scholarly journals Forecasting the Logistics Demand of Guangxi Beibu Gulf Port

2021 ◽  
Vol 9 (1) ◽  
pp. 73-89
Author(s):  
Guoyou Yue
Keyword(s):  
Moving Average ◽  
Beibu Gulf ◽  
Demand Forecast ◽  
Forecasting Models ◽  
Forecasting Method ◽  
Port Logistics ◽  
Logistics Demand ◽  
Cargo Throughput ◽  
Port Areas ◽  
Weighted Moving Average

Objective - The objective of this paper is to establish the forecasting models of port cargo throughput and container throughput in Guangxi Beibu Gulf Port in the next 5 years, and to put forward the countermeasures of port logistics development in Guangxi Beibu Gulf Port according to the forecast results. Methodology/Technique – The data of cargo throughput and container throughput of Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou in 2009-2020 are collected through the data of Guangxi Statistical Yearbook and Guangxi Statistical Bulletin. Based on 2019 and 2020, the forecasting models of cargo throughput and container throughput in Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou are establishe using a weighted moving average forecasting method. The cargo throughput and container throughput of Guangxi Beibu Gulf Port and 3 port areas of Beihai, Fangcheng and Qinzhou in 2020/2021-2025 are predicted. Findings – The forecast results show that by 2025, the cargo throughput of Guangxi Beibu Gulf Port is expected to exceed 400 million tons, and the container throughput is expected to exceed 10 million TEU. According to the fitting diagram of forecast results and actual data, it can be seen that the accuracy of the forecast results is very high. Novelty – It is innovative to select 2 base years in 2019 and 2020 to establish forecasting model. Based on the comparative analysis of the forecast results, this paper puts forward various measures to promote the development of port logistics of Guangxi Beibu Gulf port, such as strengthening the construction of port self-condition, strengthening the co-ordinated development of port and economic hinterland, speeding up the construction of port collection and distribution system, training and introducing all kinds of high-quality port logistics talents. Type of Paper: Empirical. JEL Classification: C53, R41. Keywords: Logistics Demand Forecast; Cargo Throughput Forecast; Container Throughput Forecast; Weighted Moving Average Forecasting Method; Guangxi Beibu Gulf Port Reference to this paper should be made as follows: Yue, N. (2021). Forecasting the Logistics Demand of Guangxi Beibu Gulf Port, GATR Global J. Bus. Soc. Sci. Review, 9(1): 73 – 89. https://doi.org/10.35609/gjbssr.2021.9.1(9)

scholarly journals Forecasting the Logistics Demand of Guangxi Beibu Gulf Port

2020 ◽  
Vol 11 (1) ◽  
pp. 153-153
Author(s):  
Guoyou Yue
Keyword(s):  
Moving Average ◽  
Prediction Method ◽  
Western China ◽  
Beibu Gulf ◽  
Demand Forecast ◽  
Healthy Development ◽  
Port Logistics ◽  
Asean Countries ◽  
Logistics Demand ◽  
Cargo Throughput

In February 2007, Guangxi Zhuang Autonomous Region People's Government integrated Fangcheng, Qinzhou and Beihai three coastal ports to establish Guangxi Beibu Gulf International Port Group Co., LTD. At this point, Guangxi Beibu Gulf Port integration of three, Guangxi Beibu Gulf Port ushered in a major development opportunity. Guangxi Beibu Gulf Port is one of the important ports in China's coastal cities open to the outside world. It is the meeting point of the three economic circles of South China, Southwest China and ASEAN, and also the most convenient land and sea passage between China and ASEAN countries. With the vigorous implementation of the "Belt and Road" initiative and the construction of new land and sea passages in western China, Guangxi Beibu Gulf Port has become an important gateway and connection point for the implementation of these strategies. Its strategic status keeps rising, and Guangxi Beibu Gulf Port has also developed rapidly. In recent years, how is the port logistics development of Guangxi Beibu Gulf Port? What are the changing rules and trends of its cargo throughput and container throughput? What measures can continuously and effectively promote the port logistics development of Guangxi Beibu Gulf Port? This paper will carry out research and analysis on these problems in order to better promote the healthy development of Guangxi Beibu Gulf Port. Keywords: Logistics demand forecast, Cargo throughput forecast, Container throughput forecast, Weighted moving average prediction method, Guangxi Beibu Gulf Port

