Raw Material Order Research Articles

Pengendalian Persediaan Bahan Baku Sarung Tenun Dengan Pendekatan Economic Order Quantity Kecamatan Cerme Gresik

Raw material inventory is an important factor in supporting the company's production activities. Raw material purchase scheduling can increase productivity. When the company is faced with continuous demand, supplies control should be done optimally. This research aims to carry out the control of raw materials method of Economic Order Quantity in the casing of weaving in the Jenggolok hamlet, Gedangkulut Village Cerme-Gresik subdistrict. This type of research is qualitative research. By using the Economic Order Quantity approach, the ordering of optimal raw materials, Reorder Point, and Safety stock can be found. Based on the results of the study indicate that the purchase of raw materials using the Economic Order Quantity method is more optimal than the raw material inventory method used in the previous Jenggolok hamlet. This research can be taken into consideration by the weaving sarong business in applying the EOQ method to determine the optimal order quantity of raw materials and can reduce inventory costs incurred. This research is only done on controlling the supply of raw materials of weaving holster business in Jenggolok Hamlet cerme-gresik sub-district.

Research on Raw Material Ordering and Transportation Strategy Based on Integer Programming

This paper studies the raw material ordering and transportation strategy based on the principle of integer programming. Based on the production data of a certain enterprise, the original attachment data is firstly analyzed, 6 indicators are selected and the weight coefficient of each indicator is determined by the TOPSIS entropy weight method. Then, a 0-1 integer programming model for solving the minimum number of suppliers is established, and the most economical ordering scheme model is established with the least comprehensive cost as the objective function. Finally, taking the minimum transshipment loss as the goal and the transporter's carrying capacity as the constraint condition, the transshipment plan is solved by the greedy algorithm. After analyzing the ordering plan and the transshipment plan, the ordering plan can meet the weekly production demand, and the average weekly inventory is 59540 cubic meters, the mean square error is 222.67 cubic meters; the average loss of the transshipment scheme is 8.33%, and the ratio of suppliers with two or more transshipments to the total suppliers is 3.85%.

Ordering and transportation of raw materials for manufacturing enterprises

As the necessities of production and life, construction and plate have always played an important role in China's national economy. It is also an important industry affecting China's economic development. The unit price of raw materials and the cost of transportation and storage have always been the main expenses of enterprises. Therefore, it is particularly important to study how to reduce the ordering cost and transshipment cost of raw materials. This paper makes a quantitative analysis on the supply characteristics of 402 suppliers. According to the supply quantity of suppliers, the orders with an order quantity of 0 are excluded, so as to extract the variance, range and real average value of each supplier's orders in five years. The difference between the enterprise's order quantity and the supplier's supply quantity is extracted, so that they represent four indicators: supplier reliability, sustainability and stability, And the supply intensity of the supplier. After the data is normalized through AHP, the weight of the index is assigned, the index is weighted in combination with TOPSIS, and finally the score is calculated. Then, the 0-1 programming model is established. The coefficient of the objective function is the maximum supply of each supplier every week. After analysis, it is concluded that the maximum supply is two-thirds of the average value plus one-third of the maximum value. The constraint conditions are established so that the sum of the supply of the selected suppliers is greater than the weekly capacity required by the enterprise. The 240 weeks are divided into five years. Considering the influence of practical factors, when predicting the supply of suppliers in the next 24 weeks, only the first half of each year is selected, and the grey prediction method is used to predict the average weekly supply of each supplier in the next 24 weeks. After the prediction, the grey prediction of the existing data is not ideal, and the BP neural network is used to re predict this kind of data. After the prediction, a multi-objective programming model is established to ensure the minimum sum of ordering cost and transshipment cost. Under this scheme, the minimum supply corresponding to a, B and C is calculated.

Research on Ordering and Transportation Strategy of Raw Materials for Manufacturing Companies Based on Time Series Model and Monte Carlo Stochastic Simulation

A company needs to develop a series of ordering and forwarding strategies to maximize its profitability when it purchases three raw materials from a supplier for the production of decorative building panels, which are delivered to its warehouse by a forwarder. In this paper, we analyze the past cooperation data between the company and its suppliers and forwarders to build a mathematical model and find the optimal solution. The problem is based on quantitative analysis, so it can largely avoid unnecessary expenses caused by subjective and empirical judgments of enterprise managers, and has strong practical significance. In this paper, the 50 most important suppliers selected by considering the past supply characteristics of 402 suppliers are used as the base data. To determine the minimum number of suppliers required. Integrating factors such as raw material cost and transportation loss rate, the weekly ordering and transshipment plan for the next six months will be developed based on the selected suppliers. Based on this, we introduce the goal of purchasing as much as possible A and as little as possible C, develop a new ordering and forwarding program, and conduct a predictive analysis of the program's implementation effects.

