Global R&D processes are often complex and wasteful. That’s why a company can gain a competitive advantage, if it provides the data on the wasteless performance share of its global R&D processes, exploits the full potential, and so changes the rules of the game in its industry. This requires a standardized R&D database, suitable methods and appropriate tools.
The R&D process performance consists of
- R&D value-adding performance
- R&D support performance
- R&D idle performance
- R&D value-reducing performance
The R&D value-adding performance is the wasteless performance for the customer. Examples include design and calculation, without any recurrences. The average R&D value-adding performance is estimated at 5% to 25% of total R&D performance.
The R&D support performance is required, but no customer performance and thus wasted. Examples include knowledge generation, prototyping, verification, validation, transport, communication and proportionate structures.
The R&D idle performance is not required, thus wasted, however does not harm immediately. Examples include task forces, rework, waiting and storage.
The R&D value-reducing performance is not required, thus wasted, and harms immediately. Examples include work duplication, recursions, and warranty.
Is the distribution of R&D process performance globally transparent, considerable R&D efficiency potential can be exploited by a leverage. For example, if the share of R&D waste decreases from 75% to 60% (-20%), then the share of R&D value-adding performance jumps from 25% to 40% (+ 60%). This R&D performance leap changes industry rules of the game, when it is used for growth and profit margin.
The data of the R&D value-adding performance is provided top-down in three steps. Each step has an immediate tangible benefit:
- Valid R&D project classes are uncovered and standardized. This enables direct measurement of comparable R&D expenses.
- For the R&D project classes standard costs are empirically defined. This makes the R&D productivity measurable and easy to plan.
- The root cause analysis of R&D effort uncovers the R&D value-adding performance of the work packages. From this, the potential is derived. Wasteless work packages make the plan lean and the R&D productivity controllable. So the potential is utilized.
R&D projects vary by project target and project expenses. Often they are not considered comparable by the R&D. Managers with P&L responsibility often disagree when they say that similar product developments at the end cost always the same, no matter, how they are planned. This is also contradicted by the common practice of R&D to take advantage of individual reference projects for the planning of the project expenses. So however, existing data on the variation of projects are not used efficiently. With the methodology of Statistical Engineering project classes are efficiently uncovered, validated and standardized. It is necessary to examine whether and how the existing project management tools map the standardized project classes.
Generally standard costs simplify cost accounting, productivity measurement, and planning. If standardized and valid R&D project classes exist, this method can also be applied in the R&D. So the R&D productivity is measurable. Each project plan can get a productivity target. The planning of budget and long-term plan will be substantially simplified by R&D standard costs. It must be examined whether and how the project management tools allow the use of R&D standard costs.
Projects are organized by project management and by R&D in work packages. These work packages are often heterogeneous, excessively detailed by individual R&D activities, stored in many Excel spreadsheets, and so complex. Uncovering the R&D value-adding performance at first seems like a Sisyphean task. With the methodology of Statistical Engineering, the work packages are suitably simplified, and their R&D value-adding performance is uncovered efficiently. From this, the potential is derived. The work packages are dimensioned for the project plan and budget to their value-adding performance. So the plan is lean. The R&D productivity is now controlled through transparent work packages. It is necessary to examine whether and how the existing tools represent the work packages and the R&D value-adding performance.
Thus, R&D and IT together provide data for a lean, global R&D.