Design Reuse

If You Build It They Will come

If You Build It (Using Machine Learning) Will They Come?

By | Design Reuse, PLM | One Comment

Autodesk Claims Machine Learning Technology Will Transform 3D Engineering

Autodesk announced recently the availability of a shape-based search capability in A360. A blog article titled How Machine Learning Will Transform 3D Engineering describes the new capability, called Design Graph, as a “Google search-like functionality for the world of 3D models.”

Google search functionality is probably the wrong metaphor for 3D search. Web search is fundamentally text based, whereas searching for a part or a design requires a combination of textual and geometric terms and attributes, and sufficiently deep domain semantics. In fact, the blog article makes the very same argument later, describing Design Graph’s purpose to “identify and understand designs based on their inherent characteristics—their shape and structure—rather than by any labeling (tags) or metadata” (i.e. not Google-like).
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To Reuse or Not To Reuse Parts, That is the Question

By | Automotive, Design Reuse, Manufacturing, PLM | One Comment

Design Reuse

For years I have been critical of the automotive industry and its overzealous and careless tendency to design new parts instead of reusing existing designs, inventory parts and suppliers.  The Rationale to resist the temptation to innovate and reuse tried and proven parts is broad and multifaceted. Among the chief arguments:

  • Accelerate time to market and reduce the number and severity of ECOs
  • Reuse tooling and manufacturing processes
  • Improve quality and have better estimation of volume manufacturing ramp up, service load and warranty costs
  • Reduce the need to recreate work instructions, remove and replace procedures and related documentation
  • Lower manufacturing and final product cost
  • Reduce inventory and related costs

A useful way to look at this is that in addition to the benefits of reusing a physical commodity, design reuse promotes knowledge reuse, which has broader and longer lasting benefits.

The argument for design and part reuse gets a bit more involved when we look beyond small parts and subassemblies, which the average consumer doesn’t care about. What about systems that represent the brand identity, such as the engine or transmission? Do consumers know and care whether an engine is exclusive to the brand? How does this knowledge influence buying decisions?

A recent study by Automotive News of auto dealers offers an interesting perspective on the topic. The study indicates that consumers are split almost evenly about how they feel about the brand exclusivity of the engine in the car they’re shopping for and how this knowledge influences their busying decision.

Interestingly, according to the study, consumers care much more about the brand exclusivity of the transmission: more than 50% of consumers now and care whether the car’s transmission is exclusive to that brand.

An even more surprising finding, which, quite frankly, make me somewhat leery about the reliability of this very small (n= 169) study, is that 71% of consumers know and care whether the axles are exclusive to that brand. Or, at least, this is what auto dealers believe.

For some reason, the study did not ask about common car chassis that might have a stronger impact on consumer decisions, because it makes it easier to pitch one brand against the other. A good example is Volkswagen’s A4 platform that is used in a range of vehicles from the luxury sporty Audi TT to the lower end SEAT and Skoda. While most consumers are not aware of the pervasive use common platforms, will they change their mind if they did?

The fidelity of the study’s findings aside, it’s clear that line executives and designers face a dilemma: how far can they reuse parts and systems before they start diluting the brand identity. But finding the right mix of model-exclusive and common design, especially lower level assemblies and parts, is an important step in improving operations while maintain brand’s identity and integrity.

[Photo: GrabCAD]


Design Reuse: Reusing vs. Cloning and Owning

By | Design Reuse, Manufacturing, PLM, Strategy | One Comment

Reusing vs. Cloning and Owning

As I am preparing for my presentation and panel discussion at the Product Innovation Congress in San Diego next week, I am speaking with colleagues and experts in all things PLM. I recently spoke with Charlie Krueger from BigLever on product line engineering (PLE) and how some organizations and individuals practice design reuse.

We often encounter instances in which the engineering team makes use of an existing design or an inventory part in a new product. They assign it a new part number and sometimes a new name, and move it to the new system’s bill of materials (BOM), and from that point onward the part start a product lifecycle of its own. Charlie terms this approach “cloning and owning.”

Obviously, that’s not what we mean when advocate design reuse.

In a recent blog on design reuse I discussed the importance of reuse not only for the more obvious and better understood reasons such as accelerating time to market and reducing inventory costs but, more significantly, for the ability to reuse design, manufacturing and service knowledge associated with these physical objects.

