Value-Cocreation

Service Science Service Science, Management & Engineering Service Science, Management, Engineering & Design

Paul P. Maglio UC Merced and IBM Research [email protected] [email protected]

What is service science anyway?

Service science, management, and engineering (SSME) is a term introduced by IBM to describe service science, an interdisciplinary approach to the study, design, and implementation of services systems – complex systems in which specific arrangements of people and technologies take actions that provide value for others. More precisely, SSME has been defined as the application of science, management, and engineering disciplines to tasks that one organization beneficially performs for and with another.

2 IBM has 433,362 employees worldwide 2010 Financials 21% of IBM’s revenue § Revenue - $ 106.9B in growth market countries; growing at § Net Income - $ 15.9B 13% in late 2010 § EPS - $ 13.44 § Net Cash - $19.8B More than 40% of IBM’s workforce conducts business away IBM operates in 170 countries from an office around the globe Number 1 in patent generation for 18 100 Years of Business consecutive years ; & Innovation 5,896 US patents awarded in 2010 The Smartest Machine On Earth 9 time winner of the 5 Nobel President’s National Laureates Medal of Technology & Innovation - latest “Let’s Build a Smarter award for Blue Gene Planet" Supercomputer

3 IBM Centennial – 100 Years of Innovation

IBM Centennial Film: 100 People & 100 Years http://www.youtube.com/watch?v=39jtNUGgmd4

4 IBM Icon of Progress: Invention of Service Science

http://www.technologyreview.com/printer_friendly_article.aspx?id=14403 http://www.almaden.ibm.com/asr/resources/facsummit.pdf http://www.businessweek.com/technology/content/jan2005/tc20050121_8020.htm

5 IBM’s business

2011 Pretax Income Mix Revenue Growth by Segment

SYSTEMS SERVICES (AND FINANCING) 120 100 80 Services 16% 60 41% 40 Software 20

Revenue($B) Systems 44% 0

1982 1988 1994 1998 2004 2006 2007 2008 2009 2010 2011 SOFTWARE Year

IBM Annual Reports

6 U.S. Employment

Agriculture

Manufacturing

Service

Fitzsimmons, J. A., & Fitzsimmons, M. J. (2008). Service management: Operations, strategy, and information technology (6th ed.). New York: McGraw-Hill.

7 Moments of Truth

Last year, each of our 10 million customers came in contact with approximately five Scandinavian Airlines employees, and this contact lasted an average of 15 seconds each time. Thus, Scandinavian Airlines is “created” in the minds of our customers 50 million times a year, 15 seconds at a time. These 50 million “moments of truth” are the moments that ultimately determine whether Scandinavian Airlines will succeed or fail as a company.

8 Delivering Happiness

In 1999, at the age of 24, Tony Hsieh sold LinkExchange, the company he co-founded, to Microsoft for $265 million. He then joined Zappos as an advisor and investor, and eventually became CEO, where he helped us grow from almost no sales to over $1 billion in gross merchandise sales annually, while simultaneously making Fortune magazines annual Best Companies to Work For list. In November 2009, Zappos.com, Inc. was acquired by Amazon.com in a deal valued at $1.2 billion.

9 IKEA Innovation

What’s the big idea at IKEA?

The IKEA business idea is to offer a wide range of home furnishings with good design and function at prices so low that as many people as possible will be able to afford them.

http://www.ikea.com/ms/en_US/about_ikea/our_vision/better_life.html

10 IBM’s Service Businesses

11 Modern Service Businesses

12 The World is Complex Service System

Communication Transportation $ 3.96 Tn $ 6.95 Tn Education $ 1.36 Tn

Water $ 0.13 Tn Leisure / Recreation / Electricity Clothing $ 2.94 Tn $ 7.80 Tn

Global system-of-systems $54 Trillion (100% of WW 2008 GDP)

Healthcare $ 4.27 Tn

Infrastructure $ 12.54 Tn Legend for system inputs Same Industry Business Support IT Systems Finance Govt. & Safety Energy Resources $ 4.58 Tn Food $ 5.21 Tn $ 4.89 Tn Machinery Materials Trade

IBM analysis based on OECD data. 13 Information Technology Systems

Actual Application Architecture for Consumer Electronics Company

14 Total cost of ownership

Storage: What $3 million bought in 1984 and 2000.

