Process manufacturing production planning strategy determination method, system and device

Abstract

The present invention relates to the field of workshop production planning management, and in particular to a method, system and device for determining a production planning strategy for process manufacturing. The present invention relates to the field of workshop production planning management, and formulates a production plan based on reviewing customer orders, demand forecasts and current inventory information; calculates product demand in the production plan, evaluates the delivery time for completing orders in the order process and checks net inventory; performs production scheduling simulation based on delivery period; uses discrete events for simulation; determines the simulation run length and the warm-up time of the system, and compares the model data set with the actual system data set to verify the validity of the model. The selection of the hybrid production planning strategy of the present invention can effectively reduce costs and delivery times; can be used to handle unpredictable events such as machine failures or emergency orders, and thus effectively carry out production planning arrangements, and maximize profits at the expense of efficiency. The hybrid production planning strategy is suitable for most manufacturing environments.

Description

Technical Field

[0001] The present invention relates to the field of workshop production planning management, and in particular to a method, system and device for determining a process manufacturing production planning strategy. Background Art

[0002] The selection of a production planning strategy is one of the most crucial factors in production management. For manufacturing-focused home textile companies, product quality control, on-site data collection, and workshop production planning are core concerns. Furthermore, the increasing emphasis on customer needs during production, coupled with the inevitable process characteristics and costs, compels researchers and process engineers to manage orders and select the right production planning strategy. Therefore, this paper focuses on production planning in the process manufacturing industry, specifically within the field of intelligent production systems for process manufacturing. As a key research area for smart factories, intelligent production planning must consider the entire enterprise's production, scheduling, logistics, and management.

[0003] Over the past two decades, this problem has been extensively studied in academia and industry, particularly with respect to flow shop production systems. This problem is referred to in the literature as order taking and scheduling (OAS). In an OAS, order taking and production planning are typically managed by different organizational departments. The production department's goal is to maximize resource utilization and minimize delays.

[0004] In most manufacturing systems, implementing a mix of production planning strategies is preferable to applying a single strategy in order to achieve flexibility and produce a variety of products. However, due to the uncertainty of product demand and the dynamic nature of production operations, combining these strategies is not easy. Therefore, evaluating the impact of optimizing production planning strategies becomes an important issue that needs to be addressed in real-world scenarios.

[0005] Production planning is particularly important in process industries (such as the chemical, textile, and food industries) because they involve the production of multiple products, each with its own unique storage conditions. There are four common production planning strategies in process industries:

[0006] Make-to-Stock (MTS): Under this strategy, products are produced based on forecasted demand and then stored in a warehouse for sale. The MTS strategy can reduce delivery time for customers; however, it can increase inventory and product damage costs.

[0007] Make-to-Order (MTO): In this system, production operations are carried out only after receiving an order from a customer. The MTO system is characterized by long customer delivery times, low warehousing costs, and greater production flexibility.

[0008] Engineer-to-Order (ETO) is a system used by companies that produce complex structures. Products are designed according to customer specifications, and each customer order requires a unique set of parts, bill of materials, and processes. Customers experience relatively long lead times in this strategy. ETO can reduce production and inventory costs.

[0009] Assemble to Order (ATO): In this system, semi-finished products and finished products are prepared, and when an order is received from a customer, the finished product is assembled and delivered to the customer. This method also results in a relatively short lead time for customers and lower costs for product damage.

[0010] In process industries, ETO is ineffective due to the different types of products, and ATO manifests itself as delayed variance (DD). In a DD strategy, semi-finished products (intermediate products) are stored in temporary storage and completed after receiving customer orders. The reason for using a DD strategy is that intermediate products can be used to produce multiple final products.

[0011] The Customer Order Decoupling Point (CODP) is the stage in the production process where a specific product is tied to a specific customer order. In effect, CODP separates the part of the organization that deals with customer orders from the planning and forecasting part of the organization.

