Improving the Management of Innovation Risks
- R&D risk assessment for large technology projects
Prof. Dr. Ralf Dillerup, Daniela Kappler MA, Fiona Oster MBS
Heilbronn University Graduate School,
Max-Planck-Str. 39,
74081 Heilbronn (Germany)
ralf.dillerup@hs-heilbronn.de
daniela.kappler@gmx.de
fiona.oster@gmail.com
Abstract
Global network structures of products and services are important value creators in many
companies. Complex business models include a variety of relationships and interrelationships
within and across different systems, especially in innovation processes. This leads to lower
predictability and higher behavioral deviations or, in other words, increases innovation risks.
Risk management is becoming more and more important and is crucial for the German
Machinery and Plant Engineering Industry (MPEI). Many companies are medium-sized and
are using standard static risk management methods. Use of these methods often means that
critical situations are detected late, they do not help in the understanding of problem
characteristics and their interdependencies and, therefore, lead to erroneous decisions.
With the industry focusing on its core competence in innovation, companies have complex
success factors and complex risk clusters. Therefore, the modelling of cause-and-effect
structures of innovation risks in the German MPEI facilitates the exploration and
understanding of the behavioral dynamic of risk clusters. In a comparison of standard risk
assessment with the Causal Loop Diagram and the System Dynamics Model of Innovation
Risks, the potential of System Dynamics for systemic and multi-dimensional risk management
is demonstrated.
1. Characteristics of Innovation
German Machinery and Plant Engineering Industry (MPEI) business models are aligned to the
development and production of machinery and plants in the Business-to-Business sector (B2B). Their
construct is determined by individualized equipment with high investment volumes. The industry is one
of the most important in Germany comprising more than 6,000 companies, 87% of which are Small and
Medium Enterprises. This is an exceptional characteristic and it follows that it is one of the largest
industrial employers. The industry is further characterized by capital sourcing limitations (VD MA Ful,
2014; VDMA KZK, 2015). In addition to the automotive industry, the electrical engineering and the
pharmaceutical/chemical industries, the German Machinery and Plant Engineering Industry is one of
the strongest industries for research. This is its most important success driver combined with special
conditions in terms of structure and product portfolio. The industry is highly influenced by innovation
and its associated risks. Given these special conditions, management is aware that innovation risk has
to be managed adequately and comprehensively in order to remain competitive.
Innovation is the main driver of success for today’s competition (Gassmann 2006a, 2006b). Many
challenges arise from this which are highly interconnected and turn innovation risk management into
multi-dimensional risk management (see figure 1) which is both complex and dynamic (Gassmann
2006a, Howell, 2013, Warren, 2008).
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 1
Culture
= Values and Standards
(Project-/Team Orientation)
= Behavioral pattern
Complexity
= Technological Progress
Dynamic
= Fluctuation of Demand
= Technological Progress
* Disruption of Planning
= Product Ci
= System Integration
Limited Resources
* Budget
* Personal, Experts
* Technical
Infrastructure
v
“ Degree of Novelty
Projects
= Quantum leap forward
= New for the company
Objectives
4 © Goal-/ Quality Objectives
= Time Objectives
= Cost Objectives
Specific Organisation
= Functional Organisation
<” i
= Lightweight-Project Organisation
= Heavyweight-Project Organisation
* Autonomous Team Organisation
Fig. 1: Aspects and Interconnection of innovation risks (Gassmann 2006b S.9)
2. Research Methology
A lot of research has been conducted on innovation. Common themes in innovation literature include
multiple risk categories studied from different viewpoints. They reflect on innovation risk arising out of
the market system (industry) from a meta perspective which is, in turn, influenced by the subsystems of
customers, the company and competitors (Kotler et al., 2011, Porter, 1980). Specifically in relation to
the German Machinery and Plant Engineering Industry, coopetition or cooperation partners have been
identified in previous scientific work. In order to gain a deeper understanding of the industry, a scientific
literature review was conducted and the main industry innovation risks identified. These are represented
in the following table:
Innovation Featu
k Factors
1. Technology Leadership
Technology Performance
2. Competitive Price
Innovation Budget
3. Quality Technology Rework
4. Development Time Time Delay
5.1 Internal Capacity Recruitment
5.2 External Capacity
Requirement buying in Development
6. Technical Qualification
Technology Competence
7. Knowledge Transfer
Knowledge Transfer
Fig 2: Innovation features and risks in the innovation-risk-system
for the German Machinery and Plant Engineering Industry
It is envisaged that an analysis of the connectivity between innovation risks will offer interesting in-
sights. An the results is d to identify different priorities in terms of risk management.
Due to the limitations of time and resources, only the risk of shortages in skilled workers will be
discussed from a common and System Dynamics perspective in this paper.
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 2
To manage risks systematically a standard
process was developed which has been
recommended by many authors and non- Risk ae
governmental organizations (see figure 3 Reporting 7 \aenttcation \
based on IDW PS 360; White, 1995; Crouhy R ~
et al. 2006; Olson et. al., 2010; Denk et al. R, 2 ORS
2008; Romeike/ Hager 2009; Stiefl 2010; = =
Fraser/Simkins 2010; Glei&ner 2011). The ok Ir 7%
risk analysis covers the risk identification, +f )| Sp” Risk
We Valuation
valuation and aggregation. The starting point
is risk identification where the risks are
identified and priorities are set. The methods
applied are quite often risk-checklists. The
next step is risk assessment where the
methods applied focus on the evaluation of
the probability of the occurrence of the
identified risks and the extent of potential
loss. This then determines the decisive
parameters of the function. Risk aggregation
consolidates the risks. Within risk aggregation, the models and methods of quantification applied are
based, in general, on distribution functions and their simulation (Monte Carlo Simulation). Traditional
approaches, like the arrangement within damage classes, the inquiry of maximum loss or values of
expectation of loss, are also common practice (Denk et al., 2008; Romeike/Hager, 2009; Glei&ner,
2011). The results which emanate from these analyzes affect subsequent activities. These are the most
difficult but important steps especially in the context of ing risk from a complicacy and dy ics
perspective. The objective of the risk mastery and regulation process is to avoid intolerable risks and to
bring unavoidable risks to a tolerable level. Last but not least, the risk control process has to be
completed. All in all, the risk management process is a continuous one.
