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Key issues in Supply Chain Management-

ISSUE CONSIDERATIONS

Network Planning Warehouse locations and capacities

Plant locations and production levels Transportation flows between facilities to minimize cost a

time

Inventory Control How should inventory be managed? Why does inventory fluctuate and what strategies minimiz

this?

Supply Contracts Impact of volume discount and revenue sharing

Pricing strategies to reduce order-shipment variability

Distribution Strategies Selection of distribution strategies (e.g., direct ship vs. cro

docking)

How many cross-dock points are needed?

Cost/Benefits of different strategiesIntegration and Strategic Partnering How can integration with partners be achieved?

What level of integration is best? What information and processes can be shared?

What partnerships should be implemented and in which

situations?

Outsourcing & Procurement

Strategies

What are our core supply chain capabilities and which are

not? Does our product design mandate different outsourcing

approaches?

Risk management

Product Design How are inventory holding and transportation costs affecteby product design?

How does product design enable mass customization?

Supply Chain Management Strategies

STRATEGY WHEN TO CHOOSE BENEFITS

Make to Stock standardized products,relatively predictable demand

Low manufacturing costs;meet customer demands

quickly

Make to Order customized products, many

variations

Customization; reduced

inventory; improved service

levels

Configure to Order many variations on finished

product; infrequent demand

Low inventory levels; wide

range of product offerings;simplified planning

Engineer to Order complex products, unique

customer specifications

Enables response to specific

customer requirements

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Supply chain management must address the following problems:

Distribution Network Configuration: number, location and network missions of suppliers,

production facilities, distribution centers, warehouses, cross-docks and customers.

Distribution Strategy: questions of operating control (centralized, decentralized or shared);

delivery scheme, e.g., direct shipment, pool point shipping, cross docking, direct storedelivery (DSD), closed loop shipping; mode of transportation, e.g., motor carrier, includingtruckload, Less than truckload (LTL), parcel; railroad; intermodal transport, including trailer

on flatcar (TOFC) and container on flatcar (COFC); ocean freight; airfreight; replenishment

strategy (e.g., pull, push or hybrid); and transportation control (e.g., owner-operated, privatecarrier, common carrier, contract carrier, orthird-party logistics (3PL)).

Trade-Offs in Logistical Activities: The above activities must be well coordinated in order

to achieve the lowest total logistics cost. Trade-offs may increase the total cost if only one of

the activities is optimized. For example, full truckload (FTL) rates are more economical on acost per pallet basis than LTL shipments. If, however, a full truckload of a product is ordered

to reduce transportation costs, there will be an increase in inventory holding costs which may

increase total logistics costs. It is therefore imperative to take a systems approach whenplanning logistical activities. These trade-offs are key to developing the most efficient and

effective Logistics and SCM strategy.

Information: Integration of processes through the supply chain to share valuableinformation, including demand signals, forecasts, inventory, transportation, potential

collaboration, etc.

Inventory Management: Quantity and location of inventory, including raw materials, work-

in-process (WIP) and finished goods. Cash-Flow: Arranging the payment terms and methodologies for exchanging funds across

entities within the supply chain.

Supply chain execution means managing and coordinating the movement of materials, informationand funds across the supply chain. The flow is bi-directional.

Supply chain management Activities

Supply chain management is a cross-function approach including managing the movement of raw

materials into an organization, certain aspects of the internal processing of materials into finished

goods, and the movement of finished goods out of the organization and toward the end-consumer.As organizations strive to focus on core competencies and becoming more flexible, they reduce their

ownership of raw materials sources and distribution channels. These functions are increasingly being

outsourced to other entities that can perform the activities better or more cost effectively. The effect

is to increase the number of organizations involved in satisfying customer demand, while reducingmanagement control of daily logistics operations. Less control and more supply chain partners led to

the creation of supply chain management concepts. The purpose of supply chain management is toimprove trust and collaboration among supply chain partners, thus improving inventory visibility

and the velocity of inventory movement.

