data warehousing & data mining - tu braunschweig · – provides comprehensive data security...

Data Warehousing & Data Mining& Data MiningWolf-Tilo BalkeSilviu HomoceanuInstitut für InformationssystemeTechnische Universität Braunschweighttp://www.ifis.cs.tu-bs.de

13. Decision Support Systems (DSS)

13.1 Marketing Models

13.2 Supply Chain Management

13. Decision Support Systems

DW & DM – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 2

• Decision support systems (DSS)

– Are interactive, flexible, and adaptable content based information systems

– Developed for supporting the solution of a non-structured management problem for improved

13.0 DSS - Introduction

structured management problem for improved decision-making

– It utilizes data, it provides easy user interface, and it allows for the decision maker’s own insights


– DSS evolve as they develop

– The support for the decision layer is provided by traditional approaches, data mining and data warehousing with OLAP



• Traditional approaches– Common mathematical modeling e.g., what-if-analysis– Non-rigorous modeling

• Data-driven – Rule-based systems (RBS)

• Data Warehousing


• Data Warehousing– Online Analytical Processing (OLAP)

• Data-based decision support

– Modeling• Conceptual modeling• Logical modeling• Physical modeling

– ETL-Processes


• Data Mining

– Association rule mining

– Sequence patterns and time series

– Classification


Classification

– Clustering

• In DSS the key word is decision-making


• Decision-making is a process of making the choice including– Assessing the problem

– Collecting and verifying information

– Identifying alternatives


– Identifying alternatives

– Anticipating consequences of decisions

– Making the choice using sound and logical judgment based on available information

– Informing others of decision and rationale

– Evaluating decisions


• Decision problem

13.0 Decisions

options

(alternatives)

goals


• FIND the option that best satisfies the goals

• RANK options according to the goals

• ANALYSE, JUSTIFY, EXPLAIN, …, the decision

• Types of decisions– Easy (routine, everyday)

vs. difficult (complex)

– One-time vs. recurring

– One-stage vs. sequential

13.0 Decisions

– One-stage vs. sequential

– Single objective vs. multiple objectives

– Individual vs. group

– Structured vs. unstructured

– Tactical, operational, strategic

• DSS address complex decisions


• Characteristics of complex decisions– Novelty

• There was no prior similar decision

– Unclearness• Incomplete knowledge about the problem

13.0 Complex Decisions

Incomplete knowledge about the problem

– Uncertainty• Outside events that cannot be controlled

– Multiple objectives (possibly conflicting)• Maximize economic benefits vs. minimize environmental costs

– Group decision-making

– Important consequences of the decision

– Limited resources


• Decision-making (DM)

13.0 Decision-Making

Machine DMHuman DM


Decision Systems• Switching circuits• Processors• Computer programs• Systems for routine DM• Autonomous agents• Space probes

Decision Sciences

• Decision-making

13.0 Decision-Making

Decision SystemsDecision Sciences


Normative Descriptive

Decision Support

Decision TheoryUtility TheoryGame TheoryTheory of Choice…

Cognitive PsychologySocial and Behavioral

Sciences…

Automated ControlFuzzy LogicExpert Systems…

• Decision support

– Methods and tools for supporting people involved in the decision-making process

– Central Disciplines

Operations research and management sciences

13.0 Decision Support

• Operations research and management sciences

• Decision analysis

• Decision support systems


• DSS capabilities

– Support for problem-solving phases

• Intelligence, design, choice, implementation, monitoring

– Support for different decision frequencies, e.g.:


• Ad hoc DSS: decisions that come up once in every 5 years (e.g., where should a company open a new distribution center?)

• Institutional DSS: decisions that repeat (e.g., what should the company invest in?)


– Support for different problem structures• Highly structured problems: known facts and

relationships

• Semi-structured problems: facts unknown or ambiguous, relations vague

– E.g., which person to hire for a position?

13.0 DSS Capabilities

– E.g., which person to hire for a position?

– Support for various decision-making levels • Operational level

– Daily decisions

• Tactical level– Planning and control

• Strategic level– Long-term decisions


• DSS architecture

– Information resources

– The analytical engine

– The user interface


Model management

External modelsDW


Graphical User Interface

Knowledge-based subsystem

• The database management subsystem

– Captures/extracts data for inclusion in a DSS database

– Updates (adds, deletes, edits,changes) datarecords and files

13.0 DSS Architecture

records and files

– Interrelates datafrom differentsources

– Retrieves data fromthe database forqueries and reports


– Provides comprehensive data security (protection from unauthorized access, recovery capabilities, etc.)