By completing an intensive literature review on risk
management methods, some methodical weaknesses have
Fig. 3: Extended risk management process
System Theory
to be addressed. These weaknesses refer to the risk analysis » 4
in the standard process. Most difficulties arise from the
management of cause-effect-relationships and the dynamic ° >>
of risks. Although wide reaching risk analysis methods and Ve
instruments are available, dealing with multi-dimensional
risk limits possible applications. Stemming from a system
perspective on risk which is determined by two dimen-
sions’ complicacy (System Theory) and dynamic
(Cybernetic) (see figure 4), the methods applied were duly + , |
assessed. In the dimension dynamic, the methods were
checked for their ability to cover development over time
and time delays. Thereby complicacy gives an idea of the Fig. 4: Systems Complicacy und
ability to incorporate explicit cause-and-effect-structures Systems Dynamic
and the overall linkages between the risks
(Dillerup/Kappler 2015).
To sum up previous findings, which have been discussed in previous work (Dillerup/Kappler, 2015) in
both theory and practice, the research gap identified is based on the need to have a generic, dynamic
cause-and-effect-structure for innovation risks in order to understand their interdependencies and
behavior over time.
3. Planning, Control and Risk Managing Tools in the MPEI Project Stages
Coming from a common perspective on risk now the application of methods and tools for
the German Machinery and Plant Engineering Industry is discussed. The industry is mainly influenced
by projects which are commonly determined by five phases. Each phase has different aspects and
dimensions to consider. Therefore, different planning and risk tools are applied in order to cover the
specific demands of each phase. The main tools and concepts used in the industry are (see Hilpert et. al.
2001, p. 44ff.):
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 3
= Enquiry Process Certificate
= Project Analysis
= Functional Specification Document
= Work Breakdown Structure
= Technical Data Sheets
= Installation Checklist
2
J
2
s
Amount of loss (in MBillion €)
Sy
8
= Capacity Planning (rough)
= Contract Checklist @
= Costing 46
= Schedule 5
= Engineering Change Application
= Concurrent Calculation 0
= Risk Checklists 0 7 ; 100
* Risk Analysis Probability of occurence (in %)
The examples show the complexity of the dimensions Fig. 6: Portfolio of the risk evaluation
to be managed in innovation projects. In the
Preliminary Clarification Phase, a rough project will be ducted. D ding on the
results of this phase the decision to submit a proposal will be made (see Hilpert et. al. 2001, p. 59f).
Therefore, questions in terms of technical realization, capacity for realization, customer and market
strategies, make or buy, joint ventures, etc. as well as project risks and the timing of agreements have to
be answered. These findings correlate with the findings on innovation risks in the sample industry with
the exception of the risk of “Technology Competence and Knowledge Transfer”. The risk analysis work
covers following risk types which lead to an overview of the total risk of the project (see Hilpert et. al.
2001, vl 115):
Economical > Innovation Budget
= Timing > Time Delay, Recruitment, Requirement to buy in Development
= Technological > Technology Performance, Technology Rework, Technology Competence
= Other risks > Knowledge Transfer
= Guarantee.
The preferred tool in this phase was the concept of the value analysis. This could be applied to assess
the attractiveness of the project and used in the risk identification phase in the common risk management
process. An example of how the linear risk evaluation works in shown in table 2 (See Hilpert et. al.
2001, p.66). The assessment of risks takes place through the application of a grading scale. In the
example, | up to 10 is applied.
Deal
Welan breaker
Economical - Risk far below Average Risk far above average
Timing No risk
Risk far above average
Technological Completely Controlled Risk far above average
Other risks No risk realized A lot of risk
Guarantee Minor i vn 4 Considerable
Table 2: Value analysis in innovation projects of the industry
The weighted results will be added in isolation from each other (see Hilpert et. al. 2001, p. 67). In the
context of risk management, this means that the risk has the same cause but there are no
interdependences between the risks and, risks are discussed as independent single risks (see Glei&ner
2014, p. 8). Additionally, the application of probabilities is proposed (see Hilpert et. al. 2001, p. 116).
This leads to the classical static portfolio of the risk evaluation (see figure 6).
In terms of the classical risk management approach the cycle is interrupted after risk aggregation (see
figure 7). A project will be viewed in this phase more particularly on multiple dimensions whereas the
risk is only discussed on single risk level (see Hilpert et. al. 2001, p. 115).
