Improved supply chain optimization method

Abstract

The present invention relates to the field of supply chain management, and in particular to an improved supply chain optimization method. An improved supply chain optimization method, which is characterized by comprising the following steps: S1, acquiring data, and defining variables and constraints to construct a basic mathematical model; S2, calculating profit data on the basis of known data; S3, constructing an optimization model on the basis of required objectives combined with the constraints; and S4, using an optimization algorithm to solve the optimization model, and outputting an optimization scheme. The method aims to solve the problems in the prior art of the production mode diversity being poor, not taking additional constraints into consideration and it not being possible to handle an infeasible situation, provides an improved supply chain plan optimization algorithm, and has the advantages of high efficiency, high universality, high practicability and high flexibility.

Description

An improved supply chain optimization method Technical Field

[0001] The present invention relates to the field of supply chain management, and in particular to an improved supply chain optimization method. Background Art

[0002] Currently, most domestic chemical companies have a low level of digitalization, and supply chain production planning primarily relies on manual experience and offline communication. For large chemical companies, faced with a wide variety of products, frequently changing business processes, and the need to adjust production based on market fluctuations, relying on manual experience to adjust production plans is not only labor-intensive and places high demands on production planners, but also difficult to achieve a globally optimal solution that comprehensively considers various factors. This can lead to negative consequences such as raw material shortages, inability to meet market demand, and inventory backlogs, and it is not a solution that maximizes profitability. Relying on manual experience to orchestrate production plans typically requires a significant amount of human resources and requires employees to possess extensive industry experience and in-depth expertise. This makes production planning a complex and tedious task, easily leading to a waste of human resources. Manual experience is often based on individual experiences and fragmented information, failing to fully consider the various complex factors within the supply chain. In an environment like a chemical company with a wide variety of products and frequently changing business processes, the lack of a systematic approach can easily lead to local optimization of production plans rather than global optimization. This can lead to irrational resource allocation, thus impacting production efficiency. Chinese patent publication number CN113536046B discloses a supply chain planning service optimization method, system, electronic device, and storage medium. It proposes building a supply chain process model based on actual material flow through a configuration module, taking into account constraints such as equipment, inventory, demand, and procurement, with maximizing efficiency as the optimal objective function. Chinese patent publication number CN108108994B discloses a planning optimization method for a chemical enterprise supply chain. It proposes determining the expected market demand relative to the chemical enterprise using a self-matching demand forecasting method, taking into account constraints such as procurement price, inventory price, transportation price, demand, and equipment capacity, with maximizing the overall economic profit of the supply chain while satisfying the chemical product demand plan. Chinese patent publication number CN115689255B discloses a method, device, and electronic device for determining a product production plan. It proposes building a partial supply chain process model for semi-finished products based on a bill of materials analysis configuration module, taking into account equipment capacity, inventory constraints, material demand, and procurement constraints. It generates a production plan for semi-finished products with maximizing total material production as the objective function, and then calculates the material value of the finished product using the bill of materials.

[0003] However, the above patents take into account some additional constraints on the basis of the traditional planning model. However, since the production modes of different materials in chemical enterprises are not the same, some chemical products are produced based on demand, while some chemical products are sold based on production. Therefore, the model solution under a single mode may not meet actual needs; secondly, due to the consideration of many additional constraints, the model may become infeasible. The above patents do not provide a methodological solution when it is infeasible. Summary of the Invention

[0004] The purpose of the present invention is to provide an improved supply chain optimization method with the advantages of high efficiency, versatility, high practicality and high flexibility to solve the problems of poor diversity of production modes in the existing technology, no consideration of additional constraints and inability to deal with infeasible situations.

[0005] The present invention is achieved through the following technical solutions: An improved supply chain optimization method, characterized in that it includes the following steps: S1: Acquire data, and define variables and constraints to construct a basic mathematical model; S2: Calculate profit data based on known data; S3: Construct an optimization model based on the required goals and constraints; S4: Use optimization methods to solve the optimization model and output an optimization plan.

[0006] The benefit of an improved supply chain optimization method is that it establishes a required mathematical model that reflects the key variables and constraints of the supply chain system. Through data acquisition and definition, the method can more accurately describe the actual business environment and provide a reliable foundation for subsequent optimization.

