1. 1 Management Information Systems M Agung Ali Fikri, SE. MM.

2. 2 Chapter 8 Information in Action

3. 3 Learning Objectives ► Know that a firm’s ability to develop effective information systems can be a key factor in its success. ► Recognize that the transaction processing system processes describes the firm’s basic daily operations. ► Be familiar with the processes performed by a transaction processing system for a distribution firm. ► Recognize that organizational information systems have been developed for business areas & organizational levels. ► Be familiar with architectures of marketing, human resources, manufacturing, & financial information systems.

4. 4 Learning Objectives (Cont’d) ► Know the architecture of an executive information system. ► Understand what customer relationship management is & why is requires a large computer storage capability. ► Recognize how a data warehouse differs from a database. ► Understand the architecture of a data warehouse system. ► Know how data are stored in a data warehouse data repository. ► Know how a user navigates through the data repository. ► Know what on-line analytical processing (OLAP) is. ► Know the two basic ways to engage in data mining.

5. 5 Information as a Critical Success Factor ► Critical success factor (CSF) was coined by Ronald Daniel to identify a few key activities that spell success or failure for any type of organization. ► Transaction processing system (TPS) is the information system that gathers data describing the firm’s activities, transforms the data into information, & makes the information available to users both inside & outside the firm.  1 st business application to be installed on computers. ► Also electronic data processing (EDP) system & accounting information system.

6. 6 Figure 8.1 Model of a TPS

7. 7 System Overview ► Distribution system is a TPS used by distribution firms. ► Distribution firms distribute products or services to their customers. ► We will use data flow diagrams, or DFDs, to document the system. ► Figure 8.2 represents the highest level. ► Figure 8.3 identifies the three major subsystems.

8. 8 Figure 8.2 Context Diagram of Distribution System

9. 9 Figure 8.3 Figure 0 Diagram of Distribution System

10. 10 Major Subsystems of Distribution System ► Systems that fill customer orders.  Order entry system enters customer orders into the system.  Inventory system maintains the inventory records.  Billing system prepares the customer invoices.  Accounts receivable system collects the money from the customers. ► Systems that order replenishment stock.  Purchasing system issues purchase orders to suppliers for needed stock.  Receiving system receives the stock.  Accounts payable system makes payments.

11. 11 Figure 8.4 Figure 1 Diagram of Systems that Fills Customers Orders

12. 12 Figure 8.5 Figure 2 Diagram of Systems that Order Replenishment Stock

13. 13 Major Subsystems of Distribution System (Cont’d) ► Systems that perform general ledger processes.  General ledger system is the accounting system that combines data from other accounting systems for the purpose of presenting a composite financial picture of the firm’s operations.  General ledger is the file that contains the combined accounting data.  Updated general ledger system posts records that describe various actions & transactions to the general ledger.  Prepare management reports system uses the contents of the general ledger to prepare the balance sheet, income statement, & other reports.

14. 14 Figure 8.6 Figure 3 Diagram of Systems that Perform General Ledger Processes

15. 15 Organizational Information Systems ► Organizational information systems are developed to meet the needs for information relating to those particular parts of the organization. ► Marketing information system (MKIS) provides information that relates to the firm’s marketing activities.  Consists of a combination of input & output subsystems connected by a database.

16. 16 Figure 8.7 Model of MKIS

17. 17 MKIS ► Output subsystems provide information about critical elements in marketing mix. ► Marketing mix consists of 4 main ingredients that management manages in order to meet customers’ needs at a profit.  Product subsystem provides information about the firm’s products.  Place subsystem provides information about the firm’s distribution network.  Promotion subsystem provides information about the firm’s advertising & personal selling activities.  Price subsystem helps the manager make pricing decisions.  Integrated-mix subsystem enables the manager to develop strategies that consider the combined effects of the ingredients.

18. 18 MKIS (Cont’d) ► Database is populated with data from the three MKIS input subsystems. ► Input subsystems  Transaction processing system gathers data from both internal & environmental sources & enters the data into the database.  Marketing research subsystem gathers internal & environmental data by conducting special studies.  Marketing intelligence subsystem gathers environmental data that serves to keep management informed of activities of the firm’s competitors & customers & other elements that can influence marketing operations.

19. 19 Other Organizational Information System ► Human Resources information system (HRIS) provides information to managers throughout the firm concerning the firm’s human resources. ► Manufacturing information system provides information to managers throughout the firm concerning the firm’s manufacturing operations. ► Financial information system provides information to managers throughout the firm concerning the firm’s financial activities.

