Chapter 12- Interating the Oranization From End to End- Enterprise Resource Planning

Enterprise Resource Planning (ERP)

ü    - 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

ü 

ü  -  ERP systems automate business processes

 

   ERP – bringing the organization together

-    Organization before ERP

-    ERP bring Organization together

ü  The Evolution of ERP

 

Integrating SCM, CRM, and ERP

ü    SCM, CRM, and ERP are the backbone of e-business

ü    Integration of these applications is the key to success for many companies

ü    Integration allows the unlocking of information to make it available to any user, anywhere,    anytime

ü  

     -SCM and CRM market overviews

General audience and purpose of SCM, CRM and ERP

Integration Tools

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

ü    Middleware – several different types of software which sit in the middle of and provide connectivity between two or more software applications

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

ü  Data points where SCM, CRM, and ERP integrate

 

Enterprise Resource Planning (ERP)

ü   ERP systems must integrate various organization processes and be:

·               Flexible

·               Modular and open

·               Comprehensive

·               Beyond the company

ü  Flexible – must be able to quickly respond to the changing needs of the organization

ü  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.

ü  Comprehensive – must be able to support a variety of organizational functions for a wide range of businesses

ü  Beyond the company – must support external partnerships and collaboration efforts

ü  Enterprise Resource Planning’s Explosive Growth

ü  SAP boasts 20,000 installations and 10 million users worldwide

·               ERP solutions are growing because:

·               ERP is a logical solution to the mess of incompatible applications that had sprung  up in most businesses

·               ERP addresses the need for global information sharing and reporting

·               ERP is used to avoid the pain and expense of fixing legacy systems

Chapter 11- Building a Customer-Centric Oranization- Customer Relationship Management

HOW CRM WORKS

Customer Relationship Management (CRM)

CRM enables an organization to:

v  Provide better customer service

v  Make call centers more efficient

v  Cross sell products more effectively

v  Help sales staff close deals faster

v  Simplify marketing and sales processes

v  Discover new customers

v  Increase customer revenues

Recency, Frequency, and Monetary Value

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

v  How recently a customer purchased items (Recency)

v  How frequently a customer purchased items (Frequency)

v  How much a customer spends on each purchase (Monetary Value)

The Evolution of CRM

·         CRM reporting technology – help organizations identify their customers across other applications

·         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

Customer Relationship Management’s Explosive Growth

Forecasts for CRM Spending (in billions)

  

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

·  Customer Relationship Management Success Factors

    CRM success factors include:

v  Clearly communicate the CRM strategy

v  Define information needs and flows

v  Build an integrated view of the customer

v  Implement in iterations

v  Scalability for organizational growth

Chapter 10- Extending the organization- Supply Chain Management

Three main links in Supply Chain Management

1. Material flow from suppliers and their 'upstream' suppliers at all level
2. Transformation of material into semi-finished or finished product through the organization's production process.
3. Distribution of products to customers and their 'downstream' customers at all level

Basics of Supply Chain Management

-Plan

 A company must have a plan for managing all the resources that go toward meeting customer demand for products or services.

 -  Source
Companies must carefully choose reliable suppliers that will deliver goods and services required for making products.

- Make
This is the step where companies manufacturing their product or services. This
can include scheduling the activities necessary for production, testing, packaging, and preparing for delivery

- Deliver (Logistic)
Companies must be able to receive orders from customers, fulfill the orders via
 a network of warehouses, pick transportation companies to deliver the
products, and implement a billing and invoicing system to facilitate payments.

- Return
This is typically the most problematic step in the supply chain. Companies must create a network for receiving defective and excess products and support customers who have problems with delivered products.

