Channel risk management method based on drug circulation data

Abstract

The invention discloses a channel risk management method based on drug circulation data, and relates to the technical field of drug channel risk management, and the method comprises the steps: collecting drug distribution data, pharmacy inventory data and drug sales data of a distribution link; based on the historical data and the current data, segmented risk early warning is carried out on drugs from a manufacturer, a transportation link, an inventory link and pharmacy sales, so that compensation counterfeiting risk identification in a distribution link, channel goods pressing risk identification and goods shortage and return risk identification are accurately realized, and finally, comprehensive risk assessment is carried out based on a segmented early warning result. All channel risk points of medicine circulation can be fully covered, the risk assessment period can be flexibly set, data display board display can be carried out in cooperation with a large screen of a management center with the month as the unit, the week as the unit and the day as the unit, and accurate early warning information and data pushing can be carried out on associated personnel of associated channels in cooperation with APP data intercommunication. Therefore, efficient risk identification and pushing early warning are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of drug channel risk management, and in particular to a channel risk management method based on drug circulation data. Background Art

[0002] At present, the pharmaceutical distribution industry has begun to use big data technology to conduct real-time dynamic supervision of all links. This method can effectively improve the efficiency of supervision and the accuracy of data processing. However, the application of this technology also faces challenges in data privacy and security, as well as limitations in data processing and analysis capabilities.

[0003] From the perspective of smart supply chain, the challenges in drug distribution mainly lie in the improvement of information technology, the improvement of traceability system and the integration of supply chain. Although this approach helps to optimize the supply chain process, it relies on highly complex information systems and may face problems of technical compatibility and operational difficulty during implementation.

[0004] With the development of information technology, the digital transformation of the pharmaceutical distribution industry is accelerating. This includes the use of advanced technologies such as big data, cloud computing, and the Internet of Things to improve the efficiency of drug procurement, warehousing, distribution, and management. However, digital transformation requires a lot of upfront investment and high skills requirements for employees.

[0005] Faced with compliance challenges, pharmaceutical companies are seeking to strengthen compliance and channel risk management through data empowerment. This involves aspects such as sales / promotion expense control, compliance risk identification and response, and data management model innovation. Although this approach helps improve the company's compliance level, it also increases operating costs and may become complicated due to the ever-changing regulatory environment.

[0006] In general, existing technologies have played an important role in improving the efficiency and accuracy of risk management of drug distribution data channels, but they also have defects such as data privacy, difficulty in technical implementation, cost and compliance challenges. Summary of the invention

[0007] In order to solve the technical problem of drug channel risk management, the present invention provides a channel risk management method based on drug circulation data. The following technical solutions are adopted:

[0008] A channel risk management method based on drug circulation data includes the following steps:

[0009] Step 1: Obtain historical and current drug circulation data, including drug distribution data, pharmacy inventory data, drug sales data, and channel information data;

[0010] Step 2: Perform three-stage risk identification based on historical drug circulation data and current drug circulation data at set intervals;

[0011] The three-stage risk identification includes the identification of compensation and fraud risks in the distribution link, the identification of channel pressure risks, and the identification of out-of-stock return risks;

[0012] Step 3: Provide early warning for segment matching based on the segment risk identification results;

[0013] Step 4: Conduct comprehensive risk assessment and early warning based on the drug distribution channel risk assessment model.

[0014] By adopting the above technical solution, we first need to build three main modules, which are used to collect drug distribution data, pharmacy inventory data and drug sales data in the distribution link. In addition, when collecting data, we need to identify channel information data, so that we can make segmented risk warnings for drugs from manufacturers, transportation links, inventory links and pharmacy sales based on these historical data and current data, accurately realize the risk identification of compensation and counterfeiting in the distribution link, the risk identification of channel inventory pressure and the risk identification of out-of-stock return, and finally conduct comprehensive risk assessment based on the segmented warning results.

[0015] It can achieve full coverage of risk points in all channels of drug circulation. The risk assessment cycle can be set flexibly, and can be based on months, weeks, and days. It can even conduct real-time risk assessments on some key drugs, and cooperate with the large screen of the management center to display data on the display board. It can also cooperate with APP data exchange to provide accurate early warning information and data push to related channels and related personnel, thereby achieving efficient risk identification and push early warnings.

