This document discusses improving decision making in businesses. It notes that while any single small decision may have little value, improving hundreds of thousands of small decisions can significantly increase a business's annual value. It provides examples of different types of routine decisions made in businesses and estimates the potential annual value of improving some of these decisions. The document also covers topics like the decision making process, types of decisions, information requirements for different decision makers, and tools that can help support decision making.
The document outlines the decision making process, including identifying the problem, criteria, alternatives, and implementing the chosen alternative. It discusses types of decisions like programmed and non-programmed. The 8 step decision making process is described in detail, including identifying the problem, criteria, weights, alternatives, analyzing alternatives, selecting an alternative, implementing, and evaluating. Different decision making styles like directive, analytic, conceptual and behavioral are also covered.
1. This document discusses decision support systems and business intelligence. It introduces the business pressures-responses-support model and explains how computerized support helps organizations respond quickly to changing business environments.
2. Managerial decision making is described as a complex process affected by environmental factors. Computerized decision support systems can help by providing speedy computations, improved communication and increased productivity.
3. The steps of the decision making process are outlined as intelligence, design, choice, and implementation phases. Early frameworks for computerized decision support categorize decisions and control as structured, semi-structured, or unstructured.
This document outlines the key aspects of managerial decision making. It discusses the rational and boundedly rational approaches to decision making, as well as the role of intuition. It describes the eight-step decision making process and differentiates between well-structured and poorly-structured problems. Managers must make decisions under conditions of certainty, risk, and uncertainty. Their decision making style influences the process and can be directive, analytic, conceptual, or behavioral. The ultimate goal is to choose the best alternative, implement it, and evaluate the outcome.
This document discusses various aspects of decision theory including models of decision making, decision trees, decision styles, decision theories, group decision making, and improving decision making. It describes the classical, implicit favorite, and intuitive models of decision making. It also outlines four decision styles - directive, analytical, conceptual, and behavioral. Two major decision theories - classical and behavioral - are explained along with their key aspects. Methods for improving individual and group decision making are also provided.
This document summarizes and compares several knowledge management models:
- The KM Process Framework by Bukowitz and Williams (1999) outlines four stages - get, use, learn, contribute - and emphasizes the strategic focus and context of KM.
- The KM Matrix by Gamble and Blackwell (2001) splits the KM process into four stages: locating knowledge sources, organizing knowledge, socialization, and internalization. It provides guidelines for KM implementation but focuses only on knowledge sharing.
- The Knowledge Management Process Model by Botha et al (2008) presents KM as three overlapping categories - technology, people, and processes - and includes knowledge creation, but like the other models it lacks strategic context.
The document discusses quantitative analysis and decision making in management science, outlining the 7 steps of problem solving, uses of quantitative analysis, how to develop mathematical models, and providing an example of how management science could be used to model and solve a project scheduling problem to minimize completion time. Key aspects of models include objective functions, constraints, decision variables, and whether models are deterministic or stochastic.
This document discusses decision support systems and business intelligence. It provides learning objectives about understanding decision making in turbulent business environments and the need for computerized support. It describes early frameworks for decision support and the evolution of decision support systems into modern business intelligence approaches. Key concepts covered include structured vs. unstructured decisions, decision support system architectures, and the goals and components of business intelligence systems.
Decision making involves selecting a course of action from various options. Business decision making models include SWOT analysis, buyer decision processes, and cost-benefit analysis. The decision making process involves phases like intelligence gathering, problem definition, alternative identification, choice, and implementation. Management information systems, decision support systems, executive support systems, and group decision support systems can provide information and tools to support decision making. Intelligent techniques like artificial intelligence and expert systems are also used for decision support.
The document outlines the decision making process, including identifying the problem, criteria, alternatives, and implementing the chosen alternative. It discusses types of decisions like programmed and non-programmed. The 8 step decision making process is described in detail, including identifying the problem, criteria, weights, alternatives, analyzing alternatives, selecting an alternative, implementing, and evaluating. Different decision making styles like directive, analytic, conceptual and behavioral are also covered.
1. This document discusses decision support systems and business intelligence. It introduces the business pressures-responses-support model and explains how computerized support helps organizations respond quickly to changing business environments.
2. Managerial decision making is described as a complex process affected by environmental factors. Computerized decision support systems can help by providing speedy computations, improved communication and increased productivity.
3. The steps of the decision making process are outlined as intelligence, design, choice, and implementation phases. Early frameworks for computerized decision support categorize decisions and control as structured, semi-structured, or unstructured.
This document outlines the key aspects of managerial decision making. It discusses the rational and boundedly rational approaches to decision making, as well as the role of intuition. It describes the eight-step decision making process and differentiates between well-structured and poorly-structured problems. Managers must make decisions under conditions of certainty, risk, and uncertainty. Their decision making style influences the process and can be directive, analytic, conceptual, or behavioral. The ultimate goal is to choose the best alternative, implement it, and evaluate the outcome.
This document discusses various aspects of decision theory including models of decision making, decision trees, decision styles, decision theories, group decision making, and improving decision making. It describes the classical, implicit favorite, and intuitive models of decision making. It also outlines four decision styles - directive, analytical, conceptual, and behavioral. Two major decision theories - classical and behavioral - are explained along with their key aspects. Methods for improving individual and group decision making are also provided.
This document summarizes and compares several knowledge management models:
- The KM Process Framework by Bukowitz and Williams (1999) outlines four stages - get, use, learn, contribute - and emphasizes the strategic focus and context of KM.
- The KM Matrix by Gamble and Blackwell (2001) splits the KM process into four stages: locating knowledge sources, organizing knowledge, socialization, and internalization. It provides guidelines for KM implementation but focuses only on knowledge sharing.
