CN113283870A - Engineering supply chain management method under big data environment - Google Patents
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Abstract
The invention discloses a method for managing an engineering supply chain in a big data environment, which comprises the following steps: s10: collecting data, collecting data of customer demands in history and project planning data, and collecting and storing the data to obtain a database; s20: extracting key data of each deal and making PPT for data browsing; s30: acquiring inventory data of a warehouse and supplementing the warehouse in time; s40: acquiring the requirements of customers, and extracting data meeting the requirements of the customers from a database; s50: and further optimizing the data obtained in the S40 to obtain an optimal scheme, and inputting the scheme into a database. According to the project supply chain management method under the big data environment, through extraction of key data, PPT is formed, previous works can be displayed to customers more visually, and through the adoption of a championship selection strategy and the fast non-dominated sorting of pareto, a scheme which is more satisfied by the customers is obtained.
Description
Technical Field
The invention relates to the technical field of supply chain management, in particular to a method for managing an engineering supply chain in a big data environment.
Background
The supply chain refers to a network chain structure formed by upstream and downstream enterprises which provide products or services to end user activities in the production and circulation processes; the supply chain has the most obvious effect in engineering, and each department or enterprise is often required to cooperate in one engineering, but as the number of the cooperating enterprises or departments increases, the project is not managed, the requirements of customers cannot be timely obtained, and the stock and the required conditions of materials in each department or enterprise cannot be timely obtained.
Supply chain management, meaning that the supply chain operation is optimized, with minimal cost, all the processes from procurement to fulfillment to the end customer are covered by management education such as MBA, EMBA, etc. The supply chain management is to coordinate internal and external resources of an enterprise to meet the requirements of consumers together, when the enterprise of each link on the supply chain is regarded as a virtual enterprise alliance, any enterprise is regarded as a department in the virtual enterprise alliance, and the internal management of the alliance is the supply chain management. Efficient supply chain management can help achieve four goals: the cash turnover time is shortened; reducing the risk faced by the enterprise; realizing profit growth; providing predictable revenue.
With the advent of the big data era, data analysis based on big data technology is increasingly widely applied. In the field of supply chain management, various departments or enterprises in a supply chain can be better managed and communicated through data analysis. However, how to manage the engineering supply chain with big data becomes a problem worth solving.
Disclosure of Invention
The present invention provides a method for managing an engineering supply chain in a big data environment, so as to solve the problem of managing the engineering supply chain by using big data in the existing engineering supply chain management method in the market proposed by the background art.
In order to achieve the purpose, the invention provides the following technical scheme: a method for managing an engineering supply chain in a big data environment comprises the following steps:
s10: collecting data, collecting data of customer demands in history and project planning data, and collecting and storing the data to obtain a database;
s20: extracting key data of each deal and making PPT for data browsing;
s30: acquiring inventory data of a warehouse and supplementing the warehouse in time;
s40: the method comprises the steps of obtaining the requirements of customers and extracting data meeting the requirements of the customers from a database.
S50: and further optimizing the data obtained in the S40 to obtain an optimal scheme, and inputting the scheme into a database.
Preferably, the data of the customer demand and the project planning data in the history in step S10 are data of a period, and the period may be 3 years or 5 years.
Preferably, the key data in step S20 are: the method comprises the steps that required data of a client, drawing design data, contractor engineering planning data and overall data after a project is completed are sorted according to places and then sorted according to time.
Preferably, the material information required for each collaborative department project is acquired in step S30, the name, model, and specification of the required material are obtained according to the provided material information, and then the required material is supplemented.
Preferably, in step S40, the questions required by the customer are converted into data, and the database is searched for data that approximately matches the customer' S intention.
Preferably, the specific steps in step S50 include:
a. dividing the four information of the data obtained in the step S40, the client requirement data, the drawing design data, the contractor engineering planning data and the overall data after the project is completed into M problems at random to obtain a problem population N;
b. selecting Q individuals from N to form a new population C;
c. performing cross operation on demand data of M individual customers in the population N, drawing design data, contractor engineering planning data and overall data after project completion;
d. combining M individuals in the population C and the population N to form a new population Y;
e. performing environment selection operation based on a multi-level interest area on the M population to select a new population P;
f. judging whether the current end condition is met, if so, executing the step g, otherwise, executing the step b;
g. after iteration is finished, sequencing the final population;
h. outputting the solution scheme in the non-dominated solution set and the corresponding NPV optimization target to obtain optimal data;
I. and finally modifying according to the requirements of customers to obtain a final optimal scheme.
In said step b, Q individuals are selected from N using a tournament selection strategy.
