CN113127538B - High-precision spare part demand prediction method - Google Patents
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Abstract
The invention provides a high-precision spare part demand prediction method. The method comprises the following steps: collecting metadata related to spare part demand prediction, and storing the metadata processed by the ETL into a database; preprocessing metadata stored in a database, performing data feature mining on the preprocessed metadata, determining each factor influencing spare part requirements, analyzing the influence degree of each factor on the spare part requirements, taking the metadata subjected to influence degree analysis processing, each useful influence factor and the influence degree of each influence factor after sequencing on the spare part requirements as source data in a spare part requirement prediction stage, inputting the source data into a LinearRegression, adaBoost, GBDT-based machine learning fusion modeling prediction method, performing prediction analysis on spare part requirements in a future month, and outputting a prediction result. The method can make reasonable purchase plans through the economic principle of the supply chain, is beneficial to departments to fully utilize resources, reasonably distributes and purchases the quantity of spare parts, and reduces unnecessary property cost and other operation cost.
Description
Technical Field
The invention relates to the technical field of computer application, in particular to a high-precision spare part demand prediction method.
Background
In the current fast-developing economic society, one of the ways for enterprises to obtain more competitive advantages is to more reasonably utilize the existing funds to produce greater economic benefits, which is very important for the development of enterprises. The enterprise department can determine future spare part demands through the prediction of spare part demands and key factors for adjusting the spare part demands, timely master future demand trends, make reasonable planning and decision, and finally improve the overall competitive advantage and economic benefit of the enterprise.
Under the existing mode, the traditional analysis processing mode is not only difficult, but also inaccurate in result. Therefore, aiming at the situation that a large number of factors influencing spare part demands exist and are complex and changeable, a more accurate and effective high-precision spare part demand prediction method is provided, and the method has important significance for making a more reasonable plan for a decision maker and improving the overall competitiveness of an enterprise.
Disclosure of Invention
The embodiment of the invention provides a high-precision spare part demand prediction method, which is used for accurately and effectively predicting the spare part demand. In order to achieve the above purpose, the present invention adopts the following technical scheme. A high-precision spare part demand prediction method comprises the following steps:
collecting metadata related to spare part demand prediction, and storing the metadata processed by the ETL into a database;
preprocessing metadata stored in a database, wherein the preprocessing comprises data summarization, data integration and analysis processing;
performing data feature mining on the preprocessed metadata, determining each factor influencing the spare part demand, analyzing the influence degree of each factor on the spare part demand, and sequencing each factor according to the influence degree;
in the spare part demand prediction stage, metadata with the front influence degree calculated after being processed by a key factor recognition algorithm is used as source data, the source data is input into a LinearRegression, adaBoost, GBDT-based machine learning fusion modeling prediction method, the future spare part demand is predicted and analyzed, and a prediction result is output.
The collecting metadata related to spare part demand prediction, storing the metadata processed by ETL into a database, and the method comprises the following steps:
configuring a data acquisition task, setting task attributes of the data acquisition task, wherein the task attributes comprise an acquisition object, acquisition time, acquisition period and audit level, executing the data acquisition task through a software program, and acquiring metadata from a data source of an enterprise department through data acquisition, exchange processing, data aggregation and importing loading service functions; the metadata relates to aspects of spare part information, the data sources from various links from spare part production to spare part use; and carrying out ETL processing on the acquired metadata, and storing the metadata after the ETL processing into a database.
The metadata includes: basic information of each spare part, historical workload of consumed spare parts, inventory information of spare parts, purchasing information of spare parts, working environment of spare parts, maintenance information of spare parts, classification information of spare parts, maintenance information of spare parts, supply information of spare parts, consumption information of spare parts, economic and vulnerability data of spare parts.
Preprocessing the metadata stored in the database, wherein the preprocessing comprises data summarization, data integration and analysis processing and comprises the following steps:
the metadata stored in the database is subjected to data summarization, data integration and analysis processing through a metadata management function, various data existing in each link and each stage are comprehensively described in the whole business process through metadata, the whole business process refers to production, transportation, use, consumption and replacement links of spare parts, each link comprises a supply link, a purchasing link, a transportation link, a production link, a use link and a maintenance link of the spare parts, and each stage comprises each stage of spare part use; the data summary is used to review the correctness and validity of the data.
The influencing factors include spare part market supply quantity, repair times, spare part monthly consumption quantity, repair degree, purchase quantity, spare part equipment working time, spare part supplier quantity, purchase unit price, maintenance effect, purchase times and maintenance times.