- The Knowledge Management Process Model by Botha et al (2008) presents KM as three overlapping categories - technology, people, and processes - and includes knowledge creation, but like the other models it lacks strategic context.
The document discusses quantitative analysis and decision making in management science, outlining the 7 steps of problem solving, uses of quantitative analysis, how to develop mathematical models, and providing an example of how management science could be used to model and solve a project scheduling problem to minimize completion time. Key aspects of models include objective functions, constraints, decision variables, and whether models are deterministic or stochastic.
This document discusses decision support systems and business intelligence. It provides learning objectives about understanding decision making in turbulent business environments and the need for computerized support. It describes early frameworks for decision support and the evolution of decision support systems into modern business intelligence approaches. Key concepts covered include structured vs. unstructured decisions, decision support system architectures, and the goals and components of business intelligence systems.
Decision making involves selecting a course of action from various options. Business decision making models include SWOT analysis, buyer decision processes, and cost-benefit analysis. The decision making process involves phases like intelligence gathering, problem definition, alternative identification, choice, and implementation. Management information systems, decision support systems, executive support systems, and group decision support systems can provide information and tools to support decision making. Intelligent techniques like artificial intelligence and expert systems are also used for decision support.
Machine learning involves using algorithms and large datasets to allow systems to learn from data and improve their performance. There are several types of machine learning including supervised learning for classification and prediction tasks using labeled examples, unsupervised learning like clustering to find hidden patterns in unlabeled data, and reinforcement learning where an agent learns from delayed rewards. Applications of machine learning span many domains like retail for customer segmentation, finance for credit scoring, medicine for diagnosis, and web mining for search engines. The field is growing rapidly due to increased data and computing power enabling complex models to be learned from data rather than being explicitly programmed.
The document discusses decision making and decision support systems. It covers Simon's four phases of decision making: intelligence, design, choice, and implementation. It also discusses how decision support systems can support each phase of the decision making process through tools like data mining, OLAP, expert systems, modeling, and knowledge management systems. Finally, it covers some factors that affect human decision making like personality, gender, cognitive styles, and individual decision styles.
The document discusses decision support systems for planning and scheduling. It covers application areas like manufacturing and services, system requirements, optimization techniques, and implementation issues. The key topics include common optimization techniques like dispatching rules, integer programming, and local search methods. It also discusses interface design, modular system architecture, and commercial scheduling packages.
Data governance is the practice of managing all types of enterprise data throughout its lifecycle from creation to disposal. It involves roles like stewards, users, data managers, and owners working together to define policies, data requirements, and ensure data quality, availability, and security. Data governance considerations include how data is created, distributed, used, maintained, and disposed of while balancing benefits, costs, and compliance with regulations. Regular assessments and reporting help ensure privacy, while controls and planning aim to prevent breaches and facilitate effective responses.
The document discusses management support systems and decision support systems. It defines key concepts like decision making, decision support systems, and different types of decision support systems. It also outlines various management roles, factors affecting decision making, and technologies that support decision making processes, including management information systems, data warehousing, business intelligence, knowledge management systems, and expert systems.
The document discusses decision support systems (DSS), business intelligence (BI), and an Automated Intelligent Decision Support System (AIDSS) for human resource management. It defines these concepts and outlines some of their key features. Specifically, it explains that DSS help decision-making, BI aims to support better business decisions using tools like reporting and analytics, and AIDSS is a specific DSS that uses AI to help with HR challenges like employee performance. It also provides details on the development of AIDSS, including its modules for the user interface, data input/editing, intelligent analysis, and data storage/protection.
This document provides an overview of decision support systems and business intelligence. It defines key concepts like decision support frameworks, the types of decisions that systems support, and the evolution of business intelligence tools. The document also explains how decision support systems and business intelligence are related through their architectures and goals of improving access to data and decision making.
This document discusses key information resources including data, application software, technology, information specialists, users, and facilities. It describes the role of the Chief Information Officer (CIO) in managing information resources, their responsibilities, priorities, and required competencies. The document also covers strategic planning for information systems, aligning IS strategy with organizational strategy, and issues around end-user computing.
The document discusses innovation and how it relates to new technology and business models. It provides definitions of innovation as the development of new solutions to meet customer needs. Innovation differs from improvement by doing something different rather than better. New technology is defined as tools and methods that solve problems or achieve goals, while new business models focus on increasing revenue, value, and new revenue streams. The document then gives examples of how these concepts can be applied, such as using QR codes and mobile games for marketing or forecasting models and GIS for disaster management systems.
A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management
This chapter introduces spreadsheet modeling and decision analysis as a field of management science that uses computers, statistics, and mathematics to solve business problems. It discusses how spreadsheet models represent real-world phenomena with mathematical relationships and can help analyze decisions by evaluating potential outcomes. Examples are given of companies that achieved significant cost savings and efficiency gains by developing spreadsheet and other mathematical models to optimize areas like procurement, logistics, inventory management, and operations. The chapter also covers characteristics of models, benefits of modeling approaches, categories of mathematical models, and cognitive biases that can influence decision-making.
The document discusses various types of information systems that support decision making. It describes management information systems that provide routine operational reports, decision support systems that help with semi-structured tactical decisions through modeling and analysis, and executive information systems that provide customized insights to top executives. The document also covers data warehousing, data mining, expert systems, and emerging trends like personalized decision support and what-if scenario analysis.
The document discusses various types of management support systems that can help with managerial decision making, including decision support systems, expert systems, intelligent agents, neural networks, and knowledge management systems. It covers the evolution of these computerized decision aids from early transaction processing to more advanced integrated and hybrid systems. The goal of management support systems is to improve the quality, speed, and consistency of managerial decisions through leveraging data, models, and artificial intelligence technologies.