$3 mil $3 mil $1 million $2 million Storage Storage Administration Administration $2 $2

$1 $1

$2 million $1 million System System

1984 2000

J. P. Gelb, "System-managed storage," IBM Systems Journal, Vol 28, No. 1, 1989 pp. 77-103. "Storage on Tap: Understanding the Business Value of Storage Service Providers", ITCentrix report, March 2001. "Server Storage and RAID Worldwide" (SRRD-WW-MS-9901), Gartner Group/Dataquest report, May 1999.

15 Why this shift in cost?

§ Computer speed and capabilities increase exponentially

§ Computer-cost decrease exponentially

§ Human speed and capabilities relatively constant

§ Thus, human-cost now greater proportion of TCO

Moore’s Law Computer Cognitive Laws abilities

human

abilities

1950 1990 2030 2000BC 1950 1990 2030

16 Autonomic computing

§ Self-configuring Adapt automatically to the dynamically changing environments § Self-optimizing Monitor and tune resources automatically § Self-protecting Anticipate, detect, identify, and protect against attacks from anywhere § Self-healing Discover, diagnose, and react to disruptions

Kephart, J. O. & Chess, D. M. (2003). The Vision of Autonomic Computing. Computer 36(1), 41-50.

17 What do those expensive people do?

§ Is it performance tuning?

§ Is it error recovery?

§ Is it system deployment, update, or sunset?

§ Is it organizing teams and planning work?

18 Field Studies (2001 – 2006)

Web Hosting Southbury § Web Hosting, Data Management, Operating 1 Week System, Security, and Storage Web Hosting Southbury 1 Week

Data Management Poughkeepsie 3 Days § 16 Visits, 6 sites Data Management Charlotte 3 Days Web Hosting Boulder 3 Days + 1 Eve § Surveys (~ 100 people)

Web Hosting Boulder 1 Week § Observations (~ 50 days)

Operating system Boulder § Video (~ 350 hours) 3 Days

Security Urbana § Interviews (~ 50 people) 3 Days

Storage § Boulder Diary (~ 10 months) 3 Days

Security Urbana 1 Week § Qualitative and quantitative analysis

19 George George was in trouble. A seemingly simple deployment was taking all morning, and there seemed no end in sight. His manager kept coming in to check on his progress, as the customer was anxious to have the deployment done. He was supposed to be leaving for a goodbye lunch for a departing co-worker, adding to the stress. He had called in all kinds of help, including colleagues, an application architect, technical support, and even one of the system developers. He used email, instant messaging, face-to-face contacts, his phone, and even his office mate’s phone to communicate with everyone. And George was no novice. He had been working as a Web-hosting administrator for three years, and he had a bachelor’s degree in computer science. But it seemed that all the expertise being brought to bear was simply not enough. Why was George in trouble?

Haber, E., Kandogan, E. & Maglio, P. P (2011). Collaboration in system administration. Communications of the ACM, 54(1), 46-53.

20 George’s problem

21 George’s problem

PDWeb_conﬁg -i -m

internal port: Unique port number for inter-Access Manager server communication. // Create new WebSeal instance PDWeb_config –i E1 –m 7137

// Check if new instance is shown pdadmin> server list … webseald-E1 …

// Create junctions pdadmin> server task webseald-E1 create –t tcp –h 123.456.789.100 –I –s –b ignore Could not perform the administration request. Error: Could not connect to server. (status 0x1354a324)

22 23 George’s interactions

24 George’s time

25 Shameless plug… Information technology is the foundation of modern life. When talking on the phone, using the Web, or getting money from an ATM, we rely on computers, networks, and databases – systems of information technologies. What keeps these systems running? The answer is people: computer system administrators. Most of the time, the people are invisible. They work out of sight, down in the data-center, twenty-four hours a day, seven days a week. We only notice them when there is a problem – when we cannot get our email or access our money. Most of the time, the systems are remarkably robust. How do system administrators keep systems running as well as they do? And how can we help them be better at their jobs? Taming Information Technology answers these and other questions. Through real-life stories, it documents how dynamic arrangements of people and machines work together to tame complex information technology by developing and adapting tools and practices to create effective work environments and keep systems running. Available August, 2012 26 George’s time

27 Interactions are key

As more 21st century companies come to specialize in core activities and outsource the rest, they have greater need for workers who can interact with other companies, their customers, and their suppliers. Raising the productivity of employees whose jobs can’t be automated is the next great performance challenge – and the stakes are high. Companies that get that right will build complex talent-based competitive advantages that competitors won’t be able to duplicate easily – if at all.