Research on Supplier Selection Based on Supply Characteristics

Aiming at the decision of raw material ordering and transportation of building materials manufacturing enterprises, this paper deeply excavates data and analyzes the construction model. The model is based on five secondary indexes: default rate, undersupply rate, supply deviation degree, relative supply capacity and sufficient supply rate.Finally, the supplier importance evaluation model is constructed based on TOPSIS method, and the suppliers are ranked according to the score. Then, the supplier supply quantity estimation model and the transport operator attrition rate estimation model are established based on the progressive weighted average, and the estimated quantities of supply quantity and attrition rate in the next 24 weeks are given. Considering ordering decision and transportation decision step by step, this paper calculates the number of suppliers needed by enterprises under the guarantee of weekly capacity demand.The supplier evaluation and selection model proposed in this paper can provide better solutions for related enterprises and has practical application value.

Research on Enterprise Raw material ordering and Transportation process based on Multi-objective programming and Machine Learning algorithm

This paper analyzes the various production factors that affect the building materials enterprises, including the production cost of the enterprise itself and the degree affected by suppliers and transporters. Then, according to the relevant data, the objective function of minimum production cost is established, and under the premise of ensuring production, a mathematical model is established with the influence degree of each related enterprise as the constraint. In this paper, the model is solved to help enterprises make a reasonable production and management plan. First of all, the random forest algorithm is used as a machine learning method to analyze the characteristics of the given supplier. Then we evaluate the importance of each supplier to the production of the enterprise, and then rank it according to the level of influence, and select the top 20 suppliers. Then three goals are selected based on the multi-objective programming algorithm. First, the cost of production is the lowest, that is, the total cost of purchasing all kinds of materials is the least. Second, the loss is the lowest in the process of material transportation. In this paper, the materials collected are divided into three kinds of ABC, thus it is concluded that increasing the purchase of A material and reducing the purchase of C material. Therefore, a multi-objective programming model is established to solve the ordering and transshipment scheme. Finally, the neural network algorithm is used to evaluate the supplier's supply volume and transshipment loss in the next 24 weeks. The main goal is to maximize the production capacity of the enterprise, without considering the material procurement cost and the purchase demand of An and C materials, but still keep the minimum material transfer loss. In this paper, the weight analysis and multi-objective programming algorithm around the ordering and transportation process based on the principle of production enterprises can effectively provide reliable reference analysis for this process that needs to be optimized.

Intelligent Application of Raw Material Supply Chain Planning System Based on Genetic Algorithm

For the ordering and transportation of raw materials of production enterprises, an intelligent supply chain planning system based on genetic algorithms has been researched and developed. Based on the consideration of multicycle and multiraw materials, by constructing a multiobjective function, taking into account the optimal supplier, the optimal economic ordering scheme, and the minimum loss transshipment scheme, an optimal supply planning model based on genetic algorithm is proposed, and the optimal supply chain combination of the next 24 cycles is predicted by using the model, and its applicability is verified. The study shows that the supply chain planning system has good operation convenience, fast, intuitive, practical application effect of the function and can adapt to modern intelligent logistics and transportation.

Research on Raw Material Ordering and Transportation for Enterprises Based on Goal Planning

The ordering and transportation of raw materials have an important impact on the operations of enterprises. In this paper, the importance of suppliers is measured by three indicators, namely, suppliers' supply efficiency, supply capability and reputation. Based on the three evaluation indicators, the evaluation system of supplier importance is developed, and the TOPSIS evaluation method is used to rank the suppliers. After pre-processing the given data, we set up a multi-objective optimization model with the objectives of minimum number of suppliers, minimum expected total cost of raw materials and minimum amount of loss in transit, and figured out the minimum loss of raw materials of 4526.066 cubic meters. The correctness and validity of the model were demonstrated by comparing the loss of raw materials in the last five years.

Penerapan Pengendalian Persediaan Bahan Baku Ban dengan Model Probabilistik dan Sistem Kuantitas Pemesanan Tetap

Inventory of raw materials is very important for the smooth production of the company and meet consumer demand for the company in market demand. The demand for tire production fluctuates and causes the demand for products not to need to be met to order, due to the shortage of raw materials. To solve the shortage of raw materials, the company holds special orders in consideration of savings in ordering the cost of ordering messages. To overcome the shortage of raw materials, the company holds special orders or sudden orders which will increase the cost of the message. The purpose of this study is to determine the optimum ordering of raw materials to minimize total inventory costs by using a probability model of raw material inventory control with a fixed order quantity system (Q method). From the results of research using the probabilistic model of inventory control method Q method, obtained the optimal order quantity (q*), reorder point (rop), and safety stock (ss) with total inventory costs more efficient compared to the total inventory costs of the company's current policies. what is to be achieved in inventory control is to minimize the cost of ordering, and the cost of savings to the company.

