Supply Chain Management (SCM)

Supply chain management (SCM) is the management of a network of interconnected businesses involved in the ultimate provision of (business)|product and Service (economics) packages required by end customers (Harland, 1996). Supply Chain Management spans all movement and storage of raw materials, work-in-process inventory, and finished goods from point of origin to point of consumption (supply chain).

Another definition is provided by the APICS Dictionary when it defines SCM as the "design, planning, execution, control, and monitoring of supply chain activities with the objective of creating net value, building a competitive infrastructure, leveraging worldwide logistics, synchronizing supply with demand, and measuring performance globally

   

Enterprise Resource Planning (ERP)

Enterprise Resource Planning (ERP) is a term usually used in conjunction with ERP software or an ERP system which is intended to manage all the information and functions of a business or company from shared data stores.

An ERP system typically has modular hardware and software units and "services" that communicate on a local area network. The modular design allows a business to add or reconfigure modules (perhaps from different vendors) while preserving data integrity in one shared database that may be centralized or distributed

Some organizations — typically those with sufficient in-house IT skills to integrate multiple software products — choose to implement only portions of an ERP system and develop an external interface to other ERP or stand-alone systems for their other application needs. For example, one may choose to use human resource management system from one vendor, and perform the integration between the systems themselves.[citation needed]

This is common to retailers[citation needed], where even a mid-sized retailer will have a discrete Point-of-Sale (POS) product and financials application, then a series of specialized applications to handle business requirements such as warehouse management, staff rostering, merchandising and logistics.

Ideally, ERP delivers a single database that contains all data for the various software modules that typically address areas such as

Manufacturing
    Engineering, bills of material, scheduling, capacity, workflow management, quality control, cost management, manufacturing process, manufacturing projects, manufacturing flow
Supply chain management
    Order to cash, inventory, order entry, purchasing, product configurator, supply chain planning, supplier scheduling, inspection of goods, claim processing, commission calculation
Financials
    General ledger, cash management, accounts payable, accounts receivable, fixed assets
Project management
    Costing, billing, time and expense, performance units, activity management
Human resources
    Human resources, payroll, training, time and attendance, rostering, benefits
Customer relationship management
    Sales and marketing, commissions, service, customer contact and call center support

Data services
    various "self-service" interfaces for customers, suppliers, and/or employees
Access control
    management of user privileges for various processes

Customer Relationship Management (CRM)

Customer relationship management (CRM) are methods that companies use to interact with customers. The methods include employee training and special purpose CRM software. There is an emphasis on handling incoming customer phone calls and email, although the information collected by CRM software may also be used for promotion, and surveys such as those polling customer satisfaction.

Initiatives often fail because implementation was limited to software installation, without providing the context, support and understanding for employees to learn.[1] Tools for customer relationship management should be implemented "only after a well-devised strategy and operational plan are put in place".

Other problems occur when failing to think of sales as the output of a process that itself needs to be studied and taken into account when planning automation

SAP Products

 Customer Relationship Management (CRM)

 Enterprise Resource Planning (ERP)

 Product Lifecycle Management (PLM)

 Supplier Relationship Management (SRM)

 SAP Business Objects Suite (BOBJ)
 SAP Advanced Planner and Optimizer (APO)
 SAP Apparel and Footwear Solution (AFS)
 SAP Business Information Warehouse (BW)
 SAP Business Intelligence (BI)
 SAP Catalog Content Management (CCM)
 SAP Enterprise Buyer Professional (EBP)
 SAP Enterprise Learning
 SAP Portal (EP)
 SAP Exchange Infrastructure (XI)
 Governance, Risk and Compliance (GRC)
 Enterprise central Component (ECC)
  SAP Human Resource Management Systems (HRMS)
  SAP Internet Transaction Server (ITS)
  SAP Incentive and Commission Management (ICM)
  SAP Knowledge Warehouse (KW)
  SAP Master Data Management (MDM)
  SAP Service and Asset Management
  SAP Solutions for mobile business
  SAP Solution Composer
  SAP Strategic Enterprise Management (SEM)
  SAP Test Data Migration Server (TDMS)
  SAP Training and Event Management (TEM)
  SAP NetWeaver Application Server (Web AS)
  SAP xApps
  SAP Supply Chain Performance Management (SCPM)

