Intelligent medicine inventory management and optimization system

Abstract

The invention, which relates to the technical field of medicine inventory management, discloses an intelligent medicine inventory management and optimization system comprising a demand prediction module, an inventory monitoring module, a purchase decision module and a medicine supply module. The demand prediction module is used for performing drug demand prediction according to historical sales data, seasonal factors and market trends; and analyzing and predicting by adopting a machine learning algorithm. According to the intelligent medicine inventory management and optimization system, based on a multi-objective optimization algorithm, in combination with the inventory turnover rate, the purchase cost and the medicine validity period, a scientific and reasonable purchase replenishment strategy is generated, including purchase time, quantity and supplier selection, and the scientificity and the accuracy of a purchase decision are improved; by integrating historical sales data, seasonal factors and market trends, demand prediction is performed by using a machine learning algorithm, and an accurate drug future demand trend prediction result can be generated.

Description

Technical Field

[0001] The present invention relates to the technical field of drug inventory management, and in particular to an intelligent drug inventory management and optimization system. Background Art

[0002] With the continuous development of the pharmaceutical industry and the growing demand for healthcare, the importance of drug inventory management has become increasingly prominent. Traditional drug inventory management mainly relies on manual experience and simple statistical methods, which has many problems and is difficult to meet the needs of modern pharmaceutical companies. In terms of procurement decisions, the traditional model is mainly based on experience and fixed supplier relationships or fixed time periods for procurement, lacking a scientific multi-objective optimization algorithm, which can easily lead to inventory backlogs or out-of-stocks. It is difficult to comprehensively consider factors such as inventory turnover, procurement costs, and drug expiration dates, resulting in inaccurate procurement decisions, which may increase corporate costs or affect the stability of drug supply. Summary of the invention

[0003] In order to solve the above technical problems, the present invention is implemented through the following technical solutions: an intelligent drug inventory management and optimization system, including a demand forecasting module, an inventory monitoring module, a purchase decision module and a drug replenishment module;

[0004] The demand forecasting module is used to forecast drug demand based on historical sales data, seasonal factors and market trends; use machine learning algorithms to analyze and forecast, and generate forecast results of future drug demand trends; and adjust procurement and replenishment strategies when changes in drug demand are detected;

[0005] The inventory monitoring module is used to monitor the drug inventory level in real time and update the inventory status based on the current inventory data. It also calculates the number of drugs that are about to expire based on the batch information and expiration date of the drugs, obtains drug expiration warnings, and generates clearance reminders based on drug expiration warnings. Clearance reminders are sent when the drugs are about to expire to prevent drug expiration or inventory backlogs.

[0006] The purchasing decision module is connected with the demand forecasting module and the inventory monitoring module to formulate a purchasing replenishment strategy based on a multi-objective optimization algorithm combined with demand forecasting data, and to generate a purchasing plan based on the purchasing replenishment strategy;

[0007] The drug replenishment module analyzes the drug purchase cost, inventory holding cost and out-of-stock cost in conjunction with the procurement plan to develop a replenishment plan.

[0008] Preferably, the demand forecasting module includes:

[0009] Data collection unit: used to import historical sales data of drugs from the historical sales system, and combine it with external market trend data and seasonal disease epidemic trend data to obtain a comprehensive data set;

[0010] Prediction algorithm unit: used to apply machine learning algorithms to predict drug demand based on comprehensive data sets, generate forecast results of future drug demand trends, and update corresponding prediction models based on drug characteristics and sales history.

[0011] Preferably, the demand forecasting module further includes:

[0012] Model training unit: used to regularly retrain and optimize the forecasting algorithm to improve the accuracy of demand forecasting, and automatically adjust model parameters and update the algorithm based on the error feedback between actual sales data and forecast data;

[0013] Anomaly detection unit: used to detect abnormal fluctuations in forecast results, such as sudden surges or significant drops in demand. When abnormal fluctuations are identified, alarm information is automatically generated and corresponding inventory management strategies are recommended for adjustment.

[0014] Preferably, during use of the data collection unit:

[0015] The data collection unit establishes a connection with the historical sales system, extracts the historical sales data of the drug, sorts and classifies the historical sales data, and stores the data by drug category, month, and region; the historical sales data includes sales quantity, sales time, and sales location;

[0016] Collect market research reports, news and policy changes in the pharmaceutical industry, clarify the development trend of the pharmaceutical market, and obtain trend information from the external market, including new drug research and development trends, drug price fluctuation trends, and the impact of medical insurance policy adjustments on drug demand; and analyze the potential relationship between external market trend information and drug demand, such as the impact of new drug launches on the demand for similar drugs, and the increase or decrease in demand for certain drugs caused by adjustments to medical insurance policies, to obtain market trend data;

[0017] Monitor the incidence of seasonal diseases, such as the high incidence of influenza in winter and the increase of allergic diseases in specific seasons; pay attention to the sudden trend of epidemic diseases; and analyze the correlation between seasonal diseases and epidemic trends and the demand for medicines. For example, the demand for cold medicines and antipyretics will increase during the flu season, and obtain seasonal disease epidemic trend data;

[0018] Remove outliers and erroneous data from historical sales data, market trend data, and seasonal disease epidemic trend data, and use the mean filling method to handle missing values, and then extract useful features from the collected data:

[0019] For historical sales data, the cyclical and trend characteristics of sales are extracted; for market trend data, the policy and regulatory characteristics and market competition characteristics of the market are extracted; for seasonal disease epidemic trend data, the peak incidence characteristics and epidemic intensity characteristics of the disease are extracted; the extracted features are standardized and summarized to make different features comparable, and a comprehensive data set is obtained.