[0012] The choice of production process is related to market demand and production conditions, and is closely related to the CODP position. Production planning strategies have not received much attention in the literature of the textile industry. However, these strategies have been extensively studied in other industries, which include common industry characteristics such as setup costs, limited product shelf life, uncertain product demand, and product quality.

[0013] The first study to specifically examine CODP in the production process was VanDonk's. It provided a framework for identifying factors associated with CODP and determined that various factors are effective in determining whether a product should follow an MTO or MTS strategy. Olhager explained pre- and post-CODP activity analysis by proposing decision criteria in terms of market, product, and process, and suggested demand instability and production time ratio as two primary indicators for selecting between MTO and MTS strategies. Soman studied a hybrid MTO / MTS system and provided a three-tiered decision framework (determination of production strategy, number of products saved, and production scheduling). Su stated that an MTO strategy is a form of DD strategy in which products are differentiated at the order stage; they then developed models for two types of DD structures in mass customization and measured their performance based on total supply chain cost and customer lead time. Rodgers and Nandy used discrete event simulation to develop a model of an MTO system with fixed capacity and input control, concluding that this approach did not improve customer lead time and that many orders should be rejected. Kober and Heinecke used system dynamics to introduce a hybrid MTO-MTS system. and conducted a production strategy evaluation, concluding that MTO is a very stable strategy if production capacity is not limited, but in reality, MTO cannot reach the target market. Garn and Aitken studied MTS and MTO strategies using a new outlier detection algorithm. The results of the study show that identification and use strategies such as MTO need to be implemented in conjunction with MTS. Aouam analyzed the value of using the MIP heuristic method to provide decision makers with the flexibility to accept or reject orders when the order quantity is uncertain. Gargouri suggested that a real-time scheduling method based on certain preferences and priority rules be adopted in the food industry to take into account most constraints and unforeseen events that may eventually occur. Makris proposed a probabilistic reasoning method that uses the principles of Bayesian networks to quantify the likelihood of buyers purchasing highly customized products.

[0014] While some research has examined hybrid production planning strategies, these studies have not comprehensively calculated cost criteria. Therefore, previous studies have not comprehensively compared production planning strategies for process industries. This patent utilizes discrete-time simulation techniques to identify optimal production planning strategies for process industries using realistic cost and time criteria. Summary of the Invention

[0015] Dynamic production planning in process industries is a complex process, driven by the industry's inherent structural complexity and the emergence of unpredictable situations, such as new orders, machine failures, or urgent orders. While companies prioritize new orders, they must also prioritize whether they can accept customer orders due to production capacity constraints. Therefore, optimizing production plans is crucial. While single production planning strategies typically address relatively simple scenarios, their shortcomings in more complex real-world scenarios severely impact production scheduling in process industries, failing to improve resource optimization and scheduling capabilities.

[0016] In order to solve the above problems, the present invention proposes a method for determining a process manufacturing production planning strategy, which includes the following steps:

[0017] S1: Develop production plans based on customer orders, demand forecasts, and current inventory information. Products undergo two quality tests during production, transfer, and storage. Strategies applied during information review include MTS and DD strategies.

[0018] S2: Calculate product demand in production planning, evaluate delivery time for completed orders in order processing, and check net inventory;

[0019] S3: Perform production scheduling simulation based on the delivery period. The information to be calculated in the production scheduling simulation includes delivery period, product shelf life, expiration fee, raw material cost, storage cost, and delivery time.

[0020] S4: Use discrete events for simulation, design a model in ED software, and simulate the production time and sales of each different product using EasyFit software;

[0021] S5: Determine the simulation run length and the system warm-up time, and compare the model dataset with the actual system dataset to verify the effectiveness of the model.

[0022] Furthermore, the steps of the production scheduling simulation in S3 include:

[0023] S31: Calculate the delivery date and send it to the production department;

[0024] S31: After quality control inspection, intermediate and final products are transported to temporary storage tanks and warehouses;

[0025] S32: Warehousing costs are derived through the techniques of overdue product costs, inventory costs, opportunity costs, and raw material costs.

