Tag: Banking and Financial Services Notes

Records

Records are composed of fields, each of which contains one item of information. A set of records constitutes a file. For example, a personnel file might contain records that have three fields: a name field, an address field, and a phone number field.

A single entry in a table is called a Tuple or Record or Row. A tuple in a table represents a set of related data. For example, the above Employee table has 4 tuples/records/rows.

Following is an example of single record or tuple.

Attributes

A table consists of several records(row), each record can be broken down into several smaller parts of data known as Attributes. The above Employee table consist of four attributes, ID, Name, Age and Salary.

Attribute Domain

When an attribute is defined in a relation(table), it is defined to hold only a certain type of values, which is known as Attribute Domain.

Hence, the attribute Name will hold the name of employee for every tuple. If we save employee’s address there, it will be violation of the Relational database model.

Keys

In database management systems (DBMS), keys are used to uniquely identify records within a database table. They ensure data integrity and support efficient data retrieval and manipulation. There are different types of keys in DBMS, including:

1. Primary Key: A primary key is a unique identifier for each record in a table. It ensures that there are no duplicate values and that each record can be uniquely identified. A primary key can consist of one or more columns in a table.
2. Candidate Key: A candidate key is a set of attributes (columns) that can uniquely identify a record in a table. It is similar to a primary key but may not have been designated as the primary key.
3. Foreign Key: A foreign key establishes a relationship between two tables in a relational database. It refers to the primary key of another table, creating a link between the two tables. The foreign key ensures referential integrity and maintains the relationships between tables.
4. Unique Key: A unique key ensures that the values in a column (or a set of columns) are unique and not duplicated within a table. Unlike the primary key, a unique key can allow NULL values.
5. Composite Key: A composite key is a key that consists of two or more columns in a table. Together, these columns uniquely identify a record. Individually, the columns may not be unique, but their combination makes them unique.

6. Super Key: A super key is a set of attributes that can uniquely identify a record in a table. It may contain more attributes than necessary to uniquely identify a record.

Example:

Integrity constraints

Every relation in a relational database model should abide by or follow a few constraints to be a valid relation, these constraints are called as Relational Integrity Constraints.

The three main Integrity Constraints are:

• Key Constraints
• Domain Constraints
• Referential integrity Constraints
• Entity integrity Constraints

Key Constraints

We store data in tables, to later access it whenever required. In every table one or more than one attributes together are used to fetch data from tables. The Key Constraint specifies that there should be such an attribute(column) in a relation(table), which can be used to fetch data for any tuple(row).

The Key attribute should never be NULL or same for two different row of data.

For example, in the Employee table we can use the attribute ID to fetch data for each of the employee. No value of ID is null and it is unique for every row, hence it can be our Key attribute.

Domain Constraint

Domain constraints refers to the rules defined for the values that can be stored for a certain attribute.

Like we explained above, we cannot store Address of employee in the column for Name.

Similarly, a mobile number cannot exceed 10 digits.

Referential Integrity Constraint

We will study about this in detail later. For now remember this example, if I say Supriya is my girlfriend, then a girl with name Supriya should also exist for that relationship to be present.

If a table reference to some data from another table, then that table and that data should be present for referential integrity constraint to hold true.

Entity integrity Constraints

Entity Integrity Constraint is used to ensure the uniqueness of each record or row in the data table. There are primarily two types of integrity constraints that help us in ensuring the uniqueness of each row, namely, UNIQUE constraint and PRIMARY KEY constraint.

The unique key helps in uniquely identifying a record in the data table. It can be considered somewhat similar to the Primary key as both of them guarantee the uniqueness of a record. But unlike the primary key, a unique key can accept NULL values and it can be used on more than one column of the data table.

Schema Architecture

The three-schema architecture divides the database into three-level to create a separation between the physical database and the user application. In simple words, this architecture hides the details of physical storage from the user. The database administrator (DBA) should be able to change the structure of database storage without affecting the user’s view.

This architecture contains three layers or levels of the database management system:

• External level
• Conceptual level
• Internal level

Three Schema Architecture of DBMS

1. External or View level: This is the highest level of database abstraction. External or view level describes the actual view of data that is relevant to the particular user. This level also provides different views of the same database for a specific user or a group of users. An external view provides a powerful and flexible security mechanism by hiding the parts of the database from a particular user.
2. Conceptual or Logical level: The conceptual level describes the structure of the whole database. This level acts as a middle layer between the physical storage and user view. It explains what data to be stored in the database, what relationship exists among those data, and what the datatypes are. There is only one conceptual schema per database.

Database administrator and the programmers work at this level. This level does not provide any access or storage details but concentrates on the relational model of the database. The conceptual schema also includes features that specify the checks to retain integrity and consistency.

