Month: January 2021

E-Supply chain management is practiced in manufacturing industries. E-SCM involves using internet to carry out value added activities so that the products produced by the manufacturer meets customers’ and result in good return on investment.

E-SCM is the effective utilization of internet and business processes that help in delivering goods, services and information from the supplier to the consumer in an organized and efficient way.

Improved product and material flow

• Time-to-consumer is a crucial indicator of product flow efficiency. The less time it takes for goods to reach the end customer, the more efficient the product flow. However, there are many other factors to consider such as the quality of the materials or goods that reach customers, the supply and demand balance, shipment options and costs, and inventories.
• Effective supply chain management enables companies to improve product flow through accurate demand and sales forecasting and also improve inventory management to arrest the bullwhip effect and avoid underproduction. SCM also minimizes delays and allows full traceability and visibility into the movements of goods from the supplier to the customer. SCM enables working strategies that can accelerate time-to-market and optimize business speed, while ensuring high level of product quality.

Seamless information flow

• “The effective SCM requires not only the integration of material flows but also the integration of information flows in the supply chain (Frohlich & Westbrook, 2001; Trent & Monczka, 1998).” Today, with customers constantly demanding for real-time response and easy access to product and other supply chain content, information flow should be uninterrupted. Intermittent and insufficient information flow due to a fragmented supply chain can lead to poor supplier and customer relationships and huge costs to the tune of $1.2 billion per year, according to Oracle.
• Companies with effective supply chain management can remove the bottlenecks to supply chain information flow. It can help them evaluate the quality of information sharing, then implement solutions to best fill the gaps. SCM helps design effective best practices to facilitate different types of supply chain information that usually come in different formats and structures. SCM also enables accurate, timely, complete, and relevant information flow to avoid missed opportunities and possible risks.
• Effective and seamless information flow addresses information distortion and miscommunication and promotes enhanced collaboration and relationship value among supply chain stakeholders. It also helps improve visibility into all transactions and accelerate generation of supply chain insights through past reports creation.

Enhanced financial flow

• Another pain point for supply chain players is how to improve cash flow in the value chain, which involves “thousands of invoices and payments in a given year.” The unpredictability and variability of financial inflows and outflows can add more complexity to the inherently complex supply chain financial flow.
• According to Visa, generally, financial management challenges are:

(1) Slow processing due to manual and silo processes

(2) Unreliable, unpredictable cash flows because of lack of timely information

(3) Costly processes due to compliance and lack of employee empowerment

(4) High days sales outstanding (DSO) caused by invoice reconciliation delays; and

(5) Suboptimal credit decisions due to manual processes for setting optimal limits.

• Implementing supply chain management can help companies address all these cash flow challenges, allowing them to carefully evaluate their current processes, identify the weakest links that slow down and hamper financial flow, and determine the right solutions to address the problems.

Advantages of e-supply chain management

Companies implementing E-SCM can enjoy the following advantages:

1. It improves efficiency
2. It reduces inventory
3. It reduces cost
4. It helps to take competitive advantage over competitors.
5. It increases ability to implement just-in-time delivery, increases on-time deliveries, which enhances customer satisfaction.
6. It reduces cycle time, increases revenue, by providing improved customer service.
7. It improves order fulfillment, order management, decision making, forecasting, demand planning, and warehouse/distribution activities.
8. It reduces paperwork, administrative overheads, inventory build-up, and the number of hands that handle goods on their way to the end-user i.e., the customer.

Supply Chain Management flow

SCM flows can be divided into three main activities

• Product flow
• Information flow
• Financial flow

1. Product Flow: The product flow includes the movement of goods from a supplier to a customer, and also any goods returned by customers.
2. Information flow: The information flow involves transmitting orders and updating the status of delivery.
3. Financial flow: The financial flow consists of credit terms, payment schedules, consignment and title ownership arrangements.

The activities of E-SCM include the following:

• Supply Chain Replenishment. Supply chain replenishment encompasses the integrated production and distribution processes. Companies can use replenishment information to reduce inventories, eliminate stocking points, and increase the velocity of replenishment by synchronizing supply and demand information across the extended enterprise.
• E-Procurement. It is the use of web-based technology to support the key procurement processes, including requisitioning, sourcing, contracting, ordering, and payment. E-procurement supports the purchase of both direct and indirect materials and employs several web-based functions, such as online catalogs, contracts, purchase orders, and shipping notices.
• Supply Chain Monitoring and Control Using RFID. This is one of the most promising applications of RFID (Radio-Frequency Identification).
• Inventory Management Using Wireless Devices. Many organizations are now achieving improvements in inventory management by using combinations of bar-coding technologies (or RFID) and wireless devices.
• Collaborative Planning. It is a business practice that combines the business knowledge and forecasts of multiple players along a supply chain to improve the planning and fulfillment of customer demand. Collaborative planning requires buyers and sellers to develop shared demand forecasts and supply plans for how to support demand.
• Collaborative Design and Product Development. It involves the use of product design and development techniques across multiple companies to improve product launch success and reduce time to market. During product development, engineering and design drawings can be shared over a secure network among the contract firm, testing facility, marketing firm, and downstream manufacturing and service companies.
• E-Logistics. It is the use of web-based technologies to support the material acquisition, warehousing, and transportation processes. E-logistics enables distribution to couple routing optimization with inventory-tracking information. For example, Internet-based freight auctions enable spot buying of trucking capacity.

