BUSINESS AND RESEARCH SOLUTIONS: Management Information System: In-depth

MANAGEMENT INFORMATION SYSTEMS

Previously MIS is defined as the study of ISS in business and management. The term MIS also defines a specific category of ISS that serve the management level. These systems provide managers with reports and in some cases with online access to the organization’s current performance and historical records. MIS serve the functions of: Planning, Controlling and Decision-making at the management level

They depend upon the IPSS for their data. MIS summarize and report on the company’s basic operation, the basic transaction data from TPS, compressed are usually presented in long reports that are produced on a regular schedule. MIS usually serve managers interested in weekly, monthly and yearly reports/results; not day to day activities. MIS provide answers to routine questions that have been specified in advance and have a predefined procedure for answering them.

5.3 DECISION SUPPORT SYSTEMS (DSS)

Decision Support Systems are interactive computer based systems, which help decision makers to utilize data and models to solve unstructured models. A decision support system has the following features:

· Must have a data management component which must have a database and a database management system

· Must have a user interface subsystem that is used to communicate with the user

· Must have a model management component composed of financial statistical management science and other quantitative models that provide the systems analytical capabilities and an appropriate software management program to manage the models

· Must have a knowledge management component, this is a system that can support any of the other subsystems or act as an independent component providing knowledge for he solution of a specific problem. These features can be represented by a diagram

Other computer based systems

(7)

internal &

external

databases

Dialogue Management

(4)

(6)

(8) DSS Application

System

User

(5)

5.3.1 CHARATERISTICS OF DSS

Decision Support Systems have the following characteristics:

· Provide support for decision makers at the management levels whether individuals or groups and mainly in semi-structured or structured situations by bringing together human and computerized judgment and Information

· They support several interdependent and sequential decisions

· They support all phases of decision-making process i.e. intelligence, design choice and implementation

· They are adaptable by the user overtime to deal with changing conditions

· Easy to construct and use

· Promote learning which leads to new demands and refinement of the application which leads to additional learning

· Usually utilize models, custom or standard made to allow efficient and effective solution of very complex problems

· Allow easy execution of sensitivity analysis i.e. the “what if” analysis, “goal seeking analysis”. What if analysis attempts to check the impact of a change in the assumption i.e. input data on the proposed solution e.g. what would happen to the total inventory cost if the originally assumed cost of carrying inventories is not 10% but 12%. GOAL SEEKING analysis attempts to find the value of input necessary to achieve a desired level of inputs. It represents a backward solution approach, example if in a DSS solution, if the profit yield is million shillings what sales volume would be necessary to generate the profit of 1.5 million

5.4 EXECUTIVE INFORMATION SUPPORT SYSTEMS (EISS)

The executive information support systems are computer based information system that serves the information need to the top executive. They provide rapid access to timely information and direct access to management report. They are user-friendly systems supported by graphic capabilities and provide exception reporting and drill down capabilities. They can also be easily connected online information services and electronic mail systems.

The reason for usage of EISS includes:

The ability to face external pressures to the organization that include increased competition rapidly changing decision environment
Need to access external databases
Need to be more proactive
Increasing government regulations

Internal factors which can use the demand of EIS to increase can include:

· Need for timely information

· Need for improved communication

· Need for access to operational data

· Need for rapid status updates on different activities

· Need for increased effectiveness

· Need to be able to identify historical trends

· Need for access to co-operative databases

Some of the capabilities of EISS include

· Drill down i.e. the systems have the ability to provide details of any given information by querying direct to the existing databases or even using intelligent agents to conduct a search in the internet or any available source of information that can support the drill down criteria

· The ability to identify critical success factors and determining the key performance indicators i.e. the EIS assist in identifying, monitoring, measuring a company’s standards of such factors that can be strategic, managerial or operational which will play a very important role in the organization’s success. Such factors are like comfort ability, financial, marketing, human resources, planning economic analysis and customer trends

· Status access - this is enabling the user of the system to access at any time latest data or reports on the status of key indicators or other factors

· Trend analysis - this is to enable the users of the set EIS to identify the movement of an important variable in an organization in order to be able to forecast the feature trends

· Exception reporting that enables the user of the system to pay attention to significant deviations from standards in order to enable decision maker to concentrate on areas that are extremely critical and might lead to very bad performance or very good performance

5.5 EXECUTIVE SUPPORT SYSTEM (ESS)

This is a comprehensive support system that goes beyond executive support to include analysis support communications, office automation and intelligence. However, much of its features resemble those of EIS but ESS includes the use of robotics in its support of the executives required to make decisions in an organization.

5.6 KNOWLEDGE MANAGEMENT SYSTEMS

Businesses do not run on data but they run on information and their knowledge on how to put that information to use successfully. The transformation of data into knowledge is accomplished through a process that starts with data collection from various sources. This data is stored in a database where it can be preprocessed and stored in a data warehouse. To discover knowledge the processed data may go through a transformation that makes them ready for analysis. The analysis is done with data mining tools which look for patterns and intelligent systems which support data interpretation. The results of all these activities are generated knowledge. Such knowledge can be presented using different tools of presentation and can either be stored in a knowledge base or presented to the user. This process of converting data to knowledge is known as data life cycle.

In an organization data can be internal i.e. it can be stored in the transaction databases or personal data or external environment which can be commercial database or even satellite. This data in whichever source has to be collected through methods such as observations, surveys, time studies, contributions from experts and so on. Regardless of how they are collected, they must be validated in order to ensure that the information and knowledge that is obtained from them will be relevant and dependable. The validation will be aimed at removing problems in data such as errors, delays, improper data and improper organization or unavailability. If wrong data is collected then the information or knowledge to be created will be faulty hence data control must be put else the result will be GI GO (garbage in/garbage out) situation. To ensure data quality the following attributes must be present in data.