Research on mathematical modeling of order and transshipment optimal distribution

Aiming at the problems of production arrangement and ordering, this paper conducts quantitative analysis, overall analysis and individual analysis on the supply characteristics of 402 suppliers, and constructs a ratio ranking model of 402 suppliers in the past five years, which reflects the important mathematical model to ensure the production of enterprises. And multi-index evaluation model, comprehensive use of Matlab, Excel and other software programming to solve, to determine the 50 most important suppliers. On the basis of mathematical modeling, a control scheme is given, and further analysis is completed, so as to optimize the ordering and transportation of raw materials in the production enterprise.

Transportation of Raw Materials, Recondite Question in Manufacturing Enterprise

Based on the optimization theory, this paper studies the mathematical modeling problem related to the ordering and transportation of raw materials. The paper starts with reasonable assumptions, based on which the following factors are used as index: “average supply”, “order completion rate”, “average order value” and “standard deviation of supply” of each supplier, and the weight of each index is determined by using entropy weight method. On the premise of reasonable weight, the distance method of good-bad solutions (TOPSIS) is used to evaluate and rank each supplier, and finally, the most important supplier is selected. Then, a 0-1 programming model is established, in which “minimum order price” and “minimum loss” are taken as the objective functions, and the two-week inventory of the enterprise is combined with factors such as the loss rate of the forwarder, using MATLAB programming TOPSIS algorithm to solve the model to obtain the optimal ordering and transshipment plan. Considering the possibility that the enterprise needs low utilization of raw materials A and C, a bi-objective programming model is established to determine the weekly ordering and transshipment plan within the target weeks. On the other hand, when the number of suppliers is sufficient, the capacity of the forwarders limits the expansion of the capacity of the enterprise. Therefore, combined with the existing data, a linear programming model with the maximum production capacity as the objective function is established to obtain the maximum production capacity and formulate ordering and transshipment schemes. Finally, the solution process and results are summarized and analyzed.

Research on Enterprise Raw Material Ordering and Transportation Based on Index Analysis and Entropy Method

Aiming at the raw material ordering and transportation process of production enterprises, this paper first selects the supply index and obtains 56 suppliers with large supply volume. Then, with the help of entropy method, 56 suppliers are ranked by supply variance, order quantity, payment rate and supply frequency as decision variables. In order to obtain the most economical ordering plan, the data of order quantity and supply quantity are mined, and the categories of suppliers are further divided into stable suppliers, potential emergency suppliers and general emergency suppliers. By accumulating the production capacity from high to low among 50 suppliers one by one, the threshold larger than the historical average production capacity of 670397m ³is found. Finally, it is determined that only 39 suppliers can meet the production conditions.

Raw Material Ordering and Transportation Problems in Production Enterprises

This paper analyzes from the perspective of the enterprise, establishes the corresponding objective function based on the requirements of different problems, and solves the optimal solution through the stochastic optimization algorithm combined with the corresponding constraints. Taking suppliers as the research object, this paper constructs the unordered rate index for suppliers from the perspective of enterprises. The average difference between supply and demand and the stability of the difference between supply and demand are adjusted. Finally, an evaluation model is constructed based on the grey comprehensive evaluation method to obtain the ranking of the importance of each supplier in ensuring the production of enterprises. Based on this ranking, the 50 most important suppliers are determined.

Research on Enterprise Order and Transshipment Strategy Based on Factor Analysis and Dynamic Programming

The research on ordering and transportation strategy optimization of raw materials has a profound impact on the development of enterprises. Based on factor analysis and dynamic programming method, this paper reasonably formulates raw material ordering and transshipment optimization strategy. Due to the uneven quality of each supplier, transporters transport loss rate is not the same. For enterprises, how to formulate the most economical raw material ordering plan and the least loss transshipment plan has become a key issue to be solved. In this paper, a dynamic programming model and a 0-1 programming model are established to solve the problem of enterprise transshipment ordering scheme under different needs.

