1. Introduction
Developing a financial plan is an essential component of the success of any industry. A good understanding of finance is critical for industrialists to take their businesses to new heights and survive more challenging economic conditions. In the realm of the statements mentioned above, this study illustrates an industry’s multi-objective decision-making problem. The multi-objective decision-making problem can be encountered in many applications, such as solid waste; accounting; finance; marketing; quality control; human resources; production; transportation; site selection; space studies; agriculture; telecommunication; etc. One method that can be applied to the problem of multi-objective financial planning is goal programming because it is a powerful technique for solving multi-objective decision-making problems, one of the most challenging problems. Several fields have benefited from goal programming in recent years due to its ability to generate significant results.
Undoubtedly, financial planning is one of the most critical components of any business. By considering these facts, it was decided that this study would focus on evaluating SABIC’s financial planning. Financial analysis is necessary to develop strategies for determining financial strength and identifying potential enhancements to the industry. In addition, this study aims to evaluate the company’s financial plan to maximize income and minimize costs as much as possible. As a result of the study, the following specific goals will be achieved: evaluate the maximized assets, assess the minimized total liabilities, assess maximized equity, maximized gross profit evaluation, and evaluate the maximum operating [
1,
2].
In addition to production planning, scheduling, tourism management, banking financial management, and financial institutions, goal programming techniques are now used in various fields. Ekezie and Onuoha studied goal programming for budget allocation in institutions and developed a model for analyzing the institute’s budgeting system. In addition, they emphasized that the institution should continue to use its budget allocation formula with scientific methods [
3]. F. A. Farahata and M. El Sayed proposed a goal programming model with two types of uncertainty. There are two approximation models: an upper approximation model and a lower approximation model. A lexicographical goal programming method has also been suggested for solving these upper and lower approximation models [
4]. Boppana and Jannes developed a multi-objective goal programming (MOGP) model for a real-world manufacturing situation to show the trade-off between the goals of the customer, the product, and the manufacturing process. The study’s results revealed that a planning tool could be valuable in making decisions [
5].
Thomas and Daniel examined financial management decision situations using goal programming and summarized its limitations [
6]. According to James E. Hotvedt, linear programming to solve multi-objective problems requires that all incommensurable goals be transformed into a standard unit of measure to solve the problem [
7]. Mehrdad Tamiz et al. concluded that goal programming could be used as a pragmatic and flexible approach to solving complex decision problems involving many objectives, variables, and constraints [
8]. With the help of a goal programming model, Weng Siew et al. determined several financial parameters for shipping companies. In order to enhance the developed model, the most suitable values for all goals are used as target values to enable a better comparison of achievement levels [
9]. According to Carlos Romero, it is critical to establish a bridge between the different MCDM approaches to achieve mutual benefits [
10]. In a study by Kruger et al., it was suggested that specific strategic goals include returns, risks, liquidity, capital adequacy, and growth in market share. Because these goals conflict, a simple linear programming approach will not suffice, and one must resort to a multi-objective strategy such as goal programming [
11]. A study by Luis Diaz-Balteiroa and Carlos Romero concluded that goal programming techniques efficiently integrate all the criteria into a mathematical program that could be used to solve real-life problems [
12]. Furthermore, Jamalnia and Soukhakian developed aggregate production planning in a fuzzy environment and concluded that fuzzy sets theory could be used in goal programming to specify imprecise aspiration levels [
13].
A model was developed by Chen et al. to optimize the financial management of a Malaysian Public Bank. This study found that the model was capable of achieving all goals [
14]. For the optimization of multiple criteria problems, James S. Dyer used a goal programming algorithm that requires communication between the relevant decision-maker and the algorithm [
15]. Alan used individual goals as a practical and valuable tool without a priority coefficient. This tool provided the financial planner with a powerful ‘what-if’ device to evaluate the various trade-offs among the conflicting goals and arrive at a satisfactory solution [
16].
In order to solve the personal financial planning problem more effectively than traditional approaches, Chieh-Yow proposed a generalized unique financial planning programming model with multiple fuzzy goals [
17]. The goal programming model used by Shafer and Rogers to form manufacturing cells identified that a minimum setup time, a minimum intercellular movement, a minimum investment in new equipment, and maintaining acceptable utilization levels were the multiple objectives they identified [
18]. According to Ajibola et al., UBA’s financial statement management was analyzed using a model developed based on goal programming. As a result, they concluded that the bank should convert its liabilities into earning assets as soon as possible [
19]. Romero and Rehman have noted that both lexicographic goal programming, as well as weighted goal programming are widely used as goal programming variants [
20]. Further, Marc J. Schniederjans et al. stated that Goal Programming is used as a model that utilizes the analytic hierarchy process to evaluate property attributes before making an optimal house selection decision [
21]. Eventually, by using a pre-emptive fuzzy goal programming approach, Hossein Mirzaei et al. demonstrated that a mixed-integer linear programming model could mathematically formulate problems that can then be automated using a fuzzy goal programming approach [
22].
Lam et al. used a goal programming approach to optimize the financial management of various electronic companies according to their financial parameters and the optimum management item. As a result, they found that goal programming produces the best possible solutions for each organization [
23]. A multi-objective integer linear programming model based on the Markov chain method is proposed by Devendra Choudhary and Ravi Shankar for the joint decision-making of the lot-sizing problem, the supplier selection problem, and the carrier selection problem [
24]. It was concluded by Marc J. Schniederjans and Rick L. Wilson’s analytic that the combination of the analytical hierarchy process and goal programming methodologies helped overcome some weaknesses observed when either method is used independently [
25]. Lakshmi et al. proposed financial planning to acquire incommensurable and conflicting plans using goal programming, and they concentrated on maximizing both the capital system and gain in returns [
26]. Schniederjans, Marc J. et al. proposed, in their paper, a goal programming model incorporating elements of critical path method and concurrent engineering to enhance the planning of value analysis projects [
27]. A study by Ali AlArjani and Teg Alam indicates that lexicographic goal programming has become one of the most popular approaches to multi-objective criteria. They developed a lexicographic goal programming model to analyze and optimize the performance management of Al Rajhi Bank, a Saudi Arabian bank, and they found that based on the optimal solution, Al Rajhi Bank can accomplish all its objectives [
28]. In addition, by using multi-objective optimization, Flavia and Anisor integrated organizational performance’s three primary objectives: selling more, minimizing expenses, and increasing productivity. Hence, they analyzed whether the presented system delivers something that no framework does by combining objective and subjective methods [
29]. Belaid et al. delivered a comprehensive literature study of the GP application within the accounting field. They suggested a model that acts as an approach for accountants to determine the most suitable variant of GP to deal with specific accounting-related decision-making situations [
30].
A model for SABIC’s optimal financial management is proposed in this study.
6. Conclusions
In recent years, researchers have focused on financial institutions’ financial planning to overcome the shortfalls seen in financial planning. This study evaluated SABIC’s financial performance; as a result, SABIC can accomplish all the goals outlined in this study based on the optimal solution to the developed model.
Consequently, SABIC can maximize its assets, equity, gross profit, and net income and achieve its overall goals. The study thus helps other financial institutions continue to improve by identifying updated benchmark values. In addition, this model allows financial institutions to develop strategies and make decisions based on varying economic conditions.
Future studies should examine how fiscal planning can reduce total liabilities while maximizing other objectives. Therefore, the results of this study will be crucial in overcoming future financial difficulties. In addition, future studies concerning performance management in financial institutions may also address this study’s findings. Furthermore, the proposed model will be applied to all Saudi financial institutions in subsequent research.