Optimization on the Financial Management of Construction Companies with Goal Programming Model ^†

Abstract

Financial management is important for the construction sector, as construction companies contribute to the development of countries. Malaysia encourages the construction sector to develop advanced infrastructure related to transport and housing. Financial management is a multi-criteria decision making (MCDM) problem, since companies have to consider multiple goals in order to make the optimal decision. Therefore, goal programming is proposed in financial management to solve optimization in MCDM problems. According to previous studies, there has been no comprehensive study conducted on optimization and comparison among the construction companies with a goal programming model. Thus, this study aims to propose a goal programming model to optimize and compare the financial management of listed construction companies in Malaysia for benchmarking purposes. Six goals of financial management, namely the total assets, total liabilities, equity, profit, earnings, and optimum management of construction companies are examined in this study. The results of this study show that the goal programming model is able to determine the optimal solution and goal achievement for each construction company. In addition, the model value can be further enhanced according to the optimal solution of the goal programming model. This study provides insights to the listed construction companies in Malaysia to identify the potential improvement based on the benchmarking and optimal solution of the goal programming model.

1. Introduction

Construction companies contribute to the development of countries. However, construction companies face many financial issues, such as delayed payment, contractual issues, the abuse of the Defects Liability Period, and the inability to adopt the Building Information Modelling system to plan their project cashflows [1]. Financial management in the construction industry is very important for developing advanced infrastructure, such as in transportation, housing, and commercial areas, for countries. The accuracy of the forecasting profit from construction projects is low because companies tend to suffer losses due to reworks. If a construction company makes consecutively losses in the management of projects, the company may face bankruptcy [2]. Several studies proposed the development of digital technology tools to support financial planning and decision making by construction companies [3,4,5]. Therefore, financial management is very important for construction companies.

Financial management is a multi-criteria decision making (MCDM) problem, since the companies have to consider multiple goals in order to make the optimal decision. Therefore, goal programming (GP) is proposed in financial management to solve optimization in MCDM problems. GP was started by Charnes et al. [6] and further developed by Charnes and Cooper [7]. In GP, a goal is the objective function with an aspiration level. The goals then become the soft constraints for optimization. Soft constraints are deviational variables that show incremental or decremental values that are used to determine the constraint values. The deviations should be minimized for optimality [8].

GP helps to identify the additional resources required or the reduction of the cost to meet the goal. GP also determines the degree of the achievement of goals with the current inputs. According to previous studies, there has been no comprehensive study conducted on optimization and comparison among construction companies with the goal programming model. Thus, this study aims to propose a goal programming model to optimize and compare the financial management of listed construction companies in Malaysia for benchmarking purposes. The next section explains the data and methodology, which are followed by the results and discussion, and finally, the conclusion.

2. Data and Methodology

This paper studies the financial management of listed construction companies in Malaysia, namely DKLS, TRCS, and HSL, from 2017 to 2021.

Table 1. Financial management goals of the listed construction companies.

Goal	Explanation
I	Maximize total assets
II	Minimize total liabilities
III	Maximize equity
IV	Maximize profit
V	Maximize earnings
VI	Maximize optimum management item

lists the goals of the study.

The financial management goals of the listed construction companies are to maximize the total assets, equity, profit, earnings, and optimum management item, while minimizing the total liabilities. A negative deviation in the total assets, equity, profit, earnings, and optimum management item shows the underachievement of these goals. On the other hand, the companies with a positive deviation in the total liabilities have underachieved this goal because a surplus in the total liabilities increases the business risk caused by financial distress [9].

