Knowledge Management

How knowledge management works within modla's platform and methodology. Background and common questions included.

What is knowledge management?

Traditionally businesses capture the knowledge of engineers and Subject Matter Experts (SMEs) as documentation, procedures, and processes. In the field of asset management, these include strategic and tactical plans, work instructions, assessments, reports etc.

The knowledge of those who came before us is free to leverage provided you have access to and can follow the documented processes and procedures. While it is challenging to make use of the work of others, it is even more challenging to improve on it. We often lack the original justifications and assumptions to understand and build on it. Without these insights, we cannot trace the logical path that resulted in the outcome and safely make changes free of unintended consequences

Knowledge management not only involves capturing SME knowledge, opinion and learnings. It must also capture the supporting reasoning that formed the current way of thinking. By capturing the context of decision-making, we unlock many benefits:

  • Traceability: To be able to link data and information through to decision-making processes and their outcomes.
  • Capture lessons learned: So that errors and failures do not repeat.
  • Teaching: Use of the knowledge base to quickly educate new employees or resources about the subject matter.
  • Sharing and collaboration: Structured information and knowledge can be shared and improved by multiple sources simultaneously.
  • Review: Information presented in a structured manner can easily be interrogated for flaws and improved.

How does modla capture knowledge?

Modla captures knowledge using a decision layer within our models. The decision layer contains codified engineering and SME opinion, inferences made from data, standards and research findings. It encapsulates the current thinking of industry to be leveraged and improved upon by anyone. Our decision layer and the knowledge contained within it is completely transparent. By combining a structured methodology such as RCMD with our open-source values, we have built a solid foundation for collaboration throughout the industry.

To deal with conflicting studies or opposing views from SMEs we use a Bayesian approach to statistical inference. We allow differing viewpoints to converge by using data to infer weightings and confidence parameters.

Why is this important?

While there have been various collaborative initiatives before, most businesses still work in silos. Without being able to leverage the learnings of others, the progress of industry slows as a whole. While we can only assume the many driving reasons for this division, disagreement without resolution is a definite barrier. Once collaborative efforts reach an impasse, the exercise tends to derail completely. Our structured approach and delineated requirements for each level of decision making minimize discord and facilitate continuous improvement.