[0007] Preferably, the step S2 includes: S2.1: calculating the unit inventory production cost; S2.2: calculating the unit real-time cost; S2.3: calculating the material marginal profit.

[0008] The advantage of this is that by calculating data such as unit inventory production cost, unit real-time cost, and material marginal profit, key economic indicators are provided for formulating optimization methods. The calculation of these profit data can be used as input to the optimization method, and it helps to quantify the economic benefits of the decision, making the final optimization method more in line with the company's profit maximization goal.

[0009] The step S4 includes: S4.1: adding options and updating the optimization model; S4.2: using the optimization method to solve the optimization model; S4.3: if the model is judged to be feasible and the solution result is optimal, then directly proceed to step S4.5; if the model is judged to be infeasible, the reasons for the infeasibility can be further analyzed; S4.4: if further analysis of the reasons for the infeasibility is required, the optimization method will start the model solution in the relaxation mode and return the optimal solution to the relaxation problem; S4.5: output the supply chain optimization plan.

[0010] The advantage of this is that through optimization methods, the system can find the optimal supply chain optimization method under various constraints, which helps to improve the efficiency and economy of the optimization method, reduce resource waste, and provide enterprises with practical and feasible operation plans.

[0011] Preferably, the step S2.1 includes calculating the raw material production cost based on the inventory cost information data and the production information data; and calculating the unit inventory production cost based on the product cost data and the raw material production cost.

[0012] The benefit of this is that calculating these two costs helps to more accurately assess the cost structure of the production process. By considering the cost of raw material production consumption, the system can more comprehensively analyze the economics of the supply chain and develop more reasonable optimization methods.

[0013] Preferably, the step S2.2 includes calculating the raw material purchase cost based on the raw material cost data and the production information data; and calculating the unit real-time cost based on the product cost data and the raw material purchase cost.

[0014] The benefit is that calculating these two costs helps to more accurately assess the cost structure of the procurement process. By considering the real-time costs of the procurement process, the system can more comprehensively analyze the economics of the supply chain and develop more reasonable optimization methods.

[0015] Preferably, the step S2.3 comprises calculating the material profit margin based on the product price data and the unit inventory production cost.

[0016] The benefit of this approach is that it helps quantify the profit contribution of materials, enabling companies to more specifically optimize material usage and procurement strategies. This helps optimize methods to focus more on profitability, improving the company's overall economic benefits. Furthermore, the calculated results serve as input for subsequent optimization methods.

[0017] Preferably, the added options in step S4.1 include a penalty factor and a reward factor.

[0018] The benefit of this approach is that the introduction of penalty and reward factors makes the optimization method more flexible and better adaptable to different business scenarios. This selective introduction of factors allows the method to more flexibly deal with complex decision-making problems and improves the robustness of the optimization method.

[0019] Preferably, the supply chain optimization solution includes a production internal supply optimization solution, a sales and procurement optimization solution, and an inventory optimization solution.

[0020] The benefit is that the system provides comprehensive supply chain planning solutions for enterprises by outputting methods for optimizing internal production, sales and procurement, and inventory. This enables enterprises to better coordinate various internal links, improve resource utilization, reduce costs, and enhance overall supply chain operational efficiency.

[0021] The present invention has the following beneficial effects: 1. Through an optimization model, the present invention utilizes known data and objectives to rapidly calculate a supply chain optimization method. The modularization of the steps and the optimization of the calculation method enable the method to efficiently process large amounts of data, rapidly generating an optimal solution. This high efficiency can save significant time and resources, improving overall operational efficiency.

[0022] 2. The method is highly flexible and adaptable to a variety of supply chain scenarios. It begins with data acquisition, defines variables and constraints, and constructs a basic mathematical model. This versatility allows it to be applied to supply chain systems of all types and sizes, whether optimizing production, sales, or inventory.

[0023] 3. The method carefully considered the actual business in the S2 stage and calculated the unit inventory production cost, unit real-time cost, and material marginal profit. These practical calculations help to more comprehensively understand the economic characteristics of the supply chain and provide strong support for the development of reasonable optimization methods. By combining real-time cost and profit data, the method can more accurately guide decision-making and ensure that the implementation of the optimization method is more in line with the actual situation.

