Category

Innovation

Narcissus (Carravagio, C. 1597-99)

Innovation and the Inherent Bias of Technology

By Innovation, Internet of Things (IoT), IT Strategy, Manufacturing 2 Comments

The Imherent Bias of Technology

“Technology is neither good nor bad; nor is it neutral” declared Melvin Kranzberg.

Indeed, not only is technology un-neutral, it has an intrinsic bias. In the process of defining and implementing software to perform certain tasks and solve particular problems, the designers make many assumptions and decisions—most of which are irreversible—about the intended tasks, workflows, work environment, and user profiles. Unintentionally, the marketers and designers of software tools introduce a bias.

Douglas Allchin maintains that in itself this inherent bias does not pose a problem, but it does dictate how the technology is being used, and who can and cannot use it. Consequently, the innate bias influences the ability of the organization to realize the full value of the technology.

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Knowledge Based Product Development

By Design Reuse, Innovation, Internet of Things (IoT), Manufacturing, PLM No Comments

Lack of ongoing insight into product operation and the interaction with users is a major contributor to the persistence of a knowledge gap that plagues many product organizations, reduces efficiency and stifles innovation.

What drives this knowledge gap? A number of general trends impact companies in most industry sectors and product types.

  • Technology complexity and, in particular, the increased reliance on complex embedded control software.
  • Elongated and fragmented supply chains that support global operations.
  • Meeting global markets demand results in lower volume of configuration-specific product instances.
  • Accelerated egress of an aging workforce from the workplace.

The impact of the growing knowledge gap is most recognized and frequently discussed in the context of equipment maintenance and repair, which is a knowledge intensive activity. Another area were closing the knowledge gap has significant benefits is engineering change management.

Organizations often speak about the need to collect and formalize “tribal knowledge” to close the gap between the knowledge needed to perform a complex takes or reach a decision, and the knowledge and experience the person performing this task has access to.

In a previous article I discussed the severe myopia that exists in many product companies: most engineering organizations lose sight of their products once they are sold or installed in the field. In some manufacturing companies this happens even earlier, during the transition to manufacturing engineering and before the product enters volume production. Once a product is in use, there is only a trickle of information in the form of service records and warranty claims. But many organizations dismiss critical maintenance and warranty information as merely operational and cost of doing business and aren’t leveraging its full potential.

Knowledge Based Product Development

Product organizations cannot have a true and complete view of a product and how customers are using it unless they can continue to observe it while it is in use. They must monitor and analyze products throughout their lifecycle, gauge their performance, quality and uptime, how users are interacting with the products, and how well they meet market expectations overall.

This rich multidisciplinary insight extends beyond design information. It includes multifaceted data from a variety of sources and disciplines and includes manufacturing, supply chain, filed operations and service and maintenance. The data can be aggregated from multiple sources, including real-time data such as IoT, maintenance record and customer experience.

The aggregation, classification and analysis of this data provides critical insight to embellish and enhance an organizational knowledge library for subsequent iterations of product design, manufacturing process engineering, and service planning.  reinforced by analytics, case-based reasoning (CBR), and similar tools to collect, analyze and vet information.

Product organizations cannot afford operating with blinds that prevent visibility to downstream processes. They should not ignore the value of information collected throughout the product lifecycle. They must establish knowledge processes, governed by PLM software, to maximize the utility and benefit of product lifecycle information.

(Photo: FreeDigitalPhotos.net)

Last Judgment (Hieronymus Bosch C. 1482)

The Electric Car Isn’t Disruptive Innovation

By Automotive, Autonomous, Connected, Electric, Shared Vehicles, Innovation One Comment

Disruptive Innovation

In his 1997 book The Innovator’s Dilemma about how new technologies cause seemingly well-managed companies to fail, author Clayton Christiansen coined a soon-to-become a ubiquitous term: “disruptive innovation.” This catchphrase seems to have gotten a new life over recent years. I frequently participate in client strategy sessions and attend conference presentations where the speaker does not miss an opportunity to frame a new product or service idea as “disruptive.”

Now, disruptive innovation does not mean “cool” or “better than the competition”; it does not even necessity mean “new.” The term, as Christiansen uses it, means a technology that challenges the business status quo by enabling a new and different product or business model that disrupts and shatters the hegemony of market leaders. Read More