Performance Evaluation of the Efficiency of Logistics Companies with Data Envelopment Analysis Model

Abstract

Malaysia has great geo-economic advantages, especially in becoming a major logistics and investment hub. However, as operational risk events create uncertainties, logistics companies suffer from supply and demand issues which affect their bottom lines, customer satisfaction and reputations. This is a pioneer paper to propose the optimization of the efficiency of listed logistics companies in Malaysia with operational risk factor using a data envelopment analysis (DEA) model. The basic indicator approach (BIA) is used as an output indicator for the operational risk capital requirement factor in the proposed model. This paper has practical and managerial implications with the identification of potential improvements for the inefficient listed logistics companies based on the optimal solution of the DEA model. This proposed model can be applied in emerging fields such as finance and project-based construction companies, where operational risk is a high concern.

1. Introduction

Malaysia has geo-economic advantages in connecting resources to markets for investments and business developments [1]. This natural privilege has increased the bilateral and multilateral trade volumes as major logistics companies make Malaysia a focal hub for their activities [2]. Nevertheless, local logistics companies also contribute to increasing the logistics performance in Malaysia because of the realization that logistics performance has significant relationships with the efficiency of a company and the outputs of the country [3]. Logistics companies typically manage a wide range of activities including transportation, inventories, warehouses, distribution, and information sharing [4]. All these activities are operational wherein an activity will affect subsequent processes in the logistics chain. Thus, logistics companies are highly prone to operational risk. Operational risk includes adverse activities from individuals, internal systems or activities, and some macroeconomic events [5,6,7].

Poor inventory management could lead to an inadequate number of safety stocks for sudden shift in customer demand, which cause the inability for transport planning and shipment scheduling that prolong lead time, decrease customer satisfaction, and company image, which will influence the company’s bottom line. On the other hand, inventory surplus leads to high carrying cost and wastage in times of obsoletion [8]. Many operational risk events are unrecognized while the consequences are hard to be quantified. Lochan et al. [9] found that operational risks induced by demand fluctuation and lead time are the main factors that affect logistics companies financially. This study also found that natural catastrophe, such as fire or COVID-19, prompting a logistics company to shut down for a week, could cause its customer to lose more than USD 180 million in revenue. This shows that logistics companies need to manage their operational risk well because all the activities and processes are highly interdependent. Gurtu and Johny [10] stated that operational and financial risks are the top concerns which cause major disruption in companies.

The Basel Committee on Banking Supervision (BCBS) presented the basic indicator approach (BIA) to bring down the adverse effects of operational risk [11]. Under BIA, companies could carry a capital of at least the mean of a fixed percentage (alpha) of positive gross income for the last three years [12,13]. BIA has received wide acceptance worldwide [14,15]. Operational risk has a causal relationship with the efficiency of a company, especially in terms of financial performance and should be monitored continuously [16,17]. Bai et al. [18] found that operational risk management could help shipping companies reduce financial risk exposures and increase their efficiency. Th efficiency of the logistics companies can be optimized with the data envelopment analysis (DEA) model [19,20]. DEA is non-parametric model which aims to optimize the efficiency of decision-making units (DMUs) under various inputs and outputs [21,22]. Efficiency is measured by the outputs over inputs, wherein bigger outputs or smaller inputs contribute to higher efficiency [23]. The efficiency score ranges from 0.0000 to 1.0000 under the variable return to scale (VRS) model [22,24]. This implies that the highest efficiency score is 1.0000 [17]. DMUs with this highest efficiency score is on the efficiency frontier while the distance from the frontier signals the level of inefficiency [22]. The achievement of the highest efficiency score denotes the full use of inputs for the greatest output generation under the output oriented VRS model [22].

DEA has been widely employed in the efficiency assessment in hospitals [25,26], farming [27,28], construction [29], education [30], and the power industry [31,32]. This study proposes a DEA model to optimize the efficiency of listed logistics companies in Malaysia by incorporating operational risk factor, which is the BIA. This study is the pioneer in analyzing the listed logistics companies in Malaysia over a long term with regards to operational risk factor with the DEA model. The significance of this study includes the identification of efficient and inefficient listed logistics companies in terms of operational risk factor through the proposed DEA model. Another notable contribution of this study is the ability of the DEA model for benchmarking and determining the improvement values of inefficient listed logistics companies to facilitate them to achieve efficiency score of 1.0000 and become efficient companies. Benchmarking is essential for continuous improvement especially in managerial decision-making. The flowchart of this study is presented in Figure 1.

