Influence of Structural Variation of Container Shipping Industry towards Ocean Freight Forwarders

Freight forwarders are agents who provide related services for consignment deliveries on their own or in a client’s name. They always organize the transportation of shipments with various characteristics and assumes responsibility for their delivery. Freight forwarders play the role of carriers when dealing directly with shippers; in contrast, they change their position while consigning shipments to actual carriers. Many larger OFFs have expanded their services with integrated logistics management for crosscountry operations (Burkovskis, 2008).

Service qualities or attributes of OFFs have attracted various studies seeking to clarify the factors that affect shipper selection for consigned shipments. Reliability, service, and price are primary dimensions that shippers consider (Kilibarda et al., 2016; Perlman et al., 2009). Reliability presents the gaps between the perceived and expected values of shippers. Aggregated and integrated services reveal the requirement of globalization businesses for contemporary producers. The excellent transit handlings with a rationalization of freight rates are the noticeable conditions to present the competitive advantages of forwarders (Lin and Liang, 2011). Service qualities through information tracing, availability of cargo space, the competency of emergency handling, the ability of claims, and freight rates might show a higher priority for improvement (Yang, 2012). Notably, customer response, information technology, and knowledge management affected organizational performance for OFFs (Shang and Lu, 2012). Innovation capability and firm performance of OFFs have been examined to have a positive and significant relationship (Yang, 2012).

The service conditions of OFFs are pretty crucial for their customers; however, most parts of these conditions depend on the provision of CSLs. A considerable variation of the industrial dynamics of CSLs affects the arrangement of forwarding arrangement inevitable and right.

Shipping line selection is crucial because the cooperating relationship might need to be maintained for a long time between the shipper/forwarder and any carrier. In the early operation environment, carriers’ door-to-door elapsed time and timing-related services were the unique deterministic criterion of carrier selection decision (Brooks, 1990; Lu, 2003). As the complexity of global logistics operations increased, more criteria, such as freight rates, scheduling, carrier reputation, slot availability, and on-time arrival/departure, became the additional conditions in carrier selection (Kannan, 2010; Lu, 2013). To play an intermediary role, OFFs still take shipper needs into account in carrier selection. Customs clearance efficiency and transport security are two critical factors correlated with integrated logistics (Ho et al., 2017).

The competitiveness of CSLs usually affects OFFs and shippers in selection. Schedule reliability, document accuracy, service quality, freight rate, and door-to-door service and environment significantly reflect the competitive performance among CSLs (Fanam et al., 2018). For specific markets, such as between Taiwan and southern China, different operation scales of OFFs reveal various consideration focuses. Large companies focused on the following dimensions: freight charges and information technology; whereas smaller companies preferred to focus on service performance and the reputation of the CSLs (Wen and Lin, 2016).

Although previous research (Ergin et al., 2022) indicated that the changes in world trade and maritime transport made the customers’ expectations vary, including shippers, consignees, and OFFs, the factors in selecting CSLs, reviewed from the studies mentioned above, mainly focus on freight rates, service conditions and qualities, and schedule reliabilities. Customer relations-related criteria have become more critical than the freight criterion. This study supports the phenomenon that the whole chain of maritime transport for container deliveries is changing, which is also proposed by this study.

The enormous variation from the industry of CSLs did not occur frequently. The Ocean Shipping Reform Act of 1999 in the United States induced a dramatic change to the industry of CSLs. This act almost reformed the maritime operation practice at that time. The challenges and opportunities of the intermediaries of non-vessel operating common carriers (NVOCCs) made a greater investment in information technology and other advanced tools and processes for supply chain management (Clott, 2000). Greater service values then were achieved than the traditional selling approach.

CSLs used cost-oriented competition strategies to benefit from economies of scale by ship enlargement, i.e., the more quantities of transported goods with the lower unit transport costs (Lu and Yeh, 2019; Tran and Haasis, 2015; Valentine et al., 2013). Only higher slot utilization can bring additional profits to the shipping lines. However, if deep ocean loops visit too many ports, there is a decrease in the benefits of the high load factor because extra port time and berthing cost further decrease.

