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Asian Journal of Engineering, Social and Health
Volume 4, No. 1 January 2025
Volume 4, No. 1 January 2025 - (230-248)
p-ISSN 2980-4868 | e-ISSN 2980-4841
https://ajesh.ph/index.php/gp
Pricing Strategies and Revenue Analysis of HTL International
(International Freight Forwarder) in Response to Volatile FCL (Full
Container Load) Shipping Rates from China
Mochamad Ikhsan Adityatama Wiratisna1*, Subiakto Sukarno2
Institut Teknologi Bandung, Indonesia
Emails: mochamad_wiratisna@sbm-itb.ac.id1, Subiakto@sbm-itb.ac.id2
ABSTRACT
HTL International faces dynamic variable rates attached to Full Container Load (FCL) shipments from China
hubs. HTL has no prospective pricing strategy. To stay afloat in the tide of competition, HTL employs an
integrated workforce through pricing policy, financial management, and risk disposition. If HTL considers
the cost and risks forehand, it willn’t just remain afloat and position itself on top in Indonesia’s freight
forwarding market. This study develops a financial strategy for HTL International to handle Chinese FCL
shipping rate fluctuations. The study used internal and external data. There are findings as follows:
dynamic pricing proved to be more flexible and profitable, cost-plus pricing provides stability but limits
opportunity, 40FT container volumes have the most significant impact on revenue, the freight rate 40FT
showed a stronger correlation with revenue compared to 20FT, and sensitivity analysis revealed that HTL’s
profitability is highly sensitive to changes in freight rates, variable costs, and container volumes. These
findings support the idea that implementing a pricing strategy supported by volume optimization and
operational efficiency allows HTL to navigate market volatility effectively. HTL must enhance its
operational performance, reduce variable costs, and aim at 40FT container volumes to improve its
financial situation. HTL International should implement hybrid pricing policies, maximize 40-foot container
usage, and include sensitivity in financial planning, forecasting, predictive analytics, and revenue
diversification.
Keywords: Full Container Load (FCL), HTL International, Pricing Strategies, Revenue Analysis.
INTRODUCTION
The logistics industry has a critical component: Full Container Load (FCL) shipping. FCL
shipping exposes freight forwarders to rate volatility caused by global demand fluctuations,
supply chain disruptions, and market-driven cost changes (Notteboom & Pallis, 2021). Such
volatility affects freight forwarders’ financial performance. Freight forwarders must be able to
carefully manage pricing and revenue strategies to have stable financial conditions.
Pricing Strategies And Revenue Analysis Of HTL International (International Freight Forwarder) In
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Figure 1. Global and shanghai freight index
HTL company faces challenges due to FCL shipping rate volatility. Starting from 2020, FCL
rates, especially on major routes from China, experienced fluctuations. The volatility of FCL rates
requires HTL to adopt strategic pricing models and revenue management practices. Dynamic,
Cost-Plus, and Seasonal Strategies can help freight forwarders have the flexibility to adjust to rate
fluctuations (Cahoon & Notteboom, 2019).
Figure 2. Import volume from shanghai
HTL needs the most efficient pricing models and revenue management to manage its
finances and risks. Dynamic pricing and cost-plus pricing can provide freight forwarders the
flexibility needed to adjust to rate fluctuations, protecting revenue streams, and profit margins
(Cahoon & Notteboom, 2019). HTL is financially responsible for the cargo in transit and faces
further financial risks. This is why it is valuable for HTLs to use tools for managing risk.
HTL can establish a fixed rate agreement with a specific client, provide variable pricing
contingent on market conditions, and deliver additional value-added services. Through strategic
revenue management, HTL can mitigate its vulnerability to fluctuating FCL prices (Lam & Lai,
2018). HTL can respond a little bit quicker to changes in FCL rates and provide customers with
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faster and more accurate data by using digital tools. HTL needs to adopt a pricing strategy,
manage its currency risk, and be ready to take on operational flexibility. HTL needs to focus on
growing earnings, minimizing costs, and actively managing exposure to risks.