Johnson, B., Manyika, J., & Yee, L. (2005). The next revolution in interactions. McKinsey Quarterly, 4, 20-33.

28 Jobs Change

Expert Thinking 10 Expert Thinking

Complex Communication 5 Complex Communication

Routine Manual 0 Routine Manual Non-routine Manual Non-routine Manual -5 Routine Cognitive Routine Cognitive -10 1969 1974 1979 1984 1989 1994 1999

Levy, F, & Murnane, R. J. (2004). The New Division of Labor: How Computers Are Creating the Next Job Market. Princeton University Press.

29 T-shaped Professionals

Many team-oriented service projects completed (resume: outcomes, accomplishments & awards)

Many disciplines Many systems (understanding & communications) (understanding & communications)

(analytic thinking & problem solving) solving) problem & thinking (analytic solving) problem & thinking (analytic Deep in one discipline one discipline Deep in Deep in one systemDeep in

30 IBM’s business

2011 Pretax Income Mix Revenue Growth by Segment

SYSTEMS SERVICES (AND FINANCING) 120 100 80 Services 16% 60 41% 40 Software 20

Revenue($B) Systems 44% 0

1982 1988 1994 1998 2004 2006 2007 2008 2009 2010 2011 SOFTWARE Year

IBM Annual Reports

31 What is Service?

Spohrer, J. & Maglio, P. P. (2010). Toward a science of service systems: Value and symbols. In P. P. Maglio, C. A. Kieliszewski & J. C. Spohrer (Eds.), Handbook of Service Science. New York: Springer.

32 Service Science is about Building Common Language

An analogy can be made with Computer Science. The success of CS is not in the definition of a basic science (as in physics or chemistry) but more in its ability to bring together diverse disciplines, such as mathematics, electronics and psychology to solve problems that require they all be there and talk a language that demonstrates common purpose.

Service Science may be the same thing, only bigger: an interdisciplinary umbrella that enables economists, social scientists, mathematicians, computer scientists and legislators (to name a small subset of the necessary disciplines) to cooperate to achieve a larger goal - analysis, construction, management and evolution of the most complex systems we have ever attempted to construct.

33 Service Science is the Study of Value Cocreation

Service science is an interdisciplinary approach to study, improve, create, and innovate in service. Service is value cocreation – broadly speaking, useful change that results from communication, planning, or other purposeful interactions between distinct entities. Service science is the systematic search for principles and approaches that can help understand and improve all kinds of value cocreation.

Maglio, P. P. Kieliszewski, C. A. & Spohrer, J. C. (2010). Why a handbook?. In P. P. Maglio, C. A. Kieliszewski & J. C. Spohrer (Eds.), Handbook of Service Science. New York: Springer.

34 Service System Thinking

Forms of A. Service Provider Service Relationship B. Service Client (A & B co-create value) • Individual • Individual • Organization • Organization • Public or Private • Public or Private

Forms of Service Interventions (A on C, B on C)

Forms of Forms of Responsibility Relationship Ownership Relationship (A on C) (B on C)

C. Service Target: The reality to be transformed or operated on by A, for the sake of B

• People, dimensions of • Business, dimensions of • Products, goods and material systems • Information, codified knowledge

Spohrer, J., Maglio, P. P., Bailey, J. & Gruhl, D. (2007). Steps toward a science of service systems. Computer, 40, 71-77.

35 Grand Challenge: Laws of Service?

§ 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. 36 Service-dominant logic

§ Service is the application of competences for the benefit of another entity

§ Service is exchanged for service

§ Resource Resource Value is always co-created Integrator/ Integrator/ Beneficiary Beneficiary

(“Firm”) (“Customer”) § Goods are appliances for delivery

§ All economies are service economies

§ All businesses are service businesses

Vargo, S. L. & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68, 1 – 17.

