Eric Yu & Alexei Lapouchnian

Faculty of Information and Department of Computer Science

University of Toronto

First Int’l Workshop on Advancement from Confluence of Emerging Technologies (ACET 2013) – at CASCON 2013, Markham, Ontario, Canada

Nov 19, 2013

Architecting the Enterprise to Leverage a Confluence of

Emerging Technologies

A Confluence of Emergent Technologies Mobile and social network Low-cost sensor networks Cloud and service-oriented computing Big data & advance analytics

How to leverage this confluence of emerging technologies?

[ACET workshop CfP, @CASCON’13]

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Relentless disruptive technological advances

[Manyika Dobbs Chui 2013 McKinsey Global Inst.] E.Yu 4

IT-enabled business trends

[Chui Bughin 2013 Ten-IT enabled business trends for the decade ahead. McKinsey]

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[HBR Oct 2012] 6

Momentous Shifts Dramatic rise of “sense & interpret” technologies

The (even more) crucial role of data and software in organizations

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BDA is revolutionizing “sense & interpret”

Data

Decide

Strategize

Interpret

Visualize

Act

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Seconds Machine-scale

Minutes, Hours, Days Human-scale

Data

Decide

Strategize

Interpret

Visualize

Act Conception

Requirements

Design

Construction

Operation

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Previous rounds of the Digital Revolution gave us powerful

“execution” technologies

Seconds Machine-scale

Weeks, months Human-scale

Closing the loop

Inevitable pressure for IT development/evolution/alignment to occur on same time scale as BDA/BI sense-interpret-decide-act cycle.

Increasing drive towards machine-scale

Data

Decide

Strategize

Interpret

Visualize

Act Conception

Requirements

Design

Construction

Operation Performance monitoring

External environment.

sensing

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Seconds Machine-scale

Weeks, months Human-scale

Seconds Machine-scale

Minutes, Hours, Days Human-scale

Momentous shifts Dramatic expansion of sense-&-interpret capabilities (Newly powerful) sense-&-interpret technologies + (already

powerful) execution technologies => more responsive, adaptive organizations

Adaptive loops will shift (from human-scale) towards machine-scale, (from design-time) towards run-time.

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What s/w architectures and s/w engineering processes will enable much greater adaptiveness? Legacy systems and traditional s/w engineering processes introduce many

rigidities (barriers to change) Many emerging s/w technologies enable greater flexibility and potential

machine-scale adaptation Service-oriented computing Cloud computing BPMS Process-aware IS Context-aware IS Self-adaptive software systems Personalization, customization Agent-based systems …

Software processes and software architectures are both critical for achieving enterprise adaptiveness and responsiveness They need to be analyzed within same conceptual framework 12

What abstractions will help us conceptualize the adaptive enterprise enabled by the confluence of emerging technologies?

current modeling techniques (e.g., BPMN) are inadequate for dealing with ongoing change, multi-scale dynamics global scale, enterprise-wide complexity

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Illustration: telecom

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eTOM operations processes [Ronco 2002]

Illustration: insurance

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IBM WebSphere Business Services Fabric industry content packs. http://www.ibm.com/developerworks/webservices/library/ws-cbsdev/

Enterprise Architecture Frameworks rely on modeling

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Zachman TOGAF

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LayeredArchitecture

Infrastructure

External infrastructure services

Application components and services

Roles and actors

External application services

External business services

Damage claiming process

Client Insurant InsurerArchiSurance

Registration PaymentValuationAcceptance

Customerinformation

service

Claimspaymentservice

Customeradministration

service

Paymentservice

CRM system

Financial application

Customerinformation

service

Claimregistration

service

Claimregistration

service

Claimsadministration

service

Policy administration

Claimfiles

service

zSeries mainframe

DB2database

Financialapplication

EJBs

Customerfiles

service

Sun Blade

iPlanetapp server

Claiminformation

service

Business layer

Application layer

Technology layer

Archimate

Desirable Modeling Framework Features Modeling of feedback loop elements – sensing, interpreting,

decision making, action Numerous dynamic, adaptive processes operating at different

time scales and scopes, w/ different rates of change Design-time and run-time activities represented uniformly in

same model Output of some process can be a design of another process Barriers to change (rigidities) – representation and analysis …

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Research Agenda Conceptual Modeling for a Complex and Dynamic World

Expressiveness Causal relations – producing change; closed-loop adaptation Scoping – in space, time, granularity, design-time vs. run-time, … Architecture – stability and flexibility (vs. rigidity) Goals and intentionality Scenarios Agent-orientation – localized decision making, freedom & constraints Uncertainty, emergence, autonomy, alignment Dynamic-static (process-product) interplay