scholarly journals Sales Analysis Using the Forecasting Method

bit-Tech ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 146-149
Author(s):  
Amesanggeng Pataropura ◽  
Riki Riki ◽  
Ariadi Saputra
Keyword(s):  
Current Flow ◽  
Current System ◽  
Moving Average ◽  
Scientific Work ◽  
Transaction Data ◽  
Forecasting Method ◽  
Effectiveness And Efficiency ◽  
To Come ◽  
New System ◽  
Weighted Moving Average

Sales Analysis Using Forecasting Method aims to improve effectiveness and efficiency that facilitates companies in business transaction processes, improve the delivery of information quickly, accurately, and transaction data well and minimize errors. The method used in the presentation of this scientific work is by using a forecasting method which helps determine the approximate stock of goods to come. With 3 forecasting modules are: Moving Average, Weighted Moving Average, Trend Projection is used to perform the forecasting process of the upcoming stock of goods. Can solve problems that exist in the current system so that it can help in improving its services by calculating the stock and helping by determining the average data that has been linked to the forecasting module whose results can be concluded through reports per period. It can be concluded that the results of implementing this new system can help companies in recording each transaction that occurs becomes more efficient and effective, so that it can overcome the problems that exist in the current system. With this we can predict the current flow of goods that have been calculated based on 3 (three) modules that have connections with the system

scholarly journals IMPLEMENTASI PERAMALAN PENJUALAN IKAN LAUT UNTUK OPTIMASI PERSEDIAAN BAHAN BAKU (Studi Kasus di UD Harum Bungah Gresik)

2021 ◽  
Vol 6 (2) ◽  
pp. 59
Author(s):  
Khanifatus Sa'diyah ◽  
Narto Narto
Keyword(s):  
Manufacturing Industry ◽  
Raw Materials ◽  
Moving Average ◽  
Demand Forecasting ◽  
Raw Material ◽  
Forecasting Method ◽  
National Economic ◽  
Marine Products ◽  
Sea Fish ◽  
Weighted Moving Average

Indonesian marine waters have high marine resource resources. One of Indonesia's seafood commodities is fish. With proper management and utilization, marine products become one of the promising business opportunities for the community, so that fisheries become one of the supporting sectors of national economic development. UD Harum is one of the businesses engaged in the fisheries sector as a supplier of marine fish raw material needs to meet the needs of the manufacturing industry. To optimize production planning to meet industry demand, forecasting of sea fish sales data forecasting in the previous period is needed to anticipate a shortage of raw materials. The purpose of this forecasting is to implement forecasting using the Single Moving Average (SMA), Weighted Moving Average (WMA) and Centered Moving Average (CMA) methods in forecasting sea fish sales at UD Harum and to find out the best forecasting results to increase sea fish sales at UD Harum. Forecasting results show forecasting using the Single Moving Average (3-monthly) and (5-monthly) methods respectively 8107.67 kg and 8399.4 kg. For the Weighted Moving Average (3-monthly) and (5-monthly) methods, the results of forecasting are 7268,963 kg and 7443,452, respectively. As for the Centered Moving Average (3-monthly) method with forecast results of 8107.67 kg. The forecasting method chosen to optimize sales is the Centered Moving Average method with a forecast value of 8107.67 kg and has the smallest forecasting error compared to other forecasting methods with a MAPE value of 0.30875 and MPE of -0.1720.

scholarly journals Implementasi Metode Weighted Moving Average Untuk Sistem Peramalan Penjualan Markas Coffee

