Go beyond technological navel-gazing and focus on customer value

Published on 23 November 2017 by Isaak Tsalicoglou.

Putting the horse before the cart and creating customer value with new technologies can move even an industry traditionally seen as "slow to innovate".

In an article back in June 2017 I made the following arguments:

  • The (Industrial) Internet of Things has been in a state of massive hype, because it has mainly been driven by vendors of IT systems and electronics components, instead of by pursuing validated end-customer value.
  • IoT still offers massive opportunities for niche-market players who manage to integrate increasingly commoditized hardware and software components in a meaningful way that delivers value to customers.
  • To grasp such opportunities and deliver customer value, the pursuit of innovation requires looking at a mix of value propositions of business models that are driven by data insights from IoT-enabled products.
  • The teams that do so are bound to identify further opportunities to close the loop between customers' real-life product-use patterns, updated value propositions, improved requirements and specs, towards a stronger differentiation.

These arguments were based on my customer discovery and development in two fields during my entrepreneurial endeavor with noito GmbH: 1) data-driven service business models, and 2) data-driven set-based R&D of tangible products. Moreover, to some extent, these arguments were driven by my observations that—at least on LinkedIn—"digital" and related keywords have been eroding rapidly towards a worrying state of Deft-ness since late 2015 / early 2016.

Innovation, beyond just technology push

Since I wrote that article I have joined Proceq, a company that in the past two years has created portable wireless instruments for metal hardness testing, the productivity-focused value proposition of which relies on IoT and the cloud. Essentially, the already high-quality tangible products become value-adding product-system elements at the center of "digital-enabled" business models.

Moreover, in September, Proceq announced a flurry of new value propositions that address chronic pains of non-destructive testing (NDT) equipment users. These value propositions are at the core of business models that include a cloud platform and/or tiered-pricing and rental revenue streams. For example, in metal hardness testing and structural imaging.

The tangible product-system components themselves have not lost their importance in the process—if anything, they have become even more important in serving the core functional jobs of NDT, i.e. inspections and measurements at high quality, repeatability, and reliability. And that's why tangible components, such as Ground Penetrating Radar antennas and Ultrasonic Contact Impedance probes have been, in fact, enhanced beyond the "state of the art".

At the same time, by looking beyond most companies' tech-obsessed "IoT navel-gazing", these new product systems also help to address more jobs-to-be-done better in further phases of typical NDT customer journeys, i.e. in preparation and post-processing/reporting.

True to my June article (which I wrote unbeknownst of my future career step), this progress has been achieved by taking an end-to-end view of working processes in the NDT industry through the eyes of the customers. In other words: the sought-after "business model innovation" that has been so trendy since Alexander Osterwalder's first (and, IMO, best) book first hit the market—combined with a dose of what some call Design Thinking thrown into the mix.

NDT's five major, chronic pain points

Now, some months later, we (Janko Meier, Isaak Tsalicoglou, and Ralph Mennicke) have analyzed and clustered our observations of NDT users' typical workflows and tied them to technology trends, industry trends, and drivers of "Industry 4.0" (as people in German-speaking countries like to call the increasing degree of industrial interconnectedness and automation).

This has resulted in an overview of five major and chronic pain points of NDT users. These we have described solution-neutrally, as any good product manager should:

  1. Complex user interfaces: most NDT equipment looks complex to use because it is also complex to use, as it has been designed by experts for experts.
  2. Inefficient workflows: the execution of NDT inspection is often rife with clunky procedures containing manual, error-prone steps that inevitably lead to rework and low quality.
  3. Complicated interpretation of data: in the NDT space there is a tendency to present measurement data in raw, unprocessed formats that often make the derivation of insights into a highly operator-dependent and arcane art.
  4. Incomplete traceability: NDT procedures and solutions rely excessively on users' discipline and detail-orientation for the manual documentation of inspection steps, and on the collation of results from fragmented sources of information.
  5. Obstructed sharing of data and insights: generating good reports from NDT inspections suffers from a large proportion of non-value-adding waiting time to value-adding effort during end-to-end inspection, due to the manual export and processing of ever-increasing amounts of high-density data.

Future-proof non-destructive testing

We have published our observations within the scope of the 15th Asia Pacific Conference for Non-Destructive Testing (APCNDT2017), which took place from November 13th to 17th in Singapore. In our paper, "The future of NDT with wireless sensors, A.I. and IoT", we examine these observations in closer detail and accompany them with examples of actual solutions in the market today from various industries.

Our examples of future-proof NDT can be grouped in the following five categories of major improvements:

  1. Increasing the ease-of-use of NDT solutions through sophisticated, feature-rich, yet also user-friendly and intuitive user interfaces that mimic familiar situations, such as snapping a photo with your smartphone within a matter of seconds.
  2. Increasing the accuracy and efficiency of NDT inspection workflows by reducing errors and rework through improvement of workflow steps, such as probe positioning or verification, and reducing misunderstandings by enabling one-person usage.
  3. Delivering more actionable insights of higher accuracy by supporting the user with data interpretation through providing visualization options befitting different users' skill level, and by reducing measurement error through built-in Machine Learning correction models.
  4. Establishing traceability of NDT procedures with less effort and a lower potential for errors by providing software features that help users unambiguously and transparently log all measuring data, probe settings, changes, as well as multimedia annotations.
  5. Enabling unobstructed data sharing for collaboration, quality assurance and expedited reporting by offering a secure network of wireless probes, mobile devices, cloud storage, and a web platform across locations and hardware platforms.

In our paper, we conclude that the NDT industry is—in principle—ready and well-suited for the adoption of wireless sensors, A.I. and the Internet of Things. Recent technological progress in those fields has already opened and continues to open new opportunities to address chronic pain points of NDT use cases. Furthermore, solutions are already in the market, and are gradually being preferred against legacy approaches to NDT, thanks to the customer benefits they deliver.

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