In this article, Dr. Heuer will discuss his experiences in introducing productivity metrics into a R&D organization. This “Elephant in the Bathtub” is rarely discussed openly, even though the introduction of productivity measures is a necessary step in improving the performance of R&D organizations. As a thought provoker, Dr. Heuer describes the GOAL approach for R&D.

The Elephant

Somehow, many people feel very uncomfortable when I speak about productivity metrics for R&D organizations. The inertia and push-back I encountered in my leadership assignments was substantial.

Every conceivable reason was given:

• Mathematics: “Too complex”
• Meteorology: “Too variable”
• Alchemy: “Too mysterious”
• Diplomacy: “Too diffuse”
• Arts: “Too restrictive”
• Metaphysical: “Too analytical”

How come these mathematicians, meteorologists, alchemists, diplomats, artists and meta-physicists all want to avoid being measured?

These reasons are all, at first glance, absolutely valid. I can attest that the activities of a R&D department in full swing can be quite overwhelming. Very few projects seem to be similar, there is always new lingo, everything is somewhat in flux (as in not firmly predictable) and the repeatability of the activities appears to be quite low. The system does consist of many parts linked in a nonlinear fashion (the definition of a complex system).

This is a hard management task.

Once you take a closer look, though, you can see patterns emerging. Don’t try to measure the activities (the WHAT), instead, focus on the underlying (usually less difficult) rules (The HOW). One can measure and tweak them to improve performance. I call this approach to R&D management GOAL:

Guide –Observe – Adjust – Lead.

The Elephant is there

The first step in GOAL is to guide the organization in the right direction. At a base level, R&D departments are just another way for a company to process the information the environment provides. They separate “noise” from “signals”. Noise is simply all new information (scientific discoveries, new technologies, market research, etc.) that the company cannot use profitably. Signals are those they can.

The True Job of R&D

To me, the job of R&D is to find new angles on existing consumer jobs, emerging new jobs, and emerging new consumers. The hunt is on for over-served consumers and non-consumption.

R&D’s lens is “New Technology”. What is does is simply noise removal, separating the useful new technologies from the useless.

This is a tall order, since there are so many moving parts required to find out the usefulness of new technologies. By looking at the roles within R&D, and how they contribute to the noise removal, we can break the problem down further.

The Roles within R&D

As outlined in my earlier article, “The Magnificent Six”, there are six basic roles inside R&D: Product Developer, Financial Analyst, Marketer, Application Scientist, Application Engineer, and Quality Assurance Professional. For new ideas, these roles answer specific questions:

• Product Developer
  Are requirements clearly stated and complete? Can the corporation execute this idea?
• Financial Analyst
  Is there a profitable business case for this idea? Are the assumptions explicit and verified? What is the IP worth?
• Marketer
  Where does this product fit? Will it touch existing or new consumers? Is it compatible and consistent with our brand strategies?
• Application Engineer
  Is the technology sound and understood? Can the company execute on it? Will this generate lasting value as IP?
• Application Scientist
  What is the bundle of new technologies that can deliver this idea? Are they the best, what are the alternatives?
• Quality Assurance Professional
  Can the new idea be tested successfully and with reliable data for decision making? Will the technology meet the QA guidelines?

Additionally, R&D management needs to watch process and culture:

How well do the six roles within R&D work together, how well are they connected back to their respective “core functions”? Are the participants keeping their skills fresh and sharp? How well is knowledge retained? What is the conversion rate for ideas?

These kinds of questions constitute the guidance to the organization (since they are given with a strategic intent in mind). We can now construct metrics that observe how the organization is providing answers.

The Elephant is there

Now we can observe the organization’s performance over time on how to answer these questions. It becomes quickly apparent that we need a robust data capture process that yields concrete data points. I personally prefer raw data (like time capture) as a foundation, expanded and augmented with observational data like peer ratings and other 3^rd party ratings.

The most dreaded tool of this infrastructure is time capture. Very few engineers like to be observed that closely. To make the adoption smoother, the employed tool needs to be easy to use, very fast, and elegant. I prefer a small desktop application that is started automatically during the boot of the workstation and can be managed with keyboard shortcuts as well as with the mouse. The transaction of recording activities needs to be very crisp and quick. It helps when the tool generates a skeleton status report for the week, which the engineer needs to embellish to get his personal view across.

All data capture has to be done digitally, with minimal additional work. This means that the majority of document work is migrated into a database-driven environment, with flexible forms to be filled out. Only by this means will the organization capture consistent data. Do not allow the typical PowerPoint- or MS Word-based reports, they are digital dead ends.

Taming the Elephant

Once we have hard data in our hands, we can start to adjust the organization’s decision making in the desired direction. We need to understand the meaning of the data. Don’t be afraid to change metrics ever so often to extract more meaningful data. Consistency is important, but meaning even more so.

The best adjustment strategy is to give the data to the organization itself and let it perform the necessary adjustments. This increases ownership in decisions immensely. Done right (that is with the proper authority given to the right individuals), this approach can also significantly cut processing time, resulting in quicker adjustments and hands-on management training for those involved.

Dispelling the Beast

The hard part is to convince the organization that productivity metrics are in its own interest. This is the lead component of GOAL.

I found connecting to the motivational drivers for R&D personnel to be an appropriate tool (next to the burning platform: “we better do this ourselves before it is done to us”). A good study of these can be found in the Corporate Executive Board report “The Voice of the R&D Workforce”.

It is also very beneficial to not go for the Big Bang approach of change management (see my article “R&D’s Four Riders of Apocalypse”). A staged introduction has several advantages:

· It takes into account the organization’s ability to accept and absorb structural change.

· It allows for gentle adjustments in the infrastructure

· It gives the organization an opportunity to participate in the design and implementation in much more of a hands-on fashion

This article only scratches the surface of the topic of R&D productivity measures. Any system will be specific to the needs of the organization it supports. As a guidance to the reader, I am going to make up a R&D department for the fictitious Widget Corporation in the next article “The Elephant in the Lab Part 2”.

Resources

“Seeing what’s next”, Clayton Christensen, ISBN 1591391857

“The Voice of the R&D Workforce”, Corporate Executive Board, 2005