Decision Support System for Managers
Decision Making: The Implementation Phase
"Nothing more difficult to carry out, nor more doubtful of success, nor more dangerous to handle, than to initiate a new order of things." - Solution to a problem Change Change management ?.. Implementation: putting a recommended solution to work
Three flavors of A I decisions
-Assisted intelligence -Autonomous intelligence -Augmented intelligence •Artificial brain -A people made machine "as intelligent, creative, and self-aware as humans" -To date, no one has created such a machine
Simon's Decision Making Process
-Proposed in 1977 by Herbert Alexander Simon (an American economist and political scientist) -Includes three phases: 1.Intelligence 2.Design 3.Choice 4.[+] Implementation 5.[+] Monitoring
Components of DSS
1. Data Management Subsystem Includes the database that contains the dataDatabase management system (DBMS) Can be connected to a data warehouse 2. Model Management Subsystem Model base management system (MBMS) 3. User Interface Subsystem 4. Knowledgebase Management SubsystemOrganizational knowledge base
Ten factors that potentially affect the architecture selection decision
1. Information interdependence between organizational units 2. Upper management's information needs 3. Urgency of need for a data warehouse 4. Nature of end-user tasks 5. Constraints on resources 6. Strategic view of the data warehouse prior to implementation 7. Compatibility with existing systems 8. Perceived ability of the inhouse IT staff 9. Technical issues 10.Social/political factors
A more detailed process is offered by Quain (2018)
1.Understand the decision you have to make. 2.Collect all the information. 3.Identify the alternatives. 4.Evaluate the pros and cons. 5.Select the best alternative. 6.Make the decision. 7.Evaluate the impact of your decision.
The Architecture of BI
A BI system has four major componentsa data warehouse, with its source data business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse business performance management (BPM) for monitoring and analyzing performance a user interface (e.g., dashboard)
Data Mart
A departmental small-scale "DW" that stores only limited/relevant data Dependent data mart A subset that is created directly from a data warehouse Independent data mart A small data warehouse designed for a strategic business unit or a department
What is a Data Warehouse?
A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format "The data warehouse is a collection of integrated, subject-oriented databases designed to support DSS functions, where each unit of data is non-volatile and relevant to some moment in time"
Decision Making
A process of choosing among two or more alternative courses of action for the purpose of attaining a goal(s) Managerial decision making is synonymous with the entire management process Example: Planning What should be done? When? Where? Why? How? By whom?
DSS Classifications
AIS SIGDSS Classification 1. Communication-driven and group DSS2. Data-driven DSS 3. Document-driven DSS 4. Knowledge-driven DSS 5. Model-driven DSS Often DSS is a hybrid of many classes
A Framework for Business Intelligence (BI)
BI is an evolution of decision support concepts over time Then: Executive Information System Now: Everybody's Information System (BI)BI systems are enhanced with additional visualizations, alerts, and performance measurement capabilities The term BI emerged from industry
Definition of BI
BI is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies BI is a content-free expression, so it means different things to different people BI's major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis BI helps data, to information (and knowledge), to decisions, and finally to action
Organizational Responses
Be Reactive, Anticipative, Adaptive, and Proactive Managers may take actions, such as Employ strategic planning. Use new and innovative business models. Restructure business processes. Participate in business alliances. Improve corporate information systems.
Decision-Making Disciplines
Behavioral: anthropology, law, philosophy, political science, psychology, social psychology, and sociology Scientific: computer science, decision analysis, economics, engineering, the hard sciences (e.g., biology, chemistry, physics), management science/operations research, mathematics, and statistics Each discipline has its own set of assumptions and each contributes a unique, valid view of how people make decisions
Decision-Making Disciplines 2
Better decisions Tradeoff: accuracy versus speed Fast decision may be detrimental Many areas suffer from fast decisions Effectiveness versus Efficiency Effectiveness "goodness" "accuracy"Efficiency "speed" "less resources" A fine balance is what is needed!
Changing Business Environment & Computerized Decision Support
Companies are moving aggressively to computerized support of their operations Business Intelligence Business Pressures-Responses-Support Model Business pressures result of today's competitive business climate Responses to counter the pressures Support to better facilitate the process
Business Value of BI Analytical Applications
Customer segmentation Propensity to buy Customer profitability Fraud detection Customer attrition Channel optimization
Data Management Subsystem
DSS database DBMS Data directory Query facility
Data Integration and the Extraction, Transformation, and Load Process
ETL = Extract Transform Load Data integration Integration that comprises three major processes: data access, data federation, and change capture. Enterprise application integration (EAI) A technology that provides a vehicle for pushing data from source systems into a data warehouse Enterprise information integration (EII) An evolving tool space that promises real-time data integration from a variety of sources, such as relational or multidimensional databases, Web services, etc.
