Sistem Pendukung Keputusan M. Ali Fauzi

Transcription

1 Sistem Pendukung Keputusan M. Ali Fauzi

2 Decision Support System M. Ali Fauzi

3 Decision Making, System, Modeling, and Support

4 System

5 System A System is a collection of objects such as people, resources, concepts, and procedures intended to perform an identifiable function or to serve a goal

6 The Structure of a System Three Distinct Parts of Systems > Inputs > Processes > Outputs

7 The Structure of a System Are surrounded by an environment Separated from environment by boundary Frequently include a feedback mechanism

8 The Structure of a System Environment Input(s) Processes Output(s) Feedback Boundary

9 How to Identify the Environment? Answer Two Questions 1. Does the element matter relative to the system's goals? [YES] 2. Is it possible for the decision maker to significantly manipulate this element? [NO]

10 The Environment Environmental Elements Can Be Social Political Legal Physical Economical Often Other Systems

11 The Structure of a System A human, the decision maker, is usually considered part of the system

12 The Structure of a System

13 System Types A Closed System is totally independent of other systems and subsystems An Open System is very dependent on its environment

14 System Types An open system interacts with its environment while a closed system does not.

15 System Types A Closed System is Independent Takes no inputs Delivers no outputs to the environment Black Box

16 System Types A Open System is Accepts inputs Delivers outputs to environment

17 System Types For example, the R&D department of a company may have much less interaction wit people outside the department as compared to marketing department.

18 System Types Therefore, we may consider the R&D department organization as a closed system, and Marketing department organisation as an open system.

19 System Types TABLE 2.1 A Closed Versus an Open Inventory System Factors Management Science, EOQ (Closed System) Inventory DSS (Open System) Demand Constant Variable, influenced by many factors Unit cost Constant May change daily Lead time Constant Variable, difficult to predict Vendors and users Weather and other environmental factors Excluded from analysis Ignored May be included in analysis May influence demand and lead time

20 An Information System Collects, processes, stores, analyzes, and disseminates information for a specific purpose

21 An Information System Is often at the heart of many organizations

22 An Information System Accepts inputs and processes data to provide information to decision makers and helps decision makers communicate their results

23 Performance Measurement Effectiveness is the degree to which goals are achieved Doing the right thing!

24 Performance Measurement Efficiency is a measure of the use of inputs (or resources) to achieve outputs Doing the thing right!

25 Performance Measurement Productivity (efficiency/effectiveness)

26 Models

27 Models ~ Major Component of DSS ~ Use Models instead of experimenting on the real system

28 Models A model is a simplified representation or abstraction of reality.

29 Models Reality is generally too complex to copy exactly. Much of the complexity is actually irrelevant in problem solving.

30 Degree of abstraction Degrees of Model Abstraction Less (Least to Most) ~ Iconic (Scale) Model ~ Analog Model ~ Mathematical (Quantitative) Models More

31 Models Iconic (Scale) Model: Physical replica of a system Ex. Model airplanes, bridge, dam, cars, ships

32 Models Iconic (Scale) Model Ex. Model bridges Models of bridges and dams can be subjected to multiple levels of stress from wind, heat, cold, and other sources in order to test such variables as endurance and safety.

33 Models Analog Model behaves like the real system but does not look like it (symbolic representation)

34 Models Analog Model behaves like the real system but does not look like it (symbolic representation)

35 Models Analog Model is some physical models may not look exactly like their object of representation but are close enough to provide some utility

36 Models Analog Model Ex. The use of cardboard cutouts to represent the machinery being utilized within a manufacturing facility. This allows planners to move the shapes around enough to determine an optimal plant layout.

37 Models Analog Model Ex. Organizational chart Maps Blueprints

38 Models Mathematical (Quantitative) Models use mathematical relationships to represent complexity Used in most DSS analyses

39 Models Mathematical models are perhaps the most abstract. These models do not look like their real-life counterparts at all.

40 Models Mathematical models Ex. the question of how much to order is determined by using an economic order quantity (EOQ) model.

41 Models Mathematical models Ex. EOQ models identify the optimal order quantity by minimizing the sum of certain annual costs that vary with order size

42 Models Computer graphics advances: complement math models using more iconic and analog models (visual simulation)

43 Benefits of Models 1. Time compression 2. Easy model manipulation 3. Low cost of construction 4. Low cost of execution (especially that of errors) 5. Can model risk and uncertainty

44 Benefits of Models 6. Can model large and extremely complex systems with possibly infinite solutions 7. Enhance and reinforce learning, and enhance training.

45 Modelling Approach

46 Models

47 Several Solution Approaches ~ Trial-and-Error ~ Simulation (Descriptive) ~ Optimization ~ Heuristics

48 Trial-and-Error ~ Just try one and see what you get ~ Evaluate

49 Normative models (= optimization) The chosen alternative is demonstrably the best of all possible alternatives

50 Normative models (= optimization) Assumptions : ~ Humans are economic beings whose objective is to maximize the attainment of goals ~ For a decision-making situation, all alternative courses of action and consequences are known

51 Normative models (= optimization) Assumptions : ~ Decision makers have an order or preference that enables them to rank the desirability of all consequences

52 Normative models (= optimization) Assumptions : ~ Decision makers have an order or preference that enables them to rank the desirability of all consequences

53 Normative models (= optimization) ~ Get Indomie+rice at friend s kos : 100 ~ Cooking rice + indomie : 90 ~ Cooking Indomie : 80 ~ Go to Papa jahat : 60

54 Heuristic models (= suboptimization) The chosen alternative is the best of only a subset of possible alternatives

55 Heuristic models (= suboptimization) ~ Often, it is not feasible to optimize realistic (size/complexity) problems ~ Suboptimization may also help relax unrealistic assumptions in models

56 Heuristic models (= suboptimization) ~ Cooking rice + indomie : 90 ~ Cooking Indomie : 80 ~ Go to Papa jahat : 60

57 Descriptive models They do not provide a solution but information that may lead to a solution

58 Descriptive models Describe things as they are or as they are believed to be (mathematically based)

59 Descriptive models Simulation - most common descriptive modeling method (mathematical depiction of systems in a computer environment)

60 Descriptive models Allows experimentation with the descriptive model of a system

61 Descriptive models Alternatives ~ Cooking Indomie ~ Speed : fast ~ Cost : low ~ Satisfaction : low ~ Nutrition : very low

62 Descriptive models Alternatives ~ Cooking rice + Indomie ~ Speed : medium ~ Cost : low ~ Satisfaction : high ~ Nutrition : very low

63 Descriptive models Alternatives ~ Go to Papa jahat ~ Speed : medium ~ Cost : high ~ Satisfaction : high ~ Nutrition : medium