20. 20 Figure 8.8 Model of HRIS

21. 21 Figure 8.9 Model of Manufacturing Information System

22. 22 Figure 8.10 Model of Financial Information System Figure 8.10 Model of Financial Information System

23. 23 Executive Information System ► Executive information system (EIS) is a system that provides information to upper-level managers on the overall performance of the firm; also called Executive support system (ESS). ► Drill-down capability allows for executives to bring up a summary display & then successively display lower levels of detail until executives are satisfied that they have obtained as much detail as is necessary.

24. 24 Figure 8.11 An EIS Model

25. 25 Figure 8.12 Drill-down Technique

26. 26 Customer Relationship Management ► Customer relationship management (CRM) is the management of the relationships between the firm & its customers so that both the firm & its customers receive maximum value from the relationship. ► CRM system accumulates customer data over a long term – 5 years, 10 years, or more - & uses that data to produce information for users.  Uses a data warehouse.

27. 27 Data Warehousing ► Data warehouse describes data storage that has the following characteristics:  Storage capacity is very large.  Data are accumulated by adding new records, as opposed to being kept current by updating existing records with new information.  Date are easily retrievable.  Date are used solely for decision making, not for use in the firm’s daily operations. ► Data mart is a database that contains data describing only a segment of the firm’s operations.

28. 28 Data Warehousing System ► Data warehousing is the creation & use of a data warehouse or data mart. ► Primary data sources are TPS & data obtained from other sources, both internal & environmental; any data identified as having potential value in decision making. ► Staging area is where the data undergoes extraction, transformation, & loading (abbrev. as ETL process)

29. 29 Data Warehousing System (Cont’d) ► Extraction process combines data from the various sources. ► Transformation process cleans the data, puts it into standardized format, & prepares summaries.  Data stored in both detail & summary form. ► Loading process involves the entry of the data into the data warehouse repository. ► Metadata  “Data about data”.  Data that describes the data in the data repository.  Tracks data as it flows through the data warehouse system.

30. 30 Figure 8.13 Model of Data Warehousing System

31. 31 Storing Data in the Warehouse Data Repository ► Dimension tables store the identifying & descriptive data.  Dimension provides the basis for viewing the data from various perspectives or dimensions. ► Fact tables are separate tables containing the quantitative measures of an entity.  Combined with dimension table data, various analyses can be prepared.  Users can request information that involves any combination of the dimensions & facts.

32. 32 Figure 8.14 Simple Dimension Table

33. 33 Figure 8.15 Sample Fact Table

34. 34 Storing Data … (Cont’d) ► Information package identifies all of the dimensions that will be used in analyzing a particular activity. ► Star schema - for each dimension, a key identifies the dimension & provides the link to the information package which results in a structure that is similar to the pattern of a star.  The warehouse data repository contains multiple star schemas, one for each type of activity to be analyzed.

35. 35 Figure 8.16 Information Package Format

36. 36 Figure 8.17 Sample Information Package

37. 37 Figure 8.18 Star Schema Format

38. 38 Figure 8.19 A Sample Star Schema

39. 39 Figure 8.20 Navigating the Warehouse Data Repository

40. 40 Figure 8.21 Drilling Across Hierarchies Produces Multiple Views

41. 41 OLAP ► On-line analytical processing (OLAP) enables the user to communicate with the data warehouse either through a GUI or a Web interface & quickly produce information in a variety of forms, including graphics. ► Relational OLAP (ROLAP) uses a standard relational database management system.  ROLAP data exists in detailed form.  Analyses must be performed to produce summaries.  Constrained to a limited number of dimensions. ► Multidimensional OLAP (MOLAP) uses a special multidimensional database management system.  MOLAP data are preprocessed to produce summaries at the various levels of detail & arranged by the various dimensions.  Faster summary ability, can use many dimensions – 10 or more.

42. 42 Figure 8.22 ROLAP & MOLAP Architectures

43. 43 Figure 8.23 Example Report Produced with ROLAP

44. 44 Figure 8.24 Example Report Produced with MOLAP

45. 45 Data Mining ► Data mining is the process of finding relationships in data that are unknown to the user. ► Hypothesis verification begins with the user’s hypothesis of how data are related.  Retrieval process guided entirely by user.  Selected information can be no better than user’s understanding of the data.  Traditional way to query a database. ► Knowledge discovery is when the data warehousing system analyzes the warehouse data repository, looking for groups with common characteristics.