Multivariate SPC methods for controlling manufacturing processes using predictive models – A case study in the automotive sector

2020 ◽  
Vol 123 ◽  
pp. 103307
Author(s):  
Rafael Sanchez-Marquez ◽  
José Manuel Jabaloyes Vivas
Keyword(s):  
Predictive Models ◽  
Manufacturing Processes ◽  
Automotive Sector

scholarly journals A Case Study in View of Developing Predictive Models for Water Supply System Management

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3305
Author(s):  
Katarzyna Pietrucha-Urbanik ◽  
Barbara Tchórzewska-Cieślak ◽  
Mohamed Eid
Keyword(s):  
Water Supply ◽  
Predictive Models ◽  
Failure Modes ◽  
Supply System ◽  
Water Supply System ◽  
System Management ◽  
System A ◽  
Operational Failure ◽  
Operational Data

Initiated by a case study to assess the effectiveness of the modernisation actions undertaken in a water supply system, some R&D activities were conducted to construct a global predictive model, based on the available operational failure and recovery data. The available operational data, regarding the water supply system, are the pipes’ diameter, failure modes, materials, functional conditions, seasonality, and the number of failures and time-to-recover intervals. The operational data are provided by the water company responsible of the supply system. A predictive global model is proposed based on the output of the operational data statistical assessment. It should assess the expected effectiveness of decisions taken in support of the modernisation and the extension plan.

Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector

2015 ◽  
Vol 72 ◽  
pp. 72-81 ◽  
Author(s):  
Julie Moeyersoms ◽  
David Martens
Keyword(s):  
Predictive Models ◽  
Energy Sector ◽  
Churn Prediction

The Use of Aluminium for Commercial Vehicle Structures—A Feasibility Study

1978 ◽  
Vol 192 (1) ◽  
pp. 81-92
Author(s):  
B. B. Hundy ◽  
S. Broadstock
Keyword(s):  
Structural Analysis ◽  
Aluminium Alloy ◽  
Feasibility Study ◽  
Commercial Vehicle ◽  
Load Capacity ◽  
Typical Case ◽  
Extra Cost ◽  
Manufacturing Processes ◽  
Structural Components

The use of aluminium alloy instead of steel for the structural components of a 32 ton articulated lorry has been examined. The probable manufacturing difficulties have been assessed and shown to be minimal. The savings in weight possible by using aluminium have been calculated from a structural analysis of the cab, tractor chassis and trailer and from this and an assessment of the manufacturing processes the extra cost of manufacturing in aluminium has been determined. A typical case study shows that this extra cost can be easily recovered by utilising the increased load capacity of the vehicle during the first few years of its life.

Evaluating prediction of continuous clinical values: a glucose case study (Preprint)

2021 ◽  
Author(s):  
George Hripcsak ◽  
David J Albers
Keyword(s):  
Predictive Models ◽  
Clinical Utility ◽  
Clinical Care ◽  
Glucose Measurement ◽  
Care Processes ◽  
Low Glucose ◽  
Potential Clinical Utility ◽  
Rms Error ◽  
Error Grid

BACKGROUND Background: It would be useful to be able to assess the utility of predictive models of continuous values before clinical trials are carried out. OBJECTIVE Objective: To compare metrics to assess the potential clinical utility of models that produce continuous value forecasts. METHODS Methods: We ran a set of data assimilation forecast algorithms on time series of glucose measurements from intensive care unit patients. We evaluated the forecasts using four sets of metrics: glucose root mean square error, a set of metrics on a transformed glucose value, the estimated effect on clinical care based on an insulin guideline, and a glucose measurement error grid (Parkes grid). We assessed correlation among the metrics and created a set of factor models. RESULTS Results: The metrics generally correlated with each other, but those that estimated the effect on clinical care correlated with the others the least and were generally associated with their own independent factors. The other metrics appeared to separate into those that emphasized errors in low glucose versus errors in high glucose. The Parkes grid was well correlated with the transformed glucose but not the estimation of clinical care. CONCLUSIONS Discussion: Our results indicate that we need to be careful before we assume that commonly used metrics like RMS error in raw glucose or even metrics like the Parkes grid that are designed to measure importance of differences will correlate well with actual effect on clinical care processes. A combination of metrics appeared to explain the most variance between cases. As prediction algorithms move into practice, it will be important to measure actual effects.

scholarly journals Model of Identification and Management of Technological Innovations in Product Development: A Case Study in the Automotive Industry

2018 ◽  
Vol 7 (2) ◽  
pp. 30
Author(s):  
Cristian Lucas Endler ◽  
Pedro Paulo de Andrade Júnior
Keyword(s):  
Product Development ◽  
Automotive Industry ◽  
Industrial Production ◽  
Technological Innovations ◽  
Automotive Sector ◽  
Economic Potential ◽  
New Model ◽  
Scientific Databases ◽  
Innovation Model