Characteristics of Decision Making
Groupthink Evaluating what-if scenarios Experimentation with a real system! Changes in the decision-making environment may occur continuously Time pressure on the decision maker Analyzing a problem takes time/moneyInsufficient or too much information
Decision Making: The Design Phase 4
Heuristic models (= suboptimization) The chosen alternative is the best of only a subset of possible alternatives Often, it is not feasible to optimize realistic (size/complexity) problems Suboptimization may also help relax unrealistic assumptions in models Help reach a good enough solution faster
Phases of Decision-Making Process
Humans consciously or subconsciously follow a systematic decision-making process - Simon (1977) 1) Intelligence 2) Design 3) Choice 4) Implementation 5) (?) Monitoring (a part of intelligence?)
Robotic systems
Industrial robots [for manufacturing] Service robots Example: Walmart is using robots to properly stock shelves Use of robots (or bots) in eComemrce Many are being used at Amazon.com Online shopping robots (shopbots) SoftBank - a cellphone store in Tokyo entirely staffed by robots
Other DSS Categories
Institutional and ad-hoc DSS Custom-made systems versus readymade systems Personal, group, and organizational support Individual support system versus group support system
DSS Components: User Interface Subsystem
Interface Application interface User Interface (GUI?) DSS User Interface Portal Graphical icons Dashboard Color coding Interfacing with PDAs, cell phones, etc.
The Nature of Managers' Work Mintzberg's 10 Managerial Roles
Interpersonal 1. Figurehead 2. Leader 3. Liaison Informational 4. Monitor 5. Disseminator 6. Spokesperson Decisional 7. Entrepreneur 8. Disturbance handler 9. Resource allocator 10. Negotiator
Some limitations of A I Machines
Lack human touch and feel Lack attention to non-task surroundings Can lead people to rely on A I machines too much Can be programmed to create destruction Can cause many people to lose their jobs Can start to think by themselves, causing significant damage Hypothetically ... no evidence of that! These limitations are diminishing over time
Managerial Decision Making
Management is a process by which organizational goals are achieved by using resources. Inputs: resources Output: attainment of goals Measure of success: outputs / inputs Management Decision Making Decision making: selecting the best solution from two or more alternatives
Decision-Making Process
Managers usually make decisions by following a four-step process (a.k.a. the scientific approach) 1. Define the problem (or opportunity) 2. Construct a model that describes the realworld problem. 3. Identify possible solutions to the modeled problem and evaluate the solutions. 4. Compare, choose, and recommend a potential solution to the problem.
Model Management Subsystem
Model base MBMS Modeling language Model directory Model execution, integration, and command processor
Decision Making: The Design Phase 3
Normative models (= optimization) the chosen alternative is demonstrably the best of all possible alternatives Assumptions of rational decision makers Humans are economic beings whose objective is to maximize the attainment of goals For a decision-making situation, all alternative courses of action and consequences are knownDecision makers have an order or preference that enables them to rank the desirability of all consequences
Decision Making: The Design Phase 8
Risk Lack of precise knowledge (uncertainty) Risk can be measured with probability Scenario (what-if case) A statement of assumptions about the operating environment (variables) of a particular system at a given time Possible scenarios: best, worst, most likely, average (and custom intervals)
Decision Making: The Choice Phase 2
Search approaches Analytic techniques (solving with a formula) Algorithms (step-by-step procedures) Heuristics (rule of thumb) Blind search (truly random search) Additional activities Sensitivity analysis What-if analysis Goal seeking
Decision Making: The Design Phase 2
Selection of a Principle of Choice It is a criterion that describes the acceptability of a solution approach Reflection of decision-making objective(s) In a model, it is the result variable Choosing and validating against High-risk versus low-risk Optimize versus satisfice Criterion is not a constraint!
Characteristics of DWs
Subject oriented Integrated Time-variant (time series) Nonvolatile Summarized Not normalized Metadata Web based, relational/multi-dimensional Client/server, real-time/right-time/active
Decision Making: The Choice Phase
The actual decision and the commitment to follow a certain course of action are made hereThe boundary between the design and choice is often unclear (partially overlapping phases) Generate alternatives while performing evaluationsIncludes the search, evaluation, and recommendation of an appropriate solution to the model Solving the model versus solving the problem!
