Case: Engineering competence
The customer, a major engineering company operating worldwide wanted to improve their competence management. They employ hundreds of engineers. To assemble an optimal project team was a continuous problem. They wanted to utilize their industrial knowledge more efficiently and each time assemble a team, not only with the right competence profile, but with the right "chemistry" as well. They also wanted a solution that improved their competitiveness in international sales by ensuring that the concept development phase was staffed with suitable competence. Such management systems are not available. Since they experienced increasing competition on their main markets, it would have been extremely risky to continue relying on just intuition. Therefore, BayesIT was called to develop a competence model utilizing probabilistic methods. The development continues.
The customer had a personnel management system in use. They collected data about the education, experience etc. of their employees. The team leaders were responsible for the staffing decisions, but they complained that the system did not support them in their work.
The management had realized that the conventional competence management system did not support their team assembly work well enough. This recorded data about basic characteristics such as age, education and language skills etc. The customer experienced very big profitability variations without understanding the reasons to the variations. The top management of the company suspected that one of the reasons was that they could not assemble a team with a suitable mix of competences and personalities. The problem was aggravated by a shortage of experienced project managers. With the increased competition they could not continue to use larger margins to compensate for the uncertainty, so they started to lose bids.
Bayes Information Technology Ltd. got the opportunity to develop a new approach to their competence management. To start with, BayesIT designed a competence model consisting of three data sections; the history data of executed projects from their accounting, the data the company collected about the projects and the data from the personnel database. The two last data sources, which relied on existing IT infrastructure, needed some further development to enable the knowledge model to identify the correlations between competence and the commercial success. A Bayesian Network model was created from this data.
The solution was designed so that it could easily utilize data from a CRM system. The solution is easy to use, updating the competence model with new data about a project in realization, or staff information, can be done in minutes.
The Bayes Information Technology solution produces a short list of proposed characters for each critical position to be staffed. The final decision is done by the team managers responsible for the main sections of the project. Cutting edge technology ensures that the recommendations are feasible although the task is very demanding and data is incomplete.
With the competence modelling system designed by Bayes Information Technology, the customers' engineering knowledge was captured so that the tacit knowledge "between the lines" was stored as well. As a consequence the management had a new tool to use when assembling the engineering team. This complementary information made it easier for the team managers to agree on the engineering teams for a new order, saving a lot of time. At the moment the first projects managed with the new system are about to close and results are very promising. The teams have proceeded in their task more rapidly and budgets have been kept better than in the past.
The cost of the development project was estimated to be approx. 50.000 euros. The outcome of the project is not yet available. At the time of order, the payback period was estimated to be 2 years. During the early phases of the competence model development some engineering projects that provisionally were classified as risky projects have succeeded so it is justified to believe the payback time will be shorter than planned.