Using Prescriptive Analytics to Support the Continuous Improvement Process

Schuh, Günther; Prote, Jan-Philipp; Busam, Thomas; Lorenz, Rafael; Netland, Torbjørn H.

doi:10.1007/978-3-030-30000-5_6

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…Besides wasting resources before and after such changes, such results are detrimental to the further motivation of a continuous improvement culture. Third, companies can use quantified improvement data to prescribe the value of new improvement suggestions [6]. Finally, convincing skeptical senior managers to invest money and resources in an improvement project often requires some kind of estimate of its cash flow-even if it is purely based on speculation.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Value of Process Improvement Suggestions

Netland

Mizuyama

Lorenz

2020

IFIP Advances in Information and Communication Technology

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Firms struggle to estimate the expected benefits of improvement suggestions. As a result, pointless or even damaging suggestions are sometimes implemented at the expense of potentially valuable improvement suggestions. This paper reviews, discusses, and advises on the use of available value assessment methods. Thereby, the paper contributes to the production improvement literature and practices with an overview and classification of common value assessment methods.

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Section: Introductionmentioning

confidence: 99%

Assessing the Value of Process Improvement Suggestions

Netland

Mizuyama

Lorenz

2020

IFIP Advances in Information and Communication Technology

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“…Among other things, these papers are motivated by the fact that production planning becomes more and more difficult for companies due to mass customization. In order to improve the quality of production planning, Schuh et al (2019) show that enriching production data with domain knowledge leads to an improvement in the calculation of the transition time with regression trees.…”

Section: Expert-knowledge-based Methods For Sparse-data Learning In M...mentioning

confidence: 99%

“…Indeed, He et al (2019), Lokrantz et al (2018), Nagarajan et al (2019), andZhang et al (2020) are concerned with expert knowledge in the form of cause-effect relationships and they integrate this kind of knowledge into the model's architecture. Also, Ning et al (2019), Lu et al (2017), andSchuh et al (2019) are concerned with expert knowledge in the form of explicit physical equation relationships and they integrate these equations into the models, for instance, in the form of new features.…”

Section: Expert-knowledge-based Methods For Sparse-data Learning In M...mentioning

confidence: 99%

Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing

et al. 2022

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Increasing digitalization enables the use of machine learning (ML) methods for analyzing and optimizing manufacturing processes. A main application of ML is the construction of quality prediction models, which can be used, among other things, for documentation purposes, as assistance systems for process operators, or for adaptive process control. The quality of such ML models typically strongly depends on the amount and the quality of data used for training. In manufacturing, the size of available datasets before start of production (SOP) is often limited. In contrast to data, expert knowledge commonly is available in manufacturing. Therefore, this study introduces a general methodology for building quality prediction models with ML methods on small datasets by integrating shape expert knowledge, that is, prior knowledge about the shape of the input–output relationship to be learned. The proposed methodology is applied to a brushing process with 125 data points for predicting the surface roughness as a function of five process variables. As opposed to conventional ML methods for small datasets, the proposed methodology produces prediction models that strictly comply with all the expert knowledge specified by the involved process specialists. In particular, the direct involvement of process experts in the training of the models leads to a very clear interpretation and, by extension, to a high acceptance of the models. While working out the shape knowledge requires some iterations in general, another clear merit of the proposed methodology is that, in contrast to most conventional ML, it involves no time-consuming and often heuristic hyperparameter tuning or model selection step.

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“…Among other things, these papers are motivated by the fact that production planning becomes more and more difficult for companies due to mass customization. In order to improve the quality of production planning, [33] show that enriching production data with domain knowledge leads to an improvement in the calculation of the transition time with regression trees.…”

Section: Expert-knowledge-based Methods For Sparse-data Learning In M...mentioning

confidence: 99%