The Business Environment
The environment in which organizations operate today is becoming more and more complex, creating opportunities, and problems. Example: globalization. Business environment factors: markets, consumer demands, technology, and societal...
Decision Making Process
The four step managerial process: •Define the problem •Construct a model •Identify and evaluate possible solutions •Compare, choose, and recommend a solution to the problem
Decision Support System
an interactive, flexible, computerized information system that enables managers to obtain and manipulate information as they are making decisions
A I in Production-Operation Management
•A I in manufacturing -Automation for compliance and cost reduction -React quicker and more effectively (agility) •Implementation model -Streamlining processes, smart outsourcing, work automation, improving customer experience •Intelligent factories •Logistic and transportation -Example: D H L supply-chain
A I Applications in Accounting
•A I in small accounting firms -Solve complex billing problems (especially in healthcare) §Claim processing and reimbursement -Real estate contracts, risk analysis ... -A I provides cheaper and better data-driven support -Generates needed insights from data analysis -Frees time of accountants for more complex tasks -Machine learning is often used for prediction •A I will improve and automate accounting tasks but at the same time will take away some accounting jobs.
A I Support for Decision-Making Process
•As it relates to Simon's decision making process (see Chapter 1 for the background information) •A I support in problem identification •A I support in generating or finding alternative solutions •A I support in selecting a solution •A I support in implementing the solution •A I can (and should) play a role in each and every step in the decision making process
Changing Business Environments And Evolving Needs For Decision Support And Analytics
•Big-bet, high-risk decisions. •Cross-cutting decisions, which are repetitive but high risk that require group work. •Ad hoc decisions that arise episodically. •Delegated decisions to individuals or small groups.
Knowledge and Expert Systems (2 of 2)
•Cognitive computing -Knowledge derived from cognitive science -Self learning algorithms -I B M Watson -More on this is covered in Chapter 6 •Augmented reality -Augmentation: integration of digital information within the user environment in real time -Real + virtual combined -Virtual reality
Technology Insight - Augmented Intelligence
•Combining the performance of people and machines [combining » augmenting] •Augmented machines extend human abilities •Examples -Cybercrime fighting -E-commerce decisions -High-frequency stock market trading
A Framework for Business Intelligence
•Definitions of business intelligence (B I) •A brief history of B I •The architecture of B I -Data warehousing (D W) [as a foundation of B I] -Business performance management (B P M) -User interface (dashboard) •Transaction processing versus analytics processing •Appropriate planning and alignment of B I with the business strategy
Societal Impacts of A I
•Impact on agriculture •Contribution to health and medical care •Other societal applications -Transportation -Utilities -Education -Social services
A I in Marketing, Advertising, & C R M (2 of 2)
•Improving customer experience and C R M 1.Use N L P for generating user documentation. This capability also improves the customer-machine dialogue. 2.Use visual categorization to organize images (for example, see I B M's Visual Recognition and Clarifai) 3.Provide personalized and segmented services by analyzing customer data. This includes
A I in Human Resource Management (2 of 2)
•Introducing A I to H R M operations: 1.Experiment with a variety of chatbots 2.Develop a team approach involving other functional areas 3.Properly plan a technology roadmap for both the short and long term, including shared vision with other functional areas 4.Identify new job roles and modifications in existing job roles in the transformed environment 5.Train and educate the H R M team to understand A I and gain expertise in it.
Using A I in Decision Making
•Issues & factors: -The nature of the decision [routine vs non-routine] -The method of support / technologies used §Expert systems, recommender systems §Deep learning, pattern recognition, biometrics recognition -Cos-benefit and risk analysis -Using business rules -A I algorithms -Speed
Phase 1 - The Intelligence Phase: Problem (or Opportunity) Identification
•Issues in data collection •Problem classification •Problem decomposition •Problem ownership
Knowledge and Expert Systems (1 of 2)
•Knowledge sourced intelligent systems -Knowledge acquisition §Identifying experts -Knowledge representation -Reasoning from knowledge •Chatbots •Emerging A I technologies -Effective computing -Biometric analysis
Benefits of A I
-A I has the ability to complete certain tasks much faster -The consistency of the work §A I machines do not make arbitrary mistakes -A I systems allow for continuous improvement projects -A I can be used for predictive analysis via its capability of pattern recognition -A I can manage delays and blockages in business processes -A I machines do not stop to rest or sleep
Three types of analytics
-Descriptive (or reporting) analytics ... -Predictive analytics ... -Prescriptive analytics ...
