The scenario approach based on Reibnitz

Literature in the following section on the scenario approach taken in this thesis is based on the literature review article by Mietzner and Reger (2005) and the book on Scenario Techniques by Reibnitz (1988). As mentioned, various approaches for conducting a scenario analysis exist. As recommended by Van Notten (2006), the approach chosen for the purpose of this thesis is a combination of systematic, inductive, intuitive and deductive scenario building, and both qualitative and quantitative inputs. For the most part, this thesis relies on the systematic steps proposed by Reibnitz and Geschka, and adapted by others (e.g. Heinecke and Schwager) – amongst others because this approach suggests the creation of explorative scenarios, which were determined as the scenario type with the best fit as an aid for answering the research question in the explorative context of this thesis. Since the scenario method leaves room for adaptation of the technique to the circumstances, in some instances the scenario approach taken in this thesis deviates slightly from the original approach suggested by Reibnitz. Qualitative inputs and literature research are combined with quantitative software-aided modelling.

The computer software aiding the scenario analysis in this thesis is Parmenides EIDOS™ (EIDOS). Step 1: Preparation of the Scenario Analysis: The TER

The first step in the approach by Reibnitz describes an analysis of the company, i.e. the task at hand including the company’s strengths and weaknesses. This thesis is written at the Technical University of Berlin and University of Twente, in the context of a cooperation research project with the University of Potsdam on RIMDB, and is not embedded into a corporate context. That is why this step will be skipped. However, in preparation of the scenario analysis by gaining an in-depth understanding of the technology RIMDB, its technology life cycle stage, potential competing technologies, players in the field and the research status of RIMDB, the TER was prepared. As mentioned, four expert interviews (experts E1, E2, E3 and E9) were conducted in this stage. Moreover, the TER was based on a literature review, as discussed.

Step 2: Identification of Key Drivers

The second step described by Reibnitz will, however, be part of the methodological approach in this thesis, and will be the first step in the process for this thesis. It comprises the identification and systematic analysis of the influencing actors, factors and system dynamics. Mietzner and Reger (2005) specifically highlight one major advantage of the scenario method at this stage, as “minority votes can also be taken into account” (p.228): Not only popular or dominant views, opinions and actors considered relevant to the future development of the technology under scrutiny are included, but also potentially more peripheral factors and actors. Review and scanning of various trend reports served as the primary basis for the determination of the influencing drivers, alongside the results of the previously conducted TER and further online research. After assembling a first list, a team consisting of the author of this thesis and PhD-candidate Victoria Götz of the University of Potsdam applied the brainstorming method to narrow down and group the initial list of drivers as displayed in Table 4 (p.40-42). The resulting list of 31 drivers was transferred into the situation analysis tool of EIDOS. Thereafter, each driver was analysed by the team and compared to every other driver individually to determine how strongly each driver may influence each of the other drivers. The direction of the influence is displayed by an arrow in the software, and the strength of the influence was determined by using a scale from 0 to 3 (0=no influence; 1=weak influence; 2=medium influence; 3=strong influence). This effort resulted in an interconnected web of driver connections in the surrounding environment of RIMDB (see Attachment E, p.140). Next, EIDOS was used to create an active-passive- map based on the previous efforts. The active-passive map (see Attachment E, p.141) displays those drivers as most active which are strongly influencing other drivers, and those as most passive which are strongly influenced by other drivers. The visual representation in the active-passive map makes it easy to determine the most active, i.e. most influencing drivers. For this thesis, the six most active drivers were then determined as the key drivers.

Step 3: Describing Future Projections

In the next step by Reibnitz, alternative future developments (i.e. projections) for each of the identified key drivers are described. These projections are a very essential step in the scenario building

process: Each scenario is built based on a different combination of one projection per key driver. Hence, each scenario comprises six projections in this thesis – one for each key driver. To develop the projections for each key driver, a total of four semi-structured interviews with five experts were conducted (experts E4, E5, E6, E7, E8). The expert sample and interview-technique are described in more detail in section 3.1.2 (pp.21). Great care was taken for the projections in this thesis to not only be of dichotomous nature or presenting a positive, a negative and neutral option, but as qualitatively multi-faceted as possible in order to ensure the greatest possible range of alternatives for the scenario building process later in the process. Based on the interview transcripts, the information provided by the experts that was deemed the most useful for building projections was extracted and condensed in a separate document. Based on this condensed list of bullet points for each key driver, between four and five qualitatively different projections were built for each key driver.

