Chapter 14 Decision Support Systems and Knowledge Management Book:- Waman S Jawadekar
Decision Support Systems (DSS): Concept DSS are an application of Herbert Simon Model. The DSS helps the information system in the intelligence phase where the objective is to identify the problem and then go to the design phase for solution. The DSS helps in making a decision and also in its performance evaluation. These systems can be used to validate the decision by performing sensitivity analysis on various parameters of the problem. DSS can be built around the rule in case of programmable decision situation. While in non-programmable decisions, the rules are not fixed and requires every time the user to go through the decision making cycle. The DSS refers to a class of system which support the process of decision making and does not always give a decision itself. 2
Types of Decision Support System (DSS) Status Inquiry Systems The no. of decisions in the operational management and some at the middle management are based on one or two aspects of decision making situations. It does not require elaborate computations, analysis etc. If the status is known, the decision is automatic. (the status and solution is unique relation) Data Analysis Systems These decision systems are based on comparative analysis and use of a formula or an algorithm. Eg. are The cashflow analysis, the inventory analysis and the personal inventory systems. The use of simple data processing tools and business rules are required. Information Analysis Systems The data is analysed and information reports are generated. The decision makers use these reports for assessment of the situation for decision making. Eg. are sales analysis, a/c receivable systems, the market research etc. 3
Types of Decision Support System (DSS) Accounting Systems These systems are not necessarily for decision making but they are desirable to keep track of the major aspects of the business or a function. The contents of these systems is more data processing leading to formal reporting, with exceptions if necessary. These systems account items such as cash, inventory, personnel and relate it to a norm developed by the management for control and decision. Model Based Systems These systems are simulation models or optimisation models for decision making. These decisions are one time and infrequent and provide general guidelines for operation or management. The product mix decision, the material mix, the job scheduling rules and the resource or asset or facilities planning system are the examples. 4
Example (DSS) Example of Material Management function and the variety of the decision and the type of systems used therein to support and evaluate the decision Decision Finding and selection of vendor Procurement Pricing Selections of vendor based on price, quality, performance Selection of order quantity Inventory rationalisation Management of inventory within various financial and stocking constraints Inquiry system Type of system required Performance analysis system Data analysis Information analysis system Model based system Valuation of inventory and accounting system Inventory optimisation model 5
Types of DSS (analysis) We can analyse these systems in terms of the input source, the system, the hardware and the type of user System Input source System Hardware User Inquiry Data analysis Informatio n analysis Accounting ROI Model based control a. Database b. Conventional files Database and other files Processed data files Transactions Master files and database Inventory database and external data Query system Packages of DP systems Analysis programmes and use of simple models Transactions processing system Development of OR or business models PC, servers and clients Mainframe or servers, PCs Mainframe, mini, super mini, servers, client PCs Mini, Mainframe, Client/Server Mainframe, mini, CS Clerks, assistants Operations managers Middle level managers Middle and top management Middle and top management Prepared By: Akil Z Surti 6
Facts about DSS The DSS are developed by the users and System analysis jointly The DSS uses the principles of economics, science and engineering and also the tools and techniques of management The data used in the DSS is drawn from the information systems developed in the company The DSS are developed in isolation and form an independent system subset of the management information system Common use of DSS is to test the decision alternatives and also to test the sensitivity of the result to change in the system and assumptions The data and information for the DSS are used from the internal sources such as database and files and from the external sources. 7
DSS: Deterministic Systems The most significant advantage of the DSS is its use in sensitizing the decisions and assessing its implications on the result or business performance. It focuses on the critical issues in business It provides higher management ability to delegate decision making to the lower level once the tools and the models are tested. 8
Decision Support Systems (DSS) Models The DSS can be based on three different approaches. 1. Behavioural Models 2. Management Science Models 3. Operations Research (OR) Models 9
