Jonathan S. Greipel, Regina M. Frank, Meike Huber, Ansgar Steland and Robert H. Schmitt
To ensure product quality within a manufacturing process, inspection processes are indispensable. One task of inspection planning is the selection of inspection characteristics…
Abstract
Purpose
To ensure product quality within a manufacturing process, inspection processes are indispensable. One task of inspection planning is the selection of inspection characteristics. For optimization of costs and benefits, key characteristics can be defined by which the product quality can be checked with sufficient accuracy. The manual selection of key characteristics requires substantial planning effort and becomes uneconomic if many product variants prevail. This paper, therefore, aims to show a method for the efficient determination of key characteristics.
Design/methodology/approach
The authors present a novel Algorithm for the Selection of Key Characteristics (ASKC) based on an auto-encoder and a risk analysis. Given historical measurement data and tolerances, the algorithm clusters characteristics with redundant information and selects key characteristics based on a risk assessment. The authors compare ASKC with the algorithm Principal Feature Analysis (PFA) using artificial and historical measurement data.
Findings
The authors find that ASKC delivers superior results than PFA. Findings show that the algorithms enable the cost-efficient selection of key characteristics while maintaining the informative value of the inspection concerning the quality.
Originality/value
This paper fills an identified gap for simplified inspection planning with the method for the efficient selection of key features via ASKC.
Details
Keywords
Meike Huber, Dhruv Agarwal and Robert H. Schmitt
The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid…
Abstract
Purpose
The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.
Design/methodology/approach
This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migration
Findings
The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.
Originality/value
The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.
Details
Keywords
Tobias Mueller, Meike Huber and Robert Schmitt
Measurement uncertainty is present in all measurement processes in the field of production engineering. However, this uncertainty should be minimized to avoid erroneous decisions…
Abstract
Purpose
Measurement uncertainty is present in all measurement processes in the field of production engineering. However, this uncertainty should be minimized to avoid erroneous decisions. Present methods to determine the measurement uncertainty are either only applicable to certain processes and do not lead to valid results in general or require a high effort in their application. To optimize the costs and benefits of the measurement uncertainty determination, a method has to be developed which is valid in general and easy to apply. The paper aims to discuss these issues.
Design/methodology/approach
This paper presents a new technique for determining the measurement uncertainty of complex measurement processes. The approximation capability of artificial neural networks with one hidden layer is proven for continuous functions and represents the basis for a method for determining a measurement model for continuous measurement values.
Findings
As this method does not require any previous knowledge or expertise, it is easy to apply to any measurement process with a continuous output. Using the model equation for the measurement values obtained by the neural network, the measurement uncertainty can be derived using common methods, like the Guide to the expression of uncertainty in measurement. Moreover, a method for evaluating the model performance is presented. By comparing measured values with the output of the neural network, a range in which the model is valid can be established. Combining the evaluation process with the modelling itself, the model can be improved with no further effort.
Originality/value
The developed method simplifies the design of neural networks in general and the modelling for the determination of measurement uncertainty in particular.
Details
Keywords
The purpose of this paper is to investigate the differential influence of buyer and supplier relationship-specific investments (RSI) on a buyer’s relationship governance decisions.
Abstract
Purpose
The purpose of this paper is to investigate the differential influence of buyer and supplier relationship-specific investments (RSI) on a buyer’s relationship governance decisions.
Design/methodology/approach
Based on transaction economics and social exchange theories (SET), the authors develop a framework to understand how and when buyer and supplier RSI influence governance decisions. This model was tested using a survey of 301 Information Technology (IT) procurement professionals across a multitude of industries.
Findings
This research shows that buyer and supplier RSI impact governance decisions differently. Supplier investments are positively associated with relationship formalization when goals between both parties are shared. Buyer investments are more strongly related to formalization in technologically uncertain environments.
Originality/value
This research adds to the literature by integrating arguments from both transaction cost and SET to hypothesize why buyer and supplier investments have a differential impact on relationship governance decisions. In line with these arguments, it ultimately demonstrates conditions that render such investments more/less influential.
Details
Keywords
In recent decades, it has become clear that the major economic, political, and social problems in the world require contemporary development research to examine intersections of…
Abstract
In recent decades, it has become clear that the major economic, political, and social problems in the world require contemporary development research to examine intersections of race and class in the global economy. Theorists in the Black Radical Tradition (BRT) were the first to develop and advance a powerful research agenda that integrated race–class analyses of capitalist development. However, over time, progressive waves of research streams in development studies have successively stripped these concepts from their analyses. Post-1950s, class analyses of development overlapped with some important features of the BRT, but removed race. Post-1990s, ethnicity-based analyses of development excised both race and class. In this chapter, I discuss what we learn about capitalist development using the integrated race–class analyses of the BRT, and how jettisoning these concepts weakens our understanding of the political economy of development. To remedy our current knowledge gaps, I call for applying insights of the BRT to our analyses of the development trajectories of nations.