Text Mining

Published on May 2016 | Categories: Documents | Downloads: 32 | Comments: 0 | Views: 344
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INTRODUCTION TO TEXT MINING


Text mining also known as intelligent text analytics,
text data mining or knowledge discovery in text (KDT),
refers generally to the process of extracting interesting
and non trivial information and knowledge from
unstructured text.

TEXT MINING PROCESS
Text preprocessing
 Semantic
 Text Analysis
Features Generation
 Bag of words
Features Selection
 Statistics

TEXT MINING PROCESS
Text/Data Mining
 Classification
 Clustering
 Associations
Analyzing results

TEXT MINING PROCESS
Tokenization involves converting a sentence into a
sequence of tokens i.e. words.
 Dictionary creation is used to locate occurrence of a
particular term in the documents.
 It is stored as Linked list data structure.
 It reduces retrieval time of an algorithm


DEMAND DRIVEN SUPPLY CHAIN




A system of technologies and business processes
that sense and respond to real-time demand
across a network of customers, suppliers, and
employees.
DDSN organizations are more demand sensing,
capable of more demand shaping, and able to
execute a more profitable demand response than
companies that are simply supply-centered.

DEMAND DRIVEN SUPPLY CHAIN
A DDSN requires technologies that enable
businesses to closely synchronize demand and
supply.
 DDSN technologies provide real-time demand
sensing and visibility to changing marketplace
requirements
 Technologies should enable planners global access
to actionable data, information toolsets permitting
rapid and collective decision-making based on
“what-if” alternatives, and comprehensive scoring
mechanisms that provide for clear alternatives
when choosing between competing courses of
action.


COMPONENTS OF DDSN

DEMAND/SUPPLY VISIBILITY
Demand Forecasting:
 Although actual demand signals provide critical
data driving the demand-pull, effective
forecasting is still needed for long-term planning
and new product introduction.


Today’s forecasting tools can draw upon simple
models or complex algorithms that attempt to
project future demand driven bydata from sales,
invoice, POS, lost sales, and promotion histories.

DEMAND/SUPPLY VISIBILITY
Demand Shaping:


This enabler permits supply chain nodes to
simulate demand intensity through the use of
sales techniques such as channel promotions,
bonus and incentives, pricing, and advertising
and marketing strategies.

DEMAND/SUPPLY VISIBILITY
Alert Signal Management.


Once notification of demand or supply
abnormalities occurs, the close networking of
trading partners provides for the efficient
rebalancing of channel resources. Visibility tools
include alert-driven signals revealing unplanned
product shortages (or excesses), emergency plant
shutdowns, process failures, unexpected outlier
demand, and evidence of wide variance of actual
demand and supply against the plan.

DEMAND/SUPPLY VISIBILITY


Predictive Analytics and Simulation. The ability
to capture, analyze, and simulate actual supply
chain performance is essential to provide
visibility to the health of the customer value
channel.

TEXT MINING APPLICATION IN SCM




The relational database is the model most used
in today’s software systems.
The key feature of this model can be found in the
centralization of key master records such as
items, customers, and suppliers that can be
referenced by multiple software applications
without duplication.

TEXT MINING APPLICATION IN SCM




Besides increased security, flexibility, scalability,
and performance, relational databases enable the
use of sophisticated data mining toolsets such as
structured query language (SQL) for data
reporting and analysis.
Since databases store an enormous amount of
data, they require a database management
system (DBMS) that allows users to create,
access, and query that data.



Since its inception, the linkage of SCM and
integrative information technologies has enabled
SCM to evolve to meet the changing needs of the
marketplace.

TEXT MINING APPLICATIONS IN
SCM
Companies such as P&G, Wal-Mart, and Cisco are
taking demand and supply visibility way beyond just
receiving forecasts from their customers.
 Utilizing techniques such as shopper loyalty cards and
POS data, these companies are tapping into enormous
reservoirs revealing what their customers’ retime needs
are.
 Wal-Mart, for example, provides P&G access to its huge
database of POS information.
 Other organizations are working closely with key
suppliers and customers to develop linked S&OP tools
so They can see the pulse of supply and demand in the
supply channel. P&G calls this ability to “sense”
demand as it happens.


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