## 1 Introduction
[PERSON] and [PERSON] [14] solved the JELP with the demand changing over time, imperfect production emission cost, and defective production rate. By considering the fuzzy annual demand, their mathematical model contributed to the existing inventory literature. [PERSON] and [PERSON] [15] provided a framework for the development of the fuzzy total inventory cost with uncertain demand and the used Yager's ranking method to minimize the total cost and to prevent lost-sales. [PERSON] et al. [16] introduced a multiperiod newsvendor problem, which formulated integer recourse decisions as a multistage stochastic programming model. The proposed method offered novel knowledge to optimize the trade-off between delivery, transshipment, shortage, and holding costs, which is the gap to other studies that suggested stochastic inventory models. Additionally, most studies suggested different considerations for customer service's perception, assumed that quality products, sales price, corporate social responsibility, and recycling commitment are key factors to the customer service level [17; 18; 19; 20; 21; 22]. In this paper, simulation modeling is developed to explain the ordering process for building materials in a distributor. For this case study, key system performance measures are the total cost (TC) and the customer service level (CSL). The total cost includes transportation cost, holding cost, over-storage cost, and shortage cost. On the other hand, CSL simplifies by the accumulation of total demand satisfied. Parameters of products include unit weight, unit holding cost, unit shortage cost, and unit over-storage cost. Controllable factors of the ordering process are the monthly demand, review period (T), order quantity (Q), and safety stock (SS), which are treated as independent variables. Using RSM, the best settings, which are appropriate combinations of T, Q, and SS are determined as an indicator to measure the performance of each possible setting, in which TC and CSL are response variables. The solution is to place the right order quantities at the right time, with the appropriate safety stock to achieve customer satisfaction levels between 90 and 95 percent at minimum costs. The contributions of our paper are three-fold. First, the paper proposed a computational framework to solve the order planning problem by a heuristics approach using RSM, which includes the central composite design (CCD), Box-Behnken design (BBD), and variance dispersion graph (VDG), in order to offer the best possible time-efficient and resource-saving solution in terms of key performances. Most exact methods cannot solve the problem within a satisfactory amount of time, especially for large-scale problems. In contrast, most heuristics allows decision-makers to obtain a feasible and timelier solution. For order planning problems in the construction industry, managing the demand, duration, and progress between projects that are supposed to be implemented simultaneously by a distributor is a complicated task. Thus, to facilitate the practical process and available resources, heuristics can provide decision-makers feasible short-term solutions within a reasonable amount of time. Second, the proposed heuristic model is validated by a realistic case study of a distributor in Vietnam to illustrate the effectiveness of the model's result. Third, the managerial implications of the paper could be a beneficial guide for decision-makers, purchasing managers, and warehouse managers to obtain the most suitable order policy for a robust order planning process. In addition, the practical contribution of this study is the comprehensive insight into order planning problems contributed by the case study of the building material distributor in Vietnam. The paper is divided into five sections. The introduction and relevant studies are discussed in the first section. In the second section, the research procedure and related methodology are included. This paper proposed a computational framework to solve the order planning problem.