CN111915254A - Inventory optimization control method and system suitable for automobile after-sales accessories - Google Patents
Inventory optimization control method and system suitable for automobile after-sales accessories Download PDFInfo
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Abstract
The invention provides an inventory optimization control system and method suitable for automobile after-sale accessories, which comprises the following steps: acquiring data of dealers, suppliers and accessories to obtain the whole vehicle keeping data and accessory sales summary data of the individual vehicle ages; acquiring engineering information of the accessory; according to the whole vehicle holding data, the accessory sales summary data and the engineering information of the accessories of the sub-vehicles, establishing a prediction management model for the accessory sales through a parallel calculation method, and predicting the accessory sales; making a preset strategy for the appointed accessories, and predicting sales data of each accessory according to the prediction management model to calculate an accessory inventory strategy; according to the accessory inventory strategy, calculating order strategies for each logistics node in a timed task mode; the invention realizes the automatic demand prediction of after-sales accessories and the optimization capability of prediction model parameters, and helps the after-sales department to save follow-up processing time of a large amount of invalid information.
Description
Technical Field
The invention relates to the field of after-sale accessory supply chains in the automobile industry, in particular to an inventory optimization control method and system suitable for after-sale accessories of an automobile.
Background
As the sales of the whole Chinese automobile is increased and slowed down, the market already enters the stock market. Meanwhile, with the annual reduction of sales profits of the whole vehicle, for a host factory and each after-sale service organization, the proportion of the income brought by after-sale accessories and related services to the total income of the after-sale accessories is increased year by year, and the after-sale service is more and more emphasized. With respect to full vehicle sales, after-market business management is extensive, especially with respect to after-market accessory supply chain management. Inefficient management does not enable the large aftermarket to reach the expected profit. The problems are mainly shown in the following aspects:
1) the business needs of the after-sale supply chain cannot be completely supported by the host factory, which mostly depends on some sub-functions of the ERP. After-sale supply chain management mainly depends on a senior engineer, data is obtained in various data report forms, and a decision is made according to the data, so that the processing efficiency is relatively low.
2) Because of the large amount of data and the huge amount of accessories, less accessories can be managed in proficiency, for example, after a certain brand is sold, the effective accessory catalog can reach more than 20 ten thousand, the accessories which are contacted by a 4S shop every year are more than 3 ten thousand, the accessories which are processed by an accessory manager after sale every week can reach more than 1000, in general, a dealer can only effectively manage 10-20% of the types, and the range of the proficiency management of a host factory is only about 40%. The method mainly relies on regular audit and report analysis to find risks and problems, and lacks of risk control capability.
3) Similar management software products are mainly suitable for fast-selling or electronic products, and are not suitable for the management requirements of 10 years of single accessories and great difference of various products after the automobile is sold.
Patent document CN111160819A (application number: 201910587197.6) discloses an initial inventory forecasting system for a new model of an automobile, which comprises a data acquisition module, a database module and a data analysis processing module; the data acquisition module is used for inputting data into the database module; the database module comprises a personal experience database unit, a historical database unit and a dealer database unit, and the data analysis processing module is used for carrying out big data analysis and data integration on various data in the database module and predicting the initial inventory of parts required by the new-model automobile; the patent document CN111160819A reasonably predicts the initial inventory of various parts required by the after-sale maintenance of the new-model automobile, and the problem that the new-model automobile needs to be maintained without corresponding parts is not easy to occur, so that the inventory of the parts of the new-model automobile can meet the use requirement of the after-sale maintenance, the probability of producing the parts and transporting the parts in an emergency customized manner is reduced, the emergency transportation cost is reduced, and the frequency of influencing the normal maintenance of the new-model automobile is reduced. The method is also a demand forecasting and order optimizing method applied to automobile after-market accessories, but is only suitable for accessory management in the marketing stage of a new type automobile, and cannot support the accessory forecasting and inventory optimizing requirements of the whole automobile life cycle.
Patent document CN105389406A (application number: 201410445463.9) discloses a complete vehicle design reliability evaluation method based on unit weighted cumulative number of faults, which includes: acquiring test sample data in at least a first stage in a plurality of engineering development stages; respectively calculating unit weighted cumulative numbers of the faults corresponding to at least the first stage based on the test sample data; performing grey system modeling according to the unit weighted cumulative number of the faults corresponding to each engineering development stage to form a grey matrix; and predicting the unit weighted cumulative number of the faults corresponding to the rest stages in the multiple engineering development stages by solving the gray matrix. Patent document CN105389406A predicts the unit weighted cumulative number of failures corresponding to the remaining stages in a plurality of engineering development stages by solving the grayscale matrix. The method can solve the adverse effects of small samples, poor information, complex and ambiguous system transfer functions and the like on the reliability evaluation of the whole automobile design in the durability test process of the whole automobile, is more suitable for quality estimation in the research and development stage, and is not suitable for the after-market field of automobiles.