In the spare part demand prediction stage, metadata with the front influence degree calculated after being processed by a key factor recognition algorithm is used as source data, the source data is input into a training model in a machine learning algorithm, a LinearRegression, adaBoost, GBDT-based machine learning fusion modeling prediction method is used for predicting and analyzing the spare part demand in a future month, and a prediction result is output, and the method comprises the following steps: in the spare part demand prediction stage, useless influence factors are removed by using a key factor recognition algorithm, metadata with the front influence degree calculated after being processed by the key factor recognition algorithm is used as source data, after different types of influence factors are processed in a coding mode, training is carried out by using a LinearRegression, adaBoost, GBDT-based machine learning fusion modeling prediction method, the processed source data are input into the LinearRegression, adaBoost, GBDT-based machine learning fusion modeling prediction method after training, prediction analysis is carried out on spare part demand in a month in the future, and a predicted result of the spare part demand is displayed on a front-end display platform in a report, chart and map display mode.
The key factor recognition algorithm is an XGBoost algorithm model; the machine learning model includes: GBDT prediction model, linear rregprecision prediction model, and AdaBoost prediction model.
According to the technical scheme provided by the embodiment of the invention, the spare part demand prediction obtained by the method can have positive significance for department decision and planning, and a decision maker can make a reasonable purchase plan by means of a supply chain economic principle according to the prediction result, so that the departments can fully utilize resources, the number of purchased spare parts is reasonably distributed, and unnecessary property cost and other operation cost are reduced.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an implementation of a method for predicting a spare part requirement with high accuracy according to an embodiment of the present invention;
FIG. 2 is a process flow diagram of a method for predicting the demand of spare parts with high accuracy according to an embodiment of the present invention;
FIG. 3 is a process flow diagram of a machine learning fusion modeling prediction method for predicting and analyzing the demand of spare parts based on LinearRegression, adaBoost, GBDT according to an embodiment of the present invention;
fig. 4 is a schematic diagram of various factors affecting spare part requirements according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention. As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the purpose of facilitating an understanding of the embodiments of the invention, reference will now be made to the drawings of several specific embodiments illustrated in the drawings and in no way should be taken to limit the embodiments of the invention.
The spare part demand prediction is different from the demand prediction of general materials because the spare part demand has great uncertainty and interruption, if a simple prediction technology is simply utilized, a prediction result is inevitably greatly deviated from the actual demand, so that the stock quantity of the spare part is excessive, a great amount of flowing funds are accumulated on an enterprise, or the equipment shutdown loss is caused by too little, and the economic benefit of the enterprise is influenced.
The embodiment of the invention provides a high-precision spare part demand prediction method, which aims to solve the problems that the current spare part demand prediction method is complex, the prediction result is large in actual gap, the reliability is low, and no complete spare part demand prediction system is used for systematically predicting and analyzing the demand quantity.
The spare part demand prediction in the invention predicts according to the time interval of month, and the spare part demand characteristic is mined according to the existing historical data to predict the spare part demand quantity of the future month, so that the spare part purchasing quantity can be rapidly determined according to the supply chain relation for a decision maker, and the economic benefit is improved.
The implementation schematic diagram of the high-precision spare part demand prediction method provided by the embodiment of the invention is shown in fig. 1, the specific processing flow is shown in fig. 2, and the method comprises the following processing steps:
and S10, collecting metadata related to spare part demand prediction, and storing the metadata processed by ETL (Extract-Transform-Load), extraction, conversion and loading into a database.
In the data acquisition stage, firstly, metadata related to spare part demand prediction to be acquired is defined, and the meaning and standard of the metadata are determined.
Metadata is primarily data describing and defining the business data itself and its operating environment, such as a description of databases, tables, columns, column attributes (types, formats, constraints, etc.), and the like. For the ETL process, the significant set of metadata appears as:
(1) Defining the position of a data source and the attribute of the data source;
(2) Determining a correspondence rule from the source data to the target data;
(3) Determining relevant business logic;
(4) Other necessary preparation before the actual loading of the data. Metadata generally extends through the entire data warehouse project, and all processes of the ETL must maximally refer to metadata. Reasonable metadata can effectively draw out the relevance of information, and can more effectively guide the ETL process by combining the relevance with the data quality.
The metadata in the present application mainly includes basic data such as basic information of each spare part, historical workload of consumed spare parts, inventory information of spare parts, purchasing information of spare parts, working environment of spare parts, maintenance information of spare parts, classification information of spare parts, maintenance information of spare parts, supply information of spare parts, consumption information of spare parts, economy and vulnerability of spare parts, and the like. Metadata is the "command center" of the ETL process, so that the selection, specification and management of metadata directly affect the correctness and efficiency of the ETL process.