[0026] Furthermore, the inputs of the simulation model for the discrete events in S4 include weekly production and sales data of each product, probability distribution functions of production activities, composition of intermediate products and final products, storage capacity and storage costs of products.

[0027] Furthermore, the criteria for data comparison in S5 include: (1) the percentage of orders completed on time; (2) setup costs; (3) shelf life expiration costs of intermediate and final products; and (4) inventory costs (opportunity costs and storage costs).

[0028] Furthermore, the elements used in the design model of the ED software in S4 include: arrival list, Excel data, assembler, server, multi-service, queue, distributor, and receiver.

[0029] Furthermore, the method for verifying the validity of the model in S5 is:

[0030] S51: Kolmogorov Smirnov test was used to check the normalization of the two data sets;

[0031] S52: Assuming that the two data sets are independent, use the F test to compare the variances of the two data sets. The results show that the P value is greater than 0.05, with a 95% confidence level, the two data sets have the same variance;

[0032] S53: Use t-test to test the average similarity of the two data sets;

[0033] S54: At a 95% confidence level, there is no statistically significant difference between the actual system and the simulation model, verifying the validity of the model.

[0034] In another aspect of the present invention, a system for determining a process manufacturing production plan strategy is provided, the system comprising a data input module, a data allocation module, a calculation module, a strategy analysis module, and an output module;

[0035] The data input module is used to input production plan and scheduling data and product composition;

[0036] The data allocation module is used to separate the data in the production plan and the scheduling, and the cost and storage data;

[0037] The calculation module is used to calculate product demand, net inventory, order evaluation calculation, delivery date, delivery cycle, overdue and storage costs in the production plan;

[0038] The strategy analysis module compares and verifies the model calculation data with the actual data to obtain the optimal strategy for reducing costs and delivery time and maximizing profits;

[0039] The output module is used to obtain the selected optimal production strategy.

[0040] The third aspect of the present invention also provides a device for determining process manufacturing production plan strategies, including an input device, a processor and a storage device; the input device is used to input production plan and scheduling data and product composition; the processor is used to execute the computer program in the storage device according to the input data; the storage device is used to store the program required for the computer to determine the strategy.

[0041] Beneficial effects of the present invention:

[0042] 1. The selection of mixed production planning strategy can effectively reduce costs and delivery time;

[0043] 2. It can be used to handle unpredictable events such as machine failures or urgent orders, thereby effectively scheduling production and maximizing profits at the expense of efficiency;

[0044] 3. Adopt a more structured approach to order scheduling;

[0045] 4. Hybrid production planning strategies are suitable for most manufacturing environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is the overall structural diagram of the process manufacturing production planning strategy determination system of the present invention.

[0047] Figure 2 It is a conceptual model diagram of the process manufacturing production planning strategy determination system of the present invention.

[0048] Figure 3 It is a logical flow chart of resource allocation to orders in an embodiment of the process manufacturing production planning strategy determination method of the present invention.

[0049] Figure 4 It is a system warm-up cycle diagram in an embodiment of the process manufacturing production planning strategy determination system of the present invention. DETAILED DESCRIPTION

[0050] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings. Example

[0051] The present invention proposes a method for determining a process manufacturing production planning strategy, which includes the following steps:

[0052] S1: Develop production plans based on customer orders, demand forecasts, and current inventory information. Products undergo two quality tests during production, transfer, and storage. Strategies applied during information review include MTS and DD strategies.

[0053] S2: Calculate product demand in production planning, evaluate delivery time for completed orders in the order process, and check net inventory;

[0054] S3: Perform production scheduling simulation based on the delivery period. The information to be calculated in the production scheduling simulation includes delivery period, product shelf life, expiration fee, raw material cost, storage cost, and delivery time.

[0055] S4: Use discrete events for simulation, design a model in ED software, and simulate the production time and sales of each different product using EasyFit software;

[0056] S5: Determine the simulation run length and the system warm-up time, and compare the model dataset with the actual system dataset to verify the effectiveness of the model.

