Demand Management

Kurt Y. Liu ()
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Kurt Y. Liu: University of Glasgow

Chapter 8 in Supply Chain Analytics, 2022, pp 271-318 from Springer

Abstract: Abstract In this chapter, we explore demand management for effective supply chain management. First, we introduce the concept and the SPSS (sense, predict, seize, and stabilize) model of demand management. Second, demand forecasting is discussed including both qualitative and quantitative methods. Third, we specifically look at time series forecasting using both the traditional methods such as Weight Moving Average, Exponential Smoothing, ARIMA and SARIMA, and the machine learning methods such as Random Forest Regression and Extreme Gradient Boosting (XGBoost).

Keywords: Demand forecasting; Time series forecasting; Moving average; Exponential smoothing; Autoregressive; ARMA; ARIMA; SARIMA; Random forest; XGBoost (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-92224-5_8

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DOI: 10.1007/978-3-030-92224-5_8

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