3. Internal or Physical level: This is the lowest level of database abstraction. It describes how the data is actually stored in the database and provides methods to access data from the database. It allows viewing the physical representation of the database on the computer system. The interface between the conceptual schema and the internal schema identifies how an element in the conceptual schema is stored and how it may be accessed.

If there is any change in the internal or physical schema, it needs to be addressed to the interface between the conceptual and internal schema. But there is no need to change in the interface of a conceptual and external schema. It means that the changes in physical storage devices such as hard disks, and the files organized on storage devices, are transparent to application programs and users.

Data independence

A database system normally contains a lot of data in addition to users’ data. For example, it stores data about data, known as metadata, to locate and retrieve data easily. It is rather difficult to modify or update a set of metadata once it is stored in the database. But as a DBMS expands, it needs to change over time to satisfy the requirements of the users. If the entire data is dependent, it would become a tedious and highly complex job.

Logical Data Independence

Logical data is data about database, that is, it stores information about how data is managed inside. For example, a table (relation) stored in the database and all its constraints, applied on that relation.

Logical data independence is a kind of mechanism, which liberalizes itself from actual data stored on the disk. If we do some changes on table format, it should not change the data residing on the disk.

Physical Data Independence

All the schemas are logical, and the actual data is stored in bit format on the disk. Physical data independence is the power to change the physical data without impacting the schema or logical data.

For example, in case we want to change or upgrade the storage system itself − suppose we want to replace hard-disks with SSD − it should not have any impact on the logical data or schemas.

Data Warehouse Architecture is complex as it’s an information system that contains historical and commutative data from multiple sources. There are 3 approaches for constructing Data Warehouse layers: Single Tier, Two tier and Three tier.

The basic concept of a Data Warehouse is to facilitate a single version of truth for a company for decision making and forecasting. A Data warehouse is an information system that contains historical and commutative data from single or multiple sources. Data Warehouse Concepts simplify the reporting and analysis process of organizations.

A data-warehouse is a heterogeneous collection of different data sources organised under a unified schema. There are 2 approaches for constructing data-warehouse:

Top-down approach:

The essential components are discussed below:

1. External Sources:
   External source is a source from where data is collected irrespective of the type of data. Data can be structured, semi structured and unstructured as well.
2. Stage Area:
   Since the data, extracted from the external sources does not follow a particular format, so there is a need to validate this data to load into datawarehouse. For this purpose, it is recommended to use ETL
   • E(Extracted):Data is extracted from External data source.
   • T(Transform):Data is transformed into the standard format.
   • L(Load):Data is loaded into datawarehouse after transforming it into the standard format.
3. Data-warehouse:
   After cleansing of data, it is stored in the data warehouse as central repository. It actually stores the meta data and the actual data gets stored in the data marts. Notethat data warehouse stores the data in its purest form in this top-down approach.
4. Data Marts:
   Data mart is also a part of storage component. It stores the information of a particular function of an organisation which is handled by single authority. There can be as many number of data marts in an organisation depending upon the functions. We can also say that data mart contains subset of the data stored in datawarehouse.
5. Data Mining:
   The practice of analysing the big data present in data warehouse is data mining. It is used to find the hidden patterns that are present in the database or in data warehouse with the help of algorithm of data mining.

This approach is defined by Inmon as data warehouse as a central repository for the complete organisation and data marts are created from it after the complete data warehouse has been created.

Advantages of Top-Down Approach:

1. Since the data marts are created from the data warehouse, provides consistent dimensional view of data marts.
2. Also, this model is considered as the strongest model for business changes. That’s why, big organisations prefer to follow this approach.
3. Creating data mart from data warehouse is easy.

Disadvantages of Top-Down Approach:

1. The cost, time taken in designing and its maintenance is very high.

Bottom-up approach:

1. First, the data is extracted from external sources (same as happens in top-down approach).
2. Then, the data go through the staging area (as explained above) and loaded into data marts instead of data warehouse. The data marts are created first and provide reporting capability. It addresses a single business area.
3. These data marts are then integrated into data warehouse.

This approach is given by Kinball as data marts are created first and provides a thin view for analyses and data warehouse is created after complete data marts have been created.

Advantages of Bottom-Up Approach:

1. As the data marts are created first, so the reports are quickly generated.
2. We can accommodate more number of data marts here and in this way Datawarehouse can be extended.
3. Also, the cost and time taken in designing this model is low comparatively.

Disadvantage of Bottom-Up Approach:

1. This model is not strong as top-down approach as dimensional view of data marts is not consistent as it is in above approach.

Characteristics of Data warehouse

Subject-Oriented

A data warehouse is subject oriented as it offers information regarding a theme instead of companies’ ongoing operations. These subjects can be sales, marketing, distributions, etc.

A data warehouse never focuses on the ongoing operations. Instead, it put emphasis on modeling and analysis of data for decision making. It also provides a simple and concise view around the specific subject by excluding data which not helpful to support the decision process.

