The Future of Flexible Work Arrangements for Hourly Employees

Karthikeyan Manikam

doi:doi:10.11648/j.jhrm.20231104.14

Research/Technical Note |

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The Future of Flexible Work Arrangements for Hourly Employees

Karthikeyan Manikam

Published in Journal of Human Resource Management (Volume 11, Issue 4)

Received: 5 November 2023 Accepted: 1 December 2023 Published: 22 December 2023

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Abstract

In light of the COVID-19 pandemic, the retail industry has rapidly shifted towards flexible work arrangements (FWAs) due to increased online shopping and changing consumer behavior. This paper provides a detailed analysis of the impact of FWAs on hourly retail employees, with a focus on integrating Artificial Intelligence (AI) and Machine Learning (ML) for schedule optimization and the associated challenges. The global rise in online shopping, which saw a 43% increase in 2020, has highlighted the limitations of traditional fixed schedules in meeting the fluctuating demand for retail staff. To address this issue, the paper proposes a three-module implementation strategy that includes labor focusing, automated schedule publication with shift-swapping capabilities, and a service to ensure compliance with flexible schedules. Labor forecasting is a crucial aspect of this strategy and faces complexities due to the unpredictable nature of the pandemic. Our approach utilizes a truncated dataset and AI/ML algorithms to recalibrate models in real-time, ensuring staffing levels are responsive to immediate market conditions rather than relying solely on historical patterns. Additionally, the paper discusses the development of an auto-population service for advance shift assignments, taking into account statutory notifications such as Oregon's 14-day rule. The inclusion of a 'shift swap' feature empowers employees to proactively manage their schedules, fostering a collaborative workplace culture. To minimize schedule disruptions, we propose a points-based system that penalizes postponements and non-compliance with schedule commitments. This system strikes a balance between operational requirements and employee flexibility, with Standard Operating Procedures (SOPs) in place to guide managerial responses to infractions. In conclusion, embracing FWAs, supported by innovative technologies and fair policies, positions retail businesses advantageously in the current market. This paper also calls for further research into the long-term effects of FWAs on mental health, productivity, and legislative frameworks, offering a comprehensive blueprint for the sector's evolution in the post-pandemic era.

Published in	Journal of Human Resource Management (Volume 11, Issue 4)
DOI	10.11648/j.jhrm.20231104.14
Page(s)	150-155
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Flexible Work Arrangements, Retail Workforce, Artificial Intelligence, Machine Learning, Labor Forecasting, Shift Swapping, Employee Autonomy, Schedule Optimization

