Business Modelling Agility: Turning ideas into business

Business Model Innovation is attracting more and more attention from business as well as from academics. Business Model Innovation deals with both technological and knowledge related changes that either may disrupt or sustain existing product/market strategies. Timing of Business Model Innovation both with regard to the right moment as well as speed of implementing competitive concepts becomes crucial. In this conceptual paper we discuss and evaluate possibilities for shortening the lead-time and increasing impact of Business Model Innovation aiming at low-end and new market disruptions. We are building our discussion on recent findings and identifying anomalies for further research by reflecting on exemplary business design cases

TOWARDS CROSS-ORGANISATIONAL E-GOVERNMENT: AN INTEGRATED APPROACH

One of the most challenging issues in current e-Government initiatives is the seamless exchange of information and the efficient collaboration between public administrations, companies and the private sector. Either from an intra- or cross-organisational point of view spanning processes across multiple authorities leads to a collaboration of autonomous units under consideration of law and regulations. Despite the organisational dimension current approaches are mainly technical solutions – e.g. interoperability frameworks. Within this paper we present an integrated approach which incorporates organisational aspects of the public sector and which supports the correspondent implementation of solutions for cross-organisational e-Government by adopting Model-Driven-Development practices

Challenges of Industry 4.0 in Hungarian agriculture

Although the technological revolutions in agricultural production are already at stage 5.0, the majority of Hungarian farmers are familiar with the achievements of 4.0 in theory, but most of them still use only elements of stage 2.0. The range of BigData applications goes far beyond production itself and even covers the entire supply chain. It plays a role in global issues such as food safety and sustainable management, and the results of the data from the system are used to improve efficiency. The development of the Internet of Things (IoT), which wirelessly connects agricultural production and supply chain members, will result in a lot of new, realtime data. An important challenge for these changes is to create new business models for farmers, but it also brings with it a number of open regulatory issues, such as data security and data ownership issues. Decision-making issues do not necessarily remain in the hands of farmers, but the data owner can have a major influence on the design and selection of alternatives. Sustainable integration of Big Data resources is a challenge, as it is crucial for the enterprise model. In order to introduce and apply new technologies, it is absolutely necessary to rethink and transform the existing processes. Developments should not be done in isolation, but together with innovative companies and farmers. It is important to keep in mind that in the future, the collection and sharing of data and the different work tools will be compatible with each other, and data transfer will be as simple as possible, keeping security in mind. The present study examines the theoretical effects of BigData applications in comparison to business models used in conventional technology along the business model research issue based on Lindgradt et al. (2009)

A modern approach for Threat Modelling in agile environments: redesigning the process in a SaaS company

Dealing with security aspects has become one of the priorities for companies operating in every sector. In the software industry building security requires being proactive and preventive by incorporating requirements right from the ideation and design of the product. Threat modelling has been consistently proven as one of the most effective and rewarding security activities in doing that, being able to uncover threats and vulnerabilities before they are even introduced into the codebase. Numerous approaches to conduct such exercise have been proposed over time, however, most of them can not be adopted in intricate corporate environments with multiple development teams. This is clear by analysing the case of Company Z, which introduced a well-documented process in 2019 but scalability, governance and knowledge issues blocked a widespread adoption. The main goal of the Thesis was to overcome these problems by designing a novel threat modelling approach, able to fit the company’s Agile environment and capable of closing the current gaps. As a result, a complete description of the redefined workflow and a structured set of suggestions was proposed. The solution is flexible enough to be adopted in multiple different contexts while meeting the requirements of Company Z. Achieving this result was possible only by analysing the industry’s best practices and solutions, understanding the current process, identifying the pain points, and gathering feedback from stakeholders. The solution proposed includes, alongside the new threat modelling process, a comprehensive method for evaluating and verifying the effectiveness of the proposed solution

