CHAPTER 10 MGT 300

CHAPTER 10 MGT 300

Chapter 10 Extending the Organization – Supply Chain Management

Supply Chain Management

v  The average company spends nearly half of every dollar that it earns on production

v  In the past, companies focused primarily on manufacturing and quality improvements to influence their supply chains

Basics of Supply Chain

v  The supply chain has three main links:

Ø  Materials flow from suppliers and their “upstream” suppliers at all levels

Ø  Transformation of materials into semi-finished and finished products through the organization’s own production process

Ø  Distribution of products to customers and their “downstream” customers at all levels

v  Organizations must embrace technologies that can effectively manage supply chains

v  Plan

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

v  Source

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

v  Make

Ø  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.

v  Deliver (Logistic)

Ø  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.

v  Return

Ø  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.

Information Technology’s Role in the Supply Chain

Factors Driving SCM

 Visibility

v  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

v  Supply chain visibility – the ability to view all areas up and down the supply chain

v  Bullwhip effect – occurs when distorted product demand information passes from one entity to the next throughout the supply chain

v  Supply chain visibility allows organizations to eliminate the bullwhip effect

Ø  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.

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

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

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

Ø  Eventually the one or two extra boxes ordered from a few retailers become 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

v  Companies can respond faster and more effectively to consumer demands through supply chain enhances

v  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

v  Demand planning software – generates demand forecasts using statistical tools and forecasting techniques

Competition

v  Supply chain planning (SCP) software– uses advanced mathematical algorithms to improve the flow and efficiency of the supply chain

v  Supply chain execution (SCE) software – automates the different steps and stages of the supply chain

v  SCP and SCE both increase a company’s ability to compete

v  SCP depends entirely on information for its accuracy

v  SCE can be as simple as electronically routing orders from a manufacturer to a supplier

v  Competition

v  SCP and SCE in the supply chain

Speed

v  Three factors fostering speed

v  Supply Chain Management
Success Factors

v  SCM industry best practices include:

Ø  Make the sale to suppliers

Ø  Wean employees off traditional business practices

Ø  Ensure the SCM system supports the organizational goals

Ø  Deploy in incremental phases and measure and communicate success

Ø  Be future oriented

v  SCM Success Stories

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

v  Numerous decision support systems (DSSs) are being built to assist decision makers in the design and operation of integrated supply chains

v  DSSs allow managers to examine performance and relationships over the supply chain and among:

Ø  Suppliers

Ø  Manufacturers

Ø  Distributors

Ø  Other factors that optimize supply chain performance

SUPPLY CHAIN MANAGEMENT Success Stories

CHAPTER 9 MGT 300

CHAPTER 9 MGT 300

Enabling the Organization – Decision Making

Ø  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 reality

Ø  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 organization

Ø  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

Ø  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 analysis

Interaction between a TPS and a DSS

Ø  Executive Information Systems

A 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.

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 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

CHAPTER 8 MGT 300

CHAPTER 8 MGT 300

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

Chapter 7 - Storing Organizational Information - Database

Chapter 7 - Storing Organizational Information - Database

RELATIONAL DATABASE FUNDAMENTALS

 -    Information is everywhere in an organization

-  Information is stored in databases

Ø   Database – maintains information about various types of objects (inventory), events (transactions), people (employees), and places (warehouses)

 -     Database models include;

Ø   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.

         Ø  Network database model – a flexible way of representing objects and their relationships

        Ø  Relational database model – stores information in the form of logically related two-dimensional tables

ENTITIES AND ATTRIBUTES

-    Entity – a person, place, thing, transaction, or event about which information is stored

Ø  The rows in each table contains the entities

-    Attributes (fields, columns) – characteristics or properties of an entity class

Ø  The columns in each table contain the attributes

KEYS AND RELATIONSHIPS

-    Primary keys and foreign keys identity the various entity classes (tables) in the database

Ø  Primary key – a fields (or group of fields) that uniquely identities a given entity in a table

Ø  Foreign key – a primary key of one table that appears an attribute in another table and acts to provide a logical relationships among the two tables 

RELATIONAL DATABASE ADVANTAGES

-    Database advantages from a business perspective include;

Ø  Increased flexibility

Ø  Increased scalability and performance

Ø  Reduced information redundancy

Ø  Increased information integrity (quality)

Ø  Increased information security

INCREASED FLEXIBILITY

-     A well-designed database should;

Ø  Handle changes quickly and easily

Ø  Provide users with different views

Ø  Have only one physical views

§  Physical view – deals with the physical storage of information on a storage device

Ø  Have multiple logical views

§  Logical view – focuses on how users logically access information

INCREASED SCALABILITY AND PERFORMANCE

-      A database must scale to meet increased demand, while maintaining acceptable performance levels

Ø  Scalability – refers to how well a system can adapt to increased demands

Ø  Performance – measures how quickly a system performs a certain process or transaction

REDUCED INFORMATION REDUNDANCY

-      Databases reduce information redundancy

Ø  Redundancy – the duplication of information or storing the same information in multiple places

-     Inconsistency is one of the primary problems with redundant information

INCREASED INFORMATION SECURITY

-      Information is an organization asset and must be protected

-      Databases offer several security features including;

Ø  Password – provides authentication of the user

Ø  Access level – determines who has access to the different types of information

Ø  Access control – determines types of user access, such as read-only access

DATABASE MANAGEMENT SYSTEMS

-     Database management systems (DBMS) – software through which users and application programs interact with a database

DATA-DRIVEN WEB SITES

-       Data-driven Web sites – an interactive Web site kept constantly updated and relevant to the needs of its   customers through the use of database

DATA-DRIVEN WEB SITE BUSINESS ADVANTAGES

-         Development

-         Content Management

-         Future Expandability

-         Minimizing Human Error

-         Cutting Production and Update Costs

-         More Efficient

-         Improved Stability

DATA-DRIVEN BUSINESS INTELLIGENT

-         BI in a data-driven Web site

INTEGRATING INFORMATION AMONG MULTIPLE DATABASES

-      Integration – allows separate systems to communicate directly with each other

Ø  Forward integration – takes information entered into a given system and sends it automatically to all downstream systems and processes

          Ø  Backward integration – takes information entered into a given system and sends it automatically to    all upstream systems and processes

 -          Building a central repository specifically for integrated information