Section 2 discusses the research method of the proposed DEA model. Section 3 presents the results and discussion. Section 4 concludes the paper with a summary of the results, limitation, and future directions.

2. Literature Review

The DEA is a linear programming model which assesses the efficiency of the DMUs with several inputs and outputs [33,34,35]. One of the strengths of DEA lies in the non-parametric methodology as DEA does not require assumption in the production function or distribution [36]. DEA is also powerful in converting multiple inputs to multiple outputs when assessing the DMUs. DEA was established by Charnes et al. [37] with constant return to scale assumption [38,39]. Since the change in input does not always cause a proportional difference in the outputs, Banker et al. [24] then developed the variable return to scale (VRS) model. The efficiency is the weighted sum of outputs divided by the weighted sum of inputs [40,41]. In the DEA model, efficiency ranges from 0.0000 to 1.0000 [24,41,42]. The DMU is classified as efficient if it achieves the efficiency score of 1.0000. On the other hand, the DMU is classified as inefficient if the efficiency score is below 1.0000 [36,42].

DEA has been widely applied to assess the efficiency of companies with financial ratios. When financial ratios complement DEA, the results could serve as an early warning for inefficiencies for the companies [43,44,45]. DEA is able to translate the financial ratios into a single efficiency score for evaluation and comparison [46,47]. Based on the optimal solution of the DEA model, the weights of the inputs and outputs can also be determined so that the sources of inefficiency for the inefficient DMUs can be identified [42,48]. Moreover, DEA also serves as a powerful benchmarking tool as past studies have employed DEA to determine the references for the inefficient DMUs to enhance their performances and to determine the potential improvements [46,47].

DEA complements the financial ratios to assess the performances of companies in many industries. Current ratios (CTR), debt-to-asset ratio (DAR), debt-to-equity ratio (DER), and ROE, were used to study the food and beverages companies in Europe [42]. The DAR, DER, earnings per share (EPS), ROA, and ROE, were also used to determine the efficiency of manufacturing companies in Iran [43]. The oil industry has also been assessed with the DEA model using financial ratios such as ROA and ROE to determine the strengths and weaknesses of the companies [44]. Kedžo and Lukač [45] noted that financial ratios could reflect the operations of the manufacturers in Europe when complemented with DEA. CTR, DAR, DER, EPS, ROA, and ROE have also been used to study financial institutions in Iran using DEA [49]. Hospitals were also assessed with DEA and financial ratios such as ROA and ROE [50].

The DEA model has been applied to evaluate the performances of logistics companies. Chen [51] applied the DEA model to study the companies which provide transportation, warehousing, and postal services in China. Wohlgemuth et al. [52] assessed the various types of logistics operators in Brazil with DEA. Zhang and Koutmos [53] used the DEA model to evaluate the airlines in the United States and Canada with financial ratios such as weighted average cost of capital (WACC) and return on equity (ROE). Venkadasalam et al. [54] analyzed the performances of the shipping companies in Southeast Asia with financial ratios such as return on asset (ROA) and ROE. Li et al. [55] studied container terminals in China and found the areas of inefficiency to be enhanced. Port logistics efficiency has also been evaluated with the DEA model and the study found that the efficiency was low [56]. Besides, the selection of logistics partners in Vietnam was also done using the DEA model [57]. DEA has also been applied in green logistics and third-party logistics in China and France, respectively [58,59].

Operational risk, which happens in the daily operations of a company, is caused by humans, systems, and events. The basic indicator approach (BIA) can be used to determine the capital requirement needed for operational risk based on the gross income of a company [60]. WACC involves a mix of debt and equity to finance a company’s operations. WACC is also the smallest return a company has to make from its operations. A small WACC indicates low cost of finance, whereas high WACC indicates high operational cost that would bring up a company’s operational risk. A company that experiences operational risk event requires greater cost of capital to rectify the problem and regain shareholders’ trust, which causes high volatility in the company’s equity, thus greater WACC [61]. Since past studies did not incorporate operational risk factor into the DEA model, this paper intends to fill the gap by proposing a DEA model that incorporates the operational risk factor for evaluating the efficiency of logistics companies in Malaysia.

