Titlebook: Supply Chain Analytics; Concepts, Techniques Kurt Y. Liu Textbook 2022 The Editor(s) (if applicable) and The Author(s), under exclusive lic

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Demand Management,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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Introduction,s and components of a supply chain. We discuss what supply chain management is and the main objectives of managing supply chains. In later sections, we provide an explanation on business analytics and the four basic types of analytics. Lastly, we move on to the introduction of the core concept of th

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Data Manipulation,writing, data indexing and selection, data merging and combination, data cleaning and preparation, and data computation and aggregation. In the final section of this chapter, we introduce some basic ways of working with text and datetime data.

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Data Visualization,t ways of creating a Figure and Axes. Then, the methods for customizing and formatting a figure are introduced. Next, we illustrate how to plot common charts using Matplotlib including scatter plot, bar chart, histogram, pie chart, and boxplot. The Seaborn methods for creating informative statistica

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Warehouse and Inventory Management,rst, the concept of warehouse management, associated activities, and warehouse management system are discussed, followed by warehouse performance measurement. Then, we move onto inventory management, focusing on addressing two essential questions for inventory managers, i.e., ‘how much to order?’ an

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Demand Management,ze, 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

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Logistics Management,e providers, and global logistics management. In later sections of this chapter, we focus on logistics network design. First, location decision is discussed, and a simplified store location problem is demonstrated with PuLP in Python. Then, we move onto route optimization and introduce the classic t