[0020] Preferably, the process of applying a machine learning algorithm to predict drug demand and generating a prediction result of future drug demand trend is as follows:

[0021] The forecasting algorithm unit selects the autoregressive integrated moving average model to forecast the future demand for drugs based on the drug characteristics and historical sales data, and based on the seasonality and cyclicality of drug sales;

[0022] Taking a portion of pre-processed historical sales data, market trend data, and seasonal disease epidemic trend data as input;

[0023] The future demand for drugs is taken as output, i.e., the output is the prediction result, and the selected prediction model is trained;

[0024] During the training process, cross-validation was used to obtain model parameters to improve the prediction accuracy of the model;

[0025] Use another part of the data that is not involved in the input as the test set, and evaluate the trained model based on the root mean square error; if the evaluation result is not ideal, readjust the model parameters, train and evaluate again, so as to obtain the optimal autoregressive integrated moving average model;

[0026] The model is then used to forecast drug demand for a period of time in the future, and the drug demand quantity for the next month is predicted to obtain the forecast result. The drug demand quantity for the next month or a quarter can also be predicted. Then, analysis is performed based on the forecast results to observe the changing trend, peak and trough of demand, and the forecast results are visualized by drawing a demand trend graph so that managers can more intuitively understand the future drug demand trend. The forecast model is updated in combination with the generation of new historical sales data to improve the accuracy of the forecast. The historical sales data can be updated once a month and the forecast can be re-made.

[0027] Preferably, the inventory monitoring module includes:

[0028] Inventory sensing unit: connected to the warehouse management system to monitor the current inventory data of drugs in real time and update the inventory status. The current inventory data includes inventory quantity, storage location, batch information and expiration date;

[0029] Turnover rate calculation unit: Based on historical sales data and current inventory data, the inventory turnover rate of each drug is calculated and compared with the set target turnover rate; when the turnover rate of the drug is found to be lower than expected, an inventory backlog alarm is generated;

[0030] Inventory warning unit: used to compare and analyze the inventory quantity, batch information and expiration date of drugs based on the current inventory data of the inventory sensing unit, and generate out-of-stock warnings, backlog warnings and expiration warnings.

[0031] Preferably, the process of calculating the inventory turnover rate of each drug based on historical sales data and current inventory data is as follows:

[0032] Based on historical sales data and current inventory data, the inventory turnover rate is calculated in months, combined with the calculation formula of inventory turnover rate;

[0033] The formula for calculating inventory turnover rate is: Inventory turnover rate = sales cost in a single month / average inventory cost; for drug inventory management, sales cost is sales quantity multiplied by the average purchase cost of drugs;

[0034] Average inventory cost is the sum of the beginning inventory cost and the ending inventory cost divided by two;

[0035] The beginning inventory cost is the inventory quantity at the beginning of the calculation period multiplied by the average purchase cost of the drug.

[0036] The ending inventory cost is the inventory quantity at the end of the calculation period multiplied by the average purchase cost of the drugs.

[0037] Preferably, the process of generating an inventory backlog alarm is as follows:

[0038] According to the company's business strategy, the characteristics of the drug and market demand, set a target inventory turnover rate for each drug. For commonly used drugs, a higher target inventory turnover rate can be set to ensure that there is not too much inventory backlog; for special drugs or newly launched drugs, a relatively low target inventory turnover rate can be set to avoid over-allocation and avoid waste of resources;

[0039] Compare the calculated actual inventory turnover rate of each drug with the corresponding set target inventory turnover rate; set a threshold ratio and mark the threshold ratio as F; when the inventory turnover rate is lower than F of the target inventory turnover rate, generate an inventory backlog alarm;

[0040] The inventory backlog alert includes the drug name, inventory quantity, and backlog level, and is sent to management personnel so that timely measures can be taken, including adjusting procurement strategies and increasing promotional efforts to reduce inventory backlogs.

[0041] Preferably, the purchasing decision module includes:

[0042] Strategy optimization unit: used to generate a purchase replenishment strategy based on a multi-objective optimization algorithm, combining inventory turnover rate, procurement cost and drug expiration date. The purchase replenishment strategy includes procurement time, quantity and supplier selection;

[0043] Supplier management unit: used to record and update the supplier's historical supply situation, including delivery cycle, product quality and price, so as to optimize the selection of suppliers; the supplier management unit includes a planning unit, which is used to adjust the procurement and replenishment strategy based on the supplier's delivery and price trends to obtain a procurement plan.

[0044] Preferably, the process of generating a purchasing and replenishment strategy is:

[0045] Obtain current inventory data, inventory turnover rate of each drug, and procurement cost and expiration date of the drug. At the same time, obtain the forecast results of future demand trends of drugs from the demand forecast module; understand the demand for various drugs in the future period of time;

[0046] Among them, for inventory turnover rate: based on the forecast results of future drug demand trends, determine the purchase time and purchase quantity. When the forecast results show that the demand for drug A will increase, purchase drug A in advance to meet the demand; when the forecast results show that the demand for drug A will decrease, reduce the purchase quantity or suspend purchase to reduce the inventory level;

[0047] Drug A is promoted through promotional activities and marketing, and inventory is cleaned up regularly to deal with expired and slow-moving drugs, thereby optimizing inventory turnover;