Integrated

In Data Warehouse, integration means the establishment of a common unit of measure for all similar data from the dissimilar database. The data also needs to be stored in the Datawarehouse in common and universally acceptable manner.

A data warehouse is developed by integrating data from varied sources like a mainframe, relational databases, flat files, etc. Moreover, it must keep consistent naming conventions, format, and coding.

This integration helps in effective analysis of data. Consistency in naming conventions, attribute measures, encoding structure etc. have to be ensured.

Time-variant

The time horizon for data warehouse is quite extensive compared with operational systems. The data collected in a data warehouse is recognized with a particular period and offers information from the historical point of view. It contains an element of time, explicitly or implicitly.

One such place where Datawarehouse data display time variance is in in the structure of the record key. Every primary key contained with the DW should have either implicitly or explicitly an element of time. Like the day, week month, etc.

Another aspect of time variance is that once data is inserted in the warehouse, it can’t be updated or changed.

Non-volatile

Data warehouse is also non-volatile means the previous data is not erased when new data is entered in it.

Data is read-only and periodically refreshed. This also helps to analyze historical data and understand what & when happened. It does not require transaction process, recovery and concurrency control mechanisms.

Activities like delete, update, and insert which are performed in an operational application environment are omitted in Data warehouse environment. Only two types of data operations performed in the Data Warehousing are

• Data loading
• Data access

DWH (Data warehouse) is needed for all types of users like:

• Decision makers who rely on mass amount of data
• Users who use customized, complex processes to obtain information from multiple data sources.
• It is also used by the people who want simple technology to access the data
• It also essential for those people who want a systematic approach for making decisions.
• If the user wants fast performance on a huge amount of data which is a necessity for reports, grids or charts, then Data warehouse proves useful.
• Data warehouse is a first step If you want to discover ‘hidden patterns’ of data-flows and groupings.

Most common sectors where Data warehouse is used:

Airline:

In the Airline system, it is used for operation purpose like crew assignment, analyses of route profitability, frequent flyer program promotions, etc.

Banking:

It is widely used in the banking sector to manage the resources available on desk effectively. Few banks also used for the market research, performance analysis of the product and operations.

Healthcare:

Healthcare sector also used Data warehouse to strategize and predict outcomes, generate patient’s treatment reports, share data with tie-in insurance companies, medical aid services, etc.

Public sector:

In the public sector, data warehouse is used for intelligence gathering. It helps government agencies to maintain and analyze tax records, health policy records, for every individual.

Investment and Insurance sector:

In this sector, the warehouses are primarily used to analyze data patterns, customer trends, and to track market movements.

Retain chain:

In retail chains, Data warehouse is widely used for distribution and marketing. It also helps to track items, customer buying pattern, promotions and also used for determining pricing policy.

Enhances data quality and consistency

A data warehouse converts data from multiple sources into a consistent format. Since the data from across the organization is standardized, each department will produce results that are consistent. This will lead to more accurate data, which will become the basis for solid decisions.

Telecommunication:

A data warehouse is used in this sector for product promotions, sales decisions and to make distribution decisions.

Hospitality Industry:

This Industry utilizes warehouse services to design as well as estimate their advertising and promotion campaigns where they want to target clients based on their feedback and travel patterns.

Delivers enhanced business intelligence

By having access to information from various sources from a single platform, decision makers will no longer need to rely on limited data or their instinct. Additionally, data warehouses can effortlessly be applied to a business’s processes, for instance, market segmentation, sales, risk, inventory, and financial management.

Saves times

A data warehouse standardizes, preserves, and stores data from distinct sources, aiding the consolidation and integration of all the data. Since critical data is available to all users, it allows them to make informed decisions on key aspects. In addition, executives can query the data themselves with little to no IT support, saving more time and money.

Generates a high Return on Investment (ROI)

Companies experience higher revenues and cost savings than those that haven’t invested in a data warehouse.

Provides competitive advantage

Data warehouses help get a holistic view of their current standing and evaluate opportunities and risks, thus providing companies with a competitive advantage.

Improves the decision-making process

Data warehousing provides better insights to decision makers by maintaining a cohesive database of current and historical data. By transforming data into purposeful information, decision makers can perform more functional, precise, and reliable analysis and create more useful reports with ease.

Enables organizations to forecast with confidence

Data professionals can analyze business data to make market forecasts, identify potential KPIs, and gauge predicated results, allowing key personnel to plan accordingly.

Streamlines the flow of information

Data warehousing facilitates the flow of information through a network connecting all related or non-related parties.

