Abstract
This paper presents a case study of continuous productivity improvement of an automotive parts production line using Internet of Everything (IoE) data for fault monitoring. Continuous productivity improvement denotes an iterative process of analyzing and updating the production line configuration for productivity improvement based on measured data. Analysis for continuous improvement of a production system requires a set of data (machine uptime, downtime, cycle-time) that are not typically monitored by a conventional fault monitoring system. Although productivity improvement is a critical aspect for a manufacturing site, not many production systems are equipped with a dedicated data recording system towards continuous improvement. In this paper, we study the problem of how to derive the dataset required for continuous improvement from the measurement by a conventional fault monitoring system. In particular, we provide a case study of an automotive parts production line. Based on the data measured by the existing fault monitoring system, we model the production system and derive the dataset required for continuous improvement. Our approach provides the expected amount of improvement to operation managers in a numerical manner to help them make a decision on whether they should modify the line configuration or not.
Highlights
Collecting operation data from production systems in the factory floor has been a critical task to maintain the system operation and productivity
Ref. [5] proposes to use an IoT-based architecture that collects information regarding key performance indicators to improve productivity, refs. [6,7,8] propose to build a digital twin for the production systems for multi-purpose optimization, ref. [9] suggests a smart factory framework, which has a cloud-assisted and self-organized structure to produce customized products in a real-time manner, and [10] suggests an IoT-based supply chain management system that tracks locations of goods to help managers check the status of a supply chain and its dependencies
We propose a concept of using existing fault monitoring data for the purpose of continuous improvement of production systems; We present a case study using an automotive parts production line; We develop a mathematical model of the line that predicts key performance characteristics, such as throughput, lead time, bottleneck machine, and bottleneck buffer; Based on the model, we develop a continuous improvement scenario that leads to up to 10% of productivity improvement
Summary
Collecting operation data from production systems in the factory floor has been a critical task to maintain the system operation and productivity. A method of using the data from the existing fault monitoring IoE systems for the purpose of the continuous improvement would save time and resource for the manufacturing facilities: new installation is not necessary which may avoid stopping the production for the installation. We propose a concept of using existing fault monitoring data for the purpose of continuous improvement of production systems; We present a case study using an automotive parts production line; We develop a mathematical model of the line that predicts key performance characteristics, such as throughput, lead time, bottleneck machine, and bottleneck buffer; Based on the model, we develop a continuous improvement scenario that leads to up to 10% of productivity improvement.
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