Research on Raw Material Ordering and Transportation Model Based on Multi-objective Programming and ARIMA

This paper studies the decision-making problem of raw material ordering and transportation in production enterprises, and establishes the optimal procurement scheme and optimal transportation scheme for production enterprises under different conditions based on genetic algorithm and multi-objective programming. Firstly, five factors affecting the company's decision-making are summarized; Secondly, the analytic hierarchy process (AHP) is used to determine the weight of each impact evaluation factor, and then the comprehensive effect measure matrix is obtained by combining with the multi-objective grey target decision model to rank 402 enterprises, and the 50 most important suppliers were selected.In order to better reflect the materials and transportation model required by the enterprise's production, the mean square deviation of the enterprise's output in the past five years is used to approximate its output in the next 24 weeks. The multi-objective optimization algorithm based on the combination of convergence criteria and genetic algorithm is used to obtain that at least 30 suppliers and 5 transporters should be selected; The sensitivity analysis and stability test of the model are carried out.

Study on ordering and transportation of raw materials for manufacturing companies based on importance evaluation and single-objective optimization model

Supplier preference problems and raw material ordering and forwarding problems are very common in real life. In this paper, we quantify and analyze suppliers using several indicators based on supply data, establish a supplier importance evaluation model based on the indicators, and build an optimization model by combining factors such as enterprise output, transshipment volume of transshipment providers, and raw material types to solve ordering and transshipment solutions that meet the problem. Based on the basic data, the 50 most important suppliers are identified on the basis of 402 suppliers and the results are given. In addition, for these suppliers, develop the most economical weekly ordering scheme for raw materials for the company for the next 24 weeks, and develop the least lossy transshipment scheme accordingly. An analysis of the effectiveness of the implementation of the ordering scheme and the transshipment scheme is carried out.

Research on the Optimal Scheme of Raw Material Ordering and Transportation in Manufacturing Enterprises

The ordering and transportation of raw materials in production enterprises is of great significance to ensure the stability and efficiency of the supply chain. Therefore, it is necessary to make a reasonable plan for the procurement and transportation of raw materials. Based on AHP and grey relational analysis, this paper establishes a mathematical model to ensure the importance of enterprise production, and selects the top 50 high-quality suppliers worthy of enterprise cooperation. Then, the linear programming model is used to formulate the most economical ordering scheme and the optimal transshipment scheme (with the least loss).

Multi-objective Optimization Study for Enterprise Supply and Transit Problems

This paper focuses on the ordering and transportation of raw materials for companies. This paper establishes a logistic regression equation to fit the relationship between supply quantity and order quantity based on the supply characteristic index system. In this paper, a multi-objective BP neural network model is established to solve the supply solution with the lowest purchase and storage cost, the highest supplier reliability score, as the objective function. A gray prediction model is built based on the past loss rate data of the forwarder to obtain a prediction formula to predict the loss rate. Finally, a linear programming model is built to solve for the optimal operation solution to meet the demand based on the predicted loss rate.

Nonlinear Programming Model Based on Monte Carlo Simulation

This paper focuses on planning the choice of raw material ordering and transportation solutions for enterprises, and gives the most economical ordering and forwarding solutions for enterprises through quantitative analysis of supplier supply characteristics; and predicts the future production capacity of enterprises through time series analysis models, and finally gives the optimal ordering and transportation strategies. Among them, according to the existing optimal forwarder transshipment scheme to supplement, on the basis of the original consideration of the cost required for the production of enterprises, the model is improved, by increasing the target letter material type purchase limits, the establishment of multi-objective planning model based on Monte Carlo simulation to solve the optimal procurement scheme, the development of transshipment scheme. On this basis, the supplier supply data given in the article is divided in 240 weeks, 24 weeks as a cycle, and its long-term trend fluctuation over time is judged by drawing a time series diagram through SPSS, and the enterprise capacity prediction is made in cycles. In this paper, we find out the single-week capacity by selecting the capacity factor prediction, optimize the model, and substitute it into the improved multi-objective planning model to get the optimal strategy for purchasing and forwarding.

Inventory Policy Control Using a Continuous Review Model Based on Information Systems: A Case Study in a Restaurant Industry

The development of information technology has become a necessity for every company, one of which is the optimization of inventory planning. The joint warehouse owned by the Chickenpedia Restaurant which has to supply various types of raw materials to the three restaurant branches often experiences stock outs and has to make emergency orders resulting in losses. For this reason, it is necessary to control the inventory of raw materials in the shared warehouse so that stock outs can be overcome and losses that arise can be minimized. One of them is by using the Continuous Review method. By using this method, the optimal order quantity and reorder point will be obtained. The weakness is iterative calculation and monitoring of raw material stock manually. For this reason, it is necessary to support an information system to speed up and make it easier to calculate when to place an order and how many orders of raw materials to suppliers so as to reduce the occurrence of running out of raw materials. The Information System can also monitor the stock of raw materials every day in real time based on the number of incoming and outgoing goods that enter the application.