The following shows the GP formulation [9,10,11,12,13]:

$$\min x=d_1^{-}+d_2^{+}+d_3^{-}+d_4^{-}+d_5^{-}+d_6^{-}$$

(1)

subject to:

Goal I:

$${\displaystyle \sum }_{m=1}^n{h}_{mn}{k}_m+d_1^{-}-d_1^{+}=g_1$$

(2)

Goal II:

$${\displaystyle \sum }_{m=1}^n{h}_{mn}{k}_m+d_2^{-}-d_2^{+}=g_2$$

(3)

Goal III:

$${\displaystyle \sum }_{m=1}^n{h}_{mn}{k}_m+d_3^{-}-d_3^{+}=g_3$$

(4)

Goal IV:

$${\displaystyle \sum }_{m=1}^n{h}_{mn}{k}_m+d_4^{-}-d_4^{+}=g_4$$

(5)

Goal V:

$${\displaystyle \sum }_{m=1}^n{h}_{mn}{k}_m+d_5^{-}-d_5^{+}=g_5$$

(6)

Goal VI:

$${\displaystyle \sum }_{m=1}^n{h}_{mn}{k}_m+d_6^{-}-d_6^{+}=g_6$$

(7)

where

• $x=$ objective function;
• $d_y^{+}=$ positive deviational value when goal $y=1,2,3,4,\dots ,6$;
• $d_y^{-}=$ negative deviational value when goal $y=1,2,3,4,\dots ,6$;
• $h_{mn}=$ weightage of goal in year $m=1,2,3,4,\dots ,n$;
• $k_m=$ goal in year $m=1,2,3,4,\dots ,n$;
• $g_y=$ target value when goal $y=1,2,3,4,\dots ,6$.

In this study, the computational work of GP model was performed using LINGO, which is an optimization software [14,15,16,17].

3. Results and Discussion

Table 2. Financial data of DKLS, TRCS and HSL (MYR trillion).

Goals	DKLS	TRCS	HSL
Total assets	2.8434	5.3093	5.6836
Total liabilities	0.7812	3.1716	1.6126
Equity	2.0621	2.1376	4.0711
Profit	0.0488	0.1155	0.2300
Earnings	0.9908	3.8487	2.9637
Optimum management item	6.7264	14.5828	14.5610

presents the financial data of DKLS, TRCS, and HSL from 2017 to 2021.

From Table 2, the maximum values of total assets (5.6836), equity (4.0711), profit (0.2300), earnings (3.8487), and optimum management item (14.5828) serve as the target values of the respective goals. The target value of total liabilities is the minimum value for DKLS, TRCS, and HSL, which is 0.7812.

Table 3. Optimal solution of DKLS (MYR trillion).

Goal	Target Value	Model Value	${\mathit{d}}_{\mathit{i}}^{-}$	${\mathit{d}}_{\mathit{i}}^{+}$	Achievement
Total assets	5.6836	11.4722	0.0000	5.7886	Achieved
Total liabilities	0.7812	2.4736	0.0000	1.6924	Not Achieved
Equity	4.0711	8.9986	0.0000	4.9275	Achieved
Profit	0.2300	0.3380	0.0000	0.1080	Achieved
Earnings	3.8487	3.8487	0.0000	0.0000	Achieved
Optimum management item	14.5828	27.1331	0.0000	12.5503	Achieved

,

Table 4. Optimal solution of TRCS (MYR trillion).

Goal	Target Value	Model Value	${\mathit{d}}_{\mathit{i}}^{-}$	${\mathit{d}}_{\mathit{i}}^{+}$	Achievement
Total assets	5.6836	6.3049	0.0000	0.6213	Achieved
Total liabilities	0.7812	3.1714	0.0000	2.3902	Not Achieved
Equity	4.0711	3.1336	0.9375	0.0000	Not Achieved
Profit	0.2300	0.2300	0.0000	0.0000	Achieved
Earnings	3.8487	5.6203	0.0000	1.7716	Achieved
Optimum management item	14.5828	18.4594	0.0000	3.8766	Achieved

and

Table 5. Optimal solution of HSL (MYR trillion).

Goal	Target Value	Model Value	${\mathit{d}}_{\mathit{i}}^{-}$	${\mathit{d}}_{\mathit{i}}^{+}$	Achievement
Total assets	5.6836	6.3270	0.0000	0.6434	Achieved
Total liabilities	0.7812	1.7031	0.0000	0.9219	Not Achieved
Equity	4.0711	4.6234	0.0000	0.5523	Achieved
Profit	0.2300	0.3113	0.0000	0.0813	Achieved
Earnings	3.8487	3.8487	0.0000	0.0000	Achieved
Optimum management item	14.5828	16.8136	0.0000	2.2308	Achieved

tabulates the optimal solution for DKLS, TRCS, and HSL.

