Supply chain management involves coordinating all activities involved in procuring raw materials, manufacturing products, and distributing goods to meet market demand in the most cost-effective way. The objectives of SCM are to efficiently integrate suppliers, manufacturers, warehouses and stores to produce and deliver the right products, to the right locations, at the right time. SCM software, e-supply chains, and other approaches are used to manage supply chain activities and relationships to achieve competitive advantages.

1. Supply Chain
Management

2. 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.

3. 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

4. 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.

5. E- Supply Chain
 When supply chain is managed
electronically, usually with web based
software, it is referred to as an e-supply
chain.

6. 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

7. Customer Relationship
Management (CRM)

8. 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.”

9. 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

10. 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

11. 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.

12. 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.

13. 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.

14. Process of CRM
1. Clearly identify your target market and
value proportion
2. Define your over all strategy and
consider cost
3. Define how customer type will be
handled
4. Select a CRM software to measurer
performance
5. Continue to re-engage software

15. Enterprise resource planning
system (ERP)

16. Enterprise resource planning
system (ERP)
 ERP is a set of tools and processes that
integrates department and functions
across a company into one computer
system.
 ERP runs off a single database, enabling
various depts. to share information and
communicate with each other.
 ERP system comprise function specific
modules designed to interact with other
modules, e.g. accounts receivable,
accounts payable purchasing etc.

17. Cross functional approach of ERP
Production
Planning
Customer/
Employee
Integrated
Logistics
Accounting and
Finance
Sales,
Distribution,
order
Management
Human Resources

18. ERP features:
1. Security
2. Authorization
3. Referencing
4. Responsibility
5. Implementation

19. 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

20. 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

21. Data Ware Housing

22. 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

23. Why Data Warehousing?
 Data warehousing can be considered as an important
preprocessing step for data mining
Heterogeneous
Databases
Data Warehouse
data selection
data cleaning
data integration
data summarization
 A data warehouse also provides on-line analytical
processing (OLAP) tools for interactive
multidimensional data analysis.

24. Example of a Data
Warehouse
FACT table
timeid pid sales
1 1 2
2 1 4
2 2 1
3 3 2
... ... ...
dimension 1: time
timeid day month year
1 11 4 1999
2 15 4 1999
3 2 5 1999
... ... ...
dimension 2: product
pid name type
1 chair office
2 table office
3 desk office
... ...
Employee
US-Database
eid name birthdate
... ... ...
Transaction
tid type date
1 sale 4/11/1999
2 sale 5/2/1999
3 buy 5/17/1999
... ... ...
Department
did dname
... ...
Data Warehouse
Details
tid pid qty
1 21 2
2 13 1
3 41 3
... ... ...
HK-Database
Supplier
sid name birthdate
... ... ...
Country
sid date time qty pid
1 15:4:1999 8:30 2 11
2 15:4:1999 9:30 2 11
3 ??? 3 56
4 19:5:1999 4 22
... ...
Sales
cid cname
... ...

25. Characteristics of Data
Warehouse
 Subject-Oriented
 Integrated
 Non- Volatile
 Time Variant

26. 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.

27. 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.

28. 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)

29. 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.

30. Data Mining

31. 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.

32. Components of data mining
◦ Data mining—core of
knowledge discovery
process
32
Task-relevant Data
Data Warehouse
Data
Cleaning
Data Mining
Data Integration
Databases
Selection
Pattern Evaluation

33. Process of data mining
1. Problem definition
2. Data exploration
3. Data preparation
4. Modeling
5. Evaluation
6. Deployment

34. 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.

35. 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.

36. 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.

37. 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.

38. 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.

39. 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.

41. Thank you