The blog that shapes the future of CM and unravels how to achieve process excellence through Enterprise Configuration Management.
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This article compares change control processes across aerospace, automotive, and medical device industries, highlighting their distinct approaches shaped by different failure modes. It emphasizes the need for cross-industry learning to adapt traditional frameworks to modern challenges, particularly in handling continuous software updates. A unified CM2 framework is proposed for enhanced governance.
This article compares change control processes across aerospace, automotive, and medical device industries, highlighting their distinct approaches shaped by different failure modes. It emphasizes the need for cross-industry learning to adapt traditional frameworks to modern challenges, particularly in handling continuous software updates. A unified CM2 framework is proposed for enhanced governance.
This article discusses the potential negative impact of AI on the skill development of junior configuration managers in the realm of configuration management. It highlights the risks of reliance on automation, including skill degradation and reduced critical thinking. Proposed solutions include manual practice, graduated automation, and competency gates to preserve human expertise alongside AI adoption.
This article discusses the integration of AI in configuration management, highlighting the challenges posed by probabilistic decision-making compared to deterministic systems. It emphasizes the need for governance frameworks to validate AI outputs against compliance requirements and establish confidence thresholds for manual review in decision-making processes.
This article discusses challenges of AI-assisted product changes, particularly context degradation in large language models as conversations progress. It introduces scaffolding, a structured approach to maintain contextual integrity in change management. By emphasizing task decomposition and context engineering, organizations can improve AI performance and enhance governance in engineering workflows.
This article discusses the integration of AI in configuration management, highlighting the governance gaps between probabilistic AI outputs and established deterministic standards. While AI-driven tools improve efficiency and data accuracy, concerns arise about the erosion of human expertise and the need for frameworks to validate AI-generated analyses, especially in regulated industries.
The article highlights the hidden productivity costs in engineering, estimating a $4.5 million loss due to inefficiencies and information searches that consume 30% of work time. It emphasizes the importance of Configuration Management (CM2) to make engineering decisions visible, preventing downstream issues that drive up costs and impair company margins.
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[…] conditions the resulting model can be deployed has not been addressed in published CM guidance. The intersection of machine…
Please check k also the Part re-identification decision tree I proposed in my book: https://martijndullaart-ifdql.wpcomstaging.com/part-re-identification-it-is-not-about-the-tree/ That should cover a lot…
Almost a year later, and I am only now replying. :-). Let’s take the case of early design phases. Many…
[…] Understanding the Impact of Changes introduced the need for the CM Baseline. […]
[…] design intent. This is standard change control documentation. Organizations maintaining rigorous CM baselines already have this […]