Management Information System
Management Information System
MIS is popularly known as the Management Information System. MIS is considered as one such method of generating information which is used by management of organization for decision Making, control of activities, operations etc. During the period 1940 to 1960 computers were commercially used for census and payroll work. This involved large amount of data and its processing. Since then the commercial application exceeded the scientific applications for which the computer were mainly intended for. MIS is an information System which helps in providing the management of an organization with information which is used by management for decision making. Management information systems (MIS)
Management information systems (MIS) are a combination of hardware and software used to process information automatically. Commonly, MIS are used within organizations to allow many individuals to access and modify information. In most situations, the management information system mainly operates behind the scenes, and the user community is rarely involved or even aware of the processes that are handled by the system. A computer system used to process orders for a business could be considered a management information system because it is assisting users in automating processes related to orders. Other examples of modern management information systems are websites that process transactions for an organization or even those that serve support requests to users. A simple example of a management information system might be the support website for a product, because it automatically returns information to the end user after some initial input is provided. MIS characteristics
• It supports transaction handling and record keeping. • It is also called as integrated database Management System which supports in major functional areas. • It provides operational, tactical, and strategic level managers with east access to timely but, for the most, structured information. • It supports decision –making function which is a vital role of MIS. • It is flexible which is needed to adapt to the changing needs of the organization. • It promotes security system by providing only access to authorized users.
• MIS not only provides statistical and data analysis but also works on the basis on MBO (management by objectives). MIS is successfully used for measuring performance and making necessary change in the organizational plans and procedures. It helps to build relevant and measurable objectives, monitor results, and send alerts. • Coordination: MIS provides integrated information so that all the departments are aware of the problem and requirements of the other departments. This helps in equal interaction of the different centers and connects decision centers of the organization. • Duplication of data is reduced since data is stored in the central part and same data can be used by all the related departments. • MIS eliminates redundant data.
• It helps in maintaining consistency of data. It is divided into subsystems. Handlings with small systems are much easier than an entire system. This helps in giving easy access of data, accuracy and better information production. • MIS assembles, process, stores, Retrieves, evaluates and disseminates the information.
Function of MIS
The main functions of MIS are:
• Data Processing: Gathering, storage, transmission, processing and getting output of the data. Making the data into information is a major task. • Prediction: Prediction is based on the historical data by applying the prior knowledge methodology by using modern mathematics, statistics or simulation. Prior knowledge varies on the application and with different departments. • Planning: Planning reports are produced based on the enterprise restriction on the companies and helps in planning each functional department to work reasonably. • Control: MIS helps in monitoring the operations and inspects the plans. It consists of differences between operation and plan with respect to data belonging to different functional department. It controls the timely action of the plans and analyzes the reasons for the differences between the operations and plan. Thereby helps managers to accomplish their decision making task successfully.
• Assistance: It stores the related problems and frequently used information to apply them for relative economic benefits. Through this it can derive instant answers of the related problem. • Database: This is the most important function of MIS. All the information is needs a storage space which can be accessed without causing any anomalies in the data. Integrated Database avoids the duplication of data and thereby reduces redundancy and hence consistency will be increased. • The major function of MIS lies in application of the above functions to support the managers and the executives in the organization in decision-making.
Disadvantages of MIS
The following are some of the disadvantages of MIS:
• MIS is highly sensitive: MIS is very helpful in maintaining logging information of an authorized user. This needs to monitor constantly. • Quality of outputs is governed by quality of inputs. • MIS budgeting: There is difficulty in maintaining indirect cost and overheads. Capturing the actual cost needs to have an accrual system having true costs of outputs which is extremely difficult. It has been difficult to establish definite findings. • MIS is not flexible to update itself for the changes. • The changes in the decision of top level management decrease its effectiveness. • Information accountability is based on the qualitative factors and the factors like morality, confidence or attitude will not have any base.
