5 management information system decision support system judi prajetno sugiono [email protected]...
TRANSCRIPT
2
Learning Objectives
Identify the changes taking place in the form and use of decision support in e-business enterprises.
Identify the role and reporting alternatives of management information systems.
3
Learning Objectives (continued)
Describe how online analytical processing can meet key information needs of managers.
Explain the decision support system concept and how it differs from traditional management information systems.
4
Learning Objectives (continued)
Explain how the following information systems can support the information needs of executives, managers, and business professionals: Executive information systems Enterprise information portals Enterprise knowledge portals
5
Learning Objectives (continued)
Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business.
How can expert systems be used in business decision-making situations?
6
Section I
Decision Support in Business
7
Business and Decision Support
To succeed, companies need information systems that can support the diverse information and decision-making needs of their managers and business professionals.
8
Business and Decision Support (continued)
Information, Decisions, & Management
The type of information required by decision makers is directly related to the level of management and the amount of structure in the decision situations.
9
Business and Decision Support (continued)
10
Business and Decision Support (continued)
Information Quality Timeliness
Provided WHEN it is needed Up-to-date when it is provided Provided as often as needed Provided about past, present, and future time
periods as necessary
11
Business and Decision Support (continued)
Information Quality (continued) Content
Free from errors Should be related to the information needs of a specific
recipient for a specific situation Provide all the information that is needed Only the information that is needed should be provided Can have a broad or narrow scope, or an internal or external
focus Can reveal performance
12
Business and Decision Support (continued)
Information Quality (continued) Form
Provided in a form that is easy to understand Can be provided in detail or summary form Can be arranged in a predetermined sequence Can be presented in narrative, numeric, graphic, or other
forms Can be provided in hard copy, video, or other media.
13
Business and Decision Support (continued)
14
Business and Decision Support (continued)
Decision Structure Structured decisions
Involve situations where the procedures to be followed can be specified in advance
Unstructured decisions Involve situations where it is not possible to specify
most of the decision procedures in advance
15
Business and Decision Support (continued)
Decision structure (continued)
Semistructured decisions Some decision procedures can be specified in
advance, but not enough to lead to a definite recommended decision
16
Business and Decision Support (continued)
Amount of structure is typically tied to management level Operational – more structured Tactical – more semistructured Strategic – more unstructured
17
Decision Support Trends
The growth of corporate intranets, extranets and the Web has accelerated the development and use of “executive class” information delivery & decision support software tools to virtually every level of the organization.
18
Management Information Systems
The original type of information systemProduces many of the products that support day-
to-day decision-makingThese information products typically take the
following forms: Periodic scheduled reports Exception reports Demand reports and responses Push reports
19
Management Information Systems (continued)
Management reporting alternatives Periodic scheduled reports
Prespecified format Provided on a scheduled basis
Exception reports Produced only when exceptional conditions occur Reduces information overload
20
Management Information Systems (continued)
Management reporting alternatives (continued) Demand reports and responses
Available when demanded. Ad hoc
Push reports Information is sent to a networked PC over the
corporate intranet. Not specifically requested by the recipient
21
Online Analytical Processing
Enables managers and analysts to interactively examine & manipulate large amounts of detailed and consolidated data from many perspectives Analyze complex relationships to discover