References

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[2]	M. Bohlke-Schneider et al., "Resilient Neural Forecasting Systems," in Proc. Fourth Int. Workshop Data Management for End-To-End Machine Learning, Jun. 14, 2020. [Online]. Available: https://doi.org/10.1145/3399579.3399869.
[3]	R. McLean, "Walmart Wants to Hire 150,000 Temporary Workers as the Coronavirus Pandemic Continues," CNN, Mar. 20, 2020. [Online]. Available: www.cnn.com/2020/03/20/business/walmart-hiring-coronavirus/index.html. [Accessed: Dec. 2022].
[4]	"Forecast for Global Retail Sales Growth 2017-2023," Statista. [Online]. Available: www.statista.com/statistics/232347/forecast-of-global-retail-sales-growth/.
[5]	I. Cinga Akdere, "Retail Demand Forecasting: What, How, and Why," www.inventanalytics.com, Apr. 7, 2022. [Online]. Available: www.inventanalytics.com/blog/retail-demand-forecasting-what-how-and-why/.
[6]	E. Deepa, S. Kuppusamy, and P. Kamaleswari, "Man Power Planning in Retail Sector: An Empirical Evaluation," Vivekanandha College of Engineering for Women, Tiruchengode, Jul. 7, 2013.
[7]	D. Hur et al., "Real-Time Schedule Adjustment Decisions: A Case Study," Omega, vol. 32, no. 5, pp. 333-344, Oct. 2004. [Online]. Available: https://doi.org/10.1016/j.omega.2004.01.002.
[8]	"Machine Learning in Demand Forecasting for Retail," Akkio. [Online]. Available: www.akkio.com/post/machine-learning-in-demand-forecasting-for-retail. [Accessed: Nov. 7, 2023].
[9]	"Machine Learning in Retail Demand Forecasting," RELEX Solutions, Jun. 4, 2020. [Online]. Available: www.relexsolutions.com/resources/machine-learning-in-retail- demand-forecasting/.
[10]	S. Mou and D. J. Robb, "Real-Time Labour Allocation in Grocery Stores: A Simulation-Based Approach," Decision Support Systems, vol. 124, p. 113095, Sept. 2019. [Online]. Available: https://doi.org/10.1016/j.dss.2019.113095.
[11]	"Optimizing Labor Forecasting and Scheduling Is Key to Retailers' Success," Zebra Technologies. [Online]. Available: www.zebra.com/us/en/blog/posts/2021/optimizing- labor-forecasting-and-scheduling-key-to-retail-success.html. [Accessed: Nov. 7, 2023].
[12]	N. Varma Rajesh, "How Machine Learning Improves Retail Demand Forecasting," Algonomy, Sept. 8, 2022. [Online]. Available: algonomy.com/blogs/retail/how-machine- learning-improves-retail-demand-forecasting/.
[13]	"Retail Demand Forecasting in 2022 (and Beyond)," Retalon, Sept. 11, 2020. [Online]. Available: retalon.com/blog/demand-forecasting.
[14]	G. Thompson, "Controlling Action Times in Daily Workforce Schedules," The Cornell Hotel and Restaurant Administration Quarterly, vol. 37, no. 2, pp. 82-96, Apr. 1996. [Online]. Available: https://doi.org/10.1016/0010-8804(96)83105-3. [Accessed: May 21, 2020]. Please review the references to ensure they meet your requirements.
[15]	S. R. Agnihothri and P. F. Taylor, "Staffing a Centralized Appointment Scheduling Department in Lourdes Hospital," Interfaces, vol. 21, no. 5, pp. 1-11, Oct. 1991. [Online]. Available: https://doi.org/10.1287/inte.21.5.1. [Accessed: Feb. 1, 2021].

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APA Style

Manikam, K. (2023). The Future of Flexible Work Arrangements for Hourly Employees. Journal of Human Resource Management, 11(4), 150-155. https://doi.org/10.11648/j.jhrm.20231104.14

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ACS Style

Manikam, K. The Future of Flexible Work Arrangements for Hourly Employees. J. Hum. Resour. Manag. 2023, 11(4), 150-155. doi: 10.11648/j.jhrm.20231104.14

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Manikam K. The Future of Flexible Work Arrangements for Hourly Employees. J Hum Resour Manag. 2023;11(4):150-155. doi: 10.11648/j.jhrm.20231104.14

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@article{10.11648/j.jhrm.20231104.14,
  author = {Karthikeyan Manikam},
  title = {The Future of Flexible Work Arrangements for Hourly Employees},
  journal = {Journal of Human Resource Management},
  volume = {11},
  number = {4},
  pages = {150-155},
  doi = {10.11648/j.jhrm.20231104.14},
  url = {https://doi.org/10.11648/j.jhrm.20231104.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jhrm.20231104.14},
  abstract = {In light of the COVID-19 pandemic, the retail industry has rapidly shifted towards flexible work arrangements (FWAs) due to increased online shopping and changing consumer behavior. This paper provides a detailed analysis of the impact of FWAs on hourly retail employees, with a focus on integrating Artificial Intelligence (AI) and Machine Learning (ML) for schedule optimization and the associated challenges. The global rise in online shopping, which saw a 43% increase in 2020, has highlighted the limitations of traditional fixed schedules in meeting the fluctuating demand for retail staff. To address this issue, the paper proposes a three-module implementation strategy that includes labor focusing, automated schedule publication with shift-swapping capabilities, and a service to ensure compliance with flexible schedules. Labor forecasting is a crucial aspect of this strategy and faces complexities due to the unpredictable nature of the pandemic. Our approach utilizes a truncated dataset and AI/ML algorithms to recalibrate models in real-time, ensuring staffing levels are responsive to immediate market conditions rather than relying solely on historical patterns. Additionally, the paper discusses the development of an auto-population service for advance shift assignments, taking into account statutory notifications such as Oregon's 14-day rule. The inclusion of a 'shift swap' feature empowers employees to proactively manage their schedules, fostering a collaborative workplace culture. To minimize schedule disruptions, we propose a points-based system that penalizes postponements and non-compliance with schedule commitments. This system strikes a balance between operational requirements and employee flexibility, with Standard Operating Procedures (SOPs) in place to guide managerial responses to infractions. In conclusion, embracing FWAs, supported by innovative technologies and fair policies, positions retail businesses advantageously in the current market. This paper also calls for further research into the long-term effects of FWAs on mental health, productivity, and legislative frameworks, offering a comprehensive blueprint for the sector's evolution in the post-pandemic era.
},
 year = {2023}
}