The data acquisition task is configured, the acquisition task is flexibly adjusted according to the actual condition of the data, and the acquisition object, the acquisition time, the acquisition period, the auditing level and other task attributes of the data acquisition task are set.
The data acquisition task is executed through a software program, and metadata is acquired from a data source of an enterprise department through service functions such as data acquisition, exchange processing, data summarization, import loading and the like. In practical applications, the data sources are relational databases, and other data sources also have file forms, such as txt files, excel files, xml files, PDF files, and the like.
From the above metadata we can see that the metadata to be collected relates to various aspects of spare part information, and the information needs to be collected from various links from the production to the use of spare parts. The data sources for metadata collection are thus from various links from spare part production to spare part use. The using mode includes manual recording and machine scanning, information is synchronized in a database, and the ETL tool is used for data cleaning to meet the use requirement.
Because the metadata collected from the data sources does not necessarily fully satisfy the requirements of the database, ETL processing is performed on the collected metadata. And storing the metadata processed by the ETL into a database so as to collect and analyze the metadata.
An ETL system needs to be able to complete the periodic automatic loading of daily data within a defined time, support the loading of initial data and historical data, and meet the requirements of future expansion. Tens or more target data tables and a considerable amount of source data in the system mean the complexity of the ETL program, and the huge data volume needs to fully consider the running efficiency of the system, so that a flexible and simple program structure is required for conveniently developing a complex program; the requirement of optimizing the efficiency of the program often requires personalized designs for different data. Thus, the design of ETL must balance the manageability and procedural performance of development.
And step S20, preprocessing is carried out on metadata stored in the database, wherein the preprocessing comprises data summarization, data integration and analysis processing.
Metadata stored in a database is subjected to data summarization, data integration and analysis processing through a metadata management function, and various data existing in each link and each stage are comprehensively described through the metadata in the whole business process, so that data information in the system can be read and managed in the whole process.
The whole business process refers to links of production, transportation, use, consumption, replacement and the like of spare parts, comprises a complete life cycle of the spare parts, ensures that each link of the spare parts is under consideration, and is convenient for improving the accuracy of spare part model prediction. The links mainly relate to a supply link (including information such as market supply quantity, supplier quantity, lowest supply unit price, highest supply unit price, spare part acquisition difficulty and the like), a purchasing link (including information such as spare part purchasing quantity, purchasing times, purchasing unit price, purchasing standards, spare part stock shortage cost and the like), a transportation link (including information such as spare part transportation cost, spare part storage cost and the like), a production link (including information such as spare part manufacturer, technical specifications, spare part material, whether standard, spare part material, class of the spare part, working time of the spare part equipment, working strength of the generating equipment and the like), a use link (including information such as spare part importance, spare part replaceability, stock environment, spare part working temperature, spare part working strength, spare part working position, spare part vulnerability and the like), and a maintenance link (including information such as spare part repair times, spare part repair quantity, repair degree, maintenance times, maintenance effect and the like); the stages are for the various stages of spare part use, such as manufacturing, transportation, purchasing, use, and maintenance.
The data summarization is a comprehensive method for comprehensively grasping data and auditing the data and is used for auditing the correctness and validity of the data. For the demand prediction work, the role of summarizing the inspection data is important, and the correct and effective data is the basis of the demand prediction. The data summarizing service mainly realizes the function of collecting data automatically through summarizing rules customized by the system, generates summarized data report forms and provides export printing. Then, the data is stored in a database of the system after format conversion by the data conversion loading service, and the original data of the reporting unit is finally saved and used as an archive.
The purpose of the above ETL processing and preprocessing of metadata is to ensure timeliness, legitimacy, integrity, consistency, auditability and security of the data and management of the platform.
And S30, carrying out data feature mining on the preprocessed metadata, determining each factor influencing the spare part requirement, analyzing the influence degree of each factor on the spare part requirement, and sequencing each factor according to the influence degree.
A schematic diagram of each factor affecting spare part requirements provided by the embodiment of the invention is shown in FIG. 4. As shown in fig. 4, it can be seen that the factors affecting the results are related to the spare part market supply amount, the repair number, the spare part monthly consumption amount, the repair number, the repair degree, the purchase number, the spare part equipment operating time, the spare part supplier number, the purchase unit price, the maintenance effect, the purchase number, the maintenance number, and the like.