Several models have been proposed for understanding the activities required to manage material

movements across organizational and functional boundaries. SCOR is a supply chain managementmodel promoted by the Supply Chain Council. Another model is the SCM Model proposed by the

Global Supply Chain Forum (GSCF). Supply chain activities can be grouped into strategic, tactical,

and operational levels

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Strategic level

Strategic network optimization, including the number, location, and size of warehousing,

distribution centers, and facilities.

Strategic partnerships with suppliers, distributors, and customers, creating communication

channels for critical information and operational improvements such as cross docking, direct

shipping, and third-party logistics.

Product life cycle management, so that new and existing products can be optimally integratedinto the supply chain and capacity management activities.

Segmentation of products and customers to guide alignment of corporate objectives withmanufacturing and distribution strategy.

Information technology chain operations.

Where-to-make and make-buy decisions.

Aligning overall organizational strategy with supply strategy. It is for long term and needs resource commitment.

Tactical level

Sourcing contracts and other purchasing decisions. Production decisions, including contracting, scheduling, and planning process definition.

Inventory decisions, including quantity, location, and quality of inventory.

Transportation strategy, including frequency, routes, and contracting.

Benchmarking of all operations against competitors and implementation of best practicesthroughout the enterprise.

Milestone payments.

Focus on customer demand and Habits.

Operational level

Daily production and distribution planning, including all nodes in the supply chain. Production scheduling for each manufacturing facility in the supply chain (minute by

minute).

Demand planning and forecasting, coordinating the demand forecast of all customers and

sharing the forecast with all suppliers.

Sourcing planning, including current inventory and forecast demand, in collaboration with allsuppliers.

Inbound operations, including transportation from suppliers and receiving inventory.

Production operations, including the consumption of materials and flow of finished goods.

Outbound operations, including all fulfillment activities, warehousing and transportation tocustomers.

Order promising, accounting for all constraints in the supply chain, including all suppliers,manufacturing facilities, distribution centers, and other customers.

From production level to supply level accounting all transit damage cases & arrange to

settlement at customer level by maintaining company loss through insurance company.

Managing non-moving, short-dated inventory and avoiding more products to go short-

dated.

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Customer Relationship Management (CRM)

Customer Relationship Management (CRM) can be widely defined as company activities related todeveloping and retaining customers. It is a blend of internal business processes: sales, marketing and

customer support with technology and data capturing techniques. Customer Relationship

Management is all about building long-term business relationships with customers.

CRM is an alignment of strategy, processes and technology to manage customers and all customer-

facing departments & partners. Any CRM initiative is and has the potential of providing strategic

advantages to the organization, if handled right.

It is a process or methodology used to learn more about customers' needs and behaviors in order to

develop stronger relationships with them. There are many technological components to CRM, butthinking about CRM in primarily technological terms is a mistake. The more useful way to think

about CRM is as a process that will help bring together lots of pieces of information about

customers, sales, marketing effectiveness, responsiveness and market trends.CRM helps businesses use technology and human resources to gain insight into the behavior of

customers and the value of those customers.

Advantages Of CRM

1. Using CRM, a business can:

2. Provide better customer service

3. Increase customer revenues

4. Discover new customers

5. Cross sell/Up Sell products more effectively

6. Help sales staff close deals faster

7. Make call centers more efficient

8. Simplify marketing and sales processes

Other advantages are-

CRM solutions help companies boost their business efficiency, thereby increasing profit

and revenue generation capabilities. Let us take a quick look at some of the measurable

benefits that your organization can gain by implementing a CRM solution.

Increase Customer Lifecycle Value

In most businesses, the cost of acquisition of customers is high. To make profits, it is

important to keep the customer longer and sell him more products (cross sell, up sell,

etc) to him, during his lifecycle. Customer stay, if they are provided with value, quality

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service and continuity. CRM solutions enable you to do that.

Execution Control

Once the business strategy is put into motion, the management needs feedback and

reports to judge how the business is performing. CRM solutions provide management

with control and a scientific way to identify and resolve issues. The benefits include aclearer visibility of the sales pipeline, accurate forecasts and more.

Customer Lifecycle Management

To keep the customers happy, you need to know them better. At the minimum, you need

a centralize customer database, that captures most of the information from your entire

customer facing departments and partners. Integrated CRM solutions, like CRMnextenable you to manage customer information, throughout all stages of their life cycle,

from contact to contract to customer service.