2. Explain Knowledge based system? Explain DSS and OLAP with example?
Knowledge based system are the systems based on knowledge base. Knowledge base is the database maintained for knowledge management which provides the means of data collections, organization and retrieval of knowledge. The knowledge management manages the domain where it creates and enables organization for adoption of insights and experiences. There are two types of knowledge bases.
a. Machine readable knowledge bases: The knowledge base helps the computer to process through. It makes the data in the computer readable code which makes the operator to perform easier. Such information sare used by semantic web. Semantic web is a web that will make a description of the system that a system can understand. b. Human readable knowledge bases: They are designed to help people to retrieve knowledge. The information need to be processed by the reader.
The reader can access the information and synthesize their own. Knowledge based system refers to a system of data and information used for decision making. The system is automated to work on the knowledge based data and information required in a particular domain of management activity. The processing is done based on the past decisions taken under suitable conditions. Decision making is based on the fact that the condition is similar to the past situation hence the decision is also is similar. Examples of KBS are intelligent systems, robotics, neural networks etc.
Decision Support Systems (DSS)
DSS is an interactive computer based system designed to help the decision makers to use all l the resources available and make use in the decision making. In management many a time problems arise out of situations for which simple solution may not be possible. To solve such problems you may have to use complex theories. The models that would be required to solve such problems may have to be identified. DSS requires a lot of managerial abilities and managers judgment. You may gather and present the following information by using decision support application: • Accessing all of your current information assets, including legacy and relational data sources, cubes, data warehouses, and data marts • Comparative sales figures between one week and the next • Projected revenue figures based on new product sales assumptions • The consequences of different decision alternatives, given past experience in a context that is described.
Manager may sometimes find it difficult to solve such problems. E.g. – In a sales problem if there is multiple decision variables modeled as a simple linear problem but having multiple optima, it becomes difficult to take a decision. Since any of the multiple optima would give optimum results. But the strategy to select the one most suitable under conditions prevailing in the market, requires skills beyond the model. It would take some trials to select a best strategy. Under such circumstances it would be easy to take decision if a ready system of databases of various market conditions and corresponding appropriate decision is available. A system which consists of database pertaining to decision making based on certain rules is known as decision support system. It is a flexible system which can be customized to suit the organization needs.
It can work in the interactive mode in order to enable managers to take quick decisions. You can consider decision support systems as the best when it includes high-level summary reports or charts and allow the user to drill down for more detailed information. A DSS has the capability to update its decision database. Whenever manager feels that a particular decision is unique and not available in the system, the manager can chose to update the database with such decisions.
This will strengthen the DSS to take decisions in future. There is no scope for errors in decision making when such systems are used as aid to decision making. DSS is a consistent decision making system. It can be used to generate reports of various lever management activities. It is capable of performing mathematical calculations and logical calculation depending upon the model adopted to solve the problem. You can summarize the benefits of DSS into following:
• Improves personal efficiency
• Expedites problem solving
• Facilitates interpersonal communication
• Promotes learning or training
• Increases organizational control
• Generates new evidence in support of a decision
• Creates a competitive advantage over competition
• Encourages exploration and discovery on the part of the decision maker
• Reveals new approaches to thinking about the problem space Online Analytical Processing (OLAP)
OLAP refers to a system in which there are predefined multiple instances of various modules used in business applications. Any input to such a system results in verification of the facts with respect to the available instances. A nearest match is found analytically and the results displayed form the database. The output is sent only after thorough verification of the input facts fed to the system. The system goes through a series of multiple checks of the various parameters used in business decision making. OLAP is also referred to as a multi dimensional analytical model. Many big companies use OLAP to get good returns in business.
The querying process of the OLAP is very strong. It helps the management take decisions like which month would be appropriate to launch a product in the market, what should be the production quantity to maximize the returns, what should be the stocking policy in order to minimize the wastage etc. A model of OLAP may be well represented in the form of a 3D box. There are six faces of the box. Each adjoining faces with common vertex may be considered to represent the various parameter of the business situation under consideration. E.g.: Region, Sales & demand, Product etc.