Behavioural Models These models are useful in understanding the behaviour amongst the business variables. 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. A regression model (forecasting) shows the correlation between one or more variables. It also helps in identifying the influence of one variable on the other. These models are largely used in process control, manufacturing, agricultural sciences, medicines, psychology and marketing. Behavioural analysis can be used to set the points for alert, alarm and action for the decision maker. 10
Management Science Models These models are developed on the principles of business management, accounting and econometrics. Eg. the budgetary systems, the cost accounting systems, the system of capital budgeting for better return on the investment, the ABC analysis, the control of inventory through the maximum-minimum levels, the MRP systems etc. are the examples of the use of the management science in the materials management. Production planning and control, scheduling and loading systems are the examples in production management. Manpower planning and forecasting is the example in personnel management. 11
Operations Research(OR) Models 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. In arriving the solution, methods of calculus, matrix algebra, probability and set theory are uses. The OR models address themselves to the resource usage optimisation, by balancing two or more aspects of the decision situation. The efforts are made to find the optimum solution. In manufacturing business, the maximisation of profit with an appropriate product mix, within the capacity and the market constraint, is a common problem. Queuing theory solves the designing problem, the cost of facility, its running cost, the idle time of the facility and the waiting time of the customer. Eg. of operations research models are mathematical programming techniques, Linear Programming (LP), Inventory control models, Material requirement planning (MRP) system etc. 12
Group Decision Support System (GDSS) DSS are designed for a manager who is a sole decision maker. However, many decision making situations call for involvement of a number of persons, each contributing towards the decision process. IT supports such decision making where there is a group participation. Such decision support system is termed as Group Decision Support Systems (GDSS) GDSS has same components as in DSS, namely database, models, DSS tools (Query, OLAP, Spreadsheet, Statistical Analysis). If GDSS is a group responsibility then the group needs a platform to conduct the process. Four configurations of group members are possible as listed in the next slide 13
GDSS Contd.. Group members in one room operating on network with common display screen to share the display for all members. GDSS process is transparent. Group members sit at their respective locations and use their desktop and LAN to interact with other members. GDSS process is not as transparent as in case of a. Group members are in different cities and they come together through teleconferencing or video conferencing with prior planning for GDSS operations. Group members are at remote locations may be in different countries and they come together through long distance telecommunications network. In all four configurations, GDSS support s/w is available on server for members to use. 14
GDSS Contd.. Following activities are common in GDSS Sending and receiving information in all forms, types across the network. Display of notes, graphic, drawings, pictures. Sharing ideas, choices, and indicating preferences Participate in decision making process with inputs, help and so on. In GDSS, group members interact, debate, communicate and conclude using different tools and techniques 15
Artificial Intelligence (AI) System Intelligence when supported by knowledge and reasoning abilities becomes an artificial intelligence. When such an AI is packed into a database as a system, then it is said AI system. AI systems fall into three basic categories Expert systems (Knowledge based), the Natural Language (Native language) Systems, and the Perception System (Vision, speech, touch). AI Applications Uses Human information Processing capability Uses computer intelligence For producing Human Like capacity Uses Human Capabilities In speech Recognition, Multi sensory interfacing AI Applications Robotics appln Natural interface application Expert Systems / Learning sys. etc Robot sys. Doing jobs of humans Virtual reality sys. 16
AI (Contd..) AI 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 applications are configurations, design, diagnosis, interpretation, analysis, planning, scheduling, training, testing and forecasting. The AI systems do no replace people. AI systems help to avoid making same mistakes, and to respond quickly and effectively to a new problem situation. The knowledge based expert system is a special AI System. The Goal of AI is to develop computer functions and features as close to human intelligence, described as reasoning, learning, problem solving, exhibiting creativity, respond quickly, sort quickly ambiguous and incomplete and or erroneous information or situation. Robotics application uses AI, engineering science, and physiology to produce computer intelligence to guide a computer driven machine to perform like human being, having capabilities of perception, touch, manipulation, locomotion, navigation. Robotics applications are found useful in manufacturing, material handling, and transportation. 17
