Research Article
Pattern Discovery and Forecasting of Attrition Using Time Series Analysis
@INPROCEEDINGS{10.1007/978-3-031-35081-8_7, author={Saumyadip Sarkar and Rashmi Agarwal}, title={Pattern Discovery and Forecasting of Attrition Using Time Series Analysis}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part II}, proceedings_a={ICISML PART 2}, year={2023}, month={7}, keywords={Forecasting Time series ARIMA Seasonal ARIMA Exponential Smoothing Holt-Winters Data Discovery Pareto Trend Analysis Regression Moving Average LSTM}, doi={10.1007/978-3-031-35081-8_7} }- Saumyadip Sarkar
Rashmi Agarwal
Year: 2023
Pattern Discovery and Forecasting of Attrition Using Time Series Analysis
ICISML PART 2
Springer
DOI: 10.1007/978-3-031-35081-8_7
Abstract
Attrition is a burning problem for any industry and if the rate of attrition is very high, it creates enormous pressure on the process to function effectively. This is precisely what a leading organization’s transportation Line of Business (LOB) is going through where attrition is averaging around 34% for the last three years. Time and again, it has struggled with managing a healthy attrition rate. As a result, there has been a constant occurrence of missed Service Level Agreements (SLAs) resulting in huge penalties. For managers, managing workload has become extremely tedious in the current context.
To tackle this problem, this study aims to forecast attrition using time series analysis at various levels based on only the attrition data available for the last fourteen months. The hypothesis here is, if probable attrition is forecasted well in advance, a plan can be put in place to hire employees and make them available whenever there is demand from contract managers. This in turn can help individual contract managers manage their workload efficiently, and reduce their missed SLAs, thereby reducing penalties.
The proposed solution is to compare various Time Series Forecasting techniques like Auto-Regressive Integrated Moving Average (ARIMA), Seasonal Auto-Regressive Integrated Moving Average (SARIMA), Exponential Smoothing (ES), Holt-Winters (HW), Moving Average, Ratio to Moving Average, based on attrition data and compared to arrive at the best possible solution.
The novelty of this study is the use of time series forecasting techniques to forecast future attrition trends specifically based on attrition data, which has not been explored much. This forecasted data can be used to better workload management which in turn is expected to reduce missed SLAs and penalties.
