RLS Model for Forecasting Resource Fulfilment in IT and Consulting Firms: The Global Context

Rajashekhar Karjagi and Sachin Shivakalimath

Metamorphosis: A Journal of Management Research, 2022, vol. 21, issue 1, 40-47

Abstract: Global IT and consulting firms are predominantly dependent on skilled manpower for their business. There are many skills and locations with multiple roles which may not be easily traced in the organizations where large number of employees are working in multiple locations having many skill sets. In such a situation, the resources fulfilment across the organization with diverse skill sets becomes an issue. It is an important aspect to have an efficient method to fulfil the resources appropriately without losing the revenue leakage due to delays in resource fulfilment. In that context, a Resource Location & Skill (RLS) model has been developed in the present research to provide the forecast for resource fulfilment as per the skill and location. The model forecasted about 48 employees at an average of 50% accuracy based on only five skill sets data (resource indent data at SOW [Statement of Work]/project level) from 1 May 2018 to 31 August 2018. Autoregressive integrated moving average (ARIMA) time series model in R statistical software was used for the analysis.

Keywords: RLS; AIMA; forecast; HR analytics; skill (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:metjou:v:21:y:2022:i:1:p:40-47

DOI: 10.1177/09726225221075086

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