[0016] Optionally, in step 2, potential fraudulent behavior is identified by analyzing the difference between the delivery data and the actual sales data;

[0017] The risk identification of compensation fraud in the distribution link is realized by the following formula:

[0018]

[0019] Where DDI is the drug delivery difference index, D is the drug delivery quantity parsed from the drug delivery data, S is the drug sales quantity parsed from the drug sales data, B is the drug damage quantity parsed from the drug delivery data, and B ′ It is the expected number of damaged drugs predicted based on historical drug delivery data.

[0020] Optionally, in step 3, set a difference threshold X;

[0021] If it is judged that |DDI|<X, the warning result of normal distribution link is output;

[0022] If |DDI|≥X and DDI is a positive value, then the warning result indicating the risk of fraud in the distribution process is output;

[0023] If it is judged that |DDI|≥X and DDI is a negative value, the warning result of underreporting or logistics risk in the distribution link is output.

[0024] By adopting the above technical solution, potential fraudulent behavior is identified by analyzing the difference between the distribution data and the actual sales data. The actual situation of transportation loss is taken into account in the judgment process, so the number of damaged drugs is added. The expected number of damaged drugs obtained by analysis and prediction based on historical drug distribution data is also considered here. This prediction can be based on a large model or an empirical formula.

[0025] For example: Confirmed damaged quantity B: This is the actual damaged quantity confirmed through logistics return records, on-site inspection or customer feedback;

[0026] Expected damage quantity B ′ :This is the expected breakage quantity estimated based on historical breakage rate statistics or industry standards. This value can be deducted from the delivery quantity as part of the expected loss. The value of DDI reflects the risk ratio of distribution link compensation fraud in the drug distribution process.

[0027] DDI takes into account the impact of expected damage and actual damage, and more accurately reflects the real differences in the distribution process.

[0028] If DDI is close to 0, it means that the delivery quantity is equivalent to the actual sales quantity plus the expected damage quantity, with no significant difference.

[0029] If the DDI is a positive value, it means that the delivery quantity is greater than the actual sales quantity plus the expected damaged quantity, which may indicate fraud or other problems.

[0030] If the DDI is a negative value, it means that the delivery quantity is less than the actual sales quantity plus the expected damaged quantity, which may indicate underreporting or other logistics problems.

[0031] Optionally, in step 2, the drug inventory backlog index is calculated, and whether there is a channel inventory backlog risk is identified based on whether the drug inventory backlog index exceeds a set drug inventory threshold.

[0032] Optionally, the drug inventory turnover rate is calculated using the following formula:

[0033]

[0034] Where IBI is the drug inventory turnover rate, CI is the current inventory quantity of the drug, and NI is the average inventory quantity of all drugs;

[0035] In step 3, the drug inventory threshold is set to IBIM. If IBI>IBIM, an early warning result indicating that the drug has a channel inventory pressure risk is output.

[0036] By adopting the above technical solutions, channel inventory levels can be monitored and the risk of inventory pressure caused by excessively high inventory levels can be identified.

[0037] Optionally, in step 2, by analyzing the drug sales trend and drug return data, the drug sales volatility and drug return rate are calculated to determine whether there is a risk of out-of-stock or return.

[0038] Optionally, the drug sales volatility is calculated using the following formula:

[0039]

[0040] Among them, SV is the volatility of drug sales, MX is the highest monthly sales volume of drugs obtained based on the analysis of historical drug sales data, MS is the lowest monthly sales volume of drugs, and AS is the average monthly sales volume of drugs;

[0041] The drug return rate is calculated using the following formula:

[0042]

[0043] Where RR is the drug return rate, RQ is the drug return volume, and SQ is the drug sales volume.

[0044] Optionally, a drug sales volatility threshold and a drug return rate threshold are set respectively. If the drug sales volatility is greater than the drug sales volatility threshold, an early warning result indicating a risk of out-of-stock is output;

[0045] If the drug return rate is greater than the drug return rate threshold, an early warning result indicating a return risk is output.