Engineering Resilient Space Systems

Several distinct trends will influence space exploration missions in the next decade. Destinations are becoming more remote and mysterious, science questions more sophisticated, and, as mission experience accumulates, the most accessible targets are visited, advancing the knowledge frontier to more difficult, harsh, and inaccessible environments. This leads to new challenges including: hazardous conditions that limit mission lifetime, such as high radiation levels surrounding interesting destinations like Europa or toxic atmospheres of planetary bodies like Venus; unconstrained environments with navigation hazards, such as free-floating active small bodies; multielement missions required to answer more sophisticated questions, such as Mars Sample Return (MSR); and long-range missions, such as Kuiper belt exploration, that must survive equipment failures over the span of decades. These missions will need to be successful without a priori knowledge of the most efficient data collection techniques for optimum science return. Science objectives will have to be revised ‘on the fly’, with new data collection and navigation decisions on short timescales. Yet, even as science objectives are becoming more ambitious, several critical resources remain unchanged. Since physics imposes insurmountable light-time delays, anticipated improvements to the Deep Space Network (DSN) will only marginally improve the bandwidth and communications cadence to remote spacecraft. Fiscal resources are increasingly limited, resulting in fewer flagship missions, smaller spacecraft, and less subsystem redundancy. As missions visit more distant and formidable locations, the job of the operations team becomes more challenging, seemingly inconsistent with the trend of shrinking mission budgets for operations support. How can we continue to explore challenging new locations without increasing risk or system complexity? These challenges are present, to some degree, for the entire Decadal Survey mission portfolio, as documented in Vision and Voyages for Planetary Science in the Decade 2013–2022 (National Research Council, 2011), but are especially acute for the following mission examples, identified in our recently completed KISS Engineering Resilient Space Systems (ERSS) study: 1. A Venus lander, designed to sample the atmosphere and surface of Venus, would have to perform science operations as components and subsystems degrade and fail; 2. A Trojan asteroid tour spacecraft would spend significant time cruising to its ultimate destination (essentially hibernating to save on operations costs), then upon arrival, would have to act as its own surveyor, finding new objects and targets of opportunity as it approaches each asteroid, requiring response on short notice; and 3. A MSR campaign would not only be required to perform fast reconnaissance over long distances on the surface of Mars, interact with an unknown physical surface, and handle degradations and faults, but would also contain multiple components (launch vehicle, cruise stage, entry and landing vehicle, surface rover, ascent vehicle, orbiting cache, and Earth return vehicle) that dramatically increase the need for resilience to failure across the complex system. The concept of resilience and its relevance and application in various domains was a focus during the study, with several definitions of resilience proposed and discussed. While there was substantial variation in the specifics, there was a common conceptual core that emerged—adaptation in the presence of changing circumstances. These changes were couched in various ways—anomalies, disruptions, discoveries—but they all ultimately had to do with changes in underlying assumptions. Invalid assumptions, whether due to unexpected changes in the environment, or an inadequate understanding of interactions within the system, may cause unexpected or unintended system behavior. A system is resilient if it continues to perform the intended functions in the presence of invalid assumptions. Our study focused on areas of resilience that we felt needed additional exploration and integration, namely system and software architectures and capabilities, and autonomy technologies. (While also an important consideration, resilience in hardware is being addressed in multiple other venues, including 2 other KISS studies.) The study consisted of two workshops, separated by a seven-month focused study period. The first workshop (Workshop #1) explored the ‘problem space’ as an organizing theme, and the second workshop (Workshop #2) explored the ‘solution space’. In each workshop, focused discussions and exercises were interspersed with presentations from participants and invited speakers. The study period between the two workshops was organized as part of the synthesis activity during the first workshop. The study participants, after spending the initial days of the first workshop discussing the nature of resilience and its impact on future science missions, decided to split into three focus groups, each with a particular thrust, to explore specific ideas further and develop material needed for the second workshop. The three focus groups and areas of exploration were: 1. Reference missions: address/refine the resilience needs by exploring