The 2 European clusters
3.2 Disruption theory and outliers
3.2.2 Validate the bond disruption / performance
Alex Miller (Taxonomy of Technological Settings, 1988) shows that the link is not direct between technology and firm performance.
But when he refers to technology, he solely studies production types and production methods, namely small batch, mass consumption (assembly line) and continuous process.
On our side we stick to the fact that technological disruption leads to some performance.
We subscribe in this sense to the research tradition in innovation considering the company as the unit of analysis (Brown & Eisenhardt, 1998; Ancona & Caldwell, 1992).
This “business-oriented” tradition discusses how innovative inputs are transformed into efficient innovation outputs.
And we do not examine the conditions and context of innovation: A second tradition in the literature which is economics-oriented (Brown & Eisenhardt, 1995, 343; Dosi, 1988).
This said, the big question about innovation performance is still to measure it!
As C. Christensen (2012) put it: “We need of a Theory to offer to managers how to measure”.
And E. Von Hippel (2012) is adding that Literature is insufficiently precise on where the trust (the innovation) is coming from. For we tend to measuring performance differences between SEAMINGLY similar enterprises!!!
So we adopted the following scheme to display the data:
Step 1: We will consider each company
Step 2: We will show the pattern of success, according to authors, when there is a disruption in favor of this company
Step 3: We will identify the pattern of success (if any) in the general instance of no disruption.
Step 4: We will prove that the case of any outstandingly successful company in the clusters refers to point (step) N° 2.
We also decided to use a common criterion to compare and measure the performance of firms.
Performance indicators
Considering the EBITDA evolution appeared to be an appropriate method.
Thus EBITDA is well the major reference to value a company according to the well-known Argos index (see cession-entreprise.com & Argos mid-market).
Thanks to this index, we have access to quantified data and figures about levels of margin on sales for SMES.
That will help us to bring to the foreground a quantified criterion for OP.
4Intra European Airlines
- IN SEARCH FOR OUTSTANDING PERFORMANCE IN MICRO-FLUIDICS AND BEYOND
Argos in its permanent analysis of unlisted companies is stating: “the most profitable companies on the market” have an average EBITDA amounting to 16.3%.Whereas the average EBITDA for industrial companies interesting investors is around 11%.
We can then deduce that 11% is above the real average EBIDTA of industrial SMEs.
For, and as we mentioned above, 50% of new companies disappear within 5 years after their incorporation.
And also because the targeted companies by industrial groups for a potential takeover are definitely, on average, companies that present an “above average” attraction.
So we can consider that most surviving companies after 5 years correspond to companies showing an EBITDA between 0 and 11%!
We associate these companies to a type. This type is gathering all companies with some performance but without disruption. The absence of disruption is phrased “No Effect Pattern” (NEP).
What we write the following way:
0 < EBITDA NEP < 11%
Besides, if disruptive companies lead to more performance, over 5 years the disruption effect on performance should emerge.
As a consequence, for these companies, we should get an EBITDA in the vicinity or superior to 16%, or at least in the range between 11 and 16%.
That is what we propose to check below.
We want to check this EFFECT PATTERN (Effect Pattern with disruption) among the companies belonging to the French European cluster.
Here it goes to evaluate if and when for companies within the cluster: EBITDA > 11 % And if EBITDA > 11%, we do have the Effect Pattern.
In a second step, we will countercheck this observation on the English cluster to see if the result is identical (to the established Effect Pattern).
If we consider primarily the French cluster as a whole and apply the logic described above, we obtain an average peformance for the cluster of 12 % (average EBITDA for the companies in the cluster over the period)
Therefore, on average, for the panel:
So the actual observation of the 7 companies belonging to the French cluster is showing that this group matches the Effect Pattern.
Thus, by positioning the cluster on the scale of performance previously exposed, we get the following ranking (fig. 68).
Companies in the clusters Effect Pattern
Most of the companies (after 5 years) No EP
EBITDA > 11%
- IN SEARCH FOR OUTSTANDING PERFORMANCE IN MICRO-FLUIDICS AND BEYOND
Fig. 67 Positioning of the cluster on a scale of performance
But not all the companies in the sample have achieved an EBITDA above 11 %: As shown below.
Fig. 68 « EBITDA / Turnover Performance »
So let us analyse just the companies in the sample with a positive cumulative result (EBITDA) for the period.
Company EBITDA average Best performance (Year) Comments
Jason 6% 30,3%
(Year 2)
Average performance to be put into perspective (see below)
Steady increase in the average over the 5 years and Year 5 = the best year
Imaje 20,7% 29,4%
(Year 3)
Table 18 - Companies’ performance
5%
- IN SEARCH FOR OUTSTANDING PERFORMANCE IN MICRO-FLUIDICS AND BEYOND
NB: for the other companies in the panel (those that do not have a positive cumulative result for the period), we would tend to consider them as more than likely or even “due” to fail.
Clusters like these therefore confirm that disruption through technology encourages the growth of start-ups.
Indeed, even if it is true that not all the companies have an average EBITDA for the period higher than 11%, we find that:
- All manage to generate EBITDA figures well above 11%.
- More than half of them even manage to generate EBITDA figures well above 16% (the Effect Pattern threshold), and sometimes over 20 %!