Model of OLAP
3. What are Value Chain Analysis & describe its significance in MIS? Explain what is meant by BPR? What is its significance? How Data warehousing & Data Mining is useful in terms of MIS?
Every firm performs a set of activities that helps in designing, producing, marketing, delivering and supporting its products. These activities form a process. At every stage of the process, the firm adds value. The chain of activities from raw material procurement to the after-sales service is called the value chain. It identifies nine strategic activities, i.e. five primary and four support activities, to create value, as shown in below figure. Primary activities are the activities that are involved in creating, distributing, selling and providing after sales assistance for a product. Primary activities are those activities that are involved in the physical creation of the product,
The Generic Value Chain
Why it is important MIS:
Value-addition activities like production, marketing delivery, and servicing of the product. These activities are connected in a chain. Support activities include those providing purchased inputs, technology, human resources, or overall infrastructure functions to support the primary activities. It is possible to reduce the transaction cost by proper coordination of all the activities. It should be possible to gather better information for various controls and also replace the same by less costlier activities.
It will also be possible to reduce the overall time required to complete an activity. Therefore coordination is very important to achieve competitive advantage. For this it is necessary to manage the value chain as a system rather than as separate parts. An enterprise’s value chain for competing in a particular industry is embedded in a larger stream of activities. What Porter termed as ‘value system’, may be referred to as the ‘industry value-chain’.
Business Process Re-engineering (BPR)
The existing system in the organization is totally reexamined and radically modified for incorporating the latest technology. This process of change for the betterment of the organization is called as Business process re-engineering. This process is mainly used to modernize and make the organizations efficient. BPR directly affects the performance. It is used to gain an understanding the process of business and to understand the process to make it better and re-designing and thereby improving the system. BPR is mainly used for change in the work process. Latest software is used and accordingly the business procedures are modified, so that documents are worked upon more easily and efficiently. This is known as workflow management.
Importance of BPR
Business process are a group of activities performed by various departments, various organizations or between individuals that is mainly used for transactions in business. There may be people who do this transaction or tools. We all do them at one point or another either as a supplier or customer. You will really appreciate the need of process improvement or change in the organizations conduct with business if you have ever waited in the queue for a longer time to purchase 1 kilo of rice from a Public Distribution Shop (PDS-ration shop). The process is called the check-out process. It is called process because uniform standard system has been maintained to undertake such a task.
The system starts with forming a queue, receiving the needed item form the shop, getting it billed, payment which involves billing, paying amount and receiving the receipt of purchase and the process ends up with the exit from the store. It is the transaction between customer and supplier. The above activities takes place between the customer and supplier which forms the process steps this example explains the business process. The business process may be getting admission to the college and graduating from the college, building house, and implementing new technology to an organization (Example EDUNXT in SMUDE), etc A Process can be represented by triangle:
Business process reengineering is a major innovation changing the way organizations conduct their business. Such changes are often necessary for profitability or even survival. BPR is employed when major IT projects such as ERP are undertaken. Reengineering involves changes in structure, organizational culture and processes. Many concepts of BPR changes organizational structure. Team based organization, mass customization; empowerment and telecommuting are some of the examples. The support system in any organization plays a important role in BPR. ES, DSS, AI (discussed later) allows business to be conducted in different locations, provides flexibility in manufacturing permits quicker delivery to customers and supports rapid paperless transactions among suppliers, manufacturers and retailers.
Expert systems can enable organizational changes by providing expertise to non experts. It is difficult to carry out BPR calculations using ordinary programs like spreadsheets etc. Experts make use of applications with simulations tools for BPR. Reengineering is basically done to achieve cost reduction, increase in quality, improvement in speed and service. BPR enable a company to become more competitive in the market. Employees work in team comprising of managers and engineers to develop a product. This leads to the formation of interdisciplinary teams which can work better than mere functional teams. The coordination becomes easier and faster results can be achieved. The entire business process of developing a product gets a new dimension. This has led to reengineering of much old functional process in organizations.
