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Management Support Systems: An Overview

Management Support Systems: An Overview. Learning Objectives. Understand how management uses computer technologies. Learn basic concepts of decision-making. Understand decision support systems. Recognize different types of decision support systems used in the workplace.

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Management Support Systems: An Overview

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  1. Management Support Systems:An Overview Turban, Aronson, Liang Sauter

  2. Learning Objectives • Understand how management uses computer technologies. • Learn basic concepts of decision-making. • Understand decision support systems. • Recognize different types of decision support systems used in the workplace. • Determine which type of decision support system is applicable in specific situations. • Learn what role the Web has played in the development of these systems. Turban, Aronson, Liang Sauter

  3. Opening Vignette:Casino Harrah’s The Problem • Gaming is highly competitive and profitable. • Early 90’s: gambling on riverboats • 1990-97: # casinos tripled. • In the past: star treatment for high rollers, free drink for slot m/c players. • End of 80’s: Slot m/c’s surpassed table games, 25 million players • Loyal slot players are the key to profitability! Turban, Aronson, Liang Sauter

  4. Opening Vignette: The Solution • Player-tracking system • 30% of customers who spend b/w $100-$500 account for 80% of total revenue, almost 100% of profits. • Total rewards program • Magnetic cards to capture info on How long they play, how much they spend, games preferred, winning ratios... • Incentives based on money inserted, not won!, free meals, rooms, shows • Electronically linked clubs Turban, Aronson, Liang Sauter

  5. Opening Vignette: The Solution • Magnetic card readers: read customer ID., send a personalized greeting • Electronic gaming m/c’s: capture transaction data and send to mainframe • Onsite transaction system: store all casino, hotel and dining transaction data • National data warehouse: links computer system and customer data to a server that tallies customer history and rewards. • Predictive analysis software: predict customer profile • Web site: keeps customers informed, connected entertained Turban, Aronson, Liang Sauter

  6. Harrah’s Decision Support System • Transaction Processing System (TPS) • Data Warehouse • Data Mining or Business Intelligence • Regression analysis, neural networks, cluster analysis, optimization techniques • Customer Relationship Management (CRM) • Decision Support System Turban, Aronson, Liang Sauter

  7. Resulting decisions: Some examples • Defn of the perfect player: “62 year old woman who lives in 30 min. Kansas city, Missouri and playes dollar video pocker!” • Customers who live far away receive discounts in meal, hotel, transportation • Close living customers get food, entertainment, cash incentives • Tight expiration days • Design new campaigns according to estimated response rates, return on investment Turban, Aronson, Liang Sauter

  8. 1.2 Managers and Decision Making • Airlines, • Retail organizations • Banks • Service Companies all use these methods... “To run effective business in a competitive environment, a real time, targeted and computerized DSS is essential” Turban, Aronson, Liang Sauter

  9. Factors Affecting Decision-Making • Complexity of the systems is increasing, so there are more alternative courses of action to choose from. • With new technologies and faster communication, the amount of available data is huge. • Time limitations are getting very strict; thus the cost of an erronous decision is high. • Due to several factors like political destabilization or globalization, environmental uncertainty is increasing. Thus decision making gets harder!... Turban, Aronson, Liang Sauter

  10. 1.3 Managerial Decision Making and IS • Productivity: The ratio of outputs to inputs that measures the degree of success of an organization and its individual parts • Management Support Systems increase the productivity of managerial decision making in a complex and uncertain environment by • evaluating numerous alternatives • in a very short time. • Thus, better decisions are made with lower costs of error. Turban, Aronson, Liang Sauter

  11. Decision Support Systems • Computer-based systems that • collect information from various sources, • assist in the organization and analysis of the data, • facilitate the evaluation of the alternatives by the use of specific models, • provide a good user interface through which users can easily navigate and interact. Turban, Aronson, Liang Sauter

  12. What do Decision Support Systems Offer? • Quick computations at a lower cost • Group collaboration and communication • Increased productivity • Access to multiple databases and warehouses • Ability to analyze multiple alternatives and apply risk management • Enterprise resource management and empowerment • Tools to obtain and maintain competitive advantage which is based on price, timeliness, quality, customization and support. • Overcome cognitive limits in processing and storage Turban, Aronson, Liang Sauter