- Moreover, in the case of Jason: only the impact of a negative operating surplus in its first year prevents the company from reaching the “11% threshold” . In fact, by excluding the first year of operation the calculation comes out at 10,9% (57 K€ / 521 K€).
We are well aware that the launch years, particularly the first one, often have an adverse effect on the result.
Therefore, not only are the panel results partially underestimated because they include the very first year of operation (penalizing in 5 out of 7 cases, and even 100% of cases …), but also because they only represent a period limited to the first 5 years.
- In the last case, that of APS, the analysis, though different, is in line as far as the conclusions are concerned.
Indeed, even if neither the average performance (4,9%), nor the best performance (9,7%), support an Effect Pattern presence , a more detailed analysis could nonetheless reverse this interpretation . For, as we saw earlier, the potential disruption of the APS offer is based on a service and price value. Two elements that structure a business model disruption (see Table 18 above).
But this type of disruption takes time in taking hold because it generates economies rather than gains. On the other hand it contributes to an outstanding recurrence. This is shown in the development of APS’s performance over the 5 years (see diagram below). Moreover Year 6 for APS enabled the company to achieve an EBITDA of more than 15 %. Once again a better result than the previous year and during the period of very high performance (EP ~ 16%). The latter thus confirms the power of recurrence.
Fig. 69 Development of APS’s performance (Curve)
So the performance ratio evolved from - 50% to + 9.7% between the first and the 5th year of activity.
The set of analyses above therefore validate the link between disruption and performance that we were putting forward as an assumption.
This we illustrate in the following way:
-60,00%
- IN SEARCH FOR OUTSTANDING PERFORMANCE IN MICRO-FLUIDICS AND BEYOND
Fig. 70 Summary Table Disruption / Performance
Applying the model
Once defined, we used this model to rank companies within the cluster:
Most profitable firms
Interesting firms 16%
11%
0%
Effect Pattern
Moderate Effect Pattern
Common Performance / No Effect Pattern
No Performance 5%
Performance Scale
Ordinary
firms
- IN SEARCH FOR OUTSTANDING PERFORMANCE IN MICRO-FLUIDICS AND BEYOND
Fig. 71 Performance scale between firms
Of course, the “numbers” on the scale are mostly giving a reference as far as performance is concerned.
Intervals of confidence have to be considered around these values. In other words, there is certainly a margin of error to qualify each level of the scale.
There can be some variation on the limits beyond each of the nominal values.
If indeed there is disruption and performance, we nevertheless observe wide variations in performance between the companies in the clusters. There are some companies, in particular, that stand out from the others in terms of performance (see diagrams in previous chapters that compare cluster companies).
« Outstanding Performance » is traditionally associated with disruption.
But disruption, even though from a Business Model type does not seem enough to explain Outstanding Performance. A concept that we introduce based not only on a remarkable EBITDA level but also on an exceptional growth in the turnover and number of employees.
- IN SEARCH FOR OUTSTANDING PERFORMANCE IN MICRO-FLUIDICS AND BEYOND
This (OP), moreover, is what we all have in mind when we are not specialists and when we’re thinking about a (successful) startup.
Disruption does not seem enough to explain Outstanding Performance because we can see that for an equivalent level of technical disruption there are still considerable differences in performance between the companies.
What level of disruption should be chosen or established to support this statement?
We are not looking to prioritize in details. We simply consider as « evenly » disruptive the group of companies combining:
An innovative technology AND a satisfying new application.
3 groups stand out:
a. 1 group of Oustanding Performance that includes companies from the 2 clusters : o Imaje in the French cluster
o Domino, Lynx and Willett in the English cluster
b. 1 group of actual but lower performance (already proven beforehand) : From Jason to Adaje and including Ardeje for the French cluster.
These companies, in keeping with what has been said previously, present a form of « Incomplete Disruption ».
A disruption that does not result in OP, as if the disruption was restricted by a kind of “brake”. As if there was something missing in this type of disruption: hence our qualification as incomplete.
c. Quite « ordinary » companies like Ethersial or Osmooze. Ordinary to the extent that they post negative EBITDA figures.
We therefore conclude that the performance differences sometimes originate from an « Incomplete Disruption ». In other words, an actual technology disruption for an application of proven-value.
But an incomplete disruption nevertheless, because an element is missing for the disruption to result in hyper growth for a company. The reasons for this incompleteness need to be found. They could relate to problems with the usage:
Either in the technical expectations: Lack of reliability in the technology. In the case of inkjet, difficulties for example in obtaining sufficient repeatability for the system to run smoothly.
Or in the market expectations: degree of customer demand beyond the system’s capabilities, e.g. insufficient adhesive level of the fluids (scratch resistance tests for example).
To be identified, Incomplete Disruption demands more often than not a detailed understanding of the technologies involved in the disruption process.
In the case of micro-fluidics and fluid-jetting, insufficient knowledge of the technologies involved and their basic or deep differences can lead to false conclusions about disruption. Not only can a mistake be made as to the degree of disruption (complete incomplete), but also in the evaluation of a disruption (absence presence):
Thus, some solutions are going to be incorrectly considered as disruptive while others, on the contrary, are going to be trivialized when they should be considered as disruptive compared with what is available.
What we illustrate as follows.