Posts Tagged ‘stakeholder platform’

EFP Brief No. 245: Trend Database Design for Effectively Managing Foresight Knowledge

Tuesday, January 29th, 2013

In 2010, the German Federal Government launched one of its largest research initiatives in the area of logistics and supply chain management with the central aim to secure tomorrow’s individuality, in the sense of mobility and distribution, with 75% of today’s resources. One of the projects, the ‘Competitiveness Monitor’ (CoMo) develops an innovative, webbased foresight platform, which supports strategic decision-making and contingency planning as well as competitive and environmental intelligence.

Sophisticated Architecture to Support Foresight Processes

The development of an innovative Trend Database (TDB) is part of an extensive cluster initiative that was launched by the German Federal Ministry of Education and Research in June 2010. The ‘Effizienz­Cluster LogistikRuhr’, synonym for leading-edge cluster in logistics and mobility in the German Ruhr area, aims to boost innovation and economic growth in Germany by bridging the gap between science and industry (BMBF 2010). The cluster involves 130 companies and research institutes that cooperate in a strategic partnership in order to shape a sustainable future for the region and beyond. The determined challenges of future logistics (e.g., urban supply) are currently being addressed in more than 30 joint research projects. In this way, the cluster contributes to finding new ways to growth and employment that gear not only Germany’s but the European Union’s economy towards greater sustainability (see, e.g., Schütte 2010).

One of the joint research projects is developing an innovative foresight tool, the Competitiveness Monitor (CoMo), which will contribute to the validity and robustness of foresight activities by digitally combining quantitative and qualitative forecasting methods. The CoMo aims to enhance cooperation in multi-stakeholder environments through a fully integrated web-based software solution that utilises existing knowledge and users’ conceptions. The tool links several applications for forward-looking activities as well as the development, processing and storage of foresight knowledge. The goal is to provide decision-makers from business, academia and government institutions with a valid knowledge base for future-robust decision-making.


The CoMo consists of three innovative foresight tools – Trend Database, Prediction Market app and a Future Workshop (“Zukunftswerkstatt”) app – which are implemented in an IT-based Futures Platform (Figure 1). The Futures Platform will serve as login portal in form of a dashboard and can be adapted by each user according to his or her individual interest. Within the TDB, future-oriented numbers, data, and facts on specific logistics-related topics or technologies can be stored or collaboratively developed by its users. Furthermore, the TDB shall not only include trend-related data but also handle weak signals, wildcards and disruptive events. The high practicability of the Trend Database is planned to ensure filtering of the query results through an intelligent algorithm.
Figure 1: Conceptual framework of the Competitiveness Monitor

Development of Trend Database Requirements

In the beginning of the TDB development process, we analysed and evaluated eight relevant TDBs in order to identify the state of the art. After that, we conducted several creative workshops and interviews with more than 40 interdisciplinary cluster partners and futures researchers to identify further requirements.

First of all, we compiled an extensive list of requirements and constraints in several participatory workshop sessions, which are considered relevant to our TDB. After conducting a requirement analysis according to the ‘Volere Requirements Specification Template’ (Robertson and Robertson 2006), we derived four categories and adapted them to the CoMo project concerns: (1) functional requirements, (2) non-functional requirements, (3) design requirements and (4) constraints. Whereas functional requirements describe the fundamental functions and processing actions a product needs to have, non-functional requirements are the properties that they must have, such as performance and usability. We clustered the final long list of 160 collected requirements in 9 categories as presented in the following:

In the next step of the TDB development process, we conducted a stakeholder analysis in order to generate possible use cases. Different use cases were defined according to the specific needs and organisational structures of the CoMo project partners and members of the EffizienzCluster involved. In doing so, we were able to conceptually test and complement the identi-fied requirements and constraints.
Finally, we revised the results of the trend database analysis and specification analysis and summarised our research results in a specification sheet, which now provides a clear and structured collection of TDB features for the programming process of a prototype.

