Change propagation/impact assessment is one of the most significant challenges for engineering projects. Late changes made to parts and assemblies frequently have a knock-on effect which can remain undetected until construction/assembly.
Using Net AI’s networks to provide direct information to designers within the CAD environment. Information includes parts that might be dependent and parts within an assembly that are being worked on i.e. that may change. KADlytics enterprise cloud technology enables holistic analysis across multiple CAD systems.
Reduction in concessions and last minute corrections.
To automatically appraise the completeness of CAD models and their attributes with respect to the RIBA stage-gates.
KADlytics’ cloud architecture applied to monitor the attributes and activity associated with CAD models. Taxonomies constructed to enable models to be tested against stage-gated reviews including RIBA stage-gates.
Internal cost savings arising from an automated solution and elimination of missing information and associated issues/overruns for construction projects.
Agile development programmes move quickly and while programmers can specify dependencies within the environment there is a requirement to monitor potential dependencies particularly when programmes pivot.
Application of Net AI to automatically learn about potential dependencies based on activity and events. Custom dependency graphs for each developer raises awareness of potential dependencies within the code base.
Reduced rework and right-first-time code base.
Healthcare and naval architecture
Do you really understand your business processes? Does practice conform to the documented processes?
Net AI applied to monitor and characterise existing practice. The Activity Network layer of Net AI provides a time-history of the process and through consideration of multiple projects/jobs the common structures within the network provide an ‘as-is’ business process. The Co-occurrence Network layer of Net AI learns who is working with who and how they are working together. Such understanding into levels of collaboration and types of collaboration are essential for investing in supportive tools and methods.
Revealing the 'as-is' business processes and providing evidence for business process reengineering and/or investment decisions in tools for collaborative working.
Formula Student is an international competition where students build and race a single-seat race car. Each year, a new team of students attempt this challenging task and with the previous team having graduated, there was a fundamental need to share and retain knowledge across the teams.
By analysing the technical reports and file activity of the team, a network of knowledge and understanding can be generated. As part of the preparations for the new team, the incoming project manager associated new team members with the current team activities, giving them ownership of that area of data and knowledge. In addition, the PM associated more than one member to each area in order to remove single points of failure.
The distribution of knowledge allowed work to be between distributed amongst team members, clearer identification of experts in the respective sub-teams and mitigated delays with people being away as more than one person could support a particular area of work.
Formula student came to us with a need for a tool that could monitor and highlight the emerging dependencies during the design & manufacture of their car. Given the tight time constraints and budget, the PM wanted an agile organisational structure where teams could be re-organised as dependencies and critical areas emerged.
By monitoring the structural dependencies inherent in CAD files and augmenting this by monitoring the co-occurrence of edits to models, KADlytics were able to identify the emergent dependencies and provide real-time dashboards to the PM.
The team were able to reorganise and re-structure as new dependencies emerged. Change propagation was assessed at a much more detailed level enabling the car to be built to time and cost.
To capture the current, emerging and future skills and competencies within the organisation in order to identify experts, plan successions, manage skills within teams, and identify training needs.
Application of Net AI to technical programmes in order to analyse core topics, contributing authors and levels of contribution. Network analysis allows the evolution of skills over time to be explored and experts to be identified and located by nonexperts.
Improved resource utilisation, evidence-based succession planning and new training programmes.
Integration of advanced monitoring and analysis into existing workflow.
Using KADlytics’ in-built reporting module, reports are automatically generated at the touch-of-a-button or at preset time intervals, e.g. for a design review, and emailed to team members. The interactive reports allow users to add comments, questions and explanations into the report.
KADlytics product suite integrated into the existing business processes as a service.