Demand Management

Kurt Y. Liu

Demand Management

Engineering Systems & Supply Chain Management

Research output: Chapter in Book/Published conference output › Chapter

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).

Original language	English
Title of host publication	Supply Chain Analytics Concepts, Techniques and Applications
Publisher	Palgrave Macmillan
Chapter	8
Pages	271-318
Edition	1st
ISBN (Electronic)	9783030922245
ISBN (Print)	9783030922238
Publication status	Published - 8 Apr 2022

Cite this

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title = "Demand Management",

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).",

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TY - CHAP

T1 - Demand Management

AU - Liu, Kurt Y.

PY - 2022/4/8

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N2 - 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).

AB - 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).

UR - https://link.springer.com/chapter/10.1007/978-3-030-92224-5_8

M3 - Chapter

SN - 9783030922238

SP - 271

EP - 318

BT - Supply Chain Analytics Concepts, Techniques and Applications

PB - Palgrave Macmillan

ER -

Demand Management

Abstract

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Supply Chain Analytics: Concepts, Techniques and Applications

Cite this