[0057] Furthermore, the steps of the production scheduling simulation in S3 include:

[0058] S31: Calculate the delivery date and send it to the production department;

[0059] S31: After quality control inspection, intermediate and final products are transported to temporary storage tanks and warehouses;

[0060] S32: Warehousing costs are derived through the techniques of overdue product costs, inventory costs, opportunity costs, and raw material costs.

[0061] Furthermore, the inputs of the simulation model for the discrete events in S4 include weekly production and sales data of each product, probability distribution functions of production activities, composition of intermediate products and final products, storage capacity and storage costs of products.

[0062] Furthermore, the criteria for data comparison in S5 include: (1) the percentage of orders completed on time; (2) setup costs; (3) shelf life expiration costs of intermediate and final products; and (4) inventory costs (opportunity costs and storage costs).

[0063] Furthermore, the elements used in the design model of the ED software in S4 include: arrival list, Excel data, assembler, server, multi-service, queue, distributor, and receiver.

[0064] Furthermore, the method for verifying the validity of the model in S5 is:

[0065] S51: Kolmogorov Smirnov test was used to check the normalization of the two data sets;

[0066] S52: Assuming that the two data sets are independent, use the F test to compare the variances of the two data sets. The results show that the P value is greater than 0.05, with a 95% confidence level, the two data sets have the same variance;

[0067] S53: Use t-test to test the average similarity of the two data sets;

[0068] S54: At a 95% confidence level, there is no statistically significant difference between the actual system and the simulation model, verifying the validity of the model.

[0069] In another aspect of the present invention, a system for determining a process manufacturing production plan strategy is provided, the system comprising a data input module, a data allocation module, a calculation module, a strategy analysis module, and an output module;

[0070] The data input module is used to input production plan and scheduling data and product composition;

[0071] The data allocation module is used to separate the data in the production plan and the scheduling, and the cost and storage data;

[0072] The calculation module is used to calculate product demand, net inventory, order evaluation calculation, delivery date, delivery cycle, overdue and storage costs in the production plan;

[0073] The strategy analysis module compares and verifies the model calculation data with the actual data to obtain the optimal strategy for reducing costs and delivery time and maximizing profits;

[0074] The output module is used to obtain the selected optimal production strategy.

[0075] The third aspect of the present invention also provides a device for determining process manufacturing production plan strategies, including an input device, a processor and a storage device; the input device is used to input production plan and scheduling data and product composition; the processor is used to execute the computer program in the storage device according to the input data; the storage device is used to store the program required for the computer to determine the strategy.

[0076] Specifically, according to Figure 1 It shows the overall structure of the main activities of the system, which are usually divided into production planning, production scheduling (production scheduling), intermediate production, final production and warehousing.

[0077] In production planning, weekly and monthly production volumes are determined by customer orders, demand forecasts, and current inventory. Production scheduling prioritizes mid- and late-stage production through production planning. Intermediate and final products of varying composition are produced based on production plans and schedules. In warehousing, storage capacity and costs are determined, and product inventory is updated based on sales or product damage.

[0078] Customer orders based on CODP may affect production planning or warehousing. In an MTS strategy, forecasted customer orders are reviewed by the production planning department, while actual customer orders are sent to the warehouse. In a DD strategy, the production planning department reviews both forecasted and actual customer orders.

[0079] During the production process, many intermediate products are typically stored in a warehousing environment after production. After undergoing the first quality control test (QC1), the product is transferred to the production reactor, where the final product is produced and packaged. After the second quality control test (QC2), the product is shipped to the warehouse for distribution.

[0080] The main components of the system are as follows Figure 2 The description of the conceptual model and the definition of mathematical relationships. The logical model for allocating raw materials, intermediate products and final products to orders is as follows: Figure 3 shown.

[0081] The primary input for production planning is product demand. Production planning is conducted weekly. Each order is recorded with the order's arrival date, delivery deadline, order quantity, and product ID. The first step in evaluating an order is to check the delivery time to complete the order.

