Artificial Intelligence as a Competitive Advantage in the Manufacturing Area

Abstract

Since the beginning of the industrial revolution, manufacturing has gone through different stages: the 1st technological islands, 2nd the mass production, 3rd the lean manufacturing and the 4th IIoT (for the year 2025); we must keep in mind the leadership in the production of goods today what the Eastern countries have (and are using stage 3), so that the current guidelines have the necessary meaning, which establishes a new way of producing more as the potential of technologies that are in the process of maturation such as: Artificial Intelligence, Big Data, 3D Printing and Robotics; find original solutions to the problems of productivity, customization, just in time and services.Artificial Intelligence is taking a leading role in solving manufacturing problems, with the purpose of eliminating all those areas that are blindly worked, and that therefore it is not possible to improve by suffering from data to: analyze them, obtain information, establish controls and improve. The above is achieved by establishing disruptive technologies that take control in real time.

Digitization and Lean Customer Experience Management: success factors and conditions, pitfalls and failures

“…Therefore, this reference is not included in the findings. Another paper mentions "Lean Manufacturing was born from the idea of controlling production through the computer" [39], which is highly controversial to the Toyota Production System (TPS), where Kanban was introduced in 1953 [40], a time when microprocessors and computers, as they are known now, were used in manufacturing later in 1970s. So, Kanban was used before controlling production through the computer.…”

A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems

“…The advent of Artificial Intelligence (AI) has ushered in a transformative era for manufacturing systems, catalyzing advancements in operational efficiency, quality assurance, and predictive maintenance. Machine Learning (ML) algorithms, along with conventional statistical models, have become instrumental in deciphering complex patterns within voluminous datasets, thereby facilitating more informed decision-making processes in manufacturing environments [1]. For instance, predictive maintenance, powered by ML algorithms, has enabled the proactive identification of potential equipment failures, significantly reducing downtime and associated costs [2].…”