scholarly journals A Reference Process Model for Usage Data-Driven Product Planning

2022 ◽  
Author(s):  
Maurice Meyer ◽  
Ingrid Wiederkehr ◽  
Melina Panzner ◽  
Christian Koldewey ◽  
Roman Dumitrescu
Keyword(s):  
Process Model ◽  
Data Driven ◽  
Product Planning ◽  
Reference Process ◽  
Usage Data

scholarly journals UNDERSTANDING USAGE DATA-DRIVEN PRODUCT PLANNING: A SYSTEMATIC LITERATURE REVIEW

2021 ◽  
Vol 1 ◽  
pp. 3289-3298
Author(s):  
Maurice Meyer ◽  
Ingrid Wiederkehr ◽  
Christian Koldewey ◽  
Roman Dumitrescu
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Success Factors ◽  
Data Driven ◽  
Physical Systems ◽  
Usage Phase ◽  
Product Planning ◽  
Decision Making Processes ◽  
Research Questions ◽  
Usage Data

AbstractCyber-physical systems (CPS) are able the collect huge amounts of data about themselves, their users, and their environment during their usage phase. By feeding these usage data back into product planning, manufacturers can optimize their engineering and decision-making processes. Despite promising potentials, most manufacturers still do not analyze usage data within product planning. Also, research on usage data-driven product planning is scarce. Therefore, this paper aims to identify the main concepts, advantages, success factors and challenges of usage data-driven product planning. To answer the corresponding research questions, a comprehensive systematic literature review is conducted. From its results, a detailed description of usage data-driven product planning consisting of six main concepts is derived. Furthermore, taxonomies for the advantages, success factors and challenges of usage data-driven product planning are presented. The six main concepts and the three taxonomies allow for a deeper understanding of the topic while highlighting necessary future actions and research needs.

Knowledge Discovery Process Models

2012 ◽  
pp. 72-100 ◽  
Author(s):  
Mouhib Alnoukari ◽  
Asim El Sheikh
Keyword(s):  
Life Cycle ◽  
Knowledge Discovery ◽  
Process Model ◽  
Common Factor ◽  
Final Outcome ◽  
Process Models ◽  
Data Driven ◽  
Discovery Process ◽  
The Common ◽  
Discovery Process Models

Knowledge Discovery (KD) process model was first discussed in 1989. Different models were suggested starting with Fayyad’s et al (1996) process model. The common factor of all data-driven discovery process is that knowledge is the final outcome of this process. In this chapter, the authors will analyze most of the KD process models suggested in the literature. The chapter will have a detailed discussion on the KD process models that have innovative life cycle steps. It will propose a categorization of the existing KD models. The chapter deeply analyzes the strengths and weaknesses of the leading KD process models, with the supported commercial systems and reported applications, and their matrix characteristics.

Modeling Business Process Variability for Design-Time Configuration

2011 ◽  
pp. 204-228 ◽  
Author(s):  
Macello La Rosa ◽  
Marlon Dumas ◽  
Arthur H.M. ter Hofstede
Keyword(s):  
Business Process ◽  
Process Model ◽  
Process Models ◽  
Attractive Alternative ◽  
Process Variability ◽  
Design Time ◽  
Reference Process ◽  
Multiple Variants

A reference process model represents multiple variants of a common business process in an integrated and reusable manner. It is intended to be individualized in order to fit the requirements of a specific organization or project. This practice of individualizing reference process models provides an attractive alternative with respect to designing process models from scratch; in particular, it enables the reuse of proven practices. This chapter introduces techniques for representing variability in the context of reference process models, as well as techniques that facilitate the individualization of reference process models with respect to a given set of requirements.

New Metrics for Validation of Data-Driven Random Process Models in Uncertainty Quantification

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Hongyi Xu ◽  
Zhen Jiang ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Uncertainty Quantification ◽  
Random Process ◽  
Process Model ◽  
Goodness Of Fit ◽  
Experimental Tests ◽  
Process Models ◽  
Data Driven ◽  
Gaussian Copula ◽  
High Dimensional ◽  
Marginal Distributions

Data-driven random process models have become increasingly important for uncertainty quantification (UQ) in science and engineering applications, due to their merit of capturing both the marginal distributions and the correlations of high-dimensional responses. However, the choice of a random process model is neither unique nor straightforward. To quantitatively validate the accuracy of random process UQ models, new metrics are needed to measure their capability in capturing the statistical information of high-dimensional data collected from simulations or experimental tests. In this work, two goodness-of-fit (GOF) metrics, namely, a statistical moment-based metric (SMM) and an M-margin U-pooling metric (MUPM), are proposed for comparing different stochastic models, taking into account their capabilities of capturing the marginal distributions and the correlations in spatial/temporal domains. This work demonstrates the effectiveness of the two proposed metrics by comparing the accuracies of four random process models (Gaussian process (GP), Gaussian copula, Hermite polynomial chaos expansion (PCE), and Karhunen–Loeve (K–L) expansion) in multiple numerical examples and an engineering example of stochastic analysis of microstructural materials properties. In addition to the new metrics, this paper provides insights into the pros and cons of various data-driven random process models in UQ.

scholarly journals Data-Driven Adaptive Observer for Fault Diagnosis

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Shen Yin ◽  
Xuebo Yang ◽  
Hamid Reza Karimi
Keyword(s):  
Fault Diagnosis ◽  
Process Model ◽  
Large Scale ◽  
Process Models ◽  
Physical Models ◽  
Adaptive Observer ◽  
Data Driven ◽  
Process Data ◽  
Tank System ◽  
Residual Generator

This paper presents an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process. The thresholds can be determined analytically or through estimating the probability density function of related variables. To illustrate the performance of proposed fault diagnosis approach, a laboratory-scale three-tank system is finally utilized. It shows that the proposed data-driven scheme is efficient to deal with applications, whose analytical process models are unavailable. Especially, for the large-scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.

scholarly journals A new concept to improve microwave heating uniformity through data-driven process modelling

2019 ◽  
Author(s):  
Yingguang Li ◽  
Jing Zhou ◽  
Di Li
Keyword(s):  
Microwave Heating ◽  
Process Model ◽  
Influence Factor ◽  
Data Driven ◽  
Experimental Investigations ◽  
Microwave System ◽  
Long Time ◽  
Heating Uniformity ◽  
Heating Pattern

For a long time, the heating pattern of the workpiece within a multimode microwave oven was considered to be highly sophisticated. As a consequence, the uneven microwave heating problem can only be partly alleviated by a random movement between the electromagnetic field and the workpiece. In this paper, we reported that the heating pattern has a specific correspondence with microwave system settings. The influence factor of the heating pattern and the corresponding mechanism were systematically studied by both theoretical analysis and experimental investigations. On this basis, a data-driven process model was established to learn the material’s dynamic temperature behaviors under different microwave system settings, and a new concept to improve the microwave heating uniformity by temperature monitoring and active compensation was proposed. The effectiveness of the method was demonstrated by a polymer composite microwave processing case study.

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