A tariff decrease may pressure HTL to adjust its pricing model to remain competitive. HTL
should use dynamic pricing to maintain profit margins and adapt to market changes. Data-driven
insights help HTLs improve carrier relationships, container utilization, and pricing flexibility.
Figure 3. Historical 20ft freight rate shanghai vs qindao vs guangzhou
Figure 4. Historical 40ft freight rate shanghai vs qindao vs guangzhou
Figure 5 shows inconsistent revenue due to the volatility of ocean freight from China
(Shanghai).
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Figure 5. PT HTL selling rate vs revenue (shanghai)
With HTL’s current strategies in place, the company is highly likely to lose market share and
profitability to competitors (Nguyen & Notteboom, 2020). HTL is also devoid of organized ways
of dealing with risk.
Based on the above background, this study aims to develop and evaluate financial
strategies tailored to HTL's needs, offering actionable insights in managing revenue and
mitigating risks in the face of uncertain shipping rates. so the benefits in this study to provide
practical recommendations for HTL International and other freight forwarders operating in
volatile markets. These insights can guide companies in optimizing their financial strategies,
enhancing operational efficiency, and ensuring customer satisfaction despite unpredictable
fluctuations in FCL rates. Additionally, this study contributes to the existing body of knowledge
on freight forwarding by exploring the integration of technology-driven solutions, such as digital
platforms and predictive analytics, to improve pricing strategies and risk management.
RESEARCH METHOD
Researchers collected secondary data and used a quantitative approach. Researchers use
internal data, such as historical freight rates, revenue records, and operational cost data from
2019-2023. Researchers use external data from industry reports. Quantitative techniques are
employed to process and interpret the historical data. The procedure consists of four interrelated
analyses: the Determination of Pricing Objectives, the Cost-Volume-Profit (CVP) Analysis, the
Revenue and Profitability Analysis, and the Regression Analysis.
Analysis of Volatility Against Historical FCL Rate
The analysis considers the effect of historical 40FT and 20FT rates per container from 2019
to 2023. This analysis uses the CV to estimate rate volatility:
CV = Standard Deviation of FCL Rates
Mean of FCL Rate
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Total Revenue USD
Selling Rate USD
Month
PT. HTL Selling Rate VS Revenue (Shanghai)
40FT 20FT TOTAL REVENUE
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Volume 4, No. 1 January 2025
Observing the Effect of Rate Volatility on Profitability
When the FCL rates are low, consistent, and lower than the range in which old margins are,
compare the old margins with the current ones. This assists in establishing the extent to which
rate volatility influences HTL’s ability to remain profitable.
Pricing Strategy Evaluation
This step will consider cost-plus and dynamic pricing models to establish the market prices
for HTL shares. Some of the actions that need to be taken are as follows:
a. Dynamic Pricing Analysis: This analysis examines data from 2019 to 2023. The objective is to
measure the effectiveness of this model in capitalizing on tariff increases.
b. Cost-Plus Pricing Analysis: This analysis evaluates the consistency of revenue during
predictable market conditions.
c. Scenario Simulations: The revenue effects of the two pricing models are compared, focusing
on crucial times.
Cost-Volume-Profit (CVP) Analysis
CVP analysis establishes the relationship between shipping rates, container volumes, and
HTL’s profitability. Steps that need to be taken include:
a. Profitability Assessment
Analyzing changes in transportation rates to determine their impact on HTL’s financial
performance.
Formula:
BEP = Fixed Cost
Selling Price per Unit-Variable Cost Per Unit
b. Scenario-Based Profitability
Hypothetical scenarios are utilized to simulate the impact that major events would have on
the financial system.
c. Revenue and Profitability Analysis
This analysis investigates profitability and revenue trends. It can also measure the financial
results of pricing strategies and shipping volumes.
