Using artificial neural networks to predict container flows between the major ports of Asia

Tsai, Feng-Ming; Huang, Linda J.W.

doi:10.1080/00207543.2015.1112046

Cited by 44 publications

(20 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They established a simulation model to examine the performance measures and found that the fleet size of AGVs and efficiency evaluation of the operations were crucial for planning. Tsai and Huang (2017) proposed a neural network approach with factors including GDP, interest rates, amounts of import and export trades and quantities of containers and quay cranes to predict the flow of containers. van der Spoel, Amrit, and van Hillegersberg (2017) suggested a datadriven approach for cargo arrival time prediction at distribution centres.…”

Section: Tactical Levelmentioning

confidence: 99%

From data to big data in production research: the past and future trends

Kuo

Kusiak

2018

International Journal of Production Research

153

View full text Add to dashboard Cite

Data have been utilised in production research in meaningful ways for decades. Recent years have offered data in larger volumes and improved quality collected from diverse sources. The state-of-the-art data research in production and the emerging methodologies are discussed. The review of the literature suggests that production research enabled by data has shifted from that based on analytical models to data-driven. Manufacturing and data envelopment analysis have been the most popular application areas of data-driven methodologies. The research published to date indicates that data mining is becoming a dominant methodology in production research. Future trends and opportunities for data-driven production research are presented.

show abstract

Section: Tactical Levelmentioning

confidence: 99%

From data to big data in production research: the past and future trends

Kuo

Kusiak

2018

International Journal of Production Research

153

View full text Add to dashboard Cite

show abstract

“…Also, there is an increase in the welfare level of the country with the effect of production and trade within the country. For this reason, the effect of GPD on port traffic has been also analyzed in several studies (e.g., Chou et al, 2008;Lättilä and Hilmola, 2012;Akar and Esmer, 2015;Tsai and Huang, 2017), and significant results have been obtained.…”

Section: Literature Reviewmentioning

confidence: 99%

DO SHOCKS HAVE PERMANENT OR TRANSITORY EFFECTS ON CONTAINER TRAFFIC IN TURKISH PORTS? / Şokların Türk Limanlarındaki Konteyner Trafiği Üzerindeki Etkisi Kalıcı Mıdır Geçici Midir?

Açık

2021

Uluslararası Ekonomi İşletme Ve Politika Dergisi

View full text Add to dashboard Cite

Container ports are one of the most important parts of the supply chain. Due to high investment and operating costs, the structure and predictability of current and future demands are very important. To investigate this structure, this study aims to determine whether the shocks to the export and import container quantities handled in Turkish ports are temporary or permanent. Both standard unit root tests and unit root tests with structural breaks were applied to export and import container amounts. According to the results, when the structural breaks are considered, it has been determined that both export and import container quantities are stationary. This situation shows that the effects of the shocks to the container traffic are temporary, the applied policies lose their effect in a short time and the future container traffic can be predicted using historical data.

show abstract

“…On the other side, data science leverages a direct communication with providers or distributors and permits real-time management of the supply chain orienting the business value in that case to the efficiency of the operations (Addo-Tenkorang & Helo, 2016). In the transport industry for instance, recent studies show that data science provides a better management of the container flows between ports (Tsai & Huang, 2017) or a decrease of the bullwhip effect between providers of a supply chain (Hofmann, 2017) or detects problem root cause before an incident occurs and stops the production (Chien, Liu, & Chuang, 2017). Our empirical study contributes to exploring the different combinations of causal conditions that may be linked to the improvement of the processes related to the Customer (front-office) and Provider (backoffice) management.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Exploring data conditions to improve business performance

Grimaldi

Fernández

Carrasco

2019

Journal of the Operational Research Society

View full text Add to dashboard Cite

Past researches drew from the industrial organization perspective have examined the role of the data to generate competitive advantage. Their analysis show data is a valuable resource that can leverage business partnerships, vertical integration or diversification. The emergence of data science has created new opportunities to understand better clients' needs and to manage more efficiently the organizations processes. Nevertheless, if data analytics represent an enormous potential, many organizations are still looking the conditions to obtain value from them. Our study contributes to this topical subject analysing the relationship between different combinations of data conditions and the company performance that we measure through the Customer management and Provider operations efficiency. Our methodology is novel compared to previous researches which are based in linear algebra. It is based on the use of a fuzzy-set qualitative comparative analysis (fsQCA) which allows to reveal multiple paths to achieve the possible outcomes. Our results show that the consistency, completeness and protection of the data along with a data-driven company profile are different possible solutions to a better Customer management and Provider operations efficiency. Our conclusions allow practitioners to uncover the strength of the data in the hopes of solving many of their business performance concerns.

show abstract

Using artificial neural networks to predict container flows between the major ports of Asia

Cited by 44 publications

References 15 publications

From data to big data in production research: the past and future trends

From data to big data in production research: the past and future trends

DO SHOCKS HAVE PERMANENT OR TRANSITORY EFFECTS ON CONTAINER TRAFFIC IN TURKISH PORTS? / Şokların Türk Limanlarındaki Konteyner Trafiği Üzerindeki Etkisi Kalıcı Mıdır Geçici Midir?

Exploring data conditions to improve business performance

Contact Info

Product

Resources

About