get the information they need. You owe your customers information at various stages during the engagement cycle and they never get it in a timely manner. A suitably configured PLM system can improve this dramatically.
  10. Your suppliers provide the wrong information. This can be a common problem diagnosed by your engineering staff. But do your suppliers have the right request to begin with? PLM technology can bridge this gap

Do you have three or more of these issues keeping you up at night? Time to take a serious look at a PLM system.

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