Citation: Su, X.; Lu, J.; Chen, C.; Yu,
J.; Ji, W. Dynamic Bottleneck
Identification of Manufacturing
Resources in Complex
Manufacturing System. Appl. Sci.
2022, 12, 4195. https://doi.org/
10.3390/app12094195
Academic Editors: Arkadiusz Gola,
Izabela Nielsen and Patrik Grznár
Received: 8 March 2022
Accepted: 18 April 2022
Published: 21 April 2022
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Article
Dynamic Bottleneck Identification of Manufacturing Resources
in Complex Manufacturing System
Xuan Su
1
, Jingyu Lu
1
, Chen Chen
1
, Junjie Yu
1
and Weixi Ji
1,2,
*
1
School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; susannsu@outlook.com (X.S.);
7200832004@stu.jiangnan.edu.cn (J.L.); 7190832003@stu.jiangnan.edu.cn (C.C.); yjj112395@outlook.com (J.Y.)
2
Key Laboratory of Advanced Manufacturing Equipment Technology, School of Mechanical Engineering,
Jiangnan University, Wuxi 214122, China
* Correspondence: weixiji_jiangnan@outlook.com
Abstract:
Bottleneck identification is of great interest in discrete manufacturing fields, as they limit
the system’s throughput. However, the bottlenecks are difficult to accurately identify due to the
instability and complexity of discrete manufacturing systems. This paper proposes a dynamic
bottleneck identification method (DBI-BS) that is based on effective buffers and fine-grained machine
states to identify bottlenecks accurately. First, the complex manufacturing system (CMS) with strong
coupling between elements is decoupled into several independent parts under the guidance of the
effective buffer theory. Then, the machine activity duration method is improved through further
fine-grained division, and the machine states are described by the timing flow model. The method to
quantify the degree of bottleneck that restricts the system throughput (TH) is proposed on the basis
of the turning point theory, and the one-to-one mapping relationship between the simulated and
authentic complex manufacturing systems is also studied. Simulation results show that the DBI-BS
can effectively identify dynamic bottlenecks in complex manufacturing processes, and the decoupling
of complex systems can effectively improve the accuracy of dynamic bottleneck identification.
Keywords:
complex manufacturing system; effective buffers; fine-grained states; dynamic bottlenecks;
Industry 4.0; discrete simulation
1. Introduction
The fusion of multiple technologies (manufacturing IoT [
1
], digital twins [
2
], big
data [
3
], neural networks [
4
], etc.) has brought great opportunities and challenges to the
transformation of manufacturing [
5
]. The new industrial revolution, commonly known
as Industry 4.0, originated in Germany has arrived [
6
]. Increasing productivity is one of
the explicit goals set forth by Industry 4.0 originated in Germany [
7
]. In addition, the
deteriorating new crown epidemic has dramatically damaged the production capacity
of the global manufacturing industry, so there is an urgent need to increase the output
of the current manufacturing system. Higher productivity is ranked as the number one
technology investment priority for manufacturing companies for the next decade [7].
The change in manufacturing mode and the need to improve manufacturing capa-
bilities have significantly increased the complexity of manufacturing, which has brought
considerable challenges to the production management of the workshop [
8
]. In this situa-
tion, the CMS has been widely used in manufacturing enterprises [
7
]. CMS allows a rapid
response to fluctuations inside and outside the workshop and comprehensive utilization
of existing manufacturing resources to increase system productivity. A typical CMS is a
hybrid process workshop (HFS) consisting of several serial stages with parallel machines.
The job must be processed at every stage on one machine.
According to the theory of constraints (TOC) [9], there is a stage that limits the entire
production capacity or significantly limits the throughput of the workshop, which is called
the bottleneck stage [
3
]. The bottleneck is defined as the equipment that has the most
Appl. Sci. 2022, 12, 4195. https://doi.org/10.3390/app12094195 https://www.mdpi.com/journal/applsci