If commonly used and shared parts and subsystems carry separate identities, then the ability to share lifecycle information across products and with suppliers is highly diminished, especially when products are in different phases of their lifecycle. In fact, the value of knowledge sharing can be greater when it’s done out of sync with lifecycle phase. Imagine, for example, the value of knowing the manufacturing ramp up experience of a subsystem and the engineering change orders (ECOs) that have been implemented to correct them before a new design is frozen. In an organization that practices “cloning and owning”, it’s highly likely that this kind of knowledge is common knowledge and is available outside that product line.

An effective design reuse strategy must be built upon a centralized repository of reusable objects. Each object—a part, a design, a best practice—should be associated with its lifecycle experience: quality reports, ECOs, supplier incoming inspections, reliability, warranty claims, and all other representations of organizational knowledge that is conducive and critical to making better design, manufacturing and service related decisions.

  • Organizations should strive to institute a centralized product management strategy that consolidates and exploits PLM and PDM data, ERP systems, and, in all likelihood, a myriad of informal and unstructured emails and spreadsheets.
  • To maximize the value of design reuse, information must be shared across product lines independent of the lifecycle phase of each product. In particular, incorporate late-stage knowledge such as service and warranty information in design decisions of new products.
  • Effective reuse would benefit from the ability to decompose system architectures differently. In addition to structuring product information using traditional engineering and manufacturing BOMs, encapsulating and organizing information by feature families and configurations, such as in the methodology behind PLE, is highly effective, especially in complex product architectures.


Make Informed Design Decisions; Avoid Costly Design Mistakes

By | Design Reuse, Manufacturing, Quality | No Comments

Webinar:  Chrysler, Jabil Circuits, Toshiba and  Whirlpool, avoid costly design mistakes by using a systematic approach to validating and improving product design

One of the topics I continue to research and advise manufacturing companies on is how to make informed design decisions early in the product lifecycle to improve product manufacturability and quality. In an upcoming webinar, I will disuses how numerous manufacturers, including Chrysler, Jabil Circuits, Toshiba and Whirlpool, use a systematic approach to validating and improving designs in order to reduce design iterations and accelerate time to market.

I covered the outcome of poor design decisions as reflected by the excessive number of avoidable engineering change orders (ECOs), depicted in the figure below, in a number of blog posts. The direct costs of suboptimal design are well documented:

  • Excessive design  iterations
  • Multiple tooling iterations and tool breakage
  • Longer manufacturing and assembly ramp up time
  • Unnecessary scrap, energy consumption, and other waste

Furthermore, if released to production, a poorly designed product will continue to plague the brand owner with excessive manufacturing costs, high service and warranty expenses, and tarnished brand image.

Market research shows that mature manufacturing companies use four key techniques to improve manufacturability and reduce the number of design errors and frivolous ECOs:

Improving Manufacturability

Improving Manufacturability

  • Frontload design decisions
  • Formalize and apply design and manufacturing process knowledge uniformly and consistently
  • Drive up reuse
  • Automate design checks and validation

In the webinar I will discuss the experience and the measured benefits at Chrysler, Jabil Circuits, Motorola SolutionsToshiba and Whirlpool and other manufacturers that use a formal design for manufacturing (DFM) process. I will discuss design for manufacturing excellence at Airbus, and propose a structured framework to help companies approach DFM systematically.

Most Engineering Change Orders Are Preventable

By | Design Reuse, Manufacturing, PLM | 2 Comments

Analysis of engineering change orders (ECOs) shows that the vast majority are caused by preventable design and manufacturing errors. These range from simple drawing and data entry mistakes to more complex – yet preventable – errors such as designs that are difficult to manufacture and assemble or to service. In an upcoming webinar, I will discuss two industry studies: an industrial equipment manufacturer where preventable ECOs accounted for 66% of the total, and a heavy equipment manufacturer that estimates that more than 80% of the ECOs were caused by preventable errors. (I published another ECO analysis here.)

These numbers are staggering. However, to put a more pragmatic spin on the discussion, it’s not quite realistic to expect that all of these mistakes can be detected and rectified, as I discuss in another blog post. Yet, the cost of design and manufacturing errors and just managing ECOs are so high, that eliminating even a modest portion of them would be highly valuable.

In the webinar, I will cite two examples of companies that adopted a systematic approach to detecting and preventing potential errors early in the design cycle. An electronic manufacturing services company reported 90% reduction in manufacturing line downtime and 50% reduction in scrap, and overall improvement in product quality and customer satisfaction. An automotive manufacturer reported estimated savings of $4.8M per year from improved throughput and preventing rework, reduction in tooling expenses, and savings in warranty expenses.