In step S40, in the spare part demand prediction stage, the key factor recognition algorithm may be used to remove the useless influencing factors, reduce training features, and improve the model training effect. The metadata with the front influence degree calculated after influence degree analysis processing, each useful influence factor and the influence degree of each influence factor after sequencing on spare part demands are used as source data, different types of factors are processed in a One-hotencoding, label-encoding mode and other encoding modes, the processed metadata are input into a LinearRegression, adaBoost, GBDT-based machine learning fusion modeling prediction method, the spare part demands of a certain month in the future are predicted and analyzed, and a prediction result is output.
The more useless features in the machine learning are, the more useless features are, the generalization capability and learning time of the model are affected, and the ordered influence factors are used for training based on the LinearRegression, adaBoost, GBDT machine learning fusion modeling prediction method, so that the accuracy of the machine learning can be obviously improved, the training time of the model is shortened, and the problem of dimension disasters is effectively avoided. The dimension disaster problem is alleviated: the more features, the more complex the model, and the generalization ability thereof decreases. The difficulty of learning tasks is reduced: the more features, the longer it takes to analyze the features and train the model.
The machine learning essence is function learning, the existing data features and the prediction result are subjected to model training and modeling by using a machine learning algorithm, a function which can be used for calculation, namely an algorithm model which we say, is finally obtained, and finally the data features needing to be predicted are input into the function, so that the prediction result is obtained.
In the system, an XGBoost algorithm model algorithm is used for model training, key features in data are extracted, and influence caused by useless features is reduced. And then, a GBDT prediction model, a linear prediction model and an AdaBoost prediction model are called to carry out multi-model fusion training and prediction, so that the accuracy of spare part demand prediction is improved. And then, the forecast result of the spare part demand can be displayed on the front-end display platform in the modes of report, chart, map display and the like, so that a user can simply, conveniently and quickly check the forecast result.
The front-end display platform adopts a B/S architecture, is composed of a whole set of components or services, and is connected through a powerful Web-based communication framework, so that different application requirements of users are met. The front-end display platform can adopt page display technologies such as Jquery, JS and the like.
The machine learning fusion modeling prediction method based on LinearRegression, adaBoost, GBDT is realized through an algorithm by a programming language.
The processing flow chart for predicting and analyzing the spare part demand based on the LinearRegression, adaBoost, GBDT machine learning fusion modeling prediction method is shown in fig. 3. In the whole predicting process of spare part demand, the system platform integrates related predicting functions to realize configuration management. The data, the algorithms, the functions and even the presentation forms of the analysis results required by the prediction can be defined by the user, so that an extensible analysis platform is provided for the user to perform analysis activities by the user.
A large number of mathematical operations are involved in the process of the required programming algorithm design problem and data analysis. Although modern mathematical theory of numerical computation has been well developed, most of the computation problems have efficient standard solutions, but the computational effort of simulating complex models with computers is still significant. At present, model fitting tools are covered in languages such as engineering calculation software R/Python and the like which are popular internationally, and a solution is provided for the problem.
The machine learning fusion modeling prediction method based on LinearRegression, adaBoost, GBDT utilizes Python/R language to write corresponding calculation functions, and then packages the calculation functions into jar packages for Java to call. And the system user invokes the basic data in the basic data management function module and the model of the prediction model management module to realize the algorithm, so that the prediction is realized, and the prediction data is obtained and provided for the spare part demand analysis module to be displayed to the user.
The client of the spare part demand forecasting system integrates inquiry, statistics, data mining and analysis by using a visual and user interaction technology, and on the basis of establishing and perfecting a mechanism of data exchange and data updating, the demand conditions of spare parts of enterprises are observed by fully reflecting the demand conditions of the enterprises on different spare parts in different time periods through basic information of each spare part, historical task workload of the consumed spare parts, stock information of the spare parts, purchasing information of the spare parts, working environment of the spare parts, maintenance information of the spare parts, classification information of the spare parts, maintenance information of the spare parts, supply information of the spare parts, consumption information of the spare parts, economy and vulnerability of the spare parts and other basic data and special information. Through a visual interface and a data management technology, a friendly man-machine interaction interface is directly provided for a user, and non-professional management and decision-making personnel operation are facilitated. By fusing a plurality of machine learning model prediction technologies (including GBDT prediction models, linear prediction models and AdaBoost prediction models), the data are subjected to omnibearing data model training, the data are predicted, data sharing and informatization services are provided for related departments, and support is provided for management decisions of enterprises. The system is designed according to the user requirements, the data mode and expert knowledge of the knowledge base and related software engineering standards, and is realized by utilizing development tool programming.