Most of the critical perspectives of top managers in CSLs to operate mega-ships are concerned about the ship lines themselves (Lu and Yeh, 2019), like increases in investments in building or leasing containers, changes of service networks of shipping lines, and management of empty container repositioning. Still, some factors will directly alter the logistics system operations, such as seaborne feeder services and inland transportation. The relationships between CSLs and OFFs are also affected by a significant burden to fill up a ship to capacity.

The changing features because of such chain effects affect the operation and service of OFFs. Most other partners in the transportation chain are not necessarily favorable for mega-ships (OECD, 2015). Some possible misgivings are the upsizing ships, such as service frequency reduction, risks concentrating in a single vessel, delivery disruptions or delays, port hinterland infrastructure and connectivity, peak congestion, and delay for truck companies, among others that may affect all stakeholders.

OFFs play the interface role between CSLs and shippers, but most previous studies discussed the service and selection relationships in the normal operating environment of container shipping. The CSL industry is currently situating a significant alternation on a higher concentration of service conditions because of the reduction of larger players. The influence of this phenomenon might gradually leaven to alter the service resources and even the priorities of company management of OFFs. This study attempts to dig out some valuable findings for the perceived influence of OFFs.

This study infers the similarity calculation equation for triangular fuzzy numbers to develop the operation procedure of SAM. This approach is more accessible than using trapezoid fuzzy numbers proposed by Hsu and Chen (1996). The calculation of exact similarity with triangular fuzzy numbers in the Section 3.1.1 is also the contribution of this study. Based on this calculation, the following two sections provide the operation steps of SAM and the defuzzification processes.

3.1.1 Similarity measure concept

Fuzzy techniques can aggregate opinions from multiple experts evaluating an uncertain decision environment. The fuzzy concept comes from a judgment that might be expressed in a numerical range with a different membership strength to represent the possible decision determination. Let X be a universal set. A fuzzy set A in X is characterized by a membership function u_A(x) associated as a real number in the interval between 0 and 1 with each point in X. The value of u_A(x) at x represents the membership grade of x in A.

A triangular fuzzy number is a popular method of defining the membership function to express a decision maker’s fuzziness. Equation (1) expresses the membership grades for a triangular fuzzy number A in real line ℜ referring to u_A: ℜ → (0, 1), where – ∞ < a ≤ b ≤ c < ∞(Dubois and Prade, 1978). The grade of b represents a maximal grade or the most probable value of membership in A, i.e., u_A(b) = 1. The interval (a, c) is the range of this set’s lower and upper bounds, for which length also represents A’s fuzziness. Thus, a triangular fuzzy number A can be represented by 3-tuples A = (a, b, c).

$$u_A(x)=\{\begin{array}{l}(x-a)/(b-a),a\le x\le b\\ (c-x)/(c-b),b\le x\le c\\ 0,otherwise\end{array}$$

(1)

Suppose two experts have their estimates R̃_i and R̃_j represented by triangular fuzzy numbers, but assumed they have a common intersection at some α-level cut, α ∈ (0, 1]. Figure 2 and equation (2) express that the agreement degree s(R̃_i, R̃_j) is defined by the proportion of the consistent area to the total area, also known as a similarity measure function (Zwick et al., 1987).

Figure 2. Concept of similarity measures for two triangular fuzzy numbers.

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S(R̃_i, R̃_j) can be further inferred as the flowing with the elements of two triangular fuzzy numbers R_i = (a_i, b_i, c_i) and R_j = (a_j, b_j, c_j) and expressed as Equation (3). If two estimates are the same, i.e., R̃_i = R̃_j, S(R̃_i, R̃_j) = 1. In contrast, if two estimates are entirely different, S(R̃_i, R̃_j) = 0. Higher a S(R̃_i, R̃_j), the higher the consistency or similarity.

$$\text{S}({\tilde{R}}_i,{\tilde{R}}_j)=\frac{{\displaystyle {\int }_x(\min \{{\mu }_{{\tilde{R}}_i}(x),{\mu }_{{\tilde{R}}_j}(x)\})dx}}{{\displaystyle {\int }_x(\max \{{\mu }_{{\tilde{R}}_i}(x),{\mu }_{{\tilde{R}}_j}(x)\})dx}}$$

