Individual Report: Digitisation in Shipping Assessment Answer

Why We're Your Go-To Crew

Turnitin Report
Non-AI Content Report
Expert Session
24/7 Personalized Support

🎁 Claim up to 50% Off* on All Assignments! Get a Free Quote

OFFER ENDS IN

Order Now Enjoy Upto 50% Off

Email *

Country

Phone No.*

Kindly mention your assignment details

Captcha

Verify Captcha *

I accept the T&C and all policies of the website and agree to receive offers and updates.

Subject Name : Management

Introduction

The shipping sector is vital to international trade because it allows commodities to traverse borders. Traditional shipping methods, on the other hand, can be difficult, time-consuming, and error-prone, resulting in inefficiencies, delays, and increased costs (Agarwala et al. 2021). To solve these challenges and maximize the potential of the marine sector, digital transformation has emerged as a viable option. Digital transformation has the ability to improve numerous shipping processes, including "container tracking and visibility", "logistics and supply chain management", "documentation and paperwork”, "customs clearance” and so on. Deploying technologies like artificial intelligence (AI), blockchain, Internet of Things (IoT), sensors, and virtual reality (VR), digital transformation has the potential to improve overall visibility, transparency, efficiency, and sustainability in the shipping sector (Kitada and Baum-Talmor, 2019). In this research, we will propose bringing digital transformation to an intriguing use case in the shipping industry. We will investigate the difficulties associated with traditional shipping operations as well as the potential benefits of digital transformation. We will next look at how AI, blockchain, IoT, sensors, and VR may be used to solve the problem, with specific approaches and examples. We will close with a summary of significant results, emphasising the significance of adopting digital transformation in the shipping sector to remain competitive and survive in today's business context.

Discussion

Use Case Analysis

Digital transformation has become a top priority for firms in a variety of industries, including the logistics and supply chain industries. The container tracking and visibility process is one use case where digital transformation may provide major benefits. Container monitoring and visibility are crucial for guaranteeing the efficient and secure flow of commodities across the supply chain, from origin to destination (Hirata et al. 2019). Digital technologies like the “Internet of Things (IoT)”, “big data analytics”, and “cloud computing” have the potential to revolutionise the container monitoring and visibility process (Agarwala et al. 2021). Container tracking and visibility are critical components of the shipping industry because they allow for effective supply chain management and give real-time information on the location, status, and condition of containers while they are in transit. Traditional container tracking and visibility solutions, on the other hand, frequently rely on manual procedures, paper-based documentation, and antiquated systems, which can be time-consuming, error-prone, and lack real-time visibility (Hirata et al. 2019). This can lead to delays, inefficiencies, and increased expenses, all of which have a detrimental influence on the shipping industry's overall performance.

Data

Data from shipping companies, terminals, ports, and other stakeholders are gathered, processed, and analysed in the container tracking and visibility process to provide real-time insights and visibility into the location, status, and condition of containers.

People

The process of digital transformation relies heavily on people (Baum-Talmor and Kitada, 2022). To ensure that employees are prepared for the transformation and can effectively contribute to its success, change management strategies, training programs, and communication channels must be in place.

Hardware and software

Equipment resources, like sensors, gadgets, and correspondence framework, are basic for catching and sending information progressively for holder following and permeability. The digital transformation of container visibility and track relies heavily on software (Baum-Talmor and Kitada, 2022). The data that has been gathered from a variety of sources is processed, analysed, and visualised using cutting-edge technologies like IoT, big data analytics, cloud computing, and artificial intelligence.

Challenges Without Digital Transformation of this use Case

The container tracking and visibility process can indeed reap numerous benefits from digital transformation. However, organisations may encounter difficulties when implementing such an initiative (Wang and Sarkis, 2021). Limited real-time visibility, manual and error-prone processes, limited data analysis and insights, inefficient supply chain management, limited collaboration and communication, security and risk management risks, and reduced customer experience may all issues for businesses that do not implement digital transformation. Without digital transformation, this use case may face difficulties such as:

Lack of real-time visibility

Container tracking and visibility may rely on outdated systems, paper-based documentation, and manual processes without digital transformation. This can lead to limited or delayed visibility into the location, status, and condition of containers (Wang and Sarkis, 2021). Supply chain management and decision-making can be hindered as a result of this lack of real-time visibility, which can also cause inefficiencies, delays, and an increase in operational costs.

Manual and error-prone processes

Manual data entry, paper-based documentation, and stakeholder coordination may be heavily incorporated into traditional, non-digital container tracking and visibility procedures. Data and information can become more susceptible to errors, inaccuracies, and inconsistencies as a result of this (Coronado Mondragon et al. 2021). Manual cycles can likewise be tedious and work seriously, prompting diminished efficiency and expanded functional expenses.

