Chapter 12 : Integrating the Organization from End to End - ERP

Enterprise Resource Planning

• At 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

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.
• Middle ware – several different types of software which sit in the middle of and provide connectivity between two or more software applications.
• Enterprise application integration (EAI) middle ware – packages together commonly used functionality which reduced the time necessary to develop solutions that integrate applications from multiple vendors.
• Data points where SCM, CRM and ERP integrate :

Enterprise Resource Planning

• ERP systems must integrate various organization processes and be :

1. Flexible Modular and open
2. Comprehensive
3. Beyond the company

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 - CRM

Customer Relationship Management

• CRM enables an organization to :

1. Provide better customer service.
2. Make call centres more efficient.
3. Cross sell products more effectively.
4. Help sales staff close deals faster.
5. Simplify marketing and sales processes.
6. Discover new customers Increase customer revenues.

Recency, Frequency and Monetary Value

• An organization can find its most valuable customers by using a formula that industry insiders call RFM :

1. How recently a customer purchased items (recency)
2. How frequently a customer purchases items (frequency)
3. How much a customer spends on each purchase (monetary value)

The Evolution of CRM

The Ugly Side of CRM

Customer Relationship Management's Explosive Growth

• CRM business drivers :

Using Analytical CRM To Enhance Decisions

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

CRM 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

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.

Basics 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 semi finished 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 :

Visibility

• Supply chain visibility – the ability to view all areas up and down the supply chain.
• Bull whip effect – occurs when distorted product demand information passes from one entity to the next throughout the supply chain.

Consumer Behaviour

• 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 :

Supply Chain Management Success Factors

• SCM industry best practices include :

1. Make the sale to suppliers.
2. Wean employees off 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 :

1. Suppliers
2. Manufacturers
3. Distributors
4. Other factors that optimize supply chain performance

Chapter 9 : Enabling The Organization - Decision Making

Decision Making

• 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 intelligence 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.

• Interaction between TPS and 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 :

1. Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information.
2. Drill-down – enables users to get details, and details of details, of information.
3. Slice-and-dice – looks at information from different perspectives

• Interaction between TPS and EIS :

• Digital dashboard – integrates information from multiple components and presents it in a unified display.

Artificial Intelligence

• Intelligent system – various commercial applications of artificial intelligence.
• Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn.
• The ultimate goal of AI is the ability to build a system that can mimic human intelligence :

• Four most common categories of AI include :

1. Expert system – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems.
2. Neural Network – attempts to emulate the way the human brain works Fuzzy logic – a mathematical method of handling imprecise or subjective information.
3. Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem.
4. Intelligent agent – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users.

Data Mining

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

Chapter 8 : Accessing Organizational Information - 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 Warehouse 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.
• 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.
• Cube – common term for the representation of multidimensional information.

• Data mining – the process of analysing 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 rules that predict future behaviour and guide decision making.

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 :

• Accurate and complete information :

Business Intelligence

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

1. Technology
2. People
3. Culture

Chapter 7 : Storing Organizational Information - Databases

RELATIONAL DATABASE FUNDAMENTALS

• Information is everywhere in an organization.
• Information is stored in databases.
• Database – maintains information about various types of objects (inventory), events (transactions), people (employees) and places (warehouses).
• Database models include :

1. Hierarchical database model – information is organized into a tree-like structure (using parent/child relationships) in such a way that it cannot have too many relationships.
2. Network database model – a flexible way of representing objects and their relationships.
3. Relational database model – stores information in the form of logically related two-dimensional tables.

ENTITIES & ATTRIBUTES

• Entity – a person, place, thing, transaction or event about which information is stored.
• Attributes (fields, columns) – characteristics or properties of an entity class.

KEYS & RELATIONSHIPS

• Primary keys and foreign keys identify the various entity classes (tables) in the database.

1. Primary key – a field (or group of fields) that uniquely identifies a given entity in a table.
2. Foreign key – a primary key of one table that appears an attribute in another table and acts to provide a logical relationship among the two tables.

RELATIONAL DATABASE ADVANTAGES

• Database advantages from a business perspective include :

1. Increased flexibility
2. Increased scalability and performance
3. Reduced information redundancy
4. Increased information integrity (quality)
5. Increased information security

1. Increased flexibility

• A well-designed database should :

1. Handle changes quickly and easily.
2. Provide users with different views.
3. Have only one physical view. Physical view – deals with the physical storage of information on a storage device.
4. Have multiple logical views. Logical view – focuses on how users logically access information.

2. Increased scalability and performance

• A database must scale to meet increased demand, while maintaining acceptable performance levels.
• Scalability – refers to how well a system can adapt to increased demands.
• Performance – measures how quickly a system performs a certain process or transaction.

3. Reduced information redundancy

• Databases reduce information redundancy.
• Redundancy – the duplication of information or storing the same information in multiple places.
• Inconsistency is one of the primary problems with redundant information.

4. Increased information integrity (quality)

• Information integrity – measures the quality of information.
• Integrity constraint – rules that help ensure the quality of information :

1. Relational integrity constraint
2. Business-critical integrity constraint

5. Increased Information Security

• Information is an organizational asset and must be protected.
• Databases offer several security features including :

1. Password – provides authentication of the user.
2. Access level – determines who has access to the different types of information.
3. Access control – determines types of user access, such as read-only access.

DATABASE MANAGEMENT SYSTEMS

• software through which users and application programs interact with a database.

DATA DRIVEN WEBSITES

• an interactive website kept constantly updated and relevant to the needs of its customers through the use of a database.

DATA DRIVEN BUSINESS INTELLIGENCE

• business intelligence in a data-driven website

INTEGRATING INFORMATION AMONG MULTIPLE DATABASES

• Integration – allows separate systems to communicate directly with each other.

1. Forward integration – takes information entered into a given system and sends it automatically to all downstream systems and processes.
2. Backward integration – takes information entered into a given system and sends it automatically to all upstream systems and processes.

• Forward integration and backward integration

• Building a central repository specifically for integrated information