  13. Cognitive Limits • The human mind has limited processing and storage capabilities.Any single person is therefore limited in his/her decision making abilities. • Collaboration with others allows for a wider range of possible answers, but will often be faced with communications problems. • Computers improve the coordination of these activities. • This knowledge sharing is enhanced through the use of GSS, KMS, and EIS. What is the role of web here? Turban, Aronson, Liang Sauter

  14. DSS MS/OR Techniques Business Analytics Data Mining Data Warehouse Business Intelligence OLAP CASE tools GSS EIS/EIP ERM/ERP CRM SCM KMS/KMP ES ANN Intelligent Agents E-commerce DSS Management Support Systems Technologies (Tools) Turban, Aronson, Liang Sauter

  15. Management Support Systems • Repetitive, structured problems • Linear logic • Regular reports • Low support of decision • Not Repetitive, unstructured problems • Specialized heuristics • No regular reports • System makes decisions TPS MIS DSS EIS GSS KMS ES Structured processes include routine and repetitive problems with standard solution methods Unstructured processes are fuzzy, complex problems without cut-and-dried solution methods. Turban, Aronson, Liang Sauter

  16. The Features of Management Support Systems • Decision making process has three phases: • Intellegence: Searching for conditions that call for decisions • Design: Inventing, developing and analyzing possible courses of action. • Choice: Selecting a course of action from those available • In an unstructured problem none of these phases are structured!In a semistructured problem, some of these phases are structured. • In a structured problem procedures for obtaining the best solution are known, like management science, data processing or clerical methods.Ex: Best investment plan, best production and inventory control plan. • In an unstructured problem human intuition is often the basis for decision making. Ex: Planning new services, hiring an executive, chosing a set of R&D projects for the next year.Only part of an unstructured problem can be supported by advanced decision support tools such as GDSS, ES, KMS. Turban, Aronson, Liang Sauter

  17. Turban, Aronson, Liang Sauter

  18. Components of a DSS • Database Management System (DBMS) • Collect data from various resources • Organize data • Provide user interface to access data • Model base Management System • Keep track of models • Provides run control for the models • Provides data in the format required as a model input • Provides format of the model output • Provides sensitivity analysis after the model is run • Provides the decision maker, the ability to question the assumptions of the model • User Interface • Provides input screens by which users request data and models • Provides output screens where the results are shown • Message Management System • Provide the features of web environment for data collection, modeling • Provide group support for decision making • Provides integration with other MSS’s. Turban, Aronson, Liang Sauter

  19. Management Science/Operations Research Adopts systematic approach • Define problem • Classify into standard category • Construct mathematical model • Evaluate alternative solutions • Select solution Management science process is based on mathematical modeling. Computerized methodologies are used for solution. For less structured problems methodologies are customized. Ex: In a bookstore, human judgement is necessary to predict the demand and decide on how much to order. Turban, Aronson, Liang Sauter

  20. MSS as an umbrella term • Used to describe any computerized system that supports decision making. Ex: In an organization MSS encompasses: • KMS for personnel, • DSS for marketing and accounting, • SCM System • Several expert systems for diagnostics Turban, Aronson, Liang Sauter

  21. Group Support Systems • Getting people at one place is expensive and time consuming • Time limitation to give the decision • Traditional meetings last long Systems that provide interaction and communication between people with the aid of IT are called collaborative computing systems, groupware systems, electronic systems, or simply GSS • Videoconferencing, audioconferencing, electronic brainstorming, voting, document sharing, etc.. Turban, Aronson, Liang Sauter

  22. Enterprise Information Systems • Evolved from Executive Information Systems combined with Web technologies • EIPs view information across entire organizations • Provide rapid access to detailed information through drill-down. • Provide user-friendly interfaces through portals. • Identifies opportunities and threats Turban, Aronson, Liang Sauter

  23. Enterprise Information Systems • Specialized systems include ERM/ERP, CRM, and SCM • Provides timely and effective corporate level tracking and control. • Filter, compress, and track critical data and information. What is the relation b/w ERP, CRM, SCM? Turban, Aronson, Liang Sauter

