DSS : DETERMINISTIC SYSTEMS

1. DSS : DETERMINISTIC SYSTEMS • Most of the decision situations are fairly structured and, therefore, can be put in the form of the business models. The model then has business and decision making validity. • If the management can design such models duly tested, they can be used by the decision makers, whenever the need arises. All such tools and models act as the support systems for decision making. • While designing the models, a flexible approach is taken to solve varied decision making problems. Types of Tools / Models • The decision support system can be based on the different types of tools and models. They are: DSS BEHAVIORAL MODELS MANAGEMENT SCIENCE MODELS OPERATIONS RESEARCH MODELS MIS - Chapter 11

2. Behavioral Models • These models are useful in understanding the behavior among the business variables. • The decision maker can then make decisions giving due regard to such behavioral relationships. • The trend analysis, forecasting and the statistical analysis models belong to this category. • The trend analysis indicates how different variables behave in trend setting in the past and hence in the future. • These types of models are largely used in process control, manufacturing, agricultural sciences, medicines, psychology and marketing. The behavioral analysis can be used to set the points for alert, alarm and action for the decision maker. • Management Science Models • These models are developed on the principles of business management, accounting and econometrics. • There are also several management systems, which can be converted into the DSS models. • For example, the budgetary systems, the cost accounting systems, the system of capital budgeting for better return on the investment, the ABC analysis etc., are the examples of the use of the management science in the materials management. MIS - Chapter 11

3. Some of these models can be used straightaway in the design of the DSS. While some others require the use of management principles and practices, most of the procedure based decision making models belong to this category. • Operations Research (OR) models • The OR models are mathematical models. These models represent a real life problem situation in terms of the variables, constants and parameters expressed in algebraic equations. • Since the models are mathematical, there are solutions to these problems. In arriving to the solution, methods of calculus, matrix algebra, probability, and set theory are used. • These models have a clarity to the extent that each of them has a set of assumptions which must be true in real life. • Further if the assumptions are valid, the solutions offered are realistic and practical, the model represents the real life problem situation. MIS - Chapter 11

4. Project Planning and Control Models • The PERT (Programme Evaluation and Review Technique) and the CPM (Critical Path Method) techniques have emerged as very powerful tools for planning and control of one time tasks or projects. • These systems show the inter-dependencies of each activity in the project. • Apart from planning, the model also manages to help three aspects of the project I.e., the completion time, the cost and the resource. Management Considerations of PERT/CPM • The project manager considers the sequencing of activities before implementation of the project. • Inter-dependencies between the activities are described more clearly showing the possible bottlenecks in future. • Attention is focused on selected activities which are critical for the project completion. • It provides an easy method of planning the project in a different manner, within available resources. • A running estimate is provided, of the most probable time in which a project will be completed and also the probability of its being completed in time. • Any activity which is entering into cost and time over-runs is quickly identified. MIS - Chapter 11

5. Network drawing and PERT/CPM statistics The following figure explains the rules of network drawing Network drawing rules: Activity, 0 = Event A B 0 1 5 Activity A must be completed before B starts. 0 A C Activity C can start when A and B are completed. 6 2 B 0 A and B are parallel activities, C and D are parallel activities. Activity X is a dummy, not requiring Any resource. The dummy activity X shows only dependency and does not consume time or resource. It is shown by a dotted line. A B 0 3 7 X C D 0 4 8 Activity D is not dependent on the activity X. Activity C is dependent on the activity B. MIS - Chapter 11

6. Estimating activity time • When the network is completed, every event is assigned a number for reference and identity. • Then, the next step is to estimate the completion time of each activity. • Two time estimates are: • The most likely time of completion of the activity. • The time estimate (te) based on three time estimates: The optimistic, the most likely and the pessimistic. to = The optimistic time estimate. tm = The most likely time estimate. tp = The pessimistic time estimate. Then the activity time estimate, te = (to + 4 tm + tp) / 6 Drawing the PERT network • The next step is to draw a network of the project from start to finish. • The critical path is that path which takes the longest time from start to end. Consider a sample project: MIS - Chapter 11

7. Most likely duration (Days) Immediate predecessors to activity Activity A 5 - B 8 D C 6 A, D D 11 - E 7 A, C Critical Path 3 C 4 A E 1 X 5 D 2 B MIS - Chapter 11

8. SLACK = LS – ES OR LF - EF • Critical activities are those where the slack is zero. • In the network D, X, C, E are the critical activities , while A and B are non-critical • The slack is a time resource which a project manager can use for manipulating the resource and start and finish of the activity. Project Completion Time = Sum of activity times on a critical path = D + X + C + E = 6 + 0 + 11 + 7 = 24 days. MIS - Chapter 11

