“Big data” refers to the data sets that can be described as ubiquitous or found anywhere. This article summarizes the possible sources of big data in container terminals and how this information can be used in improving the overall efficiency of container terminals.

Exploiting big data with a view to ensuring an accurate data analysis and improving responses to situations for terminals

The key software for operating a container terminal is the terminal operating system or TOS. A TOS manages processes from documentation, planning, execution of vessel operations and billing. The amount of data handled by the TOS is limited to inventory changes, work plans and sequences for dispatching jobs. Much more data is generated in terminals, including those from sensors and programmable logic controllers that have been built into cargo handling equipment deployed in such facilities. This data however, mostly remains under-processed or under-analyzed to be of real value.

Big data platform has been suggested with a view to collecting, storing, and analyzing various data that occurs from container terminals but is volatile enough to disappear quickly. This processed data can then be used meaningfully in the operation of the terminal. With big data platform in place, not only TOS information but also signals related to crane position and status and GPS position signal (if any) can be managed as data. Also, a lot of variables present in a terminal such as time, seasons, weather, temperature, humidity, and driver’s condition can be collected, processed and used as well.

Big data goes beyond its literal meaning of an enormous amount of data and may be defined as data that has a large volume, a short life cycle, and a great variety of attributes. Provided that big data is applied in container terminals, it can be exploited as a useful tool for analyzing past operational data. This can help to reduce risks and costs by viewing it in relation to the current conditions and in forecasting activities in the future.

Four stages in the exploitation of big data in terminals

If applied in a terminal, the general development of big data platform can be configured in four stages as shown below.

Stage 1 (Data Gathering): Data + System = Information

Any event or situation that can occur during operation can be a source of terminal data. This information is obtained in a three-step process: first by applying devices that can measure different conditions; second by having an environment that transmit measured data; and third by using a system that can store and manage the transmitted data.

[For example, GPS sensor is installed in a yard truck to gather location data in the formats of time stamp, sensor ID, and send these values to big data storage, using the terminal’s WiFi network.]

Stage 2 (Meaning of Information): Information + Experience = Knowledge

After the information acquired and saved in stage 1 has been identified, it can be reprocessed further by applying conditions gathered from actual experience. Thus the information is converted into knowledge.

[For example, one decides in which area (in relative location; e.g., 1A-31) a vehicle (in absolute location; e.g., 34.5678, 75.3454) is located and if it is the location for the task at hand, based on the location data for the yard truck that is acquired stage 1.]

Stage 3 (Knowledge Integration): Knowledge + Intuition = Wisdom

This is about analyzing knowledge that is accumulated through Stage 1 and Stage 2 and collecting and connecting all batches of data. The resulting system-generated information can then be in making guided decisions.

[For example, when traffic is heavy on the road leading to a destination, information is provided to help a motorist avoid crowded roads and take a less-congested one. Or, work orders are delivered to ensure speedier performance by a yard truck depending on yard status.]

Stage 4 (Wisdom-Based Prediction): Wisdom + Imagination = Creation

In this stage, one forecasts and simulates special circumstances that can develop from the current situation of a terminal. This is done by performing a comprehensive analysis of the wisdom about terminal operation that has been accumulated in Stage 3. By adding imagination (new attempts and new technology) to this wisdom, one can ensure creative terminal operation by forecasting unexpected situations on the job through simulation.

[For example, by simulating work implementation prior to the arrival of a container ship, one can predict the number of cranes and trucks that has to be allotted to tasks, operators to be assigned to the yard, and the severity of congestion at the gate.]

System support is necessary for big data exploitation

In this article we have reviewed the four stages of big data adoption in terminals. The stages are distinguished according to the level of exploitation of big data. Even if the concept of big data is applied to a terminal, it is not going to develop right away to Stage 3 or 4 immediately. To reach a higher stage, one must amass enough know-how and experience related to big data exploitation.

With the increasing interest in an automation system that can supplement the existing TOS, larger amounts of data can be accumulated and these can be converted to useful information that can be employed terminal operation. More and more terminals are rushing to introduce big data-based automation systems as a means of equipping themselves with the differentiated competitive edge resulting in reduced operation costs and improved ability to respond to situations.

Then, what factors do terminals have to have to collect big data? In our next article, we are going to take a look at terminal network and positioning technology, the basic factors of collection of various data in a terminal.