Disclosure of Invention
In view of the deficiencies in the prior art, it is an object of the present invention to provide an inventory optimization control system for after-market automotive parts.
According to the invention, the inventory optimization control system suitable for the automobile after-sales accessories comprises:
a data management module: acquiring data of dealers, suppliers and accessories to obtain the whole vehicle keeping data and accessory sales summary data of the individual vehicle ages;
an accessory engineering module: acquiring engineering information of the accessory;
a prediction management module: according to the whole vehicle holding data, the accessory sales summary data and the engineering information of the accessories of the sub-vehicles, establishing a prediction management model for the accessory sales through a parallel calculation method, and predicting the accessory sales;
a policy management module: making a preset strategy for the appointed accessories, and predicting sales data of each accessory according to the prediction management model to calculate an accessory inventory strategy;
a supply chain optimization module: according to the accessory inventory strategy, calculating order requirements which need to be issued to each supplier every day;
the predictive management model includes modeling sales of the accessory to give an expectation of future sales of the accessory.
Preferably, the data management module includes:
the dealer data includes a dealer network topology;
the provider data comprises a provider network topology;
the accessory data comprises accessory engineering data, accessory change information, accessory sales data and finished automobile sales data;
the accessory sales summary data includes feature processing accessory data, the generating including: historical monthly sales, historical quarterly sales, monthly sales growth rate, and quarterly sales growth rate;
the whole vehicle keeping data of the branch vehicle ages comprises the steps of calculating the distribution quantity of different vehicles of the current vehicle according to the sale date data of each whole vehicle, and calculating the whole vehicle keeping data of the branch vehicle ages in the future preset time in each month through a time sequence method.
Preferably, the accessory engineering module comprises: the engineering information of the accessory includes: cost of ownership, cost of loss, and pull strategy for after-market accessories; updating the change in service time and scope of the accessory due to a change in vendor or upgrade in technology; and maintaining the information of the covered vehicle type.
Preferably, the prediction management module comprises:
prediction management module M1: according to the whole vehicle holding data, the accessory sales summary data and the engineering information of the accessories of the sub-vehicles, establishing a prediction management model for the accessory sales through a parallel calculation method;
prediction management module M2: and training the prediction management model according to the historical sales data of the accessories until the prediction error reaches a preset value.
Preferably, the policy management module comprises: outputting an accessory prediction result by the homonymy prediction management model, describing the demand of the accessory by using a distribution function, and calculating the inventory level of the accessory pipeline according to the service level, wherein the formula is as follows:
wherein p is a service level requirement, I is an optimal pipe inventory to be sought, f (x, α, β) is a distribution density function of the demand for the parts, x represents a demand for the parts, α represents a mathematical expectation of the demand for the parts, and β represents a standard deviation of the demand for the parts.
Preferably, the supply chain optimization module comprises:
supply chain optimization module M1: determining an accessory inventory strategy according to a strategy management module, and calculating order strategies for each logistics node in a timed task mode;
supply chain optimization module M2: and according to the order strategy and the inventory strategy, acquiring the hand inventory quantity and the in-transit order data information, and calculating the order requirements which need to be issued to each supplier every day.
The order policy includes: order policy-inventory policy value-on-hand inventory value-on-route order value;
wherein the hand inventory value represents an amount of available parts in the warehouse; the in-transit order quantity represents the total number of parts for which an order was not delivered.
Preferably, the system also comprises a display center module and an early warning monitoring module;
the display center module comprises a display data result and an editing and confirming order result;
the early warning monitoring module prompts low stock early warning, arrival delay early warning, seasonal pulling early warning and abnormal demand early warning;
the prompt low inventory early warning is triggered when the hand inventory is lower than the safety inventory;
the arrival postponing early warning is triggered when the corresponding order completes warehousing operation at the expected expiration time;
the seasonal pull pre-warning is triggered when an accessory demand peak prediction value occurs;
the abnormal demand warning is triggered when a dealer order is greater than a historical order preset value.
