Introduction
The topic of risk in the container shipping industry has gotten a lot of coverage over the last decade.Data extraction is a hectic task in the shipping industry.The risk associated with the shipping industry is classified into: Technical risk, market risk, industry risk, and operational risk were discussed in previous studies related to container shipping. Modern and advanced analytics are becoming increasingly useful, assisting companies in lowering freight costs across the supply chain. Loss resulting from ship or machinery design and engineering, construction, technological processes, and test procedures area technical risk. Various modelling approaches for identifying risk in container shipping are discussed in this blog.
Container shipping
The shipping industry has faced many obstacles in today’s complex and time-based competitive business world. Shipping is a global industry that is inextricably linked to the growth and adversity of global economies. About 90% of foreign commerce is carried by boat, according to reports. Container shipping has grown in importance in recent decades due to its considerable advantages in loading and unloading activities and its potential to achieve intermodality. Both containerships carrying capacity and global traffic have grown at substantial rates.Consignors may be certain that their goods will be shipped to a specific port at a certain time to set line routes and written timetables. Danger has long been a significant determining force in maritime transportation, having emerged in the marine discipline. Unexpected disruptive incidents include both overt and unintentional detrimental effects on a business in various ways, like liner plan unreliability and shipping disruption or complete failure. As a result, shipping firms must understand the risks involved. Which are the most important, and how can these threats be mitigated in the shipping industry?
Risk and risk management
For the last decade, industries have paid close attention to the risks associated with container shipping.
- One impediment to risk management and prioritization of container shipping is the system’s size and sophistication. It includes various groups (such as transporters, hauliers, shippers, consignees, forwarders, and banks), all of whom have separate roles and procedures (such as trucking, loading/unloading, delivery, payment, and consolidating) that are difficult to examine thoroughly.
- Nguyen et al.[1] suggested the logistics perspective of detecting dangerous operating incidents in container shipping using cognitive appraisal methodology, keeping in mind the exceptional interaction between container shipping and logistics activities. The most general way of dealing with epistemic ambiguity is Bayesian subjective probabilities. With the addition of two-parameter stages, the risk-mapping parameter collection is enhanced using FMEA (Figure 1). To quantify Risk level (R) by using number assignment, the conventional quantification method of Failure Modes and Effects Analyses (FMEA) used only three parameters: Likelihood of event (L), Severity of outcome (S), and Probability of being observed (P). Because of the lack of a universal scale and the ineffective use of linguistic variables in consultation with data analysis experts, this approach becomes somewhat contextual.
Figure 1. Improved parameter structure based on FMEA for operating risks in container shipping [1]
2) Chang and his colleagues conducted studies to determine the degree to which each risk influences a shipping company’s results and the relative value of each risk factor. The thesis sought to address two questions: the risk factors in container shipping activities and the risk factors in a shipping business are more important than others. As seen in Figure 2, three study phases were taken: “risk assessment,” “risk measurement,” and “risk analysis.” “Risk assessment”. Figure 3 depicts the logistics flow among the relevant entities in the container shipping industry, which involve the three flows and multiple entities such as the shipping group, other transport firms, agency-related companies, consignor, consignee, and bank.
Figure 2. Research steps [2]
Figure 3. The three flows in container shipping services [2]
Each category has multiple elements (sub-categories): the information category includes information delay, information inaccuracy, and IT problems; the physical category includes transportation delay and cargo/asset damage; and the payment category includes currency exchange, payment delay, and non-payment. The risk assessment process began with supporting references, followed by multiple interviews to classify and verify possible threats in container shipping operations. Questionnaire structure information was used to gather similar data, and the risks’ impacts were then measured and ranked using the risk mapping tool.A total of 35 risk factors (Table 1) were defined and grouped into various groups. The risks associated with physical flows have more extreme risk consequences than the other forms of risks; moreover, one of the risk factors associated with information flow (shippers concealing cargo information) is the most important.
Table 1. Classification of risks in container shipping operations [2]
Future Scope:
Many kinds of research are happening on mitigating the risk in container shipping [3],[4],[5],[6].Some issues have been found, with scope for further development. First, the information base’s deficiency, which can be seen in the difficulty or incompetence of experts in providing assessments, is also not articulated mechanically. Several concerns may be posed about the expert’s ability to ascertain the knowledge base of their decisions and the method for successfully extracting and processing the input data rationally. The manner in which “confidence” is expressed and regulated and the presence of observable thresholds for “acceptable confidence” in evaluation practises are worthwhile research topics that require extensive study.Second, the proposed model lacks a method for representing customizations and expressions of relative importance among factors and experts. There is the possibility of corporating the system managing board’s vision or plan into the proposed FRBN to customize the fuzzy rules or a measuring system for the evaluating committee’s decisions. Modifications to the model, on the other hand, should be based on a sound theoretical basis.Should the model be driven by the model’s aggregated conceptual model of subjective perceptions? If the response is no, the variables that influence this feature should be explained before any customization. Unquestionably, inappropriate or arbitrary changes would have a negative impact on the risk quantitative analysis model’s efficiency and effectiveness. To improve risk evaluation accuracy, these missing items should be scrutinized and replaced by future analysis.
Reference:
[1] Son Nguyen, HaiYan Wang, (2018) “Prioritizing operational risks in container shipping systems
by using cognitive assessment technique”, Maritime Business Review, https://doi.org/10.1108/
MABR-11-2017-0029.
[2] Chia-Hsun Chang JingjingXu Dong-Ping Song , (2015),”Risk analysis for container shipping: from a logistics perspective”, The International Journal of Logistics Management, Vol. 26, Iss 1, pp. 147 – 171. https://dx.doi.org/10.1108/IJLM-07-2012-0068.
[3] Hoffmann, N., Stahlbock, R. &Voß, S. A decision model on the repair and maintenance of shipping containers. J. shipp. trd. 5, 22 (2020). https://doi.org/10.1186/s41072-020-00070-2
[4] Nguyen, S., Chen, P.S.-L. and Du, Y. (2021), “Risk identification and modeling for blockchain-enabled container shipping”, International Journal of Physical Distribution & Logistics Management, Vol. 51 No. 2, pp. 126-148. https://doi.org/10.1108/IJPDLM-01-2020-0036
[5] (2020), “Container shipping firms and risk management: Identifying the most relevant strategies”, Strategic Direction, Vol. 36 No. 5, pp. 23-25. https://doi.org/10.1108/SD-01-2020-0019.
[6]Yutong Liu, Li Cui; Risk Assessment for the Logistics of Shipping Companies: An Exploratory Study. Journal of Coastal Research 2 June 2020; 106 (SI): 463–467. doi: https://doi.org/10.2112/SI106-104.1.