[0046] By adopting the above technical solution, the potential out-of-stock or return risks can be predicted by analyzing sales trends and return data. Out-of-stock risks and return risks are risks that directly affect the drug distribution channels. In some special periods, the out-of-stock risks of key drugs, such as the influenza outbreak period, have a greater impact on the out-of-stock risks of some special cold medicines. Therefore, the assessment of out-of-stock risks and risk warnings can be discovered in time and replenishment can be carried out;

[0047] The return risk usually indicates whether there are quality problems with the drugs. Quality problems may be side effects, poor efficacy, etc. Therefore, the return risk assessment and risk warning can timely detect and conduct quality investigations on drugs, and promptly adopt substitutes or change brands.

[0048] Optionally, step 4 is implemented using a drug distribution channel risk assessment model, which is implemented based on the following formula:

[0049] R=W1·|DDI|+W2·IBI+W3·SV+W4·RR;

[0050] Among them, R represents the drug channel risk score. The higher the score, the higher the drug channel risk. W1, W2, W3 and W4 represent the weight coefficients of the risks in each link of the drug channel respectively.

[0051] By adopting the above technical solutions, this drug distribution channel risk assessment model can help managers identify the main risk points in the drug distribution channel and take corresponding preventive measures. On the basis of segmented matching warning, comprehensive risk scoring is also performed. The comprehensive risk scoring takes into account the weight influence of each link. For example, if the sum of the weight coefficients is 1, the out-of-stock risk and return risk can most intuitively represent the channel risk, and their corresponding weight coefficients are 0.4. The drug distribution difference index and drug inventory turnover rate have a weaker impact, and their weight coefficients are 0.1.

[0052] Optionally, a drug channel risk score threshold is set. If the drug channel risk score R value is greater than the drug channel risk score threshold, a comprehensive risk warning is issued while performing segment matching warning.

[0053] By adopting the above technical solutions, systematic comprehensive risk early warning can be achieved, and staff can optimize the entire drug distribution channel in a timely manner.

[0054] In summary, the present invention includes at least one of the following beneficial technical effects:

[0055] The present invention can provide a channel risk management method based on drug circulation data, which is used to collect drug distribution data, pharmacy inventory data and drug sales data in the distribution link, and based on these historical data and current data, segmented risk warning is carried out for drugs from manufacturers, transportation links, inventory links and pharmacy sales, and the risk of compensation and counterfeiting in the distribution link, the risk of channel inventory pressure and the risk of out-of-stock return are accurately identified, and finally a comprehensive risk assessment is carried out based on the segmented warning results.

[0056] It can achieve full coverage of risk points in all channels of drug circulation. The risk assessment cycle can be set flexibly, and can be based on months, weeks, and days. It can even conduct real-time risk assessments on some key drugs, and cooperate with the large screen of the management center to display data on the display board. It can also cooperate with APP data exchange to provide accurate early warning information and data push to related channels and related personnel, thereby achieving efficient risk identification and push early warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of a channel risk management method based on drug circulation data of the present invention; DETAILED DESCRIPTION

[0058] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0059] The embodiment of the present invention discloses a channel risk management method based on drug circulation data.

[0060] Reference Figure 1 , a channel risk management method based on drug circulation data, comprising the following steps:

[0061] Step 1: Obtain historical and current drug circulation data, including drug distribution data, pharmacy inventory data, drug sales data, and channel information data;

[0062] Step 2: Perform three-stage risk identification based on historical drug circulation data and current drug circulation data at set intervals;

[0063] The three-stage risk identification includes the identification of compensation and fraud risks in the distribution link, the identification of channel pressure risks, and the identification of out-of-stock return risks;

[0064] Step 3: Provide early warning for segment matching based on the segment risk identification results;

[0065] Step 4: Conduct comprehensive risk assessment and early warning based on the drug distribution channel risk assessment model.

