Using Figure 9.5 attached and the article, compile a comprehensive document explaining, in detail, using practical examples, Toyota SA would do in terms of Demand Forecasting with regards to the supply of products subsequent to South Africa being accorded junk status by S&P Global Ratings. Use Harvard referencing.

Article;

Forecasting demand of commodities after natural disasters Xiaoyan Xu, Yuqing Qi, Zhongsheng Hua * School of Management, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China article info Keywords: Natural disaster Demand forecasting Emergency management EMD ARIMA abstract Demand forecasting after natural disasters is especially important in emergency management. However, since the time series of commodities demand after natural disasters usually has a great deal of nonlinearity and irregularity, it has poor prediction performance of applying the traditional statistical and econometric models such as linear regression and autoregressive moving average (ARMA) to this kind of data. This paper tries to apply a hybrid forecasting method which is an integration of empirical mode decomposition (EMD) and autoregressive integrated moving average (ARIMA). The EMD-ARIMA forecasting methodology is then applied to the prediction of agricultural products demand after the 2008 Chinese winter storms. Forecasting results indicate that EMD can improve the prediction accuracy of classical ARIMA forecasting method for demand of commodities after natural disasters. 2009 Elsevier Ltd. All rights reserved. 1. Introduction Because of the uncertainty of disasters, fluctuation of commodities demand after natural disasters is usually drastic and irregular. It increases the difficulties of emergency resource management. In most disaster relief practices, demand information for emergency resources is mainly collected from operational elements at the regional level and then the requests flow upward and are tracked at the headquarters level (such as instructions of United States National Response Framework (NRF) Emergency Support Function (ESF) #7 (United States Department of Homeland Security, 2008) and National Incident Management System (NIMS) Chap. 4 (United States Department of Homeland Security, 2004)). Because of the uncertainty of natural disasters' evolvement, the requestors are unable to accurately describe the needed resource type and its amount at many times. And as communication conditions are usually bad, many acute requests may not be sent to the resource management center in time. For these reasons, accurate estimation and prediction of commodities demand after natural disasters is significantly important to emergency resource management which includes resource collecting, inventorying, dispatching and transporting tasks. In recent literatures, many forecasting methods have been applied to predict commodities demand after natural disasters. Expert judgment method is one the most widely used forecasting methods. Fuzzy synthetic evaluation techniques (Song, Hao, Murakami, & Sadohara, 1996), analytic hierarchy process (AHP) (Chen, Liang, & Zhang, 2009; Xiong et al., 2007) and Delphi method (Chang & Wang, 2006) can be regarded as examples of such method. Expert judgment method is a good choice in many situations especially when the structure of problem is very complicated. But the process of expert judgment is subjective and hard to interpret. Statistical methods such as linear regression, integration analysis (del Ninno, Dorosh, & Simth, 2003), autoregressive moving average (ARMA) (Balaguer, Palomares, Soria, & Martin-Guerrero, 2008) also have been widely applied in the demand forecasting. However, in the series of commodities demand after natural disasters, there is a great deal of nonlinearity and irregularity. The prediction performance may be very poor by applying these traditional statistical and econometric models to this kind of data (Weigend & Gershenfeld, 1994). AI models such as artificial neural networks (ANN) (Tsai, Lee, & Wei, 2009) also have been widely applied to demand forecasting. But, AI models also have their own shortcomings and disadvantages such as costing lots of calculating resources and some AI models are sensitive to parameter selection. To inherit the merits of common forecasting methods and avoid their defects, this study attempts to apply a hybrid methodology which is an integration of ARIMA and the empirical mode decomposition (EMD) method which is proposed by Huang, Shen, and Long (1998) to the prediction of commodities demand after natural disasters. EMD is a generally nonlinear, non-stationary data process method and has been widely applied in engineering such as fault diagnosis, computer image processing etc. The rest of this study is organized as follows: Section 2 describes a real disaster case which is going to be analyzed by our forecasting method. The forecasting method is introduced in Section 3. Section 4 proposes the forecasting results of methodology. Section 5 concludes the paper. 2. Problem description The disaster of 2008 Chinese winter storms is selected as our analysis object. The 2008 Chinese winter storms is China's worst 0957-4174/$ - see front matter 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2009.11.069

- X uLink - Student Home X Content X Bb 4667153 X Unit 7 Assignment 2 Figure 9 X Dashboard X + -> C @ File | C:/Users/Vericon/OneDrive%20-%20Toyota%20South%20Africa%20(PTY)%20Ltd/Desktop/Unit%207%20Assignment%202%20Figure%209.5%20(1).pdf YouTube V Maps E Unit 7 Assignment 2 Figure 9.5 (1).pdf / 1 100% + Figure 9.5 Forecasting techniques Forecasting techniques/approaches Qualitative Quantitative 'Soft' information - for example. "Hard information that eliminates human opinion, hunches that the personal biases associated may provide information and with qualitative approaches insights not obtainable by quantitative approaches Expert opinion Time series Market surveys Delphi method Type here to search O Fi 09:15 A (ENG 2022/08/12