1 / 71

Service Science as a Science of the Artificial

Service Science as a Science of the Artificial. Josephine Cheng, IBM Almaden Research Center, Jim Spohrer, IBM Research December 8, 2008. Support from IBM and NSF grant IIS-0527770 2006-09 is gratefully acknowledged. Outline & Thesis. Service Worlds That Compute Value

trenton
Download Presentation

Service Science as a Science of the Artificial

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Service Science as a Science of the Artificial Josephine Cheng, IBM Almaden Research Center, Jim Spohrer, IBM Research December 8, 2008 Support from IBM and NSF grant IIS-0527770 2006-09 is gratefully acknowledged.

  2. Outline & Thesis • Service Worlds That Compute Value • Sciences of the Artificial • Service Science Progress, or successful universal structural change in artificial worlds, is characterized by higher density value-cocreation interactions (service) between entities that are capable of judging, or computing the relative merits of, alternative possible future artificial worlds. Today, we are entities in one possible artificial world with money (“universal exchange medium”), rights (“universal rule of law”), and literacy (“universal education”) as three examples of progress. Money, rights and literacy are judged as progress because they support higher density value-cocreation interactions between entities. Service science calls these entities, whether people, businesses, or nations, service system entities. They interact via knowledge-based value-propositions, both formal and informal, connected into large service system networks and part of a global service system ecology. Service science studies artificial worlds, service worlds that compute value. Physics, chemistry, biology, etc. study natural worlds.

  3. Outline of next section • Service Worlds That Compute Value*,** • Economics & social science • Operations, marketing & management • Computing, software & information • Systems, networks & engineering • Psychology & experience design • Sciences of the Artificial • Service Science * = All of these service worlds matter to IBM ** = Service phenomena are value-cocreation phenomena

  4. Service Worlds: Economics and Social ScienceGlobal change in what people do and how value is created Ten Nations Total 50% of World Wide Labor A = Agriculture, G = Goods, S = Services US Employment History & Trends 2005 1980-2005 PC Age United States (A) Agriculture: Value from harvesting nature (G) Goods: Value from making products (S) Services: Value from enhancing the capabilities of things (customizing, distributing, etc.) and interactions between things The largest labor force migration in human history is underway, driven by global communications, business and technology growth, urbanization and low cost labor International Labor Organization

  5. Service Worlds: Economics and Social ScienceInformation services is where recent growth is Estimated world (pre-1800) and then U.S. Labor Percentages by Sector The Origin of Wealth by Eric D. Beinhocker 2M years as hunting clans/bands 10K years as farm families 200 years as factory workers 60 years (so far) as knowledge workers in organizations and now digital networks The Pursuit of Organizational Intelligence, By James G. March Estimations based on Porat, M. (1977) Info Economy: Definitions and Measurement

  6. Service Worlds: Economic and Social ScienceKnowledge-intensive service activities is growing most US Gross Domestic Product Products Services Material 11% 30% Information & Organization 9% 50% • Based on Uday Karmarkar, UCLA • (Apte & Karmarkar, 2006)

  7. Service Worlds: Economics and Social ScienceHistorical cross-over point recently achieved In 2006 the service sector’s share of global employment overtook agriculture for the first time, increasing from 39.5% to 40%. Agriculture decreased from 39.7% to 38.7%. The industry sector accounted for 21.3% of total employment. - International Labour Organization http://www.ilo.org/public/english/region/asro/bangkok/public/releases/yr2007/pr07_02sa.htm

  8. “…America’s service sector had a $3.7 billion trade surplus with China...” “China is now the ninth largest purchaser of American services.” Service Worlds: Economics and Social SciencePractical consequences found in trade statistics Wall Street Journal Oct 30th 2007

  9. Service Worlds: Operations, Marketing, and Management A classification of services Teboul, J. (2006). Service is Front Stage: Positioning Services for Value Advantage, Palgrave.

  10. Service Worlds: Operations, Marketing, and Management Front stage and back stage Teboul, J. (2006). Service is Front Stage: Positioning Services for Value Advantage, Palgrave.

  11. Service Worlds: Operations, Marketing, and Management The service-profit chain The service-profit chain establishes the link between profitability, customer satisfaction, and employee satisfaction. Heskett, J. L., Jones, T. O., Loveman, G. O., Sasser, W. E., Schlesinger, L. A. (1994). Putting the service profit chain to work. Harvard Business Review, 72, 164 – 174.