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 4
The proposal phase is determined to be crucial for the
success of the overall project or innovation. The treatment
of orders and also the results of orders are extensively pre-
defined. Hence, this phase is synonymous with a
conception phase. Content subjects from the preliminary
clarification phase are refined and, again, the identified
innovation risks are added to these subjects (see Hilpert
et. al. 2001, p. 61):
= Technical high-class level / specifications,
> Technology Performance
= Type and structure of the project risks,
> Technology Rework
= Milestones starting after order placement,
> Time Delay
= Capacity needs and capacity utilization,
> Recruitment, Requirement buying in
risk
Identiteaton
R, 4
R, Ry tsk
Valuation:
R, a
= R,
Fig. 7: Interrupted risk management
process in the preliminary clarification
phase
Development
= Make-or-Buy aspects,
> Technology Competence
= Perhaps cooperation’s with other enterprises
> Knowledge Transfer
= Cost volume (pre-calculations) and timeframe of occurrence > Innovation Budget
It becomes clear that different dimensions in the project
like quality, time, capacity and costs have to be considered
during the concept phase, and these are highly
interconnected. Nevertheless, checklists audit the project
feasibility from an isolated perspective (see Hilpert et. al.
2001, p. 122). Ry ‘
Simultaneously, risk analysis takes place in this phase. Ra Ruy
Single project risks are identified by means of risk
checklists (see Hilpert et. al. 2001, p. 117-119 or p. 1698). x Rey
Strongly linked is the analysis of risks in terms of potential
coverage and protections (risk control measures) and also
the costs arising from these measures, e.g. insurance
premiums, fees etc. This extends the risk management
process from the perspective of regulation measurements
(see Hilpert et. al. 2001, p.115, figure 8). If the coverage is
inapplicable (risk keeping) the prospective damage and
probability of occurrence will be defined for each single risk (see Hilpert et. al. 2001, p. 115).
These quantitative aspects of the risk analysis will be adopted in the project calculation, so that the risk
itself is only reflected in purely monetary dimensions (see Hilpert et. al. 2001, p. 80-82).
Interdependence between risks or the effect of risk measures on the overall system are not replicated in
this project phase (see Hilpert et. al. 2001, p. 122). Only in the order phase, risk management measures
(see Hilpert et. al. 2001, p. 115) and their effect on risks will be tracked (see Hilpert et. al. 2001, p. 90-
100 & 122.)
In the Transfer Phase the main focus lies on the specification of responsibility and competence in the
project. Besides the coordination of the activities, interfaces, problematic issues and the definition of
working packages, the job of the project team consists of checking the offer details with the necessary
data for the order processing consistency. The following subjects are checked content wise (see Hilpert
et. al. 2001, p. 85ff.):
= Comparison of order and offer
= Specification and actualization of targets of the project
= Planning of the implementation process and reservations
After the placement of the order the project turns in to the processing. In terms of project and risk
controlling, this phase is discussed in considerable detail in the literature. The perspectives are on
Risk
Identification
Regulation
Fig. 8: Interrupted risk management
process in the proposal phase
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 5
= Technology > Technology Performance,
= Cost > Innovation Budget
= Milestones/ Capacity and > Technology Rework, Time Delay, Recruitment,
Requirement buying in Development
= Commercial processing > Technology Performance.
They are not independent of each other and cover all
industry-specific risks with the exception of the risk of
“Technology Competence and Knowledge Transfer”.
Being aware of existing interdependence between each other,
changes (divergences = risk) in single perspectives are
Risk
Revo Identification
5 ? coe R,
brought into the respective areas. Within the scope of the f R,
technology target-performance, comparisons should be Ry y Risk
brought in in terms of costs and milestones. In the project, R OR Neweten
calculations are updated. Network plans and Gantt charts as
well as appointment lists and capacity overviews form a
fundamental basis to check the effectiveness of measures in
order to keep to the milestones (see Hilpert et. al. 2001, p.
88ff.).
Change in pr monitors the
effects on variety, scope and technical effects through the
application of checklists. The dimensions where the effects are reflected include appointments,
guarantees, penalties, costs and capacity. (see Hilpert et. al. 2001, p. 98-101). The project reporting and
project dc ion close the classical PDCA (Plan-Do-Check-Act) cycle. In this phase, the classical
risk management process fulfils all the necessary steps and so the circle is completed — the loop of the
standard risk management is closed — but not the loops within.
In terms of the interrupted risk management process in the proposal phase, it has to be pointed out that,
although the risks (changes to the project) are recognized, judged and processed from different
dimensions, the actual feedback effects are neither considered from a minute nor a holistic level. This
could be ascribed to the high number of management tools used and therefore high numbers of
dimensional interfaces. These tools were not in fact developed for application in the context of feedback
loops and time delays. On the other hand, a systemic view on the total risk assessment is prevented by
the application of these tools with all these different dimensions within the standard usage.
Within the last project phase the evaluation of the project occurs. In addition to the retrospective
calculation of the economic result, the benefit of know-how is evaluated. In any case, the know-how
transfer in the context of the technical result is judged in order to ensure continuous improvement (see
Hilpert et. al. 2001, p.108-113), The need for action and incorporation of the know-how development
and the effects in previous phases is, from a system perspective, identified.
oe
Fig. 9: Completed risk management
process in the order phase
‘al
4. Innovation Aspects and Risks in the Innovation-Risk-System
To overcome these weaknesses of the standard risk tools and to close the loops through all
the stages in the risk management process in the MPEI, the System Dynamics approach is identified as
an appropriate simulation approach for the overall risk management cycle as well as for the risk analysis,
which is the initial step in the risk management process. Within this process, System Dynamics is able
to illustrate the system linkages and time delays in the system behavior (Davis et al., 2007; Forrester,
1972; Sterman, 2000; Morecroft, 2008; Raffée/Bodo, 1979). These results are the starting point for
simulating complex and dynamic interactions. System Dynamics takes the complexity, feedback loops
and the non-linearity of social systems into account (Sterman, 2000). Another point that supports the
use of System Dynamics is the facility to simulate the interaction of quantifiable and related variables
on an aggregated overall system level (Dooley, 2002). Furthermore, the possibility to keep
multidimensional perspectives and connect them with each other without the transmission into a one
dimensional perspective militates for a System Dynamics approach.