Limited data analysis and insights

Large amounts of data from a variety of sources may not be able to be collected, processed, and analysed in real-time if digital transformation is not implemented (Sarc et al. 2019). It can be difficult to identify patterns, trends, and opportunities for optimisation and improvement because of this, which can lead to limited data analysis and insights. The absence of information-driven navigation can prevent associations from pursuing informed choices and making ideal moves.

Inadequate supply chain management

For effective supply chain management, container tracking and visibility are essential. Organisations may have trouble coordinating and optimising the movement of containers without digital transformation, which could lead to delays, inefficiencies, and higher costs (Sarc et al. 2019). Ineffectual inventory networks the board can prompt interruptions missed cutoff times and diminished consumer loyalty.

Limited collaboration and communication

There may be delays, errors, and miscommunications as a result of manual coordination and communication among various stakeholders in traditional container tracking and visibility procedures (Coronado Mondragon et al. 2021). Establishing channels of collaboration and communication among stakeholders, such as shipping carriers, terminals, ports, customs authorities, and other parties involved in the container tracking process, may be challenging for businesses in the absence of digital transformation.

Security related risks

Data security and risk management may be difficult to execute successfully in a non-digital context. Manual processes and paper-based documentation are vulnerable to mistakes, data breaches, and data leaks (Sarc et al. 2019). Organisations might be exposed to possible security threats, compliance problems, and financial losses if they do not implement comprehensive cybersecurity protections and risk management practices.

Limited customer experience

Customers expect real-time visibility and transparency in their supply chain operations in today's fast-paced business environment. It may be difficult for businesses to provide a level of customer experience that meets the growing expectations of customers without digital transformation in container tracking and visibility (Becha et al. 2020). This can bring about decreased consumer loyalty and devotion, affecting the general business execution.

Four Technologies to Address the Challenges

Several technologies can be utilised to address the aforementioned difficulties and enhance the container tracking and visibility process through digital transformation.

Artificial intelligence

Container visibility and tracking can be automated and improved with the help of AI technologies. For instance, simulated intelligence calculations can dissect information from various sources, like sensors, delivering archives, and authentic shipment information, to give constant bits of knowledge on compartment area, status, and condition (Cao, 2016). AI can likewise empower prescient examination to figure out possible deferrals or interruptions, considering proactive measures to be taken.

Blockchain

Blockchain can offer improved security, straightforwardness, and discernibility in the compartment following and permeability process. All stakeholders, including shipping companies, terminals, ports, customs agencies, and other parties participating in the supply chain, may get access to a distributed ledger that records all container-related activities and events by using blockchain (Becha et al. 2020). This makes it possible to record container movements in a secure and unbreakable way, lowering the likelihood of fraud, data breaches, and inconsistent data.

Virtual reality

Stakeholders can better understand a situation and make better decisions by using virtual reality (VR) to see how containers move in a virtual environment (Favi et al. 2019). For instance, delivering transporters can utilise VR to recreate and enhance holder steering, terminal administrators can utilise VR to design and improve compartment dealing with activities, and customs specialists can involve VR to review holders for consistency.

Sensors and IoT

Real-time data on a variety of parameters, including the container's location, temperature, humidity, vibration, and security status, can be gathered by sensors and IoT devices. The temperature of perishable goods can be monitored by temperature sensors, while security sensors can identify any unauthorised access to containers. GPS-enabled sensors, for instance, can track the precise location of containers.

Methodology to Apply These Technologies

Application of AI for Container Tracking and Visibility Process

The first thing that needs to be done is to find the relevant variables that are necessary for container visibility and tracking. The area of the compartment, its condition, temperature, dampness, and assessed season of appearance (estimated time of arrival) is only a couple of instances of these factors (Anwar et al. 2019). The next step is to locate the data sources that contain the relevant variables. GPS trackers, sensors, delivering reports, announcements from customs, climate information, vessel plans, and other applicable frameworks or data sets could all be instances of these information sources (Pfleeger, 2015). The next step is to locate the data sources that contain the relevant variables. GPS trackers, sensors, delivering reports, announcements from customs, climate information, vessel plans, and other applicable frameworks or data sets could all be instances of these information sources (Tao et al. 2017). Simulated intelligence models given the predetermined factors and information sources are in the following stage. It is possible to train models with pre-processed data and select the appropriate AI algorithms or statistical techniques. The final step is to regularly evaluate the performance and impact of the AI models in the container tracking and visibility process and to continuously monitor and optimise their performance based on the generated feedback and insights.

