A Review of Skills Relevant to Enterprise Resource Planning Implementation Projects

Cadersaib, Zarine; Ahku, Yashvinee; Sahib, Nuzhah Gooda; Khan, Maleika Heenaye-Mamode; Gobin, Baby

doi:10.1109/icimcis51567.2020.9354270

Cited by 3 publications

(1 citation statement)

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“…The researchers emphasized that publications on this subject decreased even though ERP is a multidisciplinary field in their study. Cadersaib et al (2020) aimed to analyze the skills needed in ERP implementation projects by examining the studies on ERP application skills through a systematic review. The researchers classified the skills in this regard as ERP skills, job, and problem-solving skills, interpersonal skills, knowledge management skills, strategy, and management skills according to the results of the analysis.…”

Section: Introductionmentioning

confidence: 99%

Artifıcial Intelligence in Enterprise Resource Planning Systems: A Bibliometric Study

Aktürk¹

2021

Journal of International Logistics and Trade

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Section: Introductionmentioning

confidence: 99%

Artifıcial Intelligence in Enterprise Resource Planning Systems: A Bibliometric Study

Aktürk¹

2021

Journal of International Logistics and Trade

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Enterprise Resource Planning integrated with Business Analytics in Higher Education

Cadersaib

Sta

Gobin-Rahimbux

2022

2022 IEEE Global Engineering Education Conference (EDUCON)

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Surveying the Effectiveness and Efficiency of Enterprise Resource Planning Systems

Mundra

Prakash

2022

2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)

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Adapters have been positioned as a parameterefficient fine-tuning (PEFT) approach, whereby a minimal number of parameters are added to the model and fine-tuned. However, adapters have not been sufficiently analyzed to understand if PEFT translates to benefits in training/deployment efficiency and maintainability/extensibility. Through extensive experiments on many adapters, tasks, and languages in supervised and cross-lingual zero-shot settings, we clearly show that for Natural Language Understanding (NLU) tasks, the parameter efficiency in adapters does not translate to efficiency gains compared to full fine-tuning of models. More precisely, adapters are relatively expensive to train and have slightly higher deployment latency. Furthermore, the maintainability/extensibility benefits of adapters can be achieved with simpler approaches like multi-task training via full fine-tuning, which also provide relatively faster training times. We, therefore, recommend that for moderately sized models for NLU tasks, practitioners should rely on full fine-tuning or multi-task training rather than using adapters. Our code is available at https://github.com/AI4Bharat/ adapter-efficiency.

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A Review of Skills Relevant to Enterprise Resource Planning Implementation Projects

Cited by 3 publications

References 10 publications

Artifıcial Intelligence in Enterprise Resource Planning Systems: A Bibliometric Study

Artifıcial Intelligence in Enterprise Resource Planning Systems: A Bibliometric Study

Enterprise Resource Planning integrated with Business Analytics in Higher Education

Surveying the Effectiveness and Efficiency of Enterprise Resource Planning Systems

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