The document discusses the software industry and ERP systems. It provides statistics showing the software industry was worth $407.3 billion in 2013, with the top four vendors being Microsoft, Oracle, IBM, and SAP. It also defines ERP as business management software that integrates functions like finance, manufacturing, sales, inventory, and more. The advantages of ERP include centralized data, real-time information sharing, and process standardization, while disadvantages include high costs, need for customization, and resistance to change.
A discussion of the value of Decision Management and decision modeling to the effective management of large, complex operations - including that of a large, global, financial services organization. Presented by James Taylor of Decision Management Solutions at the Building Business Capability Conference (BBCCon) 2015
The document discusses managerial decision making. It describes decision making as a process that managers go through to select actions to achieve organizational goals. The key aspects of the decision making process discussed are: identifying problems and criteria, developing alternatives, analyzing alternatives, selecting an alternative, implementing the decision, and evaluating the effectiveness. Rational and intuitive approaches to decision making are described, as are different decision styles. The document emphasizes that managers must make decisions carefully and consider the situation.
Decision support systems and business intelligenceShwetabh Jaiswal
This document discusses decision support systems and business intelligence. It describes how the modern business environment requires computerized systems to help with complex decision making. Business intelligence transforms raw data into useful information through methodologies, processes and technologies. Decision support systems couple individual expertise with computer capabilities to improve decision quality for semi-structured problems. Both systems use similar architectures of data warehouses, analytics, and user interfaces to enable analysis and informed decisions.
This document discusses various types of management decision making and information systems that support decision making. It describes strategic, tactical and operational decision making levels and different decision making models. It also summarizes transaction processing systems, decision support systems, executive support systems, group decision support systems, data mining, knowledge management systems and enterprise information portals that help managers at different levels make effective decisions. The document also provides an example of how Hertz Corp. used an executive support system to make real-time marketing decisions.
The document provides an introduction to operations management concepts including defining operations, products, services, and supply chains. It discusses different types of production systems and strategies including mass production, lean production, and agile manufacturing. The document also covers topics like facilities layout, location planning, quality management, and environmental sustainability in operations. It emphasizes the importance of operations management in business strategy and competitiveness.
Machine learning involves using algorithms and large datasets to allow systems to learn from data and improve their performance. There are several types of machine learning including supervised learning for classification and prediction tasks using labeled examples, unsupervised learning like clustering to find hidden patterns in unlabeled data, and reinforcement learning where an agent learns from delayed rewards. Applications of machine learning span many domains like retail for customer segmentation, finance for credit scoring, medicine for diagnosis, and web mining for search engines. The field is growing rapidly due to increased data and computing power enabling complex models to be learned from data rather than being explicitly programmed.
The document discusses decision making and decision support systems. It covers Simon's four phases of decision making: intelligence, design, choice, and implementation. It also discusses how decision support systems can support each phase of the decision making process through tools like data mining, OLAP, expert systems, modeling, and knowledge management systems. Finally, it covers some factors that affect human decision making like personality, gender, cognitive styles, and individual decision styles.
The document discusses decision support systems for planning and scheduling. It covers application areas like manufacturing and services, system requirements, optimization techniques, and implementation issues. The key topics include common optimization techniques like dispatching rules, integer programming, and local search methods. It also discusses interface design, modular system architecture, and commercial scheduling packages.
Data governance is the practice of managing all types of enterprise data throughout its lifecycle from creation to disposal. It involves roles like stewards, users, data managers, and owners working together to define policies, data requirements, and ensure data quality, availability, and security. Data governance considerations include how data is created, distributed, used, maintained, and disposed of while balancing benefits, costs, and compliance with regulations. Regular assessments and reporting help ensure privacy, while controls and planning aim to prevent breaches and facilitate effective responses.
The document discusses management support systems and decision support systems. It defines key concepts like decision making, decision support systems, and different types of decision support systems. It also outlines various management roles, factors affecting decision making, and technologies that support decision making processes, including management information systems, data warehousing, business intelligence, knowledge management systems, and expert systems.
The document discusses decision support systems (DSS), business intelligence (BI), and an Automated Intelligent Decision Support System (AIDSS) for human resource management. It defines these concepts and outlines some of their key features. Specifically, it explains that DSS help decision-making, BI aims to support better business decisions using tools like reporting and analytics, and AIDSS is a specific DSS that uses AI to help with HR challenges like employee performance. It also provides details on the development of AIDSS, including its modules for the user interface, data input/editing, intelligent analysis, and data storage/protection.
This document provides an overview of decision support systems and business intelligence. It defines key concepts like decision support frameworks, the types of decisions that systems support, and the evolution of business intelligence tools. The document also explains how decision support systems and business intelligence are related through their architectures and goals of improving access to data and decision making.
This document discusses key information resources including data, application software, technology, information specialists, users, and facilities. It describes the role of the Chief Information Officer (CIO) in managing information resources, their responsibilities, priorities, and required competencies. The document also covers strategic planning for information systems, aligning IS strategy with organizational strategy, and issues around end-user computing.
The document discusses innovation and how it relates to new technology and business models. It provides definitions of innovation as the development of new solutions to meet customer needs. Innovation differs from improvement by doing something different rather than better. New technology is defined as tools and methods that solve problems or achieve goals, while new business models focus on increasing revenue, value, and new revenue streams. The document then gives examples of how these concepts can be applied, such as using QR codes and mobile games for marketing or forecasting models and GIS for disaster management systems.
A decision support system (DSS) is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management
This chapter introduces spreadsheet modeling and decision analysis as a field of management science that uses computers, statistics, and mathematics to solve business problems. It discusses how spreadsheet models represent real-world phenomena with mathematical relationships and can help analyze decisions by evaluating potential outcomes. Examples are given of companies that achieved significant cost savings and efficiency gains by developing spreadsheet and other mathematical models to optimize areas like procurement, logistics, inventory management, and operations. The chapter also covers characteristics of models, benefits of modeling approaches, categories of mathematical models, and cognitive biases that can influence decision-making.
