Flexibility management and decision making in cyber-physical systems utilizing digital lean principles with Brain-inspired computing pattern recognition in Industry 4.0

Industry 4.0 and its accompanying Cyber-Physical Manufacturing Systems in digitized have recently made new approaches to optimising production operations in manufacturing. The objective of this article is to evaluate how digital lean principles can support Industry 4.0 in pursuit of reducing non-value-added tasks from production processes, flexibility management and decision-making process with machine learning techniques. This research study comes under three levels, such as data level, information level, and knowledge level. Initially, the data from the CNC machine are collected via a Wireless Sensor Network (WSN). The collected data are stored in the cloud platform where the unwanted wastes of 7 Muda are removed by using the Value Stream Mapping (VSM 4.0) tool. It is critical in the big data world to have a systematic method for gathering, managing, and analysing data to gain valuable insights from it. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related types of machinery. Accordingly, Big Data Analytics empowers the lean principle technique of Total Productive Maintenance (TPM) is suggested for avoiding potential failures to predict maintenance by allowing KPIs to be calculated in real-time. This approach requires high-performance procedures and adaptable manufacturing systems in the current digitalized lean. The information is then processed in the knowledge layer, utilizing the algorithm, rule and lean knowledge bases. The flexibility of manufacturing firms is determined by the adaptability of their shop floor processes. To meet these requirements the article developed pull control techniques of capacity slack CONWIP (CSC) control in digital lean production systems to guide the CPS deployment to offer flexible production systems. Reliable software systems are hoped to facilitate data analysis and autonomous decision-making. Finally, in the decision-making process, the article proposed the Brain-Inspired Computing of Structural and Syntactic (BIC-SS) pattern recognition method. The performance analysis of these findings is simulated in MATLAB software. Simulation can be done with the identification of ideal Kanban parameters like cycle time, lead time, delivery frequency and lot size. Furthermore, lean manufacturing improves company quality and productivity by decreasing waste and production costs, as well as adapting well to the many innovative systems that encourage the culture of change and quality inside organizations.