In summary, the spare part demand prediction obtained by the method of the embodiment of the invention has positive significance for department decision and planning, a decision maker can make a reasonable purchase plan by means of a supply chain economic principle according to the prediction result, thereby being beneficial to departments to fully utilize resources, reasonably distributing and purchasing the number of spare parts, reducing unnecessary property cost and other operation cost, achieving the purposes of saving cost, focusing on more needed spare parts and finally improving department income.
The accuracy of the spare part demand prediction result obtained by the method has great influence on department decision, and the inaccurate prediction result can cause greater loss to departments, so that the method is important for improving and innovating the traditional method, making up the defects and shortcomings or introducing a new method, and therefore, the method for reinforcing the research on spare part demand prediction is also practical and has strong social and economic values.
Those of ordinary skill in the art will appreciate that: the drawing is a schematic diagram of one embodiment and the modules or flows in the drawing are not necessarily required to practice the invention.
From the above description of embodiments, it will be apparent to those skilled in the art that the present invention may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, with reference to the description of method embodiments in part. The apparatus and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (4)
1. The method for predicting the spare part demand with high precision is characterized by comprising the following steps of:
collecting metadata related to spare part demand prediction, and storing the metadata processed by the ETL into a database;
preprocessing metadata stored in a database, wherein the preprocessing comprises data summarization, data integration and analysis processing;
performing data feature mining on the preprocessed metadata, determining each factor influencing the spare part demand, analyzing the influence degree of each factor on the spare part demand, and sequencing each factor according to the influence degree;
in the spare part demand prediction stage, metadata with the front influence degree calculated after being processed by a key factor recognition algorithm is used as source data, the source data is input into a LinearRegression, adaBoost, GBDT-based machine learning fusion modeling prediction method, the future spare part demand is predicted and analyzed, and a prediction result is output;
the collecting metadata related to spare part demand prediction, storing the metadata processed by ETL into a database, and the method comprises the following steps:
configuring a data acquisition task, setting task attributes of the data acquisition task, wherein the task attributes comprise an acquisition object, acquisition time, acquisition period and audit level, executing the data acquisition task through a software program, and acquiring metadata from a data source of an enterprise department through data acquisition, exchange processing, data aggregation and importing loading service functions; the metadata relates to aspects of spare part information, the data sources from various links from spare part production to spare part use; ETL processing is carried out on the acquired metadata, and the metadata after the ETL processing is stored in a database;
in the spare part demand prediction stage, metadata with the front influence degree calculated after being processed by a key factor recognition algorithm is used as source data, the source data is input into a training model in a machine learning algorithm, a LinearRegression, adaBoost, GBDT-based machine learning fusion modeling prediction method is used for predicting and analyzing the spare part demand in a future month, and a prediction result is output, and the method comprises the following steps:
in the spare part demand prediction stage, useless influence factors are removed by using a key factor recognition algorithm, metadata with the front influence degree calculated after being processed by the key factor recognition algorithm is used as source data, after different types of influence factors are processed in a coding mode, training is carried out by using a LinearRegression, adaBoost, GBDT-based machine learning fusion modeling prediction method, the processed source data are input into the LinearRegression, adaBoost, GBDT-based machine learning fusion modeling prediction method after training, prediction analysis is carried out on spare part demand in a month in the future, and a predicted result of the spare part demand is displayed on a front-end display platform in a report, chart and map display mode.
2. The method of claim 1, wherein the preprocessing of metadata stored in the database includes data summarization, data integration and analysis processing, including:
the metadata stored in the database is subjected to data summarization, data integration and analysis processing through a metadata management function, various data existing in each link and each stage are comprehensively described in the whole business process through metadata, the whole business process refers to production, transportation, use, consumption and replacement links of spare parts, each link comprises a supply link, a purchasing link, a transportation link, a production link, a use link and a maintenance link of the spare parts, and each stage comprises each stage of spare part use; the data summary is used to review the correctness and validity of the data.
3. The method of claim 1, wherein the factors include spare part market supply, number of repairs, spare part monthly consumption, number of repairs, degree of repairs, number of purchases, spare part equipment hours, number of spare part suppliers, number of purchases unit price, maintenance effectiveness, number of purchases, and number of maintenance.
4. The method of claim 1, wherein the metadata comprises: basic information of each spare part, historical workload of consumed spare parts, inventory information of spare parts, purchasing information of spare parts, working environment of spare parts, maintenance information of spare parts, classification information of spare parts, maintenance information of spare parts, supply information of spare parts, consumption information of spare parts, economic and vulnerability data of spare parts.
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