2021 ◽  
Vol 6 (3) ◽  
pp. 154
Author(s):  
Muchamad Rizqi ◽  
Antonius Cahya ◽  
Nova El Maida
Keyword(s):  
Information System ◽  
Calculation Result ◽  
Moving Average ◽  
Percentage Error ◽  
Arabica Coffee ◽  
Previous Period ◽  
Robusta Coffee ◽  
Forecasting Method ◽  
The Mean ◽  
Weighted Moving Average

Headquarters Coffee is one of the businesses engaged in the culinary field of coffee drinks. The problem that occurs at the Coffee Headquarters is that business activities are still carried out manually. In addition, determining sales in the next period only refers to the sales data of the previous period, resulting in owners often experiencing shortages or excess stocks of coffee to be sold due to uncertain sales. Therefore we need a forecasting method (Forecasting) that is appropriate and can be applied to an Information System in the form of a Website. The purpose of making this forecasting information system is to assist companies in recording sales to make it more practical by applying the Weighted Moving Average (WMA) method. From the results of the calculation of the WMA method, the level of accuracy will then be calculated using the Mean Absolute Percentage Error (MAPE) method. The results of forecasting by applying the WMA method and MAPE calculations on weights 3, 4 and 5 show that the Robusta coffee on the Robusta menu which has the smallest MAPE is weight 3 with a calculation result of 19.2499 and the Robusta Milk menu which has the smallest MAPE is weight 4 with the calculation result is 15.21879166 and Excelsa coffee on the excelsa menu which has the smallest MAPE is weight 3 with a calculation result of 19.1538 and the Excelsa Susu menu which has the smallest MAPE is weight 5 with a calculation of 17.27650182 while for Arabica coffee on the Arabica menu which has the smallest MAPE is weight 4 with a calculation result of 18.1735 and the Arabica Susu menu which has the smallest MAPE is weight 5 with a calculation result of 16.24012072. Where the Mape value produced by each type of coffee is still below 20%, which means the forecasting results can be categorized as good.

scholarly journals INVENTORY CONTROL USING STATISTICS FORECASTING ON MANUFACTURE COMPANY

2014 ◽  
Vol 1 (2) ◽  
Author(s):  
Rizki Tri Prasetio
Keyword(s):  
Linear Regression ◽  
Inventory Control ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Economic Order Quantity ◽  
Economic Order ◽  
Order Quantity ◽  
Naive Method ◽  
Forecasting Method ◽  
Weighted Moving Average

Abstract - Inventory Control is a main and the most crucial factor for company that can cause an efficient production process. A lot of research use different method to support inventory control. This research use several forecasting method such as, Naïve Method, Exponential Smoothing, Exponential Smoothing with Trend, Moving Average, Weighted Moving Average and Linear Regression. Economic Order Quantity is used to calculate raw materials inventory. This research results suggest that company use Linear Regression as it has the smallest MAD and MSE of the six other methods. The company also has to implement Economic Order Quantity to minimalize loss profit due to excess inventory. Keywords : Inventory Control, Forecasting Method, Economic Order Quantity Abstrak - Pengendalian inventory merupakan salah satu faktor utama dan sangat penting bagi perusahaan karena sangat berpengaruh terhadap terciptanya proses produksi yang efisien. Banyak penelitian yang menggunakan beberapa metode guna mendukung pengendalian inventory. Penelitian ini menggunakan beberapa metode peramalan (forecasting method) diantaranya, Naïve Method, Exponential Smoothing, Exponential Smoothing with Trend, Moving Average, Weighted Moving Average dan Linear Regression. Serta Economic Order Quantity (EOQ) yang digunakan untuk menghitung persediaan bahan baku yang dibutuhkan dalam proses produksi. Hasil penelitian menghasilkan bahwa metode peramalan Linear Regression memiliki tingkat kesalahan yang dihitung menggunakan MAD dan MSE paling kecil diantara 6 metode lainnya. Serta mengimplementasikan Economic Order Quantity untuk meminimalisir kerugian akibat kelebihan persediaan. Kata Kunci : Pengendalian Persediaan, Metode Peramalan, Economic Order Quantity

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