CHAPTER 12

INTEGRATING THE ORGANIZATION FROM END TO END - ENTERPRISE RESOURCE PLANNING

In this chapter, we learned about :

• Describe the role information plays in enterprise resource planning system 
• Identify the primary forces driving the explosive growth of enterprise resource planning system
• Explain the business value of integrating supply chain management, customer relationship management, and enterprise resource planning systems

ENTERPRISE RESOURCE PLANNING (ERP)

• All the heart of all ERP systems is a database, when a user enters or updates information in one module, it is immediately and automatically updated throughout the entire system

•  ERP systems automate business processes

BRINGING THE ORGANIZATION TOGETHER

• ERP - The organization before ERP

• ERP - Bringing the organization together

THE EVOLUTION OF ERP

INTEGRATING SCM, CRM, AND ERP

• SCM, CRM, and ERP are the backbone of e-business
• Integration of these applications is the key to success for many companies
• Integration allows the unlocking of information to make it available to any user, anywhere, anytime
• SCM and CRM market overviews

 

 

• General audience and purpose of SCM, CRM and ERP

INTEGRATION TOOLS

• Many companies purchase modules from an ERP vendor, an SCM vendor, and a CRM vendor and must integrate the different modules together

- Middleware : Several different types of software which sit in the middle of and provide connectivity between two or more software applications

- Enterprise application integration (EAI) middleware : Packages together commonly used functionality which reduced the time necessary to develop solutions that integrate applications from multiple vendors

• Data point where SCM, CRM, and ERP integrate

ENTERPRISE RESOURCE PLANNING (ERP)

• ERP systems must integrate various organization processes and be :

1. Flexible - Must be able to quickly respond to the changing needs of the organization
2. Modular and open - Must have an open system architecture, meaning that any module can be interface, with or detached whenever required without affecting the other modules
3. Comprehensive - Must be able to support a variety of organizational functions for a wide range of business
4. Beyond the company - Must support external partnerships and collaboration efforts

ENTERPRISE RESOURCE PLANNING'S EXPLOSIVE GROWTH

• SAP boasts 20,000 installations and 10 million users worldwide
• ERP solutions are growing because :

1. ERP is a logical solution to the mess of incompatible applications that had sprung up in most businesses
2. ERP addresses the need for global information sharing and reporting
3. ERP is used to avoid the pain and expense of fixing legacy systems 

CHAPTER 11

BUILDING A CUSTOMER - CENTRIC ORGANIZATION - CUSTOMER RELATIONSHIP MANAGEMENT

In this chapter, we learned about :

• Compare operational and analytical customer relationship management
• Identify the primary forces driving the explosive growth of customer relationship management
• Define the relationship between decision making and analytical customer relationship management
• Summarize the best practices for implementing a successful customer relationship management system 

CUSTOMER RELATION MANAGEMENT (CRM)

• CRM enables an organization to :

- Provide better customer service

- Make call centers more efficient

- Call sell products more effectively

- Help sales staff close deals faster

- Simplify marketing and sales processes

- Discover new customers

- Increase customer revenues

RECENCY, FREQUENCY, AND MONETARY VALUE

• Organizations can find their most valuable customers through "RFM" - Recency, Frequency, and Monetary value 

- How recently a customer purchased items (Recency)

- How frequently a customer purchased items (Frequency)

- How much a customer spend on each purchase (Monetary value)

THE EVOLUTION OF CRM

• CRM reporting technology : Help organizations identify their customers across other applictions
• CRM analysis technologies : Help organization segment their customers into categories such as best and worst customers
• CRM predicting technologies : Help organizations make predictions regarding customer behavior such as which customers are at risk of leaving
• Three phases in the evolution of CRM include reporting, analyzing, and predicting

THE UGLY SIDE OF CRM

CUSTOMER RELATIONSHIP MANAGEMENT'S EXPLOSIVE GROWTH

• CRM business drivers

• Forecasts for CRM spending (in billions)

USING ANALYTICAL CRM TO ENHANCE DECISIONS

• Operational CRM : Support traditional transactional processing for day-to-day front-office operations or systems that deal directly with the customers
• Analytical CRM : Support back-office operations and strategic analysis and includes all systems that do not deal directly with the customers
• Operational CRM and analytical CRM

CUSTOMER RELATIONSHIP MANAGEMENT SUCCESS FACTORS

• CRM success factors include :

1. Clearly communicate the CRM strategy
2. Define information needs and flows
3. Build an integrated view of the customer
4. Implement in iterations
5. Scalability for organizational growth

CHAPTER 10

EXTENDING THE ORGANIZATION - SUPPLY CHAIN MANAGEMENT

In this chapter, we learned about :

