Management Information Systems Chapter 11

Management Information Systems: Managing the Digital Firm - Chapter 11

• The chapter focuses on managing knowledge and artificial intelligence within a business context.
• Knowledge management systems are a rapidly growing area of software investment within the information economy.
• A significant portion of a firm's stock market value is tied to intangible assets, such as knowledge, brand reputation, and unique business processes.
• Well-executed knowledge-based projects yield substantial returns on investment (ROI).
• Knowledge is an asset, exists in various forms, has a location, and is situational.
• Knowledge-based core competencies are vital organizational assets.
• Effectively and efficiently utilizing knowledge in a way competitors cannot duplicate creates a competitive advantage.
• Specific examples include unique production systems, such as a build-to-order process.
• Organizations continually learn and gain experience through a process that includes data collection, measurement, trial and error, and feedback.
• Knowledge management is a set of business processes used to create, store, transfer, and utilize knowledge within a firm, creating a value chain to build knowledge from raw data and information.

Key Stages of the Knowledge Management Value Chain

• Knowledge Acquisition: Includes documenting tacit and explicit knowledge in various forms like reports and presentations, or unstructured data such as emails. Creating knowledge through online expert networks, and collecting external and internal data.
• Knowledge Storage: Emphasizes using databases and document management systems to store that information. Management plays a crucial role
• Knowledge Dissemination: Key tools include portals, email, instant messaging, and collaboration tools to broadly share knowledge. Training programs aid in focusing on key information that employees need to know within the organization.
• Knowledge Application: Implementation of knowledge to create new business practices and develop new products/services in new markets is highlighted.

Types of Knowledge Management Systems

• Enterprise-wide knowledge management systems: General firm-wide effort to collect, store, distribute, and apply digital content and knowledge.
• Knowledge work systems (KWS): Specialized systems created for employees whose work focuses on discovering or creating new knowledge.
• Intelligent techniques: Diverse group of techniques such as data mining for various knowledge-related goals.

Artificial Intelligence

• Grand Vision: Computer systems as "smart" as humans. Current systems do not meet this.
• Realistic Vision: Systems that take data inputs, process them, and produce outputs to accomplish complex tasks that would be difficult for humans.
• Examples: Facial recognition, interpreting medical scans, analyzing large datasets ("Big Data"), improving performance over time (learning), car navigation, responding to natural language.
• Major Types of AI: Expert systems, machine learning, neural networks, genetic algorithms, natural language processing, computer vision, and robotics.
• Business Benefits: AI captures individual and collective knowledge, and builds upon knowledge bases already established. AI extends knowledge bases in areas like knowledge capture, knowledge discovery, and automation of tasks.

Capturing Knowledge: Expert Systems

• Tools to capture tacit knowledge in specialized domains.
• Knowledge is expressed as a set of rules, used in discrete and structured decision-making.
• Expert systems typically perform limited tasks, including diagnostics and decisions, such as determining loan eligibility.

Machine Learning

• Computer programs improve performance by recognizing patterns from experience and prior learnings. Utilizes databases and algorithms
• Approaches include supervised and unsupervised learning.

Neural Networks

• Used to find patterns in large datasets, too complex for human analysis.
• "Learn" through repeated searching, building models, and correcting patterns.
• Humans feed data inputs with known outputs ("train" the network) and help neural networks learn solutions.

Genetic Algorithms

• Useful for finding optimal solutions for complex problems by searching through a vast number of possibilities.
• Inspired by evolutionary processes.

Natural Language Processing (NLP)

• Understanding and generating natural language.
• Uses machine learning with large databases to represent phrases and sentences in various languages.
• Applications include translation, spam filtering, customer service interactions, and digital assistants.

Computer Vision Systems

• Systems that create digital maps of images for identification in large datasets
• Patterns of pixels are unique to each image.

Robotics

• Machines designed to substitute for human work in various domains, such as factories, offices and homes.

Intelligent Agents

• Work without direct human intervention to perform repetitive tasks.
• Use built-in or learned knowledge bases.
• Often self-adjust, as demonstrated by digital assistants.
• Applications include modeling behavior of consumers, stock markets, and supply chains, used in predicting epidemic spread, or as part of sophisticated software agents.

Enterprise Content Management (ECM)

• Support capturing, storing, retrieving, distributing, and preserving knowledge in documentation.
• Bringing information from external sources, including news feeds, research and tools, like wikis and blogs.

Learning Management Systems (LMS)

• Tools for managing, delivering, tracking and assessing employee learning/training.
• Support various learning modes, such as CD-ROMs, web-based courses, and online forums.
• Automate course selection and delivery of learning content.

Knowledge Workers and Work Systems

• Workers who create knowledge.
• Three primary roles include keeping organizations current with knowledge, acting as internal consultants in their area of expertise, and promoting change.
• Knowledge work systems support workers to create new knowledge which can be integrated into the firm's business processes

Requirements of Knowledge Work Systems

• Sufficient computing power for graphics and calculations.
• Powerful graphics and analytical tools.
• Efficient communications and document management.
• Access to external databases
• User-friendly interface. Systems optimized for their respective tasks.

This quiz focuses on the essential concepts of managing knowledge and artificial intelligence in business as outlined in Chapter 11 of 'Managing the Digital Firm'. It discusses the significance of knowledge management systems and their role in creating competitive advantages through intangible assets. Explore how organizations can leverage knowledge-based core competencies for greater returns on investment.