CN119005914A - Intelligent production management method and system for pharmaceutical workshop - Google Patents

Abstract

The invention discloses an intelligent production management method and system for a pharmaceutical workshop, and relates to the technical field of medicine production, wherein the system realizes intelligent optimization of production planning, material flow and inventory management through three core modules; the intelligent scheduling module of the production plan dynamically adjusts the production plan by calculating a first scheduling coefficient Ddxs and a second scheduling coefficient Ddxs; the material flow and equipment state association module ensures the coordination and consistency of the material flow and the equipment operation by monitoring the supply speed, the circulation path and the equipment state in real time, so that the production efficiency is optimized; the inventory early warning and supplementing mechanism module dynamically monitors the inventory state by calculating the first inventory coefficient Kcxs and the second inventory coefficient Kcxs, and timely adjusts the strategy when the inventory is insufficient or backlog, thereby ensuring the production stability and the rationality of the inventory structure and improving the refinement level of inventory management.

Description

Intelligent production management method and system for pharmaceutical workshop

Technical Field

The invention relates to the technical field of medicine production, in particular to an intelligent production management method and system for a pharmaceutical workshop.

Background

The current pharmaceutical industry production faces the improvement of automation and intelligent demands, and the traditional mode is difficult to meet the high-efficiency production demands due to the fact that the flow is complex and the problem of data island is serious. Along with the increasingly strict regulation, the traceability of the production process and the importance of quality control are increasingly highlighted, and modern workshops are gradually transformed to digital and intelligent directions so as to improve the production efficiency, reduce the manpower dependence and improve the compliance of products. Under the background, the intelligent production management system becomes a key technical support for industry development.

In the invention patent with the application number 202010803692.9, a pharmaceutical production management system is disclosed, which relates to the technical field of production management and comprises: the map storage module is used for storing basic production maps corresponding to each production workshop; the map calling module is used for calling a corresponding basic production map according to the externally input production order and processing the basic production map to obtain a production route map; the production route map comprises production equipment which are sequentially connected according to production procedures to form a production route; the data acquisition module is used for acquiring the current production procedure in real time in the production process of the production order and acquiring the equipment state of corresponding production equipment; the map marking module is used for marking the equipment states of all production equipment on the production route map in real time to obtain a production state indication map; and the map display module is used for displaying each production state indication map of each production workshop in real time so as to be checked by a manager. The method has the beneficial effects that the manager can intuitively acquire the current production situation, and further can timely and effectively make production decisions.

However, in practical application, the technical scheme has certain defects in the aspects of dynamic scheduling optimization of a production plan, intelligent control of material circulation and inventory management, including insufficient dynamic adjustment capability, insufficient association of material flow and equipment state and insufficient inventory early warning and supplementing mechanism, and influences the overall production efficiency and resource utilization rate.

Disclosure of Invention

Technical problem to be solved

Aiming at the defects of the prior art, the invention provides an intelligent production management method and system for a pharmaceutical workshop, which solve the technical problems of insufficient dynamic adjustment capability, insufficient association of material flow and equipment state and insufficient inventory early warning and supplementing mechanism in the background art.

Technical proposal

In order to achieve the above purpose, the invention is realized by the following technical scheme: an intelligent production management system of a pharmaceutical workshop comprises a production monitoring and data acquisition module, a production plan intelligent scheduling module, a material flow and equipment state association module, an inventory early warning and supplementing mechanism module and a model building and trend prediction module;

the production monitoring and data acquisition module is used for acquiring production process related data and historical production related data in real time, wherein the production monitoring and data acquisition module comprises equipment states, production progress and material consumption; simultaneously, various information on the production line is obtained through the sensor and the data interface;

The intelligent scheduling module of the production plan is used for calculating and evaluating a first scheduling coefficient Ddxs1 and making a production plan based on the related data of the production process and the related data of the historical production; next, a second scheduling coefficient Ddxs is evaluated by calculation for dynamically adjusting the production plan; meanwhile, the system also has an emergency dispatching function, and an emergency production plan is generated when an order is changed or equipment fails;

The material flow and equipment state association module is used for associating the flow state of the material with the state of production equipment; secondly, collecting relevant data of the feeding speed and the circulation path of the materials by monitoring the state of the equipment in real time; simultaneously collecting real-time inventory management and historical inventory management related data;

The inventory early warning and supplementing mechanism module is used for monitoring the inventory conditions of raw materials, semi-finished products and finished products in real time, combining production plans and material consumption data, calculating a first inventory coefficient Kcxs and a second inventory coefficient Kcxs in a grading manner, evaluating the first inventory coefficient Kcxs and the second inventory coefficient Kcxs, making a replenishment decision according to the evaluation result of the first inventory coefficient Kcxs, sending out replenishment early warning, and finally dynamically adjusting inventory management and cost management decision according to the evaluation result of the second inventory coefficient Kcxs;

The model building and trend predicting module is used for building a predicting model, then predicting production plans and inventory management in real time by utilizing the built predicting model, predicting production related problems which possibly occur in advance by analyzing historical data and existing production data, including production bottlenecks, equipment faults, inventory shortage and backlog problems, and finally feeding back to management staff.

Preferably, the production monitoring and data acquisition module comprises a data acquisition unit and a data storage processing unit;

the data acquisition unit is used for acquiring various data on the production line in real time through the sensor connection data interface, including equipment state, production progress and material consumption data; the sensor is used for monitoring the operation parameters of key equipment, including temperature, pressure and vibration, and the data interface is used for acquiring production progress and material consumption related data from a series of external systems of the equipment control system;

The data storage processing unit is used for storing, preprocessing and primarily analyzing the acquired data; and after data cleaning, format conversion and outlier filtering, classifying, storing and dimensionless processing are carried out on the real-time data and the historical data, and the processed data is pushed to the intelligent scheduling module of the production plan according to the requirements.

