Joe Justice: Conway’s Law, Modules, and Experiments (Part 2)
In this interview, Joe Justice explains the importance of the module and a low-risk way to try out Extreme Hardware (Part 2)
Product Velocity is a new approach that enables companies to learn faster, make decisions more quickly, and deliver results.
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In this interview, Joe Justice explains the importance of the module and a low-risk way to try out Extreme Hardware (Part 2)

In this interview, Joe Justice explains how hardware development can be accelerated and why so few companies are able to do it. (Part 1)

The theory behind Product Velocity can be expressed with mathematics and optimizes the reduction of uncertainty with four variables.

In this keynote speech, Martin Eigner shows how software, systems and services are changing product development.

There is now also an Expansion Pack for the Scrum Guide, which is relevant for Systems Engineering and Product Velocity.

BMW and Daimler use different strategies to deal with increasing complexity in vehicle development.

Several start-ups are trying to solve the complexity problem in product development: Flow Engineering, Spread AI and Spicy SE.

Increasing complexity places high demands on quality management. This is particularly the case when quality is concerned with (functional) safety. Embedded systems belong to the class of products where this is a major challenge. One of the reasons for this is that software is one of the main drivers of complexity. A typical example would be...

The dust is slowly settling around log4j. As a reminder: In December, a serious security vulnerability was discovered in this open source software package. Under the name Log4Shell, this vulnerability allowed attackers to execute arbitrary software code on computers using log4j. As log4j is a widely used framework for logging application messages, the consequences were serious. In this article...

There is a lot of talk about AI at the moment, but many products are still "dumb". As a result, many development teams will be asking themselves whether AI should be built into the next generation of products. In the following, I explain why we should think twice about it.

Systems engineering is stagnating, but slowly (2025) new, innovative tools are appearing, many with SysML v2.

We have too many inhibitions about deleting. Subtractive thinking ensures a better result through simplicity and clarity.
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