Application of A I uses in Banking
-Employee surveillance (A I machines, e.g., I B M Watson). -Tax preparation/filing (H&R block uses I B M Watson). -Automated customer service; answering customer inquiries in real-time. §See Rainbird Co. ar rainbirf.ai as a company that provides such services (using I B M Watson). -Automated online support for paying bills and account inquiries using Amazon Alexa (e.g., Capital One). -Fraud detection and anti-money-laundering activities; also improving customer experience (Bank Danamon). -Victual banking assistant, Olivia at H S B C, learn from experience and helps customer better.
Examples of A I Benefits
-I S D A uses A I to eliminate tedious activities -A I revolutionizing business recruitment -A I is redefining management -Help blind people experience the world around them -Identify overlooked borrowers -Predict customer expectation -Startup A I companies are emerging in large numbers -Most impactful: customer experience and enjoyment.
Drivers of A I
-Interest in smart machines and artificial brains -The low cost of A I applications -The desire of large tech companies -The pressure on management to increase productivity -The availability of quality data -The increasing functionalities and reduced cost of computers in general -The development of new information technologies, particularly the cloud computing
Capabilities of intelligence
-Learning or understanding from experience -Making sense out of ambiguous, incomplete, or even contradictory messages and information -Responding quickly and successfully to a new situation (i.e., using the most correct responses) -Understanding and inferring in a rational way, solving problems, and directing conduct effectively -Applying knowledge to manipulate environments -Recognizing and judging the relative importance of different elements in a situation
Types of intelligence
-Linguistic and verbal, logical, spatial, body/movement, musical, interpersonal, intrapersonal, naturalist •Intelligence is not a simple concept! •Content of intelligence -Reasoning, learning, logic, problem-solving, perception, and linguistic ability
Natural language processing
-Natural language understanding -Natural language generation -Speech (voice) understanding §An interesting application cs.cmu.edu/~./listen -Machine translation of human languages §Balel fish (babelfish.com) §Google translator (translate.google.com) §Example: Sogou's travel translator
Major goals of A I
-Perceive and properly react to changes in the environment that influence specific business processes and operations. -Introduce creativity in business processes and decision making.
Schrage's Models for Using A I to Make Decisions
1.The autonomous advisor 2.The autonomous outsource 3.People-machine collaboration 4.Complete machine autonomy •Implementing these four models require appropriate management leadership and collaboration with data scientists.
The Concept of DSS
DSS - interactive computer-based systems, which help decision makers utilize data and models to solve unstructured problems Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. DS as an Umbrella Term Evolution of DS into Business Intelligence
DSS Capabilities
DSS early definition: it is a system intended to support managerial decisions in semistructured and unstructured decision situations DSS were meant to be adjuncts to decision makers extending their capabilities They are computer based and would operate interactively online, and preferably would have graphical output capabilities Nowadays, simplified via Web browsers and mobile devices
Decision Style 3
Decision-making styles Heuristic versus Analytic Autocratic versus Democratic Consultative (with individuals or groups) A successful computerized system should fit the decision style and the decision situation Should be flexible and adaptable to different users (individuals vs. groups)
An Early Decision Support Framework
Degree of Structuredness (Simon, 1977)Decisions are classified as Highly structured (a.k.a. programmed) Semi-structured Highly unstructured (i.e., nonprogrammed) Types of Control (Anthony, 1965) Strategic planning (top-level, long-range) Management control (tactical planning) Operational control
Decision Making: The Design Phase 5
Descriptive models Describe things as they are or as they are believed to be (mathematically based) They do not provide a solution but information that may lead to a solution Simulation - most common descriptive modeling method (mathematical depiction of systems in a computer environment) Allows experimentation with the descriptive model of a system
Decision Making: The Design Phase 7
Developing (Generating) Alternatives In optimization models (such as linear programming), the alternatives may be generated automatically In most MSS situations, however, it is necessary to generate alternatives manuallyUse of GSS helps generate alternatives Measuring/ranking the outcomes Using the principle of choice
Support for the Intelligence Phase
Enabling continuous scanning of external and internal information sources to identify problems and/or opportunities Resources/technologies: Web; ES, OLAP, data warehousing, data/text/Web mining, EIS/Dashboards, KMS, GSS, GIS,...Business activity monitoring (BAM) Business process management (BPM) Product life-cycle management (PLM)
Support for the Design Phase
Enabling generating alternative courses of action, determining the criteria for choice Generating alternatives Structured/simple problems: standard and/or special models Unstructured/complex problems: human experts, ES, KMS, brainstorming/GSS, OLAP, data/text mining A good "criteria for choice" is critical!