This article aims to propose a new model of technological innovations, as well as using it in a case study in the automotive industry. After an analysis of the main scientific databases, it was verified that the present work is unprecedented in presenting a unified model of identification and management of technological innovations. In methodological terms, the bibliometric and systemic analyzes were performed in order to identify the main technological innovations inherent in the automotive industry. In terms of research results, a cohesive innovation model was obtained, which, once based on the concepts of sensitive innovation and latent innovation, allows the identification and the consequent valuation of the economic potential of the main technological innovations in the area desired by the manager who will apply it. As an example, the model was applied specifically in the automotive sector, but its methodology can be generalized to any area of industrial production.

scholarly journals RENEWABLE ENERGY POTENTIAL IN THE AUTOMOTIVE SECTOR: CZECH REGIONAL CASE STUDY

2017 ◽  
Vol 6 (4) ◽  
pp. 537-545 ◽  
Author(s):  
Viera Pechancová
Keyword(s):  
Renewable Energy ◽  
Automotive Sector ◽  
Energy Potential

scholarly journals Modelling streamflow response to hydro-climatic variables in the Upper Mkomazi River, South Africa

2014 ◽  
Author(s):  
◽  
Oluwaseun Kunle Oyebode
Keyword(s):  
South Africa ◽  
Water Resources ◽  
Genetic Programming ◽  
Predictive Models ◽  
Model Development ◽  
De Algorithm ◽  
Planning And Management ◽  
Ann Models ◽  
Artificial Neural

Streamflow modelling remains crucial to decision-making especially when it concerns planning and management of water resources systems in water-stressed regions. This study proposes a suitable method for streamflow modelling irrespective of the limited availability of historical datasets. Two data-driven modelling techniques were applied comparatively so as to achieve this aim. Genetic programming (GP), an evolutionary algorithm approach and a differential evolution (DE)-trained artificial neural network (ANN) were used for streamflow prediction in the upper Mkomazi River, South Africa. Historical records of streamflow and meteorological variables for a 19-year period (1994- 2012) were used for model development and also in the selection of predictor variables into the input vector space of the models. In both approaches, individual monthly predictive models were developed for each month of the year using a 1-year lead time. Two case studies were considered in development of the ANN models. Case study 1 involved the use of correlation analysis in selecting input variables as employed during GP model development, while the DE algorithm was used for training and optimizing the model parameters. However in case study 2, genetic programming was incorporated as a screening tool for determining the dimensionality of the ANN models, while the learning process was further fine-tuned by subjecting the DE algorithm to sensitivity analysis. Altogether, the performance of the three sets of predictive models were evaluated comparatively using three statistical measures namely, Mean Absolute Percent Error (MAPE), Root Mean-Squared Error (RMSE) and coefficient of determination (R2). Results showed better predictive performance by the GP models both during the training and validation phases when compared with the ANNs. Although the ANN models developed in case study 1 gave satisfactory results during the training phase, they were unable to extensively replicate those results during the validation phase. It was found that results from case study 1 were considerably influenced by the problems of overfitting and memorization, which are typical of ANNs when subjected to small amount of datasets. However, results from case study 2 showed great improvement across the three evaluation criteria, as the overfitting and memorization problems were significantly minimized, thus leading to improved accuracy in the predictions of the ANN models. It was concluded that the conjunctive use of the two evolutionary computation methods (GP and DE) can be used to improve the performance of artificial neural networks models, especially when availability of datasets is limited. In addition, the GP models can be deployed as predictive tools for the purpose of planning and management of water resources within the Mkomazi region and KwaZulu-Natal province as a whole.

scholarly journals Supply Chain Performance Measurement: A Case Study about Applicability of SCOR® Model in Automotive Industry Firm

2018 ◽  
Vol 200 ◽  
pp. 00016
Author(s):  
Radouane Lemghari ◽  
Chafik Okar ◽  
Driss Sarsri
Keyword(s):  
Supply Chain ◽  
Performance Measurement ◽  
Practical Method ◽  
Research Problem ◽  
Automotive Sector ◽  
Business Modelling ◽  
Automotive Supply Chain ◽  
Automotive Supply ◽  
Structured Approach

The evaluation of a supply chain is a major priority of companies; it is a task that remains difficult due to the complexity of these systems [1]. This evaluation involves a selection of performance measurement indicators, which are appropriate to the management of this chain. It is then necessary to have a structured approach and adequate methodological tools [2]. Indeed, we propose in this paper a practical method that will model in the first place a Moroccan automotive supply chain, according to the SCOR® model (Supply Chain Operations Reference), proposed by the Supply Chain Council. This method will also identify at each level the appropriate indicators for the performance evaluation depending on the strategic vision. In this context our research problem is made, it is interested in the contribution of the business modelling to improve logistics performance. To the best knowledge of the authors, this is the first work that proposes a case study believed to be easy to understand, practical and suitable for the automotive sector. In short, this study is a real application leap to resolve the problematic unanswered of practical SCOR® model using an industrial application in the Moroccan automotive sector.

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