Data-driven dynamic bottleneck detection in complex manufacturing systems

Lai, Xingjian; Shui, Huanyi; Ding, Daoxia; Ni, Jun

doi:10.1016/j.jmsy.2021.07.016

Cited by 33 publications

(11 citation statements)

References 42 publications

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“…Identifying a bottleneck is the first and most crucial step in improving business productivity (Urban & Rogowska, 2020). Also, it is important to mention that the identification process requires a vast number of real-time production data and integration of factory physics knowledge (Lai et al, 2021). Firms are following various methods to identify bottlenecks which cause a significant impact on production efficiency.…”

Section: Bottleneck Identification and Eliminationmentioning

confidence: 99%

“…Bottleneck machines and improper process plan selection are some of the main factors limiting system performance (Li et al, 2008). Also, timely identification of bottlenecks enables firms to take solid decisions to effectively manage firm resources considering throughput improvement (Lai et al, 2021). As a result, greater attention is required to reach the maximum tire target the planning department sets.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, greater attention is required to reach the maximum tire target the planning department sets. According to Lai, Shui, Ding, and Ni (2021), the identification of throughput bottlenecks in manufacturing firms is not a trivial task. As a result, it is vital to analyse the whole production process to recognize the presence of bottlenecks to minimize their implications in the tire manufacturing process when targeting productivity improvements.…”

Section: Introductionmentioning

confidence: 99%

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Bottleneck Identification through Simulation Modelling: A Case of Solid Tire Manufacturing Sector

Edirisinghe,

Karunarathne

2023

Management Dynamics in the Knowledge Economy

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Businesses are constantly making productivity improvements to survive in the highly competitive marketplace. Bottlenecks have been identified as one of the main factors limiting the system performance of manufacturing firms. Thus, identifying bottlenecks in the production process is extremely important to increase productivity. Considering its importance, this case study was designed to identify causes for not meeting the tire target and determine the implications of bottlenecks in the tire manufacturing process. For this purpose, simulation analysis was carried out for the solid resilience tire-building process. Through the investigation, the cushion layer-building process was identified as the bottleneck. To validate the identified limitation, Line balancing and Pareto analysis were conducted. Analysis results confirmed the presence of a bottleneck in the cushion layer-building process. Further, to identify the root causes for not reaching the maximum tire target, Cause-and-Effect analysis and 5WHY analysis were adopted. The study revealed that inadequately maintained outdated machines and frequent power failures are the leading causes of not meeting the maximum production. By answering these issues, the target production can be increased, and the results showed the opportunity to increase the efficiency of the manufacturing process by more than 95%.

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Section: Bottleneck Identification and Eliminationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bottleneck Identification through Simulation Modelling: A Case of Solid Tire Manufacturing Sector

Edirisinghe,

Karunarathne

2023

Management Dynamics in the Knowledge Economy

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“…Several real-time case studies were performed on serial production lines by Subramaniyan et al [15]. Lai et al [16] extended the turning point method to complex systems where complex systems converted into serial lines using pseudo station replacing with the main line and detecting bottleneck using turning point method. The method has been applied to a complex industrial case study.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The digital twin is developed to experiment with an actual system using a mathematical model which imitates the real system by changing inputs to the system to see how it affects output performance. System performance is measured in terms of the utilization of the system to achieve desired goal [16]. Utilization of activity is defined as u = r/c, where r is input rate to activity and c is capacity of activity [17].…”

Section: Factory Physics-based Analyticsmentioning

confidence: 99%

Bottleneck Detection Through Data Integration, Process Mining and Factory Physics-Based Analytics

Kumbhar

Bandaru

2022

Advances in Transdisciplinary Engineering

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Production systems are evolving rapidly, thanks to key Industry 4.0 technologies such as production simulation, digital twins, internet-of-things, artificial intelligence, and big data analytics. The combination of these technologies can be used to meet the long-term enterprise goals of profitability, sustainability, and stability by increasing the throughput and reducing production costs. Owing to digitization, manufacturing companies can now explore operational level data to track the performance of systems making processes more transparent and efficient. This untapped granular data can be leveraged to better understand the system and identify constraining activities or resources that determine the system’s throughput. In this paper, we propose a data-driven methodology that exploits the technique of data integration, process mining, and analytics based on factory physics to identify constrained resources, also known as bottlenecks. To test the proposed methodology, a case study was performed on an industrial scenario were a discrete event simulation model is built and validated to run future what-if analyses and optimization scenarios. The proposed methodology is easy to implement and can be generalized to any other organization that captures event data.

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