Preferably, the intelligent scheduling module for the production plan comprises a production plan making unit, a dynamic scheduling adjustment unit and an emergency scheduling management unit;

The production plan making unit is configured to calculate and evaluate the first scheduling coefficient Ddxs by extracting production process related data and historical production related data, and finally make a new production plan based on the initial production plan according to the evaluation result of the first scheduling coefficient Ddxs, where the specific calculation and evaluation contents are as follows:

Wherein Dbl denotes order change frequency, rxq denotes medicine demand, slh denotes equipment remaining years, gqz denotes total number of equipment failures per month, sqh denotes equipment switching time, shc denotes production buffer margin, zzp denotes product turn-around time, pyc denotes average delay time of completion lot versus planned progress;

The evaluation is performed by presetting a first scheduling threshold Q1 and a first scheduling coefficient Ddxs1, which specifically comprises the following steps:

When the first scheduling coefficient Ddxs1 is smaller than or equal to the first scheduling threshold Q1, indicating that the current initial production plan is normal, executing according to the initial production plan, and not adjusting;

When the first scheduling coefficient Ddxs is larger than the second scheduling threshold Q2, the initial production plan is abnormal, and risks and fluctuations of sudden order change, equipment failure and production delay are faced at the same time; at this point, a first production plan is established, including prioritizing critical orders, scheduling spare equipment, and reassigning tasks.

Preferably, the dynamic scheduling adjustment unit is used for evaluating the effect of the first production plan in the production process; the second scheduling coefficient Ddxs is calculated and evaluated by extracting historical production related data and combining the data obtained by real-time monitoring, and finally, the first production plan is secondarily adjusted according to the evaluation result; the specific calculation and evaluation content is as follows:

Wherein Spl represents the number of acceptable products per day, dpc represents the number of unacceptable products per day, wgj represents the total amount of lag delivered products, syd represents the time of overload operation of the production node, pzh represents the ratio of actual production yield to expected maximum capacity, swb represents the number of days of difference between equipment maintenance schedule and actual maintenance time, zbd represents the frequency of reworked lot;

By presetting a second scheduling threshold value Q2 and a third scheduling threshold value Q3, respectively comparing and evaluating with a second scheduling coefficient Ddxs, the following adjustment plans are generated:

When the second scheduling coefficient Ddxs is smaller than or equal to a second scheduling threshold Q2, the production plan is maintained unchanged;

when the second scheduling threshold Q2 is smaller than the second scheduling coefficient Ddx and smaller than or equal to the third scheduling threshold Q3, generating a second production plan at the moment, wherein the second production plan comprises priority adjustment, local resource adjustment and emergency strategy presetting;

when the third scheduling threshold Q3 > the second scheduling coefficient Ddx, a third production plan is generated, including reconstructing the first production plan, enabling the alternate production scheme, and quality and cost priority rebalancing.

Preferably, the emergency dispatch management unit has an emergency response function, and when an order change, equipment failure or other emergency occurs, an emergency production plan is rapidly generated; by analyzing the current production conditions, available resources and emergency scheduling rules, an alternative production scheme is formulated, and the production process is ensured not to be interrupted; at the same time, a certain production redundancy is reserved for coordinating resources and optimizing production dead time.

Preferably, the material flow and equipment state association module comprises a material supply monitoring unit, an equipment state association unit and an inventory association and optimization unit;

the material supply monitoring unit is responsible for monitoring and recording the flow condition of materials in the production process in real time, including supply speed, circulation path and residence time, acquiring transfer information of the materials among the working procedures through a sensor and the internet of things equipment, and transmitting data to the inventory early warning and supplementing mechanism module;

the equipment state association unit is used for collecting and monitoring the operation state related data of the production equipment, including equipment load, operation speed, working time length and equipment switching frequency; meanwhile, the equipment state information is associated with the material flow data, so that the material flow and the equipment operation keep the same frequency;

The inventory association and optimization unit is responsible for collecting and integrating real-time inventory management related data and historical inventory management related data; by correlating the material flow with inventory changes, the usage and consumption of raw materials, semi-finished products and finished products in the production line are analyzed and inventory information is updated in real time.

Preferably, the inventory early warning and supplementing mechanism module comprises an inventory state monitoring unit, a replenishment decision-making unit and an inventory and cost management unit;

The stock state monitoring unit is responsible for monitoring and collecting stock related data of raw materials, semi-finished products and finished products in real time; acquiring the current stock level of each material through a sensor and a database interface, and updating stock related data periodically;

The replenishment decision making unit extracts the supply speed Gsd, the circulation path Llj, the residence time Lsj, the equipment load Sfz, the operation speed Ysd, the working time Gzc and the equipment switching frequency Sqh through the material flow and equipment state association module, performs dimensionless processing on the extracted data, and calculates and acquires a first inventory coefficient Kcxs1 through the following formula:

by presetting a first stock threshold W1 and evaluating a first stock coefficient Kcxs, making a replenishment decision and sending out replenishment early warning, the specific contents are as follows:

When the first inventory coefficient Kcxs is smaller than or equal to the first inventory threshold W1, the current inventory is normal, and replenishment is not needed; at this point, the system will continue to monitor inventory status but will not trigger restocking warning.

When the first inventory coefficient Kcxs is larger than the first inventory threshold W1, the current inventory is abnormal, a replenishment request is generated at the moment, and after replenishment early warning is sent to warehouse manager, the materials which are seriously insufficient in inventory and have influence on production are preferentially processed, and replenishment sequence or order of other materials is timely adjusted.

Preferably, the inventory and cost management unit is configured to perform dynamic adjustment of inventory management and cost management, obtain an inventory turnover rate Zzb, a material warehouse-in residence time Rzz, a low-frequency order material inventory Dcl, a historical inventory seasonal average inventory Jjx, a historical replenishment delay day Bhc, a diapause material average storage time Zxc and a raw material inventory consumption speed Kjj by extracting real-time inventory management related data and historical inventory management related data, and inventory related data of raw materials, semi-finished products and finished products, and calculate and obtain a second inventory coefficient Kcxs by the following formula after performing dimensionless processing:

Wherein w1 and w2 represent weight values, and w1+w2=1;

The evaluation is performed by presetting a second stock threshold value W2 and a second stock coefficient Kcxs, and the specific contents are as follows:

When the second inventory coefficient Kcxs2 is less than or equal to the second inventory threshold value W2, maintaining the current inventory management mode and continuously monitoring key inventory parameters including inventory turnover rate and inventory residence time;

When the second inventory coefficient Kcxs is larger than the second inventory threshold W2, indicating that the current inventory management mode is abnormal, taking measures for reducing replenishment or adjusting inventory structures for high-inventory materials and low-demand materials; secondly, reviewing the supply chain plan again, increasing the safety stock of key materials or searching for alternative supply sources; then, an inventory early warning is sent out, and a prompt management layer adjusts a production plan or modifies a purchasing strategy according to inventory fluctuation conditions; and finally, adjusting the inventory structure, including simplifying the low-frequency material inventory and improving the high-demand material inventory.

