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Transcript
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