a set of reference missions 2. Capability survey: collect, document, and assess current efforts to develop capabilities and technology that could be used to address the documented needs, both inside and outside NASA 3. Architecture: analyze the impact of architecture on system resilience, and provide principles and guidance for architecting greater resilience in our future systems The key product of the second workshop was a set of capability roadmaps pertaining to the three reference missions selected for their representative coverage of the types of space missions envisioned for the future. From these three roadmaps, we have extracted several common capability patterns that would be appropriate targets for near-term technical development: one focused on graceful degradation of system functionality, a second focused on data understanding for science and engineering applications, and a third focused on hazard avoidance and environmental uncertainty. Continuing work is extending these roadmaps to identify candidate enablers of the capabilities from the following three categories: architecture solutions, technology solutions, and process solutions. The KISS study allowed a collection of diverse and engaged engineers, researchers, and scientists to think deeply about the theory, approaches, and technical issues involved in developing and applying resilience capabilities. The conclusions summarize the varied and disparate discussions that occurred during the study, and include new insights about the nature of the challenge and potential solutions: 1. There is a clear and definitive need for more resilient space systems. During our study period, the key scientists/engineers we engaged to understand potential future missions confirmed the scientific and risk reduction value of greater resilience in the systems used to perform these missions. 2. Resilience can be quantified in measurable terms—project cost, mission risk, and quality of science return. In order to consider resilience properly in the set of engineering trades performed during the design, integration, and operation of space systems, the benefits and costs of resilience need to be quantified. We believe, based on the work done during the study, that appropriate metrics to measure resilience must relate to risk, cost, and science quality/opportunity. Additional work is required to explicitly tie design decisions to these first-order concerns. 3. There are many existing basic technologies that can be applied to engineering resilient space systems. Through the discussions during the study, we found many varied approaches and research that address the various facets of resilience, some within NASA, and many more beyond. Examples from civil architecture, Department of Defense (DoD) / Defense Advanced Research Projects Agency (DARPA) initiatives, ‘smart’ power grid control, cyber-physical systems, software architecture, and application of formal verification methods for software were identified and discussed. The variety and scope of related efforts is encouraging and presents many opportunities for collaboration and development, and we expect many collaborative proposals and joint research as a result of the study. 4. Use of principled architectural approaches is key to managing complexity and integrating disparate technologies. The main challenge inherent in considering highly resilient space systems is that the increase in capability can result in an increase in complexity with all of the 3 risks and costs associated with more complex systems. What is needed is a better way of conceiving space systems that enables incorporation of capabilities without increasing complexity. We believe principled architecting approaches provide the needed means to convey a unified understanding of the system to primary stakeholders, thereby controlling complexity in the conception and development of resilient systems, and enabling the integration of disparate approaches and technologies. A representative architectural example is included in Appendix F. 5. Developing trusted resilience capabilities will require a diverse yet strategically directed research program. Despite the interest in, and benefits of, deploying resilience space systems, to date, there has been a notable lack of meaningful demonstrated progress in systems capable of working in hazardous uncertain situations. The roadmaps completed during the study, and documented in this report, provide the basis for a real funded plan that considers the required fundamental work and evolution of needed capabilities. Exploring space is a challenging and difficult endeavor. Future space missions will require more resilience in order to perform the desired science in new environments under constraints of development and operations cost, acceptable risk, and communications delays. Development of space systems with resilient capabilities has the potential to expand the limits of possibility, revolutionizing space science by enabling as yet unforeseen missions and breakthrough science observations. Our KISS study provided an essential venue for the consideration of these challenges and goals. Additional work and future steps are needed to realize the potential of resilient systems—this study provided the necessary catalyst to begin this process