[0048] For procurement costs: establish a supplier evaluation system to evaluate suppliers’ prices, quality and delivery dates, and obtain evaluation results, which include high-quality suppliers and low-quality suppliers; based on the evaluation results, select high-quality suppliers for cooperation; at the same time, establish long-term cooperative relationships with high-quality suppliers to strive for better prices and services; and arrange bulk purchases based on demand forecasts and inventory levels to obtain price concessions as optimized procurement costs;

[0049] For drug expiration dates:

[0050] Ways to optimize the shelf life of medicines:

[0051] Procurement management: When purchasing medicines, give priority to medicines whose production date is close to the purchase date; at the same time, negotiate with suppliers to obtain medicines with long shelf life and medicine return and exchange policies;

[0052] Inventory management: Establish an inventory management system to classify and manage drugs, and arrange inventory locations and quantities based on the drug's expiration date and sales situation; drugs with a shorter expiration date can be placed in an easily accessible location and given priority for sales; drugs with a longer expiration date can be placed deep in the warehouse to reduce the number of times they are moved;

[0053] Sales management: formulate sales strategies and give priority to the sales of drugs with later production dates;

[0054] According to the multi-objective optimization algorithm, the inventory turnover rate, procurement cost and drug expiration date are weighed and analyzed to obtain the procurement replenishment strategy:

[0055] For drugs with fast demand growth and high inventory turnover rates, advance the procurement time, increase the procurement quantity, and select suppliers with reasonable prices and short delivery cycles; for drugs with stable demand but low inventory turnover rates, reduce the procurement quantity and extend the procurement cycle.

[0056] The process of weighing and analyzing inventory turnover rate, procurement cost and drug expiration date according to the multi-objective optimization algorithm is as follows:

[0057] Determine the objective function: Assume that the inventory turnover rate is The purchase cost is The drug is valid for ; The goal is to optimize these three objective functions simultaneously, which can be expressed as a multi-objective optimization problem: ;

[0058] Define decision variables: Decision variables include purchase time t, purchase quantity q, and supplier selection s; where purchase time is a specific time point; purchase quantity is the number of drugs purchased each time; supplier selection is a discrete variable, indicating selection from different suppliers;

[0059] Determine the constraints: Inventory constraint: expressed as ,in represents the inventory quantity at time t; demand constraint: expressed as ,in represents the demand forecast at time t; supplier constraints: expressed as ,in represents the delivery cycle of supplier s;

[0060] Select a multi-objective optimization algorithm: Generate an initial solution based on the scope of decision variables and constraints based on the genetic algorithm, and use the initial solution as the initial population of the algorithm; each initial solution represents a procurement replenishment strategy, wherein the procurement replenishment strategy includes procurement time, quantity and supplier selection; then calculate the corresponding objective function value and the constraint satisfaction conditions for each initial solution; then perform weighted summation of the objective function value, and punish solutions that violate the constraints, so as to design a fitness function, and select excellent solutions as the parent population based on the fitness function; then perform crossover and mutation operations on the parent population to generate a child population; merge the child population with the parent population to form a new population; then evaluate the new population according to the fitness function, and select the excellent solution as the parent population of the next generation; repeat the process until the termination condition of the algorithm is met; after iteration of the multi-objective optimization algorithm, a set of non-dominated solutions is obtained; according to the actual situation, select one solution from the non-dominated solutions as the procurement replenishment strategy.

[0061] The present invention provides an intelligent drug inventory management and optimization system, which has the following beneficial effects:

[0062] 1. This intelligent drug inventory management and optimization system is based on a multi-objective optimization algorithm, combined with inventory turnover rate, procurement cost and drug expiration date, to generate scientific and reasonable procurement and replenishment strategies, including procurement time, quantity and supplier selection, which improves the scientificity and accuracy of procurement decisions; by recording and updating the supplier's historical supply situation, it helps to select high-quality suppliers and establish long-term and stable cooperative relationships. At the same time, the procurement and replenishment strategy is adjusted according to the supplier's delivery and price trends to further optimize procurement costs and supply stability.

[0063] 2. The intelligent drug inventory management and optimization system provides comprehensive and accurate data support for inventory management by real-time monitoring of drug inventory levels, including inventory quantity, storage location, batch information and expiration date. It calculates the number of drugs approaching expiration based on the batch information and expiration date of the drugs, and generates drug expiration warnings and clearance reminders in a timely manner, effectively reducing the risk of drug expiration and waste. It generates inventory backlog alarms by calculating the inventory turnover rate of each drug and comparing it with the set target turnover rate, which helps to optimize the inventory structure and improve the efficiency of capital use.

[0064] 3. The intelligent drug inventory management and optimization system can generate relatively accurate forecasts of future drug demand trends by integrating historical sales data, seasonal factors and market trends and using machine learning algorithms to forecast demand. This helps companies prepare inventory in advance and avoid inventory shortages or backlogs caused by demand fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1This is a flow chart of an intelligent drug inventory management and optimization system of the present invention. DETAILED DESCRIPTION

[0066] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0067] like Figure 1 As shown, the present invention provides a technical solution: an intelligent drug inventory management and optimization system, including a demand forecasting module, an inventory monitoring module, a purchasing decision module and a drug replenishment module;

[0068] The demand forecasting module is used to forecast drug demand based on historical sales data, seasonal factors, and market trends; it uses machine learning algorithms to analyze and forecast, and generate forecast results of future drug demand trends; it adjusts procurement and replenishment strategies when changes in drug demand are detected;

[0069] The inventory monitoring module is used to monitor the drug inventory level in real time and update the inventory status based on the current inventory data. It also calculates the number of drugs that are about to expire based on the batch information and expiration date of the drugs, obtains drug expiration warnings, and generates clearance reminders based on drug expiration warnings. Clearance reminders are sent when the drugs are about to expire to prevent drug expiration or inventory backlogs.

