A prognostic algorithm to prescribe improvement measures on throughput bottlenecks

Mukund Subramaniyan,Anders Skoogh,Azam Sheikh Muhammad,Jon Bokrantz,Ebru Turanoğlu Bekar

doi:10.1016/j.jmsy.2019.07.004

Mukund Subramaniyan, Anders Skoogh + Show 3 more

Open Access

https://doi.org/10.1016/j.jmsy.2019.07.004

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Abstract

Throughput bottleneck analysis is important in prioritising production and maintenance measures in a production system. Due to system dynamics, bottlenecks shift between different production resources and across production runs. Therefore, it is important to predict where the bottlenecks will shift to and understand the root causes of predicted bottlenecks. Previous research efforts on bottlenecks are limited to only predicting the shifting location of throughput bottlenecks; they do not give any insights into root causes. Therefore, the aim of this paper is to propose a data-driven prognostic algorithm (using the active-period bottleneck analysis theory) to forecast the durations of individual active states of bottleneck machines from machine event-log data from the manufacturing execution system (MES). Forecasting the duration of active states helps explain the root causes of bottlenecks and enables the prescription of specific measures for them. It thus forms a machine-states-based prescriptive approach to bottleneck management. Data from real-world production systems is used to demonstrate the effectiveness of the proposed algorithm. The practical implications of these results are that shop-floor production and maintenance teams can be forewarned, before a production run, about bottleneck locations, root causes (in terms of machine states) and any prescribed measures, thus forming a prescriptive approach. This approach will enhance the understanding of bottleneck behaviour in production systems and allow data-driven decision making to manage bottlenecks proactively.

Highlights

Manufacturing companies are constantly looking for ways to improve the productivity of production systems
In the predictive algorithms category, historical machine data is used to predict the location of bottlenecks in the production system
To test the proposed algorithm’s forecasting accuracy for each active state, the individual active states’ proportion as a percentage of active time (“producing”, “down” states) for all machines are predicted for every production run in the test dataset

Summary

Introduction

Manufacturing companies are constantly looking for ways to improve the productivity of production systems. With the development of digital technologies, many manufacturing companies have started collecting machine data in digital format [6] This data enables the use of data-driven algorithms to detect throughput bottlenecks [7]. In the predictive algorithms category ( called “prognostic methods”, as prognostics is the discipline of forecasting future performance [13]), historical machine data is used to predict the location of bottlenecks in the production system. Such prediction can be made using statistically-based [12,14] or machine-learning-based methods [15]. It can be made on a real-time basis, using buffer levels [16,17]

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