  12. Service Worlds: Operations, Marketing, and ManagementCustomer input is critical & major source of process variation Sampson, S. E. (2001). Understanding service businesses: Applying principles of the unified services theory (2nd ed.). New York: John Wiley & Sons.

  13. Service Worlds: Operations, Marketing, and Management Scaling and learning curves are different in service operations IBM Revenue and Profit Mix (2005): Scaling & learning curves are different for IT manufacturing and IT services Service Innovation: How to invest to create, improve, and scale up services?

  14. Service Worlds: Operations, Marketing, and Management Scaling service operations Spohrer, J., Maglio, P. P., Bailey, J. & Gruhl, D. (2007). Steps toward a science of service systems. Computer, 40, 71-77.

  15. Reaching the Goal: How Managers Improve a Services Business Using Goldratt’s Theory of Constraints John Arthur Ricketts, IBM Service Worlds: Operations, Marketing, and Management Service innovation deals with creating, improving, and scaling • “Theory of Constraints (TOC) gets its name from the fact that all enterprises are constrained by something. If they weren’t they could grow as large and as fast as they wanted… So the first step in applying TOC is to figure out precisely where the constraints are….” • Three types of constraints: • Internal • External • Interface

  16. WSDL Structure Service Port (e.g. http://host/svc) Port Binding (e.g. SOAP) Binding Abstract interface portType operation(s) inMesage outMessage Service Worlds: Computing, Software & Information Web Services

  17. Service Worlds: Computing, Software & Information Service Oriented Architecture (SOA)

  18. New Hybrid Compute & Information Services Appear in the Cloud Service Worlds: Computing, Software & Information Cloud and IT Service Management – ITIL v3

  19. 1 Traditional 2 Open Source 3 Outsourcing 4 Hybrid 5 Hybrid+ 6 SaaS 7 Internet $4000/user (one time) $0/user $4000/user (one time) $4000/user (one time) $300/user/ month <$100/user/ month Ads Transactions Embedded (< $10/user/ Month) SW Support Management $800/user/ year $1600/user/ year $800/user/ year $800/user/ year Bid <1300/user /month $150/user/ month @H @C @H @C @H @C RedHat MySQL… IBM EDS… ORCL Blackbaud.. Callidus… Webex Salesforce WoW.. eBay Google Amazon… Service Worlds: Computing, Software & Information Tim Chou’s “Model Seven” Book – Business Models From Tim Chou’s Model Seven

  20. Value networks andservice ecosystems Service Worlds: Systems, Networks, and Engineering Ecology of service system entities that interact

  21. IBM Systems Journal Service Worlds: Systems, Networks, and Engineering Conceptual model of service value network Rouse and Basol (2008)

  22. 20th 21st Century Century Service Worlds: Systems, Networks, and Engineering IBM’s evolution to a globally integrated enterprise (GIE) A globally integrated enterprise -- business in a connected world The international era -- exporting The multinational era -- replicating • IBM is formally established in 1924 ($11M in revenue; 3384 employees) with sales operations in Canada, Latin America, Europe & Asia; 3 manufacturing facilities completed in Europe by end of decade • “Mini” IBM sales companies established in all major countries of operation, each with full-blown back-office functions e.g., HR, Finance, Marketing, Procurement • Seeded mfg in select countries to temper risk of nationalization • Over 200,000 employees Services Delivery (incl. ISC) across 50 delivery centers in 21 countries • 12 Global Shared Services Units • Nearly 2/3 of revenue outside US

  23. Optimization Service Worlds: Systems, Networks, and Engineering Supply chain and workforce optimizations

  24. Service Worlds: Psychology & Experience DesignBehavioral science for better service design

  25. Service Worlds: Psychology & Experience Design The Psychology of Waiting Lines

  26. Peer Insights Service Innovation by Design Service Worlds: Psychology & Experience Design Service Innovation by Design From Peer Insights “Seizing the Whitespace”

  27. Summary of last section • Service Worlds That Compute Value* • Economics & social science • Operation, marketing & management • Computing, software & information • Systems, networks & engineering • Psychology & experience design • Sciences of the Artificial • Service Science * = All of these service worlds matter to IBM

  28. Outline of next section • Service Worlds That Compute Value • Sciences of the Artificial • Natural and Artificial Worlds • Economic Rationality: Adaptive Artifice • Psychology & Learning • Design • Social Planning • Complexity • Service Science

  29. Sciences of the Artificial: Natural and Artificial Worlds • The world we live in today is much more a man-made, or artificial, world than a natural world. • Four indicia that distinguish the artificial from the natural: • Artificial things are synthesized (though not always or usually with full forethought) by human beings. • Artificial things may imitate appearances in natural things while lacking, in one or more respects, the reality of the latter. • Artificial things can be characterized in terms of functions, goals, adaptation. • Artificial things are often discussed, particularly when they are being designed, in terms of imperatives (“ought to”) as well as descriptives. • The computer is a member of an important family of artifacts called symbol systems, or more explicitly, physical symbol systems. Physical symbol systems can often substitute for things in the natural world and each other.

