13. Decision Support Systems Data Warehousing & Data Mining Wolf-Tilo Balke Silviu Homoceanu Institut für Informationssysteme Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de 13. Decision Support Systems (DSS) 13.1 Marketing Models DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 2 Decision support systems (DSS) Are interactive, flexible, and adaptable content based information systems Developed for supporting the solution of a nonstructured 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 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 3 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 4 Traditional approaches Common mathematical modeling e.g., what-if-analysis Non-rigorous modeling Data-driven Rule-based systems (RBS) 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 Clustering In DSS the key word is decision-making DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 5 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 6 1
Decision-making is a process of making the choice including Assessing the problem Collecting and verifying information 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 13.0 Decisions Decision problem options (alternatives) goals FIND the option that bestsatisfies thegoals RANK options according to the goals ANALYSE, JUSTIFY, EXPLAIN,, the decision DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 7 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 8 13.0 Decisions Types of decisions Easy (routine, everyday) vs. difficult (complex) One-time vs. recurring One-stage vs. sequential Single objective vs. multiple objectives Individual vs. group Structured vs. unstructured Tactical, operational, strategic DSS address complex decisions 13.0 Complex Decisions Characteristics of complex decisions Novelty There was no prior similar decision Unclearness 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 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 9 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 10 13.0 Decision-Making Decision-making (DM) 13.0 Decision-Making Decision-making Human DM Machine DM Decision Sciences Decision Systems Decision Sciences Decision Systems Switching circuits Processors Computer programs Systems for routine DM Autonomous agents Space probes Normative Decision Theory Utility Theory Game Theory Theory of Choice Descriptive Cognitive Psychology Social and Behavioral Sciences Decision Support Automated Control Fuzzy Logic Expert Systems DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 11 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 12 2
13.0 Decision Support Decision support Methods and tools for supporting people involved in the decision-making process Central Disciplines 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?) DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 13 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 14 13.0 DSS Capabilities 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? 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 DW Model management Knowledge-based subsystem External models Graphical User Interface DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 15 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 16 13.0 DSS Architecture The database management subsystem Captures/extracts data for inclusion in a DSS database Updates (adds, deletes, edits, changes) data records and files Interrelates data from different sources Retrieves data from the database for queries and reports 13.0 DBM Subsystem 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 Tracks data use within the DSS Manages data through a data dictionary DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 17 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 18 3
13.0 DSS Architecture 13.0 MMS The model management subsystem (MMS) Strategic models: non routine mergers, impact analysis, capital budgeting Tactical Models: allocation & Control labor requirements, sales promotion planning Operational Models: routine-day-to-day production 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., whatif or goal seeking analysis Manages and maintains the model base e.g., Store, access, run, update, link, catalog and query DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 19 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 20 13.0 DSS Architecture 13.0 DSS Architecture 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 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 symbolism Intelligent, context aware Customized For the non-technical user, the user interface is the system DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 21 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 22 13.1 Marketing Models 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 Massive sales transactions data are available making DSS a important tool for reaching marketing intelligence DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 23 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 24 4
13.1 Marketing Models Marketing intelligence comprises 2 prominent topics Relational marketing (RM) Sales force management (SFM) 13.1 Marketing Models 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 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 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 25 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 26 13.1 Relational Marketing 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 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 13.1 Relational Marketing RM strategies revolve around the following choices Segments Products Services Prices Relational marketing Distribution channels Sales processes Promotion channels DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 27 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 28 13.1 Relational Marketing 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 design and target marketing actions 13.1 Relational Marketing Cycle of RM analysis, phases: 1. Exploration of the data available for each customer 2. Identify market segments by using inductive learning models 3. Knowledge of customer profiles is then used to design marketing actions 4. The designed actions are translated into promotional campaigns which generate in turn new information for subsequent analyses Perform optimized and targeted actions Collect information on customers Plan actions based on knowledge Identify segments and needs DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 29 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 30 5
13.1 Customer Relations 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 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 13.1 Customer Relations 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 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 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 31 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 32 13.1 Customer Relations Important part of RM is customer relationship management (CRM) CRM The software tools which allow tracking and 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 13.1 Customer Relations E.g., XRMS Contact information screen DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 33 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 34 13.1 Customer Relations Aspects of CRM systems Operational Collaborative Analytical 13.1 CRM 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 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 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 35 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 36 6
13.1 CRM 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 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 13.1 CRM 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 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 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 37 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 38 13.1 Relational Marketing 13.1 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/her as a customer Acquisition The individual becomes customer Cross/up-selling: getting more business from current customers by selling them additional or complementary services 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 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 39 DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 40 13.1 Lifetime of a customer 13.1 Marketing Models Churn (defection): the percentage of customers who leave a business in one year Interruption: 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 roles that are involved with different tasks and responsibilities in the sales process Why SFM? It plays a critical role in: The profitability of an enterprise The implementation of the relational marketing strategy DW & DM Wolf-TiloBalke InstitutfürInformationssysteme TU Braunschweig 41 DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 42 7
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 3 components each interacting with each other Design Planning Assessment Design Sales force management Planning Design During start-up phase or during restructuring Includes 3 types of decisions Organizational structure Sizing Sales territories Assessment & control DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 43 DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 44 13.1 Design 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 necessary to analyze the complexity of customers products and sales activities Decide whether and to what extent the agents should be specialized 13.1 Design 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 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 DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 45 DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 46 13.1 Design Sales territories Deciding on assigning territories to agents Depends on factors such as The sales potential of the geographical areas The time required to travel from an area to another 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 Work time, budget E.g., market entities Products Market segments Distribution channels Customers DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 47 DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 48 8
Assessment Measure the effectiveness and efficiency of the individuals in order to decide incentives and remuneration schemes 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 Open source tools XRMS, SugarCRM, Vtiger DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 49 DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 50 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 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, DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 51 DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 52 Within a single company, internal supply chain can be modeled and optimized Contain aspects of martial purchase, production and distribution However, global supply chains may form complex networks of various material flows and costs European Suppliers European Plant Recycling 1 European Assembly Internal Supply Chain US Suppliers Main Plant Asian Assembly European Market Suppliers Purchasing Production Distribution Customers US Assembly Asian Market Asian Suppliers Asian Plant Kit Supplier Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 53 Recycling 2 US Market Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 54 9
Supply chain management is about managing and optimizing those complex supply networks Eliminating excess inventory Improvise on-time delivery performance 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 Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 55 Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 56 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) 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 customized Highly dependent on data from all branches of business Logistics Product Lifecycle Management Procurement Supply Chain Strategy Supply Chain Management Asset Management Supply Chain Planning Supply Chain Enterprise Applications Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 57 Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 58 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 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 Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 59 Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 60 10
7/10/2009 13.3 The Mondrian System Include wider verity of metrics Mondrian Stackability constraints Load and unloading rules Palletizing logic Warehouse efficiency Shipping air minimization Open source OLAP engine provided by Pentaho Based on ROLAP technology 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/ Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 61 The End Relational Database Systems 1 Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 62 13 Thank You! I hope you enjoyed the lecture and learned at least some interesting stuff Next semester s master courses: Multimedia Databases, XML Databases, GIS DW & DM Wolf-Tilo Balke Institut für Informationssysteme TU Braunschweig 63 Knowledge-Based Systems and Deductive Databases Wolf-Tilo Balke IfIS TU Braunschweig 64 11