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MD240 - MIS Oct. 13, 2005 Artificial Intelligence ERP & ‘Package’ Software Topics Covered • Artificial Intelligence – – – – Genetic Algorithms Expert Systems Neural Networks Uses, challenges, and examples • ERP – The problem of legacy systems – ERP defined – Advantages & limitations • Make vs. Buy Decisions • Successful Deployment of Large Scale Systems Types of Artificial Intelligence • Neural Networks – hunt for unseen patterns in historical data – build models based on prior history – require clean data & consistency between performance history and future events • Genetic Algorithms – search for optimal solutions based on natural selection: (1) propose solution (2) evaluate results against earlier solution (3) mutate & return to step 1 Keane’s Space Truss Design AI Continued • Expert Systems – An artificial intelligence system that uses captured human expertise to evaluate and solve problems – Characteristics: • diagnosis, configuration, and/or recommend a course of action • problems are structured and repeatable • application scope is limited to a particular problem area (domain) Legacy Systems Many firms have limited to no integration across geographic areas functional areas (v-chain) products, plants, & business units Infrastructure: general mgmt, planning, finance, IS HRM: recruiting, hiring, training, and development Tech. Development: R&D Procurement Inbound Operations Outbound Marketing logistics logistics & Sales Suppliers Service Buyers What is ERP? • ERP - Enterprise Resource Planning Software – sometimes called Enterprise Applications/Packages/Suites/Systems – connects all of the information which flows through a company to a single integrated set of systems – implemented in modules which can be integrated (all at once or at a later date) e.g. Financials, Logistics, HR – may work with a wide variety of databases, hardware, and operating systems • Leading Vendors – SAP, Oracle ERP in Action Production Staffing Inventory Purchasing Order Tracking Source: BusinessWeek Int’l Sales Planning ERP Benefits • Systems integration - enterprise data model – squeeze out waste & enable strategies • Standard software enables – inter-organizational systems (easier if buyers & suppliers use the same system, e.g. petrochem. ind.) – broad selection of add-on packages (e.g. data warehouses, etc.) • Package upgrading and new technology development is handled by vendor • Speed of deployment (possibly) The Risks • Tied to a single vendor • Flexibility limited by options offered by the vendor – may inappropriately force generic processes – may inappropriately force org. structure changes • Complexity - particularly regarding mapping and standardizing processes across the organization. Make vs. Buy Comp. Adv. Will the proposed system offer proprietary comp. adv.? Security Is the process or data highly confidential? IT Competency Is IT a core competency? Tech. Skill Does the firm have sufficient expertise with tech.? Suitability/Fit Is a suitable partner/package available? Cost/Benefit Is the package cheaper than inhouse dev.? Time Is there sufficient time to develop the system Adapted from Applegate et al., p. 61. Make Buy Yes No Yes No Yes Yes No No No Yes No Yes Yes No Successful Deployment of Large Scale Systems • Business Case – benchmark, cost justify (e.g. stop maintaining old systems) • Leadership – from the highest levels (e.g. success at Owens Corning, failure at Westinghouse) • Staffing – largely from business, not IT (users know the process) – ‘compensation handcuffs’ (e.g. end of deployment bonuses, training payback agreements) – experienced consultants - check refs., clients • Execute with proven methodologies