[0070] The purchasing decision module is connected with the demand forecasting module and the inventory monitoring module to formulate a purchasing replenishment strategy based on a multi-objective optimization algorithm combined with demand forecasting data, and to generate a purchasing plan based on the purchasing replenishment strategy;

[0071] The drug replenishment module analyzes the drug purchase cost, inventory holding cost and out-of-stock cost in conjunction with the procurement plan to develop a replenishment plan.

[0072] The demand forecasting module includes:

[0073] Data collection unit: used to import historical sales data of drugs from the historical sales system, and combine it with external market trend data and seasonal disease epidemic trend data to obtain a comprehensive data set;

[0074] Prediction algorithm unit: used to apply machine learning algorithms to predict drug demand based on comprehensive data sets, generate forecast results of future drug demand trends, and update corresponding prediction models based on drug characteristics and sales history.

[0075] The demand forecasting module also includes:

[0076] Model training unit: used to regularly retrain and optimize the forecasting algorithm to improve the accuracy of demand forecasting, and automatically adjust model parameters and update the algorithm based on the error feedback between actual sales data and forecast data;

[0077] Anomaly detection unit: used to detect abnormal fluctuations in forecast results, such as sudden surges or significant drops in demand. When abnormal fluctuations are identified, alarm information is automatically generated and corresponding inventory management strategies are recommended for adjustment.

[0078] During use of the data collection unit:

[0079] The data collection unit establishes a connection with the historical sales system, extracts the historical sales data of the drug, sorts and classifies the historical sales data, and stores the data by drug category, month, and region; the historical sales data includes sales quantity, sales time, and sales location;

[0080] Collect market research reports, news and policy changes in the pharmaceutical industry, clarify the development trend of the pharmaceutical market, and obtain trend information from the external market, including new drug research and development trends, drug price fluctuation trends, and the impact of medical insurance policy adjustments on drug demand; and analyze the potential relationship between external market trend information and drug demand, such as the impact of new drug launches on the demand for similar drugs, and the increase or decrease in demand for certain drugs caused by adjustments to medical insurance policies, to obtain market trend data;

[0081] Monitor the incidence of seasonal diseases, such as the high incidence of influenza in winter and the increase of allergic diseases in specific seasons; pay attention to sudden epidemic trends, such as the sharp increase in demand for corresponding medicines during epidemics; and conduct correlation analysis between seasonal diseases and epidemic trends and the demand for medicines, such as the increase in demand for cold and antipyretic drugs during the flu season, to obtain seasonal disease epidemic trend data;

[0082] Remove outliers and erroneous data from historical sales data, market trend data, and seasonal disease epidemic trend data, and use the mean filling method to handle missing values, and then extract useful features from the collected data:

[0083] For historical sales data, the cyclical and trend characteristics of sales are extracted; for market trend data, the policy and regulatory characteristics and market competition characteristics of the market are extracted; for seasonal disease epidemic trend data, the peak incidence characteristics and epidemic intensity characteristics of the disease are extracted; the extracted features are standardized and summarized to make different features comparable, and a comprehensive data set is obtained.

[0084] The process of applying machine learning algorithms to predict drug demand and generate prediction results of future drug demand trends is as follows:

[0085] The forecasting algorithm unit selects the autoregressive integrated moving average model to forecast the future demand for drugs based on the drug characteristics and historical sales data, and based on the seasonality and cyclicality of drug sales;

[0086] Taking a portion of pre-processed historical sales data, market trend data, and seasonal disease epidemic trend data as input;

[0087] The future demand for drugs is taken as output, i.e., the output is the prediction result, and the selected prediction model is trained;

[0088] During the training process, cross-validation was used to obtain model parameters to improve the prediction accuracy of the model;

[0089] Use another part of the data that is not involved in the input as the test set, and evaluate the trained model based on the root mean square error; if the evaluation result is not ideal, readjust the model parameters, train and evaluate again, so as to obtain the optimal autoregressive integrated moving average model;

[0090] The model is then used to predict the demand for drugs in the future, predict the demand for drugs in the next month, and obtain the forecast results. It can also predict the demand for drugs in the next month or quarter.

[0091] Then, analysis is performed based on the forecast results to observe the changing trends, peaks and troughs of demand, and the forecast results are visualized by drawing demand trend graphs so that managers can more intuitively understand the future demand trends of drugs; and the forecast model is updated in combination with the generation of new historical sales data to improve the accuracy of the forecast; historical sales data can be updated once a month and the forecast can be re-made; the above process can generate forecast results of future drug demand trends, providing a scientific basis for drug inventory management.

[0092] The Inventory Monitoring module includes:

[0093] Inventory sensing unit: connected to the warehouse management system to monitor the current inventory data of drugs in real time and update the inventory status. The current inventory data includes inventory quantity, storage location, batch information and expiration date;

[0094] Turnover rate calculation unit: Based on historical sales data and current inventory data, the inventory turnover rate of each drug is calculated and compared with the set target turnover rate; when the turnover rate of the drug is found to be lower than expected, an inventory backlog alarm is generated;

[0095] Inventory warning unit: used to compare and analyze the inventory quantity, batch information and expiration date of drugs based on the current inventory data of the inventory sensing unit, and generate out-of-stock warnings, backlog warnings and expiration warnings.