Preferably, the model building and trend prediction module comprises a data analysis and model training unit and a trend prediction and feedback unit;

The data analysis and model training unit is responsible for collecting and analyzing historical production data, inventory data and current production operation data; performing model training of various algorithms by using time sequence analysis, regression models and neural networks through data cleaning, feature extraction and data normalization processing; the generated predictive model is used for identifying potential problems in the production process, including production bottlenecks, equipment failures and inventory fluctuation key factors;

the trend prediction and feedback unit is used for inputting real-time data and generating a prediction result based on the trained model; the predicted content comprises bottleneck links in the production plan, possible faults of equipment operation and future change trend of stock level; analyzing and classifying the prediction result, feeding back the analysis report and the early warning information to the management system and simultaneously directly notifying the management personnel.

An intelligent production management method for a pharmaceutical workshop comprises the following steps:

Step one, collecting production process related data and historical production related data in real time, wherein the production process related data comprises equipment states, production progress and material consumption; simultaneously, various information on the production line is obtained through the sensor and the data interface;

Step two, calculating and evaluating a first scheduling coefficient Ddxs1 based on the production process related data and the historical production related data, and making a production plan; next, a second scheduling coefficient Ddxs is evaluated by calculation for dynamically adjusting the production plan; meanwhile, the system also has an emergency dispatching function, and an emergency production plan is generated when an order is changed or equipment fails;

Step three, correlating the flowing state of the materials with the state of production equipment; secondly, collecting relevant data of the feeding speed and the circulation path of the materials by monitoring the state of the equipment in real time; simultaneously collecting real-time inventory management and historical inventory management related data;

Step four, monitoring the stock conditions of raw materials, semi-finished products and finished products in real time, combining production plans and material consumption data, calculating a first stock coefficient Kcxs and a second stock coefficient Kcxs respectively, evaluating, making a replenishment decision according to the evaluation result of the first stock coefficient Kcxs, sending replenishment early warning, and finally dynamically adjusting stock management and cost management decision according to the evaluation result of the second stock coefficient Kcxs;

And fifthly, establishing a prediction model, predicting production plans and inventory management in real time by utilizing the established prediction model, predicting possible production related problems including production bottlenecks, equipment faults, inventory shortage and backlog problems in advance by analyzing historical data and existing production data, and feeding back to management staff.

Advantageous effects

The invention provides a communication network fault early warning method and system based on artificial intelligence. The beneficial effects are as follows:

(1) According to the intelligent production management method and system for the pharmaceutical workshop, the dynamic adjustment capability of a production plan is enhanced through the intelligent scheduling module of the production plan. Particularly, in the case of order change, equipment failure or other emergency, the system can generate and adjust the production plan in real time by calculating the first scheduling coefficient Ddxs and the second scheduling coefficient Ddxs, so as to ensure the flexibility and stability of the production flow. Meanwhile, the module is also provided with an emergency dispatch management unit, so that emergency can be responded rapidly, and the risk of production interruption is reduced. Therefore, the system improves the capacity of workshops to cope with complex production environments, and realizes the intelligent and dynamic optimization of production plans.

(2) According to the intelligent production management method and system for the pharmaceutical workshop, through the material flow and equipment state association module, the system enhances the association between materials and equipment states. The key data of the feeding speed, the circulation path and the equipment load of the materials are monitored in real time, so that the coordination and consistency of the production equipment and the material flow in time and space are ensured, and the production efficiency is optimized. The combination of the material supply monitoring unit and the equipment state association unit realizes the tight linkage of the equipment state and the material supply in the production process, reduces the conditions of equipment idling and material retention, and improves the overall operation efficiency of the production line.

(3) According to the intelligent production management method and system for the pharmaceutical workshop, the inventory early warning and supplementing mechanism module is applied to greatly improve the refinement level of inventory management. By calculating the first inventory coefficient Kcxs and the second inventory coefficient Kcxs in multiple dimensions, the system can dynamically evaluate the inventory state, and timely send out replenishment early warning or adjust inventory strategies in combination with a preset inventory threshold value, so that the occurrence of inventory shortage or backlog is avoided. The inventory state monitoring, the replenishment decision-making and the cooperative work of the inventory and cost management units enable an inventory early warning and replenishment mechanism to have more pertinence and flexibility, and ensure the stability of the production process and the rationality of the inventory structure.

Drawings

FIG. 1 is a schematic diagram of a framework structure of an intelligent production management system of a pharmaceutical plant according to the present invention;

fig. 2 is a schematic flow chart of steps of an intelligent production management method for a pharmaceutical workshop.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Example 1

Referring to fig. 1, the invention provides an intelligent production management system for a pharmaceutical workshop, which comprises a production monitoring and data acquisition module, a production plan intelligent scheduling module, a material flow and equipment state association module, an inventory early warning and supplementing mechanism module and a model building and trend prediction module;

the production monitoring and data acquisition module is used for acquiring production process related data and historical production related data in real time, wherein the production monitoring and data acquisition module comprises equipment states, production progress and material consumption; simultaneously, various information on the production line is obtained through the sensor and the data interface;

The intelligent scheduling module of the production plan is used for calculating and evaluating a first scheduling coefficient Ddxs1 and making a production plan based on the related data of the production process and the related data of the historical production; next, a second scheduling coefficient Ddxs is evaluated by calculation for dynamically adjusting the production plan; meanwhile, the system also has an emergency dispatching function, and an emergency production plan is generated when an order is changed or equipment fails;

The material flow and equipment state association module is used for associating the flow state of the material with the state of production equipment; secondly, collecting relevant data of the feeding speed and the circulation path of the materials by monitoring the state of the equipment in real time; simultaneously collecting real-time inventory management and historical inventory management related data;

The inventory early warning and supplementing mechanism module is used for monitoring the inventory conditions of raw materials, semi-finished products and finished products in real time, combining production plans and material consumption data, calculating a first inventory coefficient Kcxs and a second inventory coefficient Kcxs in a grading manner, evaluating the first inventory coefficient Kcxs and the second inventory coefficient Kcxs, making a replenishment decision according to the evaluation result of the first inventory coefficient Kcxs, sending out replenishment early warning, and finally dynamically adjusting inventory management and cost management decision according to the evaluation result of the second inventory coefficient Kcxs;

The model building and trend predicting module is used for building a predicting model, then predicting production plans and inventory management in real time by utilizing the built predicting model, predicting production related problems which possibly occur in advance by analyzing historical data and existing production data, including production bottlenecks, equipment faults, inventory shortage and backlog problems, and finally feeding back to management staff.