Strategic Consistency

Because CRM offers business and technological alignment, it enables companies toachieve strategic company goals more effectively, like enhanced sales realization, higher

customer satisfaction, better brand management and more. Additionally, the alignment

results in a more consistent customer communication creating a feeling of continuity.

Business Intelligence

Due to the valuable business insights that CRM provides, it becomes easier to identifythe bottlenecks, their causes and the remedial measures that need to be taken. For

example, CRMnext provides real-time business focus dashboards with extensive drill

down capabilities that provide the decision makers with the depth of information

required to identify the causes and spot trends.

Definition 1-Data Warehouse

A data warehouse is a collection and summarization of information from multiple databases anddatabase tables. The primary purpose of a data warehouse is not data storage, but the

collection of information for decision-making. Typically, a data warehouse extracts updated

information from operational databases on a regular basis (nightly, hourly, etc.). This forms a

snapshot of collected data that can be organized into a logical structure based on youranalytical needs.

Data warehouses allow you to express your information needs logically, without beingconstrained to database fields and records. Using the correct data mining tools, it is possible to

display information from a data warehouse in ways that are not possible using SQL or other

basic query languages. Unlike a relational database, a data warehouse can present informationin multidimensional format. This representation is called a hypercube, and contains layers of

rows and columns. Using this model a company could, for instance, track sales of multiple

products in multiple regions over a given period of time, all in the same view.

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A data warehouse can contain extremely large amounts of information, and many users will

only need to access a portion of this. Information in a data warehouse can be organized into

data marts, which are subsets of data with a specific focus. Data marts can provide an analystwith a more efficient set of working data relevant to, for instance, a specific business process

or unit of the company

Definition 2- Data Warehouse?A data warehouse is a relational database that is designed for query and analysis rather than fortransaction processing. It usually contains historical data derived from transaction data, but it can

include data from other sources. It separates analysis workload from transaction workload and

enables an organization to consolidate data from several sources.

In addition to a relational database, a data warehouse environment includes an extraction,transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP)

Supplier Database

Data warehouse

Customer DatabaseSales Database

Data Mart

Data Mart

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engine, client analysis tools, and other applications that manage the process of gathering data and

delivering it to business users

Different types of data warehouse :

Subject Oriented

Integrated

Nonvolatile Time Variant

Subject Oriented

Data warehouses are designed to help you analyze data. For example, to learn more about yourcompany's sales data, you can build a warehouse that concentrates on sales. Using this warehouse,

you can answer questions like "Who was our best customer for this item last year?" This ability to

define a data warehouse by subject matter, sales in this case, makes the data warehouse subjectoriented.

Integrated

Integration is closely related to subject orientation. Data warehouses must put data from disparate

sources into a consistent format. They must resolve such problems as naming conflicts andinconsistencies among units of measure. When they achieve this, they are said to be integrated.

Nonvolatile

Nonvolatile means that, once entered into the warehouse, data should not change. This is logical

because the purpose of a warehouse is to enable you to analyze what has occurred.

Time Variant

In order to discover trends in business, analysts need large amounts of data. This is very much in

contrast to online transaction processing (OLTP) systems, where performance requirements

demand that historical data be moved to an archive. A data warehouse's focus on change over time iswhat is meant by the term time variant

Data Warehouse Architectures

Data warehouses and their architectures vary depending upon the specifics of an organization's

situation. Three common architectures are:

Data Warehouse Architecture (Basic)

Data Warehouse Architecture (with a Staging Area)

Data Warehouse Architecture (with a Staging Area and Data Marts)

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Data Warehouse Architecture (Basic)

Figure shows a simple architecture for a data warehouse. End users directly access data derived

from several source systems through the data warehouse.

Figure 1- Architecture of a Data Warehouse

In Figure 1 the metadata and raw data of a traditional OLTP system is present, as is an additional

type of data, summary data. Summaries are very valuable in data warehouses because they pre-compute long operations in advance. For example, a typical data warehouse query is to retrieve

something like August sales.