Language, analysis and design techniques

Usage methodology and tools

Empirical grounding and evaluation E.Yu 18

Intellectual sources

From many disciplines and areas… Complex adaptive systems [Dooley] Dynamic capabilities [Teece] Organizational learning [Argyris] Sensemaking [Weick] Systems dynamics [Forrester] [Sterman] Control systems theory Adaptive software systems [Cheng] Timeline variability in software product lines [Svahnberg Gurp Bosch] …

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Illustrative example: Supply chain management

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Supply Chain Management Supply Chain Management (SCM) Definitions

SCM is the management of the flow of goods. It includes the movement and storage of raw materials, work-in-process inventory, and finished goods from point of origin to point of consumption. [Wikipedia]

SCM encompasses the planning and management of all activities involved in sourcing and procurement, conversion, and all logistics management activities. [Council of Supply Chain Management Professionals]

Management of material and information flow in a supply chain to provide the highest degree of customer satisfaction at the lowest possible cost. [BusinessDictionary.com]

SCM Components: Suppliers, distributors, transportation & logistics companies, etc.

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SCM Characteristics Extended Enterprise Suppliers, Distributors, Transport, Logistics, etc. Each company focuses on its core competencies Goal: Collection of best-in-class partners

Growing Importance due to Competition, globalization, outsourcing, etc.

Growing Complexity Extended geography, reduced control, offshoring, shorter product

lifecycles

Desired Characteristics Reliability, responsiveness, flexibility, minimal cost, customer

satisfaction

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Emerging Technologies for SCM Sensor Networks Track location, temperature, humidity, light exposure Transmit info in real-time Increase in sensing granularity (e.g., container → pallet)

Everything-as-a-Service Dynamically recruit partners, assemble supply networks Easily replace suppliers and other partners Affordable per-use payments vs. acquisition of capacity Ability to rent out excess capacity

Big Data Analytics Real-time visibility into the supply chain performance Ability to deal with the ever-growing data stream

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Multiple Levels of SCM Processes

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Process A – Produce Sourcing & Delivery

Multiple Levels of SCM Processes

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Limited Variability

Process A – Produce Sourcing & Delivery Limited variability for customization and to handle breakdowns,

emergencies

Multiple Levels of SCM Processes Process A – Produce Sourcing & Delivery Limited variability for customization and to handle breakdowns,

emergencies Fixed context, boundary from higher-level processes

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Limited Variability

Context

Multiple Levels of SCM Processes Process B – Monitors, Analyzes, and Redesigns produce

delivery Process A Monitors multiple instances of A, periodically redesigns it Improves effectiveness/efficiency of Process A Provides context/boundary for A

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Multiple Levels of SCM Processes Process C – improves supply chain across many categories of

goods for a distributor company Controls B (and similar processes for other product categories) based on: Its current performance Available budget Relative priority of the produce delivery process

Sets context for Process B E.g., budget limitations

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Multiple Levels of SCM Processes Process C – improves supply chain across many categories of

goods for a distributor company

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Handling Change – 1 New way of supplying produce. Requirements: Real-time shipment tracking Fine-grained prediction of demand

Using traditional technologies: Manual/sensor-based coarse-grained tracking In-house BI implementation

Requires: time, money, training, managerial approval

Potential barriers to change

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Handling Change – 2 Can this change be handled in Process A? I.e., Implement the new solution at runtime – per process instance Infeasible due to Long implementation time and prohibitive cost Required high-level manager approval Fixed, limited variability in Process A

Can this change be handled in B? Implementation time – OK High-level manager approval – OK Cost increase – remains a change barrier Budget for produce delivery is set in Process C.

Change must be handled in Process C!

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Handling Change – 3 Emerging technologies Sensor Networks More affordable, higher-granularity, network-connected

Cloud-based business analytics Significantly cheaper, more flexible than in-house solutions

Internet-based/cloud supply chain collaboration Increased variability: dynamically recruit/change supply chain partners for

improvement/recovery from failures

Implementing these will lead to: Likely – ability to handle this change in Process B Avoids drastic budget increase

Potentially – ability to handle it within Process A, at runtime

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Conceptual Modeling for a Complex and Dynamic World

Recent and ongoing work From BI Insights to Actions: Closing the Sense-and-Respond

Loop in Adaptive Enterprises Soroosh Nalchigar & E. Yu [PoEM’13]

Adapting to Uncertain & Evolving Requirements: the Case of Business-Driven BI

E. Yu, Alexei Lapouchnian, Stephanie Deng [RCIS’13] System Dynamics & Intentional Modeling – Evolution of a

Software Organization with Mahsa Sadi

Analyzing Architectural Rigidity using Dynamic Capabilities Theory

with Muhammad Danesh The Business Intelligence Model

John Mylopoulos, Daniele Barone, Jennifer Horkoff, Lei Jiang, Daniel Amyot, Alex Borgida, E. Yu … [PoEM’10] … [SySoM’13]

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Questions?