Challenges and Differentiators

For the identification of the key challenges, we evaluated best practices and innovative features of existing TDB concepts regarding their applicability and efficiency. For this purpose, we focused on the surrounding conditions and primary objectives of the presented TDB, determined by its purpose within the CoMo and the cross-project objectives of the leading-edge cluster. We identified four main challenges of utilising a TDB, which we will discuss in the following: (1) extent and quality of trend information, (2) cooperation within the TDB community, (3) linking mechanisms and (4) creating incentives for users.

Extensiveness and Quality of Trend Information

Most of the TDBs analysed provide an extensive set of opportunities to describe and evaluate a certain trend or future signal. Since it is hardly possible to decide without further knowledge about the user’s purpose or what the right amount of information is, we continued to compare the ways in which future knowledge is contributed to the TDB. We see two main strategies within the examined sample of TDBs: (1) input from experts and futures researchers or (2) active participation of the user community. In the latter strategy, information is revised and complemented by the community, which more accurately meets the CoMo objectives of realising cluster potentials. However, in case of low interest in a certain trend, the information may remain fragmentary and lack reliability.

The combination of both strategies seems to be promising since it ensures certain quality standards as the information provided is subject to scrutiny from two sides: an expert review process, on the one hand, and user participation, on the other. Against the background of all our analyses, we propose that providing a certain amount of trend specifications (e.g., short description, key words, time horizon etc.) should be obligatory when entering a trend into the TDB. In addition, the CoMo TDB is planned to offer a regulator for the ‘level of aggregation’, which will enable users to constrain the trend search results regarding time, geography, economic scale and further aspects.

Cooperation within the TDB community

The so-called “wisdom of the crowds” is based on the logic that many people (a “crowd”) know more than single individuals (Surowiecki 2004). Consequently, the sharing of knowledge can improve the knowledge basis of different stakeholders as well. Therefore, it is useful – particularly in dealing with future-relevant knowledge – to motivate users to co-operate and to develop their knowledge further.

Regarding our TDB architecture, users shall therefore evaluate trends in terms of impact or likelihood, participate in surveys or add further evidence or aspects to existing future-oriented knowledge (Kane and Fichman 2009). Especially the stakeholders of the leading-edge cluster, who are aiming to improve their competitive situation through collaboration, are interested in sustaining topicality, validity and relevance of future-relevant knowledge in the trend database. Our TDB is expected to contribute to an improved quality of data and provide a more accurate basis for decision-making processes.

Linking Mechanisms

The CoMo TDB will be linked in three dimensions. First, the trends within the TDB will be linked among each other. This supports users by providing a more comprehensive causal picture of the future and allows decision-makers to identify early warnings and weak signals. Second, the trend database is linked to two other CoMo apps: the Prediction Market and the Future Workshop. Both apps require raw data from the TDB for purposes of evaluation (i.e. prediction markets) or analysis (i.e. future workshops). Furthermore, they define data sources by providing new or evolved future-oriented knowledge, which needs to be re-imported into the TDB. Third, the trend database will be linked to external data pools. Facilitating the idea of linked data, relevant external information can be included, increasing the basis to be drawn on in making future-relevant decisions (Auer and Lehmann 2010). Thereby, we aim to link our dataset intelligently by attaching metadata using the Semantic Web approach. This not only facilitates the process of finding relevant and recent data but also enables identifying related topics.

Motivation of Users

In contrast to the traditional World Wide Web, the application of a Semantic Web offers information that can be sorted by relevance, topicality and quality (Berners-Lee, Hendler et al. 2001). However, the Semantic Web requires the linkage of datasets first. Therefore, users have to be encouraged to tag, for instance, the trend information as good as possible, and the community needs to be motivated to edit and complete the tagging process.

