1. Information systems and management in business Chapter 7 Using Information Systems in the Management Problems Solving and Decision Making Process

2. 7.1Introduction  Management information systems help to enhance the efficiency of the organizational decision making process  MIS are passive in nature (not interactive)  Output could be viewed but not influenced  Questions could not be asked online which influence the system’s output  Decision support systems (DSS) generates output which could be viewed and influenced by the system’s user  DSS enhances the effectiveness of the decision making process  DSS are used in many business areas such as production, operation management, marketing, finance and so on..  DSS come in a variety of flavors or classes

3. 7.2 Decision Support Systems (DSS)  DSS definition  May be broadly defined as computer based information systems which offer a more disciplined and a formal approach to making decision and solving problems  Key characteristics  A decision support system is a model based or knowledge-based and source data from a variety of sources such as relational databases or data warehouses  A DSS employs wide ranging analytical and modeling techniques  Decision support systems are interactive in nature  Allows its users to ask questions online and develop various scenarios of the problem

4. 7.2 Decision Support Systems (DSS)  DSS Classification May be broadly divided into 6 key classes  Model-driven  Data-driven  Communication-driven  Document driven  Knowledge-driven  Web-based – aka Inter and Intra-organization DSS xx

5. 7.2 Decision Support Systems (DSS)  Model-driven DSS – key characteristics  Employ various marketing, economic, accounting, and financial, optimization, simulation and many other business models  Models are typically developed by academics and generally used by managers  Tend not require access to large volume of data and hence do not require access to large databases  Mostly used to support managers solve problems of an optimization, simulation and forecasting orientation

6. 7.2 Decision Support Systems (DSS)  Data-driven Decision Support Systems – key characteristics  The emphasis is on analyzing large volume of business data  Analysis is typically performed using technologies such as data mining and on-line analytical processing (OLAP)  Typically employed to solve complex problems that may be:  Patterns and relationships based  Multi-dimensional analysis orientated  Prediction orientated  Data typically sourced from data warehouses or data marts

7. 7.2 Decision Support Systems (DSS)  Communication-driven DSS – key characteristics – aka GDSS  Primarily used to help group of people to collectively engage in the decision making process  groupware software technologies and various and networking technologies key components

8. 7.2 Decision Support Systems (DSS)  Knowledge-driven DSS – key characteristics – aka expert systems  Artificial intelligence (AI) is the Key technology employed  Typically provide suggestions and or recommendation to managers or users  They employ knowledge base and inference engines in order to deliver their decision-making objectives

9. 7.2 Decision Support Systems (DSS)  Document-driven – key characteristics  DSS Provides document retrieval and analysis with the use of various storage and processing technologies

10. 7.2 Decision Support Systems (DSS)  Web-based DSS – key characteristics  The emphasis on the use of Internet technologies  Extend the capabilities and use of model-driven DSS and other types in order to cover organization wide and inter-organizational boundaries

11. 7.3Decision Support Systems General Architecture  Decision Support Systems General Architecture  Three components make up the DSS architecture  A model  A data  A dialogue subsystem  In general the dialogue subsystem comprises an input and output functions while the model and data components are typically integrated within the processing function

12. 7.3Decision Support Systems General Architecture  Decision Support Systems Data  DSS uses a variety of data sources to deliver on its objective  The data, decision support systems uses come primarily from five different sources depending on the decision support system class  Model base  User  Data warehouse  Relational databases – transactional data  Knowledge base

13. 7.3Decision Support Systems General Architecture  Processing Function  Supports a number of model and data manipulation activities  Key activities - Within the context of model- driven systems  Communicating with the organization’s database and model base  Interacting with the input function in order to obtain user’s data necessary for establishing the modeling scenario  Carry out the modeling analysis  Interaction with both input and output functions

14. 7.4Model-Driven Decision Support Systems  Models A critical component of Model-driven DSS A model is basically a simplified and abstract representation of reality or an actual entity of a process or an object Ther are only a simplified version of their real entity Vary in complexity and are very often developed by academics

15. 7.4Model-Driven Decision Support Systems  Why Models are used? Facilitate understanding the process or the real entity Models are able to communicate information quickly and accurately using words, sounds, pictures Models are capable of searching for best or optimal solutions Capable of forecasting future changes (prediction) and enabling people to ask what-if questions (simulation)