Data Warehouse Architecture (with a Staging Area)

In above Figure1 , you need to clean and process your operational data before putting it into thewarehouse. You can do this programmatically, although most data warehouses use a staging area-instead. A staging area simplifies building summaries and general warehouse management. Figure 2

illustrates this typical architecture.

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Figure 2- Architecture of a Data Warehouse with a Staging Area

Data Warehouse Architecture (with a Staging Area and Data Marts)

Although the architecture in Figure 2 is quite common, you may want to customize your

warehouse's architecture for different groups within your organization. You can do this by adding

datamarts, which are systems designed for a particular line of business. Figure 3 illustrates anexample where purchasing, sales, and inventories are separated. In this example, a financial analyst

might want to analyze historical data for purchases and sales.

Figure 3- Architecture of a Data Warehouse with a Staging Area and Data Marts

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OLTP (On-lineTransaction Processing) is characterized by a large number of short on-line

transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on veryfast query processing, maintaining data integrity in multi-access environments and an effectiveness

measured by number of transactions per second. In OLTP database there is detailed and current

data, and schema used to store transactional databases is the entity model (usually 3NF).

- OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions.

Queries are often very complex and involve aggregations. For OLAP systems a response time is an

effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAPdatabase there is aggregated, historical data, stored in multi-dimensional schemas (usually star

schema).

The following table summarizes the major differences between OLTP and OLAP system design.

OLTP System

Online Transaction

Processing

(Operational System)

OLAP System

Online Analytical Processing

(Data Warehouse)

Source of dataOperational data; OLTPs are the original

source of the data.Consolidation data; OLAP data comes from

the various OLTP Databases

Purpose of dataTo control and run fundamental business

tasks

To help with planning, problem solving, and

decision support

What the dataReveals a snapshot of ongoing business

processes

Multi-dimensional views of various kinds of

business activitiesInserts and

Updates

Short and fast inserts and updates initiated

by end users

Periodic long-running batch jobs refresh the

data

QueriesRelatively standardized and simple

queries Returning relatively few recordsOften complex queries involving

aggregations

Processing

SpeedTypically very fast

Depends on the amount of data involved;batch data refreshes and complex queries

may take many hours; query speed can be

improved by creating indexes

SpaceRequirements

Can be relatively small if historical data isarchived

Larger due to the existence of aggregationstructures and history data; requires more

indexes than OLTP

DatabaseDesign

Highly normalized with many tablesTypically de-normalized with fewer tables;

use of star and/or snowflake schemas

Backup andRecovery

Backup religiously; operational data is

critical to run the business, data loss islikely to entail significant monetary loss

and legal liability

Instead of regular backups, someenvironments may consider simply reloading

the OLTP data as a recovery method

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Difference between data Warehouse and database

The primary difference betwen you application database and a data warehouse is that while theformer is designed (and optimized) to record , the latter has to be designed (and optimized) to

respond to analysis questions that are critical for your business.

Application databases are OLTP (On-Line Transaction Processing) systems where every transactionhas to be recorded, and super-fast at that. Consider the scenario where a bank ATM has disbursed

cash to a customer but was unable to record this event in the bank records. If this started happening

frequently, the bank wouldn't stay in business for too long. So the banking system is designed tomake sure that every trasaction gets recorded within the time you stand before the ATM machine.

This system is write-optimized, and you shouldn't crib if your analysis query (read operation) takes a

lot of time on such a system.

A Data Warehouse (DW) on the other end, is a database that is designed for facilitating querying and

analysis. Often designed as OLAP (On-Line Analytical Processing) systems, these databases containread-only data that can be queried and analysed far more efficiently as compared to your regular

OLTP application databases. In this sense an OLAP system is designed to be read-optimized.

Separation from your application database also ensures that your business intelligence solution isscalable (your bank and ATMs don't go down just because the CFO asked for a report), better

documented and managed (god help the novice who is given the application database diagrams and

asked to locate the needle of data in the proverbial haystack of table proliferation), and can answerquestions far more efficietly and frequently.