In the process of developing the CoMo TDB, we discussed several concepts and ideas to address the challenges involved in motivating users. One concept that is planned to be applied in the CoMo is the lead users approach (Leimeister, Huber et al. 2009) in which users are incentivized by an awareness of the measurability of their contributions. Considering that most of the existing trend databases use an expert-based concept instead, we infer that this was thought to be the only efficient way of providing and processing future-oriented knowledge so far. However, current tendencies, such as the disclosure of previously protected data (i.e. open source/innovation) or the increasing activity in social networks, suggest that existing concepts need to be adapted to the new requirements forward-looking activities must meet.

Metadata Approach Using the Semantic Web

Future-oriented knowledge as a basis for decision-making is always critical due to its inherent uncertainty. Therefore, innovative concepts and tools need to be developed in order to provide users with the most valid, relevant and up-to-date information possible. With our new TDB concept, we try to acknowledge current challenges such as motivation and collaboration of users, usability of information and modern linkage methods. To meet these challenges, we aim to link our dataset intelligently by attaching metadata using the Semantic Web approach. This not only facilitates finding relevant and recent data but also enables identifying related topics. However, the linkage of the data has to be conducted manually. Thus, motivating users to share their knowledge within the community is essential to provide an accurate and comprehensive picture of the future reflecting the wisdom of the crowd. Finally, we will design our TDB to present future-oriented knowledge in a sufficiently comprehensive and detailed manner with an emphasis on clarity and thereby aim to contribute significantly to the robustness and quality of future decisions.

Authors: Christoph Markmann      

Stefanie Mauksch           

Philipp Ecken                 

Dr. Heiko von der Gracht

Gianluca De Lorenzis      

Eckard Foltin                 

Michael Münnich             

Dr. Christopher Stillings              

Sponsors: German Federal Ministry of Education and Research
Type: National foresight project
Organizer: EBS Business School / Center for Futures Studies and Knowledge Management (CEFU)
Duration: 2010 – 2013
Budget: € 2,300,000
Time Horizon: Long-term
Date of Brief: October 2011

Download EFP Brief No. 245_Foresight Trend Database Design

Sources and References

Auer, S. and J. Lehmann (2010). “Creating Knowledge out of Interlinked Data.” Semantic Web Journal 1.

Berners-Lee, T., J. Hendler, et al. (2001). “The Semantic Web.” Scientific American 284(5): 34-43.

BMBF (2010). Germany’s Leading-Edge Clusters. Division for New Innovation Support Instruments and Programmes. Berlin, Bonn, Bundesministerium für Bildung und Forschung / Federal Ministry of Education and Research (BMBF).

Kane, G. and R. Fichman (2009). “The Shoemaker’s Children: Using Wikis for Information Systems Teaching, Research, and Publication.” Management Information Systems Quarterly 33(1): 1-22.

Leimeister, J. M., M. J. Huber, et al. (2009). “Leveraging Crowdsourcing: Activation-Supporting Components for IT-Based Ideas Competition.” Journal of Management Information Systems 26(10): 187-224.

Robertson, S. and J. Robertson (2006). Mastering the Requirements Process, second edition. Amsterdam, Addison-Wesley Professional

Schütte, G. (2010). Speech by. Germany’s Leading-Edge Cluster Competition – A contribution to raising Europe’s profile as a prime location for innovation. State Secretary at the Federal Ministry of Education and Research framework of the European Cluster Conference. European Cluster Conference. Brussels.

Surowiecki, J. (2004). The Wisdom of Crowds, Random House.

Note: The content of this publication is based on the joint research project “Competitiveness Monitor”, funded by the German Federal Ministry of Education and Research (project reference number: 01IC10L18 A). Joint research project partners are Bayer MaterialScience, BrainNet, dilotec, EBS Business School. Responsibility for the content is with the author(s).

EFP Brief No. 203: Competitiveness Monitor: an integrated Foresight Platform for the German Leading-edge Cluster in Logistics

Monday, December 5th, 2011

In 2010, the German Federal Ministry of Education and Research launched Germany’s biggest research initiative in the area of logistics and supply chain management. A broad range of companies and research institutes are participating in a cluster aimed at shaping a sustainable future for the region, the logistics industry and beyond. We will present the current concept of the joint research project Competitiveness Monitor, its planned architecture, and its expected contribution to the cluster, the foresight field, and the community involved.