Regression Analysis
Regression analysis is applied to determine the degree of correlation between the primary
variables and profitability. The steps that need to be taken include model construction, statistical
validation, coefficient interpretation, and scenario-based insights.
Quantitative Analysis
Quantitative analysis techniques should be employed to investigate historical data, make
predictions, and establish critical connections. Trend analysis, correlation analysis, and regression
analysis were utilized. Interpreting analysis results is the final step. Actionable insights can be
Pricing Strategies And Revenue Analysis Of HTL International (International Freight Forwarder) In
Response To Volatile FCL (Full Container Load) Shipping Rates From China
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obtained, including measuring the effectiveness of pricing strategies and observing areas to
improve risk mitigation and operations.
RESULTS AND DISCUSSION
Rate Volatility Impact HTL Revenue Stability
The following is a summary table of standard deviations and variances to highlight the level
of volatility for 40FT and 20FT container freight rates (see Table 1).
Table 1. Variance and standard deviation for 20ft and 40ft container freight rates
Year
Summary
µ
Variance
Standar Deviation
20FT
40FT
20FT
40FT
20FT
40FT
2019
0
0
71625,0
167666,7
USD 268
USD 409
2020
13
27
98018,1
246388,9
USD 313
USD 496
2021
-6
-12
80635,1
208939,4
USD 284
USD 457
2022
-5
0
57746,3
167789,4
USD 240
USD 410
2023
-10
-19
1617,8
4912,9
USD 40
USD 70
The standard deviation for 40FT containers is consistently higher than for 20FT containers.
For example, the standard deviation for a 40FT container is USD 496 and USD 457 in 2020 and
2021. The standard deviation for a 20FT container in 2020 and 2021 is USD 313 and USD 284. In
2023, the standard deviation has decreased rapidly.
Table 2. Coefficient correlation r
Year
Coefficient Correlation r
20ft
Remark
40FT
Remark
2019
0.4817
Positive Correlation
0.656
Positive Correlation
2020
0.3022
Positive Correlation
0.282
Positive Correlation
2021
0.1147
Positive Correlation
0.224
Positive Correlation
2022
-0.1089
Negative Correlation
0.501
Positive Correlation
2023
0.8099
Positive Correlation
-0.153
Negative Correlation
Table 2 shows the relationship between HTL revenue and freight rates. A positive
correlation was found between revenue and freight rates. For example:
a. In 2019, a positive correlation coefficient indicates that higher freight rates positively
contributed to revenue.
b. In 2020, a positive correlation indicates higher rates of supported revenue during volatile
periods.
c. In 2022 and 2023, the correlation became less consistent. A negative correlation was
discovered for 40-foot containers in the year 2023. There is a positive correlation for
containers that are 20 feet in length.
The increase in freight rates is in line with revenue growth. Excessive volatility can cause
instability. Freight rate fluctuations affect the revenue trend from 2019 to 2023. The fancy line
Mochamad Ikhsan Adityatama Wiratisna, Subiakto Sukarno
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on the chart shows the 40FT freight rate. Market demand drove the line to its peak in Q4 2020
and Q1 2021. Revenue increased with the rate spike, as shown by the green line.
Figure 6. Freight rate vs. revenue
The revenue trajectory appears volatile in early 2020 and mid-2022. The blue line denoting
the 20FT freight rate exhibited relative stability throughout the period. The 20FT freight rate
provides a modest but consistent revenue contribution. 40FT containers are more vulnerable to
market fluctuations and rate volatility. The analysis demonstrates that rate volatility directly
impacts revenue stability, which shows increased sensitivity to fluctuations.
Overview of Pricing Strategy Results
Pricing affects income and margin through changing tariffs. Current pricing strategies
include cost-plus and dynamic.
