Management Support Systems: An Overview

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

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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