The document discusses various types of information systems that support decision making. It describes management information systems that provide routine operational reports, decision support systems that help with semi-structured tactical decisions through modeling and analysis, and executive information systems that provide customized insights to top executives. The document also covers data warehousing, data mining, expert systems, and emerging trends like personalized decision support and what-if scenario analysis.
The document discusses various types of management support systems that can help with managerial decision making, including decision support systems, expert systems, intelligent agents, neural networks, and knowledge management systems. It covers the evolution of these computerized decision aids from early transaction processing to more advanced integrated and hybrid systems. The goal of management support systems is to improve the quality, speed, and consistency of managerial decisions through leveraging data, models, and artificial intelligence technologies.
The document discusses the software industry and ERP systems. It provides statistics showing the software industry was worth $407.3 billion in 2013, with the top four vendors being Microsoft, Oracle, IBM, and SAP. It also defines ERP as business management software that integrates functions like finance, manufacturing, sales, inventory, and more. The advantages of ERP include centralized data, real-time information sharing, and process standardization, while disadvantages include high costs, need for customization, and resistance to change.
A discussion of the value of Decision Management and decision modeling to the effective management of large, complex operations - including that of a large, global, financial services organization. Presented by James Taylor of Decision Management Solutions at the Building Business Capability Conference (BBCCon) 2015
The document discusses managerial decision making. It describes decision making as a process that managers go through to select actions to achieve organizational goals. The key aspects of the decision making process discussed are: identifying problems and criteria, developing alternatives, analyzing alternatives, selecting an alternative, implementing the decision, and evaluating the effectiveness. Rational and intuitive approaches to decision making are described, as are different decision styles. The document emphasizes that managers must make decisions carefully and consider the situation.
Decision support systems and business intelligenceShwetabh Jaiswal
This document discusses decision support systems and business intelligence. It describes how the modern business environment requires computerized systems to help with complex decision making. Business intelligence transforms raw data into useful information through methodologies, processes and technologies. Decision support systems couple individual expertise with computer capabilities to improve decision quality for semi-structured problems. Both systems use similar architectures of data warehouses, analytics, and user interfaces to enable analysis and informed decisions.
This document discusses various types of management decision making and information systems that support decision making. It describes strategic, tactical and operational decision making levels and different decision making models. It also summarizes transaction processing systems, decision support systems, executive support systems, group decision support systems, data mining, knowledge management systems and enterprise information portals that help managers at different levels make effective decisions. The document also provides an example of how Hertz Corp. used an executive support system to make real-time marketing decisions.
The document provides an introduction to operations management concepts including defining operations, products, services, and supply chains. It discusses different types of production systems and strategies including mass production, lean production, and agile manufacturing. The document also covers topics like facilities layout, location planning, quality management, and environmental sustainability in operations. It emphasizes the importance of operations management in business strategy and competitiveness.
Herbert Simon proposed a 4-phase model of problem solving: intelligence, design, choice, and review. Decision making involves either programmed or non-programmed decisions. Decision support systems (DSS) provide interactive support for semi-structured and unstructured decisions through queries, models, and reports without making the final decision. A DSS collects data, allows for group work, and can incorporate artificial intelligence. Common DSS models include behavioral, management science, and operations research models. Group decision support systems (GDSS) facilitate group problem solving through individual workstations, facilitation software, and aggregation of ideas, comments, and votes.
This document discusses how business intelligence and analytics support decision making. It describes the types of decisions managers make and how information systems can help. It explains how tools like business intelligence, business analytics, predictive analytics, and location analytics provide insights for decisions. Finally, it discusses how different roles in an organization use business intelligence and analytics to support decisions at all levels, from operational to strategic.
Healthcare Enterprise Data Model: The Buy vs. Build DebatePerficient, Inc.
Every mid-to-large-sized healthcare organization will at some point need an enterprise data warehouse to consolidate data from its many source systems. The first question most will ask is, “Do I build it from scratch or buy a model?”
For some organizations, the answer to this question is simple and obvious. For others, it may be the source of internal debate on whether someone else can create a model that will address their nuances. Some will argue that a pre-built model will save time and money and should be explored as an option, while others may curb discussions due to the belief that their electronic medical records vendor already has it figured out.
In this webinar, we explored the pros and cons of building your own data model vs. buying one and looked at real customer use cases to help weigh the pros and cons of this critical enterprise decision. Topics included:
-How experience plays into the equation
-Which solution delivers value more quickly
-Which solution helps reduce the risk to the organization
-How easy is it to integrate other solutions
-How the decision to build vs. buy can impact your internal team
Decision support systems and business intelligenceShwetabh Jaiswal
The document discusses decision support systems and business intelligence. It describes how business environments have become more complex, requiring faster and better decision-making supported by computerized systems. Business intelligence involves transforming raw data into useful information to enable strategic, tactical and operational insights. Decision support systems couple individual expertise with computer capabilities to improve decision quality for semi-structured problems.
Business analytics combines data, information technology, statistical analysis, quantitative methods, and computer-based models to provide decision makers with possible scenarios to make well-informed decisions. It refers to the skills, technologies, and practices for developing new insights into business performance based on data analysis and statistics. Business analytics has evolved over time from operations research during WWII to management science, business intelligence, decision support systems, and software tools today. It impacts organizations by improving profitability, market share, costs, and key performance indicators. Descriptive analytics describes past data, predictive analytics forecasts future events, and prescriptive analytics recommends optimal solutions.