Contribution Margin Comparison for Pricing Strategies
Figure 7. Contribution to margin – 20ft
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Volume 4, No. 1 January 2025
Figure 8. Contribution to margin – 40ft
The two charts show how the contribution margins differ across the three pricing models
over time. The Dynamic Pricing Model generated higher contribution margins, especially during
Q3 2021. With dynamic pricing, a 20FT container generated a contribution margin of around USD
700 per container. With cost-plus pricing, the contribution margin generated was less than USD
400. The limitations of the cost-plus pricing strategy can be seen during volatile markets. Since
the markup approach limits its ability to take advantage of market dynamics, the contribution
margin it generates is lower. This makes this strategy less competitive.
The dynamic pricing strategy on 40FT containers outperforms cost-plus pricing. With
dynamic pricing, the contribution margin of a 40FT container reached USD 1,200 during Q3 2021,
whereas with cost-plus pricing, it was below USD 800.
Revenue Comparison by Pricing Mode
Figure 9. Revenue comparison by pricing mode
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This chart shows the revenue trend with each pricing strategy from 2019 to 2023. The chart
indicates that the peak revenue was attained in 2021. During that period, the revenue
approached 800,000 USD. The Current Strategy and Cost-plus-pricing models exhibit relatively
stable revenue patterns, with marked stagnation during elevated demand. In 2021, both
strategies underperformed relative to dynamic pricing. The chart indicates that the peak revenue
was attained in 2021.
Rate Changes Comparison for Each Strategies
Figure 10. Rate changes comparison
This chart shows the trend of sales tariffs for 20FT and 40FT containers with the three
pricing strategies. Dynamic pricing shows fluctuations. This strategy can adapt to market changes.
HTL can use this ability to take advantage of periods of high demand with high tariffs. In early
2020 and late 2022, dynamic pricing strategies maintained competitive rates. The cost-plus
pricing strategy shows a relatively stable trajectory. This model has limitations in responding
effectively to volatile market conditions. The current strategy still cannot outperform the
capabilities of the dynamic pricing strategy.
Figure 11. 40 ft rate changes comparison
Pricing Strategies And Revenue Analysis Of HTL International (International Freight Forwarder) In
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Volume 4, No. 1 January 2025
During periods of high demand, the dynamic pricing strategy consistently outperforms the
other strategies. This strategy allows rates to reach 5000 USD per container during Q3 2021. The
cost-plus strategy rates remain stable at around 4000 USD. The current strategy for 40FT
containers fails to exploit periods of increased demand fully.
Pricing Strategy Result Analysis
These results underline the advantages of a dynamic pricing strategy when facing
fluctuating market conditions. Not only does it maximize contribution margin, but this strategy
can also increase revenue. The flexibility of this strategy was seen during Q3 2021. Dynamic
pricing also helped HTL maintain profitability and earn additional revenue. Cost-plus pricing is
static in nature, so its effectiveness is limited. Dynamic pricing is the most effective model for
maximizing profits in volatile industries.
Cost-Volume-Profit (CVP) Analysis with Pricing Strategy
Breakeven Volume by Pricing Strategy
The graphs’ analysis provides valuable insights into the impact of different pricing strategies
on HTL’s financial performance. These visualizations compare the minimum container volumes
required to cover fixed costs under Dynamic Pricing, Cost Plus Pricing, and the Actual Volumes
shipped from 2019 to 2023.
From the 20FT container chart, dynamic pricing can lower the breakeven threshold. This is
shown in the green bars. In April 2020, the breakeven volume with a dynamic pricing strategy
was around 10 containers. The breakeven volume with cost-plus pricing was 15 containers, as
shown by the red bars. In that month, HTL’s shipment volume reached around 12 containers. Its
volume exceeded the breakeven requirement for a dynamic pricing strategy. This condition
allowed HTL to achieve profitability despite the decrease in demand. However, with its higher
and more rigid breakeven threshold, the cost-plus pricing model failed to reach profitability.
