14 Oct R&D Productivity: Beyond Time-to-Market Metrics
By Dave Breda
When fastest time-to-market becomes the single most important – and sometimes the only – goal in R&D, a company can show extraordinary gains. Such was the case with many high-tech companies for most of the 1980s and 1990s. However, time-to-market can be a deceptive metric. One-time tricks like staffing priority programs with the most experienced engineers or loading the same programs with all the chronically bottlenecked resources allowed 40%+ reductions in time-to-market. This concentration of effort, while effective at face value, often came at the expense of other projects in the development pipeline or the company’s total R&D productivity.
Time-to-market will always be of paramount importance, but so is generating year-over-year improvements in the productivity of your R&D organization.
Product complexity is increasing exponentially, and R&D organizations are being asked to launch more products each year with the same or only slightly more resources. This dynamic is played out perennially between the Chief of R&D (“I need more budget for headcount to achieve the planned roadmap”) and the CFO (“we’ve increased the R&D budget by 3X in the last few years but have not achieved a 3X increase in output”). The only way to address this imbalance is through measurable annual increases in R&D productivity – that is, increasing the throughput of R&D without increasing capacity.
The operational levers that most affect R&D productivity are capacity utilization and efficiency. While these concepts are well-understood in manufacturing environments, a revelation for many companies is that both can be effectively applied in R&D environments to measurably improve R&D productivity.
Capacity Utilization: Gaining Resources at No Cost
Let’s start with capacity utilization. The concept works in manufacturing because resources are fixed and not variable, but this is relatively true in R&D as well – especially in high-tech industries. R&D resource (headcount) levels are very often established by slightly increasing the run-rate budget over the previous year. Moreover, organizations aren’t able to staff up to project demands that have significantly increased due to hiring limitations of top talent in technology fields. As is the case with manufacturing, capacity utilization for R&D focuses on getting more product development output from the same fixed level of resources. At the individual contributor level, it’s about getting more hours dedicated to the development of revenue-generating products.
Do you know what your current R&D capacity utilization is? Initially, our clients often respond: “We are really busy.” However, being busy does not equal being productive! Companies can easily assume capacity utilization is much higher than it really is without ever having measured it. Accel Management Group has been measuring R&D capacity utilization for over a decade, and we have found that the average rate is around 70-75%, with low performers hovering around 45%. A company with 100% utilization (which is not possible) would theoretically dedicate an average of 40 hours per week per headcount on revenue-generating development projects. Companies with actual 70% and 40% utilization dedicate 28 and 16 hours per week per headcount respectively on revenue-generating development projects. That is an enormous difference! In economic terms, companies with higher R&D capacity utilization produce significantly more with the same or far less investment in headcount. The unintentional downside of not knowing capacity utilization is that companies will erroneously assume resources are 100% utilized when building product development schedules (work breakdown structures). The result: Product launches will almost always be late to market. Sound familiar?
The first step in improving R&D capacity utilization is to MEASURE it. This can be accomplished by sampling hours allocation with small teams or groups of engineers, or by measuring only the chronically bottlenecked resources in your R&D organization. To be consistent with the definition of capacity utilization, engineers must report time separated by hours allocated to revenue-producing projects versus non-revenue-producing projects. Examples of the latter include conference attendance, unnecessary team meetings, administrative tasks, travel, working on unapproved scoping activities for sales and marketing, etc. Once you know where your valuable engineering resources are spent, you can work with functional managers to reduce administration and distractions to improve utilization.
Efficiency: Better Results with Less Effort
The second factor that determines R&D productivity is efficiency – the amount of output produced with each hour of engineering input. Tech companies don’t produce widgets, so measuring output must be normalized across many projects of varying complexities. Complexity is highly correlated with output more so than, say, project size. Therefore, R&D organizations should develop a straightforward and easy-to-administer complexity rating process. At the very simple end of the spectrum, this could take the form of “tee-shirt sizing” (e.g., small, medium, large, and extra-large complexity projects). The “tee-shirt sizing” approach is uncomplicated but has accuracy limitations. Accel Management Group suggests implementing a rating scale based on a small handful (less than six) of complexity criteria to produce a complexity score or index. The score of past projects can be used with resource consumption to plot a complexity-resource consumption curve. The curve can then be used to see how past and planned projects perform against an efficiency curve, with projects below the curve using resources more efficiently than projects above the curve. Once you identify which projects are relatively inefficient, you can look into ways of improving efficiency. In our experience, areas that tend to improve efficiency include requirements management and traceability, implementation of development core team structures, and reducing rework by consolidating and putting key development documents under change control.
Differentiating by Doing More with Less
In many respects, R&D organizations do behave like manufacturing operations. Given this, relatively simple and proven techniques used in manufacturing like capacity utilization and efficiency curves can be implemented to measure R&D productivity. If you can measure it, you can improve it! Substantial returns will be well worth the measurement effort.
About the Author
Dave Breda has over 25 years of strategy development and implementation experience driving breakthrough improvements with global technology companies. His expertise includes a broad range of business management areas including profitability improvement and cost reductions, innovation, product and platform strategy, portfolio and product-line management, technology management, and operations governance. Prior to joining Accel Management Group, Mr. Breda was a partner with PRTM where he was the world-wide partner lead for the Computers, Networking, and Semiconductor group. Mr. Breda earned is Engineering Degree from University of Colorado and his MBA degree from the Darden School at the University of Virginia. He is CPIM certified.