– Handles personal and unofficial data so that users can experiment with alternative solutions based on their own judgment

13.0 DBM Subsystem

their own judgment

– Tracks data use within the DSS

– Manages data through a data dictionary


• The model management subsystem (MMS)– Strategic models: non routine mergers, impact analysis,

capital budgeting– Tactical Models:

allocation & Controllabor requirements, sales promotion


sales promotionplanning

– Operational Models:routine-day-to-dayproduction scheduling,inventory control,quality control

– Analytical Models:SPSS, data mining


• Major functions of the MMS

– Creates models easily from scratch or from existing models

– Allows users to manipulate models so that they can conduct experiments and sensitive analysis e.g., what-

13.0 MMS

conduct experiments and sensitive analysis e.g., what-if or goal seeking analysis

– Manages and maintains the model base e.g.,

• Store, access, run, update, link, catalog and query


• The knowledge based subsystem

– Component of more advanced DSS

– Provides expertise in solving complex unstructured and semi-structured problems

Expertise is provided by an expert system or other


• Expertise is provided by an expert system or other intelligent system

– Leads to intelligent DSS

– Example of knowledge extraction subsystem is data mining


• The user interface

– Interactive, dialogue oriented, menu driven

– Intuitive, graphical, symbolic

– Consistent syntax and semantics, layout and


Consistent syntax and semantics, layout and symbolism

– Intelligent, context aware

– Customized

• For the non-technical user, the user interface is the system


• Applications of DSS

– Marketing Models

– Supply Chain Management



• Marketing decision processes are characterized by a high level of complexity

– Simultaneous presence of multiple objectives

– Countless alternative actions resulting from the combination of the major choice options


Countless alternative actions combination of the major choice options

• Massive sales transactions data are available making DSS a important tool for reaching marketing intelligence


• Marketing intelligence comprises 2 prominent topics

– Relational marketing (RM)

– Sales force management (SFM)



• Relational marketing as DSS application

– Designed to create, maintain, and enhance strong relationships with customers and other stakeholders

– Application of predictive models to support relational marketing strategies


relational marketing strategies

– E.g.:

• An insurance company wishes to select the most promising market segment to target for a new type of policy

• A mobile phone provider wishes to identify those customers with the highest probability of churning


• Why is RM important?

– It costs five times as much to attract a new customer as it does to keep a current one satisfied

– It is claimed that a 5% improvement in customer retention can cause an increase in profitability of

13.1 Relational Marketing

retention can cause an increase in profitability of between 25-85% depending on the industry

– Likewise, it is easier to deliver additional products and services to an existing customer than to a first-time buyer


• RM strategies revolve around the following choices


Distribution Products


Relational

marketing

Sales

processes

Distribution

channels

Products

Services

Segments

PricesPromotion

channels

• How do we implement RM?

– Using pattern recognition and machine learning models on a company’s DW it is possible to derive different segmentations of the customers which are then used to


then used to design and target marke-ting actions


• Cycle of RM analysis, phases:1. Exploration of the data available for each customer2. Identify market segments by using inductive learning

models3. Knowledge of customer profiles is then used to design

marketing actions


marketing actions4. The designed actions are

translated into promotionalcampaignswhich generatein turn new information forsubsequent analyses


Collect

information on

customers

Plan actions based

on knowledge

Identify segments

and needs

Perform optimized

and targeted

actions

• General statistics show…

– The average business never hears from 96% of its unhappy customers

• 91% never come back

• Dissatisfied customers may tell 9-10 people about their

13.1 Customer Relations

• Dissatisfied customers may tell 9-10 people about their experience

– Every positive experience is told to 4-5 people

– For every complaint received the average business in fact has 26 customers with a similar concern


– Of the customers who register a complaint, as many as 70% will do business again with your organization, if the complaint is resolved effectively

• This figure goes up to 95% if the complaint has been resolved quickly


resolved quickly

– 40% of complaints are the result from customer mistakes or incorrect expectations

– A complaint that is handled efficiently is actually better than no complaint at all

• Customers who complain and get satisfactory results are 8% more loyal than if no complaint at all


• Important part of RM is customer relationship management (CRM)

• CRM– The software tools which allow tracking and analysis of each customer's purchases, preferences,


analysis of each customer's purchases, preferences, activities, tastes, likes, dislikes, and complaints

– Enterprise vendors/products• Oracle/Siebel, SAP, Salesforce.com, Amdocs, Microsoft