The document outlines a framework for business process design projects that utilizes business process modeling, simulation, and design. It involves 8 key steps: 1) developing a case for action and vision statement, 2) identifying and selecting processes, 3) obtaining management commitment, 4) evaluating design enablers, 5) acquiring process understanding, 6) creative process design using principles like benchmarking, 7) process modeling and simulation, and 8) implementing the new process design. Process modeling and simulation allows exploration of redesign effects without disrupting current operations and helps reduce risks.
Chapter 4 Decision support Intelligent systems.pdfRAHULSINGH621665
The document discusses decision support systems (DSS), knowledge management systems, expert systems, and their components and characteristics. It describes the stages of decision making as intelligence, design, choice, and implementation. DSS are used to support business decisions and include inputs, outputs, and a user interface. Expert systems capture human expertise in a knowledge base and use an inference engine to derive answers. Knowledge management systems help organizations create and share knowledge.
Predictive Analytics & Decision Solutions [PrADS], a subsidiary of Dun & Bradstreet provides cutting edge analytics solutions and actionable insights to leading organizations globally , The following presentation provides an overview of the services offered
management information system unit 2 aktu study material quick notes easy to ...RDX29
A decision support system (DSS) aids business decision-making by analyzing large amounts of data. It has three main components: a model management system to store decision-making models, a user interface, and a knowledge base containing internal and external information. There are several types of DSS, including communication-driven, model-driven, knowledge-driven, and data-driven. While a DSS improves decision-making speed and efficiency, it also has disadvantages like high costs and potential overreliance on the system. An executive support system is a type of DSS tailored for executive-level reports. Decision-making models include the classical, administrative, and bounded rationality models proposed by Herbert Simon.
Game Changing Quality Strategies that Drive Organizational Excellencekushshah
Quality in the past was more related conforming to requirements, in lot of cases as it relates to engineering requirements and not necessarily enthusiastic customer experience. It was a very narrow definition of quality and focused more on Things Gone Wrong. Goal was to reach a level of customer accepted.
Quality definition today is much broader and winning in quality in this highly competitive environment requires deployment game changing quality strategies.
We will discuss how to infuse the voice of the customer into the way we design our products and services so that they exceed customer expectations. Organizations that engage all functions within enterprise and are customer centric will differentiate themselves from the rest of the competition. This presentation will provide an integrated roadmap on how to integrate proactive quality strategies such as Design for Six Sigma (DFSS), Advanced Product Quality Planning (APQP), Design Failure Modes and Effects Analysis (DFMEA), Process Failure Modes and Effects Analysis (PFMEA) along with reactive strategies such as Six Sigma and control plans to achieve organizational excellence.
Business analytics refers to the skills, technologies, and practices used to continuously gain insights and understand business performance based on data and statistical analysis. It combines data, information technology, statistical analysis, and quantitative methods to provide decision makers with scenarios to make well-researched decisions. Business analytics has evolved from operations research used in WWII to management science and now includes business intelligence, predictive analysis, and prescriptive analysis to describe past performance, predict future outcomes, and recommend actions respectively. It impacts organizations by improving profitability, increasing revenue and market share, and reducing costs.
Creating Value Through Digital Enterprise Transformation
Originally presented to XPX, CT Chapter. We look at what it takes to create value and reduce risk using digital enterprise transformation to improve your business processes, technology, and talent foundations.
Topics covered include building a roadmap, process improvement, systems improvement including ERP, CRM, BI/Analytics, and eCommerce, how to build a global organization, and how to build a professional management team.
The document discusses strategic consulting services from PEPR Consulting to help life science companies increase productivity and efficiency in their lab operations through a seven-step program. This includes an analysis of current processes and systems, a Multi Moment Analysis to identify inefficient tasks, development of optimized future processes and systems requirements, creation of a business case, and support in selecting and deploying new lab informatics solutions. The consulting aims to realize improvements of up to 30% for clients.
Move from Business Intelligence to Advanced Analytics by Integrating IBM SPSS...Perficient, Inc.
Standard business intelligence reports and dashboards are effective tools to describe the state of your organization or department. However, there are many questions that these tools cannot address, such as:
Why is this happening?
How will my organization be impacted if these trends continue?
What will happen next?
How can we change the outcome of this situation?
Advanced analytics tools are necessary to accurately and quickly address these questions through statistical analysis, forecasting, predictive modeling and intelligent optimization. By integrating advanced analytics solutions with existing business intelligence platforms, your organization will be better positioned to extract actionable insight from your data to gain a true competitive advantage.
Learn how your organization can extend its business intelligence investments in IBM Cognos TM1 by integrating with IBM's leading advanced analytics platform, SPSS. We'll discuss the capabilities of TM1 and SPSS, integration methodologies and strategies, and demo an integrated analytics environment.
Management information system prepared by samenasamena shawon
This presentation summarizes key topics related to information systems including unique features of e-commerce, digital markets and goods, conversion strategies for information systems, developing IS solutions, geographic information systems, knowledge management challenges, computer-aided software engineering, the system approach, and benefits of knowledge management and intelligent techniques. It was prepared by six students for their lecturer to cover these topics.
Top mailing list providers in the USA.pptxJeremyPeirce1
Discover the top mailing list providers in the USA, offering targeted lists, segmentation, and analytics to optimize your marketing campaigns and drive engagement.
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
Discover timeless style with the 2022 Vintage Roman Numerals Men's Ring. Crafted from premium stainless steel, this 6mm wide ring embodies elegance and durability. Perfect as a gift, it seamlessly blends classic Roman numeral detailing with modern sophistication, making it an ideal accessory for any occasion.