Preferably, the display data information in the display center module comprises a pipeline inventory strategy value, a safety inventory strategy value, an on-demand order, a node inventory, a sales forecast and a sales volume preset average;
the order editing and confirming result in the display center module comprises the following steps: and based on a distributed data synchronization technology, periodically sending order information to an order execution system.
Preferably, the inventory policy further comprises:
limiting a preset range of the inventory strategy according to the management requirement, and taking the currently calculated inventory strategy value when the calculated inventory strategy value is within the preset range; and when the calculated inventory strategy value is not in the preset range, taking the nearest boundary value of the preset range as the inventory strategy value to be output.
The invention provides an inventory optimization control method suitable for automobile after-market accessories, which comprises the following steps:
a data management step: acquiring data of dealers, suppliers and accessories to obtain the whole vehicle keeping data and accessory sales summary data of the individual vehicle ages;
the accessory engineering step: acquiring engineering information of the accessory;
a prediction management step: according to the whole vehicle holding data, the accessory sales summary data and the engineering information of the accessories of the sub-vehicles, establishing a prediction management model for the accessory sales through a parallel calculation method, and predicting the accessory sales;
policy management step: making a preset strategy for the appointed accessories, and predicting sales data of each accessory according to the prediction management model to calculate an accessory inventory strategy;
supply chain optimization step: according to the accessory inventory strategy, calculating order requirements which need to be issued to each supplier every day;
the predictive management model includes modeling sales of the accessory to give an expectation of future sales of the accessory.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, by adopting a machine learning technology and combining with a traditional time sequence statistical principle, the sales data accumulated after sales of the vehicle, the enterprise and the like are fully utilized, and an optimal prediction model is trained, so that the automatic demand prediction and prediction model parameter optimization capabilities of after-sales accessories are realized, and the follow-up processing time of a large amount of invalid information is saved for an after-sales department;
2. by adopting operation planning technical data and a distributed computing frame, the invention starts from the KPI of the after-sales department, and dynamically allocates the department to invest capital resources in the supply chain, so that the KPI of the department reaches a local peak value in a period of time after each model adjustment. The KPI feedback of an actual operation result is combined, and model parameters are intelligently adjusted, so that the fineness of the management of an after-sales accessory supply chain is realized, after-sales departments are helped to use invested funds to the maximum extent, and the gross profit rate level is improved;
3. the prediction error MAPE of the parts is averagely reduced by 9 percent; the once satisfaction rate of the dealer to the orders of the after-sales department reaches 95 percent; the total inventory amount of the enterprise accessories is reduced by 6%, the peak value is reduced by 13%, long-period order prediction is provided for upstream accessory suppliers, the supply on-time rate of the suppliers is improved, and the inventory pressure of finished products is reduced; the data processing working time of an enterprise after-sale team accessory planning engineer is reduced by 67% on average; the stay rate and the scrap loss of the enterprise accessories are reduced, and the stay rate is reduced by 3%.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic view of the present system;
fig. 2 is a data processing flow chart.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
According to the system, under the scene of the management business of the after-sale accessory supply chain in the automobile industry, a comprehensive machine learning model is constructed by analyzing the after-sale accessory sales historical data and the whole automobile sales historical data, the demand distribution estimation of each accessory is calculated, and the estimation is used for comprehensively planning the accessory pipeline inventory by combining the after-sale supply chain storage management cost, the logistics cost, the loss cost and other financial indexes.
The system can help the automobile enterprise after-sale departments to carry out intelligent management on the requirements of all accessories and the pipeline inventory, can optimally configure various after-sale resources under the condition that a user specifies the turnover rate or the service satisfaction rate index, and improves the receiving gross profit rate and the working efficiency of after-sale managers.
Example 1
According to the invention, the inventory optimization control system suitable for the automobile after-sales accessories comprises: as shown in figure 1 of the drawings, in which,
a data management module: acquiring data of dealers, suppliers and accessories to obtain the whole vehicle keeping data and accessory sales summary data of the individual vehicle ages;
an accessory engineering module: acquiring engineering information of the accessory;
a prediction management module: according to the whole vehicle holding data, the accessory sales summary data and the engineering information of the accessories of the sub-vehicles, establishing a prediction management model for the accessory sales through a parallel calculation method, and predicting the accessory sales;
a policy management module: making a preset strategy for the appointed accessories, and predicting sales data of each accessory according to the prediction management model to calculate an accessory inventory strategy;
a supply chain optimization module: according to the accessory inventory strategy, calculating order requirements which need to be issued to each supplier every day;
the predictive management model includes modeling sales of the accessory to give an expectation of future sales of the accessory.