How to Predict the Innovation to SMEs? Applying the Data Mining Process to the Spinner Innovation Model

Funding: This paper is financed by National Funding awarded by the FCT—Portuguese Foundation for Science and Technology to the project «UIDB/04928/2020» and NECE-UBI, R&D unit funded by the FCT —Portuguese Foundation for the Development of Science and Technology, Ministry of Education and Science, University of Beira Interior, Management and Economics Department, Estrada do Sineiro, 6200-209 Covilhã, Portugal.Despite the importance of small and medium-sized enterprises (SMEs) for the growth and development of companies, the high failure rate of these companies persists, and this correspondingly demands the attention of managers. Thus, to boost the company success rate, we may deploy certain approaches, for example predictive models, specifically for the SME innovation. This study aims to examine the variables that positively shape and contribute towards innovation of SMEs. Based on the Spinner innovation model, we explore how to predict the innovation of SMEs by applying the variables, namely knowledge creation, knowledge transfer, public knowledge management, private knowledge management and innovation. This study applied the data mining technique according to the cross industry standard process for data mining (CRISP-DM) method while the Statistical Package for the Social Sciences (SPSS_Version28) served to analyze the data collected from 208 SME employees in Oporto, Portugal. The results demonstrate how the Spinner innovation model positively influences the contributions of the SMEs. This SME-dedicated model fosters the creation of knowledge between internal and external interactions and increases the capacity to predict the SME innovation by 56%.info:eu-repo/semantics/publishedVersio

Anforderungen für einen ML- und plattformgestützten Simulationsservice

Today, with the rapid development of new technologies, many industries have adopted them to enhance their performance. Among them, additive manufacturing is known as a rapid manufacturing process that can produce products in a single step, reducing time to market. Since 2019, COVID-19 has caused significant negative impacts on the global supply chain (SC), including shortages of medical goods. Therefore, an agile and flexible medical SC is required. While machine learning (ML) methods are known for using big data to gain valuable insights through forecasting, simulation enables unlimited if-then scenarios to make informed decisions in optimising SC operations. The combination methods between ML and simulation in solving SC issues has not been investigated at a sufficient level. This paper, therefore, aims to explore the advantages of coupling ML with simulation techniques in the SC field by conducting a systematic literature review. Through an expert survey, requirements for a ML and platform-based simulation service will be investigated from a technical point of view to develop a suitable use case in the future

Conceptual Foundations for a Service-oriented Knowledge and Learning Architecture: Supporting Content, Process and Ontology Maturing

Abstract: The knowledge maturing model views learning activities as embedded into, interwoven with, and even indistinguishable from everyday work processes. Learning is understood as an inherently social and collaborative activity. The Knowledge Maturing Process Model structures this process into five phases: expressing ideas, distributing in communities, formalizing, ad-hoc learning and standardization. It is applicable not only for content but also to process knowledge and semantics. In the MATURE IP two toolsets will be develop that support the maturing process: a personal learning environment and an organisation learning environment integrating the levels of individuals, communities and organisation. The development is guided by the SER theory of seeding, evolutionary growth and reseeding and is based on generally applicable maturing services

Measuring Success for a Future Vision: Defining Impact in Science Gateways/Virtual Research Environments

Scholars worldwide leverage science gateways/VREs for a wide variety of research and education endeavors spanning diverse scientific fields. Evaluating the value of a given science gateway/VRE to its constituent community is critical in obtaining the financial and human resources necessary to sustain operations and increase adoption in the user community. In this paper, we feature a variety of exemplar science gateways/VREs and detail how they define impact in terms of e.g., their purpose, operation principles, and size of user base. Further, the exemplars recognize that their science gateways/VREs will continuously evolve with technological advancements and standards in cloud computing platforms, web service architectures, data management tools and cybersecurity. Correspondingly, we present a number of technology advances that could be incorporated in next-generation science gateways/VREs to enhance their scope and scale of their operations for greater success/impact. The exemplars are selected from owners of science gateways in the Science Gateways Community Institute (SGCI) clientele in the United States, and from the owners of VREs in the International Virtual Research Environment Interest Group (VRE-IG) of the Research Data Alliance. Thus, community-driven best practices and technology advances are compiled from diverse expert groups with an international perspective to envisage futuristic science gateway/VRE innovations

Tradespace and Affordability – Phase 1

One of the key elements of the SERC’s research strategy is transforming the practice of systems engineering – “SE Transformation.” The Grand Challenge goal for SE Transformation is to transform the DoD community’s current systems engineering and management methods, processes, and tools (MPTs) and practices away from sequential, single stovepipe system, hardware-first, outside-in, document-driven, point-solution, acquisition-oriented approaches; and toward concurrent, portfolio and enterprise-oriented, hardware-software-human engineered, balanced outside-in and inside-out, model-driven, set-based, full life cycle approaches.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046).This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046)