Business Objects

BusinessObjects is the first  business intelligence (BI) platform that delivers a complete set of market-leading BI capabilities: best-in-class performance management, reporting, query and analysis, and data integration. BusinessObjects XI introduces significant innovations that deliver BI in new ways to a much broader set of users as well as completing the integration of the Crystal and BusinessObjects product lines.
BusinessObjects XI Release 2 builds on the world’s leading business intelligence platform, BusinessObjects XI, to deliver new ways to access the information you need to do your job, allowing you to be able to say “I can answer my questions. I can trust and share my insight. And I can do everything I need on one BI standard.”
BusinessObjects XI Release 2 builds on the proven and trusted BusinessObjects XI platform. It provides substantial functional improvements and innovations across the BusinessObjects XI platform and includes full platform-level support for Desktop Intelligence™ (formerly BusinessObjects full client) to allow a smooth transition path to BusinessObjects XI for all existing customers who have invested in that technology.

Data mining

Data mining is the process of extracting patterns from data. As more data are gathered, with the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform these data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery.

While data mining can be used to uncover patterns in data samples, it is important to be aware that the use of non-representative samples of data may produce results that are not indicative of the domain. Similarly, data mining will not find patterns that may be present in the domain, if those patterns are not present in the sample being "mined". There is a tendency for insufficiently knowledgeable "consumers" of the results to attribute "magical abilities" to data mining, treating the technique as a sort of all-seeing crystal ball. Like any other tool, it only functions in conjunction with the appropriate raw material: in this case, indicative and representative data that the user must first collect. Further, the discovery of a particular pattern in a particular set of data does not necessarily mean that pattern is representative of the whole population from which that data was drawn. Hence, an important part of the process is the verification and validation of patterns on other samples of data.

The term data mining has also been used in a related but negative sense, to mean the deliberate searching for apparent but not necessarily representative patterns in large numbers of data. To avoid confusion with the other sense, the terms data dredging and data snooping are often used. Note, however, that dredging and snooping can be (and sometimes are) used as exploratory tools when developing and clarifying hypotheses.

ETL(Extract, transform, load)

• Extracting data from outside sources
• Transforming it to fit operational needs (which can include quality levels)
• Loading it into the end target (database or data warehouse)\

Extract

The first part of an ETL process involves extracting the data from the source systems. Most data warehousing projects consolidate data from different source systems. Each separate system may also use a different data organization / format. Common data source formats are relational databases and flat files, but may include non-relational database structures such as Information Management System (IMS) or other data structures such as Virtual Storage Access Method (VSAM) or Indexed Sequential Access Method (ISAM), or even fetching from outside sources such as web spidering or screen-scraping. Extraction converts the data into a format for transformation processing.An intrinsic part of the extraction involves the parsing of extracted data, resulting in a check if the data meets an expected pattern or structure. If not, the data may be rejected entirely or in part.

Transform

The transform stage applies a series of rules or functions to the extracted data from the source to derive the data for loading into the end target. Some data sources will require very little or even no manipulation of data. In other cases, one or more of the following transformation types may be required to meet the business and technical needs of the target database:

• Selecting only certain columns to load (or selecting null columns not to load)
• Translating coded values (e.g., if the source system stores 1 for male and 2 for female, but the warehouse stores M for male and F for female), this calls for automated data cleansing; no manual cleansing occurs during ETL
• Encoding free-form values (e.g., mapping "Male" to "1" and "Mr" to M)
• Deriving a new calculated value (e.g., sale_amount = qty * unit_price)
• Filtering
• Sorting
• Joining data from multiple sources (e.g., lookup, merge)
• Aggregation (for example, rollup - summarizing multiple rows of data - total sales for each store, and for each region, etc.)
• Generating surrogate-key values
• Transposing or pivoting (turning multiple columns into multiple rows or vice versa)
• Splitting a column into multiple columns (e.g., putting a comma-separated list specified as a string in one column as individual values in different columns)
• Disaggregation of repeating columns into a separate detail table (e.g., moving a series of addresses in one record into single addresses in a set of records in a linked address table)
• Applying any form of simple or complex data validation. If validation fails, it may result in a full, partial or no rejection of the data, and thus none, some or all the data is handed over to the next step, depending on the rule design and exception handling. Many of the above transformations may result in exceptions, for example, when a code translation parses an unknown code in the extracted data.

 Load

The load phase loads the data into the end target, usually the data warehouse (DW). Depending on the requirements of the organization, this process varies widely. Some data warehouses may overwrite existing information with cumulative, updated data every week, while other DW (or even other parts of the same DW) may add new data in a historized form, for example, hourly. The timing and scope to replace or append are strategic design choices dependent on the time available and the business needs. More complex systems can maintain a history and audit trail of all changes to the data loaded in the DW.

