Advances on Data Management and Information Systems

Darmont, Jérôme; Novikov, B; Wrembel, Robert; Bellatreche, Ladjel

doi:10.1007/s10796-021-10235-4

Cited by 8 publications

(3 citation statements)

References 58 publications

(44 reference statements)

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“…In a supply chain, IoT links fabrication and material handling equipment, remote sensors for freight transport, and tracking systems for vehicles and other assets 5 Data Sciences & Business Intelligence Data Science requires coding, data mining, analytical skills, and modeling to extract value and meaning from the data. In addition, human–machine interaction, quantitative skills, and understanding of information technology are regarded as essential skills in data sciences and business intelligence (Darmont et al, 2022 ) 6 Cybersecurity Cybersecurity is a measure to protect a computer or computer system against unauthorized access or attack ( https://www.merriam-webster.com/dictionary/cybersecurity ). A critical step in preventing cyber threats is finding adequate and feasible ways to encourage employees and end-users of various technologies to protect their individual and organizational information assets (Sigov et al, 2022 ) 7 Green Energy Green energy comes from renewable sources.…”

Section: Background and Literaturementioning

confidence: 99%

Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 and Beyond

2022

Inf Syst Front

172

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Industry 4.0 is revolutionizing manufacturing processes and has a powerful impact on globalization by changing the workforce and increasing access to new skills and knowledge. World Economic Forum estimates that, by 2025, 50% of all employees will need reskilling due to adopting new technology. Five years from now, over two-thirds of skills considered important in today’s job requirements will change. A third of the essential skills in 2025 will consist of technology competencies not yet regarded as crucial to today's job requirements. In this study, we focus our discussion on the reskilling and upskilling of the future-ready workforce in the era of Industry 4.0 and beyond. We have delineated top skills sought by the industry to realize Industry 4.0 and presented a blueprint as a reference for people to learn and acquire new skills and knowledge. The findings of the study suggest that life-long learning should be part of an organization’s strategic goals. Both individuals and companies need to commit to reskilling and upskilling and make career development an essential phase of the future workforce. Great efforts should be taken to make these learning opportunities, such as reskilling and upskilling, accessible, available, and affordable to the workforce. This paper provides a unique perspective regarding a future-ready learning society as an essential integral of the vision of Industry 4.0.

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Section: Background and Literaturementioning

confidence: 99%

Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 and Beyond

2022

Inf Syst Front

172

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“…Apart from answering to the process development needs, these three approaches in combination produce data of sufficient quantity and quality -significant advances have been made in data gathering and management as well as data science over the last decade (Darmont et al, 2022;Jin et al, 2015;Narayanan et al, 2020;Sarker, 2021) -which seem to render possible a fundamental change in the way that processes are being developed (von Stosch et al, 2021). The main idea is that machine learning (ML) methods allow to use the data produced by processes of different molecules to streamline the design of a process for a new molecule, i.e.…”

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confidence: 99%

Calibration design for pharmaceutical bioprocesses: A novel approach for knowledge transfer in Design of Experiments

Helleckes,

Wirnsperger,

Polak

et al. 2024

Preprint

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Modern machine learning methods, and their use alongside established paradigms such as Quality by Design, have the potential to fundamentally change the way bioprocesses are developed. In particular, horizontal knowledge transfer methods, which seek to exploit data from historical processes to facilitate process development for a new product, provide an opportunity to rethink process development workflows. In this work, we firstly assess the potential of two knowledge transfer approaches, meta learning and one-hot encoding, in combination with Gaussian process (GP) models. We compare their performance to GPs developed only on data of the new process. Using simulated mammalian cell cultivation data, we observe that both knowledge transfer approaches outperform the individual-product approach. In the second part, we address the question whether experiments for a new product could be designed more effectively by exploiting existing knowledge. In particular, we suggest to specifically design few runs for the novel product to calibrate knowledge transfer models, a task that we coin calibration design. We propose a novel, customised metric to identify a set of calibration design runs, which exploits differences in process evolutions of historical products. In two simulated case studies, we observed that training with calibration designs yields similar test set errors compared to common approaches of Design of Experiments. However, much fewer experiments are needed for the former, suggesting an interesting alternative for future bioprocess development. Overall, the results suggest that process development could be significantly streamlined when systematically carrying knowledge from one product to the next.

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“…[13] A key concept in this paradigm shift is the application of machine learning (ML) methods that have seen advances in data gathering, management, and data science. [14][15][16][17] These ML methods can leverage data from processes of many molecules to streamline the process design of new molecules-a process known as horizontal knowledge transfer. [18] An efficient way to address the challenge of horizontal knowledge transfer is transfer and meta learning techniques.…”

mentioning

confidence: 99%

Novel calibration design improves knowledge transfer across products for the characterization of pharmaceutical bioprocesses

Helleckes,

Wirnsperger,

Polak

et al. 2024

Biotechnology Journal

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Modern machine learning has the potential to fundamentally change the way bioprocesses are developed. In particular, horizontal knowledge transfer methods, which seek to exploit data from historical processes to facilitate process development for a new product, provide an opportunity to rethink current workflows. In this work, we first assess the potential of two knowledge transfer approaches, meta learning and one‐hot encoding, in combination with Gaussian process (GP) models. We compare their performance with GPs trained only on data of the new process, that is, local models. Using simulated mammalian cell culture data, we observe that both knowledge transfer approaches exhibit test set errors that are approximately halved compared to those of the local models when two, four, or eight experiments of the new product are used for training. Subsequently, we address the question whether experiments for a new product could be designed more effectively by exploiting existing knowledge. In particular, we suggest to specifically design a few runs for the novel product to calibrate knowledge transfer models, a task that we coin calibration design. We propose a customized objective function to identify a set of calibration design runs, which exploits differences in the process evolution of historical products. In two simulated case studies, we observed that training with calibration designs yields similar test set errors compared to common design of experiments approaches. However, the former requires approximately four times fewer experiments. Overall, the results suggest that process development could be significantly streamlined when systematically carrying knowledge from one product to the next.

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Advances on Data Management and Information Systems

Cited by 8 publications

References 58 publications

Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 and Beyond

Reskilling and Upskilling the Future-ready Workforce for Industry 4.0 and Beyond

Calibration design for pharmaceutical bioprocesses: A novel approach for knowledge transfer in Design of Experiments

Novel calibration design improves knowledge transfer across products for the characterization of pharmaceutical bioprocesses

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