  30. Sciences of the Artificial:Economic Rationality: Adaptive Artifice • Economics exhibits in purest form the artificial component in human behavior, in individual actors, business firms, markets, and the entire economy. • We can interpret this bare-bones theory of the firm either positively (as describing how business firms behave) or normatively (as advising how to maximize profits). • Today several branches of applied science assist the firm to achieve procedural rationality. One of them is operations research (OR); another is artificial intelligence (AI). Mathematics and heuristic search are important tools for rational actors. • Game theory’s most valuable contribution has been to show that rationality is effectively undefinable when competitive actors have unlimited computational capabilities for outguessing each other, but that problem does not arise acutely in a world, like the real world, of bounded rationality. • Each species in the ecosystem is adapting to an environment of other species evolving simultaneously with it. The evolution and the future of such systems can only be understood from a knowledge of their histories.

  31. Sciences of the Artificial:Psychology and Learning • Human beings, viewed as behaving systems, are quite simple. The apparent complexity of our behavior over time is largely a reflection of the complexity of the environment in which we find ourselves. • …behavior is adapted to goals, hence is artificial, hence reveals only those characteristics of the behaving system that limit the adaptation. • The adaptiveness of the human organism… makes it an elusive and fascinating target of our scientific inquiries – and the very prototype of the artificial. • Computers have transported physical symbol systems from the platonic heaven of ideas to the empirical world of actual processes carried out by machines or brains, or by the two of them working together.

  32. Sciences of the Artificial:Design • In the past much, if not most, of what we knew about design was intellectually soft, intuitive, informal, and cook-booky. • The artificial world is centered precisely on this interface between the inner and the outer environments; it is concerned with attaining goals by adapting the former to the latter. The proper study of those who are concerned with the artificial is the way in which that adaptation of means to environments is brought about – and central to the process of design itself. • In substantial part, design theory is aimed at broadening the capabilities of computers to aid design… • Design is a process of search for alternatives in a space of possible worlds.

  33. Sciences of the Artificial:Social Planning • As to the founding fathers it is instructive to examine their own views of their goals, reflected in The Federalist… What is striking about these documents is their practical sense and awareness they exude of the limits of foresight about large human affairs. • As we look back on such design efforts and their implementation, and we contemplate the tasks of design that are posed in the world today, our feelings are mixed. • We are energized by the great power our technological knowledge bestows on us. • We are intimidated by the magnitude of the problem it creates or alerts us to. • We are sobered by the very limited success – and some disastrous failure – of past efforts to to design on the scale of whole societies. • Data about the future – predictions – are commonly the weakest points in the armor of fact. • Who is the client? It may seem peculiar to ask, “Who is the client?” when speaking of the design of large social systems.

  34. Science of the Artificial:Complexity • Roughly, by complex system I mean one made up of a large number of parts that have many interactions. • …in such systems the whole is more than the sum of the parts in the weak but important pragmatic sense that, given the properties of the parts and the laws of their interactions, it is not trivial to infer the properties of the whole. • Business firms, governments, and universities all have a clearly visible parts-within-parts structure. • But formal organizations are not the only, or even the most common, kind of social hierarchy. Almost all societies have elementary units, called families, which may be grouped into villages or tribes, and these into larger groupings, and so on. • Information theory explains organized complexity in terms of the reduction of entropy (disorder) that is achieved when systems (organisms, for example) absorb energy from external sources and convert it to pattern or structure. • Feedback controls shows how a system can work towards goals and adapt to a changing environment, thereby removing mystery from teleology. • The notion of substituting a process description for a state description of nature has played a central role in the development of modern science. Dynamics laws… provided the clue for the simple description of the complex.

