Data-driven algorithm for throughput bottleneck analysis of production systems

Abstract

The digital transformation of manufacturing industries is expected to yield increased productivity. Companies collect large volumes of real-time machine data and are seeking new ways to use it in furthering data-driven decision making. A challenge for these companies is identifying throughput bottlenecks using the realtime machine data they collect. This paper proposes a data-driven algorithm to better identify bottleneck groups and provide diagnostic insights. The algorithm is based on the active period theory of throughput bottleneck analysis. It integrates available manufacturing execution systems (MES) data from the machines and tests the statistical significance of any bottlenecks detected. The algorithm can be automated to allow data-driven decision making on the shop floor, thus improving throughput. Real-world MES datasets were used to develop and test the algorithm, producing research outcomes useful to manufacturing industries. This research pushes standards in throughput bottleneck analysis, using an interdisciplinary approach based on production and data sciences.

Adaptive Stream-based Shifting Bottleneck Detection in IoT-based Computing Architectures

“…Compared to [20], besides that the analysis in the latter is taking place after all the data is gathered, another significant difference is due to the data structure over which data is processed; in [20], a matrix with m rows and N columns is maintained, N being the number of measurement intervals, typically large, since the measurement intervals are commonly only a few seconds long; the matrix is traversed twice to carry out the calculations, resulting to O(mN ) operations.…”

“…Machines in the production system can thus form throughput bottlenecks [16]. Improvements such as cycle time reductions and dynamic buffering are prioritizing the bottleneck machines for activities to improve the throughput [7], [15], [20].…”

Data-driven machine criticality assessment – maintenance decision support for increased productivity

“…Even though Roser, Nakano, and Tanaka (2001) developed and tested this method on a discrete event simulation environment, this method needs to be adapted to the real-time data which is collected from the shop floor to detect the bottlenecks. Subramaniyan et al (2018) proposed a manufacturing execution system (MES) based data-driven algorithm which converts the real-time data of the machines into active states and statistically detects the group of bottlenecks. More information on the details of the algorithm are available in Subramaniyan et al (2018).…”

“…Subramaniyan et al (2018) proposed a manufacturing execution system (MES) based data-driven algorithm which converts the real-time data of the machines into active states and statistically detects the group of bottlenecks. More information on the details of the algorithm are available in Subramaniyan et al (2018). Furthermore, the algorithm can also give diagnostic insights into the bottlenecks in terms of different components of active states.…”

Applications of Big Data analytics and Related Technologies in Maintenance—Literature-Based Research

“…A modern manufacturing company's processes generate vast amounts of data from its interconnected information systems, machines and equipment [8]. Moreover, the historical data stored in databases and real-time sensor data from production processes can be analysed and used as a source of information and knowledge.…”