Support for the Implementation Phase
Enabling implementation/deployment of the selected solution to the system Decision communication, explanation and justification to reduce resistance to changeResources Corporate portals, Web 2.0/Wikis Brainstorming/GSS KMS, ES
Support for the Choice Phase
Enabling selection of the best alternative given a complex constraint structure Use sensitivity analyses, what-if analyses, goal seeking Resources KMS CRM, ERP, and SCM Simulation and other descriptive models
Decision Making: The Design Phase
Finding/developing and analyzing possible courses of actions A model of the decision-making problem is constructed, tested, and validated Modeling: conceptualizing a problem and abstracting it into a quantitative and/or qualitative form (i.e., using symbols/variables) Abstraction: making assumptions for simplificationTradeoff (cost/benefit): more or less abstractionModeling: both an art and a science
Decision Making: The Design Phase 6
Good Enough, or Satisficing "something less than the best" A form of suboptimization Seeking to achieve a desired level of performance as opposed to the "best" Benefit: time saving Simon's idea of bounded rationality
Information Systems Support for Decision Making
Group communication and collaborationImproved data management Managing data warehouses and Big DataAnalytical support Overcoming cognitive limits in processing and storing information Knowledge management Anywhere, anytime support
Business Environment Factors
Markets Strong competition Expanding global markets Blooming electronic markets on the Internet Innovative marketing methods Opportunities for outsourcing with IT support Need for real-time, on-demand transactions Consumer Desire for customization demand Desire for quality, diversity of products, and speed of delivery Customers getting powerful and less loyal Technology More innovations, new products, and new services Increasing obsolescence rate Increasing information overload Social networking, Web 2.0 and beyond Societal Growing government regulations and deregulation Workforce more diversified, older, and composed of more women Prime concerns of homeland security and terrorist attacks Necessity of Sarbanes-Oxley Act and other reporting-related legislationIncreasing social responsibility of companies Greater emphasis on sustainability
Closing the Strategy Gap
One of the major objectives of computerized decision support is to facilitate closing the gap between the current performance of an organization and its desired performance, as expressed in its mission, objectives, and goals, and the strategy to achieve them.
Other DW Components
Operational data stores (ODS) A type of database often used as an interim area for a data warehouse Oper marts - an operational data mart. Enterprise data warehouse (EDW) A data warehouse for the enterprise. Metadata: Data about data. In a data warehouse, metadata describe the contents of a data warehouse and the manner of its acquisition and use
Decision Style 2
Personality temperament tests are often used to determine decision styles There are many such tests Meyers/Briggs, True Colors (Birkman), Keirsey Temperament Theory, ... Various tests measure somewhat different aspects of personality They cannot be equated!
Decision-Making Processes
Phase 2 - The Design Phase -Models Phase 3 - The Choice Phase -Evaluating alternatives Phase 4 - The Implementation Phase -Implementing the solution Phase 5 - Monitoring - Phase 4 and 5 were not part of Simons' original model
Decision Making: Intelligence Phase 2
Potential issues in data/information collection and estimation Lack of data Cost of data collection Inaccurate and/or imprecise data Data estimation is often subjective Data may be insecure Key data may be qualitative Data change over time (time-dependence)
Decision Making: Intelligence Phase 3
Problem Classification Classification of problems according to the degree of structuredness Problem Decomposition Often solving the simpler subproblems may help in solving a complex problem. Information/data can improve the structuredness of a problem situation Problem Ownership Outcome of intelligence phase
Decision Making: Intelligence Phase
Scan the environment, either intermittently or continuously Identify problem situations or opportunities Monitor the results of the implementation Problem is the difference between what people desire (or expect) and what is actually occurring Symptom versus Problem Timely identification of opportunities is as important as identification of problems
Decision Makers
Small organizations Individuals Conflicting objectives Medium-to-large organizations Groups Different styles, backgrounds, expectationsConflicting objectives Consensus is often difficult to reach Help: Computer support, GSS,
Decision Style
The manner by which decision makers think and react to problems perceive a problem cognitive response values and beliefs When making decisions, people...follow different steps/sequence give different emphasis, time allotment, and priority to each step
A Brief History of BI