Specifically, the data management module includes:
the dealer data includes a dealer network topology;
the provider data comprises a provider network topology;
the accessory data comprises accessory engineering data, accessory change information, accessory sales data and finished automobile sales data;
the accessory sales summary data includes feature processing accessory data, the generating including: historical monthly sales, historical quarterly sales, monthly sales growth rate, and quarterly sales growth rate;
the whole vehicle keeping data of the branch vehicle ages comprises the steps of calculating the distribution quantity of different vehicles of the current vehicle according to the sale date data of each whole vehicle, and calculating the whole vehicle keeping data of the branch vehicle ages in the future preset time in each month through a time sequence method.
Specifically, the accessory engineering module comprises: the engineering information of the accessory includes: cost of ownership, cost of loss, and pull strategy for after-market accessories; updating the change in service time and scope of the accessory due to a change in vendor or upgrade in technology; and maintaining the information of the covered vehicle type.
Specifically, the prediction management module includes:
prediction management module M1: according to the whole vehicle holding data, the accessory sales summary data and the engineering information of the accessories of the sub-vehicles, establishing a prediction management model for the accessory sales through a parallel calculation method;
prediction management module M2: and training the prediction management model according to the historical sales data of the accessories until the prediction error reaches a preset value.
Specifically, the policy management module includes: outputting an accessory prediction result by the homonymy prediction management model, describing the demand of the accessory by using a distribution function, and calculating the inventory level of the accessory pipeline according to the service level, wherein the formula is as follows:
wherein p is a service level requirement, I is an optimal pipe inventory to be sought, f (x, α, β) is a distribution density function of the demand for the parts, x represents a demand for the parts, α represents a mathematical expectation of the demand for the parts, and β represents a standard deviation of the demand for the parts.
Specifically, the supply chain optimization module includes:
supply chain optimization module M1: determining an accessory inventory strategy according to a strategy management module, and calculating order strategies for each logistics node in a timed task mode;
supply chain optimization module M2: and according to the order strategy and the inventory strategy, acquiring the hand inventory quantity and the in-transit order data information, and calculating the order requirements which need to be issued to each supplier every day.
The order policy includes: order policy-inventory policy value-on-hand inventory value-on-route order value;
wherein the hand inventory value represents an amount of available parts in the warehouse; the in-transit order quantity represents the total number of parts for which an order was not delivered.
Specifically, the system also comprises a display center module and an early warning monitoring module;
the display center module comprises a display data result and an editing and confirming order result;
the early warning monitoring module prompts low stock early warning, arrival delay early warning, seasonal pulling early warning and abnormal demand early warning;
the prompt low inventory early warning is triggered when the hand inventory is lower than the safety inventory;
the arrival postponing early warning is triggered when the corresponding order completes warehousing operation at the expected expiration time;
the seasonal pull pre-warning is triggered when an accessory demand peak prediction value occurs;
the abnormal demand warning is triggered when a dealer order is greater than a historical order preset value.
Specifically, the display data information in the display center module comprises a pipeline inventory strategy value, a safety inventory strategy value, an on-demand order, a node inventory, a sales forecast and a sales volume preset average;
the order editing and confirming result in the display center module comprises the following steps: and based on a distributed data synchronization technology, periodically sending order information to an order execution system.
Specifically, the inventory policy further includes:
limiting a preset range of the inventory strategy according to the management requirement, and taking the currently calculated inventory strategy value when the calculated inventory strategy value is within the preset range; and when the calculated inventory strategy value is not in the preset range, taking the nearest boundary value of the preset range as the inventory strategy value to be output.
The invention provides an inventory optimization control method suitable for automobile after-market accessories, which comprises the following steps: as shown in figure 1 of the drawings, in which,
a data management step: acquiring data of dealers, suppliers and accessories to obtain the whole vehicle keeping data and accessory sales summary data of the individual vehicle ages;
the accessory engineering step: acquiring engineering information of the accessory;
a prediction management step: according to the whole vehicle holding data, the accessory sales summary data and the engineering information of the accessories of the sub-vehicles, establishing a prediction management model for the accessory sales through a parallel calculation method, and predicting the accessory sales;
policy management step: making a preset strategy for the appointed accessories, and predicting sales data of each accessory according to the prediction management model to calculate an accessory inventory strategy;
supply chain optimization step: according to the accessory inventory strategy, calculating order requirements which need to be issued to each supplier every day;
the predictive management model includes modeling sales of the accessory to give an expectation of future sales of the accessory.