Step 4: Defining Projection Consistency

For the fourth step in Reibnitz’ approach, a consistency matrix is developed. The consistency matrix is created by conducting a cross-check of all future projections of all key drivers in pairs of two for their consistency with each other. This check serves to assess how internally consistent a scenario including both projections would be: If it does not appear plausible for two projections to occur in the same scenario, their consistency is rated lower on a scale from -3 (very low consistency) to +3 (very high consistency). The consistency matrix was first filled-in in EIDOS by the team consisting of the author of this thesis and Victoria Götz. In order to ensure the validity of the consistency matrix, three experts (E1, E4, E6) were consulted, as described in Section 3.1.2, and the matrix values were adapted accordingly in some instances. As suggested by the Reibnitz’ approach, the fourth step also includes the calculation of the scenarios with suitable software, in this case EIDOS, based on the consistency matrix. The software only presents the 100 most consistent scenarios, which are grouped in clusters according to their alikeness.

Step 5: Scenario Selection and Description

In step five, from each cluster of scenarios, the most representative scenario for each cluster is chosen utilizing a number of selection criteria: Based on previous literature, Mietzner and Reger (2005, p.233) name five criteria for scenario selection: (1) plausibility (there has to be the possibility that the scenarios can happen); (2) differentiation (scenarios should be distinctly different in their structure and not only vary on a few aspects); (3) consistency (the credibility of the scenarios has to be ensured through the combination of logics in a scenario and avoiding any potential inconsistencies); (4) decision-making utility (the selected scenarios should provide insights that contribute to decision- making within one single step); (5) challenge (conventional wisdom and old beliefs about what the future will look like should be challenged through the scenarios). These five criteria are applied in this thesis for selecting the final scenarios. The strongest focus was on consistency and differentiation, as the other criteria should automatically ensue due to the fact that the previous steps were taken very

carefully and precisely. This was done by comparing the most consistent and differentiated scenarios of each scenario cluster. A pre-selection of ten scenarios was then narrowed down to a selection of four scenarios (see Attachment E, p.142), while aiming at covering the largest possible scenario space. Due to the consistency values in the matrix, some projections did not appear in the list of the 100 most consistent scenarios. Again, a validity check was conducted with expert E10 (see Attachment D, pp.133), by presenting print-outs of a draft version for all four chosen scenarios to the expert, as already explained in section 3.1.2. Analysis of the insights the expert provided led to an iteration of the scenarios, so that finally, the originally chosen four scenarios were re-combined and condensed to three final scenarios. Iteration of the scenarios based on the expert’s opinion poses an addition to Reibnitz’s original approach. Since the sole point of validating scenarios with an expert opinion is that the scenarios may need iteration, this deviation was considered both necessary and sensible. After the three final scenarios were determined, they were fleshed out fully in a narrative fashion, using literature, statements by the interviewed experts and lastly the researcher’s imagination. The narrative explanation aims at comprehensively explaining what the future looks like given the circumstances (i.e. the projections).

Step 6: Conclusions from the Scenarios – RIMDB and Business Operations in 2025

As described by the Reibnitz approach, the sixth step consists of a consequence analysis for opportunities and risks for each scenario. Due to the formulation of the research question, i.e. how RIMDB developments by 2025 affect Industry 4.0, these topics are in focus for the consequence analysis. During the interview with E10, these topics were addressed to support the concluding arguments. A set of recommendations for resulted from this analysis, and is discussed in the conclusions of this thesis. The next step as described per Reibnitz’ approach as analysis of disruptive events and wild cards, did not apply in this thesis as none were identified. Lastly, the scenarios should be transferred into strategic planning. However, since this thesis is a research study and not part of corporate strategy planning, this step will be included in the development of possible actions and recommendations across all scenarios and for each specific scenario within step six in this process. The step of integration into strategic planning will have to be made by strategic decision-makers separate of this thesis, but the results may serve as a strong guidance in the process.

Although the methodological approach in this thesis was chosen and adapted carefully for answering the research question, there are certain limitations. The latter are discussed in detail in section 6 at the end of this thesis. However, before moving to the results, the following section provides a critical overview on the scenario method to enhance understanding of its strengths and weaknesses, and thus provide sufficient context for the results of this thesis.