patterns, trends, and exception conditions Real-time
22
Online Analytical Processing (continued)
Involves.. Consolidation
The aggregation of data. From simple roll-ups to complex groupings of
interrelated data Drill-Down
Display detail data that comprise consolidated data
23
Online Analytical Processing (continued)
Slicing and Dicing The ability to look at the database from different
viewpoints. When performed along a time axis, helps analyze
trends and find patterns
24
Decision Support Systems
Computer-based information systems that provide interactive information support during the decision-making process
DSS’s use Analytical models Specialized databases The decision maker’s insights & judgments An interactive, computer-based modeling process to
support making semistructured and unstructured business decisions
25
Decision Support Systems (continued)
Designed to be ad hoc, quick-response systems that are initiated and controlled by the decision maker
DSS Models and Software Rely on model bases as well as databases Might include models and analytical techniques used
to express complex relationships
26
Decision Support Systems (continued)
DSS models and software (continued) Can combine model components to create
integrated models in support of specific types of business decisions
27
Decision Support Systems (continued)
Geographic Information & Data Visualization Systems Special categories of DSS that integrate
computer graphics with other DSS features GIS
A DSS that uses geographic databases to construct and display maps and other graphics displays
28
Decision Support Systems (continued)
Geographic information and data visualization systems (continued)
Data visualization systems Represent complex data using interactive three-
dimensional graphic forms Helps discover patterns, links, and anomalies
29
Using Decision Support Systems
An interactive modeling processFour types of analytical modeling
What-if analysis Sensitivity analysis Goal-seeking analysis Optimization analysis
30
Using Decision Support Systems (continued)
What-If Analysis End user makes changes to variables, or
relationships among variables, and observes the resulting changes in the values of other variables
31
Using Decision Support Systems (continued)
Sensitivity Analysis A special case of what-if analysis The value of only one variable is changed repeatedly,
and the resulting changes on other variables are observed
Typically used when there is uncertainty about the assumptions made in estimating the value of certain key variables
32
Using Decision Support Systems (continued)
Goal-Seeking Analysis Instead of observing how changes in a
variable affect other variables, goal-seeking sets a target value (a goal) for a variable, then repeatedly changes other variables until the target value is achieved
33
Using Decision Support Systems (continued)
Optimization Analysis A more complex extension of goal-seeking The goal is to find the optimum value for one
or more target variables, given certain constraints
34
Contoh: PT. INDAH GELAS
PT Indah Gelas adalah suatu perusahaan yang memproduksi kaca berkualitas tinggi untuk digunakan sebagai jendela dan pintu kaca. Perusahaan ini memiliki tiga buah 'pabrik, yaitu pabrik 1 yang membuat bingkai aluminium, pabrik 2 yang membuat bingkai kayu, dan pabrik 3 yang digunakan untuk memproduksi kaca dan merakit produk keseluruhan. Saat ini perusahaan mendapat pesanan berupa dua macam produk baru yang potensial, yaitu pintu kaca setinggi 8 kaki dengan bingkai aluminium (produk 1), dan jendela berukuran 4 x 6 kaki dengan bingkai kayu (produk 2).
Produk
Pabrik
Kapasitas yang digunakan per unit ukuran produksi
Kapasitas maks
1 2
1 1 0 4
2 0 2 12
3 3 2 18
Keuntungan 3 5
35
Goal: Max. Profit: z=3x1+5x2
Criterion: x1<=4
2X2<=12
3x1+5x2<=18
x1>=0
X2>=0
36
Decision Making Under Risk
Plant
State of Nature
Wet
P(W)=0.6
Dry
P(D)=0.4
Lettuce 100 25
Tomatoes 80 50
L choose EV(T)EV(L)
68 50) x (0.4 80) x (0.6EV(T)
7025) x (0.4 100) x .60(EV(L)
(X)O.(X)PEV(X) Value Expected ii
37
Decision Tree
38
Expected Value of Imperfect Information
Consider an imperfect source of information with a prediction record as shown in the table.
In the past 100 seasons of which 60 wet (W) and 40 dry (D), the source predicted a total of 46 wet seasons (w) of which 42 were wet (W) and 4 were dry (D). Of the 54 prediction of dry (d), 36 were dry (D) and 18 were wet (W).