Figure 12. 20 ft volume comparison to break even vs actual
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Actual volume often exceeds the breakeven threshold of dynamic pricing. However, it
usually falls short of the breakeven threshold of cost-plus pricing. This pattern shows the
weakness of cost-plus pricing in volatile markets.
Figure 13. 40 ft volume comparison to break even vs actual
Dynamic pricing outperforms cost-plus pricing by maintaining significantly lower breakeven
volumes. The breakeven volume for dynamic pricing in October 2021 was about 10 containers.
The rates for the cost-plus strategy remain consistent at approximately 4000 USD.
Dynamic pricing can adapt. In early 2021, this strategy helped HTL maintain its operations.
The cost-plus pricing strategy has a static nature that makes it unable to reach the breakeven
volume.
Strategy is essential to maintain shipment volumes above breakeven. Dynamic pricing’s
ability to lower breakeven thresholds enables the company to capitalize on high-demand periods
while minimizing losses during downturns. In contrast, the inflexibility of cost-plus pricing
diminishes its efficacy in unstable market conditions, highlighting the necessity for more
adaptable pricing strategies.
Profit Sensivity Analysis
Profit Sensitivity Analysis can elucidate HL International’s profitability determinants in a
volatile market context and assess the effect of alterations in critical variables on the company’s
profit. Profit Sensitivity Analysis assesses financial opportunities and risks.
Key Assumptions and Baseline Data
The data analyzed are financial data and basic assumptions, as shown in Table 3. The
primary data highlights the following:
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40 ft Volume Comparison to Break Even Vs
Actual
Cost Plus 40FT Dyanmic 40FT Actual Volume 40FT
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Table 3. The key item table and profit & loss account
Key Item
($)
Profit & Loss Account
($)
(S/R) Freight Rate
1450
Revenue
20350
Volume
11
Cost of Sales
10912
Variable Cost
992
Gross Profit
9438
Contribution to Margin
400
SG&A
4881
Fixed Cost
4881
Operating Profit
4557
The resulting revenue is $20,350, with an Operating Profit of $4,557. Table 4 shows the
correlation between operating profit, freight rates, and container volume changes. It evaluates
several scenarios.
Table 4. Relationship between operating profit and
changes in container volume and freight rates
Operating Profit ($)
Container Volume (units)
4557
9
10
11
12
13
Freight Rates
1480
-587
-50
487
1024
1561
1665
1041
1782
2522
3262
4003
1850
2669
3613
4557
5501
6445
2035
4297
5445
6592
7739
8887
2220
5925
7276
8627
9978
11329
d. A Low Freight Rate (e.g., $1,480) combined with a low container volume (9 units) results in a
negative operating profit (-$587), emphasizing the vulnerability of HTL’s profitability during
unfavorable market conditions.
e. As the freight rate increases to $2,220 and container volume reaches 13 units, operating profit
surges to $11,329, demonstrating the significant positive impact of higher rates and volumes.
f. Increasing container volume freight rates can offset fixed costs.
Tornado Chart Analysis
Figure 15 provides an overview of the impact of changes in several factors on operating
profit. The chart evaluates the effect of a 20% increase and a 20% decrease in key variables.
Figure 14. Post sensitivity analysis
$1,368
$6,739
$2,669
$3,677
$5,533
$7,747
$2,375
$6,445
$6,437
$3,531
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Variable Cost
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Fixed Cost
Operating Profit USD
Factor
Sensitivity Analysis
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a. Selling Rate: The most influential factor, with profits ranging from $1,368 (20% decrease) to
$7,747 (20% increase).
b. Container Volume: Volume fluctuated by 20%, which significantly affected profit, which
ranged between $2,669 and $6,445.
c. Variable Cost: Variable costs increased by 20%, reducing profit to $2,375. Conversely, a 20%
reduction in variable costs could increase profits to $6,739.
d. Contribution to Margin: Changes in margin values can affect profit.
e. Fixed Cost: Fixed Cost is the least sensitive. Therefore, their impact on profits is relatively tiny.
