The article highlights the challenges of capturing tacit knowledge in engineering design, emphasizing that valuable insights often exist informally within experienced professionals. It advocates for recording reasoning during decision-making processes and acknowledges the importance of mentoring, suggesting a balanced approach between documentation and recognizing human expertise in knowledge management.
The article examines the concept of Product Memory within the context of configuration management (CM), highlighting the importance of preserving the reasoning behind engineering decisions. It emphasizes that while CM effectively manages the “what” of product data, it often overlooks the critical “why.” This gap can lead to significant risks, particularly in complex systems. The integration of AI could assist in retrieving and recording this reasoning, reinforcing the value of… Read More »The Question Your Configuration Management System Can’t Answer!
This article discusses the complexity of managing effectivity in change requests, emphasizing the need to track three stages: desired, planned, and actual effectivity. Many organizations neglect the first two stages, hindering the understanding of gaps and potential issues. It advocates for comprehensive documentation to enhance process intelligence and compliance.
The article discusses the application of AI in engineering change management (ECM), highlighting two studies that focus on predicting change propagation and adherence to effectivity dates. It emphasizes the importance of confirming the scope before effectivity and the need for documented processes to support AI modeling. Practitioners should consider the quality of process inputs when deploying AI tools.
The article discusses the concept of configuration debt in product development, highlighting how initial errors in product variant assessments can lead to compounded issues over time. Unlike technical debt, configuration debt manifests in hidden warranty claims and field failures, stemming from unaddressed applicability confirmations that impact subsequent engineering decisions.
The article discusses the importance of distinguishing between applicability and effectivity in change management. It highlights how confusion arises when both concepts are tackled simultaneously, leading to unresolved issues during meetings. The author advocates for a structured process that separates scope confirmation from effectivity assignment, enhancing productivity and decision-making in organizations.
The article discusses the challenges in reconciling discrepancies between the as-designed, as-built, and as-maintained baselines in engineering and manufacturing. It highlights how AI can systematically identify these gaps, enabling better tracking and traceability, while emphasizing the human role in interpreting these discrepancies to ensure effective configurations and risk management.
The article discusses the challenges of dataset validation within the CM2 framework, emphasizing the distinction between creator and user perspectives. It highlights how AI can enhance validation by checking technical requirements and simulating user reviews to identify ambiguities. The co-ownership model ensures accountability and clarity in dataset usage for all stakeholders.
This article emphasizes the importance of structured data in engineering change management, highlighting that 30-50% of engineering capacity is wasted on rework. It advocates for AI-assisted impact analysis within a defined framework, such as CM2, which fosters collaboration between engineers and AI. This approach enhances understanding and accountability in decision-making.
This article discusses the importance of human accountability in AI-assisted configuration management, emphasizing that governance failures, rather than technical issues, led to AI’s biggest failures in 2025. It highlights the necessity for clear ownership in processes and warns against over-relying on AI, which cannot replace human judgment, responsibility, and engagement in organizational culture.