16. 7.4Model-Driven Decision Support Systems  Model Types Physical Models Process Models  A process model falls mainly into two sub categories Descriptive Mathematical  The economic order quantity (EOQ) is a well know example of a mathematical model which is principally used in inventory management

17. 7.5 Model-Driven DSS and Mathematical Modeling  Overview  Any mathematical formula or equation is a mathematical model  DSS models vary in complexity depending on the system goals  They can range from system models that are made up of a number of mathematical equations to ones that are made up of hundreds of formulas (complex)  An example of the complex is a DSS system model that is used to work out the best scheduling arrangement for a transportation company that operates, for example, a large number of trains and serves a multitude of destinations with several hundreds of workers

18. 7.5 Model-Driven DSS and Mathematical Modeling  Decision Support Systems Analytical Modeling Activities What-if analysis  A decision maker changes one or more variables of a process then observes how the change affects its other variables Sensitivity analysis  Similar to what-if analysis but the user varies the value of one variable at a time and observes the effects on the other variables

19. 7.5 Model-Driven DSS and Mathematical Modeling  7.5 Model-Driven DSS and Mathematical Modeling Goal seeking  Goal seeking analysis sets a goal or a target for a system variable and then repeatedly changes others till the target value is realized Optimization  Used to search for the optimal solution within certain constraints Prediction  Used to forecast future changes. Techniques such as simple linear forecasting (Averaging) or time-series forecasting are typically employed here

20. 7.6The Use of Mathematical Modeling Based DSS in Business  Managers face problems of a prediction, optimization and what-if (simulation) nature for which mathematical modeling based decision support systems are most suited to help with solving  These systems are primarily used for five types of analytical modeling activities  What-if analysis  Sensitivity analysis  Goal -seeking analysis  Optimization analysis  Prediction  The various activities associated with this chapter illustrate and clarify these concepts in greater details – refer to the three end slides

21. 7.7Group Decision  Group decision support system (GDSS) Definition  A category of decision support system that is interactive and computer-based system  Principally designed to support a team or a group of people make decisions and solve problems

22. 7.7Group Decision  GDSS features Involves a special meeting arrangement duped as electronic meeting room (ERM)  Connected computers via LAN  Front screen Members of the group communicate ideas, questions and comments via their individual computers Ideas, questions and comments appears simultaneously on the front screen A facilitator is usually required to run group decision-making meetings using GDSS

23. 7.7Group Decision  Potential disadvantages with Using GDSS Cost associated with having dedicated ERM and a trained facilitator Restrictive and inhibitive to some participant  Used and familiar with traditional oral approach group discussion

24. 7.8 Expert Systems (ES)  What is an Expert System?  A category of decision support systems that employ artificial intelligence (AI) techniques in order to support the decision making process

25. 7.8 Expert Systems (ES)  Expert Systems Components  A knowledge base (KB)  Inference engine

26. 7.8 Expert Systems (ES)  Expert Systems Overview  Codes knowledge  Typically compiled by knowledge engineers from human experts and is typically referred to as a knowledge base  Provides rules to manipulate the knowledge  Processing the rules that manipulate the knowledge base is known as inference or inference engine  Knowledge manipulation delivers recommendation – decision  The ES typically collects information from the user and then employs an if-then format to reach a decision

27. 7.8 Expert Systems (ES)  Expert systems use and architecture  Employed in a wide range of business and professional fields  Medical diagnostics, resolving a variety of engineering problems in the automobile industry, resolving software application and hardware components difficulties etc…  The architecture of an expert system does not differ much from the DSS general system architecture  Involves input, output and processing functions  The processing function handles activity associated with the inference engine

28. Chapter 7 Knowledge Enhancement and Consolidation Tools and Exercises  Visit the book’s Web site www.halaeducation.com & select module 7 www.halaeducation.com  Perform Chapter 7 associated demo and case study through their respective demo and case Studies Links

29. Chapter 7 Problems Solving Skills Development  Visit the book’s Web site www.halaeducation.com & select module 7 www.halaeducation.com  Perform Chapter 7 associated skills development through their respective skills development exercises link

30. Chapter 7 Balancing Knowledge to Practice  Visit the book’s Web site www.halaeducation.com & select module 7 www.halaeducation.com  Perform Chapter 7 associated Balancing Knowledge to Practice project through its respective Hands on Project Link