[0096] Based on historical sales data and current inventory data, the process of calculating the inventory turnover rate of each drug is as follows:

[0097] Based on historical sales data and current inventory data, the inventory turnover rate is calculated in months, combined with the calculation formula of inventory turnover rate;

[0098] The formula for calculating inventory turnover rate is: Inventory turnover rate = sales cost in a single month / average inventory cost; for drug inventory management, sales cost is sales quantity multiplied by the average purchase cost of drugs;

[0099] Average inventory cost is the sum of the beginning inventory cost and the ending inventory cost divided by two;

[0100] The beginning inventory cost is the inventory quantity at the beginning of the calculation period multiplied by the average purchase cost of the drug.

[0101] Ending inventory cost is the inventory quantity at the end of the calculation period multiplied by the average purchase cost of the drug;

[0102] When using, taking a month as an example, the sales quantity of drug A is 1,000 boxes, the average purchase cost is 50 yuan / box, the inventory quantity at the beginning of the month is 2,000 boxes, and the inventory quantity at the end of the month is 1,500 boxes; the sales cost for this month is 1,000×50=50,000 yuan, and the average inventory cost is (2,000×50+1,500×50) / 2=87,500 yuan; the inventory turnover rate of drug A this month is 50,000 / 87,500=0.57 (times / month).

[0103] The process of generating an inventory backlog alert is as follows:

[0104] According to the company's business strategy, the characteristics of the drug and market demand, set a target inventory turnover rate for each drug. For commonly used drugs, a higher target inventory turnover rate can be set to ensure that there is not too much inventory backlog; for special drugs or newly launched drugs, a relatively low target inventory turnover rate can be set to avoid over-allocation and avoid waste of resources;

[0105] Compare the calculated actual inventory turnover rate of each drug with the corresponding set target inventory turnover rate; when the actual inventory turnover rate of drug B is lower than the target inventory turnover rate, it means that the inventory turnover of drug B is slow and there is a risk of inventory backlog; set a threshold ratio and mark the threshold ratio as F; when the inventory turnover rate is lower than F of the target inventory turnover rate, generate an inventory backlog alarm;

[0106] That is, when the target inventory turnover rate is 1 time / month, it is set that when the actual inventory turnover rate is lower than 80% of the target inventory turnover rate, that is, when the inventory turnover rate is less than 0.8 times / month, an inventory backlog alarm is generated;

[0107] The inventory backlog alert includes the drug name, inventory quantity, and backlog level, and is sent to management personnel so that timely measures can be taken, including adjusting procurement strategies and increasing promotional efforts to reduce inventory backlogs.

[0108] The purchasing decision module includes:

[0109] Strategy optimization unit: used to generate a purchase replenishment strategy based on a multi-objective optimization algorithm, combining inventory turnover rate, procurement cost and drug expiration date. The purchase replenishment strategy includes procurement time, quantity and supplier selection;

[0110] Supplier management unit: used to record and update the supplier's historical supply situation, including delivery cycle, product quality and price, so as to optimize the selection of suppliers; the supplier management unit includes a planning unit, which is used to adjust the procurement and replenishment strategy based on the supplier's delivery and price trends to obtain a procurement plan.

[0111] The process of generating a purchasing replenishment strategy is:

[0112] Obtain current inventory data, inventory turnover rate of each drug, and procurement cost and expiration date of the drug. At the same time, obtain the forecast results of future demand trends of drugs from the demand forecast module; understand the demand for various drugs in the future period of time;

[0113] Among them, for inventory turnover rate:

[0114] The target optimization considerations are: maintain a reasonable inventory turnover rate, avoid inventory backlogs leading to capital occupation and expiration risks, prevent low inventory from affecting supply, and increase purchase frequency or reduce purchase quantity when inventory turnover is too low;

[0115] Determine the purchase time and quantity based on the forecast results of future drug demand trends, and arrange the purchase time and quantity reasonably to avoid inventory backlogs;

[0116] Inventory turnover rate reflects the efficiency of inventory management. A high inventory turnover rate means that the drugs stay in the inventory for a short time, the capital turnover is fast, and the risk of inventory backlog and expiration is reduced.

[0117] During this period, factors affecting inventory turnover were analyzed:

[0118] Demand forecast accuracy: Accurate demand forecasts can avoid over-purchasing or under-stocking, thereby improving inventory turnover. If the demand forecast is too high, it will lead to inventory backlogs and reduce turnover. If the demand forecast is too low, there will be out-of-stocks, affecting sales and customer satisfaction.

[0119] Procurement strategy: A reasonable procurement replenishment strategy, including procurement time, procurement quantity and supplier selection, can affect inventory turnover rate; for example, timely and appropriate procurement can keep inventory levels stable and improve turnover rate; while frequent small-batch procurement or one-time large-volume procurement may have an adverse effect on turnover rate;

[0120] Sales strategy: An effective sales strategy can promote the sales of drugs and improve inventory turnover; for example, promotional activities and marketing can increase drug sales and reduce inventory backlogs;

[0121] When the forecast results show that the demand for drug A will increase in the future, drug A will be purchased in advance to meet the demand; when the forecast results show that the demand for drug A will decrease, the purchase volume will be reduced or the purchase will be suspended to reduce the inventory level;

[0122] Drug A can be promoted through promotional activities and marketing to increase drug sales and improve inventory turnover. Promotional activities can be carried out for drugs with inventory backlogs, with prices reduced and gifts provided to attract customers to buy. Marketing can also be strengthened to increase the visibility and reputation of drugs and increase sales. Inventory can be cleaned up regularly to deal with expired and unsalable drugs, thereby optimizing inventory turnover and improving inventory turnover. Expired drugs can be scrapped and unsalable drugs can be sold at a reduced price.