Actual State
W D
Prediction w 42 4 46
d 18 36 54
60 40
54/100P(d) 40/100P(D)
46/100P(w) 60/100P(W)
39
40
Example: Investment
Additional Information P(O|C)=0.8 P(O|L)=0.1 O=optimistic P(P|C)=0.2 P(P|L)=0.9 P=pessimistic
Investment Alternatives
Prospectus
Good
P(C)=0.6
Bad
P(L)=0.4
A 50 -10
B 15 60
C 100 10
41
070.93 x10)(0.077100) x (0.923EV(C)
465.18 x60)(0.07715) x (0.923EV(B)
380.45 x10)(0.077-50) x (0.923EV(A)
:2 node valueofExpected
0.48P(O)-1P(P)
0.52 0.4) x (0.1 0.6) x 0.8(P(O)
L)P(L)|P(O C)P(C)|P(OP(O)
P(OL)P(OC)P(O)
exclusiveMutually
timesame in the happendcan L and C
75.025.01)|(
25.0)|(
077.0923.01)|(
923.04.01.06.08.0
6.08.0)|(
)()|()()|(
)()|()|(
PLP
PCP
OLP
xx
xOCP
LPLOPCPCOP
CPCOPOCP
42
Using Decision Support Systems (continued)
Data Mining for Decision Support Software analyzes vast amounts of data Attempts to discover patterns, trends, &
correlations May perform regression, decision tree, neural
network, cluster detection, or market basket analysis
43
Executive Information Systems
EIS’s combine many of the features of MIS and DSS
Originally intended to provide top executives with immediate, easy access to information about the firm’s “critical success factors”
Alternative names Enterprise information systems Executive support systems
44
Executive Information Systems (continued)
Features of an EIS Information presented in forms tailored to the
preferences of the users Most stress use of graphical user interface
and graphics displays May also include exception reporting and
trend analysis
45
Enterprise Portals and Decision Support
A Web-based interface and integration of intranet and other technologies that gives all intranet users and selected extranet users access to a variety of internal & external business applications and services
46
Enterprise Portals and Decision Support (continued)
Business benefits More specific and selective information Easy access to key corporate intranet website
resources Industry and business news Access to company data for stakeholders Less time spent on unproductive surfing
47
Knowledge Management Systems
IT that helps gather, organize, and share business knowledge within an organization
Hypermedia databases that store and disseminate business knowledge. May also be called knowledge bases
Best practices, policies, business solutionsEntered through the enterprise knowledge portal
48
Section II
Artificial Intelligence Technologies in Business
49
Business and AI
“Designed to leverage the capabilities of humans rather than replace them,…AI technology enables an extraordinary array of applications that forge new connections among people, computers, knowledge, and the physical world.”
50
Artificial Intelligence
A field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, & engineering
Goal is to develop computers that can think, see, hear, walk, talk, and feel
Major thrust – development of computer functions normally associated with human intelligence – reasoning, learning, problem solving
51
Artificial Intelligence (continued)
Domains of AI Three major areas
Cognitive science Robotics Natural interfaces
52
Artificial Intelligence (continued)
Cognitive science Focuses on researching how the human brain works
& how humans think and learn Applications
Expert systems Adaptive learning systems Fuzzy logic systems Neural networks Intelligent agents
53
Artificial Intelligence (continued)
Robotics Produces robot machines with computer intelligence
and computer controlled, humanlike physical capabilities
Natural interfaces Natural language and speech recognition Talking to a computer and having it understand Virtual reality
54
Neural Networks
Computing systems modeled after the brain’s meshlike network of interconnected processing elements, called neurons
Goal – the neural network learns from data it processes
55
Fuzzy Logic Systems
A method of reasoning that resembles human reasoning
Allows for approximate values and inferencesAllows for incomplete or ambiguous dataAllows “fuzzy” systems to process incomplete
data and provide approximate, but acceptable, solutions to problems
56
Genetic Algorithms
Uses Darwinian, randomizing, & other mathematical functions to simulate an evolutionary process that can yield increasingly better solutions
Especially useful for situations in which thousands of solutions are possible & must be evaluated
57
Virtual Reality
Computer-simulated realityRelies on multisensory input/output
devicesAllows interaction with computer-simulated
objects, entities, and environments in three dimensions
58
Intelligent Agents
A “software surrogate” for an end user or a process that fulfills a stated need or activity
Uses built-in and learned knowledge base about a person or process to make decisions and accomplish tasks
59
Expert Systems
A knowledge-based information system that uses its knowledge about a specific, complex application area to act as an expert consultant
Provides answers to questions in a very specific problem area
Must be able to explain reasoning process and conclusions to the user
60
Expert Systems (continued)
Components Knowledge base Software resources
61
Expert Systems (continued)
Knowledge base Contains
Facts about a specific subject area Heuristics that express the reasoning procedures of
an expert on the subject
62
Expert Systems (continued)
Software Resources Contains an inference engine and other programs for
refining knowledge and communicating Inference engine processes the knowledge, and
makes associations and inferences User interface programs, including an explanation
program, allows communication with user
63
Developing Expert Systems
Begin with an expert system shellAdd the knowledge base
Built by a “knowledge engineer” Works with experts to capture their knowledge Works with domain experts to build the expert
system
64
The Value of Expert Systems
65
The Value of Expert Systems (continued)
Benefits Can outperform a single human expert in many
problem situations Helps preserve and reproduce knowledge of experts
Limitations Limited focus, inability to learn, maintenance
problems, developmental costs
66
Discussion Questions
Is the form and use of information and decision support in e-business changing and expanding?