37 Resources are the building blocks of service systems First foundational premise Rights No-Rights of service science Physical 1. People 2. Technology Service system entities dynamically configure 4.. Shared four types of resources Not-Physical 3. Organizations Information

The named resource is Physical Formal service systems can contract or Informal service systems can promise/commit Not-Physical Trends & Countertrends (Evolve and Balance): (physicists resolve disputes) Informal <> Formal Social <> Economic The named resource has Political <> Legal Rights Routine Cognitive Labor <> Computation Routine Physical Labor <> Technology or Transportation (Atoms) <> Communication (Bits) No-Rights Qualitative (Tacit) <> Quantitative (Explicit) (judges resolve disputes within their jurisdictions) 38

Value propositions are the building blocks of service system networks

Second foundational premise of service science Value propositions coordinate & motivate resource access

Stakeholder Measure Pricing Basic Value Perspective Impacted Decision Questions Proposition Service system entities (the players) Reasoning calculate value from multiple stakeholder perspectives 1.Customer Quality Value Should we? Model of customer: Do (Revenue) Based (offer it) customers want it? Is there a market? How large? Growth rate? A value propositions can be viewed as a request from 2.Provider Productivity Cost Can we? Model of self: Does it play (Profit) Plus (deliver it) to our strengths? Can we one service system to another deliver it profitably to customers? Can we to run an algorithm continue to improve? (the value proposition) 3.Authority Compliance Regulated May we? Model of authority: Is it (Taxes and (offer and legal? Does it compromise from the perspectives of Fines) deliver it) our integrity in any way? Does it create a moral multiple stakeholders according hazard? to culturally determined 4.Competitor Sustainable Strategic Will we? Model of competitor: Does Innovation it put us ahead? Can we (Substitute) (invest to value principles. (Market make it so) stay ahead? Does it share) differentiate us from the The four primary stakeholder competition? perspectives are: customer, provider, authority, and competitor 39 Access rights are the building blocks of service system ecology

Third foundational premise Competitor Provider Customer Authority of service science S P C A

(substitute) The access rights associated with OO OO customer and provider resources LC LC are reconfigured by mutually SA SA agreed to value propositions PA PA value-proposition relationships change-experience dynamic-configurations time § Access rights  Access to resources that are owned service = value-cocreation outright (i.e., property) B2B  Access to resource that are leased/ B2C contracted for (i.e., rental car, home B2G ownership via mortgage, insurance provider resources G2C customer resources policies, etc.) Owned Outright G2B Owned Outright  Shared access (i.e., roads, web Leased/Contract G2G Leased/Contract information, air, etc.) Shared Access C2C Shared Access  Privileged access (i.e., personal thoughts, Privileged Access C2B Privileged Access inalienable kinship relationships, etc.) C2G *** 40 Symbols are the building blocks of value cocreation

Fourth foundational premise of service science

Service system entities compute and coordinate actions with others through symbolic processes of valuing and symbolic processes of communicating.

Service is value cocreation . § Symbols guide both internal behavior and mediate interactions.

Service system entities reason about value. § Service system entities often rely on symbolic reasoning about value. Value cocreation is a kind of joint activity. § Value cocreation depends on coordination of activities across Joint activity depends on communication. individuals, organizations, and firms – often intimate relationships that involve sharing resources, risks, and rewards.

Reasoning about value and communication are § Coordination of action across a network depends on information flow. (often) effective symbolic processes. § Improvements in processes of valuing are symbolic processes that can be

shared and agreed to by service system entities. 41 Principles of Service Science

Rights No-Rights

1. People 2. Technology Physical Service system entities 4.. Shared 3. Organizations Not-Physical Information dynamically configure

Stakeholder Measure Pricing Questions Reasoning four types of resources Perspective Impacted 1.Customer Quality Value Should we? Model of customer: Based Do customers want it? Service system entities 2.Provider Productivity Cost Can we? Model of self: Does Plus it play to our compute value from multiple strengths? 3.Authority Compliance Regulated May we? Model of authority: stakeholder perspectives Is it legal? 4.Competitor Sustainable Strategic Will we? Model of competitor: Innovation Does it put us ahead? The access rights associated with entity S P C A resources are reconfigured by mutually agreed to value propositions

Service system entities compute and coordinate actions with others through symbolic processes of valuing and symbolic processes of communicating.

42 Two basic processes: “valuing” and “coordination”

§ Across entities, coordination of activities across individuals, organizations, and firms – often intimate relationships that involve sharing resources, risks, and rewards.