[0024] 4. The options and model updating mechanism in the S4 phase, as well as the analysis of infeasibility causes and the application of relaxation modes, demonstrate the high flexibility of the method. This flexibility enables the method to adapt to different constraints and objectives and to cope with changes and uncertainties in the supply chain. The adaptability and flexibility of the method make it a powerful tool that can meet the needs of different enterprises and industries, thereby better addressing the challenges in supply chain management. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG1 is a flowchart of supply chain optimization execution according to the present invention; FIG2 is a flowchart of marginal profit calculation according to the present invention; FIG3 is a flowchart of the supply chain optimization method according to the present invention. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of the present disclosure more apparent, embodiments of the present disclosure are described in further detail below with reference to the accompanying drawings. The proportions of the components herein are not drawn to scale, and the proportions and dimensions shown in the accompanying drawings are not intended to limit the essential technical solutions of the present disclosure. These embodiments do not describe all details in detail, nor do they limit the present disclosure to the specific embodiments described.

[0027] In one embodiment as shown in FIG1 , the supply chain optimization execution flowchart of the present invention is implemented using the following steps: Step 1: Acquire data; Step 2: Perform mathematical modeling with benefit maximization and production capacity maximization as multi-objective optimization functions; Step 3: Use optimization methods to solve the mathematical model and perform production-supply-marketing collaborative optimization; Step 4: Output the supply chain plan, including production plan, internal supply plan, sales plan, procurement plan, and inventory plan.

[0028] In an embodiment as shown in FIG2 , the present invention calculates profit data, and this part of the calculation results is used as input to the optimization method, including: S2.1: calculating the unit inventory production cost; S2.2: calculating the unit real-time cost; S2.3: calculating the material marginal profit based on the unit inventory production cost and the product sales price.

[0029] Step S2.1 includes calculating the raw material production consumption cost based on the updated unit inventory price and the production information table; calculating the unit inventory production cost based on the product cost price table and the raw material production consumption cost. Step S2.2 includes calculating the raw material procurement consumption cost based on the raw material purchase price table and the production information table; calculating the unit real-time cost based on the product cost price table and the raw material procurement consumption cost. Step S2.3 includes calculating the material profit margin based on the product sales price and the unit inventory production cost.

[0030] In another embodiment, the specific steps of the calculation process are as follows: Step 1: Calculate the unit inventory price of the material dimension based on the inventory information table and the unit inventory price update; Step 2: Calculate the raw material consumption cost required to produce the product based on the production information table and the updated unit inventory price; Step 3: Calculate the unit inventory production cost based on the production cost and the raw material consumption cost; Step 4: Calculate the unit real-time cost based on the production cost and the raw material procurement cost; Step 5: Calculate the marginal profit based on the market sales price and the unit inventory production cost.

[0031] The benefits of this method are as follows: First, step S2.1 involves calculating the unit inventory production cost, including the raw material production consumption cost. By calculating based on the updated unit inventory price and the production information table, the system can accurately assess the raw material production consumption cost. This helps companies fully understand resource utilization in the production process, optimize raw material use, reduce waste, and thus improve production efficiency. Second, step S2.2 calculates the unit real-time cost, taking into account the raw material procurement consumption cost. This allows companies to monitor raw material procurement costs in real time and make timely adjustments to adapt to market price fluctuations. This real-time cost monitoring helps companies formulate procurement strategies more flexibly, reduce procurement costs, and enhance their competitive advantage. Most importantly, step S2.3 calculates the material profit margin based on the product sales price and the unit inventory production cost. This step directly reveals the profit potential of each product, enabling companies to more intelligently formulate pricing strategies. Through detailed analysis of profit margins, companies can identify high-profit products, adjust market positioning, and even consider optimizing their product portfolio to maximize overall profits. The profit data calculation of the present invention further improves the accuracy and practicality of the calculation. Such a process helps enterprises better understand the contribution of each material to the overall cost and profit, so as to carry out targeted management and optimization. By quantifying the value of each material, the value brought by each material can be more intuitively seen.