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Starting a business is like embarking on an unpredictable adventure. It’s a journey filled with highs and lows, victories and defeats. But what if I told you that those setbacks and failures could be the very stepping stones that lead you to fortune? Let’s explore how resilience, adaptability, and strategic thinking can transform adversity into opportunity.
How MJ Global Leads the Packaging Industry.pdfMJ Global
MJ Global's success in staying ahead of the curve in the packaging industry is a testament to its dedication to innovation, sustainability, and customer-centricity. By embracing technological advancements, leading in eco-friendly solutions, collaborating with industry leaders, and adapting to evolving consumer preferences, MJ Global continues to set new standards in the packaging sector.
Navigating the world of forex trading can be challenging, especially for beginners. To help you make an informed decision, we have comprehensively compared the best forex brokers in India for 2024. This article, reviewed by Top Forex Brokers Review, will cover featured award winners, the best forex brokers, featured offers, the best copy trading platforms, the best forex brokers for beginners, the best MetaTrader brokers, and recently updated reviews. We will focus on FP Markets, Black Bull, EightCap, IC Markets, and Octa.
The Genesis of BriansClub.cm Famous Dark WEb PlatformSabaaSudozai
BriansClub.cm, a famous platform on the dark web, has become one of the most infamous carding marketplaces, specializing in the sale of stolen credit card data.
Storytelling is an incredibly valuable tool to share data and information. To get the most impact from stories there are a number of key ingredients. These are based on science and human nature. Using these elements in a story you can deliver information impactfully, ensure action and drive change.
Industrial Tech SW: Category Renewal and CreationChristian Dahlen
Every industrial revolution has created a new set of categories and a new set of players.
Multiple new technologies have emerged, but Samsara and C3.ai are only two companies which have gone public so far.
Manufacturing startups constitute the largest pipeline share of unicorns and IPO candidates in the SF Bay Area, and software startups dominate in Germany.
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Understanding User Needs and Satisfying ThemAggregage
https://www.productmanagementtoday.com/frs/26903918/understanding-user-needs-and-satisfying-them
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
In this webinar, we won't focus on the research methods for discovering user-needs. We will focus on synthesis of the needs we discover, communication and alignment tools, and how we operationalize addressing those needs.
Industry expert Scott Sehlhorst will:
• Introduce a taxonomy for user goals with real world examples
• Present the Onion Diagram, a tool for contextualizing task-level goals
• Illustrate how customer journey maps capture activity-level and task-level goals
• Demonstrate the best approach to selection and prioritization of user-goals to address
• Highlight the crucial benchmarks, observable changes, in ensuring fulfillment of customer needs
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2. Business Value of Improved Decision Making
• Possible to measure value of improved decision
making
• Decisions made at all levels of the firm
• Some are common, routine, and numerous.
• Although value of improving any single decision
may be small, improving hundreds of thousands of
“small” decisions adds up to large annual value for
the business
Dr. Mohamed Ezzeldin 2
3. Business Value of Improved Decision Making
Decision Maker Number
/ year
Value of
decision
Annual value
to firm
Allocate support to most
valuable customers.
Accounts manager 12 $100,000 $1,200,000
Predict call center daily
demand.
Call Center
management
4 150,000 600,000
Decide parts inventory level
daily.
Inventory manager 365 5,000 1,825,000
Identify competitive bids
from major suppliers.
Senior management 1 2,000,000 2,000,000
Schedule production to fill
orders.
Manufacturing
manager
150 10,000 1,500,000
Dr. Mohamed Ezzeldin 3
4. Types of Decisions
• Unstructured
• Decision maker must provide judgment to solve problem
• Novel, important, nonroutine
• No well-understood or agreed-upon procedure for making
them
• Structured
• Repetitive and routine
• Involve definite procedure for handling them so do not have to
be treated as new
• Semistructured
• Only part of problem has clear-cut answer provided by
accepted procedure
Dr. Mohamed Ezzeldin 4
5. Figure 10-1
Senior managers,
middle managers,
operational
managers, and
employees have
different types of
decisions and
information
requirements.
Information Requirements of Key Decision-Making
Groups in a Firm
Dr. Mohamed Ezzeldin 5
6. The Decision-Making Process
1. Intelligence
• Discovering, identifying, and understanding the problems
occurring in the organization—why is there a problem, where,
what effects it is having on the firm
2. Design
• Identifying and exploring various solutions
3. Choice
• Choosing among solution alternatives
4. Implementation
• Making chosen alternative work and monitoring how well
solution is working
Dr. Mohamed Ezzeldin 6
8. • High velocity decision-making
• Humans eliminated
• Trading programs at electronic stock exchanges
• Quality of decisions, decision making
• Accuracy
• Comprehensiveness
• Fairness
• Speed (efficiency)
• Coherence
• Due process
Dr. Mohamed Ezzeldin 8
9. The Business Intelligence Environment
• Six elements in business intelligence
environment
1. Data from business environment
2. Business intelligence infrastructure
3. Business analytics toolset
4. Managerial users and methods
5. Delivery platform
• MSS, DSS, ESS
6. User interface
Dr. Mohamed Ezzeldin 9
10. Business
intelligence and
analytics requires
a strong database
foundation, a set
of analytic tools,
and an involved
management team
that can ask
intelligent
questions and
analyze data.