In the embodiment, the real-time acquisition of the data related to the equipment state, the production progress and the material consumption is realized through the production monitoring and data acquisition module, so that the comprehensiveness and timeliness of the data are ensured, and a reliable basis is provided for subsequent production scheduling; secondly, the intelligent scheduling module of the production plan evaluates the first scheduling coefficient Ddxs and the second scheduling coefficient Ddxs through calculation, so that an initial production plan can be intelligently formulated, emergency adjustment can be rapidly carried out when an order is changed or equipment is in fault, and the flexibility and the strain capacity of the production plan are obviously improved; the material flow and equipment state association module further enhances the real-time association of the materials and the equipment states, and optimizes the overall efficiency of the production line; the inventory early warning and supplementing mechanism module ensures the accuracy and rationality of inventory management through the dynamic evaluation of multi-level inventory coefficients, and effectively avoids the problems of inventory shortage and backlog; finally, through a model building and trend prediction module, the system can identify production bottlenecks, equipment faults and inventory fluctuation in advance, provide predictive guidance and enable assistance management staff to make more scientific decisions; overall, the invention has significant improvements and advantages in improving the production flexibility of workshops, optimizing the coordination of materials and equipment, enhancing the inventory management accuracy and improving the production prediction capability.

Example 2

The production monitoring and data acquisition module comprises a data acquisition unit and a data storage processing unit;

the data acquisition unit is used for acquiring various data on the production line in real time through the sensor connection data interface, including equipment state, production progress and material consumption data; the sensor is used for monitoring the operation parameters of key equipment, including temperature, pressure and vibration, and the data interface is used for acquiring production progress and material consumption related data from a series of external systems of the equipment control system;

The data storage processing unit is used for storing, preprocessing and primarily analyzing the acquired data; and after data cleaning, format conversion and outlier filtering, classifying, storing and dimensionless processing are carried out on the real-time data and the historical data, and the processed data is pushed to the intelligent scheduling module of the production plan according to the requirements.

In the embodiment, in a specific adjustment mode, the data storage processing unit realizes standardized processing of collected data by introducing data cleaning, format conversion and outlier filtering methods; firstly, redundant or erroneous data are identified and removed through data cleaning, so that the accuracy of the data is ensured; the format conversion converts the data into unified standard according to the system requirement, so that the subsequent processing is convenient; abnormal value filtering is carried out by setting a reasonable threshold value, so that abnormal data are automatically removed; and then, carrying out dimensionless processing on the system so as to ensure that the data keep consistent under different working conditions, and pushing the data to a production plan intelligent scheduling module according to real-time production requirements after classified storage to provide an optimization adjustment basis.

Example 3

The production plan intelligent scheduling module comprises a production plan making unit, a dynamic scheduling adjustment unit and an emergency scheduling management unit;

The production plan making unit is configured to calculate and evaluate the first scheduling coefficient Ddxs by extracting production process related data and historical production related data, and finally make a new production plan based on the initial production plan according to the evaluation result of the first scheduling coefficient Ddxs, where the specific calculation and evaluation contents are as follows:

Wherein Dbl denotes order change frequency, rxq denotes medicine demand, slh denotes equipment remaining years, gqz denotes total number of equipment failures per month, sqh denotes equipment switching time, shc denotes production buffer margin, zzp denotes product turn-around time, and Pyc denotes average delay time of completion lot versus planned progress;

The evaluation is performed by presetting a first scheduling threshold Q1 and a first scheduling coefficient Ddxs1, which specifically comprises the following steps:

When the first scheduling coefficient Ddxs1 is smaller than or equal to the first scheduling threshold Q1, indicating that the current initial production plan is normal, executing according to the initial production plan, and not adjusting;

When the first scheduling coefficient Ddxs is larger than the second scheduling threshold Q2, the initial production plan is abnormal, and risks and fluctuations of sudden order change, equipment failure and production delay are faced at the same time; at this point, a first production plan is formulated, including prioritizing critical orders, scheduling standby equipment, and reassigning tasks;

the dynamic scheduling adjustment unit is used for evaluating the effect of the first production plan in the production process; the second scheduling coefficient Ddxs is calculated and evaluated by extracting historical production related data and combining the data obtained by real-time monitoring, and finally, the first production plan is secondarily adjusted according to the evaluation result; the specific calculation and evaluation content is as follows:

Wherein Spl represents the number of acceptable products per day, dpc represents the number of unacceptable products per day, wgj represents the total amount of lag delivered products, syd represents the time of overload operation of the production node, pzh represents the ratio of actual production yield to expected maximum capacity, swb represents the number of days of difference between equipment maintenance schedule and actual maintenance time, zbd represents the frequency of reworked lots;

By presetting a second scheduling threshold value Q2 and a third scheduling threshold value Q3, respectively comparing and evaluating with a second scheduling coefficient Ddxs, the following adjustment plans are generated:

When the second scheduling coefficient Ddxs is smaller than or equal to a second scheduling threshold Q2, the production plan is maintained unchanged;

when the second scheduling threshold Q2 is smaller than the second scheduling coefficient Ddx and smaller than or equal to the third scheduling threshold Q3, generating a second production plan at the moment, wherein the second production plan comprises priority adjustment, local resource adjustment and emergency strategy presetting;

when the third scheduling threshold Q3 > the second scheduling coefficient Ddx, a third production plan is generated, including reconstructing the first production plan, enabling the alternate production scheme, and quality and cost priority rebalancing.

The emergency dispatching management unit has an emergency response function, and when order change, equipment failure or other emergency occurs, an emergency production plan is rapidly generated; by analyzing the current production conditions, available resources and emergency scheduling rules, an alternative production scheme is formulated, and the production process is ensured not to be interrupted; at the same time, a certain production redundancy is reserved for coordinating resources and optimizing production dead time.

