Artificial intelligence focused on three cognitive skills: learning, reasoning, and self-correction, is a system that works by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. It is the simulation of human intelligence processes by machines, especially computer systems, where large amounts of data are filtered, and predictions are made more quickly and accurately. Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on building algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range. Industrial and manufacturing engineering challenges can be solved using a variety of machine learning methods, such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Naive Bayes, K-Nearest Neighbors, K-Means Clustering, Random Forest, and others.

Computer-aided design (CAD) is the use of computer systems to aid in the creation, modification, analysis, or optimization of a design, whereas computer-aided manufacturing (CAM) is the use of a computer to assist in all operations of a manufacturing plant, including planning, management, transportation, and storage. The CAD and CAM modules are connected through computer-aided process planning (CAPP). It offers a wide range of research scopes in design improvement, smart manufacturing, optimization, development of new tools, hybrid manufacturing, product design, robotics analysis, simulation, etc.

Ergonomics applies the knowledge of human capabilities and limitations to the analysis, design, and operation of products, services, and systems. Properties of interest include anthropometric, physiological, and psychological factors that may impact human-technology interactions. The goals of ergonomics are to increase system performance, safety, and cost effectiveness.
Safety management is a comprehensive management system designed to manage safety elements in the workplace. It includes policies, objectives, plans, procedures, organization, responsibilities, and other measures. It is used in industries that manage significant safety risks, including aviation, petroleum, chemicals, electricity generation, and others.

Industrial automation and robotics refer to the use of control systems, such as computers or robots, and information technology to substitute humans in handling processes and machines. This field overlaps with electronics, computer science, artificial intelligence, mechatronics, nanotechnology, and bioengineering. Modern industrial automation and robotics use data acquisition, distributed control, supervisory control, programmable logistics controllers, and real-time data optimization and focuses on improving manufacturing quality and flexibility.

Operations management is an area of management concerned with designing, decision-making, and controlling the processes of production and redesigning business operations in the production of goods or services. It involves the responsibility of ensuring that business operations are efficient in terms of the acquisition of fewer resources and effectively meeting customer requirements. It handles various strategic issues, including project management methods, implementing the structure of information technology networks, management of inventory levels, quality control, materials handling, and maintenance policies. Operations management is concerned with formulating realistic models of these issues and solving such decision problems, which develops new theory and techniques and thus impacts a large variety of manufacturing and service industries.

Prognostics and health management (PHM) predicts the future health state of a system by analyzing the current condition of the system under its actual operating conditions using sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering, etc. PHM is regarded as an efficient and practical approach for improving the maintenance cycle, reducing the maintenance cost, and extending the overall lifetime through evidence-based scheduled maintenance strategies. There are three different approaches of prognostics available to assess the degradation or extent of deviation from the expected performance- model-based prognostics, data-driven prognostics, and hybrid approach prognostics. In model-based prognostics, a physical model of the system or degradation process of the system is available with mathematical description to estimate the output for some definite inputs. The data-driven prognostics approach deals with previously observed data by using pattern recognition and machine learning techniques to perform prognostification. The necessity of the hybrid approach prognostics arises when the degradation system becomes too complex to express it as a mathematical model, and sufficient observed data is not present and it is harder to recognize the pattern of the data. Several applications of prognostics in practical engineering systems include capacity degradation of batteries, wear in a revolute joint, fatigue crack growth in a panel, fatigue damage in gears and bearings, and more.

Supply chain management (SCM) is the centralized control of the flow of goods and services, including all processes that transform raw materials into finished goods. The area of research can be divided into a few broad sub-research areas: supply management, purchasing, procurement, operations, demand management and logistics, transportation & distribution management, and sustainability in supply chains. Research focuses on inventory rationalization, managing upstream suppliers to obtain the highest values, understanding customer demands, forecasting future needs and identifying trends, and looking into optimum solutions for minimizing the total cost of logistics, transportation and distribution operations.

Uncertainty and risk are two fundamental terms in any decision-making framework; uncertainty is defined as imperfect knowledge in which the probabilities of possible outcomes are unknown, whereas risk exists when these probabilities are known. Risk management is the process of making and carrying out decisions from alternatives that will minimize the adverse effects of risk by focusing on the necessary resources to be controlled. Identifying and analyzing risk and uncertainty is integral to risk management processes. This field offers the understanding of uncertainty propagation, assessing probabilistic and possibilistic modelling, determining realistic and achievable targets for best project outcomes, etc.