[0032] In an embodiment as shown in Figure 3, the core steps of the optimization method process of the present invention are: S1: Acquire data, and define variables and constraints to construct a basic mathematical model; S2: Calculate profit data based on known data; S3: Construct an optimization model based on the required objectives and constraints; S4: Use the optimization method to solve the optimization model and output the optimization solution; S4.1: Add options and update the optimization model; S4.2: Use the optimization method to solve the optimization model; S4.3: If the model is judged to be feasible and the solution result is optimal, then proceed directly to step S4.5. If the model is judged to be infeasible, the reasons for the infeasibility can be further analyzed; S4.4: If further analysis of the reasons for the infeasibility is required, the optimization method will start the model solution in relaxation mode and return the optimal solution to the relaxation problem; S4.5: Output the supply chain optimization solution.

[0033] Among them, the added options in step S4.1 include penalty factors and reward factors; the data acquisition in step S1 includes: Step S1.1: Obtaining a list of material raw materials and product information from the material information; Step S1.2: Obtaining a device-input and output list from the production information table to prepare for the subsequent construction of a mathematical model; Step S1.3: Obtaining device capacity limit data.

[0034] In another embodiment, some basic data of a chemical company are as follows, including material information and production information: Table 1 Among them, Table 1 is the material information table.

[0035] Table 2 Among them, Table 2 is the corresponding production information table.

[0036] According to the data in the above table, the output of device 101 is material 1000005. In order to produce 1000005, raw materials 1000001, 1000002, 1000003, and 1000004 are needed, and their usage ratio is 2, 1, 2, 2, indicating that 2 units of 1000001, 1 unit of 1000002, 2 units of 1000003, and 2 units of 1000004 can produce 1 unit of 1000005, and 1000004 must be produced from device 000; Device 101 can also produce 1000006. In order to produce 1000006, 1 unit of 1000002 and 1 unit of 1000004 are needed, and 1000004 must be produced from device 000; and so on.

[0037] The following are business constraint data: Table 3 Among them, Table 3 is the device capacity limit table. The meaning of the device capacity limit table is as follows: The ideal capacity value of device 101 is 99,000, but it is allowed not to be turned on, that is, the capacity is 0. It is also allowed to be turned on when exceeding a certain load, that is, the capacity limit is 100,000. The restricted main product material group is [1000,1001].

[0038] Table 4 Among them, Table 4 is the demand restriction table.

[0039] The following are the cost data: Table 5 Among them, Table 5 is the sales price list.

[0040] Table 6 Among them, Table 6 is the raw material purchase price list.

[0041] Table 7 Among them, Table 7 is the production cost table.

[0042] Calculated by the marginal profit calculation formula: Marginal profit = latest market sales price - (unit inventory production cost - unit labor cost) - unit process material consumption, it is calculated that: The marginal profit of 1000004 is: 1000-(50-300)-400=850; The marginal profit of 1000005 is: 2000-(50+200+30+400-200)-250=1270; The marginal profit of 1000006 is: 500-(200+400-200)-100=0.

[0043] The optimization method is executed below. Note that inventory restrictions and purchasing restrictions are not considered in this embodiment.

[0044] First, combining the production information and the calculated marginal profit, we know that to maximize efficiency, we must first produce 1000005, then 1000004, and finally consider 1000006. However, we note that the demand for 1000006 is limited to 5000, so we abandon efficiency and consume a certain amount of 1000004 to produce 1000006.

[0045] Under the condition that the demand for 1000006 is 5000, in order to maximize the benefits, the device must be fully loaded, so the final production plan is as follows: Device 000: purchase 100000 of 1000001 to produce 10000004 of 1000000; Device 101: purchase 5000 of 1000002 and consume 5000 of 1000004 produced by device 000 to produce 10000006; Device 101: purchase 95000 of 1000001 and 10000003, purchase 47500 of 1000002 and consume 95000 of 1000004 produced by device 000 to produce 47500 of 1000005; If the lower limit of the production capacity of device 101 is set to 50000, the above plan does not meet the constraint restrictions.

[0046] At this time, the infeasibility reason analysis mode will be turned on, prompting that the maximum production is 47,500, but the lower limit of production capacity is 50,000. The difference of 2,500 is insufficient production. The lower limit of production capacity can be adjusted to 47,500 or the upper limit of production capacity of device 000 can be adjusted to 105,000.