Specifically, the data management step includes:
the dealer data includes a dealer network topology;
the provider data comprises a provider network topology;
the accessory data comprises accessory engineering data, accessory change information, accessory sales data and finished automobile sales data;
the accessory sales summary data includes feature processing accessory data, the generating including: historical monthly sales, historical quarterly sales, monthly sales growth rate, and quarterly sales growth rate;
the whole vehicle keeping data of the branch vehicle ages comprises the steps of calculating the distribution quantity of different vehicles of the current vehicle according to the sale date data of each whole vehicle, and calculating the whole vehicle keeping data of the branch vehicle ages in the future preset time in each month through a time sequence method.
Specifically, the fitting engineering step includes: the engineering information of the accessory includes: cost of ownership, cost of loss, and pull strategy for after-market accessories; updating the change in service time and scope of the accessory due to a change in vendor or upgrade in technology; and maintaining the information of the covered vehicle type.
Specifically, the prediction management step includes:
prediction management step M1: according to the whole vehicle holding data, the accessory sales summary data and the engineering information of the accessories of the sub-vehicles, establishing a prediction management model for the accessory sales through a parallel calculation method;
prediction management step M2: and training the prediction management model according to the historical sales data of the accessories until the prediction error reaches a preset value.
Specifically, the policy management step includes: outputting an accessory prediction result by the homonymy prediction management model, describing the demand of the accessory by using a distribution function, and calculating the inventory level of the accessory pipeline according to the service level, wherein the formula is as follows:
wherein p is a service level requirement, I is an optimal pipe inventory to be sought, f (x, α, β) is a distribution density function of the demand for the parts, x represents a demand for the parts, α represents a mathematical expectation of the demand for the parts, and β represents a standard deviation of the demand for the parts.
Specifically, the supply chain optimization step includes:
supply chain optimization step M1: determining an accessory inventory strategy according to the strategy management step, and calculating order strategies for each logistics node in a timing task mode;
supply chain optimization step M2: and according to the order strategy and the inventory strategy, acquiring the hand inventory quantity and the in-transit order data information, and calculating the order requirements which need to be issued to each supplier every day.
The order policy includes: order policy-inventory policy value-on-hand inventory value-on-route order value;
wherein the hand inventory value represents an amount of available parts in the warehouse; the in-transit order quantity represents the total number of parts for which an order was not delivered.
Specifically, the method also comprises a center displaying step and an early warning monitoring step;
the display center step comprises displaying data results and editing and confirming order results;
the early warning monitoring step comprises prompting low stock early warning, arrival delay early warning, seasonal pulling early warning and abnormal demand early warning;
the prompt low inventory early warning is triggered when the hand inventory is lower than the safety inventory;
the arrival postponing early warning is triggered when the corresponding order completes warehousing operation at the expected expiration time;
the seasonal pull pre-warning is triggered when an accessory demand peak prediction value occurs;
the abnormal demand warning is triggered when a dealer order is greater than a historical order preset value.
Specifically, the data information displayed in the step of displaying the center comprises a pipeline inventory strategy value, a safety inventory strategy value, an on-demand order, a node inventory, a sales forecast and a sales volume preset average;
the step of displaying the center for editing and confirming the order result comprises the following steps: and based on a distributed data synchronization technology, periodically sending order information to an order execution system.
Specifically, the inventory policy further includes:
limiting a preset range of the inventory strategy according to the management requirement, and taking the currently calculated inventory strategy value when the calculated inventory strategy value is within the preset range; and when the calculated inventory strategy value is not in the preset range, taking the nearest boundary value of the preset range as the inventory strategy value to be output.
The system is provided for the doors of the after-sale supply chain of the whole vehicle factory for use, and is applied to the part inventory management of the after-sale part center. The system is responsible for sending orders to its associated accessory suppliers daily according to a calculation strategy, while forecasting the after-market accessory requirements of the franchised dealerships for the center. The system changes the state that the enterprise depends on experience and a fixed calculation formula to predict and optimize the inventory, and has the following core indexes: starting from the inventory turnover rate and the order satisfaction rate of the parts, the inventory of all the parts is reasonably distributed to achieve the optimal index state.
Through the processing of a plurality of modules of the system, the system can effectively help the department after sales of the vehicle and the enterprise to save time and cost, improve the personnel efficiency, and help the department to make an optimal inventory strategy, thereby effectively improving the rate of after sales profit under the condition of fixed input resources.
