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Summary - Smart Industries -
- Instelling
- Universiteit Twente (UT)
summary of lecture slides of smart industries. 5 weeks of slides with a in-depht summary
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Smart Industry Course Summary - In-Depth
Week 1: Introduction to Smart Industry
Papers:
Mason, R. O., & Mitroff, I. I. (1973). A program for research on management information
systems. Management science, 19(5), 475-487.
Key Topics from Lecture Slides:
1. **What is Smart Industry?**
- Smart Industry integrates various information technologies to create a dynamic, data-
driven understanding of internal and external environments in businesses.
- It aims to enhance decision-making at multiple levels—by humans, machines, or a
combination of both—by enabling automation and smart systems.
- The main goal is to increase efficiency, reduce costs, improve responsiveness, and provide
real-time insights through the use of advanced technologies.
2. **Information Overload and Scarcity of Attention:**
- With the rise of the information age, the key challenge becomes filtering out relevant
information from the noise.
- Herbert Simon’s (1971) concept emphasizes that while information is abundant, attention
is scarce, making it critical to filter signals for effective decision-making.
3. **Decision-Making in Smart Industry:**
- The slides emphasize various decision-making methods, ranging from purely rational
approaches based on data to more intuitive methods, especially when dealing with complex
problems:
- **Rational Decision-Making**: Relies on logic and data, suitable for structured problems
with predictable outcomes.
- **Intuitive Decision-Making**: Involves relying on instincts or experiences when dealing
with more ambiguous, unstructured problems.
- **Hybrid Approaches**: The Kantian and Hegelian perspectives combine both logical and
intuitive methods to tackle complex, evolving problems.
4. **Structured vs. Unstructured Problems:**
- Structured problems: Well-defined, with clear goals and outcomes, suitable for logical,
data-driven methods.
- Unstructured problems: Often complex, dynamic, and involve unknown outcomes,
requiring more flexible, iterative approaches like systems thinking.
, - The course aims to address both types of problems by leveraging appropriate techniques
based on the problem structure.
5. **Inquiring Systems Framework:**
- The course introduces four inquiring systems, each designed to address different types of
problems:
- **Lockean (Empirical)**: Focuses on collecting sensory data and empirical evidence to
address well-structured problems. Often used in analytics and prediction models.
- **Leibnizian (Logical)**: Relies on logical, formal models for problems with clearly
defined causal relationships, commonly applied in simulations and expert systems.
- **Kantian (Mixed)**: Combines sensory experience with logical reasoning to address
semi-structured problems, using tools like digital twins and process modeling.
- **Hegelian (Dialectical)**: Tackles wicked, dynamic problems by synthesizing conflicting
viewpoints through techniques like text mining, clustering, and network analysis.
Week 2: Lockean Perspective - Data and Analytics
Papers:
Dwivedi, Y. K., et al. (2021). Artificial Intelligence: Multidisciplinary perspectives on
emerging challenges, opportunities, and agenda for research. International journal of
information management, 57, 101994.
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of
empirical research. Academy of Management Annals, 14(2), 627-660.
Key Topics from Lecture Slides:
1. **Lockean Perspective: Data and Empirical Knowledge**
- This perspective is based on empiricism, where knowledge is derived from sensory data
and experience. It is ideal for solving well-structured problems through data-driven
insights.
- The focus is on analyzing past data to derive insights, trends, and forecasts using empirical
techniques like statistics and machine learning.
2. **Types of Analytics:**
- **Descriptive Analytics**: Helps businesses understand historical data (e.g., sales trends,
customer behavior).
- **Predictive Analytics**: Uses historical data to forecast future trends (e.g., demand
forecasting, risk analysis).
- **Prescriptive Analytics**: Provides recommendations on actions to optimize decision-
making based on data (e.g., suggesting optimal pricing strategies).
3. **Machine Learning Models in Lockean Perspective:**
- Machine learning techniques are essential in predictive analytics and automation in Smart
Industry. Key methods include:
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