When Stan Davis first introduced the concept of mass customization in 1987, he predicted that it would be the future of manufacturing. But he also noted that the viability of mass customization would be constrained by technology.


Fast forward about 30 years. Davis’ vision has come to fruition. Mass customization first took hold in healthcare with customized devices, tools and fixtures. Since then, companies in a wide variety of industries have embraced mass customization, with varying degrees of success; delivering customized products without sacrificing economies of scale proved too difficult even for industry leaders like Dell, which abandoned its build-to-order (BTO) business model in 2011.

Stan Davis discusses the future of mass customization with Jay Cross of the Internet Time Group.

But the evolution of cloud technologies hold new promise for mass customization. Indeed, B2B software expert Joel York argues that “mass customization in cloud computing is more natural, more flexible, and offers more potential for competitive advantage” than SaaS solutions. Cloud CAE is particularly suited for mass customization because it allows the robust, scalable simulations required for the timely prototyping and manufacture of highly customized products.


Transition from Physical Prototyping

Bringing a single new product to market often requires a lengthy design and prototyping process. Now multiply that process for myriad permutations of each product, and mass customization quickly becomes unprofitable, if not impossible.


But even conventional approaches to simulation aren’t always up to the task of creating digital prototypes for all the possible product configurations within a reasonable time frame. Using cloud simulation software can speed up that process by ten times or more, making it much more suitable for evaluating design variations.


Capacity for Big Data

Successful mass customization relies on data. Lots of data. The explosion of IoT-enabled products and devices has resulted in a virtually endless stream of data that engineers can use to design not only the next generation of a product, but also customized variations of that product. However, the conventional on-premises cluster or individual workstation lacks the computational power to store and analyze such massive volumes of data at the speed necessary for today’s product development cycle. Cloud computing offers an excellent solution to this issue because cloud resources can be scaled to match any required computational capacity.


Greater Flexibility and Scalability

Historically engineers have conducted simulations on single applications that require conventional (and often expensive) software licenses. This model offers little flexibility; these licenses carry a cost whether they are used or not, and computational power is limited to capacity of the individual workstation, offering no scalability. These constraints are antithetical to mass customization, which requires the ability to quickly adapt to changing customer preferences.


Cloud CAE, on the other hand, provides much greater flexibility and scalability. Cloud-based software can be accessed on demand, using a pay-as-you-go model. Engineers can essentially assemble best-of-breed solutions in an online environment, and access the solutions only when they’re needed, such as when a customer requests new variations on an existing product.


Support for Parametric Design

The addition of sensors, antennae and other IoT devices that enable connectivity mean that more and more products contain electrical systems, introducing new design challenges such as ensuring that devices do not emit excessive electromagnetic waves or generate too much heat. These complexities demand more robust simulation capabilities and collaboration across engineering disciplines. Yet most engineers are still using siloed, single-physics simulation. In the automotive industry, where autonomous cars present a significant challenge, ANSYS estimates that 85% of engineers are still using single-physics simulation.


Cloud CAE enables more collaborative, comprehensive system engineering because it provides much greater computing power than most engineers can access from their workstations or on-premises clusters. Most notably, cloud CAE offers the computational power necessary to support parametric design, that is, design where a change to one parameter of a product will lead to a change in another parameter according to preset algorithms.


Four Models of Mass Customization

Mass customization still represents a novel business model for most manufacturers, and it’s important to choose the approach that best fits each company and its customers. According to Harvard Business Review, four unique approaches to mass customization have emerged.

HBR_Four_Approaches_Customization
The Harvard Business review outlines four approaches to customization. 

  • Cosmetic: Perhaps the most accessible approach, cosmetic mass customization is really just different presentations of the same standard product. For example, the packaging or advertising might be customized for specific user groups, even though all groups will essentially use the product in the same way.
  • Adaptive: Manufacturers that employ an adaptive model still produce one standard version of a product that users can alter themselves. This method is most successfully employed when customers want a product to perform in different ways at different times and when technology makes it particularly easy for users to execute their own customizations.
  • Collaborative: Most commonly associated with mass customization, this approach employs a dialogue with each individual customer to help determine exactly which product offering fits the customer’s needs. Collaborative methodology works best for businesses whose clients have trouble articulating their needs or get overwhelmed by choosing from limitless options.
  • Transparent: Perhaps the most sophisticated approach to mass customization, the transparent model is defined by providing customers unique goods and services--without explicitly asking for customization instructions from the customers or even indicating that the customer is actually receiving something customized. This method is appropriate when it is relatively easy to observe customer behavior and inconspicuously customize products accordingly, or when customers don’t want to repeatedly state their needs.

Moving toward more sophisticated mass customization will require the right technology. Cloud CAE provides the right mix of computational power and flexibility to support the next era of mass customization.

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