Example 2
Example 1 is a modification of example 2
As shown in fig. 1, the present system includes the following seven modules:
the data management module acquires dealer and accessory data from each data interface in a mode of timing tasks and the like, obtains whole vehicle keeping data and accessory sales summary data of the vehicle sharing age, and is used for:
synchronizing information such as accessory engineering data, accessory change information, dealer network topology, supplier network topology, accessory sales data, vehicle sales data and the like;
performing characteristic processing on the accessory sales data to generate the following indexes: historical monthly sales, historical seasonal sales, monthly sales growth rate, seasonal sales growth, and the like;
according to the sales date data of each whole vehicle, the distribution number of different vehicle ages of the current vehicle is calculated, and the total vehicle reserve of the vehicle divided by 12 months in the future and each month is calculated through a time series method.
The accessory engineering module acquires engineering information of accessories through file uploading and a data interface, and is used for:
importing after-sale proprietary information such as holding cost, loss cost, pulling strategy and the like of after-sale accessories into the system through file uploading;
updating the change of the service time and range of the accessory caused by the change of a supplier or the technical upgrade by establishing a data interface with the BOM system;
and (4) importing an accessory maintenance strategy in the system through file real-time transmission, namely maintaining the information of the covered vehicle type.
The prediction management module is used for modeling the sales volume of the accessories and providing the future sales volume expectation according to the sales data of the whole vehicle and the accessories which are obtained and sorted by the data management module and the accessory engineering module, and automatically adjusting model parameters according to prediction errors and obtaining a better prediction effect, and is used for:
splitting historical sales data of accessories into two parts, namely a training set and a test set, wherein in a default state, the test set is sales data of the latest natural month, and the rest is the training set;
according to the characteristics of after-sale data, the types of accessories are very many, about thirty thousand, the number of distributors is large, about one thousand, the differences between the accessories and between the distributors are huge, a model is required to be capable of coping with the sales data with various characteristics, a parallel calculation method is adopted to design a prediction model, and the method mainly comprises the following steps:
each sub-model in the frame outputs monthly prediction of future 12 months and monthly prediction and prediction errors of a test set according to requirements, and the test set is defaulted to be the latest sale month;
default in the frame includes a time series model and a support vector machine model, and the frame is allowed to be in the above;
the framework selects a model with the minimum error as the current prediction model according to the relative errors of the test set month, wherein the relative error map formula is as follows,
wherein P is the test set total forecast, and A is the monthly actual sales volume of the test set.
A policy management module providing an entry for a system user to enter custom policies for specified accessories and calculate inventory policies for each accessory in conjunction with forecast data, for:
the forecasting result of the fittings is output by the forecasting module, a proper distribution function is selected to describe the fittings, the inventory level of the fittings pipelines is calculated according to the requirement of the service level,
wherein p is a service level requirement, I is an optimal pipeline inventory to be solved, and f (x, alpha, beta) is a distribution density function of the accessory requirement; wherein x represents the part demand, α represents the mathematical expectation of the part demand, and β represents the standard deviation of the part demand;
maintaining model control limits, limiting the calculation results of the inventory strategy model according to the management requirements, giving an upper limit range and a lower limit range of inventory, training and adjusting model parameters by a system in the range according to data, and taking the closest limit boundary value as an output result of the pipeline inventory if the calculated pipeline inventory value exceeds the limit range;
the custom accessory strategy formula realizes the setting of the upper and lower limit ranges of the inventory and submits the setting to a System for calculation in a Json form, wherein Json refers to a JavaScript object numbered notation which is a light-weight data exchange format, for example, System:6 × Dmean≤I≤8*Dmean&Gcc in (BJ K), maintaining an accessory inventory strategy with the attribute of B, J or K of the accessory Gcc between 6 and 8 times of the weekly average demand, adopting a system strategy calculation result when the system strategy calculation result is between 6 and 8 weeks of the weekly average demand, taking the 6 times of the weekly demand as an inventory strategy if the system strategy calculation result is less than 6 times of the weekly demand, and taking the 8 times of the weekly demand as an inventory strategy if the system strategy calculation result is more than 8 times of the weekly demand. Wherein, System represents the intersection result of input strategy and System calculation, I represents the inventory strategy value, DmeanIndicating the mean demand of the part, part.gcc indicating the Gcc attribute of the part, the Gcc attribute describing the volume and shape of the part, in (b J k) indicating that one of the part.gcc attribute is required to be B, J, K, and the input policy is validated when the volume and shape parameters of the part are one of B, J, K.