(2)

$$\begin{array}{l}\frac{x-a_j}{b_j-a_j}=\frac{c_i-x}{c_i-b_i}\to x=\frac{c_i{b}_j-b_i{a}_j}{c_i-b_i+b_j-a_j}\text{and}{h}_x=\frac{c_i-a_j}{c_i-b_i+b_j-a_j}\\ S({\tilde{R}}_i,{\tilde{R}}_j)=\frac{\frac{1}{2}(c_i-a_j)\times \frac{c_i-a_j}{c_i-b_i+b_j-a_j}}{\frac{1}{2}(c_i-a_i)+\frac{1}{2}(c_j-a_j)-\frac{1}{2}(c_i-a_j)\times \frac{c_i-a_j}{c_i-b_i+b_j-a_j}}\\ S({\tilde{R}}_i,{\tilde{R}}_j)=\frac{{(c_i-a_j)}^2}{(c_i-a_i+c_j-a_j)\times (c_i-b_i+b_j-a_j)-{(c_i-a_j)}^2}\end{array}$$

(3)

For exploring the influence of the industrial environment change of CSLs, this study collected possible alternations from the literature to invite nine experts to preview the appropriateness of the proposed question items. The panel of this focus group consisted of five senior managers from freight forwarder companies and four professors who continuously devoted themselves to marine shipping research. Then, this study distributed the revised questionnaire to 50 industrial experts to investigate their agreement levels for all question items. Primary processes in the whole research design are described as follows.

3.2.1 Questionnaire contents

The designed questionnaire at the first phase consisted of 16 influential factors extracted from the support references. Most experts deleted the factor of “OFFs perceive the sales representatives of CSLs lacking sufficient advanced knowledge and capability.” They provided suggestions to modify the other six factors by precisely describing the situation of changes, such as revising “Choice for available cooperated carriers changes” as “Choice for available cooperated carriers decreases.” Furthermore, three factors stemmed from their observation and were implemented to be appended. These question items included “OFFs should quickly respond to the changes of logistical decision making from shippers,” “OFFs care more about risk management than before,” “OFFs track CLSs’ operation dynamics more in detail.” Table 1 shows the contents of the ready questionnaire to distribute to the next round of experts.

Table 1. Contents of the distributed questionnaire and support references of factors

No.	Factors	Support references
1	Freight rates offered by carriers are obviously changed.	Lin and Liang (2011); Lu (2013); Wen and Lin (2016); Ho et al. (2017)
2	The route mixes to provide shippers decrease.	OECD (2015); Lu and Yeh (2019); Ho et al. (2017)
3	The provisioning capability of empty containers to shippers gets better.	Ho et al. (2017); Lu and Yeh (2019)
4	Truck fees increase because of ship call changes.	OECD (2015)
5	The choice for available cooperated carriers de-creases.	Lu et al. (2006)
6	Control ability of forwarders for ship slots decreases.	Lu (2013); Wen and Lin (2016); Lu and Yeh (2019)
7	Handling and additional charges increase.	Lin and Liang (2011); Ho et al. (2017)
8	Transport reliability of CSLs decreases.	Lu (2013); Wen and Lin (2016); Lu and Yeh (2019)
9	Service quality of CSLs decreases.	Lin and Liang (2011); OECD (2015); Lu and Yeh (2019)
10	Brand reliability of CSLs decreases.	Lu et al. (2006); Lu (2013); Wen and Lin (2016); Ho et al. (2017)
11	OFFs should quickly respond to the changes in logistical decision making from shippers.	Proposed by the focus group
12	OFFs change the perception to an e-commerce application.	Lin and Liang (2011)
13	The pressure of getting more freight businesses increases.	Grimstad and Neumann-Larsen (2013); Lu and Yeh (2019)
14	OFFs care more about risk management than before.	Proposed by the focus group
15	OFFs are more concerned about the rights and obligetions for freight damage claims.	Lin and Liang (2011)
16	OFFs track CLSs’ operation dynamics more in detail.	Proposed by the focus group
17	The liaison burden between OFFs and CSLs decreases.	Lu (2013)
18	The complexity of shipment consolidation increases.	Burkovskis (2008); Lin and Liang (2011)

CSL, container shipping lines; OFF, ocean freight forwarder.

