Concurrent 22. Presentation for: Lessons Learned – incorporation of action management for inherent learning and prioritisation through opportunity matrix

Abstract

Presented on Thursday 19 May: Session 22

There are many potential improvement opportunities in corporate or industry learning to enable better and faster decisions. 'Lessons Learned' has long been a respected mechanism for ensuring mistakes of the past aren’t repeated. This may lead to operational improvements, business excellence as well as reduced health, safety and environment incidents. In the collective experience of the authors in the energy industry, as consultants and software developers working in the Lessons Learned area, although there is general approval of the Lessons Learned concept and a desire to leverage expertise, companies are not achieving their desired value and assurance of continuous improvement. To truly learn lessons, they need to have become inherently amalgamated into the processes. This may be through policies/procedures ranging from governing documentation to short-form checklists. Too often, a Lesson Learned process goes no further than airing frustrations from imperfect execution, and lessons are lost beyond the personnel directly involved or, at best, they become a repository of infrequently accessed and poorly categorised lessons of reduced value. In order to maximise knowledge transfer, workshop methodology and categorisation are key. Actions must be assigned and a prioritisation system put in place based on the level of concern and the potential opportunity for improvement. The well-accepted Risk Matrix can be applied to prioritise areas using filtering and to allow different areas of the business to focus on their speciality as well as grouping action types to aid the continuous improvement cycle. This paper provides insights into a new system of Lessons Learned, which allows accelerated execution, increased collaboration and innovation, thereby capturing company knowledge and empowering employees.

To access the presentation click the link on the right. To read the full paper click here