[0123] For procurement costs:

[0124] Procurement cost is one of the important costs in drug inventory management. Reducing procurement cost can improve the economic benefits of the enterprise.

[0125] Analyze factors affecting procurement costs

[0126] Supplier selection: Different suppliers provide different prices, quality and delivery dates for drugs. Choosing the right supplier can reduce procurement costs.

[0127] Purchase quantity: Bulk purchases can usually get price discounts, but will also increase inventory costs and risks; therefore, it is necessary to weigh the purchase quantity and inventory costs to determine the optimal purchase quantity;

[0128] Purchasing time: Market price fluctuations and supplier promotions will affect purchasing costs; choosing the right purchasing time can reduce purchasing costs

[0129] The factors to be considered in target optimization are: considering the price differences and purchase discounts of different suppliers, reducing purchase costs, negotiating with suppliers, obtaining the bottom price, and purchasing to reduce costs;

[0130] Establish a supplier evaluation system to evaluate the supplier's price, quality and delivery time, and obtain evaluation results, which include high-quality suppliers and low-quality suppliers; based on the evaluation results, select high-quality suppliers for cooperation; at the same time, establish long-term cooperative relationships with high-quality suppliers to strive for better prices and services; and arrange bulk purchases based on demand forecasts and inventory levels to obtain price discounts as optimized procurement costs; but pay attention to controlling the purchase quantity to avoid inventory backlogs; and negotiate prices with suppliers to obtain low purchase prices. You can increase the purchase volume and improve the bargaining chips through centralized procurement, joint procurement, etc.; at the same time, keep abreast of market price fluctuations, supplier promotions and other information, and choose the right time to purchase to reduce procurement costs;

[0131] For drug expiration dates:

[0132] The target optimization considerations are: give priority to purchasing drugs with longer validity periods to avoid purchasing too many drugs that are about to expire; for drugs with shorter validity periods, control the purchase quantity and time to ensure that they can be sold within the validity period;

[0133] First, determine the importance of the drug's expiration date:

[0134] The expiration date of drugs is an important guarantee of drug quality. Expired drugs cannot be sold or used, which will cause economic losses and safety risks. Therefore, in inventory management, the expiration date of drugs needs to be considered to avoid the occurrence of expired drugs.

[0135] Then, analyze the factors that affect the shelf life of drugs:

[0136] Purchase time: The closer the purchase time is to the production date of the drug, the longer the drug's shelf life will be; therefore, when purchasing drugs, you should try to choose drugs with a recent production date;

[0137] Inventory management: Reasonable inventory management can extend the shelf life of drugs. For example, keeping the inventory environment dry, ventilated, and light-proof can slow down the deterioration of drugs. Regularly cleaning up the inventory and disposing of expired drugs can avoid the impact of expired drugs on other drugs.

[0138] Sales strategy: A reasonable sales strategy can reduce the time that drugs stay in inventory, thereby extending the shelf life of drugs; for example, giving priority to selling drugs with earlier production dates can avoid drug expiration; carrying out promotional activities to increase drug sales can reduce inventory backlogs and extend the shelf life of drugs;

[0139] Finally, methods to optimize the shelf life of drugs:

[0140] Procurement management: When purchasing medicines, give priority to medicines whose production date is close to the purchase date; at the same time, negotiate with suppliers to obtain medicines with long shelf life and medicine return and exchange policies;

[0141] Inventory management: Establish an inventory management system to classify and manage drugs, and arrange inventory locations and quantities based on the drug's expiration date and sales situation; drugs with a shorter expiration date can be placed in an easily accessible location and given priority for sales; drugs with a longer expiration date can be placed deep in the warehouse to reduce the number of times they are moved;

[0142] Sales management: formulate sales strategies and give priority to the sales of drugs with later production dates;

[0143] According to the multi-objective optimization algorithm, the inventory turnover rate, procurement cost and drug expiration date are weighed and analyzed to obtain the procurement replenishment strategy:

[0144] For drugs with fast demand growth and high inventory turnover rates, advance the procurement time, increase the procurement quantity, and select suppliers with reasonable prices and short delivery cycles; for drugs with stable demand but low inventory turnover rates, reduce the procurement quantity and extend the procurement cycle.

[0145] Specifically, during the use period, the process of weighing and analyzing the inventory turnover rate, procurement cost and drug expiration date according to the multi-objective optimization algorithm is as follows:

[0146] Build a mathematical model:

[0147] Determine the objective function:

[0148] Assume the inventory turnover rate is The purchase cost is The drug is valid for ; The goal is to optimize these three objective functions simultaneously, which can be expressed as a multi-objective optimization problem: ;

[0149] Define the decision variables:

[0150] The decision variables include purchase time t, purchase quantity q, and supplier selection s; where purchase time is a specific time point; purchase quantity is the number of drugs purchased each time; supplier selection is a discrete variable, indicating selection from different suppliers;

[0151] Identify the constraints:

[0152] Inventory constraint: The inventory quantity must not exceed the maximum capacity of the warehouse, while meeting the minimum inventory requirements to ensure the stability of supply; expressed as ,in represents the inventory quantity at time t;