The term BI was coined by the Gartner Group in the mid-1990s However, the concept is much older 1970s - MIS reporting - static/periodic reports 1980s - Executive Information Systems (EIS) 1990s - OLAP, dynamic, multidimensional, ad-hoc reporting -> coining of the term "BI" 2010s - Inclusion of AI and Data/Text Mining capabilities; Web-based Portals/Dashboards, Big Data, Social Media, Analytics 2020s - yet to be seen
DW Architecture
Three-tier architecture 1. Data acquisition software (back-end) 2. The data warehouse that contains the data & software 3. Client (front-end) software that allows users to access and analyze data from the warehouse Two-tier architecture First two tiers in three-tier architecture is combined into one
Intelligent & Automated Decision Support
•Automated decision making (since 1970s) •Common examples: -Small loan approvals -Initial screening of job applicants -Simple restocking -Prices of products and services (when and how to change them) -Product recommendation (e.g., at Amazon.com) •Example: Supporting Nurses Diagnosis Decisions -An experiment conducted in a Taiwanese hospital (in 2015) -87% agreement between A I and human experts
The Classical Decision Support System Framework
•Degree of structuredness -Structured, unstructured, semi-structured problems •Type of control -Operational, managerial, strategic •The decision Support matrix •Computer support for -Structured decisions -Unstructured decisions -Semi-structured problems
A I Applications in Financial Services
•Diverse use of A I, in banking and insurance. •Examples of A I use in general financial services: -Extreme personalization (e.g., chatbots, personal assistants, etc.) -Shifting customer behavior both online and in branches -Facilitating trust in digital identity, revolutionizing payments -Sharing economic activities (e.g., person-to-person loans) -Offering financial services 24/7 and globally •Banking can also uses A I for -Face recognition (safer online banking), help customer with smart investment decisions, prevent money laundering •Insurance - mostly in issuing policies and handling claims
Technologies for Data Analysis and Decision Support
•Group communication and collaboration •Improved data management •Managing giant data warehouses and Big Data •Analytical support •Overcoming cognitive limits •Knowledge management •Anywhere, anytime support •Innovation and artificial intelligence
A I Support for Decision Making
•Jeff Bezos, the C E O of Amazon.com, said in May 2017 that A I is in a golden age ... •A I can ... -Solve complex problems that people have not been able to solve. -Make much faster decisions. -Find relevant information, even in large data sources, very fast. -Make complex calculations rapidly. -Conduct complex comparisons and evaluations in real time.
The Landscape of A I
•Major technologies -Knowledge-based technologies -Biometric related technologies •Tools and platforms •A I applications •Narrow (weak) versus general (strong) A I •The three flavors of A I decisions -Assisted intelligence -Autonomous A I -Augmented Intelligence
A I in Marketing, Advertising, & C R M (1 of 2)
•One of the richest area for A I applications: 1.Product and personal recommendations 2.Smart search engines 3.Fraud and data breaches detection 4.Social semantics 5.Web site design 6.Producer pricing 7.Predictive customer service
A I in Human Resource Management (1 of 2)
•Recruitment - talent acquisition -See Application Case 2.6 for an example •Training - A I facilitates training •Performance assessment (evaluation) •Retention -eliminating attrition -Predicting attrition way ahead of time to eliminate loss of talent •Using chatbots for supporting H R M
Analytics Examples in Selected Domains
•Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics -Example 1: Business office -Example 2: The Coach •Healthcare—Humana Examples -Example 1: Preventing Falls in a Senior Population -Example 2: Define the Right Metrics -Example 3: Predictive Models to Identify the Highest Risk Membership in a Health Insurer •Retail—Retail Value Chain ... •Image Analytics
The Influence of the External and Internal Environments on the Process
•Technology, I S, Internet, globalization •Government regulations, compliance -Political factors -Economic factors -Social and psychological factors -Environment factors Need to make rapid decision, changing market conditions
Components of a D S S
•The Data Management System -D S S database -Database management system (D B M S) -Data directory -Query facility •The Model Management Subsystem -Model base -M B M S -Modelling language -Model directory -Model execution, integration, and command processor •The User Interface Subsystem •The Knowledge-Based Subsystem
Artificial Intelligence Overview
•What Is artificial intelligence (A I)? -Technology that can learn to do things better over time. -Technology that can understand human language. -Technology that can answer questions. •The major benefits of A I -Reduction in the cost of performing work. -Work can be performed much faster. -Work is more consistent than human work. Increased productivity, profitability