3. Materials and Methods

This study aims to propose a DEA model to optimize the efficiency of the listed logistics companies in Malaysia with the incorporation of operational risk factor via BIA. The DMUs consist of 27 listed logistics companies on Bursa Malaysia [62]. This study follows the rule of thumb developed by Bowlin [63] who noted that the number of DMUs must be at least thrice the number of inputs and outputs [64,65,66]. There are four inputs and four outputs in this study. The inputs and outputs contain financial ratios and financial variables [67,68]. A combination of financial ratios including liquidity, leverage and profitability ratios could sufficiently highlight a company’s competitiveness. As such, this paper adopted current ratios (CTR), debt-to-asset (DAR), debt-to-equity ratios (DER) and weighted average cost of capital (WACC) as the inputs [44,50,69]. The outputs are earnings per share (EPS), return on assets (ROA), return on equity (ROE) and BIA [13,50,54]. The period of study is 12 years from 2010–2021 as this period is sufficient to include considerable business and product lifecycles [70,71]. Table 1 displays the proposed research framework to optimize the efficiency of the listed logistics companies with operational risk factor using the proposed DEA model.

Based on Table 1, this study aims to optimize the efficiency of the listed logistics companies with operational risk factor in Malaysia using the proposed DEA model. CTR, DAR, DER, and WACC are the inputs while EPS, ROA, ROE, and BIA are the outputs. This study includes all the 27 listed logistics companies in Malaysia from 2010 to 2021. After the identification of the objective, inputs, outputs and DMUs, this study shall evaluate and rank the efficiency of the listed logistics companies with the proposed DEA model. Then, the inefficient companies can benchmark the efficient companies based on the optimal solution of the proposed DEA model. After that, this paper shall determine the target values for the inefficient companies according to the respective optimal coefficients of the benchmarked efficient companies. Based on the target values of outputs and inputs for potential improvement, the inefficient companies can achieve the efficiency score of 1.0000 and thus, classified as efficient companies.

The efficiency is the quotient of weighted-sum of outputs to the weighted-sum of inputs, yielding efficiency of 0.0000 to 1.0000 [22,23]. The DMUs can then be ranked according to their efficiency. The highest ranking is achieved when the efficiency is 1.0000 because the DMU has garnered the greatest outcomes from its resources. When the DMU has achieved the highest ranking with 1.0000 efficiency score, this implies that the DMU is efficient. Conversely, if the efficiency score of the DMU is less than 1.0000, the DMU is inefficient and can improve based on the optimal solution of the DEA model [24]. The formulation of the proposed DEA model to optimize the efficiency of the listed logistics companies with the incorporation of operational risk factor is presented below [22,72,73].

(1)$Maximize E_p=\frac{{\sum }_{h=1}^m{t}_h{y}_{hp}-u_p}{{\sum }_{k=1}^n{w}_k{x}_{kp}}$

Subject to

(2)$\frac{{\sum }_{h=1}^m{t}_h{y}_{hp}-u_p}{{\sum }_{k=1}^n{w}_k{x}_{kp}} \le 1,p=1,2,3,\dots ,q,$

(3)$t_h\ge \{\begin{array}{c}\epsilon , if h=1,2,3,\dots ,m, \mathrm{where} h\ne BIA; \\ \alpha , if h=BIA\end{array}$

(4)$w_k\ge \epsilon , k=1,2,3,\dots ,n$

where

$E_p$ denotes the efficiency of DMU $p$,

$t_h$ denotes the weights of output $h$,

$y_{hp}$ denotes the value of $h$ output of DMU $p$,

$m$ denotes the number of outputs,

$w_k$ denotes the weights of input $k$,

$x_{\mathit{kp}}$ denotes the value of k input of DMU p,

$n$ denotes the number of inputs,

$\epsilon$ denotes positive value,

$\alpha$ denotes 15% as set by the Basel Committee on Banking Supervision,

$q$ denotes the number of DMU,

$u_p$ denotes free variable of DMU $p$,

$BIA$ denotes the hth output for Basic Indicator Approach.

Equation (1) maximizes the efficiency of the DMUs wherein the efficiency is the quotient of the weighted-sum of outputs to the weighted-sum of inputs. Equation (2) constraints the efficiency from 0 to 1. $t_h$ is the weight of output obtained from the optimal solution of DEA model for the determination of the contribution of the output among all outputs to the efficiency of the DMU. Under BIA, companies should have a capital of at least the mean of a fixed percentage (alpha) of the positive gross income over three years where alpha is 15% [14,15]. As such, $t_h$ is set to be at least 0.15 for BIA as shown in (3). $w_k$ is the input weight retrieved from the optimal solution of DEA model to quantify the contribution of the input among all inputs to the efficiency of the DMU. Since DEA is a linear programming model, the following transformation applies where the Equations (3) to (7) are the linear programming forms of the DEA model.

