Industrial and Systems Engineering Research

- Overview

Industrial and systems engineering (ISE) is a field that applies engineering, science, and mathematics to design, improve, and operate systems that deliver products and services. 

ISEs use analytics and critical thinking to optimize systems, processes, and organizations. They work to improve quality and productivity, and eliminate waste. ISEs work in many sectors, including: Energy, Finance, Healthcare, Logistics, Manufacturing
Production, Telecommunication, Transportation, etc. 

ISE is called the "Business School of Engineering". ISE is people-centric, merging technical skills, scientific knowledge, and strategic thinking. ISEs are trained to understand the connections within complex systems, and to make impactful decisions. 

Please refer to the following for more information:

- Industrial Engineering

Industrial engineering involves solving problems arising in systems management through the application of engineering science, product and process design, job analysis, human factors principles, and operations research principles.

Industrial engineering is a branch of engineering management concerned with how to make or do things better, crossing a range of disciplines associated with manufacturing industrial or consumer products. This may involve increasing efficiency, reducing production costs, improving quality control, ensuring the health and safety of employees, protecting the environment or complying with government regulations. 

Industrial Engineers apply scientific, mathematical, and engineering methods to complex systems integration and operations. Because these systems are so large and complex, Industrial engineers require knowledge and skills across a broad range of disciplines, the ability to work well with people, and a broad systems perspective. 

Industrial engineers use their knowledge and skills to improve system processes through the use of statistical analysis, interpersonal communication, design, planning, quality control, operations management, computer simulation, and problem solving.  

- Systems Engineering

Systems engineering is defined as a methodical, multi-disciplinary approach for the design, realization, technical management, operations, and retirement of a system. 

A “system” is the combination of elements that function together to produce the capability required to meet a need. The elements include all hardware, software, equipment, facilities, personnel, processes, and procedures needed for this purpose; that is, all things required to produce system-level results.

The results include system-level qualities, properties, characteristics, functions, behavior, and performance. The value added by the system as a whole, beyond that contributed independently by the parts, is primarily created by the relationship among the parts; that is, how they are interconnected. 

It is a way of looking at the “big picture” when making technical decisions. It is a way of achieving stakeholder functional, physical, and operational performance requirements in the intended use environment over the planned life of the system within cost, schedule, and other constraints. It is a methodology that supports the containment of the life cycle cost of a system. In other words, systems engineering is a logical way of thinking.

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- Industrial Engineering vs. Systems Engineering

Industrial and systems engineering involves the design, improvement, and installation of integrated systems of people, materials, information, equipment, and energy. 

Systems engineers design and optimize complex systems. They work with many other technical professionals, including software engineers, hardware engineers, and programmers. 

Industrial engineers manage and improve manufacturing processes and service operations. Industrial engineers are primarily concerned with finding ways to better utilize machines, employees, and other assets that impact operations.

 - Applications of AI in Operations Management

Operations research (OR) and machine learning (ML) often work together in a "predict-then-optimize" paradigm. OR is an analytical approach or method that can help in solving problems and making decisions.

Here are some applications of AI in operations management:

• Automated data analysis: AI-powered tools can automate the process of analyzing large volumes of data, providing real-time insights.
• Data collection and analysis: AI can enable real-time data collection and analysis, providing businesses with up-to-date information about consumer preferences and market trends.
• Analysis of historical data: AI can analyze historical data and identify trends and patterns to make accurate predictions about future outcomes.
• Decision-making: OR and optimization methods play an important role in tackling complicated problems in a wide range of fields.
• Decision support systems: Operational research can be applied in the development of decision support systems (DSS) for logistics management.
• Resource allocation: Generative AI can enhance decision-making processes, risk assessment, and performance improvement.