Revenue and Profitability Analysis
This section examines the profitability and revenue trends for two strategies: dynamic
pricing and cost-plus pricing. The data presented is from January 2019 to October 2023.
Revenue Throughout the Time
Figure 15. Revenue vs time
This graph explains the development of revenue in three scenarios: dynamic pricing, cost-
plus pricing, and actual volume.
a. The advantages of a dynamic pricing strategy were very visible in Q4 2020 and Q1 2021.
Revenue increased rapidly, even exceeding cost-plus pricing and actual pricing.
b. The cost-plus pricing strategy is relatively stable. Although the revenue generated is
predictable, this strategy cannot adapt to price spikes.
c. Actual volume revenue is lagging. This shows that dynamic pricing can increase sales and
revenue.
Profitability Analysis: Cost-Plus vs. Dynamic Pricing
The cost-plus vs. Dynamic Pricing chart compares monthly profits under the two pricing
strategies. The red line represents Dynamic Pricing, while the blue line reflects Cost-Plus Pricing.
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Pricing Strategies And Revenue Analysis Of HTL International (International Freight Forwarder) In
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Figure 16. Profit analysis cost plus vs dynamic pricing
a. Dynamic pricing outperforms Cost-Plus Pricing during periods of market volatility. The
dynamic pricing strategy generated profit benefits between Q4 2020 and Q1 2021. The profit
jump was more than USD 15,000.
b. Cost-plus pricing remains stagnant. Its profit level approaches break-even when demand is
low, especially in Q2 2020.
c. Both strategies face the challenge of maintaining profitability when demand is low. In that
situation, dynamic pricing still provides a slight advantage.
The relationship between higher actual volume and profitability has been proven. When volume
increased in 2021, the dynamic pricing strategy took advantage of the moment to gain greater
profits. During low-volume periods, such as in early 2020, profitability declined rapidly.
Regression Analysis: Interpretation of Results
Regression analysis evaluates the influence of key variables on revenue. This analysis
combines visual representation and numerical regression output.
Table 5. Regression statistic
Regression Statistics
Multiple R
0,993952502
R Square
0,987941576
Adjusted R Square
0,986576472
Standard Error
1184,113262
Observations
60
The Multiple R-value of 0.9939 concludes that there is a positive correlation between the
dependent variable and the independent variable. The R-Square value of 0.9879 means that the
independent variables can explain around 98.79% of the revenue variation. The Adjusted R-
Square value of 0.9866 takes into account the number of predictors in the model to ensure its
reliability.
-5000
0
5000
10000
15000
20000
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Sep-19
Jan-20
May-20
Sep-20
Jan-21
May-21
Sep-21
Jan-22
May-22
Sep-22
Jan-23
May-23
Sep-23
Monthly Profit USD
Month
Profit Analysis Cost Plus Vs. Dynamic Pricing
Cost Plus Dyanmic
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Table 6. ANOVA regression and residual
ANOVA
df
SS
MS
F
Significance F
Regression
6
6088398578
1014733096
723,7112686
5,46003E-49
Residual
53
74312583,55
1402124,218
Total
59
6162711162
The ANOVA results prove that the regression model is indeed robust. The F statistic value
of 723.71 shows that the regression model is very significant. The F Significance value is very low
and approaches zero, only 5.46E-49.
