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On Predicting the Work Load for Service Contractors

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Artificial Intelligence XXXIX (SGAI-AI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13652))

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Abstract

Service Industries rely on resource planning and service optimisation to improve operational efficiency. Forecasting the demand for the service with high accuracy plays a significant role in proactively planning the resources to support the expected demand. With the evolution of the Internet Of Things (IoT), the service contractors use different types of devices connected to the internet to capture the demand and monitor the historical pattern. In this work, we analyse the arrival pattern tracked using different IoT devices of personnel employed by a contractor at different zones for providing service. This arrival pattern at a specific zone is considered the service demand. We document this analysis and forecast the future arrival pattern of personnel at different zones. We compare different regression models based on their accuracy to select the best fit model and report the results. The best fit model is used for forecasting the arrival pattern by a real-life application.

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Author information

Authors and Affiliations

  1. EBTIC, Khalifa University, Abu Dhabi, UAE

    Himadri Sikhar Khargharia & Siddhartha Shakya

  2. Department of IoT and AI, Etisalat, Abu Dhabi, UAE

    Sara Sharif

  3. BT Research, British Telecom, London, UK

    Russell Ainslie & Gilbert Owusu

Authors
  1. Himadri Sikhar Khargharia
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  2. Siddhartha Shakya
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  3. Sara Sharif
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  4. Russell Ainslie
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  5. Gilbert Owusu
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Corresponding author

Correspondence to Himadri Sikhar Khargharia .

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Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. DFKI: German Research Center for Artificial Intelligence, Oldenburg, Germany

    Frederic Stahl

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Khargharia, H.S., Shakya, S., Sharif, S., Ainslie, R., Owusu, G. (2022). On Predicting the Work Load for Service Contractors. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XXXIX. SGAI-AI 2022. Lecture Notes in Computer Science(), vol 13652. Springer, Cham. https://doi.org/10.1007/978-3-031-21441-7_16

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  • DOI: https://doi.org/10.1007/978-3-031-21441-7_16

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