Creation of a DW leads to a direct increase in quality of analyses as the table structures are simpler

(you keep only the needed information in simpler tables), standardized (well-documented tablestructures), and often denormalized (to reduce the linkages between tables and the corresponding

complexity of queries). A DW drastically reduces the 'cost-per-analysis' and thus permits more

analysis per FTE. Having a well-designed DW is the foundation successful BI/Analytics initiativesare built upon.

Data Mining

Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD),a

field at the intersection of computer science and statistics,is the process that attempts to discoverpatterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine

learning, statistics, and database systems. The overall goal of the data mining process is to extractinformation from a data set and transform it into an understandable structure for further use. Aside

from the raw analysis step, it involves database and data management aspects, data preprocessing,

model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating

The term is a buzzword, and is frequently misused to mean any form of large-scale data or

information processing (collection, extraction, warehousing, analysis, and statistics) but is also

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generalized to any kind of computer decision support system, including artificial intelligence,

machine learning, andbusiness intelligence. In the proper use of the word, the key term is discovery,

commonly defined as "detecting something new". Even the popular book "Data mining: Practicalmachine learning tools and techniques with Java" (which covers mostly machine learning material)

was originally to be named just "Practical machine learning", and the term "data mining" was only

added for marketing reasons. Often the more general terms "(large scale) data analysis", or"analytics" or when referring to actual methods, artificial intelligence and machine learning are

more appropriate.

The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to

extract previously unknown interesting patterns such as groups of data records (cluster analysis),unusual records (anomaly detection) and dependencies (association rule mining). This usually

involves using database techniques such as spatial indexes. These patterns can then be seen as a kind

of summary of the input data, and may be used in further analysis or, for example, in machinelearning and predictive analytics. For example, the data mining step might identify multiple groups

in the data, which can then be used to obtain more accurate prediction results by a decision support

system. Neither the data collection, data preparation, nor result interpretation and reporting are partof the data mining step, but do belong to the overall KDD process as additional steps.

Knowledge Discovery in Databases (KDD)

Knowledge Discovery in Databases (KDD), refers to the nontrivial extraction of implicit, previously

unknown and potentially useful information from data in databases. While data mining andknowledge discovery in databases (or KDD) are frequently treated as synonyms, data mining is

actually part of the knowledge discovery process.

The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:

(1) Selection

(2) Pre-processing(3) Transformation

(4)Data Mining

(5) Interpretation/Evaluation.

It exists, however, in many variations on this theme, such as the Cross Industry Standard Process forData Mining (CRISP-DM) which defines six phases:

(1) Business Understanding

(2) Data Understanding(3) Data Preparation

(4) Modeling

(5) Evaluation

(6) Deployment

or a simplified process such as (1) pre-processing, (2) data mining, and (3) results validation.

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Explain the KDD process.

Knowledge discovery as a process is depicted and consists of an iterative sequence of the followingsteps:

1. Data cleaning: to remove noise and inconsistent data

2. Data integration: where multiple data sources may be combined

3. Data selection: where data relevant to the analysis task are retrieved from the database

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4. Data transformation: where data are transformed or consolidated into forms appropriate for

mining by performing summary or aggregation operations.

5. Data mining: an essential process where intelligent methods are applied in order to extractdata pattern.

6. Pattern evaluation to identify the truly interesting patterns representing knowledge based on

some interestingness measures;7. Knowledge presentation where visualization and knowledge representation techniques are

used to present the mined knowledge to the user.

Steps 1 to 4 are different forms of data preprocessing, where the data are prepared for mining. Thedata mining step may interact with the user or a knowledge base.

The interesting patterns are presented to the user and may be stored as new knowledge in the

knowledge base. Data mining is only one step in the entire process but an essential one because it

uncovers hidden patterns for evaluation.

Therefore, data mining is a step in the knowledge discovery process

Data Mining

Data mining is the process of extracting information from large sources of data, such as a corporatedata warehouse, and extrapolating relationships and trends within that data. It is not possible to use

standard query tools, such as SQL, to perform these operations.

There are three main categories of data mining tools: query-and-reporting tools, intelligent agents,and multidimensional analysis tools.

