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Clustering-Based Stability and Seasonality Analysis for Optimal Inventory Prediction
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Author(s): Manish Joshi (North Maharashtra University, India), Pawan Lingras (Saint Mary's University Halifax, Canada), Gajendra Wani (Bhusawal Arts, Science, and Commerce College, India)and Peng Zhang (Saint Mary's University Halifax, Canada)
Copyright: 2014
Pages: 18
Source title:
Global Trends in Intelligent Computing Research and Development
Source Author(s)/Editor(s): B.K. Tripathy (VIT University, India)and D. P. Acharjya (VIT University, India)
DOI: 10.4018/978-1-4666-4936-1.ch001
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Abstract
This chapter exemplifies how clustering can be a versatile tool in real life applications. Optimal inventory prediction is one of the important issues faced by owners of retail chain stores. Researchers have made several attempts to develop a generic forecasting model for accurate inventory prediction for all products. Regression analysis, neural networks, exponential smoothing, and Autoregressive Integrated Moving Average (ARIMA) are some of the widely used time series prediction techniques in inventory management. However, such generic models have limitations. The authors propose an approach that uses time series clustering and time series prediction techniques to forecast future demand for each product in an inventory management system. A stability and seasonality analysis of the time series is proposed to identify groups of products (local groups) exhibiting similar sales patterns. The details of the experimental techniques and results for obtaining optimal inventory predictions are shared in this chapter.
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