The Leading-edge Cluster in Logistics

The EffizienzCluster LogistikRuhr is synonymous for the leading-edge cluster in logistics and supply chain management (SCM) in the German Ruhr area (larger Rhine-Ruhr metropolitan region of more than 12 million people in North Rhine-Westphalia). Like all leading-edge clusters, it aims to boost innovation and economic growth in Germany by bridging the gap between science and industry (BMBF 2010). Through strategic partnerships between research institutions, companies, and other stakeholders, it fosters research with innovative potential relevant for future developments. Although leading-edge clusters are regional concentrations within Germany, they contribute to finding new ways to growth and employment that gear not only Germany’s but the European Union’s economy towards greater sustainability.

The global goal of the EffizienzCluster LogistikRuhr is to secure individuality in terms of mobility and distribution in the world of tomorrow with 75% of the resources required today. Supported by the German Federal Ministry for Education and Research, the cluster aims at utilising the joint innovation capacity of scientific institutions and a variety of companies, including many small and medium size enterprises. In their work, the cluster participants address the conflict between future individuality (i.e. the demand side) and resource scarcity (i.e. the supply side).

More than 130 stakeholders from academia and business are participating in order to tackle the three central challenges: (1) efficient management of resources, (2) secure urban supply and (3) facilitation of individuality in mobility. In order to reduce the complexity associated with these challenges, each joint research project belongs to one of seven lead topics. These lead topics represent the central innovation schemes enabling the cluster to realise its ambitious target. Figure 1 illustrates the seven lead topics and their strategic position in relation to the three challenges identified.

As illustrated in Figure 1, different lead topics have different strategic roles in tackling the three central challenges. In this paper, we focus on the lead topic ‘Activation of Cluster Potentials’ as this is the area where the Competitiveness Monitor (CoMo) belongs and to which it contributes. The research project CoMo has set out to develop a foresight toolbox that builds futures knowledge around the three central challenges and supports cluster stakeholders in evaluating new strategies, processes and technologies in light of these challenges. While all innovations in the EffizienzCluster ultimately result in competitive advantages, the CoMo innovation especially intends to increase foresight potential and future robustness in decision making within the cluster. The integration of three foresight tools into a future-oriented IT platform where academia, business and politics co-operate will ensure a sustainable competitive advantage for all stakeholders in the leading-edge cluster on logistics and supply chain management.

The Need for Futures Orientation in Logistics

Logistics has developed from its role of delivering the right things at the right time to deciding how the right things get there in the right time (ECM 2010). During the past 50 years, logistics has evolved from individually managed, product-flow related activities to an integrated set of processes managed across the multiple echelons of a supply chain. The future of the logistics industry is characterised by many upcoming challenges and opportunities (e.g. Ruske et al. 2010). Due to the increased competition in the industry, its business has become more volatile and uncertain. In addition, the trend towards globalisation has steadily increased resulting in longer and more complex supply chains (Meixell and Gargeya 2005). Moreover, advancements in information and communication technology are currently revolutionising logistics processes. Intelligent solutions based on information and communication technology (ICT) are an essential operation, control and support instrument of such worldwide networks. Conclusively, logistics nowadays means acting in complex networks of independent but interdependent organisations. To manage these systems efficiently is one of the major challenges for the logistics service industry today.

Given all these facts, there is a considerable need for futures orientation and innovation in logistics. Innovation is an important driver of growth and competitive advantages across all industries, and its impact has significantly increased in the course of the current cutthroat competition in the logistics service industry. In best practice, both innovation management and futures research are linked and contribute to each other (von der Gracht et al. 2010). Futures research helps to cope with uncertainty in the business environment and enables actors to react faster to future developments to realise competitive advantages.

However, the potential of futures research in logistics has by no means been fully realised yet. As a consequence of increased uncertainty, the majority of logistics planners are currently unsatisfied with their planning and forecasting tools and feel that they have to change planning practices in the future. In fact, there is a strong demand to apply new and innovative techniques in strategic logistics planning.