9. ARTIFICIAL INTELLIGENCE SYSTEM • All human beings have intelligence, which they use for problem solving. • Intelligence when supported by knowledge and reasoning abilities becomes an artificial intelligence. • When such an artificial intelligence is packed into a database as a system, then what we have is AI system. • AI systems fall into three basic categories, viz., the Expert Systems (knowledge based), the Natural Language (Native languages) Systems, and the Perception System (vision, speech, touch). ARTIFICIAL INTELLIGENCE SYSTEM NATURAL LANGUAGE EXPERT PERCEPTION USES USES USES NATIVE LANGUAGE KNOWLEDGE KNOWLEDGE SIZE, SHAPE, IMAGE, VOICE APPLIES APPLIES APPLIES SENSING ABILITIES FOR REASONING LANGUAGE REASONING HUMAN-LIKE REASONING MIS - Chapter 11

10. Artificial Intelligence is a software technique applied to the non-numeric data expressed in terms of symbols, statements and patterns. • It uses the methods of symbolic processing, social and scientific reasoning and conceptual modeling for solving the problems. • The AI systems are finding applications in configurations, design, diagnosis, interpretation, analysis, planning, scheduling, training, testing and forecasting. • The AI systems liberate experts from solving common/ simple problems, leaving the experts to solve complex problems. • The knowledge-based Expert System is a special AI system. It has wide applications in business and industry. KNOWLEDGE BASED EXPERT SYSTEM (KBES) • Decision making or problem solving is a unique situation riddled with uncertainty and complexity, dominated by the resource constraints and a possibility of several goals. • In such cases, flexible systems (open systems) are required to solve the problems. • Most of such situations, termed as the unstructured situations, adopt two methods of problem solving, generalized or the knowledge based expert system (KBES). • The generalized problem solving approach considers the generally applicable constraints, examines all possible alternatives and selects one by trial and error method with reference to a goal. MIS - Chapter 11

11. The knowledge based problem solving approach considers the specific constraints within a domain, examines the limited problem alternatives within a knowledge domain and selects the one with knowledge based reasoning with reference to a goal. • In a generalized approach, all alternatives are considered and the resolution of the problem is by trial and error, with no assurance, whether it is the best or the optimum, while in the knowledge based approach, only limited alternatives are considered and resolution made by a logical reasoning with the assurance of the local optimum. • The generalized approach is dominated by a procedure or method, while the knowledge based approach is dominated by the reasoning process based on the knowledge. • Since, the KBES considers knowledge as the base, a system is required which will hold the knowledge of experienced people and provide an application path to solve the problem. • To build a knowledge-based system certain prerequisites are required. • The first prerequisite is that a person with the ability to solve he problem with knowledge-based reasoning should be available. • The second prerequisite is that such an expert should be able to articulate the knowledge to the specific problem characteristics. The KBES has three basic components which are necessary to build the system: USER CONTROL MECHANISM KNOWLEDGE BASE INFERENCE MECHANISM KBSE Model MIS - Chapter 11

12. Knowledge base • It is a database of knowledge consisting of the theoretical foundations, facts, judgments, rules, formulae, intuition and experience. • It is a structural storage with facilities of easy storage. Inference Mechanism • It is a tool to interpret the knowledge available and to perform logical deductions in a given situation. User control mechanism • It is a tool applied to the inference mechanism to select, interpret and deduct or infer. • The user control mechanism uses the knowledge base in guiding the inference process. • In the KBES, three components are independent of each other. This helps in modifying the system without affecting all the components. • In KBES, knowledge is independent from application, I.e., inference process. • The KBES database, stores the data, the cause-and-effect relation rules, and the probability information on event occurrences. MIS - Chapter 11

13. MIS AND THE ROLE OF DSS • The Decision Support System (DSS) is a special class of system which is used as a support in decision making. Many of the decision making situations, at all levels of management, are such, that its occurrence is infrequent but the methodology of decision making is known. • These systems use data from the general MIS and they are used by a manager or a decision maker for decision support. The basic characteristic of the DSS is that it is based on some tool, techniques or model. These systems are used sometimes for testing new alternatives, training and learning. They are also used for sensitizing the various parameters of the model. • The DSS could be an internal part of the MIS. When the decision making need is in real time dynamic mode, all such systems are designed to read, measure, monitor, evaluate, analyze and act as per the decision guidance embedded in the system. • The MIS designer has to look for all such situations and design the DSS for integration in the system. The MIS would become more useful if the decision making is made person independent and executed with well-designed DSS. • When the decision situation requires multidimensional analysis using the internal and external data, then such decision suppport systems are kept out of the main MIS design scope. Most of these situations call for the use of models and the nature of decision is strategic, calling for planned activity. MIS - Chapter 11