  35. Summary of last section • Service Worlds That Compute Value • Sciences of the Artificial • Natural and Artificial Worlds • Economic Rationality: Adaptive Artifice • Psychology & Learning • Design • Social Planning • Complexity • Service Science

  36. Outline of next section • Service Worlds That Compute Value • Sciences of the Artificial • Service Science • Stages of scientific maturity • Grand challenge problem • First small steps

  37. Service Science: Stages of Scientific MaturityCollect and classify phenomena Early Stage Collect and Classify (Biology) Carl Linnaeus, the father of modern taxonomy and ecology, and a pioneer of the science of biology Mature Stage Unify and Mathematize (Electro-Magnetism)

  38. A. Informal Service Systems B. Formal Service Systems 1. Social Systems Human Systems/Sociotechnical Systems Human Cultures 2. Political Systems Governed Systems Value Systems 3. Economics Systems Markets and Organizations Firms or Hierarchies Economic Institutions Gray Markets 4. Legal Systems Legislative, Judicial, Executive Separation 5. Organizational Systems Managed Systems Open Source Communities 6. Information Systems Linguistic Systems Mathematical Systems Physical Symbol Systems 7. Engineered Systems Technological Systems Designed Systems 8. Ecological Systems Evolved Systems Nature’s Services Service Science: Stages of Scientific MaturityService system entities are diverse and complex A. 1. 2. 8. 3. B. 7. 4. 6. 5. “The goal of science is to make the wonderful and complex understandable and simple – but not less wonderful.” – Herb Simon, The Sciences of the Artificial

  39. CBM: Component Business Model WBM and RUP: Work Practices & Processes SOA: Technical Service-Oriented Architecture Key Performance Indicators (KPIs) IBM IBV: Component Business Models Service Science: Stages of Scientific MaturityIBM has begun to systematically map and classify diverse service systemsindustry by industry, component by component, measure by measure… IEEE Computer, Jan 2007

  40. Service Science: Stages of Scientific MaturityComponent business modeling (CBM) • Component Business Modeling is a framework for analyzing and modeling a business • for organizing and grouping business activities into components • In a component business map: activities are grouped along two coordinates: • business competencies (columns) and accountability levels (rows) Cherbakov, L. Galambos, G, Harishankar, R., Kalyana, S. & Rackham, G. (2005). Impact of service orientation at the business level. IBM Systems Journal, 44, 653 – 658.

  41. Service Science: Stages of Scientific MaturityComponent business modeling (CBM) • Component Business Modeling is a framework for analyzing and modeling a business • for organizing and grouping business activities into components • In a component business map: activities are grouped along two coordinates: • business competencies (columns) and accountability levels (rows) Cherbakov, L. Galambos, G, Harishankar, R., Kalyana, S. & Rackham, G. (2005). Impact of service orientation at the business level. IBM Systems Journal, 44, 653 – 658.

  42. Component Business Model to Help Decompose Your BusinessExperience and Know-how from Thousands of Client Engagements Component Business Modeling tool 2.0 • 70+ maps supporting 17 industries • 23 enhanced with key performance indicators (KPI) • Over 2,000 trained CBM specialists armed with the CBM tool • 30 CBM patents filed • CBM tool license available to clients Integrates with WebSphere Business Modeler Presentation to Gartner in October 2007, by R. Leblanc

  43. Integrating Component Business Models with Industry Process Models + = Component Business Models (CBM) and Tool Industry Process Models in WBM, built by BPM CoE, leveraging APQC’s Process Classification Framework Result: business transformation engagements delivered more quickly, through more industry-specific insights and more powerful CBM Tool IBM is bringing together its Business Process Management Center of Excellence (BPM CoE), IBM Research, and the Global Business Solution Center (GBSC) to map Component Business Models (CBM) to Industry Process Models Presentation to Forrester in November 2007, by T. Rosamilia

  44. Standard operating procedures are passed down from one generation to the next Successful processes can be copied, though transfer is not costless Learning curves Patent protection Evolution of firms … best understood through an examination of history Service Science: Stages of Scientific MaturityMechanisms of economic evolution “If the adaptation of both the business firm and biological species to their respective environments are instances of heuristic search… we will still have to account for the mechanisms that bring the adaptation about. In biology the mechanism is located in the genes and their success reproducing themselves. What is the gene’s counterpart in the business firm? Nelson and Winter suggest that business firms accomplish most of their work through standard operating procedures – algorithms for making daily decisions that become routinized and are handed down from one generation of executives and employees to the next.” - Herb Simon, Sciences of the Artificial

  45. Service Science: Stages of Scientific MaturityMechanisms of economic evolution – need for a tool • Every decade both HPC and PC platforms increase • complex simulation capabilities by 1000x. • - HPC: (2000 106), (2010 109), (2020 1012), (2030 1015) … • - PC: (2000 103), (2010 106), (2020 109), (2030 1012) … CBM-based Industry Simulations - 2013? 15 12 Heart Simulation Log Entities 9 Universe Simulation Brain Simulation Projected Simulation Capability 6 Earth Simulator 2030 2000 2010 2020

