Predictive Metrics for Projects & Programs

Measuring Competencies In Lean & Innovative Companies, October 16, 2013, focuses on metrics that are part of the Functional/Technical subgroup of metrics. Projects/Programs, Improvement, and culminating Corporate-Level metrics are the other subgroups. Competency measurement, without yet being statistically shown, will be ultimately be shown to correlate with corporate success.

Another group of metrics that will ultimately be shown to correlate with corporate success are the metrics that are “Proactive & Predictive” for projects and programs. GGI is credited with codifying a group of metrics that precede a go-no go approval decision that take risk out of projects and their resultant products, and coining them as Proactive. In sequence, Predictive metrics begin at project/product approval and generally cease when an initial form and function prototype is realized. Predictive metrics compare updates against the approved plan to track deviation or convergence. Together, both Proactive and Predictive metrics are quite useful in eliminating risk and variation in the pre, early, and mid stages of R&D investments.

The vast majority of all R&D spending across the globe is directed to project and programs. Relative to all other categories and types of R&D measurement, excepting the same types of metrics for products, metrics that enable the ability to show what might, could, or will happen to the volume of spending on projects and programs are perhaps the most important. Practically speaking, if a company performs poorly on a project, it is immediately visible within the company. If less than desired performance continues across multiple projects over time, performance usually becomes visible in the products emanating from the company and begins to affect branding, reputation, and pricing. A company can typically recover from a single product that does not live up to expectations. But, when the “product development factory of projects and programs” falls short of expectations multiple times the impact on products and the portfolio is rarely escaped.

Will we ever get to the level of process control over WIP that we now enjoy in manufacturing and other operations functions? Certainly not, nor do we wish to do so. Necessarily we wish to have more probability and variability in product development or we would quash innovation.

Today though, we still suffer way too much air time discussing the metrics that account for “what has happened.” Yes, these “reactive” metrics have a place. It is necessary to measure and record what has or did happen. Reactive metrics also play quite well to the capabilities of North American professionals, who are generally world class problem fixers. A key remaining opportunity for project and program measurement is to develop better proactive and/or predictive correction abilities.

What should or can be measured, early in the cycle, that will give actionable indications that the promised results for the project or the product are deviating from or converging to plans and promises? Is there even one measure in existence today that can be taken early in product development that has been shown to have a 1:1 correlation with the promised plan or output? Not yet. Operations, finance, sales and other company areas have such measures though. Relatively speaking, the science of R&D project and program metrics and measures still has some greenfield in front of it in comparison to the measurement proficiencies that other business functions have attained for the monies and capital entrusted top them.

Predictive Metrics for Projects and Programs [Machine Design – November 7, 2013], delves more deeply into a subject that will be on the front burner of corporations in the years ahead. The corporations that are the first to master early correlating measures of plans and promises to actual outcomes will gain a competitive advantage until the management science becomes widely understood.

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