– Data Warehouse is defined as collection of database which is referred as relational database for the purpose of querying and analysis rather than just transaction processing. Data warehouse is usually maintained to store heuristic data for future use. Data warehousing is usually used to generate reports. Integration and separation of data are the two basic features need to be kept in mind while creating a data warehousing. The main output from data warehouse systems are; either tabular listings (queries) with minimal formatting or highly formatted “formal” reports on business activities. This becomes a convenient way to handle the information being generated by various processes.
Data warehouse is an archive of information collected from wide multiple sources, stored under a unified scheme, at a single site. This data is stored for a long time permitting the user an access to archived data for years. The data stored and the subsequent report generated out of a querying process enables decision making quickly. This concept is useful for big companies having plenty of data on their business processes. Big companies have bigger problems and complex problems. Decision makers require access to information from all sources. Setting up queries on individual processes may be tedious and inefficient. Data warehouse may be considered under such situations.
Data warehouse Architecture
Data ware house is center part of data repository. Data warehousing provides a strategic approach to all the business. Data warehouse is broadly famous for its characteristics like: a. Subject oriented: Data warehouse has the ability to analyze the data. The ability to define by subject matter makes DW subject oriented. b. Integrated: This resolves the problems of conflicts and inconsistencies existing in the units of measure. c. Non volatile: Once the data is entered in the warehouse it shall not change. This characteristics is very important because after all the purpose of heuristic data is for future use. d. Time variant: The data warehouse focus on change over time. To discover new trends in business, analysts need large amount of data which is contrasting to OLTP (Online transaction Processing) which works on heuristic data. It is observed that all the companies are profit oriented and also want to exist in the market along with their competitors.
Data warehousing is of strategic value because it enables us to achieve the former while deftly avoiding the latter. This is the strategic spirit in which we should understand, implement, and manage data warehousing. A very powerful introduction to a data warehousing business case said the following: “The strategic intent of our data warehousing is to enable the business to win in the marketplace every day, with every customer, and with every purchase. By repositioning our operational data and combining it with selected foreign data, we will empower our employees so that they can routinely delight and excite our customers. Through our unique appreciation of the value of our data assets, we will elevate our data warehouses to the point where they become a compelling and durable contributor to the sustainable competitive advantage of the business.
In this way, data warehousing will enable the business to impress its attitude on the marketplace and prevail over its competitors who have already lost.” Have you implemented data warehousing with such a cogent strategic intent? Sun Tzu said: “Strategy is important to the nation-it is the ground of death and life, the path of survival and destruction, so it is imperative to examine it. There is a way of survival which helps and strengthens you; there is a way of destruction which pushes you into oblivion.” So data warehousing is a path to survival that helps and strengthens you. Our strategic understanding of data warehousing is complete. BPR uses all these technologies like data bases, data mining, and data warehousing helps the company to perform a strategic and object oriented performance.
– Data mining is primarily used as a part of information system today, by companies with a strong consumer focus – retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among “internal” factors such as price, product positioning, or staff skills, and “external” factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to “drill down” into summary information to view detail transactional data.
With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual’s purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments. Data Mining is a collaborative tool which comprises of database systems, statistics, machine learning, visualization and information science. Based on the data mining approach used, different techniques form the other discipline can be used such as neural networks, artificial intelligence, fuzzy logic, knowledge representation, high performance computing and inductive logic programming.
Data Mining Process
Data mining refers to extracting or mining knowledge from large amount of data. There may be other terms which refer data mining such as knowledge mining, knowledge extraction, data/pattern analysis, data archeology, and data dredging. The Knowledge discovery as a process may consist of following steps: 1. Data Cleaning: It removes noise and inconsistent data.