Business Applications of Data warehouse

Data warehouses have deeply rooted applications in every industry which uses structured and unstructured data from disparate sources for forecasting, analytical reporting, and business intelligence, allowing for robust decision-making. Here are some major applications of data warehouses across different industries:

Banking

• Identify the potential risk of default and manage and control collections
• Performance analysis of each product, service, interchange, and exchange rates
• Track performance of accounts and user data
• Provide feedback to bankers regarding customer relationships and profitability

Insurance

• Analyze data patterns and customer trends Maintain records of all internal and external sources, including existing participants
• Design customized offers and promotions for customers
• Predict and analyze changes in the industry

Finance

• Evaluation of customer expenses trends
• Maintain transparency in transactions
• Predict/spot defaulters and act accordingly
• Analyze and forecast different aspects of business, stock, and bond performance

Government

• Maintain and analyze tax records, health policy records, and their respective providers
• Prediction of criminal activities from patterns and trends
• Searching terrorist profile
• Threat assessment and fraud detection

Education

• Store and analyze information about faculty and students
• Maintain student portals to facilitate student activities
• Extract information for research grants and assess student demographics
• Integrate information from different sources into a single repository for analysis and strategic decision-making

Healthcare

• Generate patient, employee, and financial records
• Share data with other entities, like insurance companies, NGOs, and medical aid services
• Use data mining to identify patient trends
• Provide feedback to physicians on procedures and tests

Business Applications of Data Mining

Data mining offers many applications in business. For example, the establishment of proper data (mining) processes can help a company to decrease its costs, increase revenues, or derive insights from the behavior and practices of its customers. Certainly, it plays a vital role in the business decision-making process nowadays.

Data mining is also actively utilized in finance. For instance, relevant techniques allow users to determine and assess the factors that influence the price fluctuations of financial securities.

The field is rapidly evolving. New data emerges at enormously fast speeds while technological advancements allow for more efficient ways to solve existing problems. In addition, developments in the areas of artificial intelligence and machine learning provide new paths to precision and efficiency in the field.

DSS can theoretically be built in any knowledge domain.

One example is the clinical decision support system for medical diagnosis. There are four stages in the evolution of clinical decision support system (CDSS): the primitive version is standalone and does not support integration; the second generation supports integration with other medical systems; the third is standard-based, and the fourth is service model-based.

DSS is extensively used in business and management. Executive dashboard and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources. Due to DSS all the information from any organization is represented in the form of charts, graphs i.e. in a summarized way, which helps the management to take strategic decision. For example, one of the DSS applications is the management and development of complex anti-terrorism systems. Other examples include a bank loan officer verifying the credit of a loan applicant or an engineering firm that has bids on several projects and wants to know if they can be competitive with their costs.

A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. For example, the DSSAT4 package, developed through financial support of USAID during the 80s and 90s, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels. Precision agriculture seeks to tailor decisions to particular portions of farm fields. There are, however, many constraints to the successful adoption on DSS in agriculture.

DSS are also prevalent in forest management where the long planning horizon and the spatial dimension of planning problems demands specific requirements. All aspects of Forest management, from log transportation, harvest scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. In this context the consideration of single or multiple management objectives related to the provision of goods and services that traded or non-traded and often subject to resource constraints and decision problems. The Community of Practice of Forest Management Decision Support Systems provides a large repository on knowledge about the construction and use of forest Decision Support Systems.

A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, the Canadian National Railway system managed to decrease the incidence of derailments at the same time other companies were experiencing an increase.

DSS have been used for risk assessment to interpret monitoring data from large engineering structures such as dams, towers, cathedrals, or masonry buildings. For instance, Mistral is an expert system to monitor dam safety, developed in the 1990s by Ismes (Italy). It gets data from an automatic monitoring system and performs a diagnosis of the state of the dam. Its first copy, installed in 1992 on the Ridracoli Dam (Italy), is still operational 24/7/365. It has been installed on several dams in Italy and abroad (e.g., Itaipu Dam in Brazil), and on monuments under the name of Kaleidos. Mistral is a registered trade mark of CESI. GIS have been successfully used since the ‘90s in conjunction to DSS, to show on a map real-time risk evaluation based on monitoring data gathered in the area of the Val Pola disaster (Italy).

• DSS tends to be aimed at the less well structured, underspecified problem that upper level managers typically face;
• DSS attempts to combine the use of models or analytic techniques with traditional data access and retrieval functions;
• DSS specifically focuses on features which make them easy to use by non-computer-proficient people in an interactive mode; and
• DSS emphasizes flexibility and adaptability to accommodate changes in the environment and the decision-making approach of the user.

Typically, business planners will build a DSS system according to their needs and use it to evaluate specific operations, including

• A large stock of inventory, where DSS applications can provide guidance on establishing supply chain movement that works for a business.
• A sales process, where DSS software is a “crystal ball” that helps managers theorize how changes will affect results.
• Other specialized processes related to a field or industry.

DSS can help manage inventory

DSS can come in handy by evaluating stock held in a facility, or any other type of business asset that can be moved around or otherwise optimized. This is often one way a business can profit from “itemizing” its assets with DSS.