Based on Table 3, DKLS has achieved the goals for the total assets, equity, profit, earnings, and optimum management items. DKLS has overachieved in terms of total assets, equity, profit, and the optimum management item, with positive deviations of MYR 5.7886, 4.9275, 0.1080, and 12.5503 trillion, respectively. DKLS has underperformed in total liabilities as there is a surplus of MYR 1.6924 trillion. DKLS should reduce its total liabilities from MYR 2.4736 to 0.7812 trillion.

TRCS has achieved the total assets, profit, earnings, and optimum management item goals. TRCS outperformed in terms of total assets (0.6213), earnings (1.7716), and the optimum management item (3.8766). TRCS has not attained the total liabilities and equity goals. TRCS should bring down its total liabilities by MYR 2.3902 trillion from MYR 3.1714 to 0.7812 trillion. TRCS can increase its equity by MYR 0.9375 trillion to reach MYR 4.0711 trillion.

HSL has achieved the total assets, equity, profit, earnings, and optimum management item goals because there is no negative deviation from the target value. HSL has outperformed in terms of the total assets, equity, profit, and optimum management item goals by MYR 0.6434, 0.5523, 0.0813, and 2.2308 trillion, respectively. However, HSL has not reached the goal for total liabilities because there is an excess of MYR 0.9219 trillion. HSL should bring down its total liabilities from MYR 1.7031 to 0.7812 trillion.

Table 6. Summary of target and model values (MYR trillion).

Goals	Target Values	Model Values
		DKLS	TRCS	HSL
Total assets	5.6836	11.4722	6.3049	6.3270
Total liabilities	0.7812	2.4736	3.1714	1.7031
Equity	4.0711	8.9986	3.1336	4.6234
Profit	0.2300	0.3380	0.2300	0.3113
Earnings	3.8487	3.8487	5.6203	3.8487
Optimum management item	14.5828	27.1331	18.4594	16.8136

highlights the summary of the target and model values of DKLS, TRCS, and HSL.

DKLS, TRCS, and HSL have achieved the total assets, profit, earnings, and optimum management item goals because a negative deviation is not present. TRCS has not reached the equity goal because of the value of the negative deviation of MYR 0.9375 trillion. TRCS should have MYR 4.0711 trillion in equity. DKLS, TRCS, and HSL have not attained the total liability goal because there are positive deviations of MYR 1.6924, 2.3902, and 0.9219 trillion, respectively. All the companies should maintain their total liabilities at MYR 0.7812 trillion.

Table 7. Comparison of deviations between target values and model values (MYR trillion).

Goals	Target Values	Deviations
		DKLS	TRCS	HSL
Total assets	5.6836	0	0	0
Total liabilities	0.7812	1.6924	2.3902	0.9219
Equity	4.0711	0	0.9375	0
Profit	0.2300	0	0	0
Earnings	3.8487	0	0	0
Optimum management item	14.5828	0	0	0

shows the comparison of deviations between target values and model values for DKLS, TRCS, and HSL.

Based on Table 7, zero deviations indicate the achievement of the goals. The positive values of deviations signify the underachievement of the goals from the target values. DKLS, TRCS, and HSL have MYR 1.6924, 2.3902, and 0.9219 trillion in excess for total liabilities, respectively. The equity of TRCS is MYR 0.9375 trillion lower than the target value of MYR 4.0711 trillion.

4. Conclusions

The objective of this study is to propose a goal programming model to optimize and compare the financial management of the listed construction companies in Malaysia for benchmarking purposes. DKLS, TRCS, and HSL have achieved the total asset, profit, earnings, and optimum management item goals. TRCS has not reached the equity goal. None of the three companies attained the total liability goal. The construction companies in Malaysia should reduce their reliance on debt financing for better financial stability. This study provides insights to the listed construction companies in Malaysia to identify the potential improvement based on the benchmarking and optimal solution of the GP model. This study can also serve as an early detection of possible financial risk in the construction companies to allow the management to draft relevant strategies for continuous improvement.