2. Data integration: It is where multiple data sources are combined. 3. Data selection: Data relevant to the analysis task are retrieved from the database. 4. Data transformation: Data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance. 5. Data mining: An essential process where intelligent methods are applied in order to extract data patterns. 6. Pattern evaluation: To identify the truly interesting patterns representing knowledge based on some interesting measure. 7. Knowledge presentation: Visualization and knowledge representation techniques are used to present the mined knowledge to the users. When you look at the above step you will find that data mining is a very important step in knowledge representation. It interacts with the user for knowledge base. So it is found that there is necessity of a typical architecture for data mining as a big process.
The architecture of the data mining has the following components: 1. Database, data warehouse and information repository: This is one or a set of databases, data warehouse, and information repository which can be used for data cleaning and data integration. 2. Database server: This Server is responsible for fetching the relevant data 3. Data mining engine: This helps in accessing the user through applications. It accesses data from the warehouse with the help of standard data connectivity mechanisms. Usually database drivers are used to connect the database. 4. Patterns evaluation model: It acquires the data to be evaluated form the database, producing the pattern edge. This model scans the data. It searches and creates the interesting patterns based on the thresholds.
5. Graphical user interface: It communicates between the user and the data mining system. It allows the user to interact with the system and specifies the data mining queries or task. Data mining is applicable to any kind of information repository. Some of these may be relational databases, data warehouse, transactional databases, advanced database management systems, WWW and files. Advance database systems include object oriented databases, object relational databases, and application oriented databases. The best example for data mining which is so close to our lives is Google. The success of Google depends on the use of data mining techniques in the analysis of data in the search engine to meet your search demand. 4. Explain DFD & Data Dictionary? Explain in detail how the information requirement is determined for an organization?
Data flow diagrams represent the logical flow of data within the system. DFD do not explain how the processes convert the input data into output. They do not explain how the processing takes place. DFD uses few symbols like circles and rectangles connected by arrows to represent data flows. DFD can easily illustrate relationships among data, flows, external entities an stores. DFD can also be drawn in increasing levels of detail, starting with a summary high level view and proceeding o more detailed lower level views.
A number of guidelines should be used in constructing DFD.
• Choose meaningful names for the symbols on the diagram.
• Number the processes consistently. The numbers do not imply the sequence.
• Avoid over complex DFD.
• Make sure the diagrams are balanced.
The data dictionary is used to create and store definitions of data, location, format for storage and other characteristics. The data dictionary can be used to retrieve the definition of data that has already been used in an application. The data dictionary also stores some of the description of data structures, such as entities, attributes and relationships. It can also have software to update itself and to produce reports on its contents and to answer some of the queries. Information is useful for an Organization in many aspects. It is mainly use for planning and developing process. The information needs for the implementation of the business plan should find place in the MIS. To ensure such an alignment possibility, it is necessary that the business plan – strategic or otherwise, states the information needs. The information needs are then traced to the source data and the systems in the organization which generate such a data.
The plan of development of the MIS is linked with the steps of the implementation in a business development plan. The system of information generation is so planned that strategic information is provided for the strategic planning, control information is provided for a short term planning and execution. The details of information are provided to the operations management to assess the status of an activity and to find ways to make up, if necessary. Once the management needs are translated into information needs, it is left to the designer to evolve a plan of MIS development and implementation. While preparing the schedule due consideration is given to the importance of the system in the overall information requirement. Due regard is also given to logical system development.
For example, it is necessary to develop the accounting system first and then the analysis. Further, unless the systems are fully developed their integration is not possible. This development schedule is to be weighed against the time scale for achieving certain information requirement linked to a business plan. If these are not fully met, it is necessary to revise the time schedule and also the development schedule, whenever necessary.
