Big Data and the Product Lifecycle

Our twitter feed went silent for a few days last week as we spent some time at a conference that where chance conversations, personal reunions, and discovery were the point. In fact, this was one of the few events where attendees – like us – didn’t have our heads down buried in our computers. We’re speaking of Cyon Research’s COFES 2012 design engineering software conference, where we had the opportunity to explore the synergy of Big Data and the Product Lifecycle, why ALM and PLM systems can’t play nice, and how to keep a handle on finding the right data as product development adopts a 24/7 follow-the-sun strategy. It wasn’t an event of sessions in the conventional sense, but lots of hallways where you spent most of your time in chance, impromptu meetings. And it was a great chance to hook up with colleagues whom we haven’t caught in years.

There were plenty of contrarian views. There were a couple of keynotes in the conventional sense that each took different shots at the issue of risk. Retired Ford product life cycle management director Richard Riff took aim at conventional wisdom when it comes to product testing. After years of ingrained lean, six sigma, and zero defects practices – not to mention Ford’s old slogan that quality is job one — Riff countered with a provocative notion: sometimes the risk of not testing is the better path. It comes down to balancing the cost of defects vs. the cost of testing, the likely incidence of defects, and the reliability of testing. While we couldn’t repeat the math, in essence, it amounted to a lifecycle cost approach for testing. He claimed that the method even accounted for intangible factors, such as social media buzz or loss of reputation, when referring g to recently highly publicized quality issues with some of Ford’s rivals.

Xerox PARC computing legend Alan Kay made the case for reducing risk through a strategy that applied a combination of object-oriented design (or which he was one of the pioneers – along with the GUI of course) and what sounded to us like domain-specific languages. Or more specifically, that software describes the function, then lets other programs automatically generate the programming to execute it. Kay decried the instability that we have come to accept with software design – which reminded us that since the mainframe days, we have become all too accustomed to hearing that the server is down. Showing some examples of ancient Roman design (e.g., a 2000-year old bridge in Spain that today still carries cars and looks well intact), he insists that engineers can do better.

Some credit to host Brad Holtz who deciphered that there really was a link between our diverging interests: Big Data and meshing software development with the product lifecycle. By the definition of Big Data – volume, variability, velocity, and value – Big Data is nothing new to the product lifecycle. CAD files, models, and simulations are extremely data-intensive and containing a variety of data types encompassing graphical and alphanumeric data. Today, the brass ring for the modeling and simulation world is implementing co-simulations, where models each drive other models (the results of one drives the other).

But is anybody looking at the bigger picture? Modeling has been traditionally silo’ed – for instance, models are not typically shared across product teams, projects, or product families. Yet new technologies could provide the economical storage and processing power to make it possible to analyze and compare the utilization and reliability of different models for different scenarios – with the possible result being metamodels that provide frameworks for optimizing model development and parameters with specific scenarios. All this is highly data-intensive.

What about the operational portion of the product lifecycle? Today, it’s rare for products not to have intelligence baked into controllers. Privacy issues aside (they must be dealt with), machinery connected to networks can feed back performance data; vehicles can yield data while in the repair shop, or thanks to mobile devices, provide operational data while in movement. Add to that reams of publicly available data from services such as NOAA or the FAA, and now there is context placed around performance data (did bad weather cause performance to drop?). Such data could feed processes, ranging from MRO (maintenance, repair, and operation) and warranty, to providing feedback loops that can validate product tests and simulation models.

Let’s take another angle – harvesting publicly available data for the business. For instance, businesses could use disaster preparedness models to help their scenario planning, as described in this brief video snippet from last years COFES conference. Emerging organizations, such as the Center for Understanding Change, aim to make this reality by making available models and expertise developed through tax dollars in the national laboratory system.

Big Data and connectivity can also be used to overcome gaps in locating expertise and speed product development. Adapting techniques from the open source software world, where software is developed collaboratively by voluntary groups of experts in the field, crowdsourcing is invading design and data science (we especially enjoyed our conversation with Kaggle’s Jeremy Howard).

A personal note on the sessions – the conference marked a reunion with folks whom we have crossed paths with in over 20 years. Our focus on application development lead us to engineered systems, an area of white space between software engineering and classic product engineering disciplines. And as noted above, that in turn bought us full circle to our roots covering the emergence of CADCAM in the 80s as we had the chance to reconnect many who continue to advance the engineering discipline. What a long, fun trip it’s been.