DSS can aid sales optimization and sales projections

Decision support technology can also be a tool that analyzes sales data and makes predictions, or monitors existing patterns. Whether it’s big picture decision support tools, active or passive solutions, or any other kind of DSS tool, planners often tackle sales numbers using a variety of decision support resources.

Utilize DSS to optimize industry-specific systems

There are other uses for this powerful software option, to make good projections on the future for a business or to get an overall bird’s-eye view of events that determine a company’s progress. This can come in handy in difficult situations where a lot of financial projection may be necessary when determining expenditures and revenues.

A decision support system (DSS) is an information system that supports business or organizational decision-making activities. DSSs serve the management, operations and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance i.e., unstructured and semi-structured decision problems. Decision support systems can be either fully computerized or human-powered, or a combination of both.

Evolution

The concept of decision support has evolved mainly from the theoretical studies of organizational decision making done at the Carnegie Institute of Technology during the late 1950s and early 1960s, and the implementation work done in the 1960s. DSS became an area of research of its own in the middle of the 1970s, before gaining in intensity during the 1980s. In the middle and late 1980s, executive information systems (EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model-oriented DSS.

According to Sol (1987) the definition and scope of DSS has been migrating over the years: in the 1970s DSS was described as “a computer-based system to aid decision making”; in the late 1970s the DSS movement started focusing on “interactive computer-based systems which help decision-makers utilize data bases and models to solve ill-structured problems”; in the 1980s DSS should provide systems “using suitable and available technology to improve effectiveness of managerial and professional activities”, and towards the end of 1980s DSS faced a new challenge towards the design of intelligent workstations.

In 1987, Texas Instruments completed development of the Gate Assignment Display System (GADS) for United Airlines. This decision support system is credited with significantly reducing travel delays by aiding the management of ground operations at various airports, beginning with O’Hare International Airport in Chicago and Stapleton Airport in Denver Colorado.[5] Beginning in about 1990, data warehousing and on-line analytical processing (OLAP) began broadening the realm of DSS. As the turn of the millennium approached, new Web-based analytical applications were introduced.

The advent of more and better reporting technologies has seen DSS start to emerge as a critical component of management design. Examples of this can be seen in the intense amount of discussion of DSS in the education environment.

DSS also have a weak connection to the user interface paradigm of hypertext. Both the University of Vermont PROMIS system (for medical decision making) and the Carnegie Mellon ZOG/KMS system (for military and business decision making) were decision support systems which also were major breakthroughs in user interface research. Furthermore, although hypertext researchers have generally been concerned with information overload, certain researchers, notably Douglas Engelbart, have been focused on decision makers in particular.

The Decision Support Systems can be divided into following categories:

Model-driven DSS

A model-driven DSS was based on simple quantitative models. It used limited data and emphasized manipulation of financial models. A model-drive DSS was used in production planning, scheduling and management. It provided the most elementary functionality to manufacturing concerns.

Data-driven DSS

Data-driven DSS emphasized the access and manipulation of data tailored to specific tasks using general tools. While it also provided elementary functionality to businesses, it relied heavily on time-series data. It was able to support decision making in a range of situations.

Communication-driven DSS

As the name suggests, communication-driven DSS uses communication and network technologies to facilitate decision making. The major difference between this and the previous classes of DSS was that it supported collaboration and communication. It made use of a variety of tools including computer-based bulletin boards, audio and video conferencing.

Document-driven DSS

A document-driven DSS uses large document databases that stores documents, images, sounds, videos and hypertext docs. It has a primary search engine tool associated for searching the data when required. The information stored can be facts and figures, historical data, minutes of meetings, catalogs, business correspondences, product specifications, etc.

Knowledge-driven DSS

Knowledge-based DSS are human-computer systems that come with a problem-solving expertise. These combine artificial intelligence with human cognitive capacities and can suggest actions to users. The notable point is that these systems have expertise in a particular domain.

Web-based DSS

Web-based DSS is considered most sophisticated decision support system that extends its capabilities by making use of worldwide web and internet. The evolution continues with advancement in internet technology.

Objectives

• Support for decision makers in semi structured and unstructured problems.
• Support managers at all levels.
• Support individuals and groups.
• Support for interdependent or sequential decisions.
• Support intelligence, design, choice, and implementation.
• Support variety of decision processes and styles

A Management Information System (MIS) is a system that gathers comprehensive data, organizes and summarizes it in a form that is of value to functional managers, and provides them with information they need to carry out their work.

Decision support systems (DSS) are interactive software-based systems intended to help managers in decision-making by accessing large volumes of information generated from various related information systems involved in organizational business processes, such as office automation system, transaction processing system, etc.

DSS uses the summary information, exceptions, patterns, and trends using the analytical models. A decision support system helps in decision-making but does not necessarily give a decision itself. The decision makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions.