The selection of the architecture, the approach to the information system development and the choice of hardware and software are the strategic decisions in the design and development of the MIS in the organization. The organizations which do not care to take proper decisions in these areas suffer from over-investment, under-utilization and are not able to meet the critical information requirements. 5. What is ERP? Explain its existence before and its future after? What are the advantages & Disadvantages of ERP? What is Artificial Intelligence? How is it different from Neural Networks?
Enterprise resource planning (ERP) is a system that integrates all of these functions into a single system, designed to serve the needs of each different department within the enterprise. ERP is more of a methodology than a piece of software, although it does incorporate several software applications, brought together under a single, integrated interface.
ERP (enterprise resource planning) is an industry term for the broad set of activities that helps a business manage the important parts of its business. The information made available through an ERP system provides visibility for key performance indicators (KPIs) required for meeting corporate objectives. ERP software applications can be used to manage product planning, parts purchasing, inventories, interacting with suppliers, providing customer service, and tracking orders. ERP can also include application modules for the finance and human resources aspects of a business. Typically, an ERP system uses or is integrated with a relational database system. The deployment of an ERP system can involve considerable business process analysis, employee retraining, and new work procedures.
ERP Before and After
Prior to the concept of ERP systems, departments within an organization (for example, the human resources (HR)) department, the payroll department, and the financial department) would have their own computer systems. The HR computer system (often called HRMS or HRIS) would typically contain information on the department, reporting structure, and personal details of employees. The payroll department would typically calculate and store paycheck information. The financial department would typically store financial transactions for the organization. Each system would have to rely on a set of common data to communicate with each other.
For the HRIS to send salary information to the payroll system, an employee number would need to be assigned and remain static between the two systems to accurately identify an employee. The financial system was not interested in the employee-level data, but only in the payouts made by the payroll systems, such as the tax payments to various authorities, payments for employee benefits to providers, and so on. This provided complications. For instance, a person could not be paid in the payroll system without an employee number.
Advantages and Disadvantages
Advantages – In the absence of an ERP system, a large manufacturer may find itself with many software applications that do not talk to each other and do not effectively interface. Tasks that need to interface with one another may involve:
• A totally integrated system
• The ability to streamline different processes and workflows
• The ability to easily share data across various departments in an organization
• Improved efficiency and productivity levels
• Better tracking and forecasting
• Lower costs
• Improved customer service
Change how a product is made, in the engineering details, and that is how it will now be made. Effective dates can be used to control when the switch over will occur from an old version to the next one, both the date that some ingredients go into effect, and date that some are discontinued. Part of the change can include labeling to identify version numbers. Some security features are included within an ERP system to protect against both outsider crime, such as industrial espionage, and insider crime, such as embezzlement.
A data tampering scenario might involve a disgruntled employee intentionally modifying prices to below the breakeven point in order to attempt to take down the company, or other sabotage. ERP systems typically provide functionality for implementing internal controls to prevent actions of this kind. ERP vendors are also moving toward better integration with other kinds of information security tools.
Disadvantages – Many problems organizations have with ERP systems are due to inadequate investment in ongoing training for involved personnel, including those implementing and testing changes, as well as a lack of corporate policy protecting the integrity of the data in the ERP systems and how it is used. While advantages usually outweigh disadvantages for most organizations implementing an ERP system, here are some of the most common obstacles experienced: Usually many obstacles can be prevented if adequate investment is made and adequate training is involved, however, success does depend on skills and the experience of the workforce to quickly adapt to the new system.
• Customization in many situations is limited
• The need to reengineer business processes
• ERP systems can be cost prohibitive to install and run
• Technical support can be shoddy
• ERP’s may be too rigid for specific organizations that are either new or want to move in a new direction in the near future.
