۱- Introduction

۲- Literature review

۳- Methodology

۴- Proposed bottleneck prediction algorithm based on active period method

۵- Proposed measures for algorithm performance evaluation

۶- Industrial test case results

۷- Discussion

۸- Conclusion

References

Abstract

Smart manufacturing is reshaping the manufacturing industry by boosting the integration of information and communication technologies and manufacturing process. As a result, manufacturing companies generate large volumes of machine data which can be potentially used to make data-driven operational decisions using informative computerized algorithms. In the manufacturing domain, it is well-known that the productivity of a production line is constrained by throughput bottlenecks. The operational dynamics of the production system causes the bottlenecks to shift among the production resources between the production runs. Therefore, prediction of the throughput bottlenecks of future production runs allows the production and maintenance engineers to proactively plan for resources to effectively manage the bottlenecks and achieve higher throughput. This paper proposes an active period based data-driven algorithm to predict throughput bottlenecks in the production system for the future production run from the large sets of machine data. To facilitate the prediction, we employ an auto-regressive integrated moving average (ARIMA) method to predict the active periods of the machine. The novelty of the work is the integration of ARIMA methodology with the data-driven active period technique to develop a bottleneck prediction algorithm. The proposed prediction algorithm is tested on real-world production data from an automotive production line. The bottleneck prediction algorithm is evaluated by treating it as a binary classifier problem and adapted the appropriate evaluation metrics. Furthermore, an attempt is made to determine the amount of past data needed for better forecasting the active periods.

Introduction

Digital Manufacturing and Industrial Internet of Things (IIoT) are new emerging technologies to increase the productivity in manufacturing (Lee, Lapira, Bagheri, & Kao, 2013). Manufacturing companies collect shop floor data in digital format using Manufacturing Execution Systems (MES), sensor technologies etc. (Hedman, Subramaniyan, & Almström, 2016). For example, one of the automotive manufacturing company in Sweden collects 100 data points of machine data per hour by MES (Subramaniyan, 2015). This means that, on 8-hour operating shift, 800 data points are collected per machine. This voluminous data at a high velocity when scaled up to a production system level or a factory level can be referred to as big data (Lee et al., 2013). With the exponential growth in the data acquired from the machines, new opportunities emerge to leverage data science to enhance the state of manufacturing and enable more data-driven decision making (Shao, Shin, & Jain, 2015). To enable such data-driven decision making, companies need high informative analytical algorithms to turn high volumes of fast-moving data into meaningful insights (Lavalle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011)(Bokrantz, Skoogh, Berlin, & Stahre, 2017). This necessitates research into data analytics which can enable efficient and effective extraction of information from the raw data to derive new knowledge and insights, which can further be applied to introducing intelligence into the control of production processes and can also improve the system-level operation of manufacturing enterprises (Wuest et al., 2016). In the manufacturing domain, throughput is an important indicator used to evaluate the production system performance. The throughput of a production line depends on the individual throughput of the machine (Chang, Ni, Bandyopadhyay, Biller, & Xiao, 2007). It is often constrained by one or more machines in the production system, which are usually called bottlenecks (Goldrat & Cox, 1990).