MIS is used to transform data into useful information in order to support managerial decision-making with structured decisions or programmed decisions. In simple words, a MIS is a computer-based information system which assists managers in decision-making and control and in planning more effectively.

The typical MIS is made up of four major components data gathering, data entry, data transformation and information utilization. The modern MIS is based on a centralized database of raw data. Data is stored in the database in such a way that parts of it may be selected, altered, used in calculations, and transformed into useful information that can be used in a wide variety of applications.

MIS offers a wide spectrum of services at all levels and for all functional areas of the organization. It provides the top management with information pertaining to the external environment. To the middle management, it provides information useful for operational plans and to the first-level managers; it provides internal information useful for operations control.

A decision support system (DSS) is an interactive computer system that can be easily accessed and operated by people who are not computer specialists. It helps them to plan and make decisions. In other words, DSS is a computer-based information system that supports the process of managerial decision-making in situations that are not well structured.

Characteristics:

1. Executive Decisions are the Focal Points:

The data for the DSS and associated models are organized around the executive’s decisions rather than around existing databases.

2. Specialize in Easy-to-use Software:

The DSS specializes in easy-to-use software that uses simple English commands rather than technical computer terms

3. Employs Interactive Processing:

The rapid response time of a DSS permits interactive processing.

4. Use and Control Rests with the User:

The use and control of the DSS rests with the user and not the central information management department.

5. Flexible and Adaptable:

The DSS is flexible and adaptable to change in the executive’s style or in the external environment.

Attributes of a DSS

• Adaptability and flexibility
• High level of Interactivity
• Ease of use
• Efficiency and effectiveness
• Complete control by decision-makers
• Ease of development
• Extendibility
• Support for modeling and analysis
• Support for data access
• Standalone, integrated, and Web-based

An MIS is a DSS if, and only if, it is designed with the primary objective of managerial decision support. Thus, a DSS is a specialized MIS designed to support a manager’s skills at all stages of decision-making, namely identifying the problem, choosing the relevant data, selecting the approach to be used in making the decision, and evaluating the alternative courses of action.

Although there are similarities between a MIS and a DSS, there are also certain differences. In comparison to a MIS, a typical DSS provides more advanced analysis and greater access to various models that can be used by managers to examine a situation more thoroughly. Moreover, a DSS tends to be more interactive than a MIS.

It enables managers to communicate directly (often back and forth) with computer programs that control the system and to obtain the results of various analyses almost immediately. Finally, a DSS often relies on information from external sources as well as from the internal sources that are largely the domain of the MIS.

A typical DSS consists of the following elements:

1. An MIS that supports several methodologies for accessing and summarizing data
2. A sophisticated database that allows information to be accessed in various ways
3. A user-friendly interface that allows the user to use simple commands rather than technical computer terms when communicating with the DSS
4. A database built from both external and internal sources so that the manager can relate internal events to external forces.
5. Rapid response time, which makes DSS an easy and rewarding system to use.

Programmed and Non-programmed Decisions

There are two types of decisions programmed and non-programmed decisions.

Programmed decisions are basically automated processes, general routine work:

• These decisions have been taken several times.
• These decisions follow some guidelines or rules.

For example, selecting a reorder level for inventories, is a programmed decision.

Non-programmed decisions occur in unusual and non-addressed situations:

• It would be a new decision.
• There will not be any rules to follow.
• These decisions are made based on the available information.
• These decisions are based on the manger’s discretion, instinct, perception and judgment.

Inventory control systems are technology solutions that integrate all aspects of an organization’s inventory tasks, including shipping, purchasing, receiving, warehouse storage, turnover, tracking, and reordering. While there is some debate about the differences between inventory management and inventory control, the truth is that a good inventory control system does it all by taking a holistic approach to inventory and empowering organizations to utilize lean practices to optimize productivity and efficiency along the supply chain while having the right inventory at the right locations to meet customer expectations.

Inventory control systems, such as inventory control apps, offer a variety of functions that help companies manage various types of inventory. Inventory control systems typically consist of inventory management apps paired with barcode tagging to identify inventory assets, and information about each item is stored in a central database. Barcode labels serve as inventory trackers, allowing users to bring up information about the item on a computer system, such as the item’s price, the number of items in stock, the location of an item within a warehouse, and more.

The best inventory control apps are mobile-compatible, with companion apps that allow users to track and manage inventory while they move throughout a facility or from site to site. There are many inventory tracking apps for smartphones, some of which are mobile-exclusive, while others have desktop applications to allow users to track inventory from any device. There are also many inventory tracking apps designed specifically to meet the needs of warehouse managers. When looking for an inventory management app, look for features that accommodate your company’s needs, such as trigger alerts when inventory levels reach pre-defined thresholds, re-ordering capabilities, and analysis and reporting to support functions such as forecasting.

