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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.

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.

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.

This paper fills an identified gap for simplified inspection planning with the method for the efficient selection of key features via ASKC.

The purpose of this paper is to investigate the application of key performance indicators (KPIs) on shop floor level in German small- and medium-sized enterprises (SMEs). The paper focuses on the examination of perception differences between shop floor employees and managers with regard to collection, calculation and consolidation of KPIs as well as visualization and motivational aspects.

To examine the hypothesis on differing perceptions regarding KPIs, 27 qualitative interviews with shop floor employees and production managers within 6 SMEs from the German machinery and equipment industry were conducted on basis of a semi-structured guideline.

The findings show that shop floor employees self-assess a lack of relevant knowledge when it comes to understanding KPIs. Moreover, the results show that shop floor employees perceive the visualization of shop floor KPIs as insufficient and non-motivational. This goes along with the finding that managers are aware of the lacking benefit of KPIs resulting from the rather negative perception of shop floor employees. The interviewed managers recognize a strong potential for improvement of their KPI systems.

The interview results confirm the need to design a performance management system on the shop floor that considers and aligns both management and operations, is directed to the shop floor level, considers explicitly the perspective of employees and integrates motivational elements.

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.

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

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.

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.

In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset.

In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments.

Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model.

For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future.

This paper aims to provide an approach of modeling haptic impressions of surfaces over a wide range of applications by using multiple sensor sources.

A multisensory measurement experiment was conducted using various leather and artificial leather surfaces. After processing of measurement data and feature extraction, different learning algorithms were applied to the measurement data and a corresponding set of data from a sensory study. The study contained evaluations of the same surfaces regarding descriptors of haptic quality (e.g. roughness) by human subjects and was conducted in a former research project.

The research revealed that it is possible to model and project haptic impressions by using multiple sensor sources in combination with data fusion. The presented method possesses the potential for an industrial application.

This paper provides a new approach to predict haptic impressions of surfaces by using multiple sensor sources.

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.

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.

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.

The developed method simplifies the design of neural networks in general and the modelling for the determination of measurement uncertainty in particular.

The marketing literature uses five different experience terms that are supposed to represent different streams of research. Many papers do not provide a definition, most of the used definitions are unclear, the different experience terms have similar dimensionality and are regularly used interchangeably or have the same meaning. In addition, the existing definitions are not adequately informed from other disciplines that have engaged with experience. This paper aims to build a comprehensive conceptual framework of experience in marketing informed by related disciplines aiming to provide a more holistic definition of the term.

This research follows previously established procedures by conducting a systematic literature review of experience. From the approximately 5,000 sources identified in three disciplines, 267 sources were selected, marketing (148), philosophy (90) and psychology (29). To address definitional issues the analysis focused on enlightening four premises.

This paper posits that the term brand experience can be used in all marketing-related experiences and proposes four premises that may resolve the vagaries associated with the term’s conceptualization. The four premises address the what, who, how and when of brand experience and aim to rectify conceptual issues. Brand experience is introduced as a multi-level phenomenon.

The suggested singular term, brand experience, captures all experiences in marketing. The identified additional elements of brand experience, such as the levels of experience and the revision of emotions within brand experience as a continuum, tempered by repetition, should be considered in future research.

The multi-level conceptualization may provide a greater scope for dynamic approaches to brand experience design thus providing greater opportunities for managers to create sustainable competitive advantages and differentiation from competitors.

This paper completes a systematic literature review of brand experience across marketing, philosophy and psychology which delineates and enlightens the conceptualization of brand experience and presents brand experience in a multi-level conceptualization, opening the possibility for further theoretical, methodological and interdisciplinary promise.

Product design is an important marketing variable. Most literature about consumer preference for product design focuses on aesthetic product value. However, the appearance of a product also influences consumer perception of functionalities, quality, and ease of use. This paper therefore, seeks to assess how preference for visual complexity and symmetry depends on the type of product value that is important to people.

In a conjoint study the utility of visual complexity and symmetry in determining preference for eight VCR pictures are assessed (n=422). These utilities are used as dependent variables in regression analyses with the different product values (aesthetic, functionalities, quality, and ease of use) as independent variables.

The effects of visual complexity and symmetry on consumers' preferences depend on the product value to which consumers paid attention.

To increase insight into the relationship between design and consumer product preference, the impact of a design on consumer perception of all types of product value – not only aesthetic value – should be taken into account.

This research has direct implications for managers overseeing aspects of product development relating to aligning the design effort with target customers and determining specific product design executions.

Looks at consumer research in Greater China including Mainland China, Hong Kong and Taiwan. Maps out the contributions within this area and guides future research. Examines the state of the art over the 1979‐97 period, with particular emphasis on the topics that have been researched, the extent of the theory development in the field and the methodologies used in conducting research. Uses content analysis to review 75 relevant articles. Suggests that, while a considerable breadth of topics have been researched, there remains much to be done, there is further room for theoretical development in Chinese consumer behaviour studies; and the methodologies used need improvement and further refinement.

Several ultra-low power and gigahertz current-starved voltage-controlled oscillator (CSVCO) circuits have been proposed and compared here. The presented structures are based on the three-stage hybrid circuit of the carbon nanotube field-effect transistors (CNTFETs) and low-power MOSFETs. The topologies exploit modified and compensated Schmitt trigger comparator parts to demonstrate better consumption power and frequency characteristics. The basic idea in the presented topologies is to compensate the Schmitt trigger comparator part of the basic CSVCO for achieving faster carrier mobility of the holes, reducing transistor leakage current and eliminating dummy transistors.

This study aims to propose and compare three different comparator-based VCOs that have been implemented using the CNTFETs. The considered circuits are shown to be capable of delivering the maximum 35 tuning frequency in the order of 1 GHz to 5 GHz. A major power thirsty part of the high-frequency ring VCOs is the Schmitt trigger stage. Here, several fast and low-power Schmitt trigger topologies are exploited to mitigate the dissipation power and enhance the oscillation frequency.

As a result of proposed modifications, more than one order of magnitude mitigation in the VCO power consumption with respect to the previously presented three-stage CSVCO is reported here. Thus, a VCO dissipation power of 3.5 µW at the frequency of 1.1 GHz and the tuning range of 26 per cent is observed for the well-established 32 nm technology and the supply voltage of 1 V. Such a low dissipation power is obtained around the operating frequency of the battery-powered cellular phones. In addition, using the p-carrier mobility compensation and enhancing the rise time of the Schmitt trigger part of the CSVCO, a maximum of 2.38 times higher oscillation frequency and 72 per cent wider tuning range with respect to Rahane and Kureshi (2017) are observed. Simultaneously, this topology exhibits an average of 20 per cent reduction in the power consumption.

Several new VCO topologies are presented here, and it is shown that they can significantly enhance the power dissipation of the GHz CSVCOs.