Service data-driven method for work breakdown based on data transfer

Abstract

Disclosed in the present invention is a service data-driven method for work breakdown based on data transfer. The method relates to performing service work breakdown to form a data-driven process to implement service digitization and digital servitization. The service digitization comprises steps S1-S4: S1: breaking down a task into work orders corresponding to delivery data; S2: defining key indicators; S3: collecting, analyzing, querying, and collecting statistics about data; and S4: performing data-based real-time monitoring and adjustment. The digital servitization comprises S1 and the following steps S5-S7: S5: implementing data productization, and converting data collected from the work orders into a data product by means of data fusion, data analysis and visualization; S6: implementing data servitization; and S7: implementing data capability servitization. The service data-driven method for work breakdown based on data transfer in the present invention enables comprehensive collection of effective service data by means of specific implementations of service digitization and digital servitization, thereby implementing a service data-driven process and achieving intelligent management and efficient operation of services.

Description

A business data-driven method for work decomposition based on data transfer Technical Field

[0001] This invention relates to the fields of data management and business digitization, and in particular to a business data-driven method for work decomposition based on data transmission. Background Technology

[0002] With the rapid development of big data and internet technologies, data has become a core element of enterprise decision-making and operations. However, traditional work breakdown structures often do not prioritize data delivery; instead, they break down tasks into data-driven actions. Their biggest flaw is that non-data-driven work orders obstruct data transfer, making the data transfer path unclear. This leads to problems such as data silos, missing data, invalid data, poor data transfer, and ambiguous work breakdowns, resulting in inefficient business decision-making and significant resource waste. Therefore, effectively utilizing data transfer mechanisms to optimize work breakdown processes has become a key challenge for modern enterprises undergoing digital transformation. Summary of the Invention

[0003] To address the aforementioned issues, this invention provides a business data-driven method based on data transmission for work decomposition. This method comprehensively collects effective business data through specific methods of business digitization and digital businessization, thereby achieving business data-driven operations, as well as intelligent management and efficient operation of the business.

[0004] According to one aspect of the present invention, a business data-driven method based on data transmission for work decomposition is provided, which decomposes business work according to the direction of data transmission to form a data-driven approach, thereby realizing business digitization and digital business.

[0005] Business digitization includes the following steps S1 to S4

[0006] S1: Decompose the task into work orders with corresponding delivery data according to the direction of data transmission, where each work order corresponds to one delivery data.

[0007] S2: Define key metrics;

[0008] S3: Collect, analyze, query, and statistically analyze data;

[0009] S4: Real-time monitoring and adjustment based on data;

[0010] Digitalization of business operations includes step S1 and the following steps S5 to S7.

[0011] S5: To realize the productization of data, the data collected from work orders will be transformed into data products through data fusion, data analysis and visualization.

[0012] S6: Enable data service-oriented architecture;

[0013] S7: Enables data capability services;

[0014] Step S1 further includes the following steps:

[0015] A: Define the business categories in the task, where business categories include business category 1 to business category N;

[0016] B: In business class 1, decompose work order 1-1 with data 1-1 as the delivery data, and when data 1-1 is referenced by data 1-2, decompose work order 1-2 with data 1-2 as the delivery data, and so on, until work order 1-N with data 1-N as the delivery data is decomposed.

[0017] C: In business class 2, when data 1-1 of business class 1 is referenced by data 2-1 in business class 2, work order 2-1 is decomposed with data 2-1 as the delivery data. When data 2-1 is referenced by data 2-2, work order 2-2 is decomposed with data 2-2 as the delivery data. This process continues until work order 2-N is decomposed with data 2-N as the delivery data.

[0018] D: Following steps B) and C) in sequence, continue until work orders N-N are generated with data N-N as the delivery data.

[0019] In some implementations, data delivered in a work order can be transferred within the same business class and to other business classes. Its advantage lies in that it clearly describes the ways data can be transferred through a work order, with a very clear transfer path and logic.

[0020] In some implementations, in step S2, the key indicators include design quality indicators, design schedule indicators, and design efficiency indicators, each designed according to the nature of the business. Its advantage lies in describing the main content of the key indicators.

[0021] In some implementations, in step S3, the data includes delivery data and attribute data. Its advantage lies in describing the main content of the data to be collected, analyzed, queried, and statistically analyzed.

[0022] In some implementations, the real-time monitoring and adjustment methods in step S4 include business operations and their data visualization, as well as key performance indicator visualization. The advantage is that the main specific methods of real-time monitoring and adjustment are described.

[0023] In some implementations, step S5 includes fusing the collected model data from various business categories to generate an electronic sand table file as a visualized data product. Its advantage lies in describing a specific step in realizing data productization.

[0024] In some implementations, in step S6, the data service-oriented items include drawing mounting and sub-items. Its advantage lies in describing the main content of the data service-oriented items.

[0025] In some implementations, in step S7, the data capability service items include construction simulation and progress management, providing model version comparison services, optimizing data transmission and business processes, and data synchronization services. Its advantage lies in describing the main content of the data capability service items. Attached Figure Description

[0026] Figure 1 is a flowchart of a business data-driven method for work decomposition based on data transmission according to an embodiment of the present invention.

[0027] Figure 2 is a flowchart of the work decomposition based on data transfer shown in Figure 1. Detailed Implementation

[0028] The present invention will now be described in further detail with reference to the accompanying drawings.

[0029] As shown in Figure 1, the business data-driven method for work decomposition based on data transmission in this invention mainly includes two parts: business digitization and digital businessization. The data mentioned here is general in nature, but this embodiment uses a highway engineering BIM 3D design project as an example for illustration.

[0030] Business digitization can be achieved through the steps in S1 to S4 below.

[0031] S1: Work decomposition based on data transfer.

[0032] The work decomposition of tasks is multi-dimensional. In this invention, it refers to the work decomposition of different business categories based on the deliverables as "data" and the process of "data transfer". The task is decomposed into work orders with corresponding deliverable data, and each work order corresponds to one deliverable data.

[0033] As shown in Figure 2, the specific steps of the work breakdown are as follows:

[0034] 1. First, clarify the business categories in the task (such as road, bridge, tunnel, etc.), and let the business categories include business category 1, business category 2, ... business category N.

[0035] 2. In business category 1 (road specialty), decompose "work order 1-1" with "(route design) data 1-1" as the delivery data;

[0036] When “(Route Design) Data 1-1” is referenced by “(Subgrade Design) Data 1-2”, “Work Order 1-2” can be decomposed with “(Subgrade Design) Data 1-2” as the delivery data, that is, the data transmission path is from “Data 1-1” to “Data 1-2”.

[0037] This process continues until "(Route) Data 1-1, (Subgrade Design) Data 1-2, etc." are referenced by "(Earthwork Calculation) Data 1-N". In this case, "Work Order 1-N" can be decomposed based on "(Earthwork Calculation) Data 1-N" as the delivery data. That is, the data transmission path is from "Data 1-1", "Data 1-2" to "Data 1-N".

[0038] 3. In Business Category 2 (Bridge Specialty), when “(Route Design) Data 1-1” is referenced by “(Bridge Design) Data 2-1” in Business Category 2 (Bridge Specialty), a “Work Order 2-1” is decomposed with “(Bridge Design) Data 2-1” as the delivery data. That is, the data transmission path is from “Data 1-1” to “Data 2-1”. When “(Bridge Design) Data 2-1” is referenced by other “Data 2-2”, a “Work Order 2-2” is decomposed with “Data 2-2” as the delivery data. And so on. Thus, Business Category 2 (Bridge Specialty) also completes the work decomposition according to the internal data transmission requirements. That is, the data transmission path is from “Data 2-1” and “Data 2-2” to “Data 2-N”.

[0039] 4. Continue in this manner until all business tasks are broken down and until a "Work Order N-N" is created with "Data N-N" as the delivery data.

[0040] As can be seen, a business operation consists of multiple work orders containing delivery data, and these work orders form the work order tree for that business operation. Each work order provides additional information about each delivery data item, and all data on it (excluding the delivery data itself) are attribute data for that delivery data.

[0041] Furthermore, the data delivered in a work order decomposed from a certain business class can be transferred within the same business class (for internal business use) or transferred to other business classes (for external business use).

[0042] In this step, the work is broken down according to the "data transfer" process, which enables the collection of all delivery data and its attribute data, ensuring the high validity of the collected data. This not only forms a data-driven business digitization, but also lays the foundation for digital business.

[0043] S2: Define key metrics.

[0044] Taking design projects as an example, a series of metrics are typically used to ensure design quality, schedule, cost, and resource utilization efficiency. Data supporting these key metrics must also be integrated into the work order attribute data to be collected, analyzed, and utilized.

[0045] This invention is based on "data transmission" as the basis for work (process) decomposition, is not limited by business category, collects data covering all business data, and can support the definition of global control indicators across businesses.

[0046] Taking highway design projects as an example, key business indicators can be defined as (but are not limited to):

[0047] 1. Design quality indicators, such as:

[0048] Design error rate: The error count can be calculated by extracting review comments from the review work order and comparing it with the total design quantity extracted from the attribute data of the work order.

[0049] Number of design changes: The number of design changes can be determined by using data such as the "version number" of the delivered data in the work order, the number of times the work order "status" has changed, and the number of times it has been submitted for review.

[0050] Design review pass rate: This can be calculated using statistics on data such as the "status" of internal or external review work orders and the total number of review work orders.

[0051] 2. Design schedule indicators, such as:

[0052] Design cycle completion rate: This is the ratio of the actual completed design work to the planned work. It can be automatically calculated by extracting attribute data from relevant work orders, namely the completed amount and the planned completed amount.

[0053] Milestone achievement rate: This refers to whether key design milestones are completed on time. It can be achieved by automatically generating task visual plan charts using the start and end time data of the work order, or by statistically calculating the milestone achievement rate.

[0054] Design delay days: This refers to the difference between the actual completion time and the planned time for the design work. It can be automatically calculated by comparing the "completion time" data and "work status" data of the task in the work order, and an early warning will be pushed based on the result.

[0055] 3. Design efficiency metrics, such as:

[0056] Average output per designer: This refers to the amount of design work completed by each designer per unit of time. This indicator can be obtained by extracting, analyzing, and calculating data such as "executor, total number of work orders, completed amount, and start / end time" from work orders collected in real time.

[0057] Design drawing output efficiency: This refers to the number of design drawings completed per unit of time. This indicator can be obtained by extracting, analyzing, and calculating data such as "total number of work orders, quantity completed, and start / completion time" from collected work orders.

[0058] Design (cross-disciplinary) collaboration efficiency: This refers to the response speed and collaborative effect of multi-disciplinary collaborative design. This indicator can be obtained by extracting, analyzing, and calculating data such as "version", "submission time", and "access statistics" from the collected work orders.

[0059] 4. Other indicators, such as:

[0060] New technology application rate: the proportion of new technologies and processes used in the design.

[0061] Green design ratio: This refers to the proportion of green and environmentally friendly technologies used in the design.

[0062] Both of the above indicators can be obtained by extracting, analyzing, and calculating data such as "work tags" and "attached data" from the collected work order attribute data.

[0063] Design communication frequency: This refers to the number and quality of communication between the design team and external entities such as clients and construction companies. It can be obtained by extracting data from the "communication window" in the work order and performing statistical analysis.

[0064] On-time delivery rate of design deliverables: This refers to whether design deliverables are delivered to the client or construction party on time. This indicator can be obtained by extracting, analyzing, and calculating data such as "completed quantity" and "start / completion time" from collected work orders.

[0065] All of the above key indicators are visualized, which facilitates real-time monitoring of business progress and timely adjustment of business strategies. Business data is fed back into the business, thereby achieving data-driven refined operations.

[0066] S3: Collect, analyze, query, and statistically analyze data.

[0067] By utilizing real-time data collected from the business process, including full-process data such as work status, work time, planned workload, and completed workload, we can mine and analyze this data through data fusion, extraction, and analysis to form analysis and insights into the entire business process, providing support for business decision-making.

[0068] As shown in Figure 2, the full data for all business operations mainly includes all business categories, all work orders, and data generated throughout the entire business process. Specifically, it includes:

[0069] 1. Delivery data, i.e., business outcome data, drives business digitization.

[0070] 2. Attribute data.

[0071] The work order is an additional description of each deliverable data. All data on it (except for the deliverable data) are attribute data of this deliverable data, including but not limited to: work order number, work order name, work tag, work scope, work type, work status, start / end time, work executor, total work, completed amount, creator, work description, studio design, deliverable data version, attached data, work attachments, work members, suggestion box, this order configuration, message configuration, review order number, review order name, review process, creator, processor, creation time, update time, etc.; it also includes editable types, such as submission instructions, submission results, review comments, attachment list, communication window, etc.

[0072] Both delivery data and attribute data are dynamic and will be recorded and collected in real time.

[0073] It is evident that all delivery data collected using the method of this invention is valid, while also ensuring the high efficiency of the corresponding attribute data.

[0074] In terms of data analysis, querying, and statistics, the data-driven method based on this invention provides more detailed information, stronger data traceability, and supports multi-dimensional correlation data analysis. Specifically, by analyzing, querying, and statistically analyzing the collected full-business data, it is possible to compare and analyze production plans and actual execution within any scope of interest to identify problems and bottlenecks in the production plan, thereby achieving data-driven real-time monitoring and scientific decision-making.

[0075] Data analysis includes, but is not limited to, the following methods: integrating collected time-related attribute data to create business Gantt charts; using data analysis tools to compare and analyze production plans and actual execution; using color (data-driven) to identify business processes that do not meet the plan, facilitating the identification of bottlenecks in business lag and decision-making on resource allocation; and integrating collected model delivery data to generate project-level sandbox data; using data analysis tools to compare and analyze model data with standard data, generating comparison result reports, and providing feedback to data maintenance personnel for data maintenance.

[0076] In data querying and statistics, the data-driven method of this invention supports multi-dimensional and precise queries. Attribute data categories, keywords, etc., can all be used as query conditions, adapting to queries and statistics on business progress, quality, etc., based on any global dimension. This includes, but is not limited to, the following query methods: Precise data can track the time and responsible person for each business process. In the custom query tool, setting the "planned completion time," "completion time," and "executor" of a work order as query conditions allows for the statistical analysis and visualization of all work orders for that employee within the query time period. This enables faster identification of problems when analyzing business delays, and clicking on problematic work orders allows for in-depth analysis of work details and problem resolution, whereas traditional methods may only show the overall delay rate and cannot delve into the details.

[0077] S4: Real-time monitoring and adjustment based on data.

[0078] In this invention, because it can collect global and full-volume business data, key business indicators can be tracked and evaluated in real time. Once business problems and opportunities are discovered, business strategies can be adjusted immediately to ensure continuous business growth and optimization. Real-time monitoring and adjustment methods include business and data visualization, as well as key indicator visualization.

[0079] Regarding business operations and data visualization, this invention decomposes work based on the data transmission process, resulting in a clear data transmission logic. This not only enables business visualization but also business data visualization, facilitating real-time monitoring of work, timely identification of problems, and timely adjustments to business operations and resources.

[0080] For key indicator visualization, based on the collection of all business data, a data extraction, analysis, and application mechanism is established. Business data is integrated and transformed into key business indicators, and visualization is achieved using charts and other methods to facilitate real-time monitoring and adjustment of production plans, ensuring the accuracy and on-time performance of production plans.

[0081] Digitalization of business means using collected data, integrating it, productizing and packaging it into new business segments, and then having a professional team commercialize and operate them in a productized manner.

[0082] Digitalization of business operations can be achieved through steps S1 and S5 to S7 below.

[0083] S5: Realize data productization.

[0084] Data productization involves transforming data collected from work orders into data products, such as web-based sandbox files, through data fusion, analysis, and visualization. These web-based sandbox files can adapt to changes in the source data, thereby providing timely support for business decision-making.

[0085] By integrating the collected BIM model data from various business categories (professions) and generating an electronic sand table file, a visualized data product is obtained, which can be used for the display and reporting of the three-dimensional model data of the entire project design results.

[0086] S6: Enable data to be serviced.

[0087] Data servitization means integrating the data accumulated in business systems, finding patterns in the data, using data to drive the development of various businesses, permeating data into the operation of various businesses, allowing data to feed back into the business, and ultimately releasing the value of data to complete the operational loop of data value.

[0088] Taking highway design projects as an example, the relevant data service projects mainly include drawing mounting and sub-items.

[0089] In the drawing mounting service, mounting drawing data into the component model data is a data service provided to external users. The specific generation method for the drawing mounting service is as follows: Import BIM model data (to the tool) – Import drawing data – Identify (model attributes + drawing attributes) data – Data matching – Generate the "Drawing Mount" data service.

[0090] In the sub-item services, data analysis, transformation, and data integration methods are used to connect the collected engineering quantity data to the sub-items according to the bill of quantities for each construction phase. The engineering quantity data is then transferred from upstream to downstream applications through data transformation. Especially when design changes occur, the downstream data will promptly and accurately follow the changes after the upstream data is updated, avoiding the errors that often occur in traditional manual data updates and improving the efficiency of manual data correction.

[0091] The specific generation method for sub-items is as follows: import model, material information, material quantity, sub-item information, EBS code — import target sub-item list standard — change (iterative cycle) — generate "sub-item" engineering quantity ledger — connect to downstream data platform — generate "sub-item" data service.

[0092] S7: Enables data capability services.

[0093] Data capability services refer to the development of digital capabilities related to the organization's main business, such as intelligent analysis, simulation verification, and construction management, based on the organization's relevant data resources and knowledge.

[0094] Taking highway design projects as an example, the relevant data service capabilities mainly include the following items.

[0095] 1. Construction simulation and schedule management, which involves linking the BIM model with the construction schedule (4D) to simulate key milestones. The specific real-time steps are: extracting BIM model data – linking with the construction schedule – dynamic visualization simulation – conflict detection and optimization – schedule tracking and comparison – deviation warning – simulation iterative optimization (adjusting the schedule, multiple options must be selected) – results release (for construction briefings, reports, etc.).

[0096] 2. Provide model version comparison service, which maintains the accuracy and consistency of data through the model version comparison function provided by the data capability service. The specific real-time steps are: determine the data version number to be compared - select and extract the corresponding version of BIM model data - use the model comparison tool - generate visual comparison results.

[0097] 3. Optimize data transmission and business processes, which involves extracting data issues and improvement suggestions from team members during project execution from attribute data, and continuously optimizing data transmission and business processes. The specific real-time steps are: extracting data from "communication windows," "suggestion boxes," and "key indicators"—integrating relevant data—multi-party collaborative problem identification—using visualization methods for problem analysis and evaluation—generating and publishing optimization suggestions.

[0098] 4. Data synchronization service, which synchronizes data changes in the project in real time to ensure that all team members can access the latest data version promptly. The specific real-time steps are as follows:

[0099] (1) Data changes – triggering the “automatic update” mechanism – the studio’s delivery data is automatically updated to the latest version;

[0100] (2) Data changes - trigger the "automatic detection" mechanism - send a message to the data referencer - you can choose whether to update the referenced data.