[0066] First, it is necessary to build three main modules, which are used to collect drug distribution data, pharmacy inventory data and drug sales data in the distribution link. In addition, it is necessary to identify channel information data when collecting data, so that based on these historical data and current data, segmented risk warnings can be made for drugs from manufacturers, transportation links, inventory links and pharmacy sales, and the risk of compensation and counterfeiting in the distribution link, the risk of channel inventory pressure and the risk of out-of-stock return can be accurately identified, and finally a comprehensive risk assessment can be carried out based on the segmented warning results.

[0067] It can achieve full coverage of risk points in all channels of drug circulation. The risk assessment cycle can be set flexibly, and can be based on months, weeks, and days. It can even conduct real-time risk assessments on some key drugs, and cooperate with the large screen of the management center to display data on the display board. It can also cooperate with APP data exchange to provide accurate early warning information and data push to related channels and related personnel, thereby achieving efficient risk identification and push early warnings.

[0068] In step 2, potential fraud is identified by analyzing the differences between the delivery data and the actual sales data;

[0069] The risk identification of compensation fraud in the distribution link is realized by the following formula:

[0070]

[0071] Where DDI is the drug delivery difference index, D is the drug delivery quantity parsed from the drug delivery data, S is the drug sales quantity parsed from the drug sales data, B is the drug damage quantity parsed from the drug delivery data, and B ′ It is the expected number of damaged drugs predicted based on historical drug delivery data.

[0072] In step 3, set the difference threshold X;

[0073] If it is judged that |DDI|<X, the warning result of normal distribution link is output;

[0074] If |DDI|≥X and DDI is a positive value, then the warning result indicating the risk of fraud in the distribution process is output;

[0075] If it is judged that |DDI|≥X and DDI is a negative value, the warning result of underreporting or logistics risk in the distribution link is output.

[0076] Potential fraud is identified by analyzing the difference between delivery data and actual sales data. The actual situation of transportation loss is taken into account in the judgment process, so the number of damaged drugs is added. The expected number of damaged drugs obtained by analysis and prediction based on historical drug delivery data is also considered here. This prediction can be based on a large model or an empirical formula.

[0077] For example: Confirmed damaged quantity B: This is the actual damaged quantity confirmed through logistics return records, on-site inspection or customer feedback;

[0078] Expected damage quantity B ′ :This is the expected breakage quantity estimated based on historical breakage rate statistics or industry standards. This value can be deducted from the delivery quantity as part of the expected loss. The value of DDI reflects the risk ratio of distribution link compensation fraud in the drug distribution process.

[0079] DDI takes into account the impact of expected damage and actual damage, and more accurately reflects the real differences in the distribution process.

[0080] If DDI is close to 0, it means that the delivery quantity is equivalent to the actual sales quantity plus the expected damage quantity, with no significant difference.

[0081] If the DDI is a positive value, it means that the delivery quantity is greater than the actual sales quantity plus the expected damaged quantity, which may indicate fraud or other problems.

[0082] If the DDI is a negative value, it means that the delivery quantity is less than the actual sales quantity plus the expected damaged quantity, which may indicate underreporting or other logistics problems.

[0083] In step 2, the drug inventory backlog index is calculated, and whether there is a channel inventory backlog risk is identified based on whether the drug inventory backlog index exceeds the set drug inventory threshold.

[0084] The drug inventory turnover rate is calculated using the following formula:

[0085]

[0086] Where IBI is the drug inventory turnover rate, CI is the current inventory quantity of the drug, and NI is the average inventory quantity of all drugs;

[0087] In step 3, the drug inventory threshold is set to IBIM. If IBI>IBIM, an early warning result indicating that the drug has a channel inventory pressure risk is output.

[0088] Monitor channel inventory levels and identify the risk of overstocking caused by excessive inventory levels.

[0089] In step 2, by analyzing the drug sales trend and drug return data, the drug sales volatility and drug return rate are calculated to determine whether there is a risk of out-of-stock or return.

[0090] The drug sales volatility is calculated using the following formula:

[0091]

[0092] Among them, SV is the volatility of drug sales, MX is the highest monthly sales volume of drugs obtained based on the analysis of historical drug sales data, MS is the lowest monthly sales volume of drugs, and AS is the average monthly sales volume of drugs;

[0093] The drug return rate is calculated using the following formula:

[0094]

[0095] Where RR is the drug return rate, RQ is the drug return volume, and SQ is the drug sales volume.