As the load phase interacts with a database, the constraints defined in the database schema — as well as in triggers activated upon data load — apply (for example, uniqueness, referential integrity, mandatory fields), which also contribute to the overall data quality performance of the ETL process.

Data Mining tools

SPSS Clementine 8.5
IBM DB2 Intelligent Miner
Insightful Miner 3.0
KXEN Analytic Framework 3.0
Oracle Data Mining
Quadstone System V. 5
SAS Enterprise Miner 5.1
SPSS Clementine 8.5

Data warehouse architecture

Architecture, in the context of an organization's data warehousing efforts, is a conceptualization of how the data warehouse is built. There is no right or wrong architecture, rather multiple architectures exist to support various environments and situations. The worthiness of the architecture can be judged in how the conceptualization aids in the building, maintenance, and usage of the data warehouse.One possible simple conceptualization of a data warehouse architecture consists of the following interconnected layers:
Operational database layer
    The source data for the data warehouse - An organization's Enterprise Resource Planning systems fall into this layer.
Data access layer
    The interface between the operational and informational access layer - Tools to extract, transform, load data into the warehouse fall into this layer.
Metadata layer
    The data directory - This is usually more detailed than an operational system data directory. There are dictionaries for the entire warehouse and sometimes dictionaries for the data that can be accessed by a particular reporting and analysis tool.
Informational access layer
    The data accessed for reporting and analyzing and the tools for reporting and analyzing data - Business intelligence tools fall into this layer. And the Inmon-Kimball differences about design methodology, discussed later in this article, have to do with this layer.

Data warehouse

Data warehouse is a repository of an organization's electronically stored data. Data warehouses are designed to facilitate reporting and analysis.A Data Warehouse houses a standardized, consistent, clean and integrated form of data sourced from various operational systems in use in the organization, structured in a way to specifically address the reporting and analytic requirements.

This definition of the data warehouse focuses on data storage. However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata.

Subject-oriented
    The data in the data warehouse is organized so that all the data elements relating to the same real-world event or object are linked together.
Non-volatile
    Data in the data warehouse is never over-written or deleted - once committed, the data is static, read-only, and retained for future reporting.
Integrated

    The data warehouse contains data from most or all of an organization's operational systems and this data is made consistent.The top-down design methodology generates highly consistent dimensional views of data across data marts since all data marts are loaded from the centralized repository. Top-down design has also proven to be robust against business changes. Generating new dimensional data marts against the data stored in the data warehouse is a relatively simple task. The main disadvantage to the top-down methodology is that it represents a very large project with a very broad scope. The up-front cost for implementing a data warehouse using the top-down methodology is significant, and the duration of time from the start of project to the point that end users experience initial benefits can be substantial. In addition, the top-down methodology can be inflexible and unresponsive to changing departmental needs during the implementation phases

Benefits of data warehousing
Some of the benefits that a data warehouse provides are as follows:

    * A data warehouse provides a common data model for all data of interest regardless of the data's source. This makes it easier to report and analyze information than it would be if multiple data models were used to retrieve information such as sales invoices, order receipts, general ledger charges, etc.
    * Prior to loading data into the data warehouse, inconsistencies are identified and resolved. This greatly simplifies reporting and analysis.
    * Information in the data warehouse is under the control of data warehouse users so that, even if the source system data is purged over time, the information in the warehouse can be stored safely for extended periods of time.
    * Because they are separate from operational systems, data warehouses provide retrieval of data without slowing down operational systems.
    * Data warehouses can work in conjunction with and, hence, enhance the value of operational business applications, notably customer relationship management (CRM) systems.
    * Data warehouses facilitate decision support system applications such as trend reports (e.g., the items with the most sales in a particular area within the last two years), exception reports, and reports that show actual performance versus goals.

Disadvantages of data warehouses

There are also disadvantages to using a data warehouse. Some of them are:
    * Data warehouses are not the optimal environment for unstructured data.
    * Because data must be extracted, transformed and loaded into the warehouse, there is an element of latency in data warehouse data.
    * Over their life, data warehouses can have high costs. The data warehouse is usually not static. Maintenance costs are high.
    * Data warehouses can get outdated relatively quickly. There is a cost of delivering suboptimal information to the organization.
    * There is often a fine line between data warehouses and operational systems. Duplicate, expensive functionality may be developed. Or, functionality may be developed in the data warehouse that, in retrospect, should have been developed in the operational systems and vice versa.