Our project has just been presented via an elevator pitch and a poster. The feedback was great, with lots of interest and enthusiasm for the concept of engineering Project Health Monitoring (ePHM).
Delegates were really interested in the set of project features that we have developed and the ‘proxies’ that we use to monitor these. As a consequence, we are going to add a new section to this website that summarises each proxy and the project features for which it can provide insight. Want to know more? Check out the poster and abstract below…
As part of our efforts to understand what matters most to engineering project managers, and what they most struggle with when it comes to managing their products, we have been conducting a survey to develop an in-depth understanding of the major performance-influencing aspects of projects within various company contexts, and how well these are monitored and understood by the project management tools currently in use.
The Language of Collaborative Manufacturing is a £1.9M research project sponsored by the Engineering and Physical Sciences Research Council and led by the Universities of Bristol and Bath in collaboration with our industrial partners. We aim to deliver next-generation project dashboards that can identify potential project issues, improve productivity, and improve the management of aspects such as intellectual property, risk and cost.
The focus over this period has been two-fold: The first has dealt with consolidating the various analyses associated with each case study (data set) while the second has been to develop our approach for capturing user requirements and context(s). In the former work has continued across the four case studies associated with a Formula Student team and our other industrial partners. For the latter we have developed a combined survey and interactive workshop for potential users.
During this quarter four conference papers have been accepted for publication and are to be presented in Croatia in May and Chicago in June. In addition to this a journal article associated with the automated typing of topics in email associated with engineering projects has been submitted to the Journal of Advanced Engineering Informatics.
In addition to preparing the data set and planning analysis, Dr Emanuel has been interviewing the project manager on a monthly basis to understand the issues faced and user needs, with the aim of distilling a set of requirements for an FS dashboard. Interviews and analyses are ongoing, with two main focuses. First, requirements extraction will centre on supporting the project manager’s work flow, decision making capabilities and needs regarding issue/problem support across the 22 week build period. Second, the interviews will be used to understand the prevalence or importance of the project features, developed by Dr Snider, at different points in the build life cycle. Dr Emanuel has used the previous year’s CAD model as boundary object to communicate where work and issues are occurring as they develop this year’s car. The aim is to match these annotations to occurrences in CAD activity:
We’ve also been undertaking lots of other work in collaboration with our industrial partners, such as a tool that predicts project complexity and duration with over 75% accuracy after the project is around 30% completed. Another tool we have developed automatically connects and visualises people, topics and reports. This is being used initially as a tool to map and identify competencies, but we hope to expand it into a tool to support the creation of technology road maps also – watch this space!
New year, time to reflect on progress to date, consolidate and plan for the coming year. And brighten up a cold winter’s day with some fun activities (hint: It turns out that our mild-mannered computer scientist Leon is a bit of an expert with an airgun!)
In this period we have been verifying and validating the various analytical methods and began research to understand user contexts in order that we can begin to research dashboard concepts. Work in the area of analysis of representations has led to models to predict time to completion and stability of CAD models. For example, we can accurately predict time to completion when the model is only about 50% complete:
Through this work we have partnered the National Composite Centre (NCC) to explore transfer-ability of the techniques to Finite Element Analysis (FEA). Work with the NCC is also exploring the automatic mapping of capability and competencies.
In this quarter we have demonstrated analytical approaches for revealing previously hidden product and process dependencies through analysis of User-CAD interaction and content of technical reports/communications and novel methods for monitoring and predicting likely project complexity for routine projects.
For example, we’ve been using co-occurrence analysis to reveal model product dependencies. However, unlike traditional methods, we can also include data from representations such as CAD models: