Purchase index and price analysis visualization platform and method based on big data model

Abstract

The invention discloses a purchase index and price analysis visualization platform and method based on a big data model, and the method comprises the steps: 1, enabling a production system to obtain a purchase transaction information table for purchasing raw materials per month, and enabling the purchase transaction information table to serve as a basic data fact table; step 2, establishing or acquiring at least one feature dimension table corresponding to the target dimension index according to the need of the target dimension index; 3, establishing a year and date table through calendar, year, quarter and month functions provided by the visual platform, and taking the year and date table and the purchase transaction information table as a fact table; step 4, establishing a data model corresponding to the target dimension index by using a visual service platform, and associating the feature dimension table with the fact table to determine the relevance between the data information; and step 5, analyzing and visually displaying the specific condition of the target dimension index according to the established data model corresponding to the target dimension index, and providing corresponding early warning and data support at the same time. The method visually analyzes the data model to improve the efficiency so as to achieve the purpose of reducing cost and increasing efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data visualization analysis, and in particular to a procurement indicator and price analysis visualization platform and method based on a big data model. Background Art

[0002] In modern enterprise operations, cost reduction and efficiency improvement is an important indicator of the enterprise, which is related to the enterprise's cost control, profitability and market competitiveness. The procurement department plays an important role in the process of cost reduction and efficiency improvement. Only by formulating effective ways to achieve it can the goal of cost reduction and efficiency improvement be achieved. This includes the cost reduction KPI indicators of procurement, the execution of strategies, and the management methods of analyzing and comparing the rationality of material costs.

[0003] The traditional way to display the achievement of KPI indicators and the execution of procurement strategies is often through spreadsheets and PPT. This method requires manual data maintenance and report preparation every month, and it is impossible to present analysis across multiple years in a timely manner, which is also prone to data loss.

[0004] At the same time, in order to achieve the cost reduction KPI indicators as planned, cost control and analysis of purchased materials are important implementation measures. However, due to the large number of specifications and complex designs of components (materials) involved in consumer electronic products, it is difficult to overcome price analysis. Traditional procurement price analysis often relies on manual processing and simple spreadsheet analysis. The main disadvantages of this method are as follows: (1) Low efficiency. Due to the large number of data rows, the response speed is slow, and it may even exceed the 1.04 million rows that the spreadsheet can accommodate; (2) It is prone to errors. The human eye recognition method may cause input errors; (3) It cannot guarantee the confidentiality of data. It is easy to cause data leakage through email attachments; (4) It cannot identify the key and most fundamental cost improvement issues.

[0005] With the development of big data and business intelligence technology, enterprises urgently need an automated method to conduct detailed analysis and processing of procurement cost reduction KPI indicators, the execution of procurement strategies and the transaction prices generated in the procurement process, so as to more effectively monitor and manage procurement costs, identify cost reduction opportunities, lay the foundation for specification standardization, and achieve the goal of reducing costs and increasing efficiency. Summary of the invention

[0006] The purpose of the present invention is to provide a procurement indicator and price analysis visualization platform and method based on a big data model, intuitively analyze the data model, and solve the problem that enterprises have no idea where to start with indicators, supply chain management and price analysis during the procurement process; at the same time, further improve efficiency to achieve the goal of reducing costs and increasing efficiency.

[0007] The technical solution adopted by the present invention is:

[0008] The procurement index and price analysis visualization method based on the big data model includes the following steps:

[0009] Step 1: The production system obtains the purchase transaction information table of the raw materials purchased each month as the basic data fact table;

[0010] Step 2: Establish or obtain at least one feature dimension table corresponding to the target dimension indicator according to the needs of the target dimension indicator;

[0011] Step 3: Create a year date table through the visualization platform and use it together with the purchase transaction information table as a fact table;

[0012] Step 4: Use the visualization service platform to build a data model corresponding to the target dimension indicator, and associate the feature dimension table with the fact table to determine the correlation between the data information;

[0013] Step 5: Analyze and visualize the specific situation of the target dimension indicators based on the constructed data model of the corresponding target dimension indicators, and provide corresponding early warning and data support.

[0014] Furthermore, the transaction record information in the procurement transaction information table includes material code, product name and specification, currency, unit price, supplier code, supplier name, material voucher factory, posting date, transaction quantity, transaction amount and procurement verifier of the transaction record, providing data sources for the visualization platform to obtain and call different columns at any time.

[0015] Furthermore, the target dimension indicators include procurement KPI indicators, procurement strategy planning, key specification parameters and materials without price reduction;

[0016] Furthermore, the characteristic dimension tables corresponding to the procurement KPI indicators include the cost reduction and efficiency improvement indicator achievement status table and the product monthly order table;

[0017] The characteristic dimension table corresponding to the procurement strategy planning includes the information parameter table of the unique value of material attributes and the proportion table of the supplier strategy for each material category; the information parameter table of the unique value of material attributes is used to identify material characteristics;

[0018] The characteristic dimension tables corresponding to the key specification parameters include the information parameter table of the material attribute unique value and the key material specification parameter information table;

[0019] The characteristic dimension table corresponding to the material without price reduction includes the information parameter table of the unique values ​​of material attributes.

[0020] Furthermore, when the target dimension indicator is a procurement KPI indicator, a cost reduction and efficiency improvement indicator achievement status table is established in step 2 and a monthly product order table is obtained; the cost reduction and efficiency improvement indicator achievement status table includes product range, department group organization name, year month date, planned purchase amount of each group, planned cost reduction amount of each group, CV purchase amount of each group, and monthly CV cost reduction amount of each group; the monthly product order table includes product model, quantity and corresponding brand information data;

[0021] When the target dimension indicator is procurement strategy planning, in step 2, information parameters of unique values ​​of material attributes are established to identify material characteristics, and a strategic share table of suppliers for each material category is established; that is, information parameters of unique values ​​of material attributes are established by extracting the material code, supply code, and unique value of the transaction voucher factory in the procurement transaction information table; suppliers corresponding to strategic materials of different groups are obtained, and suppliers are distinguished as strategic manufacturers, and the planned strategic share ratio and procurement strategy description are edited to establish a strategic share table of suppliers for each material category;

[0022] When the target dimension indicator is a key specification parameter, in step 2, the information parameter of the unique value of the material attribute is established to identify the material characteristics, and the key material specification parameter information is established; the key material specification parameter information refers to the key factors that affect the material price, including the physical and chemical properties or design process indicators that affect the price change. By defining the specification parameters of the key materials and collecting the specific parameter information of each material;

[0023] When the target dimension indicator is a material without price reduction, the information parameter of the unique value of the material attribute is established in step 2 to identify the material characteristics.

[0024] Furthermore, by extracting the material code, supply code, and transaction voucher factory unique value in the purchase transaction information table, information parameters of the material attribute unique value are established; the specific steps are as follows:

[0025] Step 2-1, set the department organization scope to which the material belongs, so that the visualization platform can filter through the organization scope to identify different management levels and persons in charge;

[0026] Step 2-2, set the product range to which the material belongs, so that the product to which the material belongs can be clearly analyzed in the visualization platform;

[0027] Step 2-3, set whether the material is customer-supplied or customer-designated. Procurement does not bear the risk of price fluctuations for customer-supplied or customer-designated items.

[0028] Step 2-4, set the abbreviations of the major, medium and minor categories of the material. By setting the abbreviations of the major, medium and minor categories, you can easily see which category the material belongs to.

[0029] Specifically, in step 3, a date table is generated through related calendar, year, quarter, month and other functions.

[0030] Furthermore, when the target dimension indicator is a procurement KPI indicator, in step 4, the specific date field in the year date table is associated with the posting date field in the procurement transaction information table; the date field in the cost reduction and efficiency improvement indicator achievement status table is associated with the specific date field in the year date table; the specific date in the product order table is associated with the specific date in the year date table, so as to realize the association between the tables, and thus call the field rows and columns of each table at any time;

[0031] When the target dimension indicator is procurement strategy planning, in step 4, the specific date field in the year date table is associated with the posting date field in the procurement transaction information table; the material code, supply code, and transaction voucher factory unique value in the procurement transaction information table are associated with the material code, supply code, and transaction voucher factory unique value in the information parameter table of material attribute unique value; the department group, strategic material category, and supplier in the supplier strategy share table of each material category are associated with the department group, strategic material category, and supplier in the information parameter table of material attribute unique value to realize the association between the dimension table and the fact table;

[0032] When the target dimension indicator is a key specification parameter, in step 4, the specific date field in the year date table is associated with the posting date field in the purchase transaction information table; the material code, supply code, and transaction voucher factory unique value in the purchase transaction information table are associated with the material code, supply code, and transaction voucher factory unique value in the information parameter table of material attribute unique value; the material code in the key material specification parameter information table is associated with the material code in the information parameter table of material attribute unique value, so as to realize the association between the dimension table and the fact table;

[0033] When the target dimension indicator is a material without a price reduction, the material without a price reduction is calculated and screened out through programming code in step 4. The material without a price reduction is defined according to the period in which the transaction occurs. At the same time, the actual unit price of the material without a price reduction and the transaction amount involved are obtained through programming code to form a data model for the material without a price reduction.

[0034] Furthermore, when the target dimension indicator is the procurement KPI indicator, in step 5, based on the established data model, the actual performance of each group in the procurement department in the cost reduction process is analyzed from different dimensions such as procurement amount and proportion, order volume analysis, etc., and early warning is issued for the achievement of the target, thereby providing data support for the subsequent implementation of various procurement supply chain strategies.

[0035] Furthermore, when the target dimension indicator is the procurement strategy planning, in step 5, based on the constructed data model, early warning analysis is conducted on the procurement amount, procurement strategy share ratio and actual share, and compliance status of different suppliers under the same category, thereby providing data support for optimizing supply chain management, controlling and saving procurement costs, and improving product competitiveness.

[0036] Furthermore, when the target dimension indicator is a key specification parameter, in step 5, based on the constructed data model, the price, purchase quantity, supplier's transaction amount share, price dispersion, and purchase differences between companies of materials with the same specifications but different specifications are flexibly analyzed from different dimensions.

[0037] Furthermore, when the target dimension indicator is non-price-reduced materials, in step 5, according to the constructed data model, the non-price-reduced materials and the transaction amounts involved are obtained through programming code, thereby guiding procurement to play a greater role in promoting cost reduction.

[0038] Furthermore, the visualization platform generates various visualization charts in different switching modes by integrating the data models of various target dimension indicators through report views for visualization display.

[0039] Furthermore, the visualization platform authorizes viewing permissions to different management levels based on role management within the department organization scope, so that a certain management level can only see the scope of materials it manages, avoiding the risk of data leakage.

[0040] A procurement index and price analysis visualization platform based on big data models, which includes the following modules:

[0041] Procurement KPI indicator data model visualization analysis module: used to obtain monthly procurement transaction information of raw materials from the production system; establish a cost reduction and efficiency improvement indicator achievement status table; obtain a monthly product order table; establish a year date table; use the visualization service platform to build a procurement KPI indicator data model to determine the correlation between data information; based on the constructed procurement KPI indicator data model, analyze the actual achievement of each group in the cost reduction process in the procurement department from different dimensions such as procurement amount and proportion, order volume analysis, etc., and issue early warnings for compliance, thereby providing data support for the subsequent implementation of various procurement supply chain strategies;

[0042] Procurement strategy planning data model visualization analysis module: used to obtain monthly raw material purchase transaction information from the production system; establish information parameters of material attribute unique values ​​to identify material characteristics; establish a supplier strategy share table for each material category; establish a year date table; use the visualization service platform to build a procurement strategy planning data model to determine the correlation between data information; based on the constructed procurement strategy planning data model, conduct early warning analysis on the procurement amount, procurement strategy share and actual share of different suppliers under the same category, and compliance status, so as to provide data support for optimizing supply chain management, controlling and saving procurement costs, and improving product competitiveness;

[0043] Module for analyzing the rationality of the price of purchased materials using the key specification parameter method: used to obtain the purchase transaction information of the monthly purchase of raw materials from the production system; establish information parameters of the unique value of material attributes to identify material characteristics; establish key material specification parameter information; establish a year date table; use the visualization service platform to build a key specification parameter data model to determine the correlation between data information; based on the built key specification parameter data model, flexibly analyze the price, purchase quantity, supplier transaction amount proportion, price dispersion, and purchase differences between companies of materials with the same specifications but different dimensions;

[0044] Unreduced price material analysis module: used to obtain monthly purchase transaction information of raw materials from the production system; establish information parameters of unique values ​​of material attributes to identify material characteristics; establish a year date table; use the visualization service platform to build a data model for unreduced price materials and determine the correlation between data information; based on the built unreduced price material data model, obtain unreduced price materials and the transaction amounts involved through programming codes, so as to guide procurement to play a greater role in promoting cost reduction.

[0045] The present invention adopts the above technical scheme, and effectively solves the problem of slow response speed of data in the updating process by obtaining electronic spreadsheets and folders; solves the calculation problem of data in the statistical process by writing programming codes; at the same time, the target analysis parameters can be accurately located to the person in charge of the department organization scope; through visual presentation, targeted feature analysis is carried out, and vertical integration is used to generate intuitive data chart presentation effects, and data is updated synchronously in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments;

[0047] Figure 1 A flowchart for the analysis and visualization of the data model for procurement KPI indicators;

[0048] Figure 2Flowchart of analysis and visualization of data model for procurement strategy planning;

[0049] Figure 3 A schematic diagram of the generation process of the data relationship model of key specification parameters;

[0050] Figure 4 A flow chart for visualizing the integration of various visualization charts on the visualization platform for key specification parameters;

[0051] Figure 5 A flowchart of the data model analysis and visualization process for the price of materials that have not been reduced in price;

[0052] Figure 6 A schematic diagram of a process for authorizing viewing permissions for different management levels of the present invention. Implementation

[0053] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0054] like Figures 1 to 6 As shown in one, the present invention discloses a procurement index and price analysis visualization method based on a big data model, which includes the following steps:

[0055] Step 1: The production system obtains the purchase transaction information table of the raw materials purchased each month as the basic data fact table;

[0056] Step 2: Establish or obtain at least one feature dimension table corresponding to the target dimension indicator according to the needs of the target dimension indicator;

[0057] Step 3: Create a year date table through the visualization platform and use it together with the purchase transaction information table as a fact table;

[0058] Step 4: Use the visualization service platform to build a data model corresponding to the target dimension indicator, and associate the feature dimension table with the fact table to determine the correlation between the data information;

[0059] Step 5: Analyze and visualize the specific situation of the target dimension indicators based on the constructed data model of the corresponding target dimension indicators, and provide corresponding early warning and data support.

[0060] Furthermore, the transaction record information in the procurement transaction information table includes material code, product name and specification, currency, unit price, supplier code, supplier name, material voucher factory, posting date, transaction quantity, transaction amount and procurement verifier of the transaction record, providing data sources for the visualization platform to obtain and call different columns at any time.

[0061] Furthermore, the target dimension indicators include procurement KPI indicators, procurement strategy planning, key specification parameters and materials without price reduction;

[0062] Furthermore, the characteristic dimension tables corresponding to the procurement KPI indicators include the cost reduction and efficiency improvement indicator achievement status table and the product monthly order table;

[0063] The characteristic dimension table corresponding to the procurement strategy planning includes the information parameter table of the unique value of material attributes and the proportion table of the supplier strategy for each material category; the information parameter table of the unique value of material attributes is used to identify material characteristics;

[0064] The characteristic dimension tables corresponding to the key specification parameters include the information parameter table of the material attribute unique value and the key material specification parameter information table;

[0065] The characteristic dimension table corresponding to the material without price reduction includes the information parameter table of the unique values ​​of material attributes.

[0066] Furthermore, when the target dimension indicator is a procurement KPI indicator, a cost reduction and efficiency improvement indicator achievement status table is established in step 2 and a monthly product order table is obtained; the cost reduction and efficiency improvement indicator achievement status table includes product range, department group organization name, year month date, planned purchase amount of each group, planned cost reduction amount of each group, CV purchase amount of each group, and monthly CV cost reduction amount of each group; the monthly product order table includes product model, quantity and corresponding brand information data;

[0067] When the target dimension indicator is procurement strategy planning, in step 2, information parameters of unique values ​​of material attributes are established to identify material characteristics, and a strategic share table of suppliers for each material category is established; that is, information parameters of unique values ​​of material attributes are established by extracting the material code, supply code, and unique value of the transaction voucher factory in the procurement transaction information table; suppliers corresponding to strategic materials of different groups are obtained, and suppliers are distinguished as strategic manufacturers, and the planned strategic share ratio and procurement strategy description are edited to establish a strategic share table of suppliers for each material category;

[0068] When the target dimension indicator is a key specification parameter, in step 2, the information parameter of the unique value of the material attribute is established to identify the material characteristics, and the key material specification parameter information is established; the key material specification parameter information refers to the key factors that affect the material price, including the physical and chemical properties or design process indicators that affect the price change. By defining the specification parameters of the key materials and collecting the specific parameter information of each material;

[0069] When the target dimension indicator is a material without price reduction, the information parameter of the unique value of the material attribute is established in step 2 to identify the material characteristics.

[0070] Furthermore, by extracting the material code, supply code, and transaction voucher factory unique value in the purchase transaction information table, information parameters of the material attribute unique value are established; the specific steps are as follows:

[0071] Step 2-1, set the department organization scope to which the material belongs, so that the visualization platform can filter through the organization scope to identify different management levels and persons in charge;

[0072] Step 2-2, set the product range to which the material belongs, so that the product to which the material belongs can be clearly analyzed in the visualization platform;

[0073] Step 2-3, set whether the material is customer-supplied or customer-designated. Procurement does not bear the risk of price fluctuations for customer-supplied or customer-designated items.

[0074] Step 2-4, set the abbreviations of the major, medium and minor categories of the material. By setting the abbreviations of the major, medium and minor categories, you can easily see which category the material belongs to.

[0075] Specifically, in step 3, a date table is generated through related calendar, year, quarter, month and other functions.

[0076] Furthermore, when the target dimension indicator is a procurement KPI indicator, in step 4, the specific date field in the year date table is associated with the posting date field in the procurement transaction information table; the date field in the cost reduction and efficiency improvement indicator achievement status table is associated with the specific date field in the year date table; the specific date in the product order table is associated with the specific date in the year date table, so as to realize the association between the tables, and thus call the field rows and columns of each table at any time;

[0077] When the target dimension indicator is procurement strategy planning, in step 4, the specific date field in the year date table is associated with the posting date field in the procurement transaction information table; the material code, supply code, and transaction voucher factory unique value in the procurement transaction information table are associated with the material code, supply code, and transaction voucher factory unique value in the information parameter table of material attribute unique value; the department group, strategic material category, and supplier in the supplier strategy share table of each material category are associated with the department group, strategic material category, and supplier in the information parameter table of material attribute unique value to realize the association between the dimension table and the fact table;

[0078] When the target dimension indicator is a key specification parameter, in step 4, the specific date field in the year date table is associated with the posting date field in the purchase transaction information table; the material code, supply code, and transaction voucher factory unique value in the purchase transaction information table are associated with the material code, supply code, and transaction voucher factory unique value in the information parameter table of material attribute unique value; the material code in the key material specification parameter information table is associated with the material code in the information parameter table of material attribute unique value, so as to realize the association between the dimension table and the fact table;

[0079] When the target dimension indicator is a material without a price reduction, the material without a price reduction is calculated and screened out through programming code in step 4. The material without a price reduction is defined according to the period in which the transaction occurs. At the same time, the actual unit price of the material without a price reduction and the transaction amount involved are obtained through programming code to form a data model for the material without a price reduction.

[0080] Furthermore, when the target dimension indicator is the procurement KPI indicator, in step 5, based on the established data model, the actual performance of each group in the procurement department in the cost reduction process is analyzed from different dimensions such as procurement amount and proportion, order volume analysis, etc., and early warning is issued for the achievement of the target, thereby providing data support for the subsequent implementation of various procurement supply chain strategies.

[0081] Furthermore, when the target dimension indicator is the procurement strategy planning, in step 5, based on the constructed data model, early warning analysis is conducted on the procurement amount, procurement strategy share ratio and actual share, and compliance status of different suppliers under the same category, thereby providing data support for optimizing supply chain management, controlling and saving procurement costs, and improving product competitiveness.

[0082] Furthermore, when the target dimension indicator is a key specification parameter, in step 5, based on the constructed data model, the price, purchase quantity, supplier's transaction amount share, price dispersion, and purchase differences between companies of materials with the same specifications but different specifications are flexibly analyzed from different dimensions.

[0083] Furthermore, when the target dimension indicator is non-price-reduced materials, in step 5, according to the constructed data model, the non-price-reduced materials and the transaction amounts involved are obtained through programming code, thereby guiding procurement to play a greater role in promoting cost reduction.

[0084] Furthermore, the visualization platform generates various visualization charts in different switching modes by integrating the data models of various target dimension indicators through report views for visualization display.

[0085] Furthermore, the visualization platform authorizes viewing permissions to different management levels based on role management within the department organization scope, so that a certain management level can only see the scope of materials it manages, avoiding the risk of data leakage.

[0086] A procurement index and price analysis visualization platform based on big data models, which includes the following modules:

[0087] Procurement KPI indicator data model visualization analysis module: used to obtain monthly procurement transaction information of raw materials from the production system; establish a cost reduction and efficiency improvement indicator achievement status table; obtain a monthly product order table; establish a year date table; use the visualization service platform to build a procurement KPI indicator data model to determine the correlation between data information; based on the constructed procurement KPI indicator data model, analyze the actual achievement of each group in the cost reduction process in the procurement department from different dimensions such as procurement amount and proportion, order volume analysis, etc., and issue early warnings for compliance, thereby providing data support for the subsequent implementation of various procurement supply chain strategies;

[0088] Procurement strategy planning data model visualization analysis module: used to obtain monthly raw material purchase transaction information from the production system; establish information parameters of material attribute unique values ​​to identify material characteristics; establish a supplier strategy share table for each material category; establish a year date table; use the visualization service platform to build a procurement strategy planning data model to determine the correlation between data information; based on the constructed procurement strategy planning data model, conduct early warning analysis on the procurement amount, procurement strategy share and actual share of different suppliers under the same category, and compliance status, so as to provide data support for optimizing supply chain management, controlling and saving procurement costs, and improving product competitiveness;

[0089] Module for analyzing the rationality of the price of purchased materials using the key specification parameter method: used to obtain the purchase transaction information of the monthly purchase of raw materials from the production system; establish information parameters of the unique value of material attributes to identify material characteristics; establish key material specification parameter information; establish a year date table; use the visualization service platform to build a key specification parameter data model to determine the correlation between data information; based on the built key specification parameter data model, flexibly analyze the price, purchase quantity, supplier transaction amount proportion, price dispersion, and purchase differences between companies of materials with the same specifications but different dimensions;

[0090] Unreduced price material analysis module: used to obtain monthly purchase transaction information of raw materials from the production system; establish information parameters of unique values ​​of material attributes to identify material characteristics; establish a year date table; use the visualization service platform to build a data model for unreduced price materials and determine the correlation between data information; based on the built unreduced price material data model, obtain unreduced price materials and the transaction amounts involved through programming codes, so as to guide procurement to play a greater role in promoting cost reduction.

[0091] The specific principle of the present invention is described in detail below:

[0092] A method for establishing procurement KPI indicators, procurement strategy planning, and procurement material price analysis and non-price-reduced material price analysis and visualization based on a big data model includes the following steps:

[0093] Embodiment 1, as Figure 1 As shown in the figure, a data model visualization analysis of procurement KPI indicators is established:

[0094] Step 1: Data acquisition. The production and delivery system (the production and delivery system can be a system such as SAP system that serves order and delivery management) obtains each transaction record of the purchase of raw materials each month. The transaction record information includes material code, product name and specification, currency, unit price, supplier code, supplier name, material voucher factory, posting date, transaction quantity, transaction amount, and the purchase verification personnel of the transaction record. This data is used as the basic data fact table of the big data model, and the visualization platform can obtain and call different columns at any time to provide data sources. The number of rows is 300,000 to 400,000 rows per month.

[0095] Step 2, establish a cost reduction and efficiency improvement indicator achievement status table, which includes product range, department group organization name, year month date, each group's planned purchase amount, each group's planned cost reduction amount, each group's CV purchase amount, and each group's monthly CV cost reduction amount. The planned cost reduction amount is mainly based on the product's monthly order estimate for the next year, the planned purchase amount and the percentage of savings from the planned purchase amount, and the planned cost reduction amount is estimated based on the product of the two; the CV cost reduction amount includes two parts, one is the actual cost reduction amount as of the current month and incurred, the accounting definition is based on Q4 of each year, the accounting production delivery system (monthly purchased material unit price - the unit price of the material in Q4 of the previous year) * the purchase volume of the current month is the cost reduction amount of the material, and the weighted sum of the cost reduction amount of each piece is the actual cost reduction amount of the month; the other part is based on the actual cost reduction amount, predicting the cost reduction amount from the current month to the end of the year, the predicted cost reduction amount may deviate from the planned cost reduction amount, and is closer to the actual situation. The CV amount of purchases also consists of two parts. One part is the actual transaction amount of the production and delivery system up to the current month, which can be obtained from step 1. The other part is the predicted amount from the current month to the end of the year based on the actual transaction amount and changes in the order volume. This amount may deviate from the planned purchase amount and is closer to the actual situation.

[0096] Step 3, obtain the monthly order forecast table of the product. This table can be published by the department that has close contact with the product customers, such as the business or sales department. The order information of the product mainly includes data such as product model, quantity and corresponding brand information.

[0097] Step 4, create a year date table. The date table is mainly generated through the visualization platform. The date table is generated through related calendar, year, quarter, month and other functions, and then associated with the tables created in step 1, step 2, and step 3 through the date field;

[0098] Step 5, import the large data table generated by steps 1 to 3 into the visualization platform. Since the purchase transaction information records generated in step 1 have many levels of data rows, the data of the spreadsheets of the same format are merged in the visualization platform by obtaining folders, which effectively avoids the problem of slow response speed.

[0099] Step 6, build a relationship model for the data generated by the visualization platform in step 5. Associate the specific date field in step 4 with the posting date field of the purchase transaction record in step 1, associate the date field of the cost reduction and efficiency improvement achievement table in step 2 with the specific date field in step 4, and associate the specific date of the product order table in step 3 with the specific date in step 4. By building the above relationship model, the association between the tables is realized, so that the field rows and columns of each table can be called at any time. The flow chart of steps 1 to 6 is as follows Figure 1 shown.

[0100] Step 7: Generate a data model integrating various visualization charts in different switching modes through report views on the visualization platform. The main steps include the following:

[0101] Step 71 creates a report view generation page, and uses the year, quarter, and month in step 4 to generate a date filter, so as to dynamically view the data of each visualization view under a specific year, quarter, and month.

[0102] Step 72 creates the month, quarter, half-year, and year parameter filters through the date table generated in step 4. The subsequent steps are collectively referred to as date parameters. When you click on a month, the report displays the data for each month; when you click on a quarter, the report displays the data for each quarter; when you click on a half-year, the report displays the data for the first and second half of the year; when you click on a year, the report displays the accumulated data for the entire year;

[0103] Step 73 generates a filter based on the department group organization name and product range established in step 2, and allows viewing visualization views under different groups and products;

[0104] Step 74 programming code calculates the total of the planned cost reduction amount and the total of the CV cost reduction amount in step 2, adopts the KPI card data generation method, takes the year in the date table in step 4 as the trend axis, the target value is the planned cost reduction amount, and the card value is the CV cost reduction amount, so as to intuitively view the difference between the CV value and the target value, the difference percentage, and whether the target is met;

[0105] Step 75 uses a line chart and a column chart, with the date parameter of step 72 as the X-axis, the column Y-axis as the sum of the CV cost reduction amount of step 74, and the row Y-axis as the sum of the planned cost reduction amount of step 74. This allows you to intuitively view the difference between the CV cost reduction amount and the planned cost reduction amount under different date parameters.

[0106] The programming code in step 76 calculates the total order amount in step 3, using line and column charts, with the date parameter in step 72 as the X-axis, the column Y-axis as the total CV cost reduction amount in step 74, and the row Y-axis as the total order quantity. This allows you to intuitively view the impact of the increase or decrease in order quantity on the cost reduction amount under different date parameters.

[0107] Step 77 programming code calculates the total CV purchase amount in step 2, using a bar chart, with the department group in step 2 as the Y axis and the total CV purchase amount on the X axis. This allows you to intuitively view different department groups and predict the amount and proportion of purchases throughout the year. In principle, groups with high purchase amounts should also contribute more to cost reduction strategies.

[0108] Step 78 programming code calculates the total CV purchase amount in step 2, using a bar chart, with the department group in step 2 as the Y axis and the total CV purchase amount on the X axis. This allows you to intuitively view different department groups and predict the amount and proportion of purchases throughout the year. In principle, groups with high purchase amounts should also contribute more to cost reduction strategies.

[0109] Step 79 programming code calculates the percentage of the total CV cost reduction amount and the total planned cost reduction divided by the percentage, which is the achievement rate. The achievement rate percentage is produced in the form of a dashboard. Through different date, group, and product filters, the achievement rate in different dimensions can be viewed, so as to adjust the procurement strategy in a targeted manner.

[0110] Step 710 programming code calculates the percentage of the sum of the CV cost reduction amount and the planned cost reduction sum in step 74 divided by the percentage, which is the achievement rate. The achievement rate percentage is produced in the form of a dashboard. Through different date, group, and product filters, the achievement rate in different dimensions can be viewed, so as to adjust the procurement strategy in a targeted manner.

[0111] Step 711 programming code calculates the percentage of the total CV cost reduction amount in step 74 divided by the total CV purchase amount in step 77, which is the cost reduction percentage. Use a bar chart with the date parameter in step 72 as the Y axis and the cost reduction percentage as the X axis to view the percentage difference under different date conditions.

[0112] Step 712 programming code calculates the percentage of the total CV cost reduction amount in step 74 divided by the total CV purchase amount in step 77, which is the cost reduction percentage. Use a bar chart with the date parameter in step 72 as the Y axis and the cost reduction percentage as the X axis to view the percentage difference under different date conditions.

[0113] Step 713 changes the Y axis of step 712 to department group, and keeps other factors unchanged, so that the cost reduction percentages of different groups can be viewed. Through the above different dimensions, the changes in KPI indicators can be comprehensively analyzed, thus providing direction for the improvement and formulation of countermeasures.

[0114] In step 714, the programming code calculates the number of department groups that have achieved the target and the number of department groups that have not achieved the target, so as to issue an early warning of the achievement status and take measures to improve the strategy.

[0115] Embodiment 2, as Figure 2 As shown, the procurement strategy planning data model is visualized for analysis:

[0116] Step 1 is the same as step 1 of Example 1, which is to obtain the transaction record data of the production and delivery system. The description is not repeated here.

[0117] Step 2, establish information parameters of unique values ​​of material attributes and identify material characteristics. This step mainly extracts the unique values ​​of material code, supply code, and transaction voucher factory in the fact table of step 1 to establish material attributes. At the same time, the fact table of step 1 also needs to associate material code, supplier, and transaction voucher factory, so as to establish a one-to-many relationship between the material attribute table of step 8 and the fact table of step 1 after importing into the visualization platform. The material attribute information parameter settings include the following:

[0118] Step 21: Set the department organization scope to which the material belongs, so that different management levels and persons in charge can be identified through the organization scope screening in the visualization platform;

[0119] Step 22 sets the product range to which the material belongs. Different products and order quantities have a certain impact on the unit price and purchase quantity of the material. At the same time, the product to which the material belongs can also be clearly analyzed in the visualization platform;

[0120] Step 23 sets whether the material is customer-supplied or customer-directed. Purchasing is less likely to bear the risk of price fluctuations for customer-supplied or customer-directed materials.

[0121] Step 24 sets the abbreviations of the material's major, medium, and minor categories. Because the material name description is often written in detail and the character string is too long, it is difficult to extract during data processing. By setting the abbreviations of the major, medium, and minor categories, you can check at a glance what category the material belongs to;

[0122] Step 3, establish a strategic share table of suppliers of procurement strategy material categories; strategic materials mainly refer to materials with large purchase volume, high purchase amount ranking, and procurement that can play a dominant role in the execution of the strategy. Strategic materials are screened through the large, medium, and small categories in step 8. Based on the screened strategic materials, the suppliers corresponding to the strategic materials of different groups can be obtained in the material attribute table in step 8, and at the same time, the suppliers can be distinguished as strategic manufacturers, and the planned strategic share ratio and procurement strategy description can be edited.

[0123] Step 4, establish a year date table, the same as in Example 1.

[0124] Step 5, import the data table generated in step 1, step 2, and step 3 into the visualization platform. The transaction record data in step 1 can be obtained from other embodiments 1 and stored in the visualization platform database.

[0125] Step 6, build a relational model for the data generated on the visualization platform in step 5. The specific date field in step 4 is associated with the posting date field of the purchase transaction record in step 1. The material code, supply code, and transaction voucher factory unique value of the purchase transaction record in step 1 are associated with the material code, supply code, and transaction voucher factory unique value of the material attribute table in step 2. The department group, strategic material category, and supplier in step 3 are associated with the department group, strategic material category, and supplier in step 2. By building the above relational model, the dimension table and the fact table are associated.

[0126] Step 7: Generate a big data model integrating various visualization charts in different switching modes on the visualization platform through report views. The main steps include the following:

[0127] Step 71, create a report view generation page, use the year, quarter, and month in step 4 to generate a date filter, so as to dynamically view the big data of the next module of different years, quarters, and months;

[0128] Step 72, same as step 72 of embodiment 1, creating a parameter filter for month, quarter, half year, and year;

[0129] Step 73, through the strategic material category supplier strategy share table established in step 3, create a filter for department group name, product range, and strategic material category, and view the execution status of strategic materials under different groups and products. The filter content can be set according to specific needs.

[0130] Step 74 can be in the form of a bar chart, with the year in step 4 as the X-axis; using programming code to calculate the total transaction amount of the purchase transaction record in step 1 and use it as the Y-axis; using the supplier name as the legend to distinguish the bar chart. This allows you to intuitively see the changes in supplier transaction amounts in different years;

[0131] Step 75 can adopt the method of discount and bar chart, with the supplier name in step 3 as the X-axis; use programming code to calculate the proportion of the supplier transaction amount under a certain strategic material category in step 1 to the transaction amount of that category, and use it as the column Y-axis; use the planned strategy share ratio in step 3 as the row Y-axis, so as to analyze the difference between the actual transaction amount ratio and the planned strategy share of each supplier under a certain category.

[0132] Step 76, the supplier in step 3, whether it is a strategic manufacturer, procurement strategy description or remarks and other fields can be presented in a list format;

[0133] Step 77, use programming code to calculate the difference warning between the actual transaction amount ratio and the planned strategy share of a certain column of strategic material suppliers. The calculation logic is defined as follows: when a supplier of a certain strategy category is a strategic supplier, the difference between the actual transaction amount ratio and the planned strategy share of the strategic supplier is defined as the difference threshold, and the formula is as follows: When a warning range is set for this threshold, it is called the threshold. The threshold is defined in accordance with the actual management situation. For example, the statistics are the threshold range |X|=<15%, 15%<|X|=<20%, |X|>20%. When there is only one strategic supplier in a certain column of strategic materials whose threshold is in the range of |X|>20%, it is considered that the material as a whole does not meet the standard, and it is counted as one material category and displayed with a red light; when there is only one strategic supplier in a certain column of strategic materials whose threshold is in the range of 15%<|X|=<20% and does not exceed 20%, it is considered that the material as a whole needs to be improved, and it is counted as one material category and displayed with a yellow light; when the thresholds of all strategic suppliers in a certain column of strategic materials are in the range of |X|=<15%, it is considered that the material as a whole meets the standard, and it is counted as one material category and displayed with a green light. In this way, it is possible to count how many material categories have red, yellow, and green lights, and promote timely improvement measures for procurement.

[0134] Step 78, use programming code to calculate the percentage of the total purchase amount of strategic suppliers of a certain strategic category to the total purchase amount of all suppliers in this category, and use programming code to calculate the percentage of the number of qualified purchase material categories displayed with green lights to the total number of categories, and present the proportion of strategic supplier amounts and the compliance rate of procurement strategies in numerical form, so as to continuously formulate effective improvement measures and optimize supply chain management.

[0135] 3. If Figure 3 Or as shown in 4, the purchase material price analysis of key specification parameters:

[0136] Step 1 is the same as step 1 of Example 1, which is to obtain the transaction record data of the production and delivery system, and will not be described repeatedly.

[0137] Step 2 is the same as step 2 of Example 1 and will not be described again.

[0138] Step 3, establish the specification parameter information of key materials. Key materials mainly refer to materials with large purchase volume. The transaction amount of such materials ranks high in the total purchase amount, and the purchase can take the lead in the implementation of the procurement strategy. Key materials are obtained from the material attribute information in step 2 by screening large, medium and small categories. Specification parameters mainly refer to the key factors that affect the price of materials, which can be the main physical and chemical properties or design process indicators that can affect price changes such as material, length, area, thickness, appearance treatment, size, and design process. By defining the specification parameters of key materials and collecting the specific parameter information of each material, a one-to-many relationship is established after importing into the visualization platform.

[0139] Step 4, establishing a year date table. Same as step 4 of embodiment 1;

[0140] Step 5: Import the large data table generated in steps 1 to 4 into the visualization platform.

[0141] Step 6, build a relational model for the data generated on the visualization platform in step 5. Associate the specific date field in step 4 with the posting date field of the purchase transaction record in step 1. Associate the material code, supply code, and transaction voucher factory unique value of the purchase transaction record in step 1 with the material code, supply code, and transaction voucher factory unique value of the material attribute table in step 2. Associate the material code of the key material specification parameter table in step 3 with the material code of the material attribute table in step 2. By building the above relational model, the dimension table and the fact table are associated. The flowchart of the above steps is shown in the figure below. Figure 1 shown.

[0142] Step 7: Generate a big data model integrating various visualization charts in different switching modes on the visualization platform through report views. The main steps include the following:

[0143] Step 71, create a report view generation page, use the year, quarter, and month in step 4 to generate a date filter, so as to dynamically view the big data of the next module of different years, quarters, and months;

[0144] Step 72, three filters are generated respectively through the four fields of organization belonging, key material classification, product range, and whether it is customer supply and customer indication set in step 2, wherein the key material classification can be classified into sub-categories under the organization, so as to dynamically filter the key categories, product ranges and customer supply and customer indication conditions within different organization ranges;

[0145] Step 73, create a sunrise chart (or circle chart) generation module through the three major specification parameters of the key material specifications established in step 3. The advantage of this generation method is that the sunrise chart generates the attribution of the three major specification parameters through three layers of circles, the inner circle (first layer) is specification parameter 1, the second circle (second layer) is specification parameter 2, as a subcategory of the first layer, and the third circle (third layer) is specification parameter 3, as a subcategory of the second layer; the proportion of materials designed for each specification range in the circle is distinguished mainly by establishing a calculation formula for the total transaction amount of the transaction record in step 1, and distinguishing the proportion by color, so that it can be intuitively seen according to the different color sizes which type of specification has a higher transaction amount, and targeted screening and analysis of data;

[0146] Step 74, establishes a scattered bubble generation method for unit price and quantity under different supplier modes, through different unit price analyses with different degrees of discreteness and the distribution pattern of the purchase quantity. The generation method uses unit price as the X-axis, quantity as the Y-axis, supplier name as the scattered point of each bubble, and the transaction quantity as the size of the bubble. At the same time, an average price calculation method is established under the same specification parameter mode, which is written in programming code. The logic definition of this calculation method is: when the three specification parameters are consistent, the average price of the category is calculated. The calculation formula is as follows, assuming that the transaction unit prices of the same specification but different materials are P1, P2, P3...P n , the corresponding transaction quantities are Q1, Q2, Q3...Q n , the average price is calculated as: That is, the total transaction amount divided by the total quantity. This average price can be used as a representation feature to measure whether the purchase price is too high or too low. Use this average price as the constant line of the X-axis, and set the average price ±5% as the upper and lower limits of the unit price, and also add it to the X-axis constant line. Filter different color modules through the sunrise chart (circle chart) in step 73 to view the scattered distribution of transaction unit prices and quantities of different materials and different suppliers under the same specifications. When the unit price is higher and the quantity is more, the bubble is larger and the deviation from the average value is greater;

[0147] Step 75: Generate a non-repeating calculation formula for the part number quantity, which is written in programming code. Write the sum calculation formulas for the transaction amount and transaction quantity, and calculate the proportion of the transaction amount of a certain organization's scope in the total transaction amount. Display the part number quantity, transaction amount, transaction quantity, and transaction amount proportion through the presentation of digital cards. When the slicer in Step 72 and the sunburst chart (circle chart) in Step 73 generate interactive filtering, the digital cards perform dynamic calculations to present the part number quantity, transaction amount, transaction quantity, and transaction proportion under different materials and different specifications.

[0148] Step 76: Differentiate and count the threshold of the unit price deviation of different materials with the same specification from the average price. This generation method is mainly used to measure the warning range of price difference. It is written in programming code and is defined as when the unit price P of a certain material and the average price There is a difference, calculate the percentage of the difference, and its calculation formula is as follows: X is the threshold of the price difference. Set a warning range for this threshold, which is the threshold. The threshold is defined according to the actual situation of management. For example, count how many non-repeating counts of materials are in the three ranges of the threshold range X = <5%, 5% < X = <10%, X > 10%, and distinguish them with three colors: green, yellow, and red. This generation method can intuitively display how many materials have a unit price deviation from the average price and the proportion data of materials in a certain threshold range in this category of materials, implement the feature analysis into specific data categories, enable the management to see the problem immediately, and thus formulate corresponding countermeasures;

[0149] Step 77: Generate detailed data through a matrix table. This generation method mainly drags fields such as key parameter 1, key parameter 2, key parameter 3, material code, supplier name, unit price of each material, average price calculation formula, difference value between unit price and average price, transaction quantity, and transaction amount into the matrix table, so as to intuitively analyze the difference between the purchase unit price and the average price of each material, and set three kinds of dots: red, yellow, and green for the difference value. Green indicates that the difference between the unit price and the average price meets X = <5%, yellow indicates that the difference between the unit price and the average price falls within 5% < X = <10% and needs to be improved, and red indicates that X > 10% deviates more, which belongs to the price legacy and needs to analyze and improve the reasons for the difference;

[0150] Step 78: Calculate the actual cost reduction amount of the material, and generate a method to distinguish the cost reduction amount according to the threshold range where the actual unit price of the material differs from the average price. This generation method calculates the cost reduction amount in the form of programming code. The calculation logic is that assuming the unit price of a certain material in a certain month is P n, the unit price of the same material that was traded in January of that year is taken as the benchmark unit price. The difference between the benchmark unit price and the unit price of the current month multiplied by the purchase quantity of the current month is the cost reduction amount of the material for the current month. Instructions for setting the benchmark unit price: When there is no transaction record for the same material in January of that year, obtain the unit price of the same material that was traded in February. If it does not exist in February either, obtain the unit price of the same material in March, and so on until the transaction unit price of the previous month is obtained. This logic is used as the value of the benchmark unit price. Finally, add up the cost reduction amounts of comparable materials and distinguish the cost reduction amounts according to the threshold range of the difference between the actual unit price of the material and the average price. This generation method can intuitively analyze whether the cost reduction strategy is actually implemented for materials that are significantly different from the average price. At the same time, it also plays a certain role in promoting key performance indicators.

[0151] Step 79, through the bookmark generation method, the scattered bubble generation method of step 74 is changed to the clustered column chart generation method to view the average unit price difference generated by specification parameter 1 for different companies. The clustered column chart is set with unit price as the X-axis, specification parameter 1 as the Y-axis, and each factory as a legend to show the average price difference of the same specification under different factories. The bookmark generation method is to set a bookmark page for each module of step 71 to step 78, set step 74 to be visible, and the bar chart of step 79 to be hidden, and generate a supplier viewing interface; then generate a bookmark page, hide step 74, set the bar chart of step 79 to be visible, and generate a bookmarked company viewing interface. In this way, by clicking the supplier and company control buttons, a generation method is generated in which a module is hidden and a module is visible on the same page.

[0152] Step 710 generates a comparison method of the average price, transaction amount, and purchase volume of one of the top three specification parameters. For example, the average unit price, purchase volume, and transaction amount generated by specification parameter 1 can be generated according to specification parameter 1, so as to intuitively compare whether there are abnormal phenomena such as more purchases of high-specification items, less purchases of low-specification items, and lower prices of high-specification items than low-specification items in the content items included in specification parameter 1. This generation method is also conducive to analyzing the direction of price-oriented specification purchases in the proportion of transaction amounts, and analyzing the standardization of designed specifications, simplifying them, and achieving a procurement strategy of price-based pricing. This generation method is also a method of generating a specification viewing interface by setting steps 71 to 79 to hidden and step 710 to visible through a bookmark method to view the transaction amounts and purchase volumes under different specifications.

[0153] 4. If Figure 5 As shown, the price analysis of materials without price reduction is as follows:

[0154] Step 1, based on the information parameters of the unique value of the material attribute of step 2 of embodiment 2 and the relationship model established by the transaction record data of the production and delivery system of step 1, calculate and screen out the materials that have not been reduced in price through programming code. The materials that have not been reduced in price can be defined according to the period in which the transaction occurs. For example, it can be defined that there is no price fluctuation within a year. In the current month, there are transaction quantities in the first and last two months 11 months ago, and the price has not changed during the first and last months, which is the material that has not been reduced in price. At the same time, the actual unit price of the material that has not been reduced in price and the transaction amount involved are obtained through programming code.

[0155] Step 2: Generate a view of the non-price-reduced material counts and transaction amounts of different buyers in the form of pie charts and bar charts, based on the dimensions of material counts and transaction amounts, so as to promote procurement to continuously achieve the goal of reducing costs and increasing efficiency. The flow chart is as follows Figure 1 Shown

[0156] In addition, if Figure 6 As shown, by generating a visual platform display of the four major modules, according to the role management of the department organization scope, the viewing rights are authorized to different management levels, so that a certain management level can only see the scope of materials it manages, avoiding the risk of data leakage.

[0157] The present invention adopts the above technical scheme, and effectively solves the problem of slow response speed of data in the updating process by obtaining electronic spreadsheets and folders; solves the calculation problem of data in the statistical process by writing programming codes; at the same time, the target analysis parameters can be accurately located to the person in charge of the department organization scope; through visual presentation, targeted feature analysis is carried out, and vertical integration is used to generate intuitive data chart presentation effects, and data is updated synchronously in real time.

[0158] Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians of the art without making creative work are within the scope of protection of the present application.

Claims

1. A visualization method for purchasing indicators and price analysis based on a big data model, characterized by: It includes the following steps: Step 1: The production system obtains the purchase transaction information table of the raw materials purchased each month as the basic data fact table; Step 2: Establish or obtain at least one feature dimension table corresponding to the target dimension indicator according to the needs of the target dimension indicator; Step 3: Use the calendar, year, quarter, and month functions provided by the visualization platform to create a year date table, and use it together with the purchase transaction information table as a fact table; Step 4: Use the visualization service platform to build a data model corresponding to the target dimension indicator, and associate the feature dimension table with the fact table to determine the correlation between the data information; Step 5: Analyze and visualize the specific situation of the target dimension indicators based on the constructed data model of the corresponding target dimension indicators, and provide corresponding early warning and data support.

2. The method for visualizing procurement indicators and price analysis based on a big data model according to claim 1, characterized in that: The transaction record information in the purchase transaction information table includes material code, product name and specification, currency, unit price, supplier code, supplier name, material voucher factory, posting date, transaction quantity, transaction amount and purchase verifier of the transaction record, providing data sources for the visualization platform to obtain and call different columns at any time.

3. The method for visualizing procurement indicators and price analysis based on a big data model according to claim 1, characterized in that: Target dimension indicators include procurement KPI indicators, procurement strategy planning, key specification parameters and materials without price reduction; The characteristic dimension tables corresponding to the procurement KPI indicators include the cost reduction and efficiency improvement indicator achievement status table and the product monthly order table; The characteristic dimension table corresponding to the procurement strategy planning includes the information parameter table of the unique value of material attributes and the supplier strategy proportion table of each material category; The information parameter table of unique values ​​of material attributes is used to identify material characteristics; The characteristic dimension tables corresponding to the key specification parameters include the information parameter table of the material attribute unique value and the key material specification parameter information table; The characteristic dimension table corresponding to the material without price reduction includes the information parameter table of the unique values ​​of material attributes.

4. The method for visualizing procurement indicators and price analysis based on a big data model according to claim 3 is characterized in that: When the target dimension indicator is the procurement KPI indicator, in step 2, a cost reduction and efficiency improvement indicator achievement status table is established and a monthly product order table is obtained; the cost reduction and efficiency improvement indicator achievement status table includes product scope, department group organization name, year month date, planned purchase amount of each group, planned cost reduction amount of each group, CV purchase amount of each group, and monthly CV cost reduction amount of each group; the monthly product order table includes product model, quantity and corresponding brand information data; When the target dimension indicator is procurement strategy planning, in step 2, information parameters of unique values ​​of material attributes are established to identify material characteristics, and a strategic share table of suppliers for each material category is established; that is, information parameters of unique values ​​of material attributes are established by extracting the material code, supply code, and unique value of the transaction voucher factory in the procurement transaction information table; suppliers corresponding to strategic materials of different groups are obtained, and suppliers are distinguished as strategic manufacturers, and the planned strategic share ratio and procurement strategy description are edited to establish a strategic share table of suppliers for each material category; When the target dimension indicator is a key specification parameter, in step 2, the information parameter of the unique value of the material attribute is established to identify the material characteristics, and the key material specification parameter information is established; the key material specification parameter information refers to the key factors that affect the material price, including the physical and chemical properties or design process indicators that affect the price change. By defining the specification parameters of the key materials and collecting the specific parameter information of each material; When the target dimension indicator is a material without price reduction, the information parameter of the unique value of the material attribute is established in step 2 to identify the material characteristics.

5. The method for visualizing procurement index and price analysis based on a big data model according to claim 4 is characterized in that: The specific steps to establish the information parameters of the unique value of material attributes by extracting the material code, supply code, and transaction voucher factory unique value in the purchase transaction information table are as follows: Step 2-1, set the department organization scope to which the material belongs, so that the visualization platform can filter through the organization scope to identify different management levels and persons in charge; Step 2-2, set the product range to which the material belongs, so that the product to which the material belongs can be clearly analyzed in the visualization platform; Step 2-3, set whether the material is customer-supplied or customer-designated. Procurement does not bear the risk of price fluctuations for customer-supplied or customer-designated items. Step 2-4, set the abbreviations of the major, medium and minor categories of the material. By setting the abbreviations of the major, medium and minor categories, you can easily see which category the material belongs to.

6. The method for visualizing procurement index and price analysis based on a big data model according to claim 3 is characterized by: When the target dimension indicator is a procurement KPI indicator, in step 4, the specific date field in the year date table is associated with the posting date field in the procurement transaction information table; the date field in the cost reduction and efficiency improvement indicator achievement status table is associated with the specific date field in the year date table; the specific date in the product order table is associated with the specific date in the year date table, so as to realize the association between the tables and call the field rows and columns of each table at any time; When the target dimension indicator is procurement strategy planning, in step 4, the specific date field in the year date table is associated with the posting date field in the procurement transaction information table; the material code, supply code, and transaction voucher factory unique value in the procurement transaction information table are associated with the material code, supply code, and transaction voucher factory unique value in the information parameter table of material attribute unique value; the department group, strategic material category, and supplier in the supplier strategy share table of each material category are associated with the department group, strategic material category, and supplier in the information parameter table of material attribute unique value to realize the association between the dimension table and the fact table; When the target dimension indicator is a key specification parameter, in step 4, the specific date field in the year date table is associated with the posting date field in the purchase transaction information table; the material code, supply code, and transaction voucher factory unique value in the purchase transaction information table are associated with the material code, supply code, and transaction voucher factory unique value in the information parameter table of material attribute unique value; the material code in the key material specification parameter information table is associated with the material code in the information parameter table of material attribute unique value, so as to realize the association between the dimension table and the fact table; When the target dimension indicator is a material without a price reduction, the material without a price reduction is calculated and screened out through programming code in step 4. The material without a price reduction is defined according to the period in which the transaction occurs. At the same time, the actual unit price of the material without a price reduction and the transaction amount involved are obtained through programming code to form a data model for the material without a price reduction.

7. The method for visualizing procurement index and price analysis based on a big data model according to claim 3 is characterized by: When the target dimension indicator is a procurement KPI indicator, in step 5, based on the established data model, the actual achievement of each group in the procurement department in the cost reduction process is analyzed from different dimensions, including procurement amount, proportion, and order volume, and early warning is issued for the achievement of the target, thereby providing data support for the subsequent implementation of various procurement supply chain strategies; When the target dimension indicator is procurement strategy planning, in step 5, based on the established data model, early warning analysis is conducted on the procurement amount, procurement strategy share ratio and actual share, and compliance status of different suppliers under the same category, thereby providing data support for optimizing supply chain management, controlling and saving procurement costs, and improving product competitiveness; When the target dimension indicator is a key specification parameter, in step 5, based on the established data model, the price, purchase quantity, supplier transaction amount ratio, price dispersion, and purchase differences between companies of materials with the same specifications but different specifications are flexibly analyzed from different dimensions; When the target dimension indicator is non-price-reduced materials, in step 5, according to the established data model, the programming code is used to obtain the non-price-reduced materials and the transaction amounts involved, thereby guiding procurement to play a greater role in promoting cost reduction.

8. The method for visualizing procurement index and price analysis based on a big data model according to claim 7, characterized in that: The visualization platform generates various visualization charts in different switching modes by integrating the data models of various target dimension indicators through report views for visual display.

9. The method for visualizing procurement index and price analysis based on a big data model according to claim 1, characterized in that: The visualization platform authorizes viewing permissions to different management levels based on role management within the department organization scope, so that a certain management level can only see the scope of materials it manages, avoiding the risk of data leakage.

10. A procurement indicator and price analysis visualization platform based on a big data model, applying the procurement indicator and price analysis visualization method based on a big data model according to any one of claims 1 to 9, characterized in that: The platform includes the following modules: The data model visualization analysis module of the procurement KPI indicators is used to obtain the monthly procurement transaction information of raw materials purchased from the production system; establish a cost reduction and efficiency improvement indicator achievement status table; Obtain monthly product order tables; establish year-date tables; use the visualization service platform to build a procurement KPI indicator data model to determine the correlation between data information; based on the built procurement KPI indicator data model, analyze the actual performance of each group in the procurement department in the cost reduction process from different dimensions such as procurement amount and proportion, order volume analysis, etc., and issue early warnings on the performance, thereby providing data support for the subsequent implementation of various procurement supply chain strategies; Procurement strategy planning data model visualization analysis module: used to obtain monthly procurement transaction information of raw materials from the production system; establish information parameters of unique values ​​of material attributes and identify material characteristics; Establish a supplier strategy share table for each material category; establish a year date table; use the visualization service platform to build a procurement strategy planning data model to determine the correlation between data information; According to the established procurement strategy planning data model, early warning analysis is conducted on the procurement amount, procurement strategy share and actual share, and compliance status of different suppliers under the same category, thereby providing data support for optimizing supply chain management, controlling and saving procurement costs, and improving product competitiveness; Module for analyzing the rationality of the price of purchased materials using the key specification parameter method: used to obtain the purchase transaction information of the monthly purchase of raw materials from the production system; establish information parameters of the unique value of material attributes to identify material characteristics; establish key material specification parameter information; establish a year date table; use the visualization service platform to build a key specification parameter data model to determine the correlation between data information; based on the built key specification parameter data model, flexibly analyze the price, purchase quantity, supplier transaction amount proportion, price dispersion, and purchase differences between companies of materials with the same specifications but different dimensions; Unreduced price material analysis module: used to obtain the monthly purchase transaction information of raw materials from the production system; establish information parameters of unique values ​​of material attributes and identify material characteristics; Establish a year-date table; use the visualization service platform to build a data model for materials that have not been reduced in price and determine the correlation between data information; based on the constructed data model for materials that have not been reduced in price, obtain materials that have not been reduced in price and the transaction amounts involved through programming codes, thereby guiding procurement to play a greater role in promoting cost reduction results.

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