IMPC - Topic 3 Space 4.0 and the Evolution of the (Aero) Space - PowerPoint PPT Presentation

IMPC - Topic 3 Space 4.0 and the Evolution of the (Aero) Space Sector Sergio Bras, Fabio Fabozzi, Shahrzad Hosseini, Stephan Jahnke, Narayan Nagenda, Kobkaew Opasjumruskit, Alice Pais de Castro, Ting Peng, Malgorzata Solyga, Jeffrey Stuart, Tatiana Volkova

Topic 3 Members Kobkaew Opasjumruskit Computer Scientist at DLR Ph.D., Electrical and Computer Alice Pais de Castro Narayan Nagenda Engineering working on smart system and semantic technology ESA/ESTEC, YGT in Technology Management Space engineer & space policy Major in Physics and Master in Astrophysics and Experience in supply chain management Space Instrumentation Cofounder at satsearch.co Ting Peng Stephan Jahnke Graduate in Aeronautical and Astronautical Systems Engineer at DLR Science & Technology Graduate in space engineering DLR, Working for on-board system software Research in design processes Experience in embedded system Shahrzad Hosseini Malgorzata Solyga Aerospace Engineer Graduate in Aerospace/Space Engineering PhD Candidate in Space Engineering & Neuroscience ESTEC, Working as thermal engineer ESA / ESTEC , Working in the Lunar Exploration team In general experience in thermal engineering Fabio Fabozzi +research of thermal-related technologies ISAE-SUPAERO/Airbus D&S, PhD candidate Jeffrey Stuart in GNSS signal processing Graduate in space engineering JPL, Mission design & navigation systems Experience in AOCS and MBSE Research, mission development, & operations, particularly for SmallSat Sergio Bras Tatiana Volkova ESA/ESTEC, AOCS Performance Engineer in EO Ph.D.,Electrical and Computer Engineering focused EPFL, Switzerland, Ph.D. candidate, space architecture on position and attitude estimation ENSAPLV, Paris, MSc space architecture 2 Bauman MSTU, Moscow, MSc space engineering

Space 4.0 Global Connectivity Launch Capability Miniaturization Spacecraft 3

Space 4.0 Artificial Intelligence Model Based Systems Engineering Demographics & Disruptive Technologies Inspiration 4

Artificial Intelligence A.I. 5

Prospect of evolution of AI in PM (LAHMANN, 2018) Chatbots Machine learning (ML)-based Autonomous PM Integration & KEY ELEMENT (PM) assistants (CA) Automation Combining the previous elements Enabling predictive analytics and giving Integration and automation with Streamlining and automating tasks advice to the project manager based on additional human- through integration and process what worked in past projects computer interaction automation >give the increased visibility into >enhance the quality of >enhance the quality of PM >take over basic PM tasks the projects smaller, standardized processes >relieve project teams of PROSPECT >enhance the quality of projects >reduce the effort and labour repetitive tasks decision-making >reduce the quantity of costs human interaction Project managers can be Project manager will be ML will give intelligent advice on Autonomous project project scheduling and tasks focused on complex project increasingly replaced by managers seem unlikely activities project assistants within the next 10-20 years Where we expect AI to support project management skills? TECHNICAL PM YES STRATEGIC & BUSINESS MANAGEMENT 6 LEADERSHIP NO

Infusing AI algorithms into PM tasks (LAHMANN, 2018) COST RISK PERFORMANCE PROJECT MANAGEMENT ESTIMATION MANAGEMENT PLANNING Knowledge Based Expert System (KBSE) Estimate the suitable markup to Estimate the probability of Assess claims and provide expert decisions Provide estimates of the duration occurrence for project risks increase the possibility of winning and resource requirements for project activities tenders Artificial Neural Network (ANN) Predict the possible cost overruns Mimic the human procedure of Predict the performance of future projects Automate the sequencing of risk evaluation and adaptation based on the project parameters based on the project parameters project activities based on functional requirements Genetic Algorithm (GA) Get accurate forecast of project cost Supports simulation of risk Analyze past projects and resources to Optimize the schedule of factors produce an optimal performance construction project activities from past data management Fuzzy Logic (FL) Optimize the cost-time trade-offs in Assess risks in construction Improve project management efficiency in Determine project priorities in the projects to model probability construction projects portfolio management process construction projects distributions 7