(5)$Maximize E_p={\displaystyle \sum }_{h=1}^m{t}_h{y}_{hp}-u_p$

Subject to

(6)$-{\displaystyle \sum }_{h=1}^m{t}_h{y}_{hp}+{\displaystyle \sum }_{k=1}^n{w}_k{x}_{kp}+u_p\ge 0, p=1,2,3,\dots ,q,$

(7)$$\begin{gathered} {\displaystyle \sum }_{k=1}^n{w}_k{x}_{kp}=1, \\ \mathrm{Equations} \left(3\right) \mathrm{and} \left(4\right). \end{gathered}$$

The computational work of the proposed DEA model is solved with LINGO [74,75]. The efficient DMUs may form the reference units to be benchmarked by the inefficient DMUs. The optimal solution of the DEA model will also provide the optimal coefficient for the calculation of target values for the inefficient DMUs in terms of the outputs and inputs [76]. The potential improvements show the amount that an input should be reduced and the value that an output should be increased for the inefficient DMUs in order to achieve 1.0000 efficiency score and thus classified as efficient DMUs [77].

The target value of the output of inefficient DMU is shown below:

(8)$Y_{hp}={\displaystyle \sum }_{g=1}^z{r}_g{v}_{hg}, h=1,2,3,\dots ,m$

where

$Y_{hp}$ denotes the target value of output $h$ of DMU $p$,

$r_g$ denotes the optimal coefficient of benchmarked DMU $g$,

$v_{hg}$ denotes the initial value of output $h$ of benchmarked DMU $g$,

$z$ denotes the number of benchmarked DMU.

The determination of the target value of the output is shown in Equation (8). The target value can be determined using the summation of the product of the optimal coefficient of the benchmarked DMU and the initial value of the output of the benchmarked DMU.

The target value of the input of inefficient DMU is shown below:

(9)$X_{kp}={\displaystyle \sum }_{g=1}^z{r}_g{x}_{kg}, k=1,2,3,\dots ,n$

where

$X_{kp}$ denotes the target value of input $k$ of DMU $p$,

$r_g$ denotes the optimal coefficient of benchmarked DMU $g$,

$x_{kg}$ denotes the initial value of input $k$ of benchmarked DMU $g$,

$z$ denotes the number of benchmarked DMU.

For the input, the determination of the target value is shown in Equation (9). The target value can be computed using the summation of the product of the optimal coefficient of the benchmarked DMU and the initial value of the input of the benchmarked DMU. Based on the target value of output and input as presented in the Equations (8) and (9) respectively for potential improvement, the inefficient DMU can achieve the efficiency score of 1.0000 and thus, classified as efficient DMU.

4. Results and Discussion

The efficiencies and ranks of the listed logistics companies in Malaysia with the inclusion of operational risk factor are tabulated in Table 2.

From Table 2, listed logistics companies with efficiency equals to 1.0000 are ranked first because they are efficient. Out of the 27 listed logistics companies, 15 companies achieve full efficiency of 1.0000. They are AIRPORT, COMPLET, GDEX, HUBLINE, ILB, MISC, MMCCORP, NATWIDE, POS, PDZ, PRKCORP, SEEHUP, SYSCORP, TNLOGIS, and TOCEAN. These 15 listed logistics companies have made full use of their inputs for the greatest output generation. Since these 15 companies are efficient and lie on the efficiency frontier, these companies can serve as the benchmarks and reference units for the remaining inefficient companies. On the other hand, there are 12 listed logistics companies which have not managed to achieve full efficiency. From higher ranks in descending order, these companies are SEALINK (0.9906), HARBOUR (0.9860), BIPORT (0.9737), SURIA (0.9666), BHIC (0.9649), FREIGHT (0.9400), GCAP (0.9026), TASCO (0.8966), CJCEN (0.8889), LITRAK (0.8875), MAYBULK (0.8497), and TAS (0.6725). SEALINK is very close to the efficiency frontier since its efficiency is 0.9906 while TAS, with an efficiency of 0.6725, requires more improvement since it is the furthest from the efficiency frontier. All these 12 listed logistics companies have not maximized their input utilization for the greatest outputs.