Figure 1: Automated container tracking

(Source: https://spotworx.com)

Application of Blockchain for container tracking and visibility process

The first step is to determine the relevant variables that must be recorded on the Blockchain and are essential for container tracking and visibility. Container ID, location, condition, temperature, humidity, timestamps, and other relevant parameters might be examples of these variables (Pfleeger, 2015). Next, locate the data sources that contain the relevant variables, such as sensors, GPS trackers, shipping documents, declarations from customs, invoices, bills of lading, and other relevant databases or systems (Anwar et al. 2019). The third step is to define the smart contracts that will govern the interactions and transactions related to the container tracking and visibility process and choose the right Blockchain network for your use cases, such as a consortium, public, or private blockchain. Integrating the pertinent data from the identified data sources into the Blockchain network is the next step (Donepudi et al. 2014). The consensus mechanism can be used to define how to validate the integrity and accuracy of the recorded data within the Blockchain network in the subsequent step.

Application of VR for container tracking and visibility process

The first step is to determine the relevant variables for container visibility and tracking, such as the container's location, condition, temperature, humidity, and status (Lauronen et al. 2021). The relevant variables, such as real-time data from GPS trackers, sensors, and other Internet of Things devices, as well as historical data from previous container tracking and visibility records, can be identified in the subsequent step (Tao et al. 2017). The VR environment should then incorporate relevant data from the identified data sources and define the user interactions that allow users to interact with virtual objects and carry out tasks related to container tracking and visibility. Finally, the VR environment can be used to train and simulate the purposes.

Application of Sensors and IoT for container tracking and visibility process

The first thing to do is figure out the variables, like where the container is, temperature, humidity, shock, vibration, and door status (Lauronen et al. 2021). The variables can be captured by taking into account things like the kind of containers, the environment, the options for connectivity, the power requirements, and the devices' data transmission capabilities (Donepudi et al. 2014). The next step is to set up the process for collecting and transmitting data from the IoT devices and deploying sensors to the specified data sources. Then, integrate the collected data into the container tracking and visibility procedure and analyse the data to gain insights and make decisions based on accurate information.

Figure 2: Smart monitoring of container with IoT

(Source: https://hyperthings.net)

Identification of Assets, Threats, and Vulnerabilities

Sl. No.	Assets	Threats	Vulnerabilities	Control
1	Container data	Malicious actors' unauthorised access to the data in the container	Lack of multi-factor authentication or weak passwords as authentication and authorization measures for accessing container data (Haraldson et al. 2019)	Encryption, multi-factor authentication (Cassar et al. 2019)
2	Sensors and IoT devices	Altering or control of sensor and IoT gadgets, prompting off base or misdirecting information	Physical security protections for sensor and IoT equipment are lacking, such as insufficient tamper-proofing or simple physical access to devices (Cassar et al. 2019)	Regular security audits and timely software updates are all robust data security measures.
3	Data transmission network	During the transmission of data from sensors to data sources, data interception or eavesdropping can occur (Haraldson et al. 2019)	Data transmission without encryption or protocols with weak encryption leaves it open to interception or unauthorised access.	Regular security audits and timely software updates are all robust data security measures
4	Cloud-based platforms and data repositories	Information breaks or digital assaults on cloud-based stages or information archives where compartment information is put away (Cassar et al. 2019)	Inadequate security measures, such as inadequate authentication or vulnerabilities in cloud-based platforms or data repositories have not been fixed.	Encryption, multi-factor authentication
5	Communication channels	Interference or disruption of the communication channels used to transmit sensor data, such as disruption of cellular networks or interference with radio signals	Relying solely on one communication channel with no redundancy or secure communication protocols (Coronado Mondragon et al. 2021)	Evaluating and upgrading the infrastructure on a regular basis
6	Personal and user access	Insider threats, such as employees or authorised users gaining unauthorised access to or misusing container data	Unauthorised access to or misuse of container data is caused by inadequate access controls, such as granting excessive permissions or failing to conduct regular access reviews (Haraldson et al. 2019)	Putting comprehensive employee training programs into place and giving security awareness training on a regular basis (Coronado Mondragon et al. 2021)
7	Physical infrastructure	Container tracking and visibility-supporting infrastructure, such as ports, warehouses, or data centres, is physically damaged or destroyed (Agarwala et al. 2021)	The infrastructure is vulnerable to physical threats due to the absence of adequate physical security measures like surveillance systems, access controls, or backup power supplies.	Putting strong physical security measures in place
8	Third-party integration malicious actors' unauthorised access to the data in the container	Security weaknesses in outsider reconciliations or APIs utilised for compartment following and permeability, prompting unapproved access or information breaks.	Lack of thorough security assessments and monitoring of third-party APIs or integrations, resulting in integration point vulnerabilities (Agarwala et al. 2021)	Encryption, multi-factor authentication

Conclusion

In conclusion, the logistics industry can significantly improve efficiency, transparency, and security by implementing digital transformation in the container tracking and visibility process. AI, blockchain, virtual reality, sensors, and the Internet of Things (IoT) are promising new technologies that offer opportunities for improved data accuracy, real-time monitoring, and automated decision-making to address the issues associated with container visibility and tracking. A comprehensive and proactive approach to risk management is required for the successful implementation of digital transformation technologies in the container tracking and visibility process.