4.1 Causal Loop Diagram on innovation risks
The starting point for the research project was an analysis of scientific and specialized literature, the
general views of consultants, auditors, as well as representatives of the German Engineering Association
and leading companies, all of whom informed the following research questions:
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 6
a) How can the innovation risks in the machinery and plant engineering be defined?
b) What does the structure of the relevant innovation risks look like?
c) How do they affect each other?
d) Is there a need for adjusting single risks depending on the results of the simulation?
For questions a) to c) a Causal Loop Diagram was developed which was the starting point for the
development of the System Dynamics Model and which was used to answer question d).
As previously mentioned, the innovation aspects in the German Machinery and Plant Engineering
Industry have been identified and the appropriate risk factors where matched to previous work. By
applying the approach of “Standard Cases: Standard Structures (see Standard Models by Kim Warren,
2014 and also other leading System Dynamics Experts e.g. Brossel, 2004a; Bossel, 2004b; Warren,
2014) a literature review of generic business architectures on innovation models, market models,
knowledge and project in the System Dynamics literature was conducted. By
matching them to the findings of the industry research on risks, the list was consolidated to the industry
specific approaches which are highlighted in bold in fig 10.
Potential Standard Structures & Selected Structures (bold)
1. Technology Leadership: Maier (1998); Milling (1996) auf Basis von Bass (1969); Dillerup (1999); Milling (2002); Morecroft (2008);
Warren (2008).
2. Price Competitiveness: Maier (1998); Bossel (2004); Milling (2002).
3. Quality: Lyneis & Ford (2007); Rahmandada & Weiss (2009); Rahmandad & Hu (2010); Ford & Sterman (1998); Lyneis et al.
(2001); Love et al. (2002).
4, Time for Development: Rodrigues & Williams (1998); Lyneis et al. (2001); Love et al. (2002); Lyneis & Ford (2007); Richardson
(2014).
5.1 Internal Capacity Expansion: Lyneis & Ford (2007); Rodrigues & Bowers (1996); Ford & Sterman (1998); Rodrigues & Williams
(1998); McGray & Clark (1999); Lyneis et al. (2001); Morecroft (2008).
5.2 External Capacity Expansion: Ford & Sterman (1998)
6. Technical Qualification: McGray & Clark (1999); Lyneis & Ford (2007); Warren (2008); Lyneis et al. (2001); Rodrigues &
Williams (1998).
7. Knowledge Transfer: Georgantzas & Katsamakas (2008); Warren (2008); McGray & Clark (1999); Luna-Reyes et al. (2008);
Rahmandada & Weiss (2009).
Fig. 10: Modelling standard risk(s) with standard structures
These results extended the initial figure 1 from the perspective of the identified feedback loops which
shows the system approach and therefore the system behavior of innovation risks.
Innovation Feature Feedback loops Risk Factors
1. Technology Leadership R1.1 R&D Policies Technology Performance
R1.2 Competition
B1.3 Market
2. Competitive Price B2 Pricing Innovation Budget
3. Quality R3.1/2 Internal/External Rework Cycle Technology Rework
4. Development Time Time Delay
5.1 Internal Capacity B5.1 Internal Capacity Expansion Recruitment
5.2 External Capacity R5.2 External Acquisition Requirement buying in
R5.3 External R&D Placing Development
6. Technical Qualification B6.1 Internal Acquisition of Knowledge Technology Competence
B6.2 External Acquisition of Knowledge
7. Knowledge Transfer B7.1 Drain Reverse Engi Transfer
B7.2/3 Knowledge Drain External/ Internal
Fig. 11: Innovation features, risks and feedback loops in an innovation-risk-system for the industry
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 7
Further
Training
Requirement.
further Training
Personnel
Capacity
@
Development Deadline
Productivity
+
‘e21) } JU tai
New or Further ——* 27> Period
t
Davelopinisrt Development
Order fH buying-in
4 \ development
/ ¢ achievement
2.2
fe)
Requirement
Development Capacity
4
w)
R&D Budget
Engineering
Change Order
External
Development
Innovative
Technol
tes2) ‘echnology
>
Non PR
Customer
Varieties
—ta~
Products/
Processes
with Innovative
81.3) Technology
be
Innovation
Demand
Price Preasure
+ Sales
ON Market a7
Potential
2): oy
Probability of -
Purchase
Innovation Demand #
Competition
Fig. 12: Holistic Innovation-Risk-Net for the Machinery and Plant Engineering
(see Dillerup/Kappler, 2015)
By matching these findings with the findings of the literature on the German Machinery and Plant Engi-
neering Industry an innovation-multi-causal-dynamic-risk-system called INNO_CLD-Model (see figure
12) was developed. This Causal Loop Diagram has been assessed in several workshops and meetings
by System Dynamic experts, i for dard risk methods, auditors, the German
Engineering Association and their risk experts as well as leading companies in the industry.
Also in accordance with the approach “Standard Cases: Standard Structures: Standard Models “the
System Dynamics model INNO_SIM was created. With the support of several System Dynamics experts
the generic structures and models were adjusted, extended and aggregated to the System Dynamics
Model INNO_ SIM.