Table 7. The regression output of each predictor variable
Coefficients
Standard Error
t Stat
P-value
Intercept
-27393,57503
4765,335089
-5,748509709
4,53644E-07
Freight Rate 20FT
3,589683663
3,89742472
0,921039897
0,361203156
Freight Rate 40FT
10,67143637
2,640660494
4,041199692
0,000172849
Contribution to
Margin 20FT
10,53905788
6,670575078
1,579932428
0,120072621
Contribution to
Margin 40FT
5,348037024
4,508583017
1,186190208
0,240837651
Volume 20FT
1360,537828
209,3818574
6,4978783
2,89503E-08
Volume 40FT
2121,84696
52,06848099
40,75108241
1,09318E-41
The 40FT freight rate has the highest coefficient. Its value is 10.67. This means that if the
40FT freight rate increases by $ 1, revenue will increase by $ 10.67. A P-value of 0.00017 indicates
strong statistical relevance. The coefficient of 2121.8 and a P-value of 1.09E-41 indicate its
substantial influence on revenue. So, it is important to maintain the volume of 40FT containers.
Contribution to Margin 40FT has a positive coefficient of 5.34. However, this is not
significant, with a P-value of 0.2408. The coefficient of 20FT Transportation Rate is lower, which
is 3.58. The P-value is higher, which is 0.3612. These coefficients yield the following regression
equation:
Y = Intercept + (3,589 β1) + (10,67 β2) + … + βnXn
This equation shows how each independent variable affects income. The statistical
significance of 40FT freight rates and volumes implies that tactics should enhance both to
optimize income. Figures 18 and 19 show the graphical interpretation of the regression analysis:
Pricing Strategies And Revenue Analysis Of HTL International (International Freight Forwarder) In
Response To Volatile FCL (Full Container Load) Shipping Rates From China
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Asian Journal of Engineering, Social and Health
Volume 4, No. 1 January 2025
Figure 17. Freight rate 20 ft line fit plot
Figure 17 shows the fit plot of the 20FT freight rate line, and Figure 19 shows the
relationship between 20FT freight rates and revenue. The scatter plot shows actual revenue data
as blue dots and expected revenue as red boxes. The data points are evenly distributed around
the trend line, indicating a mediocre fit.
Higher 20FT freight rates generally result in higher revenue. The spread of the data points
shows variability; the previous regression results show that the coefficient of the 20FT Freight
Rate is 3.58, and the P-value is 0.3613, indicating statistical insignificance. This suggests that while
increasing 20FT rates contributes positively to revenue, the impact is inconsistent or as strong as
other predictors like 40FT freight rates and volume.
Figure 18. Freight rate 40 ft line fit plot
The Freight Rate 40FT Line Fit Plot reveals a stronger and more consistent positive
relationship between 40FT freight rates and revenue. Compared to the 20FT plot, the steeper
slope of the line indicates greater revenue sensitivity. The tighter distribution of data points
$-
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$- $500 $1,000 $1,500 $2,000
Revenue
Freight Rate 20FT
Freight Rate 20FT Line Fit Plot
Revenue Predicted Revenue
$-
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$- $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500
Revenue
Freight Rate 40FT
Freight Rate 40FT Line Fit Plot
Revenue Predicted Revenue
Mochamad Ikhsan Adityatama Wiratisna, Subiakto Sukarno
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Asian Journal of Engineering, Social and Health
Volume 4, No. 1 January 2025
around the regression line indicates a more reliable correlation. An increase in the 40FT fare
directly results in an increase in revenue. The 40FT fare shows a high level of statistical
significance with a P-value close to zero.
Figure 19. Volume 20 ft line fit plot
The 20FT volume line figure shows a weak income and 20FT container volume relationship.
Many data points are scattered around the regression line since the contribution of 20FT
containers to total income differs. The dispersed data imply that increasing 20FT volume does
not significantly influence income. The coefficient of 20FT volume is positive but relatively minor,
and the P-value indicates moderate significance. The plot suggests that reliance on 20FT
containers may not be enough to generate significant revenue increases.
The 40FT Volume Line Fit graph correlates income and 40FT container volume. The data
points and rigorous regression line clearly and consistently display more 40FT containers, which
results in more income. The results showed that the coefficient of 40FT volume is much higher
and highly significant. The line’s steepness indicates the importance of 40FT containers in
generating revenue. In order to maintain financial performance, it is critical to maintain 40FT
volume above the breakeven level.