[0096] Set the drug sales volatility threshold and drug return rate threshold respectively. If the drug sales volatility is greater than the drug sales volatility threshold, output a warning result indicating the risk of out-of-stock.

[0097] If the drug return rate is greater than the drug return rate threshold, an early warning result indicating a return risk is output.

[0098] By analyzing sales trends and return data, potential out-of-stock or return risks can be predicted. Out-of-stock risks and return risks are risks that directly affect drug distribution channels. In some special periods, the out-of-stock risks of key drugs, such as influenza outbreaks, have a greater impact on the out-of-stock risks of some special cold medicines. Therefore, the assessment of out-of-stock risks and risk warnings can be used to detect and replenish stocks in a timely manner;

[0099] The return risk usually indicates whether there are quality problems with the drugs. Quality problems may be side effects, poor efficacy, etc. Therefore, the return risk assessment and risk warning can timely detect and conduct quality investigations on drugs, and promptly adopt substitutes or change brands.

[0100] Step 4 is implemented using the drug distribution channel risk assessment model, which is based on the following formula:

[0101] R=W1·|DDI|+W2·IBI+W3·SV+W4·RR;

[0102] Among them, R represents the drug channel risk score. The higher the score, the higher the drug channel risk. W1, W2, W3 and W4 represent the weight coefficients of the risks in each link of the drug channel respectively.

[0103] This drug distribution channel risk assessment model can help managers identify the main risk points in the drug distribution channel and take corresponding preventive measures. On the basis of segmented matching warning, a comprehensive risk score is also conducted. The comprehensive risk score takes into account the weight influence of each link. For example, if the sum of the weight coefficients is 1, the out-of-stock risk and return risk can most intuitively represent the channel risk, and their corresponding weight coefficients are 0.4. The drug distribution difference index and drug inventory turnover rate have a weaker impact, and their weight coefficients are 0.1.

[0104] Set a drug channel risk score threshold. If the drug channel risk score R value is greater than the drug channel risk score threshold, a comprehensive risk warning will be issued while performing segment matching warnings.

[0105] It can achieve early warning of systematic comprehensive risks, and staff can optimize the entire drug distribution channel in a timely manner.

[0106] The following uses a specific example to illustrate the implementation principle of a channel risk management method based on drug circulation data:

[0107] The disease center predicted the prevalence of a certain cold virus in a certain area. The specific medicine for the cold virus is an antiviral cold medicine. Therefore, the center implemented key channel risk management for the cold medicine. The channel risk warning data is updated every day.

[0108] First, three data collection modules are built to collect drug distribution data, pharmacy inventory data, and drug sales data in the distribution link. In addition, when collecting data, it is necessary to identify the information data of channel dealers. Based on these historical data and current data, segmented risk warnings are carried out for drugs from manufacturers, transportation links, inventory links, and pharmacy sales.

[0109] On a specific monitoring day, the risk identification of compensation fraud in the distribution link is achieved using the following formula:

[0110]

[0111] The distribution is carried out on a daily basis, where DDI is the drug distribution difference index, D is the drug distribution quantity parsed from the drug distribution data, and the daily transportation and distribution quantity is 10,000 boxes, S is the drug sales quantity parsed from the drug sales data, and 12,000 boxes are obtained. B is the drug damage quantity parsed from the drug distribution data. 150 boxes of this batch were damaged due to logistics handling. ′ is the expected number of damaged drugs predicted based on historical drug delivery data. Based on historical data analysis, the average number of damaged drugs in a batch during transportation is about 100 boxes. Therefore, B ′ Take 100;

[0112] Substituting the above data into

[0113] Set the difference threshold to 0.001;

[0114] If |DDI|≥X and DDI is negative, the warning result of underreporting or logistics risk in the distribution link is output. At this time, the logistics channel is marked with a red warning on the large screen of the management center, and the relevant data is displayed synchronously, and the relevant warning data is transmitted to the person in charge of the logistics channel through a handheld smart terminal.