4.2 Validation Milestones
For validation purposes the common accepted validation processes in the System Dynamics literature
were applied (see Barlas 1996; Forrester/Senge 1980; Sterman 2010). Due to the requirements of the
research proposal the INNO_SIM-Model has to be a generic simulation model of innovation risks for the
industry. Not all validation tests could be applied within this theory-driven simulation model and a focus
was set on the validation tests of the model structure. The validation process incorporated several
methods:
1. Workshops and meetings by System Dynamic experts and system perspective experts, the German
Engineering Association and their risk experts.
2. Comparison to reference modes where available and also the use of similar equations set ups.
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 8
During the modeling process the model passed these testing phases several times. The structure
validation test in particular was applied iteratively. The final results of all tests are presented in fig. 13:
Competences) Development & Market
Construction
Boundary Adequacy
(Structure) Test level level level level level
SOUR Cui umCeam Model structure | Model structure | Model structure | Model structure | Model structure
reality reality reality reality reality
compliant compliant compliant compliant compliant
Parameter Verification
Test reality reality reality reality reality
compliant compliant compliant compliant compliant
Dimensional Consisten: Di Di Di Di Di
Test
Extreme Conditions Test Behaviour Behaviour Behaviour Behaviour Behaviour
reality reality reality reality reality
compliant compliant compliant compliant compliant
Behaviour Prediction Test
5 Behaviour valid | Behaviour valid | Behaviour valid’ | Behaviour valid’ | pe aviour valid
3 reproduced reproduced
£4 Behaviour Anomaly Test Medel structs
= valid
3
fy Boundary Adequacy Structure Structure Structure Structure Structure
(Behaviour) Test
Fig. 13: Applied validations test and final result after the testing phase
Extracts of the modelling process of the Causal Loop Diagram INNO_CLD and System Dynamics model
INNO_SIM are presented in previous work (see Dillerup/Kappler 2015). The current paper catches up
at this point by presenting the risk “shortage of skilled workers” from an isolated and system perspective.
5. Simulation Case and Transfer Results
5.1 Parametrization proposal
The starting point for the simulation study is the academically derived INNO_SIM-Model of innovation
risks in the German Machinery and Plant Engineering Industry which was partly presented in the
previous chapter and also partly discussed in a previous paper (see Dillerup/Kappler 2015). In order to
differentiate between standard risk behavior and simulated risk behavior, the simulation structures were
developed in order to show system behavior which was deactivated for the standard approach. Therefore,
the simulation model is able to generate risk behavior based on an isolated and linear understanding and
anticipation through the application of classical risk tools di: d in previous chapters.
Due to the fact that the model has more than 110 parameters there has to be a focus on the main variables.
In order to give a generic and consolidated view on the risk behavior, the comparison focuses on:
= Market launch, which reflects the risk of time delays arising out of the system independently of
the sector where it originated
= Costs and actual margins, which reflect the risk in increasing or shrinking innovation budgets.
The decision to allocate i ing costs to s can be also defined in the INNO_SIM
model.
= Customers, who indicate a willingness to buy the innovation. This is reflected in the number of
customers who adopt the innovation. The factors that influence their decision are the market
launch, the innovative technology (quality technical), the quality (quality functional) and also
the price derived from the costs. These will be compared to the offerings of the competitor.
The parametrization proposal is based on an intense data analysis of several statistical studies. These
studies are conducted regularly by the German Engineering Association and are exclusively available
for association members. The studies cover different sectors of a company in the industry (see Authorless
15 ZEW 2015, Authorless 25 Mbau 2015; Hilpert et al. 2001; Lott/Lutz 2012; VDMA Ful 2014; VDMA
HR 2014; VDMA HR 2015; VDMA KO 2014; VDMA PP 2014; VDMA QM 2014, VDMA Vertrieb 2015;
VDMA KZ EukK 2012):
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 9
= VDMA KPIs - Comparison, Understanding and Changing:
— Development and Construction, 2012
— Cost Management, 2014
— Human Resource Management, 2014
— Human Resource Structure, 2015
— Quality Management, 2014
— Sales, 2015
= Research and Innovation, 2014
= Product Piracy, 2014
= MPI in Figures and Graphs (2015)
= Industry Report of innovation — Machinery Engineering Industry (2014, 2015)
= Product Management in the Machinery Engineering Industry (2012)
5.2 Initial Settings and Standard Base Run (SBR)
The standard case was derived from the studies mentioned before. The case developed is based on a
company size of less than 250 employees and a new product development project. For the base run of
the simulation model, which is the reference mode to evaluate the risk behavior, is defined as followed:
= Number of experts in the human resource sector
(HR-sector): 6 employees (no recruitment risk,
no risk regarding requirement buying in
development)
= Time needed for a new product development
(plan): 23.5 months (no time delay)
= Proportion of own development: 88.7%
= Proportion that has to be changed (rework
buffer): 6% (risk of technology rework is
considered)
= Quality (functional = performance): plan 100%
= Quality (technical = output): plan 100 tasks
(relatively 100%)
= Margin: 0.6% (No risk of innovation budget)
Development &
Construction
SBR
Market
SBR
Fig. 14: Standard base run configuration
= Total innovation cycle (milestone market introduction): after 49.5 months
= Market introduction competitor: after 77 months (match with the duration of a further
development which is round about 27 months after period 49.5 which was the market launch of
the company)
= The competitor offers the same product
regarding quality, price and output.