The CoMo addresses the need for innovative foresight methods in strategic logistics planning. Importantly, this is facilitated in an innovative environment provided by the leading-edge cluster in logistics. Thus, the CoMo is not only an innovation in itself but establishes a direct link between the futures field, the cluster and innovation management for a hundred innovations of the future.

Competitiveness Monitor

The CoMo is, in the first instance, a joint research project aiming to create and convey future-oriented knowledge within the cluster. It comprises a future-oriented IT platform where science, business and politics cooperate to ensure a sustainable competitive advantage for all stakeholders and support innovations in the leading-edge cluster. This translates into four major challenges for CoMo:

(1) Creating, linking and processing information about future macro- and microeconomic developments in logistics and its environment

(2) Providing educative information on futures studies and teaching future skills

(3) Incentivizing stakeholders to systematically deal with their futures and foster innovation

(4) Stimulating cooperation among stakeholders

In order to address these challenges, we developed a CoMo architecture that integrates three innovative foresight tools. The structure and interrelation of the tools is illustrated in Figure 2 and elaborated on in the subsequent sections.

Since June 2010, the joint project team has been involved in intense scientific desk research and analysis, requirement analyses, conceptual development as well as multiple participatory workshop sessions with external experts. So far, a solid foundation for the CoMo joint research project has been established. Roughly 1,000 ideas (or requirements) for tool functionalities and interfaces have been identified, classified and prioritised. Requirements have been classified according to applications (Futures Platform, Trend Database, Future Workshop, Prediction Markets, or app-interlinkages), type (functional, non-functional or constraint) and categorical purpose (e.g. user collaboration). A three dimensional framework consisting of (1) feasibility, (2) innovativeness and (3) importance was developed to narrow down and evaluate requirements. The outcome and status quo of our analysis together with related theoretical foundations are discussed in the following sections.

Futures Platform

Our Futures Platform is intended to serve as the users’ personalised login portal. Users can interactively individualise their Futures Platform according to their interests by, for instance, saving trend favourites, displaying related information or following a certain Prediction Market. This flexible and individualised structure offers an individual decision-making environment that increases ease and encourages overall use. Furthermore, users communicate directly through the Futures Platform to elaborate on future-relevant topics. The three applications Trend Database, Future Workshop and Prediction Market are linked to the platform and can be accessed from there; users can ask experts to help them get started and assist them in applying these tools.

Since the provided tools are of an innovative kind, the platform will include an educational self-learning package, structured in a curricular form. This educational part will reduce uncertainty and assure that newcomers to strategic planning and foresight can use the platform to build foresight competencies.

Trend Database

Our Trend Database concept represents the quantitative and qualitative pool of future-relevant knowledge that is provided to and by the cluster actors. A user may query future-oriented numbers, data and facts about specific logistics-related topics or weak signals, wildcards and disruptive events. Similarly to the Futures Platform, the Trend Database embodies elements for users to cooperate. By allowing and encouraging users to share individual wisdom, overall wisdom increases (Surowiecki 2004). Another characteristic feature of the Trend Database is the linkage of its architecture in three dimensions using a semantic structure: (1) the linkage of trends among each other, (2) the linkage of the Trend Database with the tools Prediction Market and Future Workshop, and (3) the linkage of the Trend Database with external data pools.

In sum, the Trend Database will perform the function of an intelligent unit within the CoMo that generates and links future-relevant knowledge facilitating cooperation among the stakeholders of the cluster and reducing complexity. The possibility to acknowledge trends early and systematically creates significant competitive advantages for the cluster and ensures sustainable management and action in the field of logistics.