[0082] LT=ODD-(PTMP+PTFP+TQCTT)

[0083] LT = time between order and delivery (lead time), ODD = order due date, PTMP = time to produce intermediate products, PTFP = time to produce final products, TQCTT = total time for quality control

[0084] The next step in the order evaluation process is to check net inventory.

[0085] NIH=TPW+TPQCT+PIP

[0086] NIH = Net Inventory on Hand, TPW = Total Products in Warehouse, TPQC = Total Products in Quality Control, PIP = Work in Progress

[0087] Production scheduling simulation based on lead time. Production orders for products are sent to the production department based on when the product can be delivered to the customer. The lead time is calculated as follows:

[0088] MPLT=ODD-(PTMP+PTFP+TQCTT)

[0089] FPLT=ODD-(PTFP+TQCTT)

[0090] MPLT = intermediate product lead time, FPLT = final product lead time

[0091] After quality control inspection, intermediate and final products are transported to temporary storage tanks and warehouses. These products increase warehouse inventory levels. The shelf life of the products is monitored daily. When a batch of product is produced, the production time is immediately recorded with a timestamp to track the product's shelf life.

[0092] The product is discarded if:

[0093] CT>SL+TS

[0094] CT=current time, SL=shelf life, TS=time stamp

[0095] Late delivery (more than 1 day) is considered delayed delivery: OOF = On-Time Order Fulfillment, i is the product index, =Total number of completed orders for product i, = Total number of orders received for product i

[0096] The maintenance cost of related equipment after each production is considered a production preparation cost: SC = production preparation cost, m = different storage methods, = the average cost of equipment maintenance after m production times, = average number of m conversions

[0097] Expiration costs for intermediate and final products SLEC=Shelf Life Expiration Charge, Cost of intermediate products in temporary storage units until their shelf life expires, = Cost of expiration of shelf life of final product in warehouse = expiration cost of intermediate product k, = Total expired quantity of intermediate product k (kg); = shelf life expiry cost of final product i, = Total quantity of final product i that expires (kg). Inventory cost is the cost associated with inventory that remains in the warehouse. Inventory cost includes storage costs and opportunity costs. OC=Opportunity Cost, = opportunity cost of intermediate products, =Opportunity cost of final product. = direct raw material cost of intermediate product k, = the total amount of intermediate product k (kg), = the remaining time that final product i remains in the warehouse. WHC = Warehousing Cost, = Storage cost of intermediate products, = Storage cost of final product = storage cost of intermediate product k, = Storage cost of final product k

[0098] Due to the structural complexity and parameter uncertainty of the model, this patent uses discrete event simulation. To verify and implement the simulation experiments, data from a chemical fiber manufacturing company in Hangzhou was used. This factory was selected because of its production process (first producing intermediate products and then different final products).

[0099] Simulation is a set of methods that simulate the behavior of real systems over time with the help of computers. Its purpose is to analyze and evaluate the performance of the system and improve the system.

[0100] The goal is to maximize delivery time to the customer and minimize total costs. To this end, the performance of MTS and DD strategies is evaluated, and then two hybrid DD-MTS strategies (MD1 and MD2) are created, focusing on reducing the cost of product damage. The described process was designed using discrete event simulation in the ED software of ISS Corporation.

[0101] To compare strategies, four main criteria were calculated: (1) the percentage of orders completed on time, (2) setup costs, (3) shelf life expiration costs for intermediate and final products, and (4) inventory costs (opportunity costs and storage costs). We used three years of production and sales data for a chemical fiber production line. The plant adopted the MTS strategy.

[0102] The model inputs included weekly production and sales data for each product, a probability distribution function for production activity, the composition of intermediate and final products, and storage capacity and storage costs for each product. To implement the simulation model, the data needed to be converted into a probability distribution function. Therefore, the production schedule and sales profile for each product were modified using EasyFit software.

[0103] The model was designed in ED software considering the physical limitations of the system, the detailed processes and the operational characteristics of the system.