Artificial Intelligence and Neural Networks
Artificial intelligence is a field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics and engineering. The goal of AI is to develop computers that can simulate the ability to think, see, hear, walk, talk and feel. In other words, simulation of computer functions normally associated with human intelligence, such as reasoning, learning and problem solving. AI can be grouped under three major areas: cognitive science, robotics and natural interfaces. [Source MIS 7th edition, James O Brien and George M Marakas- Tata Mc Graw Hill]. Cognitive science focuses on researching on how the human brain works and how humans think and learn. Applications in the cognitive science area of AI include the development of expert systems and other knowledge-based systems that add a knowledge base and some reasoning capability to information systems.
Also included are adaptive learning systems that can modify their behavior based on information they acquire as they operate. Chess-playing systems are some examples of such systems. Fussy logic systems can process data that are incomplete or ambiguous. Thus, they can solve semi-structured problems with incomplete knowledge by developing approximate inferences and answers, as humans do. Neural network software can learn by processing sample problems and their solutions. As neural nets start to recognize patterns, they can begin to program themselves to solve such problems on their own. Neural networks are computing systems modeled after the human brain’s mesh like network of interconnected processing elements, called neurons. The human brain is estimated to have over 100 billion neuron brain cells.
The neural networks are lot simpler in architecture. Like the brain, the interconnected processors in a neural network operate in parallel and interact dynamically with each other. This enables the network to operate and learn from the data it processes, similar to the human brain. That is, it learns to recognize patterns and relationships in the data. The more data examples it receives as input, the better it can learn to duplicate the results of the examples it processes. Thus, the neural networks will change the strengths of the interconnections between the processing elements in response to changing patterns in the data it receives and results that occur.
For example, neural network can be trained to learn which credit characteristics result in good or bad loans. The neural network would continue to be trained until it demonstrated a high degree of accuracy in correctly duplicating the results of recent cases. At that point it would be trained enough to begin making credit evaluations of its own. Genetic algorithm software uses Darwinian (survival of the fittest), randomizing and other mathematics functions to simulate evolutionary processes that can generate increasingly better solutions to problems.
Robotics: Ai, engineering and physiology are the basic disciplines of robotics. This technology produces robot machines with computer intelligence and computer-controlled, humanlike physical capabilities. This area thus includes applications designed to give robots the power of sight, or visual perception; touch or tactile capabilities; dexterity or skill in handling and manipulation; locomotion, or the physical ability to move over any terrain; and navigation, or the intelligence to properly find one’s way to a destination. Natural interfaces: The development of natural interfaces is essential to the natural use of computers by humans.
Development of natural languages and speech recognition are major thrusts in this area of AI. Being able to talk to computers and robots in conversational human languages and have them understand us as easily as we understand each other is a goal of AI research. This involves research and development in linguistics, psychology, computer science and other disciplines. Other natural interface research applications include the development of multi-sensory devices that use a variety of body movements to operate computers. This is related to the emerging application area of virtual reality. Virtual reality involves using multi-sensory human– computer interfaces that enable human users to experience computer simulated objects, spaces activities and worlds as if they actually exist.
6. Distinguish between closed decision making system & open decision making system? What is ‘What – if‘ analysis? Why is more time spend in problem analysis & problem definition as compared to the time spends on decision analysis?
The decision-making systems can be classified in a number of ways. There are two types of systems based on the manager’s knowledge about the environment. If the manager operates in a known environment then it is a closed decision-making system. The conditions of the closed decision-making system are: a) The manager has a known set of decision alternatives and knows their outcomes fully in terms of value, if implemented. b) The manager has a model, a method or a rule whereby the decision alternatives can be generated, tested, and ranked for selection.
c) The manager can choose one of them, based on some goal or objective criterion. Few examples are a product mix problem, an examination system to declare pass or fail, or an acceptance of the fixed deposits. If the manager operates in an environment not known to him, then the decision-making system is termed as an open decision-making system. The conditions of this system in contrast closed decision-making system are: a) The manager does not know all the decision alternatives.
b) The outcome of the decision is also not known fully. The knowledge of the outcome may be a probabilistic one. c) No method, rule or model is available to study and finalise one decision among the set of decision alternatives. d) It is difficult to decide an objective or a goal and, therefore, the manager resorts to that decision, where his aspirations or desires are met best. Deciding on the possible product diversification lines, the pricing of a new product, and the plant location, are some decision-making situations which fall in the category of the open decision-making systems. The MIS tries to convert every open system to a closed decision-making system by providing information support for the best decision. The MIS gives the information support, whereby the manager knows more and more about environment and the outcomes, he is able to generate the decision alternatives, test them and select one of them. A good MIS achieves this.
