Monday, 14 September 2015

CRM : Chapter 11


CRM enables an organization to: 

  • Provide better customer service
  • Make call centers more efficient
  • Cross 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 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

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

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

Friday, 11 September 2015

Supply Chain Management 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:

  • Materials flow from suppliers and their “upstream” suppliers at all levels
  • Transformation of materials into semifinished and finished products through the organization’s own production process
  • Distribution of products to customers and their “downstream” customers at all levels

Basics of Supply Chain Management

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

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

This is the step where companies manufacture their products 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.

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.


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
To explain the bullwhip effect to your students discuss a product that demand does not change, such as diapers.  

The need for diapers is constant, it does not increase at Christmas or in the summer, diapers are in demand all year long.  

The number of newborn babies determines diaper demand, and that number is constant.
Retailers order diapers from distributors when their inventory level falls below a certain level, they might order a few extra just to be safe

Distributors order diapers from manufacturers when their inventory level falls below a certain levelthey might order a few extra just to be safe

Manufacturers order diapers from suppliers when their inventory level falls below a certain level, they might order a few extra just to be safe

Eventually the one or two extra boxes ordered from a few retailers becomes several thousand boxes for the manufacturer. 
 This is the bullwhip effect, a small ripple at one end makes a large wave at the other end of the whip.

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


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

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

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

The End :)

Tuesday, 8 September 2015

Chapter 9 ; Streaming Business Operations

Chapter Nine – Enabling the Organization – Decision Making

Chapter Ten – Extending the Organization – Supply Chain Management

Chapter Eleven – Building a Customer-centric Organization – Customer Relationship Management

Chapter Twelve – Integrating the Organization from End to End – Enterprise Resource Planning

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

Transaction Processing Systems

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

Models information to support managers and business professionals during the decision-making process

Three quantitative models used by DSSs include:

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 hurricane in South Carolina reduces holding inventory from 30% to 10%?

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.

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.

Executive Information System

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 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.

Artificial Intelligence (AI)

Intelligent system – various commercial applications of artificial intelligence

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

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.

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.

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 retailer’s websites and provide a comparison of each retailers’s offering including prive and availability.

Data Mining

Common forms of data-mining analysis capabilities include:
Cluster analysis
Association detection
Statistical analysis

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

Accessing Organizational Information-Data Warehouse Chapter 8

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

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

Business Intelligence

Business intelligence – information that people use to support their decision-making efforts

Principle BI enablers include:


Even the smallest company with BI software can do sophisticated analyses today that were unavailable to the largest organizations a generation ago.
The largest companies today can create enterprisewide BI systems that compute and monitor metrics on virtually every variable important for managing the company. 

How is this possible? The answer is technology—the most significant enabler of business intelligence. 


A key responsibility of executives is to shape and manage corporate culture. 

The extent to which the BI attitude flourishes in an organization depends in large part on the organization’s culture. 

Perhaps the most important step an organization can take to encourage BI is to measure the performance of the organization against a set of key indicators.