Table 3 presents the summary of the efficiency of the listed logistics companies with the incorporation of operational risk factor using DEA model.

From Table 3, the percentage of efficiency is 55.56%, which means that 55.56% of listed logistics companies have achieved full efficiency of 1.0000. This percentage of efficiency is in line with past studies where the range of efficiency is from 48.00% to 62.00% [50,78,79]. The average efficiency is 0.9600 and this also in accordance with past studies which have average efficiency of 0.9220 to 0.9710 [54,66,80,81]. Moreover, out of the 27 companies, 20 companies (74.07%) managed to achieve above average efficiency [82]. They are AIRPORT, COMPLET, GDEX, HUBLINE, ILB, MISC, MMCCORP, NATWIDE, POS, PDZ, PRKCORP, SEEHUP, SYSCORP, TNLOGIS, TOCEAN, SEALINK, HARBOUR, BIPORT, SURIA, and BHIC.

From the optimal solution of the DEA model, the benchmarks which form the reference units, together with the respective optimal coefficients for the inefficient listed logistics companies are identified and shown in Table 4. The benchmarks are made up of the efficient companies.

BHIC, BIPORT, CJCEN, FREIGHT, GCAP, HARBOUR, LITRAK, MAYBULK, SEALINK, SURIA, TAS, and TASCO are the 12 inefficient listed logistics, thus require improvements. These 12 companies can benchmark the 15 efficient companies, which include AIRPORT, COMPLETE, GDEX, HUBLINE, ILB, MISC, MMCCORP, NATWIDE, POS, PDZ, PRKCORP, SEEHUP, SYSCORP, TNLOGIS, and TOCEAN. Out of the 15 efficient listed logistics companies, only 11 companies serve as benchmarks for these inefficient companies. AIRPORT, GDEX, NATWIDE, and PDZ do not form the benchmarks for any inefficient companies. POS appears the most in the reference units of the inefficient companies as POS serves as the benchmark for nine inefficient companies. This is followed by ILB, MISC and SEEHUP which are the benchmarks for seven inefficient companies, respectively. SYSCORP and TOCEAN only appear as the benchmarks for one company, which are SEALINK and MAYBULK, respectively.

From Table 4, BIPORT has six benchmarks in the reference unit, which means that BIPORT is less efficient compared to these six companies. These six companies are MISC, MMCCORP, POS, PRKCORP, SEEHUP, and TNLOGIS. The optimal coefficients of these six companies are then used to compute the target values for the outputs and inputs of BIPORT based on Equations (8) and (9), respectively. For example, the target value of the EPS can be computed based on the sum of the product of the optimal coefficients of MISC (0.1580), MMCCORP (0.1239), POS (0.0001), PRKCORP (0.0406), SEEHUP (0.1368), and TNLOGIS (0.5405) and the initial values of the EPS of MISC, MMCCORP, POS, PRKCORP, SEEHUP, and TNLOGIS, respectively.

SEALINK, which is the closest to the efficiency frontier among all the inefficient companies because of the efficiency of 0.9906, can benchmark two companies, which are HUBLINE and SYSCORP to improve its efficiency. The target values of the outputs and inputs for SEALINK shall be based on the optimal coefficients of HUBLINE (0.1194) and SYSCORP (0.8806) and the initial values of the outputs and inputs of HUBLINE and SYSCORP, according to Equations (8) and (9), respectively.

Table 5 explains the potential improvements of the inefficient listed logistics companies based on the reference units and optimal coefficients in Table 4.

From Table 5, the inefficient logistics companies can obtain the target values based on the benchmarks and optimal coefficients as provided by the optimal solution of the DEA model. Upon obtaining the target values, the potential improvements can be calculated by the difference between the target values and the initial values. The potential improvements for the outputs should be at least zero or a positive value because the outputs should be maximized; for inputs, the potential improvements should be zero or less than zero so that there can be input reduction [77]. The inefficient listed logistics companies are BHIC, BIPORT, CJCEN, FREIGHT, GCAP, HARBOUR, LITRAK, MAYBULK, SEALINK, SURIA, TAS, and TASCO.

