This blog post examines the role industrial engineering plays within the complex modern industrial structure and why it is gaining attention as an important field of study.
Examining the modern industrial structure reveals that diverse fields are intertwined to form a vast system. For example, large Korean corporations like Samsung and LG not only operate in various business sectors but also expand their operations globally. This necessitates demand for complex elements that connect these diverse business areas and enable smooth communication within the organization’s hierarchical structure. Furthermore, as business structures and manufactured products become increasingly diverse and complex, companies increasingly collaborate externally for tasks they cannot handle internally. This has led to a surge in efforts to efficiently manage complex organizational structures, logistics, and distribution. Currently, companies are exploring various methodologies to operate these efficiently.
Industrial engineering is a discipline created to meet these demands. It explores methods for coordinating the various fields that constitute industrial systems. Unlike other engineering majors that focus on the research and development of specific technologies, industrial engineering aims to innovate and systematize business processes by combining scientific principles with management strategies. It is also a discipline that deals with scientific theories and methodologies for deriving rational methods and establishing comprehensive management strategies. The fields covered in industrial engineering can be broadly divided into five: management and economic systems, systems analysis and optimization, operations management, product and service design, and information systems. Each of these fields contains several detailed research areas.
Among these, systems analysis and optimization is a field that particularly well describes industrial engineering. As industrialization progressed and various industries developed, corporate systems became increasingly complex and large-scale. Consequently, inefficient processing procedures emerged in various areas, and addressing these inefficiencies is the task of system analysis and optimization. The foundation of industrial engineering originated from Taylor’s work management theory. While this forms the basis for fields like ergonomics and human interface systems, the original purpose of work management is to enhance efficiency, improve productivity, and design optimal systems. Designing an optimal system means ensuring workers, processes, and organizational operations achieve maximum efficiency. This can be accomplished through mathematical planning or optimization tasks.
Optimization is a discipline for solving real-world problems. During World War II, it was a field of study developed by mathematicians and economists within the military to solve problems in planning, transportation, and other areas. This discipline provides methodologies to maximize productivity across various industrial sectors. For example, optimization models and solutions are applied to KTX vehicle routing, determining economical cutting patterns for paper companies, effective resource allocation in wireless communication networks, constructing investment portfolios that minimize risk, and classifying genome sequencing. Current research trends in optimization focus on pursuing optimality by applying optimization models and solutions across the entire spectrum of mobile communications—design, operation, quality assurance, and pricing policies—which holds the greatest economic potential and scale. As these examples illustrate, optimization is now expanding its application scope to encompass the entire spectrum of growth-driving industries, based on increasingly sophisticated and mathematical methodologies. Businesses utilizing optimization are also growing in potential scale within Korea. As the pool of researchers capable of utilizing this grows, demand for optimization will emerge in broader fields based on the utility of optimization solutions.
Optimization solves real-world problems using mathematical and scientific methods. Therefore, modeling—extracting each element of the problem and logically reconstructing it—is essential. At first glance, optimization can be seen as the study of the structure and solutions of mathematical models. Depending on the mathematical model, it can be divided into several subfields. Major subfields include nonlinear programming, network flow, large-scale problem solving, stochastic programming, integer programming, LCP, and practical polynomial solutions for linear programming. Most of these remain important research topics. For example, in logistics systems, research focuses on finding the optimal transportation route at minimal cost. Optimization is performed by treating factors such as cost, transport vehicles, and locations as variables, and optimal solutions are found using methods like linear programming and the simplex algorithm.
Recently, the field of optimization has seen multifaceted research into models with discrete structures. This is because modern systems increasingly exhibit discrete characteristics at the granular level. Optimizing discrete mathematical models mostly involves problems with NP-hard properties, making it difficult to develop solutions. NP-hard refers to problems where finding an exact solution requires examining all possible cases, with no other feasible method. Consequently, structural analysis and solution theories for related integer programming and combinatorial optimization problems have become major topics in contemporary optimization. Indeed, solutions for integer and combinatorial optimization continue to advance. Optimization evolves through direct engagement with real-world problems. Thanks to accelerated computing technology, problems of sizes unthinkable just 5-6 years ago can now be solved. Whereas mathematics was once a theoretical discipline studied at a desk, optimization mathematics has transformed into a discipline solving real-world problems. This practical nature of optimization is maximized when combined with computer theory. It is a well-known fact that the decisive early factor in optimization’s development was the advancement of computers. In particular, theories proposing solutions for problems with NP-hard properties are evolving into theories related to approximate solutions for optimization problems.
Thus, the field studied in industrial engineering requires knowledge of each element within complex systems to adapt to changing environments. Industrial engineering helps decision-making across the entire system by efficiently utilizing each component and enables effective service delivery when needed. Industrial engineering can be described as a discipline that allows one to see both the trees and the forest.