[0153] Demand constraints: The quantity and time of purchases meet future demand forecasts; expressed as ,in represents the demand forecast at time t;

[0154] Supplier constraints: The supplier's supply capacity and delivery cycle must meet the requirements of the procurement plan; the supplier's delivery cycle cannot exceed the specified time limit, expressed as ,in represents the delivery cycle of supplier s;

[0155] Select a multi-objective optimization algorithm:

[0156] Generate initial solutions based on the scope and constraints of decision variables based on genetic algorithms, and use the initial solutions as the initial population of the algorithm; each initial solution represents a purchase and replenishment strategy, which includes purchase time, quantity and supplier selection; then calculate the corresponding objective function value and the satisfaction conditions of the constraints for each initial solution; then perform weighted summation of the objective function values, and punish solutions that violate the constraints, so as to design a fitness function, and select excellent solutions as the parent population based on the fitness function; then, perform crossover and mutation operations on the parent population to generate a child population; the crossover operation can combine some features of the two parent solutions to generate a new solution; the mutation operation can randomly change certain features of the solution to increase the diversity of the population; the child population is combined with The parent populations are merged to form a new population; then, the new population is evaluated according to the fitness function, and the excellent solution is selected as the parent population of the next generation; the process is repeated until the termination condition of the algorithm is met; after iteration of the multi-objective optimization algorithm, a set of non-dominated solutions is obtained; the decision maker selects a solution from the non-dominated solutions as a procurement and replenishment strategy based on the actual situation; factors such as the company's business strategy, risk preference, and the urgency of market demand can be considered; if the company pays more attention to reducing procurement costs, a solution with a lower procurement cost can be selected; if the company pays more attention to ensuring the stability of supply, a solution with a higher inventory turnover rate can be selected; through the above process, the inventory turnover rate, procurement cost and drug expiration date can be comprehensively analyzed and weighed according to the multi-objective optimization algorithm to generate the optimal procurement and replenishment strategy.

[0157] By analyzing the procurement cost, inventory holding cost and out-of-stock cost of drugs and formulating replenishment plans, it helps to reduce the total cost of inventory management; the entire system is based on a large amount of data for analysis and decision-making, which improves the scientific nature and accuracy of decision-making and reduces the errors and risks of human decision-making; at the same time, the system has a high degree of automation and intelligence, which can improve the efficiency of inventory management and reduce labor costs.

[0158] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. An intelligent drug inventory management and optimization system, characterized in that: It includes demand forecasting module, inventory monitoring module, purchasing decision module and drug replenishment module; The demand forecasting module is used to forecast drug demand based on historical sales data, seasonal factors and market trends; Use machine learning algorithms to analyze and predict, generate forecasts of future drug demand trends; adjust procurement and replenishment strategies when changes in drug demand are detected; The inventory monitoring module is used to monitor drug inventory levels in real time and update inventory status based on current inventory data; It also calculates the number of drugs that are about to expire based on the batch information and expiration date of the drugs, obtains drug expiration warnings, and generates clearance reminders based on drug expiration warnings; The purchasing decision module is connected with the demand forecasting module and the inventory monitoring module to formulate a purchasing replenishment strategy based on a multi-objective optimization algorithm combined with demand forecasting data, and to generate a purchasing plan based on the purchasing replenishment strategy; The drug replenishment module analyzes the drug purchase cost, inventory holding cost and out-of-stock cost in conjunction with the procurement plan to develop a replenishment plan.

2. The intelligent drug inventory management and optimization system according to claim 1, characterized in that: The demand forecasting module comprises: Data collection unit: used to import historical sales data of drugs from the historical sales system, and combine it with external market trend data and seasonal disease epidemic trend data to obtain a comprehensive data set; Prediction algorithm unit: used to apply machine learning algorithms to predict drug demand based on comprehensive data sets, generate forecast results of future drug demand trends, and update corresponding prediction models based on drug characteristics and sales history.

3. The intelligent drug inventory management and optimization system according to claim 2 is characterized by: The demand forecasting module also includes: Model training unit: used to regularly retrain and optimize the forecasting algorithm to improve the accuracy of demand forecasting, and automatically adjust model parameters and update the algorithm based on the error feedback between actual sales data and forecast data; Anomaly detection unit: used to detect abnormal fluctuations in forecast results, such as sudden surges or significant drops in demand. When abnormal fluctuations are identified, alarm information is automatically generated and corresponding inventory management strategies are recommended for adjustment.

4. The intelligent drug inventory management and optimization system according to claim 3 is characterized by: The data collection unit is used during: The data collection unit establishes a connection with the historical sales system, extracts the historical sales data of the drug, sorts and classifies the historical sales data, and stores the data by drug category, month, and region; the historical sales data includes sales quantity, sales time, and sales location; Collect market research reports, news and policy changes in the pharmaceutical industry, clarify the development trend of the pharmaceutical market, and obtain trend information from the external market; and analyze the potential relationship between trend information from the external market and drug demand to obtain market trend data; Monitor the incidence of seasonal diseases, pay attention to sudden epidemic trends, and analyze the correlation between seasonal diseases and epidemic trends and drug demand to obtain seasonal disease epidemic trend data; Remove outliers and erroneous data from historical sales data, market trend data, and seasonal disease epidemic trend data, and use the mean filling method to handle missing values, and then extract useful features from the collected data: For historical sales data, the cyclical and trend characteristics of sales are extracted; for market trend data, the policy and regulatory characteristics and market competition characteristics are extracted; for seasonal disease epidemic trend data, the peak incidence characteristics and epidemic intensity characteristics of the disease are extracted; the extracted characteristics are standardized and summarized to obtain a comprehensive data set.