[0104] Given that the strategy adopted by the studied enterprise is the MTS strategy, the MTS model was first designed and implemented. After verifying and validating the model, a scenario was designed to modify the existing system.

[0105] Model assumptions:

[0106] For the DD and hybrid strategies, the cost of creating a temporary storage unit needs to be calculated.

[0107] Processing times and production capacities are precisely measured.

[0108] Since the raw material conditions of various strategies are similar, the strategy selection is inefficient and the raw materials are considered to be infinite and without damage.

[0109] Elements used in problem modeling in ED software include:

[0110] Arrival list (input weekly and monthly production schedule data)

[0111] Excel (input production planning and scheduling data and product composition)

[0112] Assemblers (equipment that produces intermediate and final products)

[0113] Server (Quality Control)

[0114] Multiple services (customer order decoupling point)

[0115] Queues (warehouses and temporary storage units)

[0116] Distributor (input and separation of raw materials)

[0117] Receivers (number of registered outputs)

[0118] Then determine the simulation run length and the system warm-up time. System warm-up refers to the time required for the system to enter a relatively stable state. Since the simulation model does not have the same basic conditions in the initial stage and is implemented on the actual production line, a certain system warm-up time needs to be determined. In this model, a system warm-up specific diagram is used to determine the warm-up time, such as Figure 4 As shown, the warm-up time of the system is estimated to be 14 days (recorded by the software after the model is executed 30 times).

[0119] Because the decision criteria were based on annual costs, the simulation run length (without a system warm-up period) was considered to be one year. The simulation process was performed 30 times for each scenario. The production speed, costs, and times recorded in the software were compared with the actual data of the factory during all simulation stages.

[0120] The model dataset was compared with the actual system dataset to verify the model's validity. First, the Kolmogorov-Smirnov test was used to check the normalization of the two datasets. Second, assuming the two data sets are independent, the next step was to compare the variances of the two datasets using an F-test. The results showed that, with a P value greater than 0.05 and a 95% confidence level, the two datasets had identical variances. Finally, a t-test was used to test the mean similarity of the two datasets. The results showed that, at a 95% confidence level, there were no statistically significant differences between the actual system and the simulation model, validating the model's validity.

[0121] After checking the model's validation and verification, examine the impact of other strategies (D, MD1, and MD2). To transition a production system from the MTS strategy to other strategies in different scenarios, first determine the optimal storage capacity for the DD and hybrid strategies, as well as the product selection for the hybrid strategy. Because storage units significantly impact production costs (especially shelf life expiration costs), the optimal capacity of temporary storage units for intermediate products needs to be calculated before modeling the hybrid strategy.

[0122] For this purpose, initially, the number of temporary storage units for each intermediate product (for the DD strategy) starts from the minimum demand, the cost criterion is evaluated as a function of the number of temporary storage units, and finally it is concluded that changing the storage units can effectively reduce the cost, and the optimal capacity of the temporary storage units is determined.

[0123] The goal of this patent is to achieve the optimal conditions for all criteria by creating a hybrid strategy. Therefore, Pareto analysis is used to identify the products (in the MTS policy) that have the largest share of shelf life expiration costs.

[0124] In the hybrid MD1 strategy, the corresponding products are produced under the DD strategy, while the other products are produced under the MTS strategy. For these products, choosing the DD strategy will increase installation costs and customer delivery time, but this is negligible compared to the significant reduction in shelf life expiration costs. For the hybrid MD2 strategy, the selection criterion is increased to 94% of the total expiration cost.

[0125] The results show that the single MTS strategy has the lowest customer delivery time and setup costs, but the highest shelf life expiration costs and inventory costs. In contrast, the DD strategy has lower inventory costs and shelf life expiration costs than the MTS strategy. The results show that the mixed strategy significantly reduces shelf life expiration costs and inventory costs compared to the MTS strategy.

[0126] The MTS strategy outperformed the other strategies in terms of on-time order completion percentage, but the mixed strategy achieved a slight improvement over the DD strategy. To compare these strategies, an analysis of variance was performed. Given a P value less than 0.05, the equilibrium between the average total cost and on-time order completion percentage across the different strategies was rejected.

