3 THREE STEP APPROACH TO INCREASING EFFICIENCY AND EFFECTIVENESS IN R&D

Most of the initiatives to improve the R&D process are structured according to the analysis - conception – implementation pattern.

This approach frequently includes some fundamental weaknesses:

 Countless shortcomings are identified in the analysis. As long as no clear output and target parameters are implemented, no systematic optimization in terms of targets can be made, i.e. the prioritization of weaknesses with regard to effectiveness is usually carried out in a very subjective way. A company implements, for example, an idea data bank, although the execution of projects is the real problem.

 Strong ‘internal’ thinking is involved in the elaboration of concepts. In many cases, proven best practice is not systematically incorporated into the process.

 The evaluation of the success of R&D optimization initiatives is very limited due to the lack of output measurement.

A three step approach is thought to overcome these weaknesses and to ensure successful R&D optimization initiatives:

Figure 1: The Three step approach to ensure successful R&D optimization initiatives

3.1 Step 1: Innovation input and output transparency

Innovation input and output transparency is the first step in the described approach. The authors have observed two reasons why many companies have problems in achieving transparency on innovation input and output measuring.

The first reason is that innovation output measurement has many facets, and many attempts to measure the innovation output fail due to this complexity.

Therefore, the expectations of an innovation output measurement system should, in practice, be more similar to an 80/20 approach. The easiest way to make an output measurement system a failure is to address all of the potential exceptions.

The second reason is that an output measurement approach will differ between industries and innovation types. The five innovation types have already been described above.

In analogy to strategy development a clear segmentation is a key success factor. As long as they are measured with the same approach, which does not consider the differences, the result will be of limited validity.

This means that input and output for the different innovation types needs to be transparent (see figure 2).

Figure 2: Segmentation of innovation types

Differences also need to be considered from an industry perspective: for instance industries such as automotive, electronics or medical technology can be characterized by frequent model changes which generally lead to smaller proportions of ‘old’ products. In these industries management pays great attention to new products or at least new models. But the proportion of new products has limited relevance since it highly depends on scheduling of market introduction.

In industries like the chemical industry, in the pharmaceutical or in food and beverage industries one can observe partially very long product lifecycle times. The proportion of new products and models is generally lower, therefore, management attention towards product innovation is, on the whole generally lower. This means that in such industries the proportion of new products is a relevant indicator for innovativeness.

Therefore an input and output measurement approach needs to be pragmatic and at the same time has to be customized and tailored to the specific situation. Input measurement is much easier than output management. However a few things need to be considered:

- Innovation budget contains more than the R&D budget alone. Costs from other functions, e.g. procurement resources supporting project supply, also need to be considered.

- Due to the existence of different innovation types the input needs to be allocated to the above described segments.

To reduce complexity the authors will be focusing only on new products and new applications (NPD) in this paper. For these two innovation types the profit made with new products or new applications is the most important KPI.

From a high level perspective it is obvious that new products need to

generate profits to cover the development and market introduction efforts and therefore justify an innovation-based business model. In practice some challenges occur in applying this metric.

 The period of time in which a product is considered to be new needs to be aligned with the product lifecycle of the industry. It can be as short as a few months, or several years.

 In order to differentiate between new products and facelifts, customized criteria, depending mainly on the industry, need to be defined to distinguish between these innovation types.

 Strong backward focus - progress within the innovation pipeline is not indicated by measuring profitability.

 Market introduction strategies with the aim of ‘occupying’ a market need to be covered with additional KPIs.

 In order to work with ‘new products’, they need to be ‘flagged’ as new in the ERP system.

 Profitability at a product level needs to be available in the ERP system.

 Disruptive technologies need to fit into an appropriate strategy and they therefore need a different set of metrics.

Due to these challenges other KPIs can complement the innovation output measurement:

 Sales with new products

 Net present value (NPV) of innovation portfolio

 Development of market share

 Competitor benchmarks

However, the profitability of new products is the core KPI for an innovation based business model. If new products do not generate sufficient profit in the mid or long term there will be not enough budget to finance new development projects.

3.2 Step 2: Analysis of output-relevant shortcomings

The second step is generating potential shortcomings transparency. For this purpose multiple approaches have been developed, based on different frameworks and scorecards, as shown in [8], [9] and [10].

However many companies attempt to do this by ‘gut feeling’ and common sense today, by analyzing processes or by comparing the status-quo with best practices. These approaches lead to optimization levers. However they do not support a prioritization towards impact on output.

In order to obtain a stronger focus on effectiveness and therefore on output we propose to apply an approach called de-bottlenecking, which is used to optimize production processes. The overall idea of de-bottlenecking is to identify which step in a multi-step process is the one that most impairs the overall output performance.

Innovations can also be viewed as a sequence of four steps: ideas are the raw materials which are first refined to project proposals, then to projects, then to products/services, and finally they lead to ‘sales with new products.’

If the ‘raw material’ is identified as the bottleneck, it does not make much sense to put much energy into the improvement of project management. On the other hand a company should not invest in a new idea database if the launch process is the bottleneck. The last example illustrates the value of this approach. Using the conventional, topic focused, approach the analysis would show that idea management has flaws. It is very likely that a recommendation would follow to implement a new idea database.

The detection of the bottleneck is tricky. When, for example, many launches fail it does not necessarily mean that the launch process is the bottleneck, this is only one potential reason. The other potential reason is a weakness in the project proposal phase: when the specification of the innovation and the customer needs are challenged. Weaknesses in this phase could also lead to the situation that the innovation is properly executed and launched, however, it is based on false assumptions.

In the end an identification of the innovation bottleneck is not possible with 100% accuracy. Nevertheless it is a major advantage for the management team of a company when they agree which of the four steps is the bottleneck. According to the experience of the authors, the following indications support identification of the weak parts of the innovation chain.

1. Not enough high-quality ideas

 Limited ‘competition’ between new ideas and running projects: no

‘waiting list’ for promising project proposals

 Low new ideas transparency

 Low ratio (less than 5:1) of ideas to detailed project proposals 2. Weaknesses in project definition and prioritization

 Low project target achievement

 Low target achievement in commercialization

 Low ratio (less than 3:1) of detailed project proposals to started projects

 Low formal and information quality of project proposals 3. Weaknesses in project execution

 Low project target achievement

 Low running projects transparency

 Many or no project terminations 4. Weaknesses in launch

 Low target achievement in commercialization

 Launch planning is not part of project definition

 Low commercialization process transparency 3.3 Step 3: Implementation of best practices

The third step is to detect and close gaps between the status quo and best practices on the operational level in the areas with the most significant

shortcomings, as identified in step 2. A comprehensive overview on best practices and experiences from other companies support the gap analysis and delivers the right choice of improvement actions.

4 ADVANTAGES OF THE METHOD AND OUTLOOK INTO THE FUTURE