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Predictable Measurement Framework (PMF) for Agile

Ashwani Nandini, GlobalLogic

How did your improvement help the organization achieve its goals?

Predicting release schedule is tricky in the agile world where change is welcomed and is the norm. In product engineering services, where we deal with digital transformation engagements, delivering on time with promised business value and quality is critical. We introduced and mandated a concept of predictable measurement framework for agile methodologies. With this, we could manage change within permissible limits keeping business value intact, keep a check on quality and still managed to predict release dates more often than not winning the trust of the customer. If the committed release date was going to be delayed, we could predict the magnitude of delay and take action as needed.

How did adoption or use of the CMMI contribute to the improvement?

CMMI Process areas OPM & QPM mandated teams using Agile methodology to submit data as defined by minimum metrics framework. Data collection and analysis helped monitor, baseline, identify weak areas at engagement and organization level and in tightening processes around weak areas. CMMI process adoption helped establish mechanisms to institutionalize local improvements, applying learning from one engagement to another, behavioral change for achieving business objective and reducing process variation. PPM helped in identifying critical sub-processes and predicting results by connecting past performance with future outcomes. Statistical concepts allowed control over false interpretations.

Why should this implementation be considered successful?

Due to frequent changes and high agility, release schedule prediction is a challenge in the Agile world. Digital transformation engagements are complex with several moving parts but delivering high quality product to committed schedule keeping business value intact is the expectation. With the introduction of Predictable Measurement Framework for Agile and mandating all engagements to submit timely data, we managed to improve release prediction. Product release on time or predicting release schedule with data provided the visibility and transparency to customer. This helped build trust and improved the agile process implementation wherever it was weak and minimized breaking of processes.

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