AI SWOT analysis STRENGTHS WEAKNESSES - No human creativity INTERNAL - Reduce costs and mistakes, time to treat project/clients requests - Not able to balance the capabilities and emotions of diverse set of humans (empathy) - Facilitates routine operations - Analyze risks and lead them toward success - Require special training for the team (online - Improves the analysis method - Keep projects on time and on budge t courses, corporate training) - Require continuous monitoring/adaptation - Additional research needed into ethical, legal, and social aspects THREATS OPPORTUNITIES - Significant disruption to business models - Integration with Apps not used in PM field EXTERNAL - Requires a large investment (e.g., Even.com predictive budgeting tool) - Over-reliance on AI as a sole source of truth - Incorporate AI into PM portfolio as a way of - Security, reliability and confidence in the AI facilitating predictive steering of complex system transformation projects - Development of standards and platforms for - Global cloud services testing 8

Conclusions & recommendations Conclusions ● AI will assist, not replace, project managers ● AI can help increase project success rates ● AI can add real strategic value and drive positive change in PM and business transformations ● Scaling AI is a company-wide transformation ● AI implementation in PM requires a large company investments Recommendations ● Company must invest in highly qualified data scientists, systems engineers, solution architects when integrate AI ● The project managers needs to master AI based tools to be successful ● Company should conduct the trainings and seminars for a team prior the implementation of AI ● Respect clever distribution of the roles between AI assistant tools and project managers 9

Model Based Systems Engineering 10

MBSE introduction NASA presentation, Daniel L Dvorak, Model-Centric Engineering, MBSE 3 pillars implementation (Badache N. & Roques P., 2018). part I: An introduction to model-based System Engineering, 2013 11

MBSE benefits for PM across the project life cycle Key benefits for PM: ● Consistency ● Traceability ● Reuse ● Information sharing ● Knowledge capture 12 [Hause, 2013]

MBSE maturity status and prospects ● MBSE is still at early stage of maturation ● Forecast: MBSE transition needs another 10 - 15 years What needs to be done in the meantime: ● Encourage team and project managers! ● Improve interoperability! ● Support by INCOSE and OMG! MBSE Maturity Road Map, INCOSE IW (Chakraborty, 2016) 13

MBSE interoperability issues Proposed implementation roadmap Mediators between tools Interoperability issues between MBSE tools concern: ● Modeling ● Simulation ● Collaboration activities Semantic MBSE models & APIs Current solutions for resolving the model exchange issue (Lu, 2018): ● Linked data → add semantic meaning Globally harmonized ● Meta-model integration → create common flexible templates dataset ● Tool-based integration via Application Programming Interfaces (APIs) → common standard / protocol Advanced learning algorithms 14

Recommendations for PM ● Create Guidelines to help implement MBSE in Donec risus dolor porta venenatis ● 03 Lorem ipsum dolor sit amet at organisations Support ● Pharetra luctus felis nec at adipiscing ● ● Provide comprehensive training for future users Proin in tellus felis volutpat ● Provide assistance during implementation Include MBSE in existing standards (e.g. ECSS / ● NASA PM Handbook) Standardisation Define standards for the three main elements of ● MBSE specifically for PM (--> MBPM) ● Intensify collaboration (e.g. with INCOSE, OMG or IPMA) to increase efficiency & learn best Collaboration practices ● Support step-wise introduction of MBSE & MBPM Testing / Communication ● Verify and communicate the benefits! 15

Disruptive Technologies and PM in Space 4.0 16

Traditional Space vs. Space 4.0 Traditional Space Space 4.0 ● High reputation agencies ● Venture capital ● Large projects ● Market needs Key Drivers ● Human missions ● Disruptive technologies ● Very risk averse ● Acceptance of risks ● Limited adoption of new technology ● Fast / iterative development Characteristics ● Dependent on political environment ● Lower costs ● Mostly scientific oriented ● Open-market oriented 17