Supply Chain

Management

Supply Chain

Management(SCM) A firm’s Supply chain consists of all processes and

activities that are necessary to bring products to market.

It includes:-

1. Procurement to acquire raw material;

2. Manufacturing to convert raw materials into components and final products; and distribution to respond to market demand;

3. The objective of supply chain management is to coordinate and integrate all these processes and activities so as to meet customers’ expectations in the most cost-effective way

SCM is a set of approaches to manage the SC, i.e.,

To efficiently integrate suppliers, manufacturers, warehouses, and stores, so that merchandise is produced and distributed at the right quantity, to the right location, and at the right time.

“Efficiently means “minimizing” the system-wide cost while satisfying service level requirement, or maximizing the total profit.

Definition

SCM is the integration of all activities

associated with the flow and

transformation of goods from raw

materials through to end user, as well

as information flows, through

improved supply chain relationships,

to achieve a sustainable competitive

advantage.

Handfield and

Nichols

SCM Software

SCM software refers to software that

supports specific segments of the

supply chain, especially in

manufacturing, inventory control,

scheduling and transportation. This

software is designed to improve

decision making, optimization, and

analysis.

E- Supply Chain

When supply chain is managed

electronically, usually with web based

software, it is referred to as an e-

supply chain.

Seven Principles of Supply

Chain Management Segment customers based on service

needs

Listen to signals of market demand and plan accordingly

Develop a supply-chain-wide technology strategy

Customize the logistics network

Differentiate product closer to the customer

Source strategically

Adopt channel-spanning performance measures

Customer Relationship

Management (CRM)

Customer Relationship

Management (CRM) “It is a business strategy to select and

manage customers to optimize long-term value.”

“It requires a customer-centric business philosophy and culture to support effective marketing, sales, and service processes.”

“CRM applications can enable effective Customer Relationship Management, provided that an enterprise has the right leadership, strategy, and culture.”

Definition

CRM “is the process of managing

detailed information about individual

customers and carefully managing all

customer ‘touch points’ to carefully

managing all customer touch points to

maximize customer loyalty”

Kotler & Keller

Benefits

Instill greater customer loyalty

Increased efficiency through automation

Deeper understanding of customers

Increased marketing and selling opportunities

Identifying the most profitable customers

Receiving customer feedback that leads to new and improved products or services

Obtaining information that can be shared with business partners

Components of CRM

1. People Management:- People Management is nothing but the effective use of people in the right place at the right time. It imperative to adopt the right measures to ensure the people skills their job profiles.

2. Lead management:- Basically involves tracking and distribution of sales leads. This benefits the sales., call centers and marketing industries as well.

3. Sales forces automation:- Sales forces automation is by far one of the most essential components of customer relationship Management and also of the first. It is nothing but a software solution that includes forecasting, Tracking, potential interaction and processing of sale.

4. Customer service :- the Customer service component in CRM. This is because CRM focuses on collection of customer data, gathering in formation about their purchase patterns and provides this information to every department that requires it.

5. Marketing:- Marketing is nothing but the promotional activities that are involve in promoting a product either to a general public or to specific group.

6. Work flow automation:- Work flow

processes include cutting cost and

streaming lings processes. It basically

save several people form doing the

same jobs again.

7. Business reporting:- This is nothing

but being able to identify the exact

position of your company at given point

of time.

8. Analytics:- It involve the study of data

so tat information can used to study

market trends.

Process of CRM

5. Continue to re-engage software

4. Select a CRM software to measurer performance

3. Define how customer type will be handled

2. Define your over all strategy and consider cost

1. Clearly identify your target market and value proportion

Enterprise resource planning

system (ERP)

Enterprise resource planning

system (ERP) ERP is a set of tools and processes thatintegrates department and functionsacross a company into one computersystem.

ERP runs off a single database, enablingvarious depts. to share information andcommunicate with each other.

ERP system comprise function specificmodules designed to interact with othermodules, e.g. accounts receivable,accounts payable purchasing etc.

Cross functional approach of ERP

Customer/Employee

ProductionPlanning

Integrated Logistics

Accounting andFinance

Sales, Distribution, order Management

Human Resources

ERP features:

1. Security

2. Authorization

3. Referencing

4. Responsibility

5. Implementation

Benefits

Help in integrating applications for

decision making and planning

Allow departments to talk to each

other

Easy to integrate by using processed

built into ERP software.

Better management of resources

reducing the cost of operations.

Increases in the productivity of the

business possible

Implementation of ERP

The Implementation stage of ERP life cycle involve a number of activities that must be managed effectively in order for the project to be success. Those activities are:-

1. Installation

2. Confrigration

3. Customization

4. Testing

5. Change management

6. Training

Data Ware Housing

Data Ware Housing

Data Ware House is a repository which stores integrated information for efficient querying and analysis.

“A data warehouse is simply a single, complete, and consistent store of data obtained from a variety of sources and made available to end users in a way they can understand and use it in a business context.”

-- Barry Devlin, IBM Consultant

Why Data Warehousing?

Data warehousing can be considered as an important preprocessing step for data mining

A data warehouse also provides on-line analytical processing (OLAP) tools for interactivemultidimensional data analysis.