[0101] The business data-driven method for work decomposition based on data transmission in this invention has the following advantages:

[0102] 1. The work decomposition method based on "data transfer" can make the work decomposition objectives very clear and less likely to be missed. The "data transfer" path is clear. Moreover, it can not only collect all the data of the business process, but also break down the data silos between businesses, realize the integration and communication of upstream and downstream data, and ensure the efficient operation of the business process through the comprehensive datafication of the business process, laying a data foundation for the optimization of the entire business process.

[0103] 2. Using 'data transfer' as the basis for work (process) decomposition is not limited by business, thus breaking down data barriers between business units and facilitating overall business control;

[0104] 3. Based on the work orders after the work is decomposed from the data transmission, the process data is recorded and captured in real time across the entire chain, which can be used for data analysis and as a basis for management decision-making;

[0105] 4. Based on the work order decomposition of data transfer, the delivered data is classified, defined, and planned from the beginning of its creation, and all of it is valid data that can be used directly;

[0106] 5. Enables accurate and comprehensive collection of business data through work orders, allowing for more diverse and advanced data services that can be provided externally, such as drawing mounting and component segmentation.

[0107] The above descriptions are merely some embodiments of the present invention. For those skilled in the art, various modifications and improvements can be made without departing from the inventive concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A business data-driven method for work decomposition based on data transmission, characterized in that: Decompose business tasks according to the direction of data transmission to form a data-driven approach and realize business digitization and digital business. Business digitization includes the following steps S1 to S4 S1: Decompose the task into work orders corresponding to the data delivery direction, where each work order corresponds to... A delivery data should be provided; S2: Define key metrics; S3: Collect, analyze, query, and statistically analyze data; S4: Real-time monitoring and adjustment based on data; Digitalization of business operations includes step S1 and the following steps S5 to S7. S5: To productize data, data collected from work orders will undergo data fusion, data analysis, and visualization. Segments are transformed into data products; S6: Enable data service-oriented architecture; S7: Enables data capability services; Step S1 further includes the following steps: A: Define the business categories in the task, where business categories include business category 1 to business category N; B: In business class 1, decompose work order 1-1 with data 1-1 as the delivery data, and when data 1-1 is referenced by data 1-2, decompose work order 1-2 with data 1-2 as the delivery data, and so on, until work order 1-N with data 1-N as the delivery data is decomposed. C: In business class 2, when data 1-1 of business class 1 is referenced by data 2-1 in business class 2, work order 2-1 is decomposed with data 2-1 as the delivery data. When data 2-1 is referenced by data 2-2, work order 2-2 is decomposed with data 2-2 as the delivery data. This process continues until work order 2-N is decomposed with data 2-N as the delivery data. D: Following steps B) and C) in sequence, continue until work orders N-N are generated with data N-N as the delivery data.

2. The business data-driven method for work decomposition based on data transmission according to claim 1, characterized in that: Data delivered in a work order can be transferred within the same business class and to other business classes.

3. The business data-driven method for work decomposition based on data transmission according to claim 1, characterized in that: In step S2, the key indicators include design quality indicators, design schedule indicators, and design efficiency indicators, each of which is designed according to the nature of the business.

4. The business data-driven method for work decomposition based on data transmission according to claim 1, characterized in that: In step S3, the data includes delivery data and attribute data.

5. The business data-driven method for work decomposition based on data transmission according to claim 1, characterized in that: In step S4, real-time monitoring and adjustment methods include business and its data visualization and key indicator visualization.

6. The business data-driven method for work decomposition based on data transmission according to claim 1, characterized in that: Step S5 includes fusing the collected model data from various business categories to generate an electronic sand table file as a visualized data product.

7. A business data-driven method for work decomposition based on data transmission according to claim 1, characterized in that: In step S6, the data service-oriented items include drawing mounting and sub-items.

8. The business data-driven method for work decomposition based on data transmission according to claim 1, characterized in that: In step S7, the data capability services include construction simulation and progress management, providing model version comparison services, optimizing data transmission and business processes, and data synchronization services.

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