In this embodiment, the order change frequency Dbl reflects the adjustment frequency of orders in the production cycle, and is used to evaluate the stability of the production plan and the response capability to the change demand; drug demand Rxq represents the adaptability of the production system to market demand fluctuations, and measures the flexibility of the production plan; the service life and aging degree of the equipment are evaluated by the residual life Slh of the equipment, so that the performance degradation and potential faults of the equipment are predicted; the total number Gqz of equipment faults per month evaluates the premonition degree of the equipment before the faults according to the historical data, adjusts the production plan in time and reduces the production interruption caused by the faults; the equipment switching time Sqh reflects the time required by the equipment in switching different products or processes, and influences the switching efficiency of the production plan; the production buffer margin Shc measures the buffer time or material stock set in production; the product turnover time Zzp reflects the circulation efficiency of the product among links of the production line and influences the overall production rhythm; the average delay time Pyc of the completed batch compared with the planned progress is used for measuring the delay degree caused by various factors in the production of each batch, so that a reference is provided for a dispatching decision;

The daily qualified product quantity Spl is quantified to be the daily qualified product quantity, and is used for evaluating the influence of frequent switching of equipment on the production efficiency; the daily unacceptable product quantity Dpc is quantified as the daily unacceptable product quantity, reflecting the deviation from the plan in the order execution process; the total amount Wgj of the lag delivered product is quantified as the delay of the material in the feeding process by evaluating the amount of the lag delivered product; the overload operation time Syd of the production node is quantized into overload operation time of each process node caused by uneven resource allocation or improper scheduling in the production process; the ratio Pzh of actual production yield to expected maximum capacity is quantified as the ratio of actual production yield to expected maximum capacity, and the complexity and integration efficiency in the batch merging or splitting process are measured; the difference days Swb of the equipment maintenance plan and the actual maintenance time represent the approaching degree of the equipment to enter a maintenance or service period, and the production plan is adjusted in advance to avoid equipment shutdown; the frequency Zbd of reworked lots is used to quantify the production quality stability of the drug product.

Example 4

The material flow and equipment state association module comprises a material supply monitoring unit, an equipment state association unit and an inventory association and optimization unit;

the material supply monitoring unit is responsible for monitoring and recording the flow condition of materials in the production process in real time, including supply speed, circulation path and residence time, acquiring transfer information of the materials among the working procedures through a sensor and the internet of things equipment, and transmitting data to the inventory early warning and supplementing mechanism module;

the equipment state association unit is used for collecting and monitoring the operation state related data of the production equipment, including equipment load, operation speed, working time length and equipment switching frequency; meanwhile, the equipment state information is associated with the material flow data, so that the material flow and the equipment operation keep the same frequency;

The inventory association and optimization unit is responsible for collecting and integrating real-time inventory management related data and historical inventory management related data; by correlating the material flow with inventory changes, the usage and consumption of raw materials, semi-finished products and finished products in the production line are analyzed and inventory information is updated in real time.

In the embodiment, the material supply monitoring unit not only records the supply speed, the circulation path and the residence time of the material in the production process in real time, but also automatically acquires the material transfer information among the working procedures through the sensor and the internet of things equipment, so that the timeliness of data transmission to the inventory module is ensured; the equipment state association unit is used for realizing synchronous adjustment of material flow and equipment operation by associating equipment load, operation speed and working time length state data, so that production bottleneck is effectively reduced; the inventory association and optimization unit dynamically adjusts inventory strategies by integrating real-time and historical inventory data and combining material flow information, optimizes replenishment and storage schemes and finally improves the cooperative efficiency of the whole production flow.

Example 5

The inventory early warning and supplementing mechanism module comprises an inventory state monitoring unit, a replenishment decision making unit and an inventory and cost management unit;

The stock state monitoring unit is responsible for monitoring and collecting stock related data of raw materials, semi-finished products and finished products in real time; acquiring the current stock level of each material through a sensor and a database interface, and updating stock related data periodically;

The replenishment decision making unit extracts the supply speed Gsd, the circulation path Llj, the residence time Lsj, the equipment load Sfz, the operation speed Ysd, the working time Gzc and the equipment switching frequency Sqh through the material flow and equipment state association module, performs dimensionless processing on the extracted data, and calculates and acquires a first inventory coefficient Kcxs1 through the following formula:

by presetting a first stock threshold W1 and evaluating a first stock coefficient Kcxs, making a replenishment decision and sending out replenishment early warning, the specific contents are as follows:

When the first inventory coefficient Kcxs is smaller than or equal to the first inventory threshold W1, the current inventory is normal, and replenishment is not needed; at this point, the system will continue to monitor inventory status but will not trigger restocking warning.

When the first inventory coefficient Kcxs is larger than the first inventory threshold W1, the current inventory is abnormal, a replenishment request is generated at the moment, after replenishment early warning is sent to warehouse manager, materials which are seriously insufficient in inventory and have influence on production are preferentially processed, and replenishment sequence or order of other materials is timely adjusted;

the inventory and cost management unit is used for dynamically adjusting inventory management and cost management, acquiring inventory turnover fluctuation rate Zzb, material warehouse-in retention time Rzz, low-frequency order material inventory Dcl, historical inventory season average inventory Jjx, historical replenishment delay days Bhc, average stock time Zxc of the lost materials and raw material inventory consumption speed Kjj by extracting real-time inventory management related data, historical inventory management related data and raw material, semi-finished product and finished product inventory related data, and acquiring a second inventory coefficient Kcxs by calculating the following formula after dimensionless processing:

Wherein w1 and w2 represent weight values, and w1+w2=1;

The evaluation is performed by presetting a second stock threshold value W2 and a second stock coefficient Kcxs, and the specific contents are as follows:

When the second inventory coefficient Kcxs2 is less than or equal to the second inventory threshold value W2, maintaining the current inventory management mode and continuously monitoring key inventory parameters including inventory turnover rate and inventory residence time;

When the second inventory coefficient Kcxs is larger than the second inventory threshold W2, indicating that the current inventory management mode is abnormal, taking measures for reducing replenishment or adjusting inventory structures for high-inventory materials and low-demand materials; secondly, reviewing the supply chain plan again, increasing the safety stock of key materials or searching for alternative supply sources; then, an inventory early warning is sent out, and a prompt management layer adjusts a production plan or modifies a purchasing strategy according to inventory fluctuation conditions; and finally, adjusting the inventory structure, including simplifying the low-frequency material inventory and improving the high-demand material inventory.

