Time keeping and Labor Cost Optimization through Predictive Analytics and Environmental Intelligence
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P106Keywords:
Timekeeping, Labor Optimization, Predictive Analytics, Environmental Intelligence, Workforce Forecasting, Machine Learning, Weather Data, Economic Indicators, Ai Scheduling, Workforce Agility, Labor Cost Management, Operational EfficiencyAbstract
Inaccurate work forecasting has traditionally caused problems in many other different fields that lead to unneeded expenses, scheduling conflicts & also poor staff optimization. By using predictive analytics & also environmental information, this study presents a dynamic approach to address these problems. By using historical workforce data, actual time operational measurements & also more contextual environmental factors such as weather patterns, regional events & also more economic indicators our method creates complex forecasting models that adapt to changing their conditions. We assessed several work demand scenarios in various circumstances using a combination of more scenario-based simulations and also ML techniques. The results showed significant increases in predicting accuracy, which would save employment expenses, enhance shift coordination & also increase their agility in handling unanticipated demand variations. The structure improves operational agility so that companies may go from more reactive to proactive planning. Retail, shipping, manufacturing, and hospitality among other sectors might benefit greatly from these insights as even little efficiency gains can result in major financial savings. Our findings show that integrating environmental awareness into work management systems helps companies to increase their staff utilization & also improve their resistance against outside shocks. This article emphasizes the growing need of smart, data-driven solutions in the effective management of human capital in a dynamic & erratic environment
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References
[1] Malikireddy, Sai Kiran Reddy. "Transforming SME cloud cost management with artificial intelligence." International Journal of Cloud Computing and Services Science 9.3 (2020): 112-124.
[2] Williams, Steve. Business intelligence strategy and big data analytics: a general management perspective. Morgan Kaufmann, 2016.
[3] Balafoutis, Athanasios T., Frits K. Van Evert, and Spyros Fountas. "Smart farming technology trends: Economic and environmental effects, labor impact, and adoption readiness." Agronomy 10.5 (2020): 743.
[4] Veluru, Sai Prasad. “Flink-Powered Feature Engineering: Optimizing Data Pipelines for Real-Time AI”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 1, Nov. 2021, pp. 512-33
[5] Isson, Jean Paul, and Jesse S. Harriott. People analytics in the era of big data: Changing the way you attract, acquire, develop, and retain talent. John Wiley & Sons, 2016.
[6] Talakola, Swetha. “The Importance of Mobile Apps in Scan and Go Point of Sale (POS) Solutions”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 1, Sept. 2021, pp. 464-8
[7] Kaur, Ishnoor, et al. "Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic." Environmental Science and Pollution Research 28.30 (2021): 40515-40532.
[8] Vasanta Kumar Tarra, and Arun Kumar Mittapelly. “AI-Driven Fraud Detection in Salesforce CRM: How ML Algorithms Can Detect Fraudulent Activities in Customer Transactions and Interactions”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 2, Oct. 2022, pp. 264-85
[9] Crawford, Kate. The atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press, 2021.
[10] Veluru, Sai Prasad. “Streaming MLOps: Real-Time Model Deployment and Monitoring With Apache Flink”. Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 2, July 2022, pp. 223-45
[11] Falahat, Mohammad, et al. "Big data analytics capability ecosystem model for SMEs." Sustainability 15.1 (2022): 360.
[12] Syed, Ali Asghar Mehdi, and Shujat Ali. “Multi-Tenancy and Security in Salesforce: Addressing Challenges and Solutions for Enterprise-Level Salesforce Integrations”. Newark Journal of Human-Centric AI and Robotics Interaction, vol. 3, Feb. 2023, pp. 356-7
[13] Silva, António João, et al. "Business analytics in Industry 4.0: A systematic review." Expert systems 38.7 (2021): e12741.
[14] Paidy, Pavan. “Log4Shell Threat Response: Detection, Exploitation, and Mitigation”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 1, Dec. 2021, pp. 534-55
[15] Biswas, Avijit. Lean Optimization of newly established Assembly Line using Data Analysis of Quality by the feed from Image recognition Industry 4.0 and Jishuken Study. Diss. Politecnico di Torino, 2021.
[16] Anand, Sangeeta. “AI-Based Predictive Analytics for Identifying Fraudulent Health Insurance Claims”. International Journal of AI, BigData, Computational and Management Studies, vol. 4, no. 2, June 2023, pp. 39-47
[17] Moro Visconti, Roberto, and Donato Morea. "Big data for the sustainability of healthcare project financing." Sustainability 11.13 (2019): 3748.
[18] Talakola, Swetha. “Automating Data Validation in Microsoft Power BI Reports”. Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 3, Jan. 2023, pp. 321-4
[19] Nowak, Małgorzata. "Use of Business Intelligence Tool in Health Protection Entities." Zeszyty Naukowe Politechniki Częstochowskiej. Zarządzanie 32 (2018): 274-284.
[20] Atluri, Anusha. “Extending Oracle HCM Cloud With Visual Builder Studio: A Guide for Technical Consultants ”. Newark Journal of Human-Centric AI and Robotics Interaction, vol. 2, Feb. 2022, pp. 263-81
[21] Yasodhara Varma. “Graph-Based Machine Learning for Credit Card Fraud Detection: A Real-World Implementation”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 2, June 2022, pp. 239-63
[22] Spring, Martin, James Faulconbridge, and Atif Sarwar. "How information technology automates and augments processes: Insights from Artificial‐Intelligence‐based systems in professional service operations." Journal of Operations Management 68.6-7 (2022): 592-618.
[23] Atluri, Anusha. “Data Security and Compliance in Oracle HCM: Best Practices for Safeguarding HR Information”. Newark Journal of Human-Centric AI and Robotics Interaction, vol. 1, Oct. 2021, pp. 108-31
[24] Kupunarapu, Sujith Kumar. "AI-Driven Crew Scheduling and Workforce Management for Improved Railroad Efficiency." International Journal of Science And Engineering 8.3 (2022): 30-37.
[25] Frame, J. Davidson, and Yanping Chen. "Why Data Analytics in Project Management?." Data Analytics in Project Management. Auerbach Publications, 2018. 7-22.
[26] Paidy, Pavan. “Scaling Threat Modeling Effectively in Agile DevSecOps”. American Journal of Data Science and Artificial Intelligence Innovations, vol. 1, Oct. 2021, pp. 556-77
[27] Syed, Ali Asghar Mehdi. “Edge Computing in Virtualized Environments: Integrating Virtualization and Edge Computing for Real-Time Data Processing”. Essex Journal of AI Ethics and Responsible Innovation, vol. 2, June 2022, pp. 340-63
[28] Disselkamp, Lisa, ed. Workforce asset management book of knowledge. John Wiley & Sons, 2013.
[29] Buyya, Rajkumar, et al. "Scheduling parameter sweep applications on global Grids: a deadline and budget constrained cost–time optimization algorithm." Software: Practice and Experience 35.5 (2005): 491-512.
[30] Srinivas Chippagiri, Savan Kumar, Sumit Kumar,” Scalable Task Scheduling in Cloud Computing Environments Using Swarm Intelligence-Based Optimization Algorithms”, Journal of Artificial Intelligence and Big Data (jaibd), 1(1),1-10,2016.