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The questionnaire asked respondents to express the agreement in perceived recognition from their practical experience for each factor. A 7-point scale of linguistic variables, from 1 for extremely disagree to 7 for extremely agree, were applied to measure respondent opinions. Table 2 shows the settings of triangular fuzzy numbers for every linguistic variable and their membership function.

Table 2. Triangular fuzzy numbers and membership function for linguistic variables

Linguistic variables for importance	Triangular fuzzy numbers	Membership function
Extremely disagree (ED)	(0, 0, 0.2)
Very disagree (VD)	(0, 0.2, 0.4)
Disagree (D)	(0.1, 0.3, 0.5)
Moderate (M)	(0.3, 0.5, 0.7)
Agree (A)	(0.5, 0.7, 0.9)
Very disagree (VA)	(0.6, 0.8, 1)
Extremely agree (EA)	(0.8, 1, 1)

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The given value of β in Equation (7) is a critical setting to distinguish the individual weights for respondents. While β = 0 means the RAD is considered only without taking the relative weights of each respondent’s importance levels (w_i), i.e., CDC = RAD. This section first reports the case of β = 0 to neglect the different individual conditions of all respondents. Table 4 shows that the linguistic expression for every respondent opinion was first transformed into the system of triangular fuzzy numbers.

Following the steps of SAM operation, we constructed eighteen 50 × 50 matrixes of agreement degrees for every factor. Each S_ij follows equation (3) calculation to complete the mutual consensus evaluation for respondents. Then, the AAD (A(E_i)), RAD, and CDC for each respondent can be calculated as Equations (4), (5), and (7). For example, Table 5 illustrates the calculation results for question item 1. Considering the fuzzy opinions of all respondents, the aggregation result, following as Equation (8), R̃ = (0.467452, 0.667452, 0.867048) for question item 1.

The same steps were conducted for other question items. Table 5 collects all aggregation results of every question item and their defuzzification for ranking results. Items 11 to 16, with defuzzification values over 0.7, were clustered as an item group with significant changes.

For exploring the possibility of different results from individual conditions, this study set the relative weights with a variety of respondent identities. The analysis categories included their work experience, main managing business markets, and job titles. The given relative weights were set from 1 to the number of the investigated group. Table 5 shows that the greater the importance of the respondent group, the larger the setting weight. It was noted that the influence of the shipping lines industry occurred more for deep ocean services than for short sea markets because of growing ship sizes. The relative weights of businesses markets were set as larger as the scope of respondent management of the deep ocean. After a normalization process of the given weights, each respondent has a given relative weight of the importance (r_i) in carrying on the SAM operations.

As equation (6), every respondent’s important degree (w_i) can be calculated. Further, let β = 0.5 express a neutral preference so that the required processes in SAM can be implemented. Table 6 shows the example for all indexes of the first question item that has been weighted by work experience for all respondents.

Table 7 shows the results using the same operation of SAM for three kinds of weighted categories. The defuzzification figures slightly differ, but the ranks of the top seven and question items 4, 8, and 17 for the three types of weighting schemes are the same. For comparison with the result of setting β = 0 in Table 6, the sequence of the top three factors is presented differently. The weighting schemes for different individual background conditions of respondents slightly altered the evaluation results to obtain a more distinguished and reliable group consensus. After the industrial change of upstream, OFFs cared more about their rights and obligations when damage claims occurred. The dynamics of operation details from the changes of carriers attracted OFFs more attention. Meanwhile, such enormous variation from upstream actually generated more significant pressure on the freight businesses to OFFs. This result might be why OFFs experienced no reduction in liaison burden with their cooperated CSLs.