5. The intelligent drug inventory management and optimization system according to claim 4 is characterized by: The process of applying the machine learning algorithm to predict drug demand and generate the prediction results of the future demand trend of drugs is as follows: The forecasting algorithm unit selects the autoregressive integrated moving average model to forecast the future demand for drugs based on the drug characteristics and historical sales data, and based on the seasonality and cyclicality of drug sales; Taking a portion of pre-processed historical sales data, market trend data, and seasonal disease epidemic trend data as input; The future demand for drugs is taken as output, i.e., the output is the prediction result, and the selected prediction model is trained; During the training process, cross-validation was used to obtain the model parameters; Use another part of the data that is not involved in the input as the test set, and evaluate the trained model based on the root mean square error; if the evaluation result is not ideal, readjust the model parameters, train and evaluate again, so as to obtain the optimal autoregressive integrated moving average model; The model is then used to predict the demand for medicines in the future and the demand for medicines in the next month to obtain the forecast results.

6. The intelligent drug inventory management and optimization system according to claim 5, characterized in that: The inventory monitoring module includes: Inventory sensing unit: connected to the warehouse management system to monitor the current inventory data of drugs in real time and update the inventory status. The current inventory data includes inventory quantity, storage location, batch information and expiration date; Turnover rate calculation unit: Based on historical sales data and current inventory data, the inventory turnover rate of each drug is calculated and compared with the set target turnover rate; when the turnover rate of the drug is found to be lower than expected, an inventory backlog alarm is generated; Inventory warning unit: used to compare and analyze the inventory quantity, batch information and expiration date of drugs based on the current inventory data of the inventory sensing unit, and generate out-of-stock warnings, backlog warnings and expiration warnings.

7. The intelligent drug inventory management and optimization system according to claim 6 is characterized by: The process of calculating the inventory turnover rate of each drug based on historical sales data and current inventory data is as follows: Based on historical sales data and current inventory data, the inventory turnover rate is calculated in months, combined with the calculation formula of inventory turnover rate; The formula for calculating inventory turnover rate is: Inventory turnover rate = sales cost in a single month / average inventory cost; For drug inventory management, the cost of sales is the number of sales multiplied by the average purchase cost of the drug; The average inventory cost is the sum of the beginning inventory cost and the ending inventory cost divided by two; the beginning inventory cost is the inventory quantity at the beginning of the calculation period multiplied by the average purchase cost of the drugs, and the ending inventory cost is the inventory quantity at the end of the calculation period multiplied by the average purchase cost of the drugs.

8. The intelligent drug inventory management and optimization system according to claim 7 is characterized by: The process of generating an inventory backlog alert is as follows: Set a target inventory turnover rate for each drug based on the company's business strategy, drug characteristics, and market demand; compare the calculated actual inventory turnover rate of each drug with the corresponding target inventory turnover rate; Set a threshold ratio and mark the threshold ratio as F; When the inventory turnover rate is lower than the target inventory turnover rate F, an inventory backlog alarm is generated; The inventory backlog alarm includes the drug name, inventory quantity, and backlog degree, and the inventory backlog alarm is sent to the management personnel.

9. An intelligent drug inventory management and optimization system according to claim 8, characterized in that: The purchasing decision module includes: Strategy optimization unit: used to generate a purchase replenishment strategy based on a multi-objective optimization algorithm, combining inventory turnover rate, procurement cost and drug expiration date. The purchase replenishment strategy includes procurement time, quantity and supplier selection; Supplier management unit: used to record and update the supplier's historical supply situation, including delivery cycle, product quality and price; the supplier management unit includes a planning unit, which is used to adjust the procurement and replenishment strategy based on the supplier's delivery and price trends to obtain a procurement plan.

10. An intelligent drug inventory management and optimization system according to claim 9, characterized in that: The process of generating a purchasing and replenishment strategy is as follows: Obtain current inventory data, inventory turnover rate of each drug, and drug purchase cost and expiration date. At the same time, obtain the forecast results of future drug demand trends from the demand forecast module; Among them, for inventory turnover rate: based on the forecast results of future drug demand trends, determine the purchase time and purchase quantity. When the forecast results show that the demand for drug A will increase, purchase drug A in advance; when the forecast results show that the demand for drug A will decrease, reduce the purchase quantity or suspend purchase, so as to optimize the inventory turnover rate. For procurement costs: establish a supplier evaluation system, evaluate suppliers’ prices, quality and delivery dates, and obtain evaluation results, which include high-quality suppliers and low-quality suppliers; based on the evaluation results, select high-quality suppliers for cooperation; at the same time, establish long-term cooperative relationships with high-quality suppliers; and arrange bulk purchases based on demand forecasts and inventory levels to obtain price concessions as optimized procurement costs; Optimize the validity period of drugs: Procurement management: When purchasing medicines, give priority to medicines whose production date is close to the purchase date; at the same time, negotiate with suppliers to obtain medicines with long shelf life and medicine return and exchange policies; Inventory management: Establish an inventory management system, classify and manage drugs, and arrange inventory locations and quantities based on drug expiration dates and sales conditions; Sales management: formulate sales strategies and give priority to the sales of drugs with later production dates; Based on the multi-objective optimization algorithm, a trade-off analysis is performed on inventory turnover rate, procurement cost and drug expiration date to obtain the procurement replenishment strategy.

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