§ Within entities, processes of valuing enable decisions about what to coordinate Service system: Customer-provider dyad Service system Service constellation The World is a Complex Service System

Communication Transportation $ 3.96 Tn $ 6.95 Tn Education $ 1.36 Tn

Water $ 0.13 Tn Leisure / Recreation / Electricity Clothing $ 2.94 Tn $ 7.80 Tn

Global system-of-systems $54 Trillion (100% of WW 2008 GDP)

Healthcare $ 4.27 Tn

Infrastructure $ 12.54 Tn Legend for system inputs Same Industry Business Support IT Systems Finance Govt. & Safety Energy Resources $ 4.58 Tn Food $ 5.21 Tn $ 4.89 Tn Machinery Materials Trade IBM analysis based on OECD data.

The food system is complex, and interventions often have unintended and deleterious effects on food security, or have major consequences that affect GHS emissions. Agricultural, economic, and climate modelers must compare their models more systematically, share results, and integrate their work to meet the needs of policy-makers.

Challenge

§ Develop technology to help experts across diverse domains collaborate to solve complex policy problems. ? § No single dataset, model, or knowledge base can capture all factors ? ?

§ Compose individual models and data from different disciplines to develop comprehensive models for understanding different policies.

§ It’s hard! § Domain experts have different worldviews, use different vocabularies, sit in different organizations, and develop models using different platforms and technologies Splash: A Computational Platform for Collaborating to Solve Real-World Problems

Paul P. Maglio IBM Research – Almaden

Peter J. Haas Cheryl A. Kieliszewski Patricia G. Selinger Wang-Chiew Tan Ignacio Terrizzano

Splash Smarter Planet Platform for Analysis and Simulation of Health www.almaden.ibm.com/asr/projects/splash Splash

Fundamental idea: Loosely couple models via data exchange

Simulation model

Statistical model

Decision/optimization model

Dataset

Data transformation

Reuse heterogeneous models and heterogeneous data that are created and curated by different domain experts. Splash Vision A platform and service through which IBM and partners can integrate existing data, models, and simulations to gain insight needed for complex decision making related to health policy, planning, and investment.

Key Research Question Can such integration of independently created deep- domain models be made feasible, practical, flexible, cost-effective, attractive, and usable? Health is a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.

Worldwide obesity has more than doubled since 1980. In 2008, 1.5 billion adults were overweight. In 2010, 43 million children under five were overweight.

State of the Art in Health System Model Combination Public Policy Investment Decision Support Obesity incidence and treatment

Model of access Model of to physical activity transportation Diet choices and costs model

Dunn and Bradstreet US Census data business data

Insight: Nearby location of large chain grocery stores reduced obesity rates Tax incentives for chain stores to move to obesity “hotspots”?

Chaloupka FJ, Powell LM. Price, availability, and youth obesity: evidence from Bridging the Gap. Prev Chronic Dis 2009; 6(3). Obesity Example

Data source Dataflow Simulation model Dataflow Data Transformation

Transportation (VISUM simulation model) Geospatial alignment GIS data

Buying and eating (Agent-based simulation model) Time alignment Demographic and data merging data Exercise (Stochastic discrete-event simulation)

Facility data BMI Model (Differential equation model)

Results Sample Results

If we open a new “healthy” food store in a “bad” neighborhood…

BMI by rich/poor BMI by rich/poor

poor poor rich rich

Without traffic model Including traffic model

* Many assumptions, sample only, your mileage may vary … Splash

Models are loosely coupled via data exchange Metadata - Model inputs and outputs SPLASH REPOSITORY contains - Access and execution - Data schemas - Model and data locations SPLASH MODULES Provide - Model and data semantics models and data Model and Data Registration

Model, Data, and Mapping describes Discovery Use models and data Model and Data Composition Model Data Model Execution Composite Model Experiment Manager Model

Collaborative Reporting and Data Mapping Visualization

Multi-disciplinary users Splash Interface

§ Schema mapping design tools § State-of-the-art: Semi-automate the generation of structural transformations of data from one schema to another. § Enhance to semi-automate the generation of simulation-specific transformations. Data Transformations

§ Zone-Coordinate Mapper § Output of transportation model: zone-by-zone travel time matrix § Input to buying-and-eating model: coordinate- by-coordinate travel time matrix. § A Java program that maps a zone-by-zone travel time matrix to a coordinate-by- coordinate travel time matrix.

§ Join Demographics Mapper § Enhanced Clio++ tool to interactively design data transformation.