Dynamics

– Open source tools• Opentaps, Tunesta, Compiere, XRMS, SugarCRM


• E.g., XRMS

– Contactinformationscreen



• Aspects of CRM systems

– Operational

– Collaborative

– Analytical



• Operational CRM– Provides support to "front office" business processes,

including sales, marketing and service

– Each interaction with a customer is generally added to a customer's contact history, and staff can

13.1 CRM

to a customer's contact history, and staff can retrieve information on customers from the database when necessary

– Main benefits is that customers can interact with different people in a company over time without having to describe the history of their interaction each time


• Collaborative CRM

– Covers aspects of a company's dealings with customers that are handled by various departments within a company

• E.g., sales, technical support and marketing

– Staff members from different departments can share

13.1 CRM

– Staff members from different departments can share information collected when interacting with customers

• E.g., feedback received by customer support agents can provide other staff members with information on the services and features requested by customers

– Goal of collaborative CRM is to use information collected by all departments to improve the quality of services provided by the company


• Analytical CRM– Analyzes customer data for a variety of purposes:

• Design and execution of targeted marketing campaigns to optimize marketing effectiveness

• Design and execution of specific customer campaigns, including customer acquisition, cross-selling, up-selling, retention

13.1 CRM

customer acquisition, cross-selling, up-selling, retention

• Analysis of customer behavior to aid product and service decision making e.g., pricing, new product development

• Management decisions, e.g. financial forecasting and customer profitability analysis

• Prediction of the probability of customer defection (churn)

• Acquisition? Cross-selling? Up-selling? Retention? Churn? Let’s see the lifetime of a customer


• Lifetime of a customer

– Lost proposal

• Before becoming a customer, an individual may receive repeated proposals from the enterprise to win him/heras a customer


as a customer

– Acquisition

• The individual becomes customer


– Cross/up-selling:getting more business from current customers by selling them additional or complementaryservices

– Retention:

13.1 Lifetime of a customer

– Retention: the continuous attempt to satisfy and keep current customers actively involved in conducting business

• Highly satisfied customers are

– Less price sensitive

– More likely to talk favorably about you

– More likely to refer you to others

– Remain loyal for longer


– Churn (defection):the percentage of customers who leave a business in one year

– Interruption:customers leaving a business. Possible reasons are that

13.1 Lifetime of a customer

customers leaving a business. Possible reasons are that they:

• Die

• Move away

• Leave for competitive reasons

• Are dissatisfied

• Quit because of an attitude of indifference


• Sales force management (SFM)

– Management of the whole set of people and rolesthat are involved with different tasks and responsibilities in the sales process

• Why SFM?


• Why SFM?

– It plays a critical role in:

• The profitability of an enterprise

• The implementation of the relational marketing strategy


• Designing the sales network and planning agents activities involve complex decision making tasks– Remaining activities are operational and sales force

automation (SFA) software can be used

• SFM decision-making process can be grouped in

13.1 Sales force management

• SFM decision-making process can be grouped in 3 components each interacting with each other– Design

– Planning

– Assessment


Sales force

management

Assessment &

control

PlanningDesign

• Design

– During start-up phase or during restructuring

– Includes 3 types of decisions

• Organizational structure


• Sizing

• Sales territories


– Organizational structure

• May take different forms corresponding to hierarchical agglomerations of agents by group, products, brand or geographical area

• In order to determine the organizational structure it is

13.1 Design

• In order to determine the organizational structure it is necessary to analyze the complexity of customers products and sales activities

– Decide whether and to what extent the agents should be specialized


– Sizing

• Decide the number of agents that should operate in the selected structure

• Depends on several factors

– Number of customers, prospects, sales area coverage estimated

13.1 Design

– Number of customers, prospects, sales area coverage estimated time for each call, the agents traveling time, etc.

• Conflicting goals

– Reduction in costs due to decreasing sales force size is often followed by a reduction in sales and revenues


– Sales territories

• Deciding on assigning territories toagents

• Depends on factors such as

– The sales potential of the geographical areas

13.1 Design

– The sales potential of the geographical areas

– The time required to travel from an area toanother

– The availability time of each agent

• Purpose of assignment is to determine a balanced situation between sales opportunities in each territory to avoid disparities among agents


• Planning

– Decision-making process involving the assignment of sales resources structured and sized during design phase, to market entities

• E.g., sales resources


• E.g., sales resources

– Work time, budget

• E.g., market entities

– Products

– Market segments

– Distribution channels

– Customers


• Assessment

– Measure the effectiveness and efficiency of the individuals in order to decide incentives and remuneration schemes

• Define adequate evaluation criteria that take into


• Define adequate evaluation criteria that take into account the personal contribution of each agent having removed effects due to area or product characteristics


