2 research outputs found
Twin tools for intelligent manufacturing: a case study
The article deals with a case of an industrial plant for which a balanced mix of flexibility and production slenderness is sought together with high quality, transparency and production effectiveness. The study is based on virtual plant assessments (by means of virtual engineering) and considers industrial artefacts (automotive components) aiming at economics of scale figures on short horizons, but undergoing fast updating requests of high flexibility of new hybrid and electric vehicles; the sought solution profits of shop-floor resources modularity and robotic cells use; example simulation issues are given, and the advantages, offered by the use of digital twins, are analyzed
Analysis and Evaluation of the Impacts of Predictive Analytics on Production System Performances in the Semiconductor Industry
Problem Statement: Predictive Analytics (PA) may effectively support semiconductor
industry (SI) companies in order to manage the special challenges in SI value chains. To
discover the implications of PA, the realistic benefits as well as its limitations of its
application to semiconductor manufacturing, it is necessary to assess in which ways the
application of PA affects the production system (PS) performances. However, based on the
literature survey, the influences of PA on the various performance characteristics of an SI PS
are not as clear as expected for the efficiently operative application. Besides, the existing
performance models are not effective to predict the impacts of PA on the SI PS
performances. Therefore, the overall aim of this thesis is to analyse and evaluate the
impacts of PA on the SI PS performances and to identify under which conditions a PA
application would generate the most significant performance improvements. The focus of this
thesis is predictive maintenance (PdM).
Research Methodology: Based on a post-positivist philosophy, the thesis applies a
deductive research approach using mixed-methods for data collection. The research design
has the following stages: (1) theory, (2) hypothesis, (3) state of research, (4) case study and
(5) verification.
Main Achievements: (1) The systematic literature review is carried out to identify the gaps
of the existing research and based on these findings, a conceptual framework is proposed
and developed. (2) The existing performance models are analysed and evaluated against
their applicability to this study. (3) A causal loop model for SI PS is generated based on the
assessment of experts with industrial engineering and equipment maintenance expertise. (4)
An expert system is developed and evaluated in order to investigate transitive and
contradictory effects of PdM on SI PS performances. (5) A simulation model is developed
and validated for investigating the strengths and limitations of PdM regarding SI PS
performances under different circumstances.
Results: The results of the logical inference study show that PdM has 34 positive effects as
well as 4 contradictory effects on SI PS performance characteristics. Based on the various
simulation experiments, it has been found that (1) ’Mean Time to Repair’ decreases only if
PdM supports proportionate reduction of failures and repair times. (2) Logistics performance
improves only if the underlying workcenter is limited in capacity or the four partners are nonsynchronous.
(3) PdM supports optimal cost decreases for workcenters where the degree of
exhausting wear limits can be most effectively improved and (4) the degree of yield
improvement gained by PdM is dependent on the operation scrap rate. However, (5) if a
workcenter has overcapacity, PdM will potentially worsen PS performances, even if the
particular workcenter performance can be improved. These new insights advance existing
knowledge in production managements when adopting predictive technologies at SI PS in
order to improve PS performances. The findings above enable SI practitioners to justify a PdM investment and to select suitable workcenters in order to improve SI PS performances
by applying the proposed PdM.
Contributions: The main contributions of this PhD project can be divided into practical
application and theoretical work.
The contributions from the theoretical perspective are:
1) The critical review and evaluation of the state of the research for PA in the context of
semiconductor manufacturing and the models for predicting and evaluating SI PS
performances.
2) A new framework for investigating the implications of PA on the challenges such as
gaining high utilizations and controlling the variability in production processes in SI
value chains.
3) The new knowledge about transitive and contradictory effects of PdM on SI PS
performances, which indicates that PdM can be used to improve PS performances
beyond a single machine.
4) The new knowledge about strengths and limitations of PdM in order to improve SI
PS performances under particular circumstances.
The contributions from the practical application perspective are:
1) A practical method for identifying workcenters where PdM delivers the most
significant benefits for SI PS performances.
2) An expert system that provides a comprehensive knowledge base about causes and
effects within SI PS in order to justify a PdM investment.
3) A concise review of important PA applications, their capabilities for the wafer
fabrication and the most suited PA methods. These findings can be adopted by SI
practitioners