Business Solution for Current Issue
Optimize Dynamic Pricing Strategies
Dynamic pricing significantly outperforms cost-plus pricing. Our recommendations are:
a. Segmentation-Based Pricing: Inform your customers about the service’s requirements
regarding shipment, time, and category.
b. Rate Flexibility During Low Demand: Consider adjusting the rates to draw in large volumes of
containerized cargo during periods of low demand while still breaking even.
$-
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
0 1 1 2 2 3 3 4
Revenue
Volume 20FT
Volume 20FT Line Fit Plot
Revenue Predicted Revenue
Pricing Strategies And Revenue Analysis Of HTL International (International Freight Forwarder) In
Response To Volatile FCL (Full Container Load) Shipping Rates From China
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Asian Journal of Engineering, Social and Health
Volume 4, No. 1 January 2025
Focus on 40FT Container Volume Growth
The regression and sensitivity analysis results suggest that 40FT containers impact sales
performance the most. Suggestion actions are:
a. Volume-Based Incentives: Offer discounts or incentives to customers who ship larger volumes,
especially for 40FT containers.
b. Capacity Planning: Increasing the capacity of 40FT containers will improve revenue,
operational efficiency, and financial stability.
Utilize Data Analytics for Strategic Decision-Making
This study shows data-driven decision-making benefits cost management, volume
planning, and pricing. Suggestion actions are:
a. Regular Performance Reviews: Conduct monthly or quarterly performance reviews to track
revenue, profitability, and pricing effectiveness.
b. Scenario Analysis: Use sensitivity analysis to model “what-if” scenarios for freight rates,
volumes, and costs, enabling proactive decision-making in volatile markets.
Utilize Data Analytics for Changing Market Conditions
The regression results and line fit plots indicate that pandemic disruptions and seasonal
trends affect revenue performance. The proposed solutions are:
a. Seasonal Planning: Optimize capacity and pricing strategies ahead of high-demand seasons.
b. Market Diversification: Increase market diversification to include other shipping routes and
markets, reducing reliance on a single port and mitigating risks.
c. Contingency Plans: Make plans to deal with unexpected setbacks.
HTL International must adopt dynamic pricing, focus on 40FT container volumes, manage costs,
and implement data analytics.
CONCLUSION
The profitability of HTL is prone to change not only due to changes in rates but also due to
demand and supply. The turnaround in the first quarter of 2021 allowed the firm to achieve its
targets on profitability that were set for 2020. A significant rise in revenue is noted after rate
increases, as demonstrated by historical data from 2019 to 2023. From 2019 to 2023, there was
an increase in income during periods of rate surges.
Dynamic prices that exploit the price swings allow HTL to enhance its profitability. Raising
the transit dynamic pricing saves quite a lot. Cost plus pricing ensures a steadier but more
inflexible income flow. The most suitable strategy for HTL would be to have a mixed strategy that
entails dynamic pricing when the markets are volatile and cost-plus pricing when the markets are
due to their stability.
The 40-foot containers consistently contributed approximately 70% of HTL’s FCL revenue,
while 20-foot containers accounted for 30%. In 2021, the 40-foot container’s revenue grew more
Mochamad Ikhsan Adityatama Wiratisna, Subiakto Sukarno
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Asian Journal of Engineering, Social and Health
Volume 4, No. 1 January 2025
substantially. HTL must prioritize strategies that enhance 40-foot container operations. The 40FT
container volume reduces variable costs and increases operating efficiency.
The sensitivity analysis demonstrated that slight changes in freight rates, variable costs, or
shipment volumes could significantly impact revenue and profit margins. This study
demonstrates the importance of having an operational plan and a sound financial model.
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Mochamad Ikhsan Adityatama Wiratisna, Subiakto Sukarno (2025)
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