What if analysis
Decisions are made using a model of the problem for developing various solution alternatives and testing them for best choice. The model is built with some variables and relationship between variables. In reality, the considered values of variables or relationship in the model may not hold good and therefore solution needs to be tested for an outcome, if the considered values of variables or relationship change. This method of analysis is called ‘what if analysis.’ For example, in decision-making problem about determining inventory control parameters (EOQ, Safety Stock, Maximum Stock, Minimum Stock, Reorder level) lead time is assumed fairly constant and stable for a planning period.
Based on this, the inventory parameters are calculated. Inventory manager wants to know how the cost of holding inventory will be affected if lead time is reduced by one week or increased by one week. The model with changed lead time would compute the cost of holding inventory under new conditions. Such type of analysis can be done for purchase price change, demand forecast variations and so on. Such analysis helps a manager to take more learned decisions. ‘What if analysis’ creates confidence in decision-making model by painting a picture of outcomes under different conditions?
Behavioural Concepts in Decision-making
The manager, being a human being, behaves in a peculiar way in a given situation. The response of one manager may not be the same as that of the two other managers, as they differ on the behavioural platform. Even though tools, methods and procedures are evolved, the decision is many a times influenced by personal factors such as behaviour. The managers differ in their approach towards decision-making in the organisation, and, therefore, they can be classified into two categories, viz., the achievement-oriented, i.e., looking for excellence and the task-oriented, i.e., looking for the completion of the task somehow. The achievement-oriented manager will always opt for the best and, therefore, will be enterprising in every aspect of the decision-making. He will endeavour to develop all the possible alternatives.
He would be scientific, and therefore, more rational. He would weigh all the pros and cons properly and then conclude. The manager’s personal values will definitely influence ultimately. Some of the managers show a nature of risk avoidance. Their behaviour shows a distinct pattern indicating a conservative approach to decision-making – a path of low risk or no risk. Further, even though decision-making tools are available, the choice of the tools may differ depending on the motives of the manager. The motives are not apparent, and hence, are difficult to understand. A rational decision in the normal course may turn out to be different on account of the motives of the manager. The behaviour of the manager is also influenced by the position he holds in the organisation.
The behaviour is influenced by a fear and an anxiety that the personal image may be tarnished and the career prospects in the organisation may be spoiled due to a defeat or a failure. The managerial behaviour, therefore, is a complex mix of the personal values, the atmosphere in the organisation, the motives and the motivation, and the resistance to change. Such behaviour sometimes overrides normal decisions based on business and economic principles. The interplay of different decision-making of all the managers in the organization shapes up the organizational decision-making. The rationale of the business decision will largely depend upon the individuals, their positions in the organization and their inter-relationship with other managers.
If two managers are placed in two decision-making situations, and if their objectives are in conflict, the managers will arrive at a decision objectively, satisfying individual goals. Many a times, they may make a conscious decision, disregarding organization’s objective to meet their personal goals and to satisfy their personal values. If the manager is enterprising, he will make objectively rational decisions. But if the manager is averse to taking risk, he will make a decision which will be subjectively rational as he would act with limited knowledge and also be influenced by the risk averseness. Thus, it is clear that if the attitudes and the motives are not consistent across the organization, the decision-making process slows down in the organization.
Subject: Decision theory,
University/College: University of Arkansas System
Type of paper: Thesis/Dissertation Chapter
Date: 13 October 2016
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