That being said, there are two different types of inventory control systems available today: perpetual inventory systems and periodic inventory systems. Within those systems, two main types of inventory management systems barcode systems and radio frequency identification (RFID) systems used to support the overall inventory control process:

Main Inventory Control System Types:

• Perpetual Inventory System
• Periodic Inventory System

Types of Inventory Management Systems within Inventory Control Systems:

• Barcode System
• Radio Frequency Identification (RFID) System

Role

Reports on Inventory Costs

Your management information system can tie your inventory costs to sales. In other words, each unit that sells can have a price assigned to it that is based on what you paid for it. This tells you if your purchase prices for inventory are leaving you enough room for profit.

Reports on Inventory Levels

You can monitor your inventory levels with a management information system by asking the database for reports from manufacturing or purchasing, inventory and sales. This will show you how quickly products move through your company. If you discover that products sit in your warehouse for extended periods, you know you need to find ways to move them, either through putting them on sale or advertising them to your customers.

Value of Inventory

The value of your inventory is not what you paid for it. It’s what you could sell it for. A management information system can calculate the sales value of your existing inventory. This counts as an asset for your company, and this figure can be useful when determining what you can offer as collateral for a loan.

Increasing Your Inventory Levels

A management information system can show you an increase in sales because it monitors sales on a daily basis. If you see an upward trend, you can stock up on items that sell faster than other items.

Generally, inventory can be grouped into four primary classifications:

• Raw materials: Raw materials are inventory items used in the manufacturing process to create finished goods. What is considered a raw material to one company may be considered finished goods to another. For example, a company that creates parts or components for machinery or equipment would consider those components finished goods. A manufacturer that purchases those components for use in their manufacturing process would consider the same components raw materials. Raw materials may consist of things like paper or steel, nuts and bolts, chemicals, wheels, and other items.
• Work-in-progress: Work-in-progress (WIP) inventory includes items that are currently being processed. WIP inventory can include raw materials and components that are going through the manufacturing process to produce finished goods as well as finished items that are waiting for final inspection or quality control. After those final steps are complete, these finished items would be considered finished goods.
• Finished goods: Finished goods are comprised of all completed items that are ready for sale to the final customer.
• MRO goods: MRO stands for maintenance, repair, and operating supplies. MRO inventory consists of items necessary to operate, such as equipment and machinery, and the items needed for maintaining equipment and infrastructure. That means MRO inventory can also include items that are sometimes considered raw materials but in this case are essentially spare parts. Nuts and bolts are a good example. When nuts and bolts are on hand to assemble finished products, they’d be classified as raw materials. Extra nuts and bolts a company keep in storage to repair equipment, on the other hand, are classified as MRO. Other examples of MRO inventory include janitorial supplies such as cleaning solutions, mops, and brooms, tools, packaging materials, uniforms and gloves, and office supplies such as paper, pens, calculators, printer ink, and other items.

Types

Perpetual Inventory System

When you use a perpetual inventory system, it continually updates inventory records and accounts for additions and subtractions when inventory items are received, sold from stock, moved from one location to another, picked from inventory, and scrapped. Some organizations prefer perpetual inventory systems because they deliver up-to-date inventory information and better handle minimal physical inventory counts. Perpetual inventory systems also are preferred for inventory tracking because they deliver accurate results on a continual basis when managed properly. This type of inventory control system works best when used in conjunction with warehouse inventory database of inventory quantities and bin locations updated in real time by warehouse workers using barcode scanners. Inventory management apps are perpetual inventory systems.

There are some challenges associated with perpetual inventory systems. First, these systems cannot be maintained manually and require specialized equipment and software that results in a higher cost of implementation, especially for businesses with multiple locations or warehouses. Periodic maintenance and upgrades are necessary for perpetual inventory systems, which also can become costly. Another challenge of using a perpetual inventory system is that recorded inventory may not reflect actual inventory as time goes by because they do not conduct periodic physical inventory counts, a necessary activity even when inventory trackers are used. The result is that errors, stolen items, and improperly scanned items impact the recorded inventory records and cause them not to match actual inventory counts.

Periodic Inventory System

Periodic inventory systems do not track inventory on a daily basis; rather, they allow organizations to know the beginning and ending inventory levels during a certain period of time. These types of inventory control systems track inventory using physical inventory counts. When physical inventory is complete, the balance in the purchases account shifts into the inventory account and is adjusted to match the cost of the ending inventory. Organizations may choose whether to calculate the cost of ending inventory using LIFO or FIFO inventory accounting methods or another method; keep in mind that beginning inventory is the previous period’s ending inventory.

There are a few disadvantages of using a periodic inventory system. First, when physical inventory counts are being completed, normal business activities nearly become suspended. As a result, workers may hurry through their physical counts because of time constraints. Periodic inventory systems typically don’t use inventory trackers, so errors and fraud may be more prevalent because there is no continuous control over inventory. It also becomes more difficult to identify where discrepancies in inventory counts occur when using a periodic inventory control system because so much time passes between counts. The amount of labor that is required for periodic inventory control systems make them better suited to smaller businesses.