For BHIC, in terms of outputs, EPS, ROA and BIA can be kept at the initial values of 0.0690, 0.0192 and 0.0545, respectively. ROE can be increased by 0.0027 from 0.0414 to 0.0441. However, all the inputs should be reduced to move towards greater efficiency. CTR of BHIC should be reduced from 0.9947 to a target value of 0.9598, with a difference of −0.0349. DAR should be brought down by 0.0308 from its initial value of 0.5779 to attain the target value of 0.5471. Since BHIC has a very high DER of 1.8922, BHIC could bring down its DER by 0.2116 to 1.6809. WACC can have a reduction of 0.0026 from the initial value of 0.0733 to reach 0.0707.

TAS has the lowest efficiency of 0.6725. All the outputs should be increased while all the inputs should be reduced for higher efficiency. EPS and BIA of TAS are very low, therefore, TAS should increase its EPS by 0.2127 from 0.0405 to 0.2532 while its BIA should be increased by 0.2559 from the initial value of 0.0018 to the target value of 0.2577. The initial values of ROA and ROE of TAS are 0.0266 and 0.0450, respectively. The potential improvements of ROA and ROE of TAS are 0.0037 and 0.0026, so that the ROA and ROE can reach 0.0303 and 0.0477, respectively. Among all the inputs, TAS has a very high initial values of CTR and DER amounting to 2.1528 and 1.5783. Therefore, based on the optimal solution of DEA model, its CTR and DER can be lowered by 0.6222 and 0.9695 to arrive at the values of 1.5306 and 0.6088, respectively. DAR and WACC of TAS could be reduced from 0.5080 to 0.3612 and from 0.1028 to 0.0731, respectively.

5. Conclusions

This study has successfully optimized the efficiency of the listed logistics companies in Malaysia with the incorporation of operational risk factor, which is the BIA, using DEA model from 2010 to 2021. The percentage of efficiency is 55.56% and the efficient companies are AIRPORT, COMPLET, GDEX, HUBLINE, ILB, MISC, MMCCORP, NATWIDE, POS, PDZ, PRKCORP, SEEHUP, SYSCORP, TNLOGIS, and TOCEAN. The average efficiency is 0.9600 with 74.07% of the companies managed to achieve above average efficiency. The potential improvements of the inefficient listed logistics companies, which include BHIC, BIPORT, CJCEN, FREIGHT, GCAP, HARBOUR, LITRAK, MAYBULK, SEALINK, SURIA, TAS, and TASCO have also been obtained with the powerful benchmarking ability of the DEA model.

Based on the optimal solution of the proposed DEA model, the inefficient listed logistics companies can identify the inputs to be reduced and outputs to be raised. To increase its EPS, ROA, and ROE, the inefficient companies can reduce its expenses, such as participating in global sourcing to procure from markets with high quality materials at lower cost or by lowering its cost of goods sold and expenses through Kaizen costing for continuous improvement. Besides, the inefficient companies can set aside the respective capital for operational risk hedging based on the BIA determined by the optimal solution of the proposed model. A high CTR can reduce the company’s operating capital in the short term. Therefore, the inefficient listed logistics companies can delay their capital purchases or restructure their short-term debts to reduce their CTR. For DAR and DER reduction, the inefficient companies can perform effective inventory management for better utilization of its assets and equities. It is important for companies to assess their risks as lower market risks would bring down its cost of equity, thereby lowering WACC for the inefficient logistics companies.

This paper is highly significant as the benchmarking process could facilitate the decision-making processes by the top management for financial structure and investment insights. This proposed model is also the pioneer in including BIA to optimize the efficiency of listed logistics companies in Malaysia. For the limitation of this research, the proposed model is only applicable for the efficiency evaluation of listed companies that provide the annual financial reports for data analysis. In the future, this model can be adopted in various prominent fields such as finance, project-based construction, engineering, manufacturing, and retail industries. This proposed model can also be applied in other countries to evaluate the efficiency of companies.

Author Contributions

Conceptualization, P.F.L., W.S.L. and W.H.L.; methodology, P.F.L., W.S.L. and W.H.L.; software, P.F.L.; validation, W.S.L. and W.H.L.; formal analysis, P.F.L., W.S.L. and W.H.L.; investigation, P.F.L., W.S.L. and W.H.L.; resources, P.F.L., W.S.L. and W.H.L.; data curation, P.F.L.; writing—original draft preparation, P.F.L., W.S.L. and W.H.L.; writing—review and editing, P.F.L., W.S.L. and W.H.L.; supervision, W.S.L. and W.H.L.; project administration, W.S.L. and W.H.L. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

This research is supported by the Universiti Tunku Abdul Rahman, Malaysia.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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Figure and Tables

Figure 1. Research flowchart.

