Real-Time Capacity Planning and Control in Smart Factory Environments

Abstract

In the 4.0 industry revolution, smart factories have emerged as advanced production systems which utilize real-time
information, data analysis, and automation to refine the manufacturing value chain. One of the main issues is efficient
capacity planning and control due to resource allocation and production scheduling deadlines that, if not optimally
met, can lead to delays or bottlenecks. In this paper, we propose a real-time control system for capacity planning and
management optimal for smart factory settings. With this approach, IIoT (Industrial Internet of Things) data sensors,
cloud computing, and predictive resource allocation analytics are employed to facilitate rapid responsiveness to
demand changes. A hybrid approach is used where a model with deterministic planning heuristic algorithms and
adaptive control strategies provides the system. Experimental results obtained from simulated data of smart factories
indicate that the proposed system exceeds the expected value in traditional factories in terms of throughput, machine
utilization, and responsiveness. Feedback and real-time decisions greatly boost operational efficiency, which were
core in the research outcomes. This intelligent manufacturing system is resilient and easily scalable, making it a
valuable addition to the collaborative ecosystem of contemporary production facilities.