• Sales Force Automation software

– Most CRM tools include SFA functionality

– Enterprise vendors/products

• Oracle/Siebel, SAP, Salesforce.com, Microsoft Dynamics, Netsuite


Netsuite

– Open source tools

• XRMS, SugarCRM, Vtiger


• For producing industries, another field of business operation is of great importance:– Supply chain management (SCM)

• A supply chain summarizes the logistic and production processes of a single enterprise as


production processes of a single enterprise as well as a network of companies– Covers the flow of materials and products from

the raw material down to the end product at the customer

• Contains acquisition of raw materials, production, transportation, storage,


• Within a single company, internal supply chain can be modeled and optimized

– Contain aspects of martial purchase, production and distribution


Relational Database Systems 1 – Wolf-Tilo Balke – Institut für Informationssysteme – TU Braunschweig 53

Internal Supply Chain

Purchasing Production DistributionSuppliers Customers

• However, global supply chains may form complex networks of various material flows and costs


European Plant

Recycling 1

European AssemblyEuropean Suppliers


Main Plant

European Plant

Asian PlantAsian Suppliers

US Assembly

US Market

Asian Market

European Market

Recycling 2

Asian Assembly

European Assembly

Kit Supplier

European Suppliers

US Suppliers

• Supply chain management is about managingand optimizing those complex supply networks

– Eliminating excess inventory

– Improvise on-time delivery performance


on-time delivery

– Maximize the value of procurement

– Minimize transport costs

– Minimize storage costs

– Etc.


• Steps of SCM– Plan (strategic portion of SCM)

• Strategy for managing all the resources that go toward meeting customer demand

• Developing a set of metrics to monitor the performance of the supply chain so that it is efficient, costs less and delivers high quality


– Source• Choose suppliers to deliver the goods and services

• Develop a set of pricing, delivery and payment processes with suppliers

• Create metrics for monitoring and improving the relationships

• Put together processes for managing goods and services inventory, including receiving and verifying shipments, transferring them to the manufacturing facilities and authorizing supplier payments


– Make (manufacturing step)• Schedule the activities necessary for production, testing, packaging and preparation for delivery

• Most metric-intensive portion of the supply - measure quality levels, production output and worker productivity

– Deliver (the logistics part)


– Deliver (the logistics part)• Coordinate the receipt of orders, develop a network of warehouses, pick carriers to get products to customers and set up an invoicing system to receive payments

– Return• Receive and manage defective or excess products

• Recycle used products



• For solving these tasks, SCM has to span across most other enterprise management areas

– Thus, software solutions are usually very diverse and

Supply Chain

Strategy


very diverse and customized

– Highly dependenton data fromall branches ofbusiness

Supply Chain

Management

Supply Chain

Planning

Supply Chain

Enterprise

Applications

Asset

ManagementProcurement

Product

Lifecycle

Management

Logistics

• The traditional approach for optimizing supply chains was severely hampered by the unavailability of necessary data– Thus, usually only future demand was forecast as good

as possible, using statistical trending and “best fit” techniques


techniques• Only high level data necessary

– e.g. by weekly data by product category and customer group

• For dealing with unpredictability, security margins are added• Based on the estimates, the supply chain could be optimized

– Capacity Planning– Bill of Material problems– Network flow optimization– etc.


• However, due to improved data warehouse strategies, more dynamic and fine-grained optimizations are possible

– Forecasting at much finer-granularity


• e.g. calculate the best inventory level per article for each store

• So called model stock

– Allows for new optimization techniques

• Simulation

• Stochastic models


– Include wider verity of metrics

• Stackability constraints

• Load and unloading rules

• Palletizing logic

• Warehouse efficiency


• Warehouse efficiency

• “Shipping air” minimization


• Mondrian

– Open source OLAP engine provided by Pentaho

– Based on ROLAP technology

– Is able to work with any major DBMS

13.3 The Mondrian System

Is able to work with any major DBMS

• Terradata, Oracle, IBM DB2, Sybase, Microsoft SQL Server, Microsoft Access, MySQL, Informix, PostgreSQL, etc.

• http://is59.idb.cs.tu-bs.de/mondrian/


The End


• I hope you enjoyed the lecture and learned at least some interesting stuff…

– Next semester’s master courses: Multimedia Databases, XML Databases, GIS

13 Thank You!

Knowledge-Based Systems and Deductive Databases – Wolf-Tilo Balke – IfIS – TU Braunschweig 64

data warehousing & data mining - tu braunschweig · – provides comprehensive data security...

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