Business Intelligence and Analytics for Decision Support
Dr. Mohamed Ezzeldin 10
11. Business Intelligence and Analytics Capabilities
• Production reports
• Predefined, based on industry standards
• Parameterized reports
• E.g. pivot tables
• Dashboards/scorecards
• Ad-hoc query/search/report creation
• Drill-down
• Forecasts, scenarios, models
• “What-if” scenario analysis, statistical analysis
Dr. Mohamed Ezzeldin 11
12. Examples of Business Intelligence Pre-Defined Reports
Business Functional Area Production Reports
Sales Sales forecasts, sales team performance, cross selling, sales cycle times
Service/Call Center Customer satisfaction, service cost, resolution rates, churn rates
Marketing Campaign effectiveness, loyalty and attrition, market basket analysis
Procurement and Support Direct and indirect spending, off-contract purchases, supplier
performance
Supply Chain Backlog, fulfillment status, order cycle time, bill of materials analysis
Financials General ledger, accounts receivable and payable, cash flow, profitability
Human Resources Employee productivity, compensation, workforce demographics,
retention
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13. Predictive Analytics
• Use statistical analytics and other techniques
• Extracts information from data and uses it to
predict future trends and behavior patterns
• Predicting responses to direct marketing campaigns
• Identifying best potential customers for credit cards
• Identify at-risk customers
• Predict how customers will respond to price changes and
new services
• Accuracies range from 65 to 90%
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14. Data Visualization, Visual Analytics, and GIS
• Data visualization, visual analytics tools
• Rich graphs, charts, dashboards, maps
• Help users see patterns and relationships in
large amounts of data
• GIS—geographic information systems
• Visualization of data related to geographic
distribution
• E.g., GIS to help government calculate
response times to emergencies
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15. Casual users are
consumers of BI
output, while
intense power
users are the
producers of
reports, new
analyses,
models, and
forecasts.
Business Intelligence Users
Dr. Mohamed Ezzeldin 15
16. Support for Semi-Structured Decisions
• Decision-support systems (DSS)
• BI delivery platform for “super-users” who want to create own
reports, use more sophisticated analytics and models
• What-if analysis
• Sensitivity analysis
• Backward sensitivity analysis
• Pivot tables: spreadsheet function for multidimensional
analysis
• Intensive modeling techniques
Dr. Mohamed Ezzeldin 16
17. This table displays the results of a sensitivity analysis of the effect of
changing the sales price of a necktie and the cost per unit on the product’s
break-even point. It answers the question, “What happens to the break-even
point if the sales price and the cost to make each unit increase or
decrease?”
Sensitivity Analysis
Dr. Mohamed Ezzeldin 17
18. In this pivot table, we
are able to examine
where an online training
company’s customers
come from in terms of
region and advertising
source.
A Pivot Table That Examines Customer Regional
Distribution and Advertising Source
Dr. Mohamed Ezzeldin 18
19. Decision Support for Senior Management
• Executive support systems
• Balanced scorecard method
• Leading methodology for understanding information
most needed by executives
• Focuses on measurable outcomes
• Measures four dimensions of firm performance
• Financial
• Business process
• Customer
• Learning and growth
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20. The Balanced Scorecard Framework
In the balanced score-
card framework, the
firm’s strategic
objectives are
operationalized along
four dimensions:
financial, business
process, customer,
and learning and
growth. Each
dimension is
measured using
several KPIs.
Dr. Mohamed Ezzeldin 20
21. • Business performance management (BPM)
• Management methodology
• Based on firm’s strategies
• E.g., differentiation, low-cost producer, market share
growth, scope of operation
• Translates strategies into operational targets
• Uses set of KPI (key performance indicators) to measure
progress toward targets
• ESS combine internal data with external
• Financial data, news, etc.
• Drill-down capabilities
Dr. Mohamed Ezzeldin 21
22. Group Decision-Support Systems (GDSS)
• Interactive, computer-based systems that facilitate solving
of unstructured problems by set of decision makers
• Used in conference rooms with special hardware and
software for collecting, ranking, storing ideas and
decisions
• Promotes a collaborative atmosphere by guaranteeing
contributors’ anonymity
• Supports increased meeting sizes with increased
productivity
• Software follows structured methods for organizing and
evaluating ideas
Dr. Mohamed Ezzeldin 22
23. • Intelligent techniques for enhancing decision making
• Many based on artificial intelligence (AI)
• Computer-based systems (hardware and software) that
attempt to emulate human behavior and thought
patterns
• Include:
• Expert systems
• Case-based reasoning
• Fuzzy logic
• Neural networks
• Genetic algorithms
• Intelligent agents
Dr. Mohamed Ezzeldin 23
24. • Expert systems
• Model human knowledge as a set of rules that are
collectively called the knowledge base
• From 200 to 10,000 rules, depending on complexity
• The system’s inference engine searches through the rules
and “fires” those rules that are triggered by facts gathered
and entered by the user
• Useful for dealing with problems of classification in which
there are relatively few alternative outcomes and in which
these possible outcomes are all known in advance
Dr. Mohamed Ezzeldin 24
25. An expert system contains a set
of rules to be followed when
used. The rules are
interconnected; the number of
outcomes is known in advance
and is limited; there are multiple
paths to the same outcome; and
the system can consider multiple
rules at a single time. The rules
illustrated are for a simple credit-
granting expert system.
Rules in an Expert System
Dr. Mohamed Ezzeldin 25
26. • Case-based reasoning
• Knowledge and past experiences of human specialists
are represented as cases and stored in a database for
later retrieval
• System searches for stored cases with problem
characteristics similar to new one, finds closest fit, and
applies solutions of old case to new case
• Successful and unsuccessful applications are tagged and
linked in database
• Used in medical diagnostic systems, customer support
Dr. Mohamed Ezzeldin 26
27. Case-based reasoning
represents knowledge as
a database of past cases
and their solutions. The
system uses a six-step
process to generate
solutions to new
problems encountered by
the user.