To show the extent to which the results are different
from those of the INNO_SIM model by the application
of classical risk analysis methods as described in chapter
3 and which further findings can be derived from the
INNO_SIM model, a comparison of the results of both
methods is presented. Two simulation scenarios were
defined:
The first simulation corresponds to an isolated "linear
cause-and-effect relationship" with no feedback and
time-delay effects. This scenario is referred to as the
"SBR Plan". This scenario is compared with the "SBR
System". The same simulation model is used to
200
Techn]
logy
ofloss (T€)
2
Inhovation_| gy
Bhdget Risk] —
Krfowledge
@ Fakster Risk
in Risk
(%) 100
Fig. 15: SBR Plan Perspective
determine the results for the plan and system scenario. In the plan perspective, the simulation is adjusted
to a non-feedback perspective which shows the isolated and linear way of the standard risk perspective.
If the parameters of the standard base run are entered into the model, the system calculates the manner
shown in figure 14. This perspective is isolated and static and the effects are treated as linear and refers
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks
to the Risk Matrix were risks are presented by the volume of loss and probability of occurrence (see. fig.
15).
Starting from the identified innovation risk systems, the risks and results are formed in the dynamic risk
analysis in a multidimensional manner (see Table 3). These effects are then activated for the SBR
System and reflect the non-linear and interconnected perspective (see fig. 16).
Perspective
Knowledge Transfer Risk
Recruitment Risk Innovation Budget Risk
Knowledge Transtar Risk
= Performance = ans! —
anaes Competence
fo cm ror
‘Actual Margin
‘without * 40 Fk
pone Bayram Deeoprant echnology Rework Risk
iene Delay Risk Knowledge Transfer Risk
Innovation fudget Risk
Competitor
i
g
é
u
ae
=
i (nowiedge Transfer Rish
2 Competitor Arnaut ee m
Tab. 3: SBR Plan Perspective Fig. 16: SBR Plan Perspective
5.3 Simulation Results — Standard Base Run (SBR) Plan and System
In the Standard Base Run System, a comparison of both perspectives with the same parameterization
initially demonstrated a coherence in quality and technology (see fig. 17). Following a second review, a
risk in the development time was discovered. This was due to developments in the HR sector.
Development Quality Plan Development Quality System
8 0,060 lError Rate 0,060) Error Rate
= oss pas 2 oos =
2 0030 § 0.030
: !
0.015 0.015) Error Rate Plan
ool _._| , ool, a 0
) 10 ° 10 40 60
[ D Plan ] D System
0.97 Technology Performance Plan
g g 10 2 :
$0 2 o7s
g $ A
H ¥ 2 050 _~ Technology Performance Company
& “af 097
é § 025) /
00 | |
10 20 20 0 ® ° 10 2 3% 40 Co
Fig.17 Standard Base Run (SBR) Plan and System
In the scenario plan, no systemic effects or other substantive differentiations were included in the
analysis. In the scenario system, however, these effects are taken into account. This is the reason for the
various developments in the personnel sector.
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 1
Development HR Plan Development HR System
Former Experts
0.69
© Rookies
06
Experts in Training 0
Offer of Employment 0
Former Experts
Rookies 0
f «
y ] Experts
Zz af Offer of
U +z Employment
° 54 10.8 16,2 21.6 27.0 ° 108 16.2 216 0
nea Month: 27
Fig.18 Development in the HR-Sector
The declining stock of experts is due to experts-in-training and fluctuating numbers of experts. With a
time delay, jobs are advertised and inexperienced employees are hired. The number of employees is
dynamic. The different competencies lead to different productivities.
Coming from a “state of the art risk management perspective” only the following scenario would be
identifiable in the market (see table 5 left column “Plan”). Due to the late market entry of the
competitor, our ‘own’ company was able to harvest 41 customers out of 100 in period 121 which defined
the approximate tipping point in the innovation-adoption-process in the SBR Plan. Sterman’s (2010)
infection theory was applied to show the reactions of customers in terms of their choice after the launch
of the innovation. The adoption rate of the company doesn’t adjust to the competitor level due to this
phenomenon.
Market Development System
aout Standard Base Run
slic L_System_| Risk (Deviation in %)
Market launch 51,5 +40
Quality (Funktional) in % 100 0
Company Technology Performance 97
situation Costs in TE 2.412
Actual Margin without penalty in
% 6 0,7
Market Situation Customer Amount a 38
121,5 Month Competitor Customer 69 7,3 +58
Table 5: SBR results
However, as can also be seen in the table, the risk development is different from a systemic perspective.
The market launch date has shifted by 4% in the personal sector alone.The system inherent risks and the
associated effects on the overall result are already apparent in the basic scenario. In the system scenario,
internal capacity risks lead to risks of timing and costs as well as long-term lack of customer potential
(competition risks) presented in the following figure.
Adoption Rate
Company
140
Month: 121,5
Fig.19 Market Development in the system perspective
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 12
The graph shows the commonly known innovation-phase-shape (Rogers 1983). For the purpose of
comparison, the result or the market graph will be offered also in further iterations. For the purpose of
comparing the results of the risk shortage of skilled workers the results shown in fig. 19 is the reference
mode which will guide the comparison.
5.4 Standard Base Run Risk (SBRR) - Shortages in skilled workers
The scenarios that present best the differences between the standard view on risk management and the
systemic view are defined as “base run risk “-scenarios. For the purpose of this paper the human resource
risk “shortage of skilled workers” was chosen to show the main risk of innovation. This risk affects, in
reality, all five sectors in the INNO_SIM Model, but not the common risk management thinking in the
German Machinery and Plant Engineering Industry.
The cause of shortages in skilled workers has several aspects in the Base Run risk:
= More tasks in research and development as expected (higher technology performance = output)
= Fluctuation (capacity)
= Missing knowledge (productivity, ability of specification)
.
In the simulation model the human resource 2. Recruitment
capacity is reduced by one person: therefore 5
pemionment &
experts are available for development &
construction. The circumstance of missing workers
Construction
SBRR
leads to an anticipated time delay which initializes
a demand for workers and therefore a recruiting
need if the people are not available in the company.