A supply chain optimization module to:
according to the accessory inventory strategy determined by the strategy management module, calculating an order strategy for each logistics node in a timed task mode, wherein the order strategy is an inventory strategy value-on-hand inventory value-on-the-way order value, the on-hand inventory value refers to the available accessory quantity in a warehouse, and the on-the-way order value refers to the total quantity of accessories without delivery of orders;
according to the inventory strategy and the order strategy, a data interface with the logistics storage system is established to acquire the information of the inventory quantity of the accessories in hand and the quantity of the orders in transit, and the order requirements which need to be issued to each supplier every day are calculated.
A presentation center module to:
displaying data results, including: pipeline inventory strategy values, safety inventory strategy values, on-demand orders, node inventory, sales volume prediction average and the like;
and editing and confirming the order result, and periodically sending order information to an order execution system based on a distributed data synchronization technology.
An early warning monitoring module for:
prompting low stock early warning, and triggering when the hand stock is lower than the safety stock;
the method comprises the steps of early warning of delay of arrival, and triggering when the corresponding order completes warehousing operation at the expected expiration time;
seasonal pulling early warning, which is triggered when the predicted value of the accessory demand peak appears;
and (4) abnormal demand early warning, which is triggered when the order of the dealer is greater than the thousand deciles of the historical order.
As shown in fig. two, the data processing flow of the present system includes the following steps:
a demand forecasting stage:
training data: the system divides the historical sales data of the accessories into a training set and a verification set according to the time sequence, wherein the verification set is the sales data of the latest month under the default condition, and the rest are the training sets;
model framework: a mode of distributing and paralleling multiple prediction models is adopted, multiple modeling methods are tried for each accessory data, and a time series model and a support vector machine model are used by default;
and (3) determining parameters of the model: training each available model in the model framework by using the training set and the verification set data, and respectively calculating model parameters and error estimation;
error data: actual prediction error data of each model accumulated in the system operation process;
selecting a model: selecting a model with the minimum average value of model error estimation and historical error data as a current prediction model;
and (3) prediction calculation: and according to the result of the selection of the prediction model, executing a calculation process to obtain the prediction data of the 12 months of the accessory.
And (3) an inventory optimization stage:
and (3) calculating the upper limit of the distribution model: calculating the inventory upper limit requirement of each part according to the random inventory model of the operational research inventory theory and the prediction calculation result;
customizing an inventory strategy: a user inputs a custom inventory requirement to the system in a Json mode;
stock data: acquiring the on-site and on-route data of the accessories through a data interface of a logistics storage system TMS;
and (3) order calculation: combining II.A, II, B and II.C, and calculating the obtained accessory order sent to the supplier on the same day according to the supplier order requirements uploaded by the user, such as package, minimum order taking amount and the like;
order approval: and the user confirms the order data calculated by the model in the system and calls the relevant interfaces of the order execution system and the enterprise operation management system to send the order.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. An inventory optimization control system for automotive after-market accessories, comprising:
a data management module: acquiring dealer, supplier and accessory data through computer network or manual input to obtain whole vehicle keeping data and accessory sales summary data of vehicle sharing age;
an accessory engineering module: acquiring engineering information of the accessory through a file uploading and data interface;
a prediction management module: according to the whole vehicle keeping data of the vehicle sharing age, the accessory sales summary data and the engineering information of the accessories, establishing a prediction management model for the accessory sales through a parallel calculation method, and predicting the accessory sales;
a policy management module: making a preset strategy for the specified accessories, predicting sales data of each accessory according to the prediction management model, calculating an accessory inventory strategy, and storing the accessory inventory strategy into an accessory inventory strategy database;
a supply chain optimization module: acquiring an accessory inventory strategy from an accessory inventory strategy database, and calculating order requirements which need to be issued to various suppliers every day;
the predictive management model includes modeling sales of the accessory to give an expectation of future sales of the accessory.
2. The inventory optimization control system for after-market automotive parts according to claim 1, wherein said data management module comprises:
the dealer data includes a dealer network topology;
the provider data comprises a provider network topology;
the accessory data comprises accessory engineering data, accessory change information, accessory sales data and finished automobile sales data;
the accessory sales summary data includes feature processing accessory data, the generating including: historical monthly sales, historical quarterly sales, monthly sales growth rate, and quarterly sales growth rate;
the whole vehicle keeping data of the branch vehicle ages comprises the steps of calculating the distribution quantity of different vehicles of the current vehicle according to the sale date data of each whole vehicle, and calculating the whole vehicle keeping data of the branch vehicle ages in the future preset time in each month through a time sequence method.