In this embodiment, the feeding speed Gsd reflects the speed of the material from the supplier to the production line, directly affects the timeliness of inventory replenishment, and is a key factor for ensuring production continuity; the flow path Llj describes the flow path of the material in the production flow, and is used for analyzing the efficiency and possible bottleneck of material flow, optimizing the logistics link to reduce unnecessary stock backlog; residence time Lsj is used for measuring residence time of materials among working procedures, and excessive residence time can cause low inventory turnover efficiency and is an important index for influencing inventory level; the equipment load Sfz reflects the actual work load of the production equipment, and the excessive high or low load can influence the material demand prediction, so that the setting of stock level is influenced; the running speed Ysd represents the running speed of the equipment, influences the consumption rate of materials, and further influences the demand prediction of the inventory and the establishment of a supplement plan; the working time Gzc records the actual working time of the equipment, and the production fluctuation condition is evaluated by comparing with the planned working time, so that the stock level of the material is regulated; the significance of the acquisition device switching frequency Sqh is that frequent device switching is usually accompanied with adjustment of a production plan, and can cause fluctuation of requirements of different materials to influence adjustment of inventory strategies;

inventory turnover volatility Zzb is used to reflect the magnitude and frequency of inventory changes, higher volatility means that inventory management is unstable and more frequent replenishment or adjustment of inventory strategies may be required; the material warehouse-in retention time Rzz is used for measuring the time from warehouse-in to actual consumption of materials, and excessively long retention time may mean excessive inventory or inaccurate demand prediction, and increase inventory cost; the low frequency order stock Dcl is used to monitor stock of materials with low demand frequency, excessive backlog may cause capital occupation or stock expiration, requiring special management; the historical stock season average stock amount Jjx analyzes seasonal fluctuation of stock according to the historical data and is used for coping with periodic change of material demands and avoiding excessive stock or shortage; historical replenishment delay days Bhc evaluate the reliability of the replenishment program by analyzing the delay condition in the replenishment history, and provide a reference for the adjustment of future inventory strategies; average shelf life Zxc for the sold materials measures the average shelf life of those materials that are sold or used slowly, and excessive shelf life may mean that the sold or managed poorly; raw material inventory consumption rate Kjj is used to monitor the inventory risk of critical raw materials, and too low an inventory may result in production interruptions, thus requiring special care to ensure that production continues.

Example 6

The model building and trend predicting module comprises a data analysis and model training unit and a trend predicting and feedback unit;

The data analysis and model training unit is responsible for collecting and analyzing historical production data, inventory data and current production operation data; performing model training of various algorithms by using time sequence analysis, regression models and neural networks through data cleaning, feature extraction and data normalization processing; the generated predictive model is used for identifying potential problems in the production process, including production bottlenecks, equipment failures and inventory fluctuation key factors;

the trend prediction and feedback unit is used for inputting real-time data and generating a prediction result based on the trained model; the predicted content comprises bottleneck links in the production plan, possible faults of equipment operation and future change trend of stock level; analyzing and classifying the prediction result, feeding back the analysis report and the early warning information to the management system and simultaneously directly notifying the management personnel.

In the embodiment, through deep processing of historical data and real-time data, a highly accurate prediction model is established by utilizing various algorithms, and the model can identify potential problems in a production process, including production bottlenecks, equipment faults and inventory fluctuation, so that omnibearing prediction and early warning of production plans, equipment states and inventory management are realized; the model building and trend predicting module has the unique advantages of improving the accuracy of problem prejudgment, greatly optimizing the production efficiency and resource allocation, avoiding decision delay and resource waste caused by data lag, solving the problems of difficult dynamic adjustment, early warning lag and uncontrollable bottleneck links in the traditional production management, and providing reliable guarantee for intelligent and refined management of a pharmaceutical workshop.

Example 7

Referring to fig. 2, an intelligent production management method for a pharmaceutical workshop includes the following steps:

Step one, collecting production process related data and historical production related data in real time, wherein the production process related data comprises equipment states, production progress and material consumption; simultaneously, various information on the production line is obtained through the sensor and the data interface;

Step two, calculating and evaluating a first scheduling coefficient Ddxs1 based on the production process related data and the historical production related data, and making a production plan; next, a second scheduling coefficient Ddxs is evaluated by calculation for dynamically adjusting the production plan; meanwhile, the system also has an emergency dispatching function, and an emergency production plan is generated when an order is changed or equipment fails;

Step three, correlating the flowing state of the materials with the state of production equipment; secondly, collecting relevant data of the feeding speed and the circulation path of the materials by monitoring the state of the equipment in real time; simultaneously collecting real-time inventory management and historical inventory management related data;

Step four, monitoring the stock conditions of raw materials, semi-finished products and finished products in real time, combining production plans and material consumption data, calculating a first stock coefficient Kcxs and a second stock coefficient Kcxs respectively, evaluating, making a replenishment decision according to the evaluation result of the first stock coefficient Kcxs, sending replenishment early warning, and finally dynamically adjusting stock management and cost management decision according to the evaluation result of the second stock coefficient Kcxs;

fifthly, establishing a prediction model, and predicting the production plan and inventory management in real time by utilizing the established prediction model; by analyzing the historical data and the existing production data, the possible production related problems including production bottlenecks, equipment faults, inventory shortages and backlog problems are predicted in advance and finally fed back to the manager.

In the embodiment, through five key steps, intelligent management of the whole production process is realized, firstly, production data are collected and analyzed in real time, the core links of equipment state, production progress and material consumption are effectively covered, the accuracy and timeliness of the data are ensured, secondly, the optimization and flexible adjustment of a production plan are realized through the dynamic evaluation of two scheduling coefficients Ddxs and Ddxs, the emergency treatment when an order is changed and equipment fails is more efficient, the deep association of material flow and equipment state and the double-coefficient evaluation of inventory management are realized, the high efficiency of material supply and the rationality of inventory level are further ensured, the problem of inventory shortage or backlog is avoided, and finally, the establishment and application of a prediction model realize the forward prospective early warning and optimization adjustment of production bottlenecks, equipment faults and inventory fluctuation; on the whole, the method particularly solves the problems of inflexible scheduling, disconnection of materials and equipment states, inventory management lag and the like in the traditional production, obviously improves the fine management level and response speed of the production, and ensures that the pharmaceutical production is more intelligent, efficient and stable.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (10)

