Trends

Risks and safety – (just) an issue in times of crisis?

Germany and almost all other countries are facing a crisis! Within a very short time we had to realize that an infectious organic structure with a size of a few nanometers is causing gigantic damage worldwide and is turning our lives upside down. We suddenly realize how vulnerable our existence is and how our own need for security always comes to the fore when everything around us can no longer be taken for granted.

History teaches us that it doesn’t matter what the name of a disaster is – COVID-19, 9/11, Fukushima or Tsunami, safety and risks have to be reconsidered and reassessed in each case. Because too much security makes any system slow and cumbersome. By contrast, too many risks, however, entail a high degree of danger – not only for the individual, but also for society as a whole.

The consideration and evaluation of product risks is a familiar topic in the PLM world. Risk management is often perceived as an annoying accessory, which essentially causes a lot of paper to be filled. However, the concrete added value is not always immediately apparent. This is where the change of perspective from producer to consumer helps: Countless test and quality seals in every area of daily life ensure that products are safe to use, for example the TÜV for the car, the CE mark on the notebook or the GS seal on the office chair. Nobody wants to do without the fact that the product he uses is sufficiently safe and that he can use it without major risks.

Three fundamental insights have become apparent for my consulting work:

Think the unthinkable
Science and technology make us feel more and more that nothing can happen to us. But this certainty can be deceptive. Sometimes we only feel safe because we are not (yet) aware of risks or because we misjudge them.
You cannot think as stupidly as it may come
It will never be possible to anticipate every risk and to be prepared for all eventualities or even be insured against them. Nevertheless, risk management trains the eye for possible dangers and effective countermeasures. Regularly thinking about risks and sounding out parameters for security in times of no crisis trains thought structures and action patterns. These consequently form a solid basis for sound crisis management.
Necessity is the mother of invention
Unknown situations paralyze one’s own thinking and acting at first, but as a consequence they stimulate creativity in particular. There is enormous potential here for innovative and efficient ideas on how to achieve great things with existing onboard resources. So every crisis also offers the chance for change and improvement.

Corona ensures that the golden mean between safety and risk in all areas of life can once again be balanced under the given parameters. Perhaps the changed perspectives will even make it possible to take a more positive view of risk management in PLM and give free rein to one’s own creativity both in assessing risks and developing safe products?

AI – Where we are in the Hype Cycle and how it continues

While the artificial intelligence index shows that the increase of research articles and conferences in the field of AI continues, the media is slowly showing some fatigue in the face of the hype. So it’s time to take stock: What has been achieved? What is practically possible? And what is the way forward?

What has been achieved?

In the years 2018 and 2019 the previously developed methods for the application of neural networks (this is how I define AI here) were further refined and perfected. Whereas the focus was initially (2012-2016, Imagenet competition) on methods for image classification and processing and then on audio methods (2015-2017, launch of Alexa and other language assistants), major advances in text processing and generation were made in 2019 (NLP = natural language processing). Overall, the available technologies have been further improved and combined with a great deal of effort, especially from the major players (Google, Facebook, OpenAI, Microsoft).

What is practically possible?

The use of AI is still essentially limited to four areas of application:

Images: image recognition and segmentation
Audio: Conversion from speech to text and vice versa
NLP: word processing and generation
Labeled Data: Prediction of the label (e.g. price) from a set of features

This list is surprisingly short, measured by the attention AI receives in the media. The most impressive successes of AI, however, result from a combination of techniques such as speech assistants using a combination of audio, NLP and labeled data to convert the input into text, recognition of text intention with NLP and prediction of the speaker’s wish by using huge amounts of labeled data, meaning previous evaluations of similar utterances.

Decisive for the development of precisely these AI application fields were

the existence of large quantities of freely available benchmark data sets (data sets for machine learning) on which algorithms have been developed and compared
a large community of researchers who have jointly agreed on the benchmark data sets and compared their algorithms in public competitions (GLUE, Benchmarks AI, Machine Translation, etc.)
a free availability of the developed models, which serve as a starting point for the practical application (exemplary Tensorflow Hub)

Based on these prerequisites one can quickly assess how realistic some marketing fantasies are. For example, there are neither benchmark data sets nor a community of researchers for the often strikingly presented field of application of predictive maintenance, and accordingly there are no models.

What’s next?

On the one hand, it is foreseeable that the further development in the AI area will certainly continue initially in the above-mentioned fields of application and continue to develop in the peripheral areas. On the other hand, areas are emerging which, similar to the above-mentioned fields of application, will be driven forward at the expense of large public and private funds (e.g. OpenAI and Deepmind are being subsidised by Elon Musk and Google with billions of euros respectively). An example of large investments in this area is certainly autonomous driving, but also the area of IoT. In total, I see the following areas developing strongly in 2020-2022:

The combination of reinforcement learning with AI areas for faster learning of models
A further strengthening in the area of autonomous driving resulting from the application and combination of AI and reinforcement learning
Breakthroughs in the generalization of the knowledge gained from image processing to 3D (Geometric Deep Learning and Graph Networks)
A fusion of traditional methods from statistics with neural networks
IoT time series (see below)

I see a big change coming with the rise of IoT and the associated sensor technology and data. By their very nature, IoT data are time series that must be filtered, combined, smoothed and enriched for evaluation. Relatively little specific has been done to date for this purpose. It could be that from 2020 – 2022, this topic could hold some surprising twists and breakthroughs for us. German industry in particular, which has benefited rather little from the initial developments in the field of AI, should find a promising area of application here.

Agile physical product development?

My last blog post was about teams that only become really agile through experience. Today, the focus is on the challenges that agility brings to engineering.

Almost 20 years after the Agile Manifesto, agile software development has become widely accepted. It is no longer about whether, but only about best practices in detail and agile scalability. The success and ease of use of task boards, for example, have led to agile procedures also finding enthusiastic users outside software development where tasks are processed in a team.

This finally led to the increasingly intensively discussed question of whether physical products could also be developed more efficiently with an agile approach.

Why?

For many years, there have been established product development processes that have reached great maturity and support successful development. Why abandon them and take on the risks of a completely new approach?

The more unclear the requirements on the product are and the less known the technology to be used, the less suitable classical project management methods are, because they are very strongly forward-planning. It is precisely this tendency to start projects despite initially incomplete requirements that we are increasingly observing. Digitization and new technologies require new business models or new technological capabilities. This speaks for an agile approach, as it was invented to deal with ambiguity and not-yet-knowledge.

Is that even possible?

The decisive question here is: Are agile methods from software development at all suitable for mastering the challenges of “classical” product development? In contrast to software, physical products are developed with a much greater division of labour. The production of faulty components causes high consequential costs and the validation depends on physical prototypes. It is not possible to present a new, functioning and potentially deliverable stand every two weeks. Solutions for such problems require a creative further development of the known agile process models. A very simple example: The teams of different domains use different sprint durations. While the software team delivers every 2 weeks, the mechanics team delivers every 6 weeks. It is important to synchronize the sprints so that a common increment is achieved every 6 weeks.

The challenge

The challenge of introducing agile methods is therefore twofold: On the one hand, it is necessary to adapt the agile methods from software development to the conditions in product development. On the other hand – and this brings me back to my previous contribution – a lot of agile experience is needed to successfully make such adjustments. In order to resolve this contradiction, one should bring together the pioneers who dare to venture into new territory with experienced “agilicans” who master their craft in software development. Mutual learning and sharing of knowledge leads to a better mastery of product development under rapidly changing conditions.