Selection of the Most Suitable Statistical Process Control Approach for Short Production Runs: A Decision-Model

Marques, Pedro Alexandre; Cardeira, Carlos; Paranhos, Paula; Ribeiro, S.M.P.; Gouveia, Helena

doi:10.7763/ijiet.2015.v5.521

Cited by 16 publications

(10 citation statements)

References 53 publications

(54 reference statements)

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“…Like in any manufacturing paradigm, SPC plays an important part in FMS to ensure that manufacturing processes operate in their in-control state. However, it has been found that traditional SPC methods are not appropriate for situations of small lots or where a high variety of products exist [38]. Although flexible production systems may manufacture large volumes, the production in these types of environments is intermittent because the change to other product variants is easy.…”

Section: Related Workmentioning

confidence: 99%

“…First, the lack of available data to estimate reliable process parameters due to smaller production runs. This is a typical scenario in Just-intime (JIT) systems, where low levels of inventory are kept, and during start-up of a process or initiation of a new process, where there is an insufficient number of subgroups of measurements under different conditions available [38]. Second, there is an increasing risk of false acceptance in SPC because of measurement errors [10].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the development of new SPC methods to deal with the challenges of FMS (e.g. change-point methods, pattern recognition using Machine Learning), these normally solve some but not all the issues [15,33,38] and are often very difficult to implement by the non-expert. In addition, there is little literature related to the implementation of these new SPC methods in industry.…”

Section: Introductionmentioning

confidence: 99%

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Process Monitoring in a Flexible Manufacturing Cell through Data Analytics and a Visualisation Tool

Arellano

Nguyen

Hinton

et al. 2021

Preprint

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With the need of more responsive and resilient manufacturing processes for high value, customised products, Flexible Manufacturing Systems (FMS) remain a very relevant manufacturing approach. Due to their complexity, quality monitoring in these types of systems can be very difficult, particularly in those scenarios where the monitoring cannot be fully automated due to functional, safety and legal characteristics. In these scenarios, quality practitioners concentrate on monitoring the most critical processes and leaving out the inspection of those that are still meeting quality requirements but showing signs of future failure. In this paper we introduce a methodology and visualisation tool based on data analytics that allows the practitioner to anticipate out of control processes and take action. By identifying a reference model or best performing machine, and the occurring patterns in the quality data, the presented approach identifies the adjustable processes that are still in control, allowing the practitioner to decide if any changes in the machine's settings are needed (tool replacement, repositioning the axis, etc). An initial deployment of the tool at BMW Plant Hams Hall to monitor a focussed set of part types and features has shown a reduction in scrap of 97% throughout 2020 in relation to the monitored features compared to the previous year. This in the long run will reduce reaction time in following quality control procedure, reduce significant scrap costs and ultimately reduce the need for measurements and enable more output in terms of volume capacity.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Process Monitoring in a Flexible Manufacturing Cell through Data Analytics and a Visualisation Tool

Arellano

Nguyen

Hinton

et al. 2021

Preprint

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“…Another reason is that the required confidence intervals of the capability indices are dependent on the sample size and become too large to be informative. To overcome the problem of too small sample sizes different approaches exist [4]. Change point models [5], control charts with greater sensitivity [6], self-starting charts [7], pre-control-charts [8], Bayesian approaches [9], control charting process and parameters instead of product features [10] are exclusively for control charts.…”

Section: Introductionmentioning

confidence: 99%

Comparison of grouping algorithms to increase the sample size for statistical process control

2020

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In this study, we present and compare four grouping algorithms to combine samples from low volume production processes. This increases their sample sizes and enables the application of Statistical Process Control (SPC) to low volume production processes. To develop the grouping algorithms, we define different grouping criteria and a general grouping process. To identify which algorithm is optimal, we deduct following requirements on the algorithms from real production datasets: their ability to handle different amount of characteristics and sample sizes within each characteristic as well as being able to separate characteristics possessing distributions with different spreads and locations. To check the fulfillment of these requirements, we define two performance indices and conduct a full-factorial Design of Experiments. We achieve the performance indices for each algorithm by using simulations with artificial data incorporating the aforementioned requirements. One index rates the achieved group sizes and the other one the compactness within groups and the separation between groups. To validate the applicability of grouping algorithms within SPC, we apply real production data to the grouping algorithms and control charts. The result of this analysis shows that the grouping algorithm based on cluster analysis and splitting exceeds the other algorithms. In conclusion, the grouping algorithms enable the application of SPC to small sample sizes. This provides companies, which produce in low volumes, with new means of reducing scrap, generating process knowledge and increasing quality.

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“…This is not possible without using properly selected methods and tools specific to the organization. Analysis of literature shows general issues and ideas in SPC [8,20,21], practical examples of the use of selected SPC methods and tools in conditions of mass and batch production [1,2,10,18,19] or own SPC tools and models proposals [3,7,9,14]. Yet, it is difficult to find examples relating to the piece and small lot production, especially based on adapting conventional SPC tools.…”

Section: Introductionmentioning

confidence: 99%

Statistical process control in conditions of piece and small lot production

Kołosowski¹,

Duda²,

Tomasiak³

2016

DAAAM Proceedings

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The aim of the article is to present the essence of the adaptation of common SPC solutions appropriate for mass and batch production, to the conditions of piece and small lot production on the example of the selected production process. The article presents the possibility of using Shewhart control charts in improving piece and small lot production processes. The principles of data collection and transformation, to be useful to prepare charts, have been pointed out. Moreover, determination of capability indices for piece and small lot production has been shown. The proposed solution of using statistical process control tools in the analyzed company enables the board a fair assessment of the processes from the point of view of quality assurance, and thus reduce the cost of poor quality and well targeted improvement actions. Keywords

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Selection of the Most Suitable Statistical Process Control Approach for Short Production Runs: A Decision-Model

Cited by 16 publications

References 53 publications

Process Monitoring in a Flexible Manufacturing Cell through Data Analytics and a Visualisation Tool

Process Monitoring in a Flexible Manufacturing Cell through Data Analytics and a Visualisation Tool

Comparison of grouping algorithms to increase the sample size for statistical process control

Statistical process control in conditions of piece and small lot production

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