• List and describe the components of a typical supply chain
• Define the relationship between decision making and supply chain management
• Describe the four changes resulting from advances in IT that are driving supply chains
• Summarize the best practices for implementing a successful supply chain management system

SUPPLY CHAIN MANAGEMENT

• The average company spends nearly half of every dollar that it earns on production
• In the past, companies focused primarily on manufacturing and quality improvements to influence their supply chains

BASIC OF SUPPLY CHAIN

• The supply chain has three main links : 

1. Materials flow from suppliers and their "upstream" suppliers at all levels 
2. Transformation of materials into semifinished and finished products through the organization's own production process
3. Distribution of products to customers and their "downstream" customers at all levels

• Organizations must embrace technologies that can effectively manage supply chains

INFORMATION TECHNOLOGY'S ROLE IN THE SUPPLY CHAIN

• It's primary role is to create integrations or tight process and information linkages between functions within a firm

• Factors driving SCM

VISIBILTY

• Supply chain visibilty : The ability to view all areas up and down the supply chain
• Bullwhip effect : Occurs when distorted product demand information passes from one entity to the next through out the supply chain

CONSUMER BEHAVIOR

• Companies can respond faster and more effectively to consumer demands through supply chain enhances
• Demand planning software : Generates demand forecasts using statistical tools and forecasting techniques

COMPETITION

• Supply chain planning (SCP) software : uses advanced mathematical algorithms to improve the flow and efficiency of the supply chain
• Supply chain execution (SCE) software : automates the different steps and stages of the supply chain
• SCP and SCE in the supply chain

SPEED

• Three factors fostering speed

SUPPLY CHAIN MANAGEMENT SUCCESS FACTORS

 

• SCM industry best practices include :

1. Make the sale to suppliers
2. Wean employees of traditional business practices
3. Ensure the SCM system supports the organizational goals
4. Deploy in incremental phases and measure and communicate success
5. Be future oriented

SCM SUCCESS STORIES

• Top reasons why more and more executives are turning to SCM to manage their extended enterprises

• Numerous decision support systems (DSSs) are being built to assist decision makers in the design and operation of integrated supply chains
• DSSs allow managers to examine performance and relationships over the supply chain and among :

- Suppliers

- Manufacturers

- Distributors

- Other factors that optimize supply chain performance

CHAPTER 9

ENABLING THE ORGANIZATION - DECISION MAKING

In this chapter, we learned about :

• Define the systems organizations use to make decisions and gain competitive advantages
• Describe the three quantitative models typically used by decision support systems
• Describe the relationship between digital dashboards and executive information systems
• List and describe four types of artificial intelligence systems
• Describe three types of data-mining analysis capabilities

DECISION MAKING

• Reasons for the growth of decision-making information systems

- People need to analyze large amounts of information

- People must make decisions quickly

- People must apply sophisticated analysis techniques, such as modelling and forecasting, to make good decisions

- People must protect the corporate asset of organizational information

• Model - A simplified representation or abstraction of reality
• IT systems in an enterprise

 

TRANSACTION PROCESSING SYSTEMS

• Moving up through the organizational pyramid users move from requiring transactional information to analytical information

 

• Transaction processing system - The basic business system that serves the operational level (analysts) in an organization 
• Online transaction processing (OLTP) - The capturing of transaction and event information using technology to (1) process the information according to defined business rules, (2) store the information, (3) update existing information to reflect the new information
•  Online analytical processing (OLAP) - The manipulation of information to create business intelligent in support of strategic decision making

 

DECISION SUPPORT SYSTEMS

 

• Decision support system (DSS) - Models information to support managers and business professionals during the decision-making process
• Three quantitative models used by DSSs include :

1. Sensitivity analysis - The study of the impact that changes in one (or more) parts of the model have on other parts of the model
2. What-if analysis - Checks the impact of a change in an assumption on the proposed solution
3. Goal-seeking analysis - Finds the inputs necessary to achieve a goal such as a desired level of output

• What-if analysis

 

• Goal-seeking analysis

 

• Interaction between a TPS and a DSS

EXECUTIVE INFORMATION SYSTEMS

• Executive information system (EIS) - A specialized DSS that supports senior level executives within the organization
• Most EISs offering the following capabilities :

- Consolidation - Involves the aggregation of information and features simple roll-ups to complex grouping of interrelated information

- Drill-down - Enables users to get details, and details of details, of information

- Slice-and-dice - Looks at information from different perspectives

• Interaction between a TPS and an EIS

 

• Digital dashboard - Integrates information from multiple components and presents it in a unified display

 

 

ARTIFICIAL INTELLIGENCE (AI)