These are:

- Accuracy - Deliverability - Accessibility

- Objectivity - Reputation - Security

- Relevance - Value added - Timeliness

- Completeness - Interpretability - Ease of understanding

- Causes representation - Consistent representation

Data that has been preprocessed and stored in a data warehouse can be accessible for analysis and representation. A data warehouse is a single depository place for keeping all types of databases. It enables data to be accessed quickly as they are located in one place and the users of data can access such data easily and frequently. Data warehouses are organized to allow for the storage of metadata.

Metadata also known as a data mart is a replicated subset of the data warehouse and it is dedicated to a functional or regional area, for example, a company may keep data marts for different functions such as human resources, marketing, engineering and so on. Such data marts and data warehouses support analytical processing which is done in order to discover trends in data which is the basis of trusting and knowledge creation.

The process of extracting useful knowledge from volumes of data is known as knowledge discovery in databases or just knowledge discovery.

This process starts with identifying which data to consider in the data ware then processing this data to be ready for analysis. The objective is to identify valid, novel potentially useful and ultimately understandable patterns in data. In order to get the patterns, the knowledge discovery process can use any of the following three:

· Massive data collection

· Powerful multiprocessor computers

· The data mining algorithms

Data mining is searching for valuable business information in large databases. It can follow techniques such as case based reasoning where historic cases can be used to recognize patterns.

It can also follow neural computing. This is a machine learning approach by which historical data can be examined for pattern co-ordination or it can follow intelligent agents which in modern times uses internet to discover the right information in the internet or from the internet based databases or it can use association analysis which is in most cases expressions of statistical rules among items. In massive data collection knowledge discovery provides with huge volumes of data from where it can be believed that from those large volumes of data from where it can be believed that from those large volumes, knowledge can be discovered using any of the techniques that can be available to the user.

By use of powerful computers, knowledge based systems can be applied to look for trends in data which can be then applied to various uses that the user is interested. Once knowledge has been discovered, it has to be presented. If presentation has to be effective, then visualization technologies have to be used to communicate such knowledge to the users. Technologies such as digital images, geographical information systems, graphical user interfaces, multidimensional tables and graphs, virtual reality and animation make the knowledge presentation more attractive and understandable to users. When this presentation is done among the employees of an organization, it is said that an organization learns. This is critical because it enables an organization to survive and to sustain competitive advantage over its competitors.

5.7 EXPERT SYSTEMS

Expert systems are computerized advisory programs that attempt to imitate the reasoning process of experts in solving difficult problems. These systems can be used by organizations to increase productivity and to argument work force in specialty areas where human experts are becoming increasingly difficult to find and retain or are too expensive to use.

An expert system attempts to mimic human experts. Experts have specific knowledge and experience in a specific problem area. This specific knowledge and experience can be programmed and stored in computer software. Technically an expert system is decision-making software that can reach a level of performance comparable to or even exceeding that of a human expert. Such a system stores the expertise and it can make inferences and a conclusion. Then like human experts, it advices non-experts and explains if necessary the logic behind advice.

Expertise in the extensive is the task specific knowledge acquired from reading and experience. It enables experts to make better and faster decisions than non-experts in solving complex problems. Expertise takes a long time usually several years to acquire and it is distributed in organizations in an uneven manner. The transfer of expertise from an expert to a computer and then to the user involves four steps:

· Knowledge acquisition from experts and other source

· Knowledge representation in the computer

· Knowledge referencing

· Knowledge transfer to the user

Organizations can benefit from expert systems in the following ways:

· Increased output and productivity which always support mass customization

· Increased quality as expert systems can increase the quality of providing consistent advise and reducing error rates

· Capture of scarce expertise and its dissemination

· Expert systems can operate in hazardous environment where a human being may not be able to work

· Expert systems can make knowledge to several people in many occasions e.g. can be implemented at a help desk where people acquire and receive advice

· Reliability as expert systems do not become tired or fall sick and always pay consistent attention to details and do not overlook relevant information and potential solutions

· Increased capabilities of other computerized systems i.e. expert systems can be made even more effective as they can easily be integrated in other systems in organization

· Provide training to novice users

· They have the ability to work with incomplete or uncertain information

· They enhance problem solving capabilities and also decrease decision-making time

Limitations

· Knowledge to be captured is not always readily available

· Expertise is hard to extract from humans

· The approach of each expert to a situation may be different nevertheless correct

· It is hard even for highly skilled experts to accurately access situations under time pressure

· Users of expert systems have natural cognitive limits so they may not use the benefits of the system to the fullest extent

· Lack of trust by end users may be barrier to expert system use

· Knowledge transfer is subject to perceptual and judgmental basis

5.7.1 COMPONENTS OF AN EXPERT SYSTEM

An expert system is composed of five main components:

1. Knowledge based component which contains knowledge necessary for understanding, formulating and solving problems. It includes two basic elements. These are:

· Facts of the problem areas

· Rules that direct the use of knowledge to solve specific in a particular domain

2. Blackboard that an area of working memory set aside for the description of a current problem as specified by the input data and also used for recording intermediate results

3. Brain or inference engine that is a computer program that provides a methodology for researching formulating conclusions

4. The interface that allows a computer user to dialog in any language as natural as possible in order to enable the inference engine to match the problem symptoms in the knowledge base and generate advice

5. Explanation subsystems which is responsible for explaining the expert systems behaviour and also explain why certain questions are asked, how a conclusion was reached, why one rejected and the plan to reach the solution

A knowledge refining system can be included that enables to analyze their own performance and learn from it in order to improve their future consultations.