Based on the findings of the analysis for the
purpose of parametrization, the average vacancy
time is 1.8 months until the job vacancy is filled.
In the standard perspective, there is a linear filling
after 1.8 months. This circumstance could be
identified in the graph which shows the result in Fig. 20: Risk management in the simulation
the HR sector (see figure 21). There, a step of 1 skills shortage
1. Time Delay
expert is seen in after 1.8 months.
The overall effects on the whole system are
marginal. Costs decrease by round about €1,000
due to fewer employees applying for development
& construction in order to reach the same output
level and same performance level. Nevertheless,
out of the recruitment risk another time delay risk
evolved. There is a delay of 0.4 months in terms of
the market launch. Potential penalties (extent of
losses) are not considered in the calculation due to
missing numerical information. This penalty has to
be included in the risk calculation in real life
projects! The assumption in the simulation model
is, that higher costs will not be passed to the |
customers in the short term (the overall C) 54 10.8 162 21.6 27,0
assumptions have been discussed in the Moet ©
development of the causal loop diagram).
The question if this “longer” cause and effects
Experts 6
Fig. 21 SBRR — Plan Shortages in skilled works
chain is tracked in the standard view can’t be
discussed further. Nevertheless, it is assumed that the process will be handled in a linear manner. Also
the human resource capacity is reduced by one person: therefore, 5 experts are available for development
& construction in the beginning. In the systemic simulation the loop B internal capacity extension (see
figure 22) is activated.
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 13
Market launch
49.9 Period
Costs 2,412 TE
0.66% +
Actual Margin without penalty penalty for
Time Delay
Market launch Competitor 77. Period
Market Results after Period 122
Customers 41%
| Customers of the Competitor 6.9% |
Table 6: SBRR- Plan in the scenario shortages in skilled workers
The question if this “longer” cause and effects chain is tracked in the standard view can’t be discussed
further. Nevertheless, it is assumed that the process will be handled in a linear manner. Also the human
resource capacity is reduced by one person: therefore, 5 experts are available for development &
construction in the beginning. In the systemic simulation the loop B internal capacity extension (see
figure 22) is activated.
The kind of further systemic cir-
cumstance in the HR-Sector has a
Human Resource”
Yo Recruitmen|
significant influence on output and / Internal capacity
. : . / extension
performance in the innovation : (ah
project shown in figure 13. This Development =
graph reflects the system behavior Time delays Need for
which has evolved over time and af capacity
which should be considered in the Time for development
risk analysis if the risk of shortage
of skilled workers is analyzed. The
identified effects feature also on the
analysis work of the studies:
4
New Innovation A
no Ss
Development tasks
7
Innovation
budget
Fig. 22: risk Recruitment Loop B Internal capacity extension
= Several main focuses: development and construction, other activities (among other things e.g.
train the trainers
= Different classifications of the human resource
= Fluctuation rate of newly occupied and continuance employee’s vacancy
= Vacancy times and non-occupation
= Advancement of human resources
If only these circumstances are included in the HR-sector the following development arises in the
simulation model (figure 23):
Final Amount of
Employees
Former Experts
‘ /expetsin
3 Sa Training
J Bret
)) /
we . Rookies
; 5
Pree > Offer of
5 N 098 |___ Employment
an 54 108 162 26 (270
Fig. 23: Systemic base run risk - Shortages in skilled workers
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks
All these non-linear behaviors are ascribed to time delays and feedback loops. The model considers a
time delay between advertisement of the vacancy and its subsequent occupation (see line Offer of
Employment and line Rookies).
In addition, the model includes a delay until a
rooky becomes an expert. Training on the job ~ =
affects the available capacity of experts (see Advanced Training ‘SS
line 4 Experts in Training). These effects are J, a A 4B) Technolo
. / Innovation f, = iompeté
ascribed to the technology competence loop B / budget Internal Knowledge
internal capacity extension (see figure 24). / / Extension g 4
Within the HR-sector the average productivity | lo Nesd for
is modeled. The different productivity rates of Human Advanced Training
rookies and experts further affects 1 & Resource 4
productivity. Based on the focus of this paper D Sec
one will be discussed in more detail. The train tale an
the trainers concept, which was already | ig
tioned, effects productivity. The startin; g Nesd for
mentioned, P ry. s ig capacity
point is the assumption that the advancement Development s¢
of the rookies happens in the project phase tasks = ——
(training on the job). Therefore, the human
resource capacity in terms of the Final amount Fig. 24: Risk technology competence loop and
of Employees is not affected. Nevertheless, it is internal capacity extension
considered that training measurements of the
experts limits their productivity and therefore the development rate.
Also, the risk of fluctuation is processed in the model at a monthly rate based on the current stock of
rookies and experts (see line Former Experts. The effect on the rookies is not present in order to keep
an appropriated overview).
To sum up all the findings, it has to be pointed out that it is not only the shortage of skilled workers has
to be considered when the available capacity is analyzed. Also, time delays and other effects affect the
capacity although it did not seem to be considerable from an isolated perspective. The analysis forms a
systemic view showing the significance of all these effects. If only the effects in the HR-sector are
considered another reaction could be identified in the market (figure 25):
ompetitorCustomer
Customer
~ adoption Rate
‘Adoption Rate
‘Company
Poietial Customer
180
Months: 121,53
Fig. 25: Systemic base run risk market scenario
The systemic development within in the HR-sector leads to a time delay of 4.2 months (time delay risk).