The key activities of e-SCM use a variety of infrastructure and tools. The following are the major infrastructure elements and tools of e-SCM:

• Electronic data interchange (EDI). It is the major tool used by large corporations to facilitate supply chain relationships. Many companies are shifting from traditional EDI to Internet-based EDI.
• Its major purpose is to support inter organizational communication and collaboration.
• These are the corporate internal networks for communication and collaboration.
• Corporate portals. These provide a gateway for external and internal collaboration, communication, and information search.
• Workflow systems and tools. These are systems that manage the flow of information in organizations.
• Groupware and other collaborative tools. Many tools facilitate collaboration and communication between two parties and among members of small as well as large groups. Various tools, some of which are collectively known as groupware, enable such collaboration. Blogs and wikis are beginning to play an important role. A major purpose of these tools is to provide visibility to all, namely, let people know where items are and when they arrive at certain locations.
• Identification and tracking tools. These tools are designed to identify items and their location along the supply chain.

Components:

1. Planning

This is one of the most important stages. Before the beginning of the entire supply chain, it is essential to finalise the strategies and put them into place. Checking the demand for the product or service, checking the viability, costing, profit, and manpower etc., are vital. Without a proper plan or strategy in place, it will be well-nigh impossible for the business to achieve effective and long term benefits. Therefore, enough time has to be devoted to this phase. Only after the finalisation of the plans and consideration of all pros and cons, can one proceed further. Every business needs a plan or blueprint or a roadmap based on which the strategies are made. Planning helps to identify the demand and supply trends in the market and this, in turn, helps to create a successful supply chain management system.

2. Information

The world today is dominated by a continuous flow of information. In order to be successful, it is essential that a business stays abreast with all the latest information about the various aspects of its production. The market trends of supply and demand for a particular product can be best understood if the information is properly and timely disseminated through the many levels of the business. Information is crucial in a knowledge-based world economy, and ignorance about any aspect of business may actually spell doom for the prospects of the business.

3. Source

Suppliers play a very crucial role in supply chain management systems. Products and services sold to the end user are created with the help of different sets of raw materials. It is therefore necessary that suitable quality raw materials are procured at cost effective rates. If a supplier is unable to supply on time, and within the stipulated budget, the business is bound to suffer losses and gain a negative reputation.

It is crucial that a company procures good quality resources so it can create good quality products and maintain its reputation in the market. This necessitates a strong role for suppliers in the supply chain management system.

4. Inventory

For a highly effective supply chain management system it is essential that an inventory is kept and thoroughly maintained. An inventory means the ready list of items, raw materials and other essentials required for the product or service. This list has to be regularly updated to demarcate available stock and required stock. Inventory management is critical to the function of supply chain management, because without proper inventory management the production, as well as sale of the product, is not possible. Businesses have now started to pay more attention to this component simply because of its impact on the supply chain.

5. Production

Production is one among the most important aspects of this system. It is only possible when all the other components of the supply chain are in tandem with each other. For the process of production to start it is essential that proper planning and supply of goods, as well as the inventory, are well maintained. The production of goods is followed by testing, packaging and the final preparation for delivery of the finished product.

6. Location

Any business, that wants to survive as well as flourish, needs a location which is profitable for the business. Take for example, a carbonated drink factory is set up in an area where water supply is scarce. Water is a basic necessity of such business. The lack of water could hamper the production as well as affect the goodwill of the company. A business cannot survive if it has to share an already scarce raw material with the community. Hence, a suitable location, which is well connected, and very close to the source of essential resources for production is vital to a business’ prosperity. The requirement and availability of manpower must also be considered while setting up a business unit.

7. Transportation

Transportation is vital in terms of carrying raw materials to the manufacturing unit and delivering the final product to the market. At each stage, timely transportation of goods is mandatory to sustain a smooth business process. Any business which pays attention to this component, and takes good care of it, will benefit from the production and transportation of its goods on time.

It is essential that a company works towards a safe and secure transportation process. Be it in-house or a third-party vendor, the transportation management system must ensure zero damage and minimal loss in transit. A well-managed logistics system along with flawless invoicing are the two pillars of secure transportation.

8. Return of goods

Among the various components that create a strong supply chain is the facility for the return of faulty/malfunctioning goods, along with a highly responsive consumer grievance redress unit.

No one is infallible. Even a machine may malfunction once in a million times if not more. As a part of a strong business process, one may expect the return of goods under various circumstances. Even the best quality control processes may have unavoidable momentary lapses. In the case of such lapses, inevitably followed by consumer complaints, a business must, instinctively, recall the product/s and issue an apology. This not only creates a good customer bonding, but also maintains goodwill in the long run.

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 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.