The Use of Big Data in Ports and Terminals
According to Forbes, 87% of enterprises believe that big data will redefine the competitive landscape in the next three years, and 89% of them believe that not using big data could lead to loss of market share. While the world is gung-ho about the possibilities with big data, at the end of the day, many companies, cutting across sectors, are still to apply Big Data in their operations in a big way including ports and terminals.
Big Data analytics has made considerable headway in many industries. 61% aviation companies consider it as a high priority area, 42% of manufacturing concerns think likewise. However, big data is still relatively new and untested in the port industry, even as the stakeholders remain well aware of its potential.
Big Data Sources
Big Data from ports come from two major sources:
- The terminal operating system (TOS), the nerve center of any modern port operations. The TOS manages all processes, from documentation to planning and from execution of vessel operations to billing. It is a treasure house of rich data, especially on inventory changes, work plans and sequences for dispatching jobs.
- Data from the field, from sensors and programmable logic controllers have been built into cargo handling equipment.
Usually, such data mostly remains under-processed or under-analyzed. The application of big data analytics unlocks great insights which may be put to many uses.
Big Data Uses
The use of big data analytics in ports and terminals goes beyond analyzing past operations to forecast future activities. The following are some enhanced possibilities:
Unlocking big data from port operations makes it easier to optimise usage of resources and infrastructure. For example, a typical crane operator works only one-quarter of the time, remaining idle for three-quarters of the time, waiting to get a container ready to load or for an empty truck to unload a container. Increasing the number of trucks may not be a viable solution owing to the congestion it would cause. Rather, big data analysis could synchronise movements, so that the crane operator works more time. For instance, signals related to crane position, status, and GPS position signals could sync movement of trucks and containers, to reduce idling time.
Also, cranes show different performance levels according to various factors such as skill of driver, workload, weather, container type, and yard density. Understanding such patterns makes it possible to either find solutions to overcome the roadblocks, or sync operations to factor such limitations, ultimately enhancing productivity.
The application of statistical analytics to the data stream uncovers operational insights, such as an underutilised barge mooring space.
Preventive maintenance of cranes, and other machinery:
Harvesting operational data from sensors placed inside machines makes it possible to predict when a part might fail, paving way for a more effective maintenance schedule as opposed to following the maintenance schedule recommended by the manufacturer. Such an approach allow for timely replacements, pre-empting the catastrophic effects, including spoil over effects of disruption of operations caused by machinery breakdown or parts failure, and result in significant direct and indirect savings.
Big data analytics unlocks data hitherto not visible, and consolidates information from various sources, including vessels, machinery, and terminal operating software. This leads to insights otherwise difficult or impossible to fathom. Unlocking relevant operational patterns generates actionable, allowing decision makers to not just optimise operations, but also anticipate events.
Data from sensors placed in port equipment could help port operators design a predictive model for each type of machine, maximise the efficiency of port equipment, leading to cost savings.
Sensors and monitoring cameras could identify patterns of container stacking according to vessels, and such information could find use to simulate future terminal operations and performance predictions, allowing for optimised plans for yard space and equipment, and making it possible to predict the number of cranes, yard trucks and other container handling equipments required, with accuracy.
Big Data analytics also have the power to predict demand and supply of port infrastructure, and thereby suggest new business models.
The positive benefits of data analytics helps not just the port operators, but extend to the complete ecosystem as well. For instance, shipping companies with access to the port’s big data insights can predict costs and turnaround times with accuracy. Logistics companies with access to the port’s big data can anticipate the expected demand of trucks on any given day, and schedule likewise.
One of the most high profile applications of big data to-date has been done by the port of Rotterdam. The port has established a new spatially enabled network to support performance-based asset management, and thereby optimise the use of its resources. At the core of the new infrastructure is a geo-database of 150 layers, encompassing everything from port assets, land records, boundaries, ortho-imagery, transportation data, nautical charts, and everything else relevant to the port, with such data updated on a real time basis. The network is connected to mobile devices and business systems used across the port, allowing the stakeholders to leverage the full power of big data.
The three essential cogs of infrastructure required for the successful application of big data in any port environment are:
- Devices that can measure different conditions, such as GPS sensors and RFID tags
- An environment that transmits measured data reliably and in real time, mostly wi-fi connection
- A system that can store and manage the transmitted data, and offer a platform to analyze it.
The success of big data interventions on the port site depends on reliability of the communication network, and its real-time responsiveness. Networks in port cannot rely on wired connections and require high speed wi-fi, with stability, or means to bypass the network infrastructure into an alternative route if problems crop up. A key challenge specific to port environment is radio waves that carry wi-fi connection bouncing off instead of passing through iron equipments such as cranes and containers. The work around is to factor in placement of machinery and stacking of containers when designing the network. Using recently developed Wireless Mesh network technology offers another solution.
Big Data has the potential to reinvent companies cutting across industries. In the port arena, such transformation would be heralded by harnessing big data to improving the efficiency of port equipment, TOS operations, and for boosting the overall logistics process.