  46. Service Science: Grand Challenge ProblemDiscover a Moore’s Law for service system improvement Service System/Network 1. People 2. Technology 3. Shared Information 4. Organizationsconnected by value propositions Computational System More win-win interactions, more value Requires investment roadmap More transistors, more powerful Requires investment roadmap

  47. Service Science: Grand Challenge Problem Towards a Moore’s Law – “A Smarter Planet” • Computational power doubles at a predictable rate. • Are there analogous capability-doubling laws that apply in services? • Suppose that traces of human activity in particular service systems double at some rate, and that these human activity data lead to specific opportunities for improved or increased service productivity or quality. • Consider Amazon.com: The quality of recommendations depends on accurate statistics – the more purchases made, the better the statistics for recommendations. • Three improvement “laws” that might be applicable in services: • The more an activity is performed (time period doubling, demand doubling), the more opportunities to improve. • The better an activity can be measured (sensor deployment doubling, sensor precision doubling, relevant measurement variables doubling) and modeled, the more opportunities to improve. • The more activities that depend on a common sub-step or process (doubling potential demand points), the more likely investment can be raised to improve the sub-step.

  48. Supply:Knowledgecreation rate Demand:Customeradoption rate Servicesystem/networkgrowth Howtoinvest? 100 Television Electricity Telephone Radio Automobile VCR 50 PC % Adoption Cellular Internet 0 25 50 75 100 125 150 Years Service Science: Grand Challenge Problem Towards a Moore’s Law – accelerating adoption

  49. Service Science is emerging as a distinct field. Its vision is to discover the underlying logic of complex service systems and to establish a common language and shared frameworks for service innovation. To this end, an interdisciplinary approach should be adopted for research and education on service systems. Service Science: Grand Challenge Problem Towards a Moore’s Law – “Succeeding through Service Innovation” • For education: Enable graduates from various disciplines to become T-shaped professionals or adaptive innovators; promote SSME education programmes and qualifications; develop a modular template-based SSME curriculum in higher education and extend to other levels of education; explore new teaching methods for SSME education. • For research: Develop an interdisciplinary and intercultural approach to service research; build bridges between disciplines through grand research challenges; establish service system and value proposition as foundational concepts; work with practitioners to create data sets to understand the nature and behaviour of service systems create modelling and simulation tools for service systems. • For business: Establish employment policies and career paths for T-shaped professionals; review existing approaches to service innovation and provide grand challenges for service systems research; provide funding for service systems research; develop appropriate organisational arrangements to enhance industry-academic collaboration; work with stakeholders to include sustainability measures. • For government: Promote service innovation and provide funding for SSME education and research; demonstrate the value of Service Science to government agencies; develop relevant measurements and reliable data on knowledge- intensive service activities; make public service systems more comprehensive and citizen-responsive; encourage public hearings, workshops and briefings with other stakeholders to develop service innovation roadmaps. http://www.ifm.eng.cam.ac.uk/ssme/

  50. Service Science: Grand Challenge Problem Towards a Moore’s Law – Call to create Service Innovation Roadmaps (SIR) reports 5. Call for actions 1. Emerging demand 2. Define the domain 3. Vision and gaps 4. Bridge the gaps Service Innovation Growth in service GDP and jobs Service quality & productivity Environmental friendly & sustainable Urbanisation & aging population Globalisation & technology drivers Opportunities for businesses, governments and individuals Service Systems Customer-provider interactions that enable value cocreation Dynamic configurations of resources: people, technologies, organisations and information Increasing scale, complexity and connectedness of service systems B2B, B2C, C2C, B2G, G2C, G2G service networks Stakeholder Priorities Education Research Business Government The white paper offers a starting point to - Service Science To discover the underlying principles of complex service systems Systematically create, scale and improve systems Foundations laid by existing disciplines Progress in academic studies and practical tools Gaps in knowledge and skills Develop programmes & qualifications Skills & Mindset Encourage an interdisciplinary approach Knowledge & Tools Develop and improve service innovation roadmaps, leading to a doubling of investment in service education and research by 2015 Employment & Collaboration Policies & Investment Glossary of definitions, history and outlook of service research, global trends, and ongoing debate “Succeeding through Service Innovation” Whitepaper: A Framework for Progress (http://www.ifm.eng.cam.ac.uk/ssme/)

More Related