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TY  - JOUR
T1  - The Future of Flexible Work Arrangements for Hourly Employees
AU  - Karthikeyan Manikam
Y1  - 2023/12/22
PY  - 2023
N1  - https://doi.org/10.11648/j.jhrm.20231104.14
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AB  - In light of the COVID-19 pandemic, the retail industry has rapidly shifted towards flexible work arrangements (FWAs) due to increased online shopping and changing consumer behavior. This paper provides a detailed analysis of the impact of FWAs on hourly retail employees, with a focus on integrating Artificial Intelligence (AI) and Machine Learning (ML) for schedule optimization and the associated challenges. The global rise in online shopping, which saw a 43% increase in 2020, has highlighted the limitations of traditional fixed schedules in meeting the fluctuating demand for retail staff. To address this issue, the paper proposes a three-module implementation strategy that includes labor focusing, automated schedule publication with shift-swapping capabilities, and a service to ensure compliance with flexible schedules. Labor forecasting is a crucial aspect of this strategy and faces complexities due to the unpredictable nature of the pandemic. Our approach utilizes a truncated dataset and AI/ML algorithms to recalibrate models in real-time, ensuring staffing levels are responsive to immediate market conditions rather than relying solely on historical patterns. Additionally, the paper discusses the development of an auto-population service for advance shift assignments, taking into account statutory notifications such as Oregon's 14-day rule. The inclusion of a 'shift swap' feature empowers employees to proactively manage their schedules, fostering a collaborative workplace culture. To minimize schedule disruptions, we propose a points-based system that penalizes postponements and non-compliance with schedule commitments. This system strikes a balance between operational requirements and employee flexibility, with Standard Operating Procedures (SOPs) in place to guide managerial responses to infractions. In conclusion, embracing FWAs, supported by innovative technologies and fair policies, positions retail businesses advantageously in the current market. This paper also calls for further research into the long-term effects of FWAs on mental health, productivity, and legislative frameworks, offering a comprehensive blueprint for the sector's evolution in the post-pandemic era.

VL  - 11
IS  - 4
ER  -

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Author Information

Karthikeyan Manikam

Amazon.com, Inc., Prosper, USA

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APA Style

Manikam, K. (2023). The Future of Flexible Work Arrangements for Hourly Employees. Journal of Human Resource Management, 11(4), 150-155. https://doi.org/10.11648/j.jhrm.20231104.14

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ACS Style

Manikam, K. The Future of Flexible Work Arrangements for Hourly Employees. J. Hum. Resour. Manag. 2023, 11(4), 150-155. doi: 10.11648/j.jhrm.20231104.14

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AMA Style

Manikam K. The Future of Flexible Work Arrangements for Hourly Employees. J Hum Resour Manag. 2023;11(4):150-155. doi: 10.11648/j.jhrm.20231104.14