  24. Knowledge Management Systems Do not reinvent the wheel each time! • Knowledge that is organized and stored in a repository for use by an organization • Can be used to solve similar or identical problems in the future • ROIs as high as a factor of 25 within one to two years • Web technologies feature prominantly • Provides access to knowledge repository, a textual database Turban, Aronson, Liang Sauter

  25. Issues in Knowledge Management Systems • Where to find knowledge • How to classify it • How to ensure its quality • How to store it • How to maintain it • How to use it • Motivate people to contribute their knowledge • People who leave the organization take their knowledge with them Turban, Aronson, Liang Sauter

  26. KMS Application: Xerox Experience • Problem: With decreasing demand for copying, Xerox strugled to survive the digital revolution. • Solution Method: Developed an intranet based knowledge repository in 1996 to support sales people to quickly answer customers’ queries. • Result: Days of investigations have decreased to a few minutes. • Implications: • Questions and solutions are indexed to easily retrieve information in the latter requests. So the system improves itself. • Accumulated knowledge is analyzed to learn the products strenghts, weaknesses, customer trends, etc. • Challenges in organizational culture change: • Persuade people to share knowledge. • Learn to use intranet and KMS. Turban, Aronson, Liang Sauter

  27. Expert Systems Decison makers ask for expert opinions! • ES Attempts to mimic human experts’ problem solving • Uses technologies that apply reasoning methodologies in a specific domain • Examples include: • Artificial Intelligence Systems • Artificial Neural Networks (neural computing) • Genetic Algorithms • Fuzzy Logic • Intelligent Agents • Most ES software is implemented on the web tools (java applets), installed on web servers and use web browsers for interfaces. Ex: Corvid Exsys Turban, Aronson, Liang Sauter

  28. Expert Systems • Expertise is transferred from expert to computer • The knowledge is stored in the computer • Users run the computer whenever advice is needed • The ES asks for facts, make inferences, arrive at a conclusion like a human consultant • May explain the logic behind the advice Turban, Aronson, Liang Sauter

  29. Methodologies of ES: Artificial Neural Networks • Application of decision methodologies requires explicit data, information or knowledge stored in a computer ad manipulated when needed. • In complex real world where the environment changes rapidly, people make decisions based on partial, incomplete or inexact information, by using their “experiences”. • In the absence of explicit data, ANN recall similar experiences, learn from them in a computerized system. • Uses pattern recognition approach, i.e., learns patterns in data presented during training and can apply it to new cases, predict the future behaviors of systems, people, markets, etc. Ex: Detecting unusual credit card expenditures and bank loan approvals Turban, Aronson, Liang Sauter

  30. Methodologies of ES: • Genetic algorithms: mimic the process of evolution and search for an extremely good solution by survival of the fittest rule Ex: Max. Advertising profit at tv stations • Fuzzy logic: assist decision makers in solving problems with imprecise statements of parameters, approaches the problems the way people do. Ex: “The weather is really hot”. How hot is hot? • Intelligent agents learn what you want to do, take over some tasks like travel agents, real estate agents. Turban, Aronson, Liang Sauter

  31. Hybrid Support Systems • Integration of different computer system tools to resolve problems • Tools perform different tasks, but support each other • Work together to produce smarter answers Ex: United Sugars Corporation revises its marketing and distribution plans to gain access to new markets and serve the existing customers more efficiently. • Model is developed (millions of decn. var’s and 250,000 constraints) to find the minimum cost solution for packaging, inventory and distribution. • SAP and and DB system provides data • Web based GIS graphically displays reports for optimal solution. • Results are uploaded to SAP and subsequent optimization models are run for inventory control. Turban, Aronson, Liang Sauter

  32. Emerging Technologies • Grid computing • Cluster computing power in an organization and utilize unused cycles for problem solving and other data processing needs. • Improved GUIs • Due to improvements in web, expectations have risen. • Model-driven architectures with code reuse • Software reuse and machine generated software by the computer aided software engineering tools has become prevalent. • M-based and L-based wireless computing • As cellular phones and wireless pc cards are getting less expesive, m-commerce is evolving. Ex: FedEx uses mobile computer to track shipping packages and analyze patterns • Intelligent agents: • help users and assist in e-commerce negotiations. • Genetic algorithms, heuristics and new problem-solving techniques • Distributed as part of Java middleware and other platforms. Turban, Aronson, Liang Sauter

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