Query-and-reporting tools offer functionality similar to query and report generators for standard

databases. These tools are easy to use, but their scope is limited to that of a relational database, andthey do not take full advantage of the potential of a data warehouse.

The term 'intelligent agents' encompasses a variety of artificial intelligence tools which haverecently emerged into the field of data manipulation. Two of these tools are neural networks and

fuzzy logic. An intelligent agent can sift through the contents of a database, finding unsuspected

trends and relationships between data.

Multidimensional analysis tools allow a user to interpret multidimensional data (i.e., a hypercube

data set) from different perspectives. For example, if a set of data includes products sold in various

regions over time, multidimensional analysis allows you to view the data in different ways. Forinstance, you could display all sales in all regions for a given time, or all sales over time in a given

region

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Data mining involves six common classes of tasks:[1]

Anomaly detection (Outlier/change/deviation detection) The identification of unusual data

records, that might be interesting or data errors and require further investigation.

Association rule learning (Dependency modeling) Searches for relationships between

variables. For example a supermarket might gather data on customer purchasing habits.Using association rule learning, the supermarket can determine which products are frequently

bought together and use this information for marketing purposes. This is sometimes referred

to as market basket analysis.

Clustering is the task of discovering groups and structures in the data that are in some wayor another "similar", without using known structures in the data.

Classification is the task of generalizing known structure to apply to new data. For

example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".

Regression Attempts to find a function which models the data with the least error.

Summarization providing a more compact representation of the data set, includingvisualization and report generation.

Data Warehouse

Engine

Query And reporting ToolsMultidimensional Analysis

Tools

Intelligent Agent

Data warehouse

http://en.wikipedia.org/wiki/Data_mining#cite_note-Fayyad-0http://en.wikipedia.org/wiki/Anomaly_detectionhttp://en.wikipedia.org/wiki/Association_rule_learninghttp://en.wikipedia.org/wiki/Cluster_analysishttp://en.wikipedia.org/wiki/Statistical_classificationhttp://en.wikipedia.org/wiki/Regression_analysishttp://en.wikipedia.org/wiki/Automatic_summarizationhttp://en.wikipedia.org/wiki/Data_mining#cite_note-Fayyad-0http://en.wikipedia.org/wiki/Anomaly_detectionhttp://en.wikipedia.org/wiki/Association_rule_learninghttp://en.wikipedia.org/wiki/Cluster_analysishttp://en.wikipedia.org/wiki/Statistical_classificationhttp://en.wikipedia.org/wiki/Regression_analysishttp://en.wikipedia.org/wiki/Automatic_summarization

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Architecture of a typical Data Mining System-

The architecture of a typical data mining system may have the following major components :

Database, data warehouse, WorldWideWeb, or other information repository:

This is one or a set of databases, data warehouses, spreadsheets, or other kinds

of information repositories. Data cleaning and data integration techniques may be performed on the data.

Database or data warehouse server:

The database or data warehouse server is responsible for fetching the relevant

data, based on the users data mining request.

Knowledge base:

This is the domain knowledge that is used to guide the search or evaluate the

interestingness of resulting patterns. Such knowledge can include concept hierarchies, used to organize attributesor attribute values into different levels of abstraction.

Knowledge such as user beliefs, which can be used to assess a patterns

interestingness based on its unexpectedness, may also be included.

Other examples of domain knowledge are additional interestingness

constraints or thresholds, and metadata (e.g., describing data from multiple

heterogeneous sources).

Data mining engine:

This is essential to the data mining system and ideally consists of a set offunctional modules for tasks such as characterization, association and correlation

analysis, classification, prediction, cluster analysis, outlier analysis, and evolution

analysis.

Pattern evaluation module:

This component typically employs interestingness measures and interacts with

the data mining modules so as to focus the search toward interesting patterns. It may

use interestingness thresholds to filter out discovered patterns.

Alternatively, the pattern evaluation module may be integrated with the

mining module, depending on the implementation of the data mining method used. For efficient data mining, it is highly recommended to push the evaluation of

pattern interestingness as deep as possible into the mining process so as to confine thesearch to only the interesting patterns.