How Case-Based Reasoning Works
Dr. Mohamed Ezzeldin 27
28. • Fuzzy logic
• Rule-based technology that represents imprecision in
categories (e.g., “cold” versus “cool”) by creating rules
that use approximate or subjective values
• Describes a particular phenomenon or process
linguistically and then represents that description in a
small number of flexible rules
• Provides solutions to problems requiring expertise that is
difficult to represent in the form of IF-THEN rules
• E.g., Sendai, Japan subway system uses fuzzy logic
controls to accelerate so smoothly that standing
passengers need not hold on
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29. • Neural networks
• Use hardware and software that parallel the processing
patterns of a biological brain.
• “Learn” patterns from large quantities of data by searching
for relationships, building models, and correcting over and
over again the model’s own mistakes
• Humans “train” the network by feeding it data for which the
inputs produce a known set of outputs or conclusions
• Machine learning
• Useful for solving complex, poorly understood problems for
which large amounts of data have been collected
Dr. Mohamed Ezzeldin 29
30. Figure 10-11 A neural network uses rules it “learns” from patterns in data to construct a
hidden layer of logic. The hidden layer then processes inputs, classifying them
based on the experience of the model. In this example, the neural network has
been trained to distinguish between valid and fraudulent credit card purchases.
How a Neural Network Works
Dr. Mohamed Ezzeldin 30
31. • Genetic algorithms
• Find the optimal solution for a specific problem by
examining very large number of alternative solutions for
that problem
• Based on techniques inspired by evolutionary biology:
inheritance, mutation, selection, and so on
• Work by representing a solution as a string of 0s and 1s,
then searching randomly generated strings of binary
digits to identify best possible solution
• Used to solve complex problems that are very dynamic
and complex, involving hundreds or thousands of
variables or formulas
Dr. Mohamed Ezzeldin 31
32. This example illustrates an initial population of “chromosomes,” each representing a different solution. The
genetic algorithm uses an iterative process to refine the initial solutions so that the better ones, those with
the higher fitness, are more likely to emerge as the best solution.
The Components of a Genetic Algorithm
32
33. • Intelligent agents
• Programs that work in the background without direct
human intervention to carry out specific, repetitive, and
predictable tasks for user, business process, or
software application
• Shopping bots
• Procter & Gamble (P&G) programmed group of
semiautonomous agents to emulate behavior of
supply-chain components, such as trucks, production
facilities, distributors, and retail stores and created
simulations to determine how to make supply chain
more efficient
Dr. Mohamed Ezzeldin 33
34. Intelligent
agents are
helping Procter
& Gamble
shorten the
replenishment
cycles for
products, such
as a box of
Tide.
Intelligent Agents in P&G’s Supply Chain Network
Dr. Mohamed Ezzeldin 34
35. • Knowledge management
• Business processes developed for creating,
storing, transferring, and applying knowledge
• Increases the ability of organization to learn
from environment and to incorporate knowledge
into business processes and decision making
• Knowing how to do things effectively and
efficiently in ways that other organizations
cannot duplicate is major source of profit and
competitive advantage
Dr. Mohamed Ezzeldin 35
36. • Three kinds of knowledge
• Structured: structured text documents
• Semistructured: e-mail, voice mail, digital pictures, etc.
• Tacit knowledge (unstructured): knowledge residing in heads
of employees, rarely written down
• Enterprise-wide knowledge management systems
• Deal with all three types of knowledge
• General-purpose, firm-wide systems that collect, store,
distribute, and apply digital content and knowledge
Enterprise-Wide Knowledge Management Systems
Dr. Mohamed Ezzeldin 36
37. • Enterprise content management systems
• Capabilities for knowledge capture, storage
• Repositories for documents and best practices
• Capabilities for collecting and organizing
semistructured knowledge such as e-mail
• Classification schemes
• Key problem in managing knowledge
• Each knowledge object must be tagged for retrieval
Dr. Mohamed Ezzeldin 37
38. An enterprise content management
system has capabilities for classifying,
organizing, and managing structured
and semistructured knowledge and
making it available throughout the
enterprise.
An Enterprise Content Management System
Dr. Mohamed Ezzeldin 38
39. • Digital asset management systems
• Manage unstructured digital data like photographs,
graphic images, video, audio
• Knowledge network systems (expertise location
and management systems)
• Provide online directory of corporate experts in well-
defined knowledge domains
• Use communication technologies to make it easy for
employees to find appropriate expert in firm
Dr. Mohamed Ezzeldin 39
40. A knowledge network maintains
a database of firm experts, as
well as accepted solutions to
known problems, and then
facilitates the communication
between employees looking for
knowledge and experts who
have that knowledge. Solutions
created in this communication
are then added to a database of
solutions in the form of
frequently asked questions
(FAQs), best practices, or other
documents.
An Enterprise Knowledge Network System
Dr. Mohamed Ezzeldin 40
41. • Collaboration tools
• Social bookmarking: allow users to save their
bookmarks publicly and tag with keywords
• Folksonomies
• Learning management systems (LMS)
• Provide tools for management, delivery,
tracking, and assessment of various types of
employee learning and training
Dr. Mohamed Ezzeldin 41
42. Knowledge Work Systems (KWS)
• Specialized systems for knowledge workers
• Requirements of knowledge work systems:
• Specialized tools
• Powerful graphics, analytical tools, and
communications and document management
• Computing power to handle sophisticated
graphics or complex calculations
• Access to external databases
• User-friendly interfaces
Dr. Mohamed Ezzeldin 42
43. Knowledge work systems
require strong links to
external knowledge bases
in addition to specialized
hardware and software.
Requirements of Knowledge Work Systems
Dr. Mohamed Ezzeldin 43
44. Knowledge Work Systems (KWS)
• Examples of knowledge work
systems:
• Computer-aided design (CAD) systems
• Virtual reality (VR) systems
• Virtual Reality Modeling Language (VRML)
• Augmented reality (AR) systems
• Investment workstations
Dr. Mohamed Ezzeldin 44