The penalties (extent of losses) are also not considered in the calculation. Nevertheless, the extent of
losses was significant, increasing due to longer processing times which are ascribed to the limited
resource. Up to €33,000 have been spent in addition for the HR-capacity applied for the project. These
additional investments are ascribed to the systemic perspective in the HR-sector. Only these additional
costs reduce the margin by 1.31% to -0.71% (risk innovation budget).
The effects on the market arise out of the market entry delay. The assumptions in terms of quality,
technology and pricing in comparison to the competitor are not adjusted and therefore equal to our ‘own’
company. In period 122, the acquisition of customers decreased by 8% in comparison to the base run
risk (see table 7).
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 15
Risk Situation 5 Employees — Systemic Run
Market launch 54.1 Period
Costs 2,445 TE
Actual Margin without penalty!!! -0.71%
Market launch Competitor 77. Period
Market Results after Period 122
Customers 33%
Customers of the Competitor 7.8%
Table 7: Systemic run results in the scenario shortages in skilled workers
To conclude, there is a need to differentiate between standard risk behavior and the System Dynamics
risk behavior. The risk of time delays increases and can be ascribed to delays and loops considered in
the system. Also, the budget is affected by an increase of approximately €33K. Potential penalties have
not yet been considered, but should be added. There is a loss of 8 customers (%) due to the risk of the
time delay (see table 8).
Base Run Risk Situation 5 Employees Risk
Base Run Syster
ituation 5 Employees —
Run
Market launch 49.5 Period 49.9 Period 54.1 Period
Costs 2,413 TE 92,412 TE 2,445 TE
Actual Margin without a ofa 0.719
penalty!!! 0.6% 10.66%’ 71%
Mere launch 77, Period 77. Period 77. Period
Competitor
Market Results after Period 122
Customers 41% 41% 33%
Customers of the _
Competitor 6.9% 6.9% 7.8%
Table 8: Results comparing standard and systemic risk behavior
Last but not least, there are some further aspects emerging from the systemic run which have to be
considered form a medium and long term perspective. If the single project perspective is left, there will
be other additional risks which would affect the total risk position of the company.
Coming from an internal perspective the delay of the project would influence the available HR-capacity
in other projects. The time needed in development & construction ties up 5.7 employees for 4.6 months.
Therefore, the HR-effect is only partial in the original project but has significant effects in subsequent
projects.
On the market side the project risk has also further impacts. From a medium term perspective, a reduced
customer base could influence the potential base if further devel of the innovative
product are considered. This would activate the loop Competition and close the loop of the overall
innovation risk system.
4. Conclusion
The starting point of the research project INNOMOD was the identification of a gap in the considerations
of all plans and the development of each element over time, for example:
1. The missing causalities between the plans and therefore the causalities of risks;
2. The multidimensional perspective on performance and tl the missing multidi ional per-
spective on risks (Dillerup/Kappler 2015, p.8).
To close the research gap it was determined that the development of a specific System Dynamic model
could overcome this problem and also incorporate multi-causal i ‘ions and multidi ional
views on risk (Dillerup/Kappler 2015, p.9). Based on the adapted approach of “Standard Cases:
Standard Structures: Standard Models “by Kim Warren, 2014, the Causal Loop Diagram INNO_CLD
and also the simulation model INNO_SIM, was developed and which now covers all of these aspects.
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 16
It can be concluded from the closing findings of the simulation and the research conducted that a
systemic view on risks leads to other assessments of innovation risks and their behavior over time. It
can also be pointed out that the isolated planning, control and risk managing tools in the industry specific
project stages can be aggregated by the INNO_SIM-Model throughout all stages by keeping the
multidimensional perspectives.
Through the application of the INNO_CLD and the INNO_SIM-Model, risks can be discussed, assessed
and evaluated in more detail in terms of relevance (intensity of risk effects), probability of occurrence
(linked to linkages between the risks) and their 'gverall effectiveness by considering the risks in their
multi-causal interconnections, multi-di i ives and the ic time delays.
Both INNO-Models provide project-specific and realistic risk management tools that meet the
requirements of holistic perspectives, complexity assessment and decision support, and can improve the
quality of the risk assessment.
Market Development
Systemszenarier| System Szenario
Result ; Total Allin
Basis | Risk | Risk Deviation
MarketLaunch [eos cay gag =
Quality
100 © 100-9963 -0,37
>. | Technology
&S | Performance fo7 97 80,6 16,9
3
5= | costs
cs] 2.412 2.485 2636 «+93
Actual Margin :
without 0 |_Row
penalty o
—_ 0,7 07 -86 = 1.328
Customer
Amount 38 33 21 -44,7
Competitor
Customer 73.78 12 +644
Furthermore, risk measurements can be tested and evaluated in terms on their risk effectiveness if system
behavior is considered.
Although the research gap identified seems to be closed, some limitations have to be considered and
should be tracked in further research work. Only effects which have been explored in System Dynamics
literature as well the studies of the German Machinery and Plant Engineering Industry where considered.
Further research could continue at this stage by applying field search in order to assess these remaining
effects. There is also a lot of movement in the industry due to the trend of digitalization. Industry 4.0 is
discussed intensely and could influence the HR-sector by having a more detailed view on the
classification of the employees. Also, the development & construction and competence-sector will be
probably influenced. Therefore, the further development of this issue has to be tracked and processed.
Key words
Innovation Risk, Holistic Risk Management, Complexity, Dynamic, Risk Systems, Risk Analysis, Risk
Aggregation, System Dynamics
Dillerup/Kappler/Oster: Improving the Management of Innovation Risks 7
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