[0115] Through a thorough investigation by the person in charge, it was discovered that a large number of drugs from illegal channels had been mixed into the logistics channels, so the police were quickly notified to avoid causing greater losses.

[0116] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A channel risk management method based on drug circulation data, characterized in that: The following steps are involved: Step 1: Obtain historical and current drug circulation data, including drug distribution data, pharmacy inventory data, drug sales data, and channel information data; Step 2: Perform three-stage risk identification based on historical drug circulation data and current drug circulation data at set intervals; The three-stage risk identification includes the identification of compensation and fraud risks in the distribution link, the identification of channel pressure risks, and the identification of out-of-stock return risks; Step 3: Provide early warning for segment matching based on the segment risk identification results; Step 4: Conduct comprehensive risk assessment and early warning based on the drug distribution channel risk assessment model.

2. A channel risk management method based on drug circulation data according to claim 1, characterized in that: In step 2, potential fraud is identified by analyzing the differences between the delivery data and the actual sales data; The risk identification of compensation fraud in the distribution link is achieved using the following formula: Where DDI is the drug delivery difference index, D is the drug delivery quantity parsed from the drug delivery data, S is the drug sales quantity parsed from the drug sales data, B is the drug damage quantity parsed from the drug delivery data, and B ′ It is the expected number of damaged drugs predicted based on historical drug delivery data.

3. A channel risk management method based on drug circulation data according to claim 2, characterized in that: In step 3, set the difference threshold X; If it is judged that |DDI|<X, the warning result of normal distribution link is output; If |DDI|≥X and DDI is a positive value, then the warning result indicating the risk of fraud in the distribution process is output; If it is judged that |DDI|≥X and DDI is a negative value, the warning result of underreporting or logistics risk in the distribution link is output.

4. The channel risk management method based on drug circulation data according to claim 1 is characterized by: In step 2, the drug inventory backlog index is calculated, and whether there is a channel inventory backlog risk is identified based on whether the drug inventory backlog index exceeds the set drug inventory threshold.

5. A channel risk management method based on drug circulation data according to claim 4, characterized in that: The drug inventory turnover rate is calculated using the following formula: Where IBI is the drug inventory turnover rate, CI is the current inventory quantity of the drug, and NI is the average inventory quantity of all drugs; In step 3, the drug inventory threshold is set to IBIM. If IBI>IBIM, an early warning result indicating that the drug has a channel inventory pressure risk is output.

6. A channel risk management method based on drug circulation data according to claim 5, characterized in that: In step 2, by analyzing the drug sales trend and drug return data, the drug sales volatility and drug return rate are calculated to determine whether there is a risk of out-of-stock or return.

7. A channel risk management method based on drug circulation data according to claim 6, characterized in that: The drug sales volatility is calculated using the following formula: Among them, SV is the volatility of drug sales, MX is the highest monthly sales volume of drugs obtained based on the analysis of historical drug sales data, MS is the lowest monthly sales volume of drugs, and AS is the average monthly sales volume of drugs; The drug return rate is calculated using the following formula: Where RR is the drug return rate, RQ is the drug return volume, and SQ is the drug sales volume.

8. A channel risk management method based on drug circulation data according to claim 7, characterized in that: Set the drug sales volatility threshold and drug return rate threshold respectively. If the drug sales volatility is greater than the drug sales volatility threshold, output a warning result indicating the risk of out-of-stock. If the drug return rate is greater than the drug return rate threshold, an early warning result indicating a return risk is output.

9. The channel risk management method based on drug circulation data according to claim 1 is characterized in that: Step 4 is implemented using the drug distribution channel risk assessment model, which is based on the following formula: R=W1·|DDI|+W2·IBI+W3·SV+W4·RR; Among them, R represents the drug channel risk score. The higher the score, the higher the drug channel risk. W1, W2, W3 and W4 represent the weight coefficients of the risks in each link of the drug channel respectively.

10. A channel risk management method based on drug circulation data according to claim 9, characterized in that: Set a drug channel risk score threshold. If the drug channel risk score R value is greater than the drug channel risk score threshold, a comprehensive risk warning will be issued while performing segment matching warnings.

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