Has the growth of self-directed teams to manage work in organizations changed the need for strategic, tactical, and operational decision making in business?
67
Discussion Questions (continued)
What is the difference between the ability of a manager to retrieve information instantly on demand using an MIS and the capabilities provided by a DSS?
In what ways does using an electronic spreadsheet package provide you with the capabilities of a decision support system?
68
Discussion Questions (continued)
Are enterprise information portals making executive information systems unnecessary?
Can computers think? Will they EVER be able to?
69
Discussion Questions (continued)
What are some of the most important applications of AI in business?
What are some of the limitations or dangers you see in the use of AI technologies such as expert systems, virtual reality, and intelligent agents? What could be done to minimize such effects?
70
Real World Case 1 – AmeriKing & Others
AmeriKing’s old system Relied on an antiquated corporate information
system. Involved mailing or faxing paper reports to managers.
AmeriKing’s new system An intranet-based enterprise information portal Enables employees to use Web browsers to instantly
access financial, marketing, human resource, and other reports.
71
Real World Case 1 (continued)
What is the business value to a company of an enterprise portal like AmeriKing’s?
What are several ways AmeriKing could improve the business value of its portal?
72
Real World Case 1 (continued)
How might an enterprise portal help you as a business professional or manager in your work activities?
Is it becoming necessary for all companies to provide an enterprise information portal to their employees?
73
Real World Case 2 – BAE Systems
Problems Wasted time trying to find information to do the job. Duplication of effort Information overload Inadequate search capability
SolutionAn intranet-based knowledge management
system
74
Real World Case 2 (continued)
What problems was BAE having in knowledge sharing? Are such problems common to many companies?
How does BAE’s knowledge management system help solve such problems?
75
Real World Case 2 (continued)
What are some of the business benefits and potential limitations of BAE’s knowledge management system?
What is the difference between a corporate intranet and a knowledge management system? What is the difference in their business value?
76
Real World Case 3 – Cisco Systems, NetFlix, & Office Depot
What are the business benefits and limitations of Cisco’s Web-based system for its channel managers?
Do you agree that NetFlix’s real-time personalization system is critical to their success?
77
Real World Case 3 (continued)
Do you think salespeople will appreciate and benefit from the real-time alert system envisioned for Office Depot?
78
Real World Case 4 – Producers Assistance, Kinko’s, & Champion Printing
Using Spatial Information Systems to… Find workers Find services Find customers
79
Real World Case 4 (continued)
What is the business value of spatial information systems?
How else could spatial information systems be used in business?
80
Real World Case 4 (continued)
How helpful is Kinko’s location finder service? What else can they do to improve this spatial information management application?
81
Real World Case 5 – Schneider National
The business value of business intelligence (BI)
“We were drowning in data but starving for information.”
82
Real World Case 5 (continued)
What problem was Schneider National having with their business data?
How did business intelligence solve the problem?
83
Real World Case 5 (continued)
What are the benefits and limitations of business intelligence software as demonstrated by Schneider National?
What is the business value of business intelligence as defined by Cognos?