[0047] This design of the present invention offers the following benefits: First, the data acquisition and model building phases in step S1 do not rely on configuration diagrams, significantly improving modeling efficiency and simplifying computational complexity. While traditional modeling methods may require complex graphical tools, this process directly extracts necessary information from the data to construct the basic mathematical model by clearly defining variables and constraints. This simplifies the entire process, enabling companies to more quickly respond to market changes and adjust plans. Second, by quantifying the value of each material, this process more intuitively reveals each material's contribution to overall profit. This provides in-depth insight for business decision-makers, enabling them to more targetedly adjust production and supply strategies. This sophisticated profit data calculation enables companies to optimize resource allocation, increase profitability, and better meet market demand. Third, by allowing for the optimization of multiple strategies, this method fully accounts for the different production models of different materials, offering broader applicability. This versatility means that companies can flexibly address supply chain challenges across different products and market conditions, thereby better adapting to the ever-changing business environment. Most importantly, if the model is infeasible, the method automatically calculates the optimal solution to the relaxation problem and provides users with recommended data modifications. This automated exception handling mechanism enables companies to find solutions more quickly when faced with plan unfeasibility, improving the speed and accuracy of decision-making.

[0048] In summary, the planning optimization method process of this embodiment has significant advantages in improving efficiency, accuracy, versatility, and dealing with unfeasible situations, providing more powerful tools and decision support for the company's supply chain planning.

[0049] In summary, the present invention proposes an optimization method for the supply chain master plan of a chemical enterprise. The method is based on the production information of each material, combined with the attribute characteristics of the material, to build an operations optimization model, comprehensively considers the device production capacity, material safety stock and maximum storage capacity, raw material procurement restrictions, sales demand and other restrictions, takes output maximization, benefit maximization, demand maximization and other multiple objectives as optimization functions, solves the operations optimization model, quickly customizes the master production plan, and analyzes the effectiveness of the master production plan in multiple dimensions by returning the material production plan, raw material procurement plan, internal supply plan, raw material consumption plan, material sales plan, and material inventory plan, thereby solving the problems of low efficiency and incomplete consideration in the actual formulation of the master production plan.

[0050] The present invention is not limited to the above-mentioned embodiments. Regardless of any changes in shape or material composition, any structural design provided by the present invention is a variation of the present invention and should be considered within the scope of protection of the present invention.

Claims

1. An improved supply chain optimization method, characterized in that: The following steps are involved: S1: Obtain data and define variables and constraints to build a basic mathematical model; S2: Calculate profit data based on known data; S3: Build an optimization model based on the desired objectives combined with constraints; S4: Solve the optimization model using an optimization method and output an optimization solution.

2. An improved supply chain optimization method according to claim 1, characterized in that: The step S4 comprises: S4.1: Add options and update the optimization model; S4.2: solving the optimization model using an optimization method; S4.3: If the model is judged to be feasible and the solution is optimal, then proceed directly to step S4.

5. If the model is judged to be infeasible, the reasons for the infeasibility can be further analyzed; S4.4: If further analysis of the infeasibility reasons is required, the optimization method will start the model solution in the relaxation mode and return the optimal solution of the relaxation problem; S4.5: Output supply chain optimization plan.

3. The improved supply chain optimization method according to claim 1, characterized in that: The step S2 comprises: S2.1: Calculate unit inventory production cost; S2.2: Calculate unit real-time cost; S2.3: Calculate material contribution margin.

4. An improved supply chain optimization method according to claim 1 or 3, characterized in that: The step S2.1 includes calculating the raw material production cost based on the inventory cost information data and the production information data; Based on the product cost data and the raw material production cost, the unit inventory production cost is calculated.

5. An improved supply chain optimization method according to claim 1 or 3, characterized in that: The step S2.2 includes calculating the raw material purchase cost based on the raw material cost data and the production information data; Based on the product cost data and the raw material procurement costs, the unit real-time cost is calculated.

6. The improved supply chain optimization method according to claim 3, characterized in that: The step S2.3 includes calculating the material profit margin based on the product price data and the unit inventory production cost.

7. An improved supply chain optimization method according to claim 2, characterized in that: The added options in step S4.1 include a penalty factor and a reward factor.

8. An improved supply chain optimization method according to claim 2, characterized in that: The supply chain optimization plan includes a production internal supply optimization plan, a sales and procurement optimization plan, and an inventory optimization plan.

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