User interface:

This module communicates between users and the data mining system,

allowing the user to interact with the system by specifying a data mining query or

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task, providing information to help focus the search, and performing exploratory data

mining based on the intermediate data mining results.

Also, it allows the user to browse database and data warehouse schemas ordata structures, evaluate mined patterns, and visualize the patterns in different forms.

Figure-Typical data Mining Architecture

Classification of Data mining System

There are many data mining systems available or being developed. Some are specialized systemsdedicated to a given data source or are confined to limited data mining functionalities, other aremore versatile and comprehensive. Data mining systems can be categorized according to various

criteria among other classification are the following:

Classification according to the type of data source mined: this classification categorizesdata mining systems according to the type of data handled such as spatial data, multimedia

data, time-series data, text data, World Wide Web, etc.

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Classification according to the data model drawn on: this classification categorizes data

mining systems based on the data model involved such as relational database, object-oriented

database, data warehouse, transactional, etc.

Classification according to the king of knowledge discovered: this classification

categorizes data mining systems based on the kind of knowledge discovered or data mining

functionalities, such as characterization, discrimination, association, classification,clustering, etc. Some systems tend to be comprehensive systems offering several data mining

functionalities together. Classification according to mining techniques used: Data mining systems employ and

provide different techniques. This classification categorizes data mining systems according

to the data analysis approach used such as machine learning, neural networks, genetic

algorithms, statistics, visualization, database-oriented or data warehouse-oriented, etc. The

classification can also take into account the degree of user interaction involved in the datamining process such as query-driven systems, interactive exploratory systems, or

autonomous systems. A comprehensive system would provide a wide variety of data mining

techniques to fit different situations and options, and offer different degrees of userinteraction.

Enterprise Resource planning (ERP)

ERP is a software architecture that facilitates the flow of information among the different functions

within an enterprise. Similarly, ERP facilitates information sharing across organizational units andgeographical locations.3 It enables decision-makers to have an enterprise-wide view of the

information they need in a timely, reliable and consistent fashion.

ERP provides the backbone for an enterprise-wide information system. At the core of this enterprisesoftware is a central4 database which draws data from and feeds data into modular applications that

operate on a common computing platform, thus standardizing business processes and data

definitions into a unified environment. With an ERP system, data needs to be entered only once. Thesystem provides consistency and visibilityor transparencyacross the entire enterprise. A primarybenefit of ERP is easier access to reliable, integrated information. A related benefit is the elimination

of redundant data and the rationalization of processes, which result in substantial cost savings. The

integration among business functions facilitates communication and information sharing, leading todramatic gains in productivity and speed.

The Components of an ERP System - The components of an ERP system are the commoncomponents of a Management Information System (MIS).

ERP Software - Module based ERP software is the core of an ERP system. Each software

module automates business activities of a functional area within an organization. Common

ERP software modules include product planning, parts purchasing, inventory control,product distribution, order tracking, finance, accounting and human resources aspects of an

organization.

Business Processes - Business processes within an organization falls into three levels -strategic planning, management control and operational control. ERP has been promoted as

solutions for supporting or streamlining business processes at all levels. Much of ERP

success, however, has been limited to the integration of various functional departments.

ERP Users - The users of ERP systems are employees of the organization at all levels, from

workers, supervisors, mid-level managers to executives.

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Hardware and Operating Systems - Many large ERP systems are UNIX based. Windows

NT and Linux are other popular operating systems to run ERP software. Legacy ERP

systems may use other operating systems.

The Boundary of an ERP System - The boundary of an ERP system is usually small than the

boundary of the organization that implements the ERP system. In contrast, the boundary of supply

chain systems and ecommerce systems extends to the organization's suppliers, distributors, partnersand customers. In practice, however, many ERP implementations involve the integration of ERP

with external information systems.

ERP vs. CRM and SCM

CRM (Customer Relationship Management) and SCM (Supply Chain Management) are two other

categories of enterprise software that are widely implemented in corporations and non-profit

organizations. While the primary goal of ERP is to improve and streamline internal businessprocesses, CRM attempts to enhance the relationship with customers and SCM aims to facilitate the

collaboration between the organization, its suppliers, the manufacturers, the distributors and the

partners.