Future Workshop

The Future Workshop app represents the element of CoMo where trends are systematically projected into individual futures and recommended options and actions can be derived. The fundamental idea of a Future Workshop was developed by Robert Jungk, Ruediger Lutz and Norbert R. Muellert in the 1970s (originally termed “Zukunftswerkstatt” in German; Jungk and Muellert 1988). Our internal analysis as well as experience from the expert workshops has shown that scenario planning, roadmapping, backcasting and Imagineering provide valuable elements for a Future Workshop. This led us to consider best practices from these four approaches in designing the Future Workshop in order to establish a valid and reliable web-based foresight process.

Our Future Workshop app will allow users to use the Trend Database as a discussion basis and digitally collaborate in global or private workshop environments. Stakeholders of the cluster, for example from a certain company, are led through a process of problem identification, innovation and creativity towards problem solving while spatial boundaries are overcome. In the process, Future Workshops will facilitate a future-oriented strategic logistics planning.

Prediction Market

The requirement analysis for the CoMo Prediction Market app revealed promising applications for stakeholders in the leading-edge cluster. Our CoMo Prediction Market app will supplement Future Workshops and the Trend Database by providing an innovative foresight method that generates futures knowledge and by complementing the CoMo platform. Prediction markets originally evolved in psephology and proved to provide significantly better forecasts than classical opinion polls – for this reason, they have recently been transferred into the business world (Ho and Chen 2007). In the Prediction Market app, CoMo users will be able to bet on the outcome of future events in a virtual environment. A single stock price represents the aggregated wisdom/knowledge of all market players while competition in the market ensures efficiency in aggregating asymmetrically distributed information.

Platform to Enhance Future-oriented Decision-making

The CoMo will provide a platform that utilises the cluster’s unique combination of more than 130 partners from business, academia and politics in order to share complementary resources, specifically to share knowledge that is relevant to their future-oriented decisions. The combination of a Trend Database, a Future Workshop app, and a Prediction Market app will facilitate cooperation, will provide a shared future-relevant knowledge base, and individual future-oriented decision support. Ultimately, the CoMo contributes to the major goal of the leading-edge cluster by enhancing the quality of the stakeholders’ future-oriented decisions.

Authors: Dr. Heiko von der Gracht

Stefanie Mauksch         

Philipp Ecken                                      

Christoph Markmann     

Gianluca De Lorenzis              

Eckard Foltin                            

Michael Münnich         

Dr. Christopher Stillings            

Sponsors: German Federal Ministry of Education and Research (BMBF)1
Type: National Foresight Project
Organizer: EBS Business School / Center for Futures Studies and Knowledge Management (CEFU)
Duration: 06/10-05/13 Budget: 2.3 m € Time Horizon: long-term Date of Brief: Oct 2011  


Download EFP Brief No. 203_Competitiveness Monitor


BMBF (2010). Germany’s Leading-Edge Clusters. Division for New Innovation Support Instruments and Programmes. Berlin, Bonn, Bundesministerium für Bildung und Forschung / Federal Ministry of Education and Research (BMBF).

ECM (2010). 100 Innovationen für die Logistik von Morgen. Mülheim an der Ruhr, Dortmund, EffizienzCluster Management GmbH.

Ho, T.-H. und K.-Y. Chen (2007). New Product Blockbusters: The Magic and Science of Prediction Markets California Management Review 50(1): 144-158.

Jungk, R. and N. Muellert (1988). Future workshops: How to Create Desirable Futures. London, Institute for Social Inventions.

Meixell, M. J. and V. B. Gargeya (2005). Global supply chain design: A literature review and critique. Transportation Research Part E: Logistics and Transportation Review 41(6): 531-550.

Ruske, K.-D., P. Kauschke et al. (2010a). Transportation and Logistics 2030 – Volume 2: Transport infrastructure — Engine or hand brake for global supply chains? Duesseldorf, PricewaterhouseCoopers.

Surowiecki, J. (2004). The Wisdom of Crowds: Why the Many Are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations. New York Doubleday.

von der Gracht, H. A., R. Vennemann et al. (2010). Corporate Foresight and Innovation Management: A Portfolio-Approach in Evaluating Organizational Development. Futures – The journal of policy, planning and futures studies 42(4): 380-393.