Best Practice Sharing for Complexity Management in Supply Chains of the Semiconductor Industry

Sun, Chuang; Rose, Thomas; Ehm, Hans; Herbig, Tobias

doi:10.1016/j.procir.2015.12.004

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…The current globalization and innovation trend in the business environment has brought many external and internal challenges for modern companies, such as the volatile and changing market, large organizations, different product choices, etc. [15]. In addition, this change also increases the complexity and, therefore, threatens companies' performance.…”

Section: Hypothesis External Knowledge Transfermentioning

confidence: 99%

The Role of Employees’ Performance and External Knowledge Transfer on the Supply Chain Flexibility

Díaz-Reza¹,

García-Alcaráz²,

Avelar-Sosa³

et al. 2019

RCS

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In this article structural equation models are reported, which relate four latent variables associated with employee performance, knowledge transfer, and supply chain flexibility, which incorporate 17 observed variables. In addition, the latent variables are related through 6 hypotheses that were tested with data from 269 questionnaires applied to the maquiladora industry in Ciudad Juarez, Mexico. Moreover, this model was executed in WarpPLS 6.0 software using the partial least squares technique to analyze the direct, indirect, and total effects. Additionally, the results show that the external knowledge transfer is crucial within the supply chains, since it explains 44.6% of the complexity, 19.5% of the employees' performance, as well as 10.6% of the supply chain flexibility.

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Section: Hypothesis External Knowledge Transfermentioning

confidence: 99%

The Role of Employees’ Performance and External Knowledge Transfer on the Supply Chain Flexibility

Díaz-Reza¹,

García-Alcaráz²,

Avelar-Sosa³

et al. 2019

RCS

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“…Latos et al [8] Interview Organizational projects Leang et al [10] Not specified Manufacturing industry Piya et al [3] Literature review, expert review Supply chain Schmid et al [11] Not specified Production equipment Schuh et al [12] Literature review, expert review Organizational projects Schuh et al [13] Literature review Organizational projects Schuh et al [7] Not specified Organizational projects Sun et al [4] Questionnaire, expert review, workshop Supply chain Asadi et al [6] acknowledges the need for adapting the complexity driver identification method to specific contexts by allowing survey respondents to add missing complexity drivers. Regardless of the fundamental method used for identifying complexity drivers, Kohr et al [5] notes that the analysts must determine the relevant sources to use, further implying a focus on context specificity.…”

Section: Manufacturing Industrymentioning

confidence: 99%

“…In response, companies have increased the complexity of their own operations to accommodate these changed conditions [1,2], often resulting in increased costs and reduced performance [3]. Such costs are identified across organizations in supply chain management [3,4], production systems [5,6], and various internal projects [7,8]. However, complexity is not always undesirable, and may even be a source of competitive advantage [8], if managed properly.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Identification of Complexity Drivers in Manufacturing Companies

Andersen

Brunoe

Nielsen

2019

IFIP Advances in Information and Communication Technology

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The initial issue of complexity management in companies is the identification of the drivers of complexity. However, current literature lacks methods for assisting practitioners in the initial identification of such drivers. This paper, therefore, presents a novel framework for assisting practitioners with identifying complexity drivers in manufacturing companies. The framework uses a generic value chain and a generic product structure as its two dimensions. Multiple workshops are then conducted with company representatives across different value chain fields focusing on two main parts: First, surveys are used to assign complexity ratings to different generic product structure elements. Secondly, the complexity ratings are elaborated on by workshop participants. The process provides valuable insights into the perceived complexity drivers. The framework is then verified through a case study in the process industry. Based on the case study, multiple complexity drivers were identified across both value chain fields and product structure elements. The case study findings show that the framework facilitates practitioners in identifying organization-wide perceived complexity drivers. The framework contributes to both industry and research by addressing a neglected aspect of complexity management. It achieves this by providing a comprehensive and structured approach for the initial identification of complexity drivers across product elements and value chain fields.

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“…In the area of 'Data and IT', Sun et al (2016) pointed out that observations show that SI managers and engineers tend to prioritize urgent operational needs rather than standardization. This type of prioritization leads to a growing number of so-called 'quick fixes' that solve a particular problem fast, but leads to high risk and uncertainties for the whole value chain.…”

Section: 2 the Semiconductor Industrymentioning

confidence: 99%

Analysis and Evaluation of the Impacts of Predictive Analytics on Production System Performances in the Semiconductor Industry.

Preis¹

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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.

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Best Practice Sharing for Complexity Management in Supply Chains of the Semiconductor Industry

Cited by 3 publications

References 8 publications

The Role of Employees’ Performance and External Knowledge Transfer on the Supply Chain Flexibility

The Role of Employees’ Performance and External Knowledge Transfer on the Supply Chain Flexibility

A Framework for Identification of Complexity Drivers in Manufacturing Companies

Analysis and Evaluation of the Impacts of Predictive Analytics on Production System Performances in the Semiconductor Industry.

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