AI (Contd..) Natural interface application uses AI to build natural, realistic, multi sensory human computer interface. This interface enables you to build a Virtual Reality. Eg. 3D games, or when an architect rotates the model of house for different views. 18
DSS application in E-enterprise DSS are data driven and model driven. DSS are used in Supply Chain Management (SCM) and Customer Relationship Management (CRM). The decisions in SCM are of two types Structural:- They are strategic in nature and need a careful data analysis, problem definition, modeling, developing alternatives and selecting best. Typical Structural decisions are Deciding number of warehouses, service centres, manufacturing units and their locations based on minimum cost or fastest delivery. Use of mechanised and automated material handling systems in warehouse, use of computer aided manufacturing systems to speed up process Use of inventory models to decide stock keeping units (SKU) etc. Operational:- Those are in areas of stock allocation to work orders in head, deciding on inventory control parameters (EOQ, SS, ROL). The objective of DSSs in SCM is to reduce the cost of supply chain operations. 19
DSSs structure and architecture for SCM Transaction Processing Systems Enterprise Mgmt Systems External Data Processing Systems Data Warehouse Business data DB for DSS Customer Vendor DB DSSs Software Solutions Model Based system Simulation Models Data Driven Systems Use of rules & Spread Sheet Appln of OR models Business Models OLAP and queries Data Mining Tools 20
Contd.. Another major area of DSS application is CRM. DSSs for CRM focus on meeting customer centric decision requirements namely pricing, product differentiation, deciding payment options, and credit facilities, deciding method of problem resolutions, customer segmentation etc. Another major application of DSS is executive decision support. The executives responsible for strategic management of business need DSS (also known as Executive information support systems (EIS) which are conceptualised; modelled and used by them to look into every new problem. 21
Knowledge Management Knowledge is the ability of a person to understand the situation and act effectively. Knowledge in the work place is the ability of people and organizations to understand and act effectively. Intellectual Capital (IC) is defined as a body of knowledge, which is well structured in text and numeric form and has commercial and economic value significance to the person or organization. In general terms, Knowledge and IC is a set made of information, ability, experience, and wisdom which gives the organization a competitive advantage and expertise. The human brain has ability to respond to a communication through following different inputs as shown in the next slide. 22
Contd.. Signal Data Input Information Expertise Knowledge Intellectual Capital Components (IC) Indication of something Human Response / understanding Some measure of this indication Measure in context of some other thing adding focus and clarity Building information sets based on principles, scientific reasons for problem solving Developing capabilities using cognitive science for effective behaviour Set of knowledge components uniquely possessed by the person OR Organisation having strategic and competitive unbeatable advantage The scope of knowledge management depends on the perspective the organization would take. One broader perspective is that KM includes strategic, tactical and operational knowledge management. A limited perspective is KM for improving operational efficiency of the organization 23
Forces driving KM initiatives Driving Force External Internal Competition Incr. effectiveness Customization Tech - breakthroughs Cognitive behaviour Knowledge intense work Changing capabilities Of business partners Need to move to Sharable intelligence These forces create initiatives to build Knowledge Management System (KMS). KMS is a logical extension to sophisticated MIS, which uses latest information technology to improve process capabilities, customer service and business performance. 24
Knowledge Management KM has following processes Define, capture, manipulate, store and develop. Develop information systems for knowledge creation. Design applications for improving organization s effectiveness. Create knowledge set, i.e. intellectual capital to increase economic value of the organization. Keep IC continuously on upgrade to use it as a central resource. Distribute and share to concerned Physical view of KMS is shown below Data Capture Information processing Principles, practices, Applications Problem solving exp., wisdom Problem solving models Tools for modeling Process for Knowledge Components & IC Knowledge Assets IC Expert System 25
