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Fakultät für Informatik Technische Universität München A Quantitative Perspective on Systems of Systems Formerly: Upscaling for Systems of Systems Astrid,

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Presentation on theme: "Fakultät für Informatik Technische Universität München A Quantitative Perspective on Systems of Systems Formerly: Upscaling for Systems of Systems Astrid,"— Presentation transcript:

1 Fakultät für Informatik Technische Universität München A Quantitative Perspective on Systems of Systems Formerly: Upscaling for Systems of Systems Astrid, Iman, Jens, Judith, Steffen, Florian 22.10.2009, 12:20 1 091022-Dagstuhlintern

2 Glossary of Terms  Systems = Organizational Systems (social & economic) + IT Systems  Key Performance Indicators (KPIs) are (arbitrary) useful measures for business performance Different from technical throughput, response time, resource consumption intern091022-Dagstuhl2

3 Geschäfts-Service-Schicht Infrastruktur-Service-Schicht Localizing the Problem: Aligning Business & IT  You can’t manage what you can’t measure  Precision of metrics vs. understandability & commitment & control  Tension between players in the business layer (department egoism)  Gap: Linking and integrating multiple models from the contributing domains intern091022-Dagstuhl3 Business Layer Application & Information Layer Infrastructure Layer Strategies & Objectives KPI Business Service Layer Infrastructure Service Layer Quality Metrics ?!

4 Problem Statement Simply combining existing metrics of multiple systems is not sufficient to arrive at useful recommendations for key performance indicators (KPIs) in organizations. Reasons  Missing common terminology  Complexity in nature of the socio-technical systems we are measuring Example  Scalability as an aggregated metrics might be more attractive/useful than individual fine-grained metrics  Cost, skills & time are “universal measures” that are neglected today Possible solutions  Interaction between KPI view & systems quantity attributes Business to IT mapping of requirements IT to Business mapping of measurements intern091022-Dagstuhl4

5 Illustration by a specific example intern091022-Dagstuhl5 Enterprise Architecture Management IT Architecture Management Synchronization Management Project Portfolio Management Strategy & Goal Management IT Project Lifecycle Define Measure Plan Measure Prioritize & Commit Implement Measure Deploy & Migrate Requirements Management Identify Measure SCENARIO: Internet Book shop Stakeholder: Sales Mgr. for the US KPI: # of Customers in the US Goal: Increase in 2010 by 50% Current Value: 1 Mio Customers

6 Illustration by a specific example  Inspect / update capability map  Collect proposals from marketing  New kind of products mp3 sales through immediate delivery leads to 40% new customers of in 2010  # of MP3 downloads per customer characteristics of work Cost: 1M € in 2010 (setup & operations) Earnings: 100K €in 2010 Time: Shop opening in January 2010: “Immediate”  buy not make  Competing project proposals (modify existing processes) Availability of performance data through monitoring in the past  Are there limiting factors in the existing systems? Missing functionality: Search Engine with Engl. Thesaurus Lack of capacity (# of Engl. Books)  Allocate Budget intern091022-Dagstuhl6

7 Lessons learned  Business information can to some degree provide useful information for performance management  Can we refine the high-level indicators, e.g. to provide statistical distributions over time and deviations, e.g. Christmas sales, marketing campaign on opening?  How do we estimate value / risk?  How do we handle risk? Oversizing, flexible architecture, simple architecture?  Currently: Low quality, usage, storage, and analysis of logged data  Main motivation for logging today is bug tracking, rarely other goal-directed logging  It is not always clear, which data is relevant for which goal. This can depend on the business context the system is used in.  Need for service management interfaces in addition to service interfaces to perform monitoring & adaptation.  Hypothesis: Fuzzy quantitative models might be more helpful than classical process description languages tuned for automated process execution. intern091022-Dagstuhl7

8 What can Software Engineering bring to the table?  possible research topics  Quantitative annotation of visual process models (instead of Excel / Visio separately) Similar to tool to describe software processes & analysis of graph, now also applied to medical processes (Lee Osterweil & Lori Clarke, UMass) Dependency Structure Matrixes (DSM) could also applied to business architecture (describe or constrain dependencies between business units / business processes)  Bring together experts with knowledge about marketing models and experts with knowledge about performance models. Is it possible to formalize the marketing models, e.g. to use them in model transformations?  At which level of abstraction could simulation based on behavioral specifications take place? intern091022-Dagstuhl8


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