Heterogeneous

Databases

Data Warehouse

data selection

data cleaning

data integration

data summarization

Example of a Data

Warehouse

timeid pid sales1 1 2

2 1 42 2 1

3 3 2... ... ...

timeid day month year1 11 4 19992 15 4 19993 2 5 1999... ... ...

pid name type1 chair office2 table office3 desk office... ...

FACT table

dimension 1: time

dimension 2: product

tid type date1 sale 4/11/1999

2 sale 5/2/19993 buy 5/17/1999

... ... ...

Transaction

eid name birthdate... ... ...

Employeedid dname... ...

DepartmentData Warehouse

tid pid qty1 21 2

2 13 13 41 3

... ... ...

Details

sid date time qty pid1 15:4:1999 8:30 2 11

2 15:4:1999 9:30 2 11

3 ??? 3 564 19:5:1999 4 22

... ...

Sales

sid name birthdate... ... ...

Supplier

cid cname... ...

Country

US-Database

HK-Database

Characteristics of Data

Warehouse

Subject-Oriented

Integrated

Non- Volatile

Time Variant

Data Warehouse—Subject-

Oriented

Organized around major subjects, such as customer,

product, sales.

Focusing on the modeling and analysis of data for

decision makers, not on daily operations or

transaction processing.

Provide a simple and concise view around particular

subject issues by excluding data that are not useful in

the decision support process.

Data Warehouse—Integrated

Constructed by integrating multiple, heterogeneous data sources◦ relational databases, flat files, on-line

transaction records

Data cleaning and data integration techniques are applied.◦ Ensure consistency in naming conventions,

encoding structures, attribute measures, etc. among different data sources E.g., Hotel price: currency, tax, breakfast covered,

etc.

◦ When data is moved to the warehouse, it is converted.

Data Warehouse—Time

Variant The time horizon for the data warehouse is

significantly longer than that of operational

systems.

◦ Operational database: current value data.

◦ Data warehouse data: provide information from a historical

perspective (e.g., past 5-10 years)

Every key structure in the data warehouse

◦ Contains an element of time, explicitly or implicitly

◦ But the key of operational data may or may not contain

“time element” (the time elements could be extracted from

log files of transactions)

Data Warehouse—Non-

Volatile

A physically separate store of data transformed from

the operational environment.

Operational update of data does not occur in the

data warehouse environment.

◦ Does not require transaction processing, recovery, and

concurrency control mechanisms

◦ Requires only two operations in data accessing:

initial loading of data and access of data.

Data Mining

Data Mining

Data mining is the process of

analyzing data from different

perspectives and summarizing it into

useful information. The information

that can be used to increase revenue.

Data mining is primarily used today by

companies with a strong consumer

focus- retail, financial, communication,

and marketing organization.

Components of data mining

◦ Data mining—core of knowledge discovery process

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Data

Cleaning

Data Integration

Databases

Data Warehouse

Task-relevant Data

Selection

Data Mining

Pattern Evaluation

Process of data mining

1. Problem definition

2. Data exploration

3. Data preparation

4. Modeling

5. Evaluation

6. Deployment

Problem definition

A data mining project starts with the understanding of the business problem. Data mining experts, business experts, and domain experts work closely together to define the project objectives and the requirements from a business perspective. The project objective is then translated into a data mining problem definition. In the problem definition phase, data mining tools are not yet required.

Data exploration

Domain experts understand the meaning of the metadata. They collect, describe, and explore the data. They also identify quality problems of the data. A frequent exchange with the data mining experts and the business experts from the problem definition phase is vital. In the data exploration phase, traditional data analysis tools, for example, statistics, are used to explore the data.

Data preparation

Domain experts build the data model for the modeling process. They collect, cleanse, and format the data because some of the mining functions accept data only in a certain format. They also create new derived attributes, for example, an average value. In the data preparation phase, data is tweaked multiple times in no prescribed order. Preparing the data for the modeling tool by selecting tables, records, and attributes, are typical tasks in this phase. The meaning of the data is not changed.

Modeling

Data mining experts select and apply various mining functions because you can use different mining functions for the same type of data mining problem. Some of the mining functions require specific data types. The data mining experts must assess each model. In the modeling phase, a frequent exchange with the domain experts from the data preparation phase is required.

The modeling phase and the evaluation phase are coupled. They can be repeated several times to change parameters until optimal values are achieved. When the final modeling phase is completed, a model of high quality has been built.

Evaluation

Data mining experts evaluate the model. If the model does not satisfy their expectations, they go back to the modeling phase and rebuild the model by changing its parameters until optimal values are achieved. When they are finally satisfied with the model, they can extract business explanations and evaluate the following questions: Does the model achieve the business objective?

Have all business issues been considered?

At the end of the evaluation phase, the data mining experts decide how to use the data mining results.

Deployment

Data mining experts use the mining results by exporting the results into database tables or into other applications, for example, spreadsheets. The Intelligent Miner™ products assist you to follow this process. You can apply the functions of the Intelligent Miner products independently, iteratively, or in combination.

The following figure shows the phases of the Cross Industry Standard Process for data mining (CRISP DM) process model.

Thank you