[0127] According to the results of the software output and the ranking of the strategies in the Tukey test, the MTS strategy is more disadvantageous than the other strategies in terms of total cost, so the MTS strategy will be eliminated. Among the remaining three strategies, MD2 is selected as the best strategy.

[0128] The beneficial effects of the present invention are as follows: 1. The selection of a hybrid production planning strategy can effectively reduce costs and delivery times; 2. It can be used to handle unpredictable events such as machine failures or emergency orders, thereby effectively scheduling production and maximizing profits at the expense of efficiency; 3. A more structured approach is used to implement order scheduling; 4. The hybrid production planning strategy is applicable to most manufacturing environments.

[0129] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for determining a production planning strategy for process manufacturing, characterized by The method comprises the following steps: S1: Develop production plans based on customer orders, demand forecasts, and current inventory information. Products undergo two quality tests during production, transfer, and storage. Strategies applied during information review include MTS and DD strategies. S2: Calculate product demand in production planning, evaluate delivery time for completed orders in the order process, and check net inventory; S3: Perform production scheduling simulation based on the delivery period. The information to be calculated in the production scheduling simulation includes delivery period, product shelf life, expiration fee, raw material cost, storage cost, and delivery time. S4: Use discrete events for simulation, design a model in ED software, and simulate the production time and sales of each different product using EasyFit software; S5: Determine the simulation run length and the system warm-up time, and compare the model dataset with the actual system dataset to verify the effectiveness of the model; The steps of the production scheduling simulation in S3 include: S31: Calculate the delivery date and send it to the production department; S31: After quality control inspection, intermediate and final products are transported to temporary storage tanks and warehouses; S32: Determine storage costs using techniques including overdue product costs, inventory costs, opportunity costs, and raw material costs; The inputs of the discrete event simulation model in S4 include weekly production and sales data of each product, probability distribution function of production activities, composition of intermediate products and final products, storage capacity and storage cost of products.

2. The process manufacturing production planning strategy determination method according to claim 1, characterized in that: The criteria compared in S5 include: the percentage of orders completed on time, setup costs, shelf life expiration costs of intermediate and final products, and inventory costs, which include opportunity costs and storage costs.

3. The process manufacturing production planning strategy determination method according to claim 1, characterized in that: The elements used in the design model of the ED software in S4 include: arrival list, Excel data, assembler, server, multi-service, queue, distributor, and receiver.

4. The process manufacturing production planning strategy determination method according to claim 2, characterized in that: The method for verifying the validity of the model in S5 is: S51: Kolmogorov Smirnov test was used to check the normalization of the two data sets; S52: Assuming that the two data sets are independent, use the F test to compare the variances of the two data sets. The results show that the P value is greater than 0.05, with a 95% confidence level, the two data sets have the same variance; S53: Use t-test to test the average similarity of the two data sets; S54: At a 95% confidence level, there is no statistically significant difference between the actual system and the simulation model, verifying the validity of the model.

5. A system using the process manufacturing production planning strategy determination method according to any one of claims 1 to 4, characterized in that: The system includes a data input module, a data distribution module, a calculation module, a strategy analysis module, and an output module; The data input module is used to input production plan and scheduling data and product composition; The data allocation module is used to separate the data in the production plan and the scheduling, and the cost and storage data; The calculation module is used to calculate product demand, net inventory, order evaluation calculation, delivery date, delivery cycle, overdue and storage costs in the production plan; The strategy analysis module compares and verifies the model calculation data with the actual data to obtain the optimal strategy for reducing costs and delivery time and maximizing profits; The output module is used to obtain the selected optimal production strategy.

6. The system for determining a process manufacturing production plan strategy according to claim 5, characterized in that: It includes an input device, a processor and a storage device; the input device is used to input production plan and scheduling data and product composition; the processor is used to execute the computer program in the storage device according to the input data; and the storage device is used to store the program required by the computer for strategy determination.

Images