Barcode Inventory Systems

Inventory management systems using barcode technology are more accurate and efficient than those using manual processes. When used as part of an overall inventory control system, barcode systems update inventory levels automatically when workers scan them with a barcode scanner or mobile device. The benefits of using barcoding in your inventory management processes are numerous and include:

• Accurate records of all inventory transactions
• Eliminating time-consuming data errors that occur frequently with manual or paper systems
• Eliminating manual data entry mistakes
• Ease and speed of scanning
• Updates on-hand inventory automatically
• Record transaction histories and easily determine minimum levels and reorder quantities
• Streamline documentation and reporting
• Rapid return on investment (ROI)
• Facilitate the movement of inventory within warehouses and between multiple locations and from receiving to picking, packing, and shipping

Radio Frequency Identification (RFID) Inventory Systems

Radio frequency identification (RFID) inventory systems use active and passive technology to manage inventory movements. Active RFID technology uses fixed tag readers throughout the warehouse; RFID tags pass the reader, and the movement is recorded in the inventory management software. For this reason, active systems work best for organizations that require real-time inventory tracking or where inventory security has been an issue. Passive RFID technology, on the other hand, requires the use of handheld readers to monitor inventory movement. When a tag is read, the data is recorded by the inventory management software. RFID technology has a reading range of approximately 40 feet with passive technology and 300 feet with active technology.

RFID inventory management systems have some associated challenges. First, RFID tags are far more expensive than barcode labels; thus, they typically are used for higher value goods. RFID tags also have been known to have interference issues, especially when tags are used in environments with a lot of metal or liquids. It also costs a great deal to transition to RFID equipment, and your suppliers, customers, and transportation companies need to have the required equipment as well. Additionally, RFID tags carry more data than barcode labels, which means your system and servers can become bogged down with too much information.

When choosing an inventory control system for your organization, you first should decide whether a perpetual inventory system or periodic inventory system is best suited to your needs. Then, choose a barcode system or RFID system to use in conjunction with your inventory control system for a complete solution that will enable you to have visibility into your inventory for improved accuracy in scanning, tracking, recording, and reporting inventory movement.

Many organizations use MIS successfully, others do not. Though the hardware and the software are the latest and has appropriate technology, its use is more for the collection and storage of data and its elementary processing. There are some factors which make the MIS a success and some others, which make it a failure. These factors can be summarized as follows:

Factors Contributing to Success

• The MIS is integrated into the managerial functions. It sets clear objectives to ensure that the MIS focuses on the major issues of the business.
• An appropriate information processing technology required to meet the data processing and analysis needs of the users of the MIS is selected.
• The MIS is oriented, defined and designed in terms of the user’s requirements and its operational viability is ensured.
• The MIS is kept under continuous surveillance, so that its open system design is modified according to the changing information needs.
• MIS focuses on the results and goals, and highlights the factors and reasons for non-achievement.
• MIS is not allowed to end up into an information generation mill avoiding the noise in the information and the communication system.
• The MIS recognizes that a manager is a human being and therefore, the systems must consider all the human behavioral factors in the process of the management.
• The MIS recognizes that the different information needs for different objectives must be met with. The globalization of information in isolation from the different objectives leads to too much information and information and its non-use.
• The MIS is easy to operate and, therefore, the design of the MIS has such features which make up a user-friendly design.
• MIS recognizes that the information needs become obsolete and new needs emerge. The MIS design, therefore, has a basic potential capability to quickly meet new needs of information.
• The MIS concentrates on developing the information support to manager critical success factors. It concentrates on the mission critical applications serving the needs of the top management.

Factors Contributing to Failures

Many a times MIS is a failure. The common factors which are responsible for this are as follows:

• The MIS is conceived as a data processing and not as an information processing system.
• The MIS does not provide that information which is needed by the managers but it tends to provide the information generally the function calls for. The MIS then becomes an impersonal system.
• Underestimating the complexity in the business systems and not recognizing it in the MIS design leads to problems in the successful implementation.
• Adequate attention is not given to the quality control aspects of the inputs, the process and the outputs leading to insufficient checks and controls in the MIS.
• The MIS is developed without streamlining the transaction processing systems in the organization.
• Lack of training and appreciation that the users of the information and the generators of the data are different, and they have to play an important responsible role in the MIS.
• The MIS does not meet certain critical and key factors of its users such as a response to the query on the database, an inability to get the processing done in a particular manner, lack of user-friendly system and the dependence on the system personnel.
• A belief that the computerized MIS can solve all the management problems of planning and control of the business.
• Lack of administrative discipline in following the standardized systems and procedures, wrong coding and deviating from the system specifications result in incomplete and incorrect information. 
• The MIS does not give perfect information to all the users in the organization.