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					       ERP / APS and the
         Supply Chain
          Strategic and Operational Things




ERP?

       APS ?
Let’s Shift Gears and Move from Operational
  Stuff to Decision Support Stuff and APS
Data Warehousing and Decision
          Support



           data
ERP and APS (Advanced Planning and Scheduling) Systems


 APS Systems work with ERP systems by providing business analysis
 and decision support capabilities. The majority of ERP systems are
 still transactions-oriented and have limited decision support features.
 An APS system leverages the data residing in a company’s ERP
 system to provide decision support for production planning, demand
 planning and transportation planning.
                  Operational vs Informational Systems



Characteristic            Operational Systems       Informational Systems

Primary Purpose           Run Current Business      Support Decision Making
Type of Data              Current Data              Historical or Point-In-Time
Primary Users             Operational People        Management, Analysts
Scope of Use              Narrow Simple Queries     Broad Complex Queries
Design Goals              Performance               Ease of Access and Use



                                                  feeds all types of custom and
                                                    standard decision models
                   Basic Concepts of Data Warehousing


A data warehouse is a subject-oriented, integrated, time-variant, nonvolatile
collection of data used in support of management decision making processes.
Where:

          subject-oriented  organized around the key subjects (entities) of
          the enterprise, eg. customer, products, vendors, etc.

          integrated  data defined using consistent formats, naming,
          structures, and related characteristics.

          time-variant  warehouse data contains a time dimension so that
          can be used as a historical record of the business.

          nonvolatile  warehouse data are loaded and refreshed from
          operational systems, but cannot be updated by end users.
The DSS components that form a system are:

         The data store (data warehouse) component is basically a DSS
         database that contains business data and business-model data. These
         data represent a snapshot of the company situation.

         The data extraction and filtering component is used to extract,
         consolidate, and validate the data store.

         The end user query tool is used by the data analyst to create the
         queries used to access the database.

         The end user presentation tool is used by the data analyst to organize
         and present the data.
  Operational Environment                                Decision Support Environment


             Source
              (file)




  Source
(database)

                        Transformation and Integration
                                                         Data Warehouse



   Source
 (database)




                        Source
                       (external)
  Operational Environment                                     Decision Support Environment


             Source
              (file)
                                                         End-User Query Tools



  Source
(database)

                        Transformation and Integration
                                                              Data Warehouse



   Source
 (database)




                        Source
                       (external)
  Operational Environment                                          Decision Support Environment

                                                         Presentation_Interface Tools
             Source
              (file)




  Source
(database)

                        Transformation and Integration
                                                                   Data Warehouse



   Source
 (database)




                        Source
                       (external)
In addition to the time frame and aggregation differences between operational
and decision support informational data, the most distinguishing characteristic of
DSS data is its dimensionality.

For Example Assume a table with three dimensions: product, location, and time.
Data Warehouse Architectures

The most basic architecture for a data warehouse is a two-level physical architecture.

           1.data are extracted from the various source system files and databases,

           2.data are transformed and integrated before loaded into the data warehouse,

           3.data are read-only within the warehouse,

           4. user access is supported by a variety of query languages and analytical tools.
 For most organizations it is essential to separate informational processing from
operational processing by creating a data warehouse!

          a data warehouse logically centralizes data that are scattered through-out
          disparate operational systems and makes them readily available for
          decision-support applications,

          the existence of a data warehouse forces improved data quality and
          consistency,

          a separate data warehouse reduces the competition for data resources
          among operational and informational needs.
What are Data Marts? Why would a company be interested in Data Marts as
a substitute for a Data Warehouse?

Knowing that Data Warehousing requires time, money, and considerable
managerial effort, many companies create Data Marts, instead. Data Marts are
smaller, more manageable data sets that are targeted to fit the special needs of
small groups within the organization. In other words, Data Marts are small,
single-subject Data Warehouse subsets. Data Mart development and use costs
are lower and the implementation time is shorter. Once the Data Marts have
demonstrated their ability to serve the DSS, they can be expanded to become
Data Warehouses or they can be migrated into larger existing Data Warehouses.
  Operational Environment                                 Decision Support Environment


             Source
              (file)                A 3 Level Structure                 mart1




  Source
(database)

                        Transformation and Integration
                                                                                mart2
                                                           Data Warehouse



   Source
 (database)



                                                                       mart3
                        Source
                       (external)
                                                         Selection and Aggregation
Data Warehouse Principle:


Large organizations with many heterogeneous data sources should adopt a 3 level
data warehouse architecture that includes Data Marts.
The Star Schema
Location                                                Product
  LocationId       Name                Etc                ProductId       Name                  Etc
L1             Marion         stuff1                    P1            Product1         stuff1
L2             Charlotte      stuff2                    P2            Product2         stuff2
L3             Raleigh        stuff3                    P3            Product3         stuff3



SalesFact
SalesDollars    LocationId    ProductId       SalesId     TimeId
   $1,234.00   L1            P1              S01        T1               Time
   $3,211.00   L1            P1              S02        T2
   $2,234.00   L1            P1              S03        T3                    TimeId              Date             Etc
   $1,112.00   L1            P2              S04        T1               T1                          1/1/97 stuff1
   $5,432.00   L1            P2              S05        T2               T2                          7/1/97 stuff2
   $2,376.00   L1            P2              S06        T3               T3                         1/14/97 stuff3
   $2,134.00   L1            P3              S07        T1
   $1,156.00   L1            P3              S08        T2
   $3,256.00   L1            P3              S09        T3
   $1,245.00   L2            P1              S10        T1
   $6,432.00   L2            P1              S11        T2
   $3,245.00   L2            P1              S12        T3
   $1,890.00   L2            P2              S13        T1
   $2,245.00   L2            P2              S14        T2
   $2,643.00   L2            P2              S15        T3
   $7,654.00   L2            P3              S16        T1
   $1,234.00   L2            P3              S17        T2
Query 1:


SELECT Location.Name, Product.Name, SalesFact.SalesDollars, Time.Date
FROM [Time] INNER JOIN (Product INNER JOIN (Location INNER JOIN SalesFact ON
Location.LocationId = SalesFact.LocationId) ON Product.ProductId = SalesFact.ProductId) ON
Time.TimeId = SalesFact.TimeId
WHERE Location.Name = 'Marion' and Product.Name = 'Product2' and Time.Date = #1/1/97#;


      Location.Name Product.Name SalesDollars          Date
      Marion        Product2         $1,112.00            1/1/97
Query 2:
SELECT Location.Name, Sum(SalesFact.SalesDollars) AS SalesDollars
FROM [Time] INNER JOIN (Product INNER JOIN (Location INNER JOIN SalesFact ON
Location.LocationId = SalesFact.LocationId) ON Product.ProductId = SalesFact.ProductId)
ON Time.TimeId = SalesFact.TimeId
WHERE Location.Name = 'Marion' and Time.Date = #1/1/97#
GROUP BY Location.Name;




             Name       SalesDollars
         Marion             $4,480.00




                                                                  Show Access
The User Interface

A well designed data warehouse or data mart, loaded with relevant data may not be
used unless users are provided with a powerful, intuitive interface that allows them to
easily access and analyze those data.

A variety of tools are available to query and analyze data stored in data warehouses
and data marts. These tools may be classified as follows:

          .traditional query and reporting tools,
          .on-line analytical processing (OLAP) tools,
          .data-mining tools,
          .data-visualization tools.

The first requirement for building a user-friendly interface is a set of metadata that
describes the data in the data mart in terms that users can easily understand. The
metadata associated with data marts are often referred to as a 'data catalog', 'data
dictionary', etc.
Data Mining and Decision Support
            Systems
How does data mining work? The different phases in the data mining
process.

Data mining is subject to four phases:

In the data preparation phase, the main data sets to be used by the data mining
operation are identified and cleansed from any data impurities. Because the data
in the data warehouse are already integrated and filtered, the Data Warehouse
usually is the target set for data mining operations.

The data analysis and classification phase objective is to study the data to
identify common data characteristics or patterns. During this phase the data
mining tool applies specific algorithms to find:

data groupings, classifications, clusters, or sequences, data dependencies, links,
or relationships, data patterns, trends, and deviations.

The knowledge acquisition phase uses the results of the data analysis and
classification phase. During this phase, the data mining tool (with possible
intervention by the end user) selects the appropriate modeling or knowledge
acquisition algorithms. The most typical algorithms used in data mining are based
on neural networks, decision trees, rules induction, genetic algorithms,
classification and regression trees, memory-based reasoning, or nearest neighbor
and data visualization. A data mining tool may use many of these algorithms in any
combination to generate a computer model that reflects the behavior of the target
data set.
Although some data mining tools stop at the knowledge acquisition phase, others
continue to the prognosis phase. In this phase, the data mining findings are used to
predict future behavior and forecast business outcomes. Examples of data mining
findings can be:
Although some data mining tools stop at the knowledge acquisition phase, others
continue to the prognosis phase. In this phase, the data mining findings are used to
predict future behavior and forecast business outcomes. Examples of data mining
findings can be:

65% of customers who did not use the credit card in six months are 88% likely to
cancel their account
Although some data mining tools stop at the knowledge acquisition phase, others
continue to the prognosis phase. In this phase, the data mining findings are used to
predict future behavior and forecast business outcomes. Examples of data mining
findings can be:

65% of customers who did not use the credit card in six months are 88% likely to
cancel their account

82% of customers who bought a new TV 27" or bigger are 90% likely to buy a
entertainment center within the next 4 weeks.
Although some data mining tools stop at the knowledge acquisition phase, others
continue to the prognosis phase. In this phase, the data mining findings are used to
predict future behavior and forecast business outcomes. Examples of data mining
findings can be:

65% of customers who did not use the credit card in six months are 88% likely to
cancel their account

82% of customers who have bought a new TV 27" or bigger 2 years ago or before are
90% likely to buy a entertainment center within the next 4 weeks.

If age < 30 and income <= 25,0000 and credit rating < 3 and credit amount > 25,000
then the minimum term is 10 years.
Although some data mining tools stop at the knowledge acquisition phase, others
continue to the prognosis phase. In this phase, the data mining findings are used to
predict future behavior and forecast business outcomes. Examples of data mining
findings can be:

65% of customers who did not use the credit card in six months are 88% likely to
cancel their account

82% of customers who bought a new TV 27" or bigger are 90% likely to buy a
entertainment center within the next 4 weeks.

If age < 30 and income <= 25,0000 and credit rating < 3 and credit amount > 25,000
then the minimum term is 10 years.

The complete set of findings can be represented in a decision tree, a neural net, a
forecasting model or a visual presentation interface which is then used to project future
events or results. For example the prognosis phase may project the likely outcome of a
new product roll-out or a new marketing promotion.
End

				
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posted:8/23/2011
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