Introduction:

• Over the past decade, there has been significant attention given to the topic of risk within the container shipping industry.
• Extracting data in the shipping sector is a demanding task.
• Previous studies have addressed the classification of risks associated with the shipping industry, including technical risk, market risk, industry risk, and operational risk.
• These studies have also highlighted the growing importance of modern and advanced analytics in helping companies reduce freight costs throughout the supply chain.
• Technical risk encompasses losses resulting from ship or machinery design, engineering, construction, technological processes, and test procedures.

This blog examines various modeling approaches that have been discussed for identifying risks in container shipping.

Container Shipping:

• In today’s complex and highly competitive business world, the shipping industry has encountered numerous challenges.
• Being a global industry, it is intricately connected to the growth and fluctuations of the global economies.
• Reports indicate that approximately 90% of international trade is carried out through shipping.
• Over the past few decades, container shipping has gained significant importance due to its notable advantages in loading and unloading operations, as well as its potential for intermodality.
• Both the carrying capacity of containerships and global shipping traffic have experienced substantial growth rates.

• Container shipping provides consignors with the assurance that their goods will be transported to a specific port within a designated time frame, thanks to established line routes and published schedules.
• However, the maritime transportation sector has always been influenced by risks, which play a pivotal role in shaping its operations.
• Unforeseen disruptive incidents can have both intentional and unintentional adverse effects on businesses, such as disruptions or complete failures in liner plans and shipping operations.
• Consequently, shipping companies need to have a comprehensive understanding of the risks involved in order to identify the most critical ones and implement effective mitigation strategies within the industry.

Risk and Risk Management:

• Container shipping has been a subject of significant attention regarding risk management in the past decade.
• However, one challenge in effectively managing and prioritizing risks in container shipping is the scale and complexity of the system.
• It involves multiple stakeholders, including transporters, hauliers, shippers, consignees, forwarders, and banks, each with their distinct roles and procedures such as trucking, loading/unloading, delivery, payment, and consolidation.
• These complexities make it difficult to comprehensively examine the entire process.

• Nguyen et al. (reference [1]) proposed a logistics perspective for identifying hazardous operational incidents in container shipping, utilizing the cognitive appraisal methodology while considering the unique interaction between container shipping and logistics activities.
• Bayesian subjective probabilities are commonly used to address epistemic uncertainty.
• The authors enhanced the risk-mapping parameter collection using Failure Modes and Effects Analysis (FMEA) with the addition of two-parameter stages.
• The conventional quantification method employed in FMEA involves assigning numbers to three parameters: Likelihood of event (L), Severity of outcome (S), and Probability of being observed (P) to determine the Risk level (R).
• However, this approach is somewhat contextual due to the lack of a universal scale and limitations in the use of linguistic variables without sufficient consultation with data analysis experts.

Chang and his colleagues conducted a series of studies to assess the influence of different risks on the performance of a shipping company and determine their relative significance. The main objective of the research was to address two key questions: the importance of risk factors in container shipping activities and the relative importance of risk factors in the overall shipping business. The study consisted of three phases: “risk assessment,” “risk measurement,” and “risk analysis.”   During the “risk assessment” phase, the researchers depicted the logistics flow within the container shipping industry, which involved multiple entities such as the shipping group, other transport firms, agency-related companies, consignor, consignee, and bank. This phase identified three primary flows: information flow, physical flow, and payment flow. Each flow was further categorized into specific elements or sub-categories. For the information flow, these included information delay, information inaccuracy, and IT problems. The physical flow encompassed transportation delay and cargo/asset damage. The payment flow included currency exchange, payment delay, and non-payment.   The risk assessment process began by referring to existing literature and conducting multiple interviews to identify and verify potential threats in container shipping operations. A structured questionnaire was designed to collect relevant data, and a risk mapping tool was utilized to measure and rank the impacts of the identified risks. In total, 35 risk factors were defined and categorized into various groups.   The findings revealed that risks associated with physical flows had more severe consequences compared to other types of risks. Additionally, one specific risk factor related to the information flow, namely shippers concealing cargo information, emerged as the most significant risk factor.

Future Scope:

• Extensive research is currently being conducted to mitigate risks in container shipping, as evidenced by various studies.
• While progress has been made, there are certain areas that require further development.
• One of the identified issues is the lack of an adequate information base, leading to difficulties or limitations in experts providing assessments.
• The process of extracting and processing input data in a rational manner is not clearly defined.
• Research should explore the experts’ ability to determine the knowledge base for their decisions and develop effective methods for data extraction and processing.

• Another aspect that requires attention is the representation of customizations and expressions of relative importance among factors and experts within the proposed model.
• It is essential to incorporate the vision or plan of the system management board into the model, allowing for customization of fuzzy rules or the development of a measurement system for evaluating committee decisions.
• Any modifications made to the model should be grounded in a solid theoretical foundation.
• It is crucial to establish whether the model should be driven by an aggregated conceptual model of subjective perceptions.
• If not, the variables influencing this aspect should be clearly explained before any customization is implemented.
• Care must be taken to avoid inappropriate or arbitrary changes, as they could negatively impact the efficiency and effectiveness of the risk quantitative analysis model.

To enhance the accuracy of risk evaluation, it is imperative to thoroughly examine and address these missing elements through future research. By doing so, the risk evaluation model can be further refined and improved