Container Shipment Demand Forecasting in the Australian Shipping Industry: A Case Study of Asia–Oceania Trade Lane

Demand forecasting has a pivotal role in making informed business decisions by predicting future sales using historical data. Traditionally, demand forecasting has been widely used in the management of production, staffing and warehousing for sales and marketing data. However, the use of demand forecasting has little been studied in the container shipping industry. Improved visibility into the demand for container shipments has been a long-held objective of industry stakeholders. This paper addresses the shortcomings of both short-term and long-term shipment demand forecasting for the Australian container shipping industry. In this study, we compare three forecasting models, namely, the seasonal auto-regressive integrated moving average (SARIMA), Holt–Winters’ seasonal method and Facebook’s Prophet, to find the best fitting model for short-term and long-term import demand forecasting in the Australian shipping industry. Demand data from three years, i.e., 2016–2018, is used for the Asia–Oceania trade lane. The mean absolute percentage error (MAPE), root mean squared error (RMSE) and 2-fold walk-forward cross-validation are used for the model evaluation. The experiment results observed from the selected metrics suggest that Prophet outperforms the other models in its comparison for container shipment demand forecasting.

Economic Impact of Container-loading Problem

Thousands of containers with different types of cargo are loaded every day in multiple manufacturing and logistics centres in the world. The main problem arising from these handlings is how to make the maximum use of all the available container capacities, while keeping the overall costs of transport per cargo unit as low as possible. The previous research mostly focuses on studying different algorithms for optimising container loading with cargo that has already been assigned based on its dimensions and weight. However, this paper will emphasise the importance of using algorithms in the planning and preparation of the cargo itself during the manufacturing processes before it is dispatched for loading into containers. Besides the length, width, height, and weight of the cargo itself, a fifth component influencing the overall transport costs will be considered, i.e. the manner of loading a container. The research will be carried out on an example of a container shipment of wooden sawn timber materials.

Analysis of Labor Needs at Perawang Port 2013 - 2018

Shipment of container has become a phenomenon in the world of logistics it is seen as more efficient and effective means of transportation. Changes in shipment patterns from break bulk cargo to containers also occur in the port of Perawang along with the construction of bridges in Siak Sri Indrapura so that large vessels can no longer enter and carry out loading and unloading activities at the Perawang port, and this has resulted in companies having to invest in mechanical equipment because ships that enters the perawang private jetty is no longer supported by the crane on the ship. Based on data owned by PT. Indah Kiat Perawang container shipments continue to increase from year to year but not accompanied by an increase in the number of labor. This study aims to see the effect of Container Shipment, Mechanical Equipment Investment and Labor Wages on Labor Demand at perawang port. This study uses secondary data with data from 12 semesters from 2013 to 2018. With independent variables namely Container Shipment, Investment and Wages. The analysis technique used is multiple regression. The results of the research shows that container shipment and investment has no significant effect on labor demand, whilts wages has a significant and negative effect on labor demand