Join Demographics mapping process Join Demographics – time alignment Join Demographics – structural alignment Obesity Example

Data source Dataflow Simulation model Dataflow Data Transformation

Transportation (VISUM simulation model) Geospatial alignment GIS data

Buying and eating (Agent-based simulation model) Time alignment Demographic and data merging data Exercise (Stochastic discrete-event simulation)

Facility data BMI Model (Differential equation model)

Results Multi-level, End-to-End Modeling

Socio-Economic Models 4 Business Models 3 Healthcare Ecosystem (Society) 5

System Structure Lever1 Lever2 Lever3 (Organizations) 2 Policy “Flight Simulator”

Delivery Operations Careflow Models (Processes) (Flow of Patients, Money, Information) 1 Clinical Practices 6 (People) Personalized Medicine (Targeted interventions) Disease Progression Models

Rouse, W. B. & Cortese, D. A. (2010). Introduction, in W. B. Rouse & D. A. Cortese (Eds.), Engineering the System of Healthcare Delivery. IOS Press. Cross-domain, Syndemic Modeling

Richard Rothenberg et al., Georgia State University, 2011 Composite model for traffic safety

Emergency Data Collision Heatmaps Impact of… x …on collisions

Emergency Response IBM Deep Thunder Model (Client) weather model Heavy Snow Game Day IBM Megaffic traffic simulation model

Weather Data

Collision Data Volume Data Pay Day Combined Geographic Model (ESRI) Intervention Scenarios Legend Component Model A: Roadway design changes GIS Data Demographic Data Data Source Transformations B: Placement variable speed limits

Data Flow C: Enforcement Research Opportunities

§ Data search → model-and-data search § Find compatible models, data, and mappings (using metadata) § Involves semantic search technologies, repository management, privacy and security

§ Data integration → model integration § Simulation-oriented data mapping § Time, space, unit alignment § Hierarchical models with different resolutions § Complex data transformations (e.g., raw simulation output to histogram)

§ Query optimization → simulation-experiment optimization § Optimally configure workflow among distributed data and models Model A Model B § Factoring common operations across different mappings in the workflow § Avoiding redundant computations across experiments § Statistical issues: managing pseudorandom numbers and Monte Carlo replications Some Deep Problems

§ Causality approximation § Fixed-point + perturbation Transportation Buying & Eating approaches Model Model § System support & ftnnn( )= Λ11( ftg ( ),− ( t )) § Theoretical support & gtnnn( )= Λ21( f− ( t ), gt ( ))

& ! ft( )= ΛΔ1 ( ft ( ), gnt ( )) $ § ' for tntn∈Δ(& ,(+ 1)Δ t) Deep collaborative analytics gt&( ) f ( n t ), gt ( ) = ΛΔ2 ( ))$ § Visualizing and mining the results § Understanding and explaining results: § Dashboarding of parameters § Provenance § Root-cause analysis § Sensitivity analysis § Trusting results § Model validation § ManyEyes++, Swivel++ Splash Publications

§ Tan, W. C., Haas, P. J., Mak, R. L., Kieliszewski, C. A., Selinger, P., Maglio, P. P., Glissmann, S., Cefkin, M. & Li, Y. (2012). Splash: A platform for analysis and simulation of health. Proceedings of ACM SIGHIT International Health Informatics Symposium (IHI 2012).

§ Cefkin, M., Glissmann, S., Haas, P. Kieliszewski, C. A., Maglio, P. P., Mak, R. L., Selinger, P., Tan, W. C. (2011). SPLASH: A Progress Report on Combining Simulations for Better Health Policy. INFORMS Healthcare 2011, Montreal, Canada.

§ Cefkin, M., Kieliszewski, C. A. & Maglio, P. P. (2011). When are calories like furniture? Modeling service systems to improve health. Service Research and Innovation Institute Global Conference, 2011.

§ Haas, P. J., Maglio, P. P., Selinger, P. G. & Tan, W. C. (2011). Data is dead… Without what-if models. Proceedings of the VLDB Endowment, vol 4.

§ Cefkin, M., Glissmann, S., Haas, P. Jalali, L., Maglio, P. P.,Selinger, P., Tan, W. C. (2010). Splash: A Progress Report on Building a Platform for a 360 Degree View of Health. 5th INFORMS Workshop on Data Mining and Health Informatics, Austin, TX.