From the investigation summary, the variation of industrial structure among CSLs generated influences for OFFs. Several factors were ranked higher in the consensus, belonged to inner awareness, and were event-driven. The sudden broken-down announcement from the Hanjin Lines caused onboard containers detained on its ships because they were prohibited from sailing into harbors. Port administrators and operators worried this company could no longer pay off port tariffs and charges. Panicked shippers were confronted with a complex situation in which their cargo could not be delivered to fulfill the trade contract and could not perform redelivery by other shipping lines. There is only one solution that shippers can use to claim compensation for their consigned forwarders with this event. In the same situation, OFFs also play a role as shippers to carriers; their rights are also hampered by bankruptcy, which falls on freight forwarders. OFFs might have related knowledge on handling this situation, but they cannot do anything about it because they are inexperienced. Involuntarily, freight forwarders must pay greater attention to the rights and obligations regarding cargo damage claims as part of their processes. OFFs are often willing to avoid such things and wish never to have them happen again by reminding employees to observe the details of the operational dynamics of CSLs closely. Such experiences induce OFFs to care more about risk management than before.

The hidden factor of continuous growth of containership sizes puts more significant pressure on CSLs to maximize their capacity as cost-saving is measured as unit transportation for containers. These targets were also transferred to cooperating OFFs before the close date for each stop of mega-ship voyages. This pressure was also passed on from online forwarders of larger ships to off-line OFFs like a cascading effect, which led OFFs to uncertainty and hesitancy on ship slot control. Also, the changes of port call for routes because larger ships or strategical alliance deployment might result in the alternation of shippers’ logistical chains and will exert OFFs pressure to rapidly respond to the changes of logistical decision making from shippers. OFFs might require rerouting seaborne and inland transports for shippers at those ports without mega-ship calls. Any additional charges in a new routing operation, such as truck fees and stevedoring charges, may appear different. A large scale of port rotation alternation and service network change because of cascading effects of mega-ships or the reshuffling of SAs, at the same time, might influence the freight rates that CSLs offered to OFFs.

As information and communication technologies progressed, industrial restructure made carriers emphasize the use and development of modern information techniques, such as the appearance of blockchain technology. The cooperation among runners and other stakeholders or even transaction platform providers, such as Alibaba, let OFFs alter e-commerce applications’ perception. This development is the channel through which OFFs can observe the detailed operation dynamics. In Taiwan, some OFFs even build up a transaction platform to enforce the integration capabilities of freight consolidation in advance. The function of e-commence has developed to maximization of logistics integration.

Noted that carrier services on transport reliability and quality did not worsen because of reduced CSLs or reshuffling SA members. The brand confidence of existing CSLs did not slack to OFFs. OFFs did not detect the consolidated operations becoming more complicated than before. However, OFFs were still required to keep intensive relationships with shipping lines. The connections to complement one another between OFFs and carriers were not changed.

Although the survey results contributed from the sampling to the 50 respondents of OFFs in Taiwan, recent rapid and considerable changes in the global supply chain support the robustness of the main findings summarized in this study. The recent case that a giant containership named ‘EVER GIVEN’ blocked the Suez Canal and resulted in the largest general average claim in global maritime history (https://theloadstar.com/). This event deepens global OFFs risk recognitions for their relationships with CSLs. The outbreak of the COVID-19 pandemic made a severe shortage of ship slots and empty containers because of worsened logistical speed and lengthened cycle time for load equipment of CSLs (https://home.kuehnenagel.com/). Slot control and reservation became the best advantage of OFFs to attract their businesses despite shippers being aware of extraordinarily forwarding handlings.

Stakeholders in the container transportation chain launched the new digital logistics platform for providing customers with instant online cargo booking and tracking (https://container-news.com/). Such development puts forwarders or NOVCCs a heavy erodent pressure on their businesses. Global logistical integrators vigorously appeal to customers to use their networks for a suitable choice of transportation alternatives with lower risk (https://home.kuehnenagel.com/). This tendency also expresses the importance of e-commerce platforms for OFFs in future operations.

Although the phenomena above are accompanied by special events occurring, the significant relationships with the structural variation of the CSL industry are apparent. However, transport reliability and quality in ocean container deliveries almost collapsed because of the unstoppable COVID-19 pandemic. Port congestion stalling the regular cycle times of ships and containers induced the extraordinary soaring of freight rates (https://www.lloydsloadinglist.com/). These event-driven influences are unexpected after the structural variation of the CSL industry.