References

Agarwala, P., Chhabra, S. and Agarwala, N., 2021. Using digitalisation to achieve decarbonisation in the shipping industry. Journal of International Maritime Safety, Environmental Affairs, and Shipping, 5(4), pp.161-174.

Anwar, M., Henesey, L. and Casalicchio, E., 2019. Digitalization in container terminal logistics: A literature review. In the 27th annual conference of the international association of maritime economists, Athens (pp. 1-25).

Baum-Talmor, P. and Kitada, M., 2022. Industry 4.0 in shipping: Implications to seafarers' skills and training. Transportation research interdisciplinary perspectives, 13, p.100542.

Becha, H., Schröder, M., Voorspuij, J., Frazier, T. and Lind, M., 2020. Global data exchange standards: The basis for future smart container digital services. In Maritime informatics (pp. 293-307). Cham: Springer International Publishing.

Cao, F.H., 2016. A Ship Driving Teaching System Based on Multi-level Virtual Reality Technology. International Journal of Emerging Technologies in Learning, 11(11).

Cassar, C., Simpson, R., Bradbeer, N. and Thomas, G., 2019, September. Integrating virtual reality software into the early stages of ship design. In Proceedings of the 19th International Conference on Computer Applications in Shipbuilding ICCAS, The Royal Institution of Naval Architects, Rotterdam, The Netherlands (pp. 24-25).

Coronado Mondragon, A.E., Coronado Mondragon, C.E. and Coronado, E.S., 2021. Managing the food supply chain in the age of digitalisation: A conceptual approach in the fisheries sector. Production Planning & Control, 32(3), pp.242-255.

Donepudi, P.K., 2014. Technology growth in the shipping industry: an overview. American Journal of Trade and Policy, 1(3), pp.137-142.

Favi, C., Moroni, F., Manieri, S., Germani, M. and Marconi, M., 2019. Virtual Reality-enhanced configuration design of customised workplaces: a case study of ship bridge systems.

Haraldson, S., Lind, M., Karlsson, M. and Bach, A., 2019. Digitalisation and automation in small and medium-sized Swedish ports (SMPs).

Hirata, E., 2019. Service characteristics and customer satisfaction in the container liner shipping industry. The Asian Journal of Shipping and Logistics, 35(1), pp.24-29.

Kitada, M. and Baum-Talmor, P., 2019, October. Maritime digitisation and its impact on seafarers’ employment from a career perspective. In Proceedings of the International Association of Maritime Universities (IAMU) Conference: AGA20 (pp. 259-267). International Association of Maritime Universities.

Lauronen, J., Sakari, L. and Lehtonen, T., 2021. How Simulation Training Can Benefit from Virtual Reality Extensions? Case: A Virtual Reality Extension to a Simulated Ship Bridge for Emergency Steering Training. In Advances in Usability, User Experience, Wearable and Assistive Technology: Proceedings of the AHFE 2021 Virtual Conferences on Usability and User Experience, Human Factors and Wearable Technologies, Human Factors in Virtual Environments and Game Design, and Human Factors and Assistive Technology, July 25-29, 2021, USA (pp. 149-156). Springer International Publishing.

Pfleeger, P. et al. 2015. Security in Computing (5th Edition), Prentice Hall, Chapter 1.

Sarc, R., Curtis, A., Kandlbauer, L., Khodier, K., Lorber, K.E. and Pomberger, R., 2019. Digitalisation and intelligent robotics in the value chain of circular economy oriented waste management–A review. Waste Management, 95, pp.476-492.

Tao, R., Ren, H.X. and Peng, X.Q., 2017. Ship fire-fighting training system based on virtual reality technique. In Modelling, Design and Simulation of Systems: 17th Asia Simulation Conference, AsiaSim 2017, Melaka, Malaysia, August 27–29, 2017, Proceedings, Part II 17 (pp. 249-260). Springer Singapore.

Wang, Y. and Sarkis, J., 2021. Emerging digitalisation technologies in freight transport and logistics: Current trends and future directions. Transportation Research Part E: Logistics and Transportation Review, 148, p.102291.

You Might Also Like:-

Management Assignment Help

How to do an In-Text Citation APA Style?

DEM311: Emergency and Disaster Management in Context - Assessment Answer