Maintenance Strategies and Innovative Approaches in the Pharmaceutical Industry: An Integrated Management System (IMS)

Felice, Fabio De; Petrillo, Antonella; Autorino, Claudio

doi:10.5772/59023

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Maintenance and production planning are interdependent as the degradation of the production system is dependent on the production rate (Hajej et al ., 2014) and maintenance times may interfere with production (Ao et al ., 2019). Insufficiently integrated planning for maintenance and production can pose a major problem for maintenance management systems (De Felice et al ., 2014; Bokrantz et al ., 2020).…”

Section: Opportunities For Internal Integration Supported By Data-dri...mentioning

confidence: 99%

“…(2009) highlight the importance of standardized data models to easily exchange information between different production and maintenance systems and applications. Information exchange between different parts of the organization is necessary to increase transparency and establish process control within operations (De Felice et al ., 2014). E3: There are always projects involved in making sure the standard systems all work together.…”

Section: Enablers For Internal Integration Supported By Data-driven D...mentioning

confidence: 99%

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Data-driven decision-making in maintenance management and coordination throughout the asset life cycle: an empirical study

Hinrichs,

Prifti,

Schneegass

2023

JQME

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PurposeWith production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.Design/methodology/approachFifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.FindingsThe main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.Originality/valueThis paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

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Section: Opportunities For Internal Integration Supported By Data-dri...mentioning

confidence: 99%

Section: Enablers For Internal Integration Supported By Data-driven D...mentioning

confidence: 99%

Data-driven decision-making in maintenance management and coordination throughout the asset life cycle: an empirical study

Hinrichs,

Prifti,

Schneegass

2023

JQME

View full text Add to dashboard Cite

show abstract

“…Furthermore, traditional ML approaches such as neural networks, LR, SVM, and decision tree supported transferring scenarios in regression and classification problems become more realistic than improving independently (Zhu, 2008;Thrun et al,1998;Thrun, 1997;Zhang, Hu, and Fang, 2010). Eventually, researchers studied the practice of TL in deep/convolutional neural networks for complex applications (De Felice et al, 2014;Paul, Rottensteiner, and Heipke, 2015;Shen, Chen, Yan, and Gao, 2016;Gideon, Khorram, Aldeneh, Dimitriadis, and Provost, 2017). Then, the diagnosis model, trained in virtual space, was migrated to physical space using TL.…”

Section: Transfer Learning (Tl) Based Fault Diagnosis (Fd) and Predic...mentioning

confidence: 99%

Similarity-based Multi-source Transfer Learning Approach for Time Series Classification

Senanayaka

Mamun

Bond

et al. 2022

IJPHM

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This study aims to develop an effective method of classification concerning time series signals for machine state prediction to advance predictive maintenance (PdM). Conventional machine learning (ML) algorithms are widely adopted in PdM, however, most existing methods assume that the training (source) and testing (target) data follow the same distribution, and that labeled data are available in both source and target domains. For real-world PdM applications, the heterogeneity in machine original equipment manufacturers (OEMs), operating conditions, facility environment, and maintenance records collectively lead to heterogeneous distribution for data collected from different machines. This will significantly limit the performance of conventional ML algorithms in PdM. Moreover, labeling data is generally costly and time-consuming. Finally, industrial processes incorporate complex conditions, and unpredictable breakdown modes lead to extreme complexities for PdM. In this study, similarity-based multi-source transfer learning (SiMuS-TL) approach is proposed for real-time classification of time series signals. A new domain, called "mixed domain," is established to model the hidden similarities among the multiple sources and the target. The proposed SiMuS-TL model mainly includes three key steps: 1) learning group-based feature patterns, 2) developing group-based pre-trained models, and 3) weight transferring. The proposed SiMuS-TL model is validated by observing the state of the rotating machinery using a dataset collected on the Skill boss manufacturing system, publicly available standard bearing datasets, Case Western Reserve University (CWRU), and Paderborn University (PU) bearing datasets. The results of the performance comparison demonstrate that the proposed SiMuS-TL method outperformed conventional Support Vector Machine (SVM), Artificial Neural Network (ANN), and Transfer learning with neural networks (TLNN) without similarity-based transfer learning methods.

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Design of condition-based maintenance framework for process operations management in pharmaceutical continuous manufacturing

Ganesh

et al. 2020

International Journal of Pharmaceutics

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Maintenance Strategies and Innovative Approaches in the Pharmaceutical Industry: An Integrated Management System (IMS)

Cited by 3 publications

References 22 publications

Data-driven decision-making in maintenance management and coordination throughout the asset life cycle: an empirical study

Data-driven decision-making in maintenance management and coordination throughout the asset life cycle: an empirical study

Similarity-based Multi-source Transfer Learning Approach for Time Series Classification

Design of condition-based maintenance framework for process operations management in pharmaceutical continuous manufacturing

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