True / False of KM (Facts of KM) Facts KM leads to more additional work KM is an additional function and a high overhead KM requires substantial investment in hardware and software People do not like to share knowledge, more so the knowledge which gives them power in organization Knowledge has to be secret and can not be shared. It is a secret formula, process. It should be with one or two personnel called as confidential and should be stored in an inaccessible place. Comments Reduces problem solving time in routine and non routine situation No, it is additional extended effort on existing system but no significant increase in overhead compared to benefit No, knowledge for operation and tactical effectiveness does not need add. Invest. Yes, there is a resistance. But it can be managed with other incentives to people to share. It is a temporary problem. Knowledge define here is of different types. It deals with management of business ie. Diagnostics, problem solving, decision making, etc. therefore, the treatment given to R&D secrets is not recommended here 26
Contd.. Facts Information set, once declared as knowledge, persists forever. KM is a static system Knowledge is an analytical information, processed for specific goal Knowledge once created, its use is automatic straight away with assured benefits Comments No, today s Knowledge is a general knowledge of tomorrow. No, it is dynamic to handle knowledge obsolescence No, knowledge has the capability, understanding permitting to handle different complex situations. It enables the user to anticipate and judge the implications and effects of the same No, it is not automatic. User is required to use cognitive abilities to envision, choose and apply. 27
Knowledge Management Systems To bring knowledge as critical input in the management process, it is necessary to have knowledge management systems. The KMS deals with definition, acquisition, construction, storage, delivery and application of knowledge. KMS handles two types of knowledge: 1) TACIT 2) EXPLICIT The knowledge has a structure and character. Knowledge type Nature Owners of knowledge Source Skills Tacit Individual Individual Capability Tacit Individuals or groups Individuals or groups Knowhow Tacit Individuals or groups Individuals or groups Information Explicit Individual Individual Organized info. Explicit Databases System Facts Explicit Databases/individuals System Process Explicit Organization System Proprietary(Patent) Explicit Organization System 28
Contd.. Knowledge is an essence of business management intelligence, residing in individuals, group of individuals, systems in the form of information set, models, processes and databases. Use of knowledge is critical to the organization, hence knowledge creation, storage, distribution and delivery is very important calling for establishing formal KMS. 29
KMS Architecture KMS architecture deals with knowledge identification, generation and delivery for application in business. KMS Identification of knowledge Knowledge Generation Knowledge Delivery Defn & Categorisation Processing for acquisition Access Control Surveying & Locating Manipulation & Modeling Application Methods Build knowledge structure Creation of KDB Storage & Security 30
Identification of knowledge In a given business scenario, knowledge needs to be defined and identified for further processing. On identification of knowledge in terms of scope and category next step is to survey for locating the source for such knowledge in the orgn. Category Knowledge Purpose Impact Tacit Explicit Workplace knowledge --- Yes Acting effectively Speed Business knowledge Yes Yes Functioning effectively Performance Intellectual capital components (IC) --- Yes Performing effectively Growth On locating the valid source, it is necessary to put into structure for understanding and application. 31
Knowledge Generation After identification, definition and structuring, the knowledge process must be set for acquisition of knowledge. On acquisition, knowledge needs to be manipulated for understanding, presentation and usage. Next step is to integrate knowledge sets to build knowledge databases for access and distribution. The important thing is to give meaningful definition and presentation to tacit knowledge for ease of use or application. Knowledge generation can be achieved through training of concerned personnel in the organization. 32
Knowledge Delivery Knowledge needs to be protected and secured and also simultaneously made available to users for viewing, manipulating and application. The system for access control, authorisation and authentication of knowledge for the purpose of update, alter, delete etc. are necessary. Knowledge packaging and delivery for ready to use is also necessary 33
Tools for knowledge Management Database Management tools For data management and seeking knowledge through SQL queries Data warehousing, data mart, data mining tools For business information creation and using Data Mining tools, OLAP tools to seek knowledge on different views and scenarios Process modeling and management tools For recording standard process as an explicit knowledge for use in the orgn. Work flow management tools For recording the process of workflow as an explicit knowledge for group workers Search engine tool For locating specific information through search algorithms Document management tools like Lotus notes. DBMS tools for documents. Useful to search and manipulate documents Web based tools Eg. www.infoseek.com, www.eloquent.com 34
