1. Productivity in the Enterprise through OR-CI Synthesis and Integration Organizers: Bob Fourer, Steve Wright, Jorge Moore, Karthik Ramani OR: Suvrajeet Sen Shared CI: Sangtae Kim Workshop held in Washington, D.C. August 30-31, 2004 Sponsored by the National Science Foundation

2. OR as an Infrastructure OR: Science of Decision making Strengths Integrates theory, algorithms, and software Provides modeling and analysis tools Underlies and facilitates productive activity in design, manufacturing, services, supply-chain management... serves many purposes not originally envisioned Weaknesses Lack of standards for interoperability of tools Limited accessibility to modeling and analysis tools... ad hoc, awkward interfaces

3. DMII Opportunities Current Product Development Processes are extremely iterative, communication intensive (data driven), and linear. Challenge areas to be addressed Enterprise applications in such areas as design optimization and configuration management total supply network management production planning over product lifecycles simulation of decentralized services Dynamic representations rather than static... bypassed by Atkins report

4. Integrating OR within Cyberinfrastructure Remedies for current weaknesses Create an infrastructure to enable research collaboration across institutions, locations, time, and fields of endeavor Ensure that the data and software acquired at great expense and effort are available for future researchers Replace incompatible software tools and structures and take the lead in fostering “coordinated” interoperability Invest in maintenance and usability of successful OR tools... echoing dangers cited in Atkins report

5. Making OR a Cyberinfrastructure Benefits of prompt action Steer CI towards Meta Models, rather than complete reliance on Meta Data Focus time and talent on breaking new ground rather than reproducing past efforts Reduce isolation of the OR community and among investigators within the community Preserve-reuse valuable data (models and knowledge) for future research

6. Combining top-down and bottom-up approaches at multiple levels and scales

7. System LevelFunctionalityExamples User Interaction & Interfaces Working level of interaction standards with users and user systems. Interface to various computing environments that enable access from various current and future operating platforms Applications Core tools for design, analysis, manufacture and supply chain coordination, etc. (Proprietary, open source, and shared). CADD, structural analysis programs, flow visualization, simulation tools, enterprise management, collaborative communications, optimization etc. Community Resources Algorithms and analytical tools available to the applications and user levels. Platform for community tool development & management. Data analysis tools, image processing, statistical analysis functions, data mining, search functions, language translators, optimization languages. System Interoperation Architecture Low level operating software and standards, security, and communication protocols. Frameworks for interaction, communications, and resource sharing (DATA, INFORMATION, MODELS, KNOWLEDGE). “Low Level” Architectures “High Level” Architectures Systems view of OR-CI

8. CI-OR-EA and Engineering Design Data/Knowledge/Models are the foundation of design (repositories, libraries, catalogs) CI and OR can facilitate:  Access to remote and more current data/knowledge/models  Organization of data, data mining and searching methodologies  More compute cycles => explore a much larger scenario space, anticipate more exceptions and failure modes => more robust designs  More powerful, distributed algorithms for design under uncertainty design of flexible entities with many more degrees of freedom

9. Supply-Network Management The supply “chain” Design and production  transportation and warehousing  marketing and delivery Really a supply “network” Part owned, part outsourced Decision-making on wide range of time scales, real-time control to short-term scheduling to long-term planning Reconfigurable network Robust optimization to deal with uncertainties... a company must be able to easily use the network

10. Challenges in Supply-Network Management Diverse tools required Drawing on statistical, simulation, and optimization techniques Communicating with each other, with varied data sources, with human analysts at different levels and locations Difficult degree of integration required Approached by some costly and specialized proprietary systems Still out of the reach of most researchers and practitioners... great potential for an operations cyberinfrastructure

11. CI-OR for Supply-Network Management Standards for web services Enable quick and reliable connections between diverse analytical methods and data sources Free time for experimentation with new computational ideas and new software components Accessibility of the CI Speed integration of new research ideas into practice Disseminate new supply-network ideas to a broader variety of companies, especially relatively small ones Promote use of the most challenging approaches, such as optimization under uncertainty, distributed simulation and optimization, global optimization on noisy data, optimization of simulations

12. Example: CI-OR in Product Lifecycle Challenges of outsourcing Steadily increasing demands for customization of products, but... Further increases in complexity Management of interfaces between suppliers threatens to become expensive and inefficient Dispersion of engineering and production increases opportunities for breakdown in multi-tier supply networks Hidden logistical and inventory costs and increased lead times Consequences for design and development Highly iterative Communication-intensive Reliant on suppliers from prototyping to production

13. Example: CI-OR in Product Customization Consequences for the supply network Significant time and cost to develop a stable, reliable supply network for a product Intensive coordination between different tiers Rigid networks, unresponsive to dynamically changing markets High inventory costs, borne mainly by lower-tier suppliers already under pressure to cut costs What CI-OR in enterprise applications can provide Competitive advantages through productivity improvements at all levels More competitive based on speed and responsiveness of the supply network Solve large-scale distributed optimization and constraints Handle large scale systems at multiple levels and scales... not just for the biggest players …

14. CI-OR-EA and Enterprise Design and Services Includes design of  Physical entities (e.g. electrical grid, data networks)  Virtual entities (alliances and markets) Grid and network design:  Design for robust (decentralized?) control, to allow for continuing operation after disruptions (Big algorithmic challenges for OR)  Placement of sensors, handling of sensor data are major issues Market design (electricity markets, health information alliances):  CI: Standards for information exchange  OR: Use models/algorithms to design policies and pricing mechanisms to facilitate efficient and fair operation CI will enable the componentization of business infrastructures and result in service oriented IT models Pervasive connectivity between physical infrastructure and CI will enable new service models

15. Modeling System Modeling Language OR-Cyberinfrastructure: Example Local Agent Distributed Solver Interface Function Evaluator Solver Centralized Registry Function Simulator Local Analyzer Distributed

16. Conclusions The demands of real time and competitive decisions  designed CI platform. The CI-OR-EA computational engines and service specific data repositories and libraries  increased DMS productivity. Innovation in design and manufacturing as well as R&D. Capture commonalities and eliminate duplication  increase quality and reliability. OR – CI: resource location, network flow and assignment problems. Target applications in areas where technologies can be gainfully employed. CI and OR are essential partners to drive enterprise wide productivity.