1. An intelligent production management system of pharmaceutical workshop, its characterized in that: the system comprises a production monitoring and data acquisition module, a production plan intelligent scheduling module, a material flow and equipment state association module, an inventory early warning and supplementing mechanism module and a model building and trend prediction module;

the production monitoring and data acquisition module is used for acquiring production process related data and historical production related data in real time, wherein the production monitoring and data acquisition module comprises equipment states, production progress and material consumption; simultaneously, various information on the production line is obtained through the sensor and the data interface;

The intelligent scheduling module of the production plan is used for calculating and evaluating a first scheduling coefficient Ddxs1 and making a production plan based on the related data of the production process and the related data of the historical production; next, a second scheduling coefficient Ddxs is evaluated by calculation for dynamically adjusting the production plan; meanwhile, the system also has an emergency dispatching function, and an emergency production plan is generated when an order is changed or equipment fails;

The material flow and equipment state association module is used for associating the flow state of the material with the state of production equipment; secondly, collecting relevant data of the feeding speed and the circulation path of the materials by monitoring the state of the equipment in real time; simultaneously collecting real-time inventory management and historical inventory management related data;

The inventory early warning and supplementing mechanism module is used for monitoring the inventory conditions of raw materials, semi-finished products and finished products in real time, combining production plans and material consumption data, calculating a first inventory coefficient Kcxs and a second inventory coefficient Kcxs in a grading manner, evaluating the first inventory coefficient Kcxs and the second inventory coefficient Kcxs, making a replenishment decision according to the evaluation result of the first inventory coefficient Kcxs, sending out replenishment early warning, and finally dynamically adjusting inventory management and cost management decision according to the evaluation result of the second inventory coefficient Kcxs;

The model building and trend predicting module is used for building a predicting model, then predicting production plans and inventory management in real time by utilizing the built predicting model, predicting production related problems which possibly occur in advance by analyzing historical data and existing production data, including production bottlenecks, equipment faults, inventory shortage and backlog problems, and finally feeding back to management staff.

2. The intelligent production management system of a pharmaceutical plant of claim 1, wherein: the production monitoring and data acquisition module comprises a data acquisition unit and a data storage processing unit;

the data acquisition unit is used for acquiring various data on the production line in real time through the sensor connection data interface, including equipment state, production progress and material consumption data; the sensor is used for monitoring the operation parameters of key equipment, including temperature, pressure and vibration, and the data interface is used for acquiring production progress and material consumption related data from a series of external systems of the equipment control system;

The data storage processing unit is used for storing, preprocessing and primarily analyzing the acquired data; and after data cleaning, format conversion and outlier filtering, classifying, storing and dimensionless processing are carried out on the real-time data and the historical data, and the processed data is pushed to the intelligent scheduling module of the production plan according to the requirements.

3. The intelligent production management system of a pharmaceutical plant of claim 2, wherein: the production plan intelligent scheduling module comprises a production plan making unit, a dynamic scheduling adjustment unit and an emergency scheduling management unit;

The production plan making unit is configured to calculate and evaluate the first scheduling coefficient Ddxs by extracting production process related data and historical production related data, and finally make a new production plan based on the initial production plan according to the evaluation result of the first scheduling coefficient Ddxs, where the specific calculation and evaluation contents are as follows:

Wherein Dbl denotes order change frequency, rxq denotes medicine demand, slh denotes equipment remaining years, gqz denotes total number of equipment failures per month, sqh denotes equipment switching time, shc denotes production buffer margin, zzp denotes product turn-around time, pyc denotes average delay time of completion lot versus planned progress;

The evaluation is performed by presetting a first scheduling threshold Q1 and a first scheduling coefficient Ddxs1, which specifically comprises the following steps:

When the first scheduling coefficient Ddxs1 is smaller than or equal to the first scheduling threshold Q1, indicating that the current initial production plan is normal, executing according to the initial production plan, and not adjusting;

When the first scheduling coefficient Ddxs is larger than the second scheduling threshold Q2, the initial production plan is abnormal, and risks and fluctuations of sudden order change, equipment failure and production delay are faced at the same time; at this point, a first production plan is established, including prioritizing critical orders, scheduling spare equipment, and reassigning tasks.

4. A pharmaceutical shop intelligent production management system according to claim 3, wherein: the dynamic scheduling adjustment unit is used for evaluating the effect of the first production plan in the production process; the second scheduling coefficient Ddxs is calculated and evaluated by extracting historical production related data and combining the data obtained by real-time monitoring, and finally, the first production plan is secondarily adjusted according to the evaluation result; the specific calculation and evaluation content is as follows:

Wherein Spl represents the number of acceptable products per day, dpc represents the number of unacceptable products per day, wgj represents the total amount of lag delivered products, syd represents the time of overload operation of the production node, pzh represents the ratio of actual production yield to expected maximum capacity, swb represents the number of days of difference between equipment maintenance schedule and actual maintenance time, zbd represents the frequency of reworked lots;

By presetting a second scheduling threshold value Q2 and a third scheduling threshold value Q3, respectively comparing and evaluating with a second scheduling coefficient Ddxs, the following adjustment plans are generated:

When the second scheduling coefficient Ddxs is smaller than or equal to a second scheduling threshold Q2, the production plan is maintained unchanged;

when the second scheduling threshold Q2 is smaller than the second scheduling coefficient Ddx and smaller than or equal to the third scheduling threshold Q3, generating a second production plan at the moment, wherein the second production plan comprises priority adjustment, local resource adjustment and emergency strategy presetting;

when the third scheduling threshold Q3 > the second scheduling coefficient Ddx, a third production plan is generated, including reconstructing the first production plan, enabling the alternate production scheme, and quality and cost priority rebalancing.

5. A pharmaceutical shop intelligent production management system according to claim 3, wherein: the emergency dispatching management unit has an emergency response function, and when order change, equipment failure or other emergency occurs, an emergency production plan is rapidly generated; by analyzing the current production conditions, available resources and emergency scheduling rules, an alternative production scheme is formulated, and the production process is ensured not to be interrupted; at the same time, a certain production redundancy is reserved for coordinating resources and optimizing production dead time.