Approaches to Develop KM Systems The knowledge body, tacit or explicit is an output of two basic approaches. Conventional: Data to information (untested) Knowledge Refined: Result to information (tested) Data Information Action Result KMS becomes successful in the orgn when developers and users recognise and appreciate the barriers in the system implementation. The barriers are at four different locations People in the orgn Resistance to change Lack of motivation to learn Turnover of people Resistance to share knowledge Management of the orgn Ego problem Loss of power of possession Fear of loosing to competition 35
Contd.. Organisation structure Complext, distributed, based on different principles of structuring posing problems in storage, distribution sharing and security. Knowledge itself Universal defn of knowledge with respect to context Unanimity in coding, classifying and storing of knowledge Decision on specific knowledge to declare it as a general knowledge 36
Knowledge based Expert System (KBES) Decision making or problem solving is a unique situation 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, generalised or the KBES. The generalised 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. 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. To build a KBES certain prerequisites are required A person with the ability to solve the problem with knowledge based reasoning should be available. An expert should be able to articulate the knowledge to the specific problem characteristics. 37
KBES Contd.. Knowledge in the KBES is defined as a mix of theory of the subject, knowledge of its application, organised information and the data of problems and its solutions, and an ability to generate the new avenues to solve the problem. The KBES has 3 basic components: KBES MODEL User Control Mechanism Knowledge base Inference mechanism Knowledge base: It is a database of knowledge consisting of the theoretical foundations, facts, judgments, rules, formulae, intuition & experience. 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 inter. The user control mechanism uses the knowledge base in guiding the inference process. 38
Knowledge based Expert System (KBES) In the KBES, the knowledge data base uses certain methods of knowledge representation. These methods are Semantic Networks, Frames and Rules. Semantic Network:- Knowledge is represented on the principle of predicate functions and the symbolic data structures which have a meaning built into it are known as semantic. A semantic network is a network of nodes and arcs connecting the nodes. The node represents an entity and the arc represents association with a true and false meaning built into it. Eg. If table in the room is big and made of wood with lamination and has elliptical shape, it would be inferred as conference room table. Frames:- The concept of frame is to put the related knowledge in one area called a frame. The frame is an organized data structure of a knowledge. The frames can be related to other frames. Rules:- A rule is a conditional statement of an action that is supposed to take place, under certain conditions. Eg. If an item is made of tungsten carbide then the item is excisable. 39
Inference Mechanism This mechanism is based on the principle of reasoning. When reasoning is goal driven, it is called backward chaining to goal and when it is data driven it is called forward chaining to goal. For eg. If there is a breakdown in the plant, then looking backward for the symptoms and causes, based on the knowledge database is backward chaining. The data when collected during plant operations to predict whether the plan will stop or work at low efficiency is used to infer the performance or the plant and this is called forward chaining. The Choice between backward or forward chaining really depends on the kind of situation. To resolve a problem after the event one has to go from goal to data it is case of backward chaining. But if the question is of preventing a breakdown, then the data would be monitored in such a way if it is towards a goal then it is a case of forward chaining. KBES uses both the methods of reasoning. The success of the KBES depends on a degree of knowledge, the confidence in the knowledge and the quality of inference mechanism. 40
Benefits of DSS Ability to view data/information in different dimensions and sensing the problem, trend, pattern through different views. Ability to understand and assess business performance and various results in terms of cause and effect, and enabling to define the problem. Ability to understand the problem and its ramifications(effect), and ability to judge the impact on business. Ability to assess the impact of any change in the business performance and enabling to focus on the areas where impact is negative. Ability to view a complex scenario or problem and to design a model to analyse the problem, develop alternatives to solve the problem, test the solution and to conduct sensitivity analysis. Ability to make better decisions due to quick analysis, modelling, developing alternatives and testing for selection. Ability to control the risk exposure in decisions. 41