Work Transformation Method Application

As mentioned in a previous post, one of the projects I have completed as part of the MIT SDM program has been to look at the process of diesel engine aftertreatment system calibration.  The project team for this assignment was Genevieve Flanagan (lead), Candice Engler, and I.  Also supporting us was Oliver de Weck and Arun Balasubramaniam.

After-treatment systems are those components on diesel engines that remove particulate matter (soot) and nitrous oxides (NOx) from the exhaust.  Since the introduction of the U.S. Clean Air act, there have been several levels of decreasing particulates and NOx levels mandated; these are sometimes referred to by tiers (Tier I, Tier II, Tier III, Interim Tier IV, Final Tier IV).  Because these are regulatory mandates, a producer of engines must either achieve the mandate or not ship engines.  Producers of engines also do not want to compromise performance of the engines to meet regulatory requirements.  From a project perspective, this is a project that has fixed scope and schedule.

As producers of these cleaner engines have moved up the tiers they have come to rely more and more on software to control the after-treatment systems in order to meet the regulatory requirements.  Part of the process of preparing these engines is to calibrate them and their software in a test environment.  Calibration activities are resource intensive, both in terms of human labor and capital for the test cells.

When talking with the after-treatment team the calibration activity was largely treated as one large task, with lots of iteration within the task.  The variability in the effort and duration for the calibration of each engine configuration was considered unacceptable by stakeholders.  We also learned that the after-treatment team had built a design structure matrix showing the calibration and other dependencies between the components (physical and software) of the after-treatment system.  The DSM was highly coupled, with few small independent components and one large meta-component.

We investigated various mechanism to better plan the calibration activities, including a signal flow graph approach and visibility matrices.  We settled on using a work transformation approach to create the engineering iteration model.  We used the DSM to determine a series of 24 design steps (things that were later labeled "sensor calibration" and "determine soot model", as examples) required for engine calibration.  These calibration steps still happen iteratively (e.g., an activity done during step 2 may require rework in step 1), but now there are identified calibration modes (as opposed to one large task before).  The next step will be to build a probabilistic schedule (most likely using the signal flow graph approach) for the whole calibration activity based on the twenty-four calibration modes.

Another interesting outcome of this project was that a DSM that represented the system architecture of the system was able to be reused, after mathematical transformation, as a project planning tool.  This means that changes to the system architecture can be used to update the project plan with mathematical linkages to the underlying architectural change, including determining project impact of proposed changes.