6. The intelligent production management system of a pharmaceutical plant of claim 1, wherein: the material flow and equipment state association module comprises a material supply monitoring unit, an equipment state association unit and an inventory association and optimization unit;

the material supply monitoring unit is used for monitoring and recording the flow condition of the material in the production process in real time, including the supply speed, the circulation path and the residence time; the sensor and the internet of things equipment are used for acquiring transfer information of materials among the working procedures, and transmitting data to the inventory early warning and supplementing mechanism module;

the equipment state association unit is used for collecting and monitoring the operation state related data of the production equipment, including equipment load, operation speed, working time length and equipment switching frequency; meanwhile, the equipment state information is associated with the material flow data, so that the material flow and the equipment operation keep the same frequency;

The inventory association and optimization unit is responsible for collecting and integrating real-time inventory management related data and historical inventory management related data; by correlating the material flow with inventory changes, the usage and consumption of raw materials, semi-finished products and finished products in the production line are analyzed and inventory information is updated in real time.

7. The intelligent production management system of a pharmaceutical plant of claim 1, wherein: the inventory early warning and supplementing mechanism module comprises an inventory state monitoring unit, a replenishment decision making unit and an inventory and cost management unit;

The stock state monitoring unit is responsible for monitoring and collecting stock related data of raw materials, semi-finished products and finished products in real time; acquiring the current stock level of each material through a sensor and a database interface, and updating stock related data periodically;

The replenishment decision making unit extracts the supply speed Gsd, the circulation path Llj, the residence time Lsj, the equipment load Sfz, the operation speed Ysd, the working time Gzc and the equipment switching frequency Sqh through the material flow and equipment state association module, performs dimensionless processing on the extracted data, and calculates and acquires a first inventory coefficient Kcxs1 through the following formula:

by presetting a first stock threshold W1 and evaluating a first stock coefficient Kcxs, making a replenishment decision and sending out replenishment early warning, the specific contents are as follows:

when the first inventory coefficient Kcxs is smaller than or equal to the first inventory threshold W1, the current inventory is normal, and replenishment is not needed; at this time, the system can continuously monitor the stock state, but can not trigger the replenishment early warning;

When the first inventory coefficient Kcxs is larger than the first inventory threshold W1, the current inventory is abnormal, a replenishment request is generated at the moment, and after replenishment early warning is sent to warehouse manager, the materials which are seriously insufficient in inventory and have influence on production are preferentially processed, and replenishment sequence or order of other materials is timely adjusted.

8. The intelligent production management system of a pharmaceutical plant of claim 1, wherein: the inventory and cost management unit is used for dynamically adjusting inventory management and cost management, acquiring inventory turnover fluctuation rate Zzb, material warehouse-in retention time Rzz, low-frequency order material inventory Dcl, historical inventory season average inventory Jjx, historical replenishment delay days Bhc, average stock time Zxc of the lost materials and raw material inventory consumption speed Kjj by extracting real-time inventory management related data, historical inventory management related data and inventory related data of raw materials, semi-finished products and finished products, and acquiring a second inventory coefficient Kcxs by calculating the following formula after dimensionless processing:

Wherein w1 and w2 represent weight values, and w1+w2=1;

The evaluation is performed by presetting a second stock threshold value W2 and a second stock coefficient Kcxs, and the specific contents are as follows:

When the second inventory coefficient Kcxs2 is less than or equal to the second inventory threshold value W2, maintaining the current inventory management mode and continuously monitoring key inventory parameters including inventory turnover rate and inventory residence time;

When the second inventory coefficient Kcxs is larger than the second inventory threshold W2, indicating that the current inventory management mode is abnormal, taking measures for reducing replenishment or adjusting inventory structures for high-inventory materials and low-demand materials; secondly, reviewing the supply chain plan again, increasing the safety stock of key materials or searching for alternative supply sources; then, an inventory early warning is sent out, and a prompt management layer adjusts a production plan or modifies a purchasing strategy according to inventory fluctuation conditions; and finally, adjusting the inventory structure, including simplifying the low-frequency material inventory and improving the high-demand material inventory.

9. The intelligent production management system of a pharmaceutical plant of claim 1, wherein: the model building and trend predicting module comprises a data analysis and model training unit and a trend predicting and feedback unit;

The data analysis and model training unit is responsible for collecting and analyzing historical production data, inventory data and current production operation data; performing model training of various algorithms by using time sequence analysis, regression models and neural networks through data cleaning, feature extraction and data normalization processing; the generated predictive model is used for identifying potential problems in the production process, including production bottlenecks, equipment failures and inventory fluctuation key factors;

the trend prediction and feedback unit is used for inputting real-time data and generating a prediction result based on the trained model; the predicted content comprises bottleneck links in the production plan, possible faults of equipment operation and future change trend of stock level; analyzing and classifying the prediction result, feeding back the analysis report and the early warning information to the management system and simultaneously directly notifying the management personnel.

10. An intelligent production management method for a pharmaceutical workshop, according to claim 1-9, characterized in that: the method comprises the following steps:

Step one, collecting production process related data and historical production related data in real time, wherein the production process related data comprises equipment states, production progress and material consumption; simultaneously, various information on the production line is obtained through the sensor and the data interface;

Step two, calculating and evaluating a first scheduling coefficient Ddxs1 based on the production process related data and the historical production related data, and making a production plan; next, a second scheduling coefficient Ddxs is evaluated by calculation for dynamically adjusting the production plan; meanwhile, the system also has an emergency dispatching function, and an emergency production plan is generated when an order is changed or equipment fails;

Step three, correlating the flowing state of the materials with the state of production equipment; secondly, collecting relevant data of the feeding speed and the circulation path of the materials by monitoring the state of the equipment in real time; simultaneously collecting real-time inventory management and historical inventory management related data;

Step four, monitoring the stock conditions of raw materials, semi-finished products and finished products in real time, combining production plans and material consumption data, calculating a first stock coefficient Kcxs and a second stock coefficient Kcxs respectively, evaluating, making a replenishment decision according to the evaluation result of the first stock coefficient Kcxs, sending replenishment early warning, and finally dynamically adjusting stock management and cost management decision according to the evaluation result of the second stock coefficient Kcxs;

And fifthly, establishing a prediction model, predicting production plans and inventory management in real time by utilizing the established prediction model, predicting possible production related problems including production bottlenecks, equipment faults, inventory shortage and backlog problems in advance by analyzing historical data and existing production data, and feeding back to management staff.