3. The inventory optimization control system for after-market automotive parts according to claim 1, wherein said parts engineering module comprises: the engineering information of the accessory includes: cost of ownership, cost of loss, and pull strategy for after-market accessories; updating the change in service time and scope of the accessory due to a change in vendor or upgrade in technology; and maintaining the information of the covered vehicle type.
4. The inventory optimization control system for after-market automotive parts according to claim 1, wherein said forecast management module comprises:
prediction management module M1: according to the whole vehicle holding data, the accessory sales summary data and the engineering information of the accessories of the sub-vehicles, establishing a prediction management model for the accessory sales through a parallel calculation method;
prediction management module M2: and training the prediction management model according to the historical sales data of the accessories until the prediction error reaches a preset value.
5. The inventory optimization control system for after-market automotive parts according to claim 1, wherein said policy management module comprises: outputting an accessory prediction result by the homonymy prediction management model, describing the demand of the accessory by using a distribution function, and calculating the inventory level of the accessory pipeline according to the service level, wherein the formula is as follows:
wherein p is a service level requirement, I is an optimal pipe inventory to be sought, f (x, α, β) is a distribution density function of the demand for the parts, x represents a demand for the parts, α represents a mathematical expectation of the demand for the parts, and β represents a standard deviation of the demand for the parts.
6. The inventory optimization control system for automotive after-market accessories according to claim 1, wherein the supply chain optimization module comprises:
supply chain optimization module M1: determining an accessory inventory strategy according to a strategy management module, and calculating order strategies for each logistics node in a timed task mode;
supply chain optimization module M2: according to the order strategy and the inventory strategy, acquiring the inventory quantity of the on-hand and the in-transit order data information, and calculating the order requirements which need to be issued to each supplier every day;
the order policy includes: order policy-inventory policy value-on-hand inventory value-on-route order value;
wherein the hand inventory value represents an amount of available parts in the warehouse; the in-transit order quantity represents the total number of parts for which an order was not delivered.
7. The inventory optimization control system for after-market automotive parts according to claim 1, further comprising a presentation center module and an early warning monitoring module;
the display center module comprises a display data result and an editing and confirming order result;
the early warning monitoring module prompts low stock early warning, arrival delay early warning, seasonal pulling early warning and abnormal demand early warning;
the prompt low inventory early warning is triggered when the hand inventory is lower than the safety inventory;
the arrival postponing early warning is triggered when the corresponding order completes warehousing operation at the expected expiration time;
the seasonal pull pre-warning is triggered when an accessory demand peak prediction value occurs;
the abnormal demand warning is triggered when a dealer order is greater than a historical order preset value.
8. The inventory optimization control system for after-market automotive parts according to claim 7, wherein said display data information in said presentation center module includes piping inventory strategy values, safety inventory strategy values, on-demand orders, nodal inventory, sales forecasts and sales volume predetermined averages;
the order editing and confirming result in the display center module comprises the following steps: and based on a distributed data synchronization technology, periodically sending order information to an order execution system.
9. The inventory optimization control system for automotive after-market accessories of claim 5, the inventory strategy further comprising:
limiting a preset range of the inventory strategy according to the management requirement, and taking the currently calculated inventory strategy value when the calculated inventory strategy value is within the preset range; and when the calculated inventory strategy value is not in the preset range, taking the nearest boundary value of the preset range as the inventory strategy value to be output.
10. An inventory optimization control method for automobile after-market accessories, comprising:
a data management step: acquiring dealer, supplier and accessory data through computer network or manual input to obtain whole vehicle keeping data and accessory sales summary data of vehicle sharing age;
the accessory engineering step: acquiring engineering information of the accessory through a file uploading and data interface;
a prediction management step: according to the whole vehicle keeping data of the vehicle sharing age, the accessory sales summary data and the engineering information of the accessories, establishing a prediction management model for the accessory sales through a parallel calculation method, and predicting the accessory sales;
policy management step: making a preset strategy for the specified accessories, predicting sales data of each accessory according to the prediction management model, calculating an accessory inventory strategy, and storing the accessory inventory strategy into an accessory inventory strategy database;
supply chain optimization step: acquiring an accessory inventory strategy from an accessory inventory strategy database, and calculating order requirements which need to be issued to various suppliers every day;
the predictive management model includes modeling sales of the accessory to give an expectation of future sales of the accessory.
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