Table 1

Proposed research framework.

Objective	To Optimize the Efficiency of the Listed Logistics Companies with Operational Risk Factor in Malaysia Using Proposed DEA Model
Inputs	Current ratios (CTR)
	Debt-to-asset ratios (DAR)
	Debt-to-equity ratios (DER)
	Weighted average cost of capital (WACC)
Outputs	Earnings per share (EPS)
	Return on asset (ROA)
	Return on equity (ROE)
	Basic indicator approach (BIA)
Decision Making Units (DMUs)	AIRPORT	HUBLINE	PRKCORP
	BHIC	ILB	SEALINK
	BIPORT	LITRAK	SEEHUP
	CJCEN	MAYBULK	SURIA
	COMPLET	MISC	SYSCORP
	FREIGHT	MMCCORP	TAS
	GCAP	NATWIDE	TASCO
	GDEX	POS	TNLOGIS
	HARBOUR	PDZ	TOCEAN

Table 2

Efficiencies of the listed logistics companies.

DMUs	Efficiency	Rank	Categorization
AIRPORT	1.0000	1	Efficient
BHIC	0.9649	20	Inefficient
BIPORT	0.9737	18	Inefficient
CJCEN	0.8889	24	Inefficient
COMPLET	1.0000	1	Efficient
FREIGHT	0.9400	21	Inefficient
GCAP	0.9026	22	Inefficient
GDEX	1.0000	1	Efficient
HARBOUR	0.9860	17	Inefficient
HUBLINE	1.0000	1	Efficient
ILB	1.0000	1	Efficient
LITRAK	0.8875	25	Inefficient
MAYBULK	0.8497	26	Inefficient
MISC	1.0000	1	Efficient
MMCCORP	1.0000	1	Efficient
NATWIDE	1.0000	1	Efficient
POS	1.0000	1	Efficient
PDZ	1.0000	1	Efficient
PRKCORP	1.0000	1	Efficient
SEALINK	0.9906	16	Inefficient
SEEHUP	1.0000	1	Efficient
SURIA	0.9666	19	Inefficient
SYSCORP	1.0000	1	Efficient
TAS	0.6725	27	Inefficient
TASCO	0.8966	23	Inefficient
TNLOGIS	1.0000	1	Efficient
TOCEAN	1.0000	1	Efficient

Table 3

Summary of efficiency.

	Efficiency
Minimum efficiency	0.6725
Maximum efficiency	1.0000
Average efficiency	0.9600
Percentage of efficiency (%)	55.56
Percentage of inefficiency (%)	44.44

Table 4

Benchmarks for inefficient DMUs.

Inefficient DMUs	Benchmarks (Efficient DMUs)
	Complet	Hubline	ILB	MISC	Mmccorp	POS	Prkcorp	Seehup	Syscorp	Tnlogis	Tocean
BHIC		0.5682			0.1476	0.0001	0.0025			0.2816
BIPORT				0.1580	0.1239	0.0001	0.0406	0.1368		0.5405
CJCEN			0.0894		0.5247	0.0012		0.3847
FREIGHT			0.2039	0.4376		0.0025		0.3560
GCAP			0.7071			0.0857		0.2072
HARBOUR			0.1507	0.2119		0.0021		0.6353
LITRAK					0.1478	0.0039	0.0375			0.8108
MAYBULK	0.4004			0.1028		0.0032	0.0003				0.4933
SEALINK		0.1194							0.8806
SURIA	0.8525		0.0336	0.0992			0.0147
TAS			0.0379	0.6720				0.2901
TASCO			0.0334	0.3635		0.0022	0.0049	0.5960

Table 5

Potential improvement for inefficient DMUs.