§ Maglio, P. P., Cefkin, M., Haas, P., & Selinger, P. (2010). Social factors in creating an integrated capability for health system modeling and simulation. In S-K Chai, J.J. Salerno, & P.L. Mabry (Eds.), Advances in Social Computing: Third International Conference on Social Computing, Behavioral Modeling, and Prediction, SBP10, New York: Springer, 44-51. The World is a Complex Service System

Communication Transportation $ 3.96 Tn $ 6.95 Tn Education $ 1.36 Tn

Water $ 0.13 Tn Leisure / Recreation / Electricity Clothing $ 2.94 Tn $ 7.80 Tn

Global system-of-systems $54 Trillion (100% of WW 2008 GDP)

Healthcare $ 4.27 Tn

Pervasive Force: Leveraging Technology to Advance Service

Strategy Development Execution Priorities Priorities Priorities

Fostering Service Stimulating Effectively Branding Infusion and Growth Service Innovation and Selling Services

Improving Well-Being Enhancing the Service Enhancing through Experience through Service Design Transformative Service Cocreation

Optimizing Measuring and Creating and Maintaining Service Networks Optimizing the Value of a Service Culture and Value Chains Service

Ostrom, A. L. et al. (2010). Moving forward and making a difference: Research priorities for the science of service. Journal of Service Research, 13(1), 4-36.

78 Progress toward Service Science

Worldwide movement in service science that has led to substantial increases in government funding for service research and innovation (> $1B today) creation of a new discipline (establishment of a vast number university courses, programs, degrees, research centers, books, journals, articles, professional associations) impact on government leaders, academic leaders, and the general public (support received and press generated) Details The Service Science movement has influenced ~$1B service research investment worldwide. The IBM- Cambridge SSME report advocated nations double investment in service research by 2015. More than 18 governments held service innovation workshops or produced service innovation roadmaps. South Korea released a report indicating intention to double by 2012 (from ~100M/year). Government investment for service research and service innovation in Europe is more than 500 million Euros (Finland, Germany, and the European Commission). >400 universities in 55 countries created SSME-related programs and >25 SSME-related research centers were established in the last five years. Professional organizations added service subgroups. Journals on service science were established, and journal special issues appeared. Service Science is now a full-fledged journal of INFORMS. Springer has two book series (in business and LNCS) on service science. More than 500 SSME-related press articles, including NY Times, Wall Street Journal, International Herald Tribune, USA Today, Chronicle of Higher Education, Investors Business Daily, Business Week, Information Week.

79 Journals and More

http://www.research.ibm.com/journal/sj47-1.html http://cacm.acm.org/magazines/2006/7 http://www.springerlink.com/content/1617-9846/9/2/ http://www.springerlink.com/content/1617-9846/8/1/ http://www.springer.com/series/8080 http://www.springer.com/series/8680 http://www.informs.org/Pubs/Service-Science http://www.springer.com/business+%26+management/journal/12927 http://www.emeraldinsight.com/products/journals/journals.htm?id=ijqss http://www.inderscience.com/browse/index.php?journalCODE=ijssci http://www.scirp.org/journal/jssm/ http://www.igi-global.com/journal/international-journal-service-science-management/1132 http://www.aicit.org/aiss/home/index.html

80 So what is service science? § Service is value-cocreation, that is, useful changes that result from communication, planning, or other purposeful interactions between distinct entities.

§ A service system is a collection of entities and interactions that cocreate value, that is, a set of distinct configurations of resources (including people, organizations, shared information, and technology) that are better off working together than working alone.

§ Service Science aims to create a body of knowledge that describes, explains, predicts, and improves value- cocreation between entities as they interact, that is, relying on methods and standards used by a community to account for observable phenomenon with conceptual frameworks, theories, models, and laws that can be empirically tested.

§ So the object of study value-cocreation, the basic abstraction is the service system, and the ultimate goal is develop methods and theories that can be used to explain and improve value-cocreation in service systems.

81 Some Collaborators

§ Jim Spohrer, Cheryl Kieliszewski, Wendy Murphy

§ Eser Kandogan, Eben Haber, John Bailey

§ Peter Haas, Cheryl Kieliszewski, Pat Selinger, Wang-Chiew Tan, Ignacio Terrizzano

Thanks to all!

82 83