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@article{10.11648/j.jhrm.20231104.14,
  author = {Karthikeyan Manikam},
  title = {The Future of Flexible Work Arrangements for Hourly Employees},
  journal = {Journal of Human Resource Management},
  volume = {11},
  number = {4},
  pages = {150-155},
  doi = {10.11648/j.jhrm.20231104.14},
  url = {https://doi.org/10.11648/j.jhrm.20231104.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jhrm.20231104.14},
  abstract = {In light of the COVID-19 pandemic, the retail industry has rapidly shifted towards flexible work arrangements (FWAs) due to increased online shopping and changing consumer behavior. This paper provides a detailed analysis of the impact of FWAs on hourly retail employees, with a focus on integrating Artificial Intelligence (AI) and Machine Learning (ML) for schedule optimization and the associated challenges. The global rise in online shopping, which saw a 43% increase in 2020, has highlighted the limitations of traditional fixed schedules in meeting the fluctuating demand for retail staff. To address this issue, the paper proposes a three-module implementation strategy that includes labor focusing, automated schedule publication with shift-swapping capabilities, and a service to ensure compliance with flexible schedules. Labor forecasting is a crucial aspect of this strategy and faces complexities due to the unpredictable nature of the pandemic. Our approach utilizes a truncated dataset and AI/ML algorithms to recalibrate models in real-time, ensuring staffing levels are responsive to immediate market conditions rather than relying solely on historical patterns. Additionally, the paper discusses the development of an auto-population service for advance shift assignments, taking into account statutory notifications such as Oregon's 14-day rule. The inclusion of a 'shift swap' feature empowers employees to proactively manage their schedules, fostering a collaborative workplace culture. To minimize schedule disruptions, we propose a points-based system that penalizes postponements and non-compliance with schedule commitments. This system strikes a balance between operational requirements and employee flexibility, with Standard Operating Procedures (SOPs) in place to guide managerial responses to infractions. In conclusion, embracing FWAs, supported by innovative technologies and fair policies, positions retail businesses advantageously in the current market. This paper also calls for further research into the long-term effects of FWAs on mental health, productivity, and legislative frameworks, offering a comprehensive blueprint for the sector's evolution in the post-pandemic era.
},
 year = {2023}
}

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TY  - JOUR
T1  - The Future of Flexible Work Arrangements for Hourly Employees
AU  - Karthikeyan Manikam
Y1  - 2023/12/22
PY  - 2023
N1  - https://doi.org/10.11648/j.jhrm.20231104.14
DO  - 10.11648/j.jhrm.20231104.14
T2  - Journal of Human Resource Management
JF  - Journal of Human Resource Management
JO  - Journal of Human Resource Management
SP  - 150
EP  - 155
PB  - Science Publishing Group
SN  - 2331-0715
UR  - https://doi.org/10.11648/j.jhrm.20231104.14
AB  - In light of the COVID-19 pandemic, the retail industry has rapidly shifted towards flexible work arrangements (FWAs) due to increased online shopping and changing consumer behavior. This paper provides a detailed analysis of the impact of FWAs on hourly retail employees, with a focus on integrating Artificial Intelligence (AI) and Machine Learning (ML) for schedule optimization and the associated challenges. The global rise in online shopping, which saw a 43% increase in 2020, has highlighted the limitations of traditional fixed schedules in meeting the fluctuating demand for retail staff. To address this issue, the paper proposes a three-module implementation strategy that includes labor focusing, automated schedule publication with shift-swapping capabilities, and a service to ensure compliance with flexible schedules. Labor forecasting is a crucial aspect of this strategy and faces complexities due to the unpredictable nature of the pandemic. Our approach utilizes a truncated dataset and AI/ML algorithms to recalibrate models in real-time, ensuring staffing levels are responsive to immediate market conditions rather than relying solely on historical patterns. Additionally, the paper discusses the development of an auto-population service for advance shift assignments, taking into account statutory notifications such as Oregon's 14-day rule. The inclusion of a 'shift swap' feature empowers employees to proactively manage their schedules, fostering a collaborative workplace culture. To minimize schedule disruptions, we propose a points-based system that penalizes postponements and non-compliance with schedule commitments. This system strikes a balance between operational requirements and employee flexibility, with Standard Operating Procedures (SOPs) in place to guide managerial responses to infractions. In conclusion, embracing FWAs, supported by innovative technologies and fair policies, positions retail businesses advantageously in the current market. This paper also calls for further research into the long-term effects of FWAs on mental health, productivity, and legislative frameworks, offering a comprehensive blueprint for the sector's evolution in the post-pandemic era.

VL  - 11
IS  - 4
ER  -

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