DMU	Output/Input	Initial Value	Target Value	Potential Improvement
BHIC	EPS	0.0690	0.0690	0.0000
	ROA	0.0192	0.0192	0.0000
	ROE	0.0414	0.0441	0.0027
	BIA	0.0545	0.0545	0.0000
	CTR	0.9947	0.9598	−0.0349
	DAR	0.5779	0.5471	−0.0308
	DER	1.8922	1.6809	−0.2113
	WACC	0.0733	0.0707	−0.0026
BIPORT	EPS	0.3648	0.3648	0.0000
	ROA	0.0689	0.0689	0.0000
	ROE	0.1501	0.1746	0.0245
	BIA	0.1102	0.1102	0.0000
	CTR	3.2174	1.2836	−1.9338
	DAR	0.5415	0.5273	−0.0142
	DER	1.2977	1.2638	−0.0339
	WACC	0.0602	0.0586	−0.0016
CJCEN	EPS	0.1121	0.1145	0.0024
	ROA	0.0456	0.0374	−0.0082
	ROE	0.0739	0.0722	−0.0017
	BIA	0.0116	0.1768	0.1652
	CTR	1.8669	1.6569	−0.2100
	DAR	0.3959	0.5228	0.1268
	DER	0.6771	1.3977	0.7207
	WACC	0.0772	0.0612	−0.0160
FREIGHT	EPS	0.1018	0.1932	0.0914
	ROA	0.0633	0.0641	0.0008
	ROE	0.1023	0.1023	0.0000
	BIA	0.0160	0.1710	0.1550
	CTR	2.1968	2.1162	−0.0807
	DAR	0.3794	0.3513	−0.0281
	DER	0.6125	0.5900	−0.0225
	WACC	0.0731	0.0704	−0.0027
GCAP	EPS	0.0362	0.0991	0.0629
	ROA	1.1417	1.1417	0.0000
	ROE	0.0650	1.8885	1.8235
	BIA	0.0049	0.0166	0.0117
	CTR	13.9511	3.8790	−10.0721
	DAR	5.2244	0.3059	−4.9185
	DER	0.5446	0.4934	−0.0513
	WACC	0.0775	0.0702	−0.0073
HARBOUR	EPS	0.1041	0.1232	0.0191
	ROA	0.0550	0.0550	0.0000
	ROE	0.0907	0.0911	0.0003
	BIA	0.0122	0.0899	0.0777
	CTR	1.9375	1.9331	−0.0044
	DAR	0.3852	0.3755	−0.0097
	DER	0.6587	0.6572	−0.0015
	WACC	0.0657	0.0655	−0.0001
LITRAK	EPS	0.3192	0.3192	0.0000
	ROA	0.0753	0.1150	0.0398
	ROE	0.2539	0.2539	0.0000
	BIA	0.0608	0.0608	0.0000
	CTR	3.0011	1.2042	−1.7969
	DAR	0.6972	0.5896	−0.1076
	DER	2.9152	1.5080	−1.4072
	WACC	0.0603	0.0535	−0.0068
MAYBULK	EPS	0.0771	0.0771	0.0000
	ROA	0.0668	0.0730	0.0062
	ROE	0.1121	0.1121	0.0000
	BIA	0.0425	0.0425	0.0000
	CTR	2.3389	1.9873	−0.3516
	DAR	0.3433	0.2917	−0.0516
	DER	0.7257	0.4414	−0.2843
	WACC	0.0997	0.0801	−0.0195
SEALINK	EPS	0.0122	0.0286	0.0165
	ROA	0.0067	0.0129	0.0063
	ROE	0.0136	0.0208	0.0071
	BIA	0.0034	0.0081	0.0048
	CTR	1.0091	1.0004	−0.0088
	DAR	0.4391	0.4094	−0.0297
	DER	0.8154	0.8083	−0.0071
	WACC	0.0719	0.0682	−0.0037
SURIA	EPS	0.1925	0.1925	0.0000
	ROA	0.0439	0.0712	0.0274
	ROE	0.0620	0.1157	0.0537
	BIA	0.0159	0.0404	0.0245
	CTR	2.8923	2.8063	−0.0861
	DAR	0.2793	0.2710	−0.0083
	DER	0.4018	0.3875	−0.0143
	WACC	0.0848	0.0822	−0.0025
TAS	EPS	0.0405	0.2532	0.2127
	ROA	0.0266	0.0303	0.0037
	ROE	0.0450	0.0477	0.0026
	BIA	0.0018	0.2577	0.2559
	CTR	2.1528	1.5306	−0.6222
	DAR	0.5080	0.3612	−0.1468
	DER	1.5783	0.6088	−0.9695
	WACC	0.1028	0.0731	−0.0297
TASCO	EPS	0.1886	0.1886	0.0000
	ROA	0.0597	0.0597	0.0000
	ROE	0.1032	0.1058	0.0026
	BIA	0.0160	0.1463	0.1303
	CTR	1.6644	1.5248	−0.1396
	DAR	0.4191	0.3840	−0.0351
	DER	0.8357	0.6753	−0.1604
	WACC	0.0734	0.0672	−0.0062

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