• Intelligent system - Various commercial applications of artificial intelligence
• Artificial intelligence (AI) - Stimulates human intelligence such as the ability to reason and learn

- Advantages : Can check info on competitor

• The ultimate goal of AI is the ability to build a system that can mimic human intelligence

 

 

 

• Four most common categories of AI include :

- Expert system - Computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems

- Neural network - Attempts to emulate the way the human brain works

- Fuzzy logic - A mathematical method of handling imprecise or subjective information

 - Genetic algorithm - An artificial intelligent system that mimics the evolutionary, survival-of-the-fitters process to generate increasingly better solutions to a problem

 - Intelligent agent - Special-purposed knowledge - based information system that accomplishes specific tasks on behalf of its users

• Multi-agent systems

• Agent-based modelling

DATA MINING

• Data-mining software includes many forms of AI such as neural networks and expert systems

 

• Common forms of data-mining analysis capabilities include :

- Cluster analysis

- Association detection

- Statistical analysis

CLUSTER ANALYSIS

• Cluster analysis - A technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible
• CRM systems depend on cluster analysis to segment customer information and identify behavorial traits

 

ASSOCIATION DETECTION

• Association detection - Reveals the degree to which variables are related and the nature and frequency of these relationships in the information

- Market basket analysis - Analyzes such items as Websites and checkout scanner information to detect customers' buying behavior and predict future behavior by identifying affinities among customers choices of products and services

STATISTICAL ANALYSIS

• Statistical analysis - Performs such functions as information correlations, distributions, calculations, and variance analysis

- Forecast - Predictions made on the basis of time-series information

- Time-series information - Time-stamed information collected at a particular frequency

CHAPTER 8

ACCESSING ORGANIZATIONAL INFORMATION - DATA WAREHOUSE

 

In this chapter, we learned about :

 

• Descibe the roles and purposes of data warehouses and data marts in an organization
• Compare the multidimensional nature of data warehouses (and data marts), with the two-dimensional nature of databases
• Identify the importance of ensuring the cleanliness of information throughout an organization
• Explain the relationship between business intelligence and a data warehouse

 

HISTORY OF DATA WAREHOUSING

 

• Data warehouses extend the transformation of data into information
• In the 1990's executives became less concerned with the day-to-day business operations and more concerned with overall business functions 
• The data warehouse provided the ability to support decision making without disrupting the day-to-day operations

 

DATA WAREHOUSES FUNDAMENTALS

 

• Data warehouse - A logical collection of information - gathered from many different operational databases - that supports business analysis activities and decision-making tasks
• The primary purpose of a data warehouse is to aggregate information throughout an organization into a single repository for decision-making purposes
• Extraction, transformation, and loading (ETL) - A process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse
• The ETL process gathers data from the internal and external databases and passes it to the data warehouse
• The ETL process also gathers data from the data warehouse and passes it to the data marts
• Data mart - Contains a subset of data warehouse information

 

 

MULTIDIMENSIONAL ANALYSIS AND DATA MINING

 

• Databases contain information in a series of two-dimensional tables
• In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows

- Dimension - A particular attribute of information

• Each layer in a data warehouse or data mart represents information according to an additional dimension
• Dimensions could include such things as :

- Products

- Promotions

- Stores

- Category

- Region

- Stock price

- Date

- Time

- Weather

• Cube - Common term for the representation of multidimensional information

 

 

- Users can slice and dice the cube to drill down into the information

- Cube A represents store information (the layers), product information (the rows), and promotion information (the columns)

- Cube B represents a slice of information displaying promotion II for products at all stores

- Cube C represents a slice of information displaying promotion III for product B at store 2

• Data mining - The process of analyzing data to extract information not offered by the raw data alone
• To perform data mining users need data mining tools

- Data mining tool - Uses a variety of techniques to find patterns and relationships in large volumes of information and infers ruler that predict future behavior and guide decision making

• Data mining can begin at a summary information level (course granularity) and progress through increasing levels of detail (drilling down), or the reverse (drilling up)
• Data-mining tools include query tools, reporting tools, multidimensional analysis tools, statistical tools, and intelligent agents

 

INFORMATION CLEANSING OR SCRUBBING

 

• An organization must maintain high-quality data in the data warehouse
• Information cleansing or scrubbing -  A process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information
• Contact information in an operational system

 

  

 

• Standardizing customer name from Operational Systems

 

 

•  Information cleansing activities

 

 

- Information cleansing allows an organization to fix these types of inconsistencies and cleans the data in the data warehouse

• Accurate and complete information

 

 

BUSINESS INTELLIGENCE

• Business intelligence - Information that people use to support their decision-making efforts
• Principle BI enablers include :

 - Technology

- People

- Culture