Introduction:
Terminals play a crucial role in the global logistics chain, ensuring the efficient transfer of goods between ships and land-based networks. A notable trend within these infrastructures is the relentless pursuit of operational efficiency. This continuous quest for improvement stems from several factors, including the exponential growth of international trade, the increase in the volume of handled goods, and the need to reduce production times. Terminals strive to optimize their processes to expedite ship loading and unloading, minimize waiting times, and enhance overall productivity. This trend also arises from the escalating competitive pressure among ports, prompting operators to adopt innovative technologies and advanced logistic practices to remain competitive on the global stage. Thus, efficiency becomes a cornerstone in the management of Terminals, driven by economic and logistical imperatives.
While Terminals are actively pursuing operational efficiency, there is a parallel challenge associated with the lack of standardized metrics. Despite the emphasis on optimization, the absence of universally recognized standards makes it challenging to accurately assess and compare the efficiency levels of different equipment within the Terminals. This lack of standardized metrics can impede efforts to identify best practices, hindering the industry's ability to collectively address inefficiencies. As terminals strive for enhanced efficiency, there is a concurrent need for the development and adoption of comprehensive and standardized metrics to effectively gauge and improve their operational performance. This dual focus on efficiency enhancement and metric standardization highlights the complexity of managing and benchmarking performance within the dynamic landscape of port operations.
From an operational perspective, the need for standardized efficiency metrics in port terminals is crucial for streamlining processes and benchmarking performance. Consistent metrics allow terminal operators to identify bottlenecks, inefficiencies, and areas for improvement. It facilitates a more transparent evaluation of operational effectiveness, enabling terminals to implement targeted strategies to enhance productivity. This fosters a culture of continuous improvement and ensures that operational efforts are directed towards universally recognized objectives.
From an equipment health perspective, the need for standardized efficiency metrics in port terminals is paramount for ensuring the well-being and longevity of the machinery and technology deployed. Standardized metrics provide a systematic approach to monitor the health and performance of equipment, allowing for timely identification of potential issues. This proactive stance enables maintenance teams to implement preventive measures, reducing the risk of unexpected breakdowns and minimizing downtime. By having consistent metrics, terminal operators can establish baseline performance levels for each piece of equipment, facilitating the creation of maintenance schedules and predictive maintenance strategies.
For equipment teams standardized metrics serve as a valuable tool for designing robust and resilient machinery. Understanding the operational benchmarks enables technical teams to optimize equipment maintenance and or design, and implement features that enhance reliability. These metrics act as a feedback loop, informing teams about the real-time performance of equipment and guiding them in refining future priorities.
This alignment with standardized efficiency metrics ensures that equipment is not only efficient in terms of operational productivity but also sustainable and capable of withstanding the demands of continuous usage in a port terminal environment. Ultimately, the integration of standardized metrics from an equipment health perspective contributes to the overall reliability and performance of port terminal operations.
Problem Statement
In the previous health whitepapers, we introduced:
How health as a concept has to be used to enhance the terminal visibility
How maintenance should play an important role to drive health
Today, the industry is missing an efficient and accurate way to provide health as measurable information. We have a variety of information that is not aggregated at present to provide both operational and technical teams with an accurate statement of the equipment health. How healthy is my machine? 70%? Can I perform the next 8 hours with that level of cargo moves without any problem?
On the other hand, the way to process the data in order to be able to calculate and reflect the health of a piece of equipment or a fleet is not yet clearly defined.
How can TIC4.0 improve value?
To enhance Terminals capabilities, we already created two data models. We started with the CHE health data model and we later added the Maintenance data model to empower the health information with the reality of maintenance tasks.
CHE data model structure to ensure health calculation
Now 'we talk health' and we have the prerequisites regarding which data we need to process the health, however we still have to progress and bring health to the next level: being a metric information.
To do this, we first need a conceptual insight, as a reminder.
The CHE data model has been built to monitor all equipment activities. All the actions such as hoist, trolley, gantry and others are considered as subjects. All those subjects are ‘parents’ for their concepts.
Below, the CHE data model that includes health as a subject as it was.
Focusing on the health subject, we observe the concept healthy as below
The advantage of the data model is not only its flexibility but also the infinite measurability it can offer. This concept of healthy could be repeated for different subject. So, from the data model point of view, the solution to bring health to the next level is to copy this concept within every subject. We will then be able to have:
An example of how the data model will look is below (with spreader focus):
Signals need to be well processed in order to deliver a valuable interpretation & results. Thanks to CHE data model and its flexibility, we managed to integrate healthy in every subject.
Thanks to the data model arrangement and flexibility, we automatically have the right way to classify the information that is coming from different signals and we are thus able to organize the health calculation process accordingly.
What is the healthy level?
TIC 4.0 aims to represent any reality in digital format. The way to represent an instantaneous reality (a frozen image of reality) is through the “status” (position, speed, etc.) in a specific timestamp.
(e.g. at 06-10-2023 09:00 Straddle Carrier 01 is working bay 01A 02 01 and driving with speed of 10 km/h)
In the KPI whitepaper, we defined:
‘KPI = Represents in a consolidated way a reality, during a period of time, filtered, grouped and split in such a way that allows the reader to understand it is creating value.’
This is exactly what health level has to achieve. Being a value that represents the CHE state of health. This value, thanks to the data model flexibility, can be utilized in every systems and subsytems (concepts) of a CHE.
Healthy is a scalable concept that could also be used to measure a process such as the following examples:
Operation layers (planning, execution) → ‘How Healthy am I in the dispatching? How often am I cancelling a job instruction?’
Preventive maintenance → ‘How healthy is my right on time maintenance? How healthy is my scehduling process? Is the workforce intervention healthy?'. As we introduce in the previous white paper, we introduce new concepts in the maintenance data model where healthy as a concept could be introduced as well to ensure maintenance performance & reliability measurement through Health.
Get Health as a KPI should be seen as the objective. We have to handle and standaridize the way to process and compute the information first to reach the aim of having a value that could be aggreated across the time and splitted by CHE or Process.
As per the definition, health concept is declined in sub concepts that allow to understand the machine behavior. These concepts are:
What is impacting healthy level?
List the different parameters that should be taken in consideration to proceed.
What are we missing now?
Measure health as a value = express the healthy concept for every subject… → Data model modification (Curro to update)
Signal classification → Signals need to be well processed in order deliver a valuable interpretation & results. Thanks to CHE data model and its flexibility, we managed to integrate Healthy in every Subject. …
CHE & Maintenance data models to ensure equipment health visibility
Below chart to be reviewed and updated with Maintenance + new way to integrate health in the DM (healthy concept below every subject in the CHE DM)
Appendixes
Maintenance data model.
Definition proposals:
Repairing: Restoring something to a functional or original state after damage or malfunction.
Diagnosing: Identifying and analyzing the nature or cause of a problem or condition, especially in a technical or medical context.
Inspecting: Examining something closely and systematically to assess its condition, quality, or compliance with standards.
Certifying: Officially verifying or confirming that something meets specific requirements, standards, or qualifications.
Testing: Evaluating the performance, functionality, or characteristics of something through systematic trials or experiments.