Factors Driving SCM

 Visibility

- Visibility – more visible models of different ways to do things in the supply chain have emerged.  High visibility in the supply chain is changing industries, as Wal-Mart demonstrated- Supply chain visibility – 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 throughout the supply chain-Supply chain visibility allows organizations to eliminate the bullwhip effect

Consumer Behavior-  Companies can respond faster and more effectively to consumer demands through supply chain enhances- Once an organization understands customer demand and its effect on the supply chain it can begin to estimate the impact that its supply chain will have on its customers and ultimately the organizations performance-  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 both increase a company’s ability to compete-  SCP depends entirely on information for its accuracy-  SCE can be as simple as electronically routing orders from a manufacturer to a supplier-  Competition
-  SCP and SCE in the supply chain

Speed
- Three factors fostering speed

- Supply Chain Management 
Success Factors

  SCM industry best practices include:

-  Make the sale to suppliers

-  Wean employees off traditional business practices

-  Ensure the SCM system supports the organizational goals

-  Deploy in incremental phases and measure and communicate success

-  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

SUPPLY CHAIN MANAGEMENT Success Stories

Chapter 9- Enabling the organization- Decision Making

Enabling the Organization – 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 modeling and forecasting, to make good decisions-  People must protect the corporate asset of organizational information
ØModel – a simplified representation or abstraction of realityØ  IT systems in an enterprise
 Transaction Processing Systems(TPS)Ø  Moving up through the organizational pyramid users move from requiring transactional information to analytical information
Ø  Transaction processing system - the basic business system that serves the operational level (analysts) in an organizationØ  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 informationØ  Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making
Ø  Decision Support Systems(DSS)Models information to support managers and business professionals during the decision-making process.Ø  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. Eg: What will happen to the supply chain if a tsunami in Sabah reduces holding inventory from 30% to 10%?2.       What-if analysis – checks the impact of a change in an assumption on the proposed solution. Eg: Repeatedly changing revenue in small increments to determine it effects on other variables.3.       Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of output. Eg: Determine how many customers must purchase a new product to increase gross profits to $5 million.
    What-if analysis                           Goal-seeking analysisInteraction between a TPS and a DSS

Ø  Executive Information SystemsA specialized DSS that supports senior level executives within the organization
Ø  Most EISs offering the following capabilities:§  Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information. Eg: Data for different sales representatives can be rolled up to an office level. Then state level, then a regional sales level.§  Drill-down – enables users to get details, and details of details, of information. Eg: From regional sales data then drill down to each sales representatives at each office.§  Slice-and-dice – looks at information from different perspectives. Eg: One slice of information could display all product sales during a given promotion, another slice could display a single product’s sales for all promotions.
Interaction between a TPS and an EIS

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


Ø  Intelligent system – various commercial applications of artificial intelligence
Ø  Artificial intelligence (AI) – simulates 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

Ø  Four most common categories of AI include:

• Expert system – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems. Eg: Playing Chess.

• Neural Network – attempts to emulate the way the human brain works. Eg: Finance industry uses neural network to review loan applications and create patterns or profiles of applications that fall into two categories – approved or denied.

•  Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem.

            Eg: Business executives use genetic algorithm to help them decide which combination of projects a firm should invest.

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

          Multi-agent systems
•          Agent-based modeling

             Eg:  Shopping bot: Software that will search several retailers’ websites and provide a comparison of each retailers’ offering including prive and availability.

• 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 – 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 behavioral traits

• Eg: Consumer goods by content, brand loyalty or similarity

• 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 Web sites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services

Eg: Maytag uses association detection to ensure that each generation of appliances is better than the previous generation.

• 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-stamped information collected at a particular frequency

Eg: Kraft uses statistical analysis to assure consistent flavor, color, aroma, texture, and appearance for all of its lines of foods

Fuzzy logic – a mathematical method of handling imprecise or subjective information. Eg: Washing machines that determine by themselves how much water to use or how long to wash

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

DATA WAREHOUSE FUNDAMENTALS

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 multi-dimensional; 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 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 rules that predict future behavior 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 – refers to applications and technologies that are used to gather, provide access, analyze data, and information to support decision making effort.
These systems will illustrate business intelligence in the areas of customer profiling, customer support, market research, market segmentation, product profitability, statistical analysis, and inventory and distribution analysis to name a few
Eg: Excel, Access

Principle BI enablers include:

·         Technology

·         People

·         Culture