In the step g, the sequence is as follows: a pareto-based fast non-dominated sorting is performed on the last population.
Compared with the prior art, the invention has the beneficial effects that: according to the engineering supply chain management method under the big data environment, through extraction of key data, PPT is formed, previous works can be displayed to customers more intuitively, a scheme which is satisfied by the customers is obtained through a championship selection strategy and pareto fast non-dominated sorting, material information required by each collaborative department project can be obtained in time, the product name, the model and the specification of required materials are obtained according to the provided material information, and then the required materials are supplemented.
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FIG. 1 is a schematic view of the step structure of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a technical scheme that: a method for managing an engineering supply chain in a big data environment comprises the following steps:
s10: collecting data, collecting data of customer demands in history and project planning data, and collecting and storing the data to obtain a database;
s20: extracting key data of each deal and making PPT for data browsing;
s30: acquiring inventory data of a warehouse and supplementing the warehouse in time;
s40: the method comprises the steps of obtaining the requirements of customers and extracting data meeting the requirements of the customers from a database.
S50: and further optimizing the data obtained in the S40 to obtain an optimal scheme, and inputting the scheme into a database.
Further, the data of the customer demand and the project planning data in the history in step S10 are data of a period, and the period may be 3 years or 5 years.
Further, the key data in step S20 are: the method comprises the steps that required data of a client, drawing design data, contractor engineering planning data and overall data after a project is completed are sorted according to places and then sorted according to time.
Further, in step S30, material information required by each collaborative department project is acquired, and the name, model, and specification of the required material are obtained according to the provided material information, and then the required material is supplemented.
Further, in step S40, the questions required by the customer are converted into data, and the database is searched for data that approximately matches the customer' S intention.
Further, preferably, the specific steps in step S50 include:
a. dividing the four information of the data obtained in the step S40, the client requirement data, the drawing design data, the contractor engineering planning data and the overall data after the project is completed into M problems at random to obtain a problem population N;
b. selecting Q individuals from N to form a new population C;
c. performing cross operation on demand data of M individual customers in the population N, drawing design data, contractor engineering planning data and overall data after project completion;
d. combining M individuals in the population C and the population N to form a new population Y;
e. performing environment selection operation based on a multi-level interest area on the M population to select a new population P;
f. judging whether the current end condition is met, if so, executing the step g, otherwise, executing the step b;
g. after iteration is finished, sequencing the final population;
h. outputting the solution scheme in the non-dominated solution set and the corresponding NPV optimization target to obtain optimal data;
I. and finally modifying according to the requirements of customers to obtain a final optimal scheme.
In said step b, Q individuals are selected from N using a tournament selection strategy.
In the step g, the sequence is as follows: a pareto-based fast non-dominated sorting is performed on the last population.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that various changes in the embodiments and/or modifications of the invention can be made, and equivalents and modifications of some features of the invention can be made without departing from the spirit and scope of the invention.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (9)
1. A method for managing an engineering supply chain in a big data environment is characterized by comprising the following steps: the method comprises the following steps:
s10: collecting data, collecting data of customer demands in history and project planning data, and collecting and storing the data to obtain a database;
s20: extracting key data of each deal and making PPT for data browsing;
s30: acquiring inventory data of a warehouse and supplementing the warehouse in time;
s40: acquiring the requirements of customers, and extracting data meeting the requirements of the customers from a database;
s50: and further optimizing the data obtained in the S40 to obtain an optimal scheme, and inputting the scheme into a database.
2. The method for engineering supply chain management in big data environment according to claim 1, wherein: in the step S10, the data of the customer demand and the project planning data in the history are data of one cycle, and the cycle is 3 years.
3. The method for engineering supply chain management in big data environment according to claim 1, wherein: in step S10, the data of the customer demand and the project planning data in the history are data of one cycle, and the cycle is 5 years.
4. The method for engineering supply chain management in big data environment according to claim 1, wherein: the key data in step S20 are: the method comprises the steps that required data of a client, drawing design data, contractor engineering planning data and overall data after a project is completed are sorted according to places and then sorted according to time.
5. The method for engineering supply chain management in big data environment according to claim 1, wherein: in step S30, material information required by each collaborative department project is acquired, and the name, model, and specification of the required material are obtained according to the provided material information, and then the required material is supplemented.
6. The method for engineering supply chain management in big data environment according to claim 1, wherein: in step S40, the questions required by the customer are converted into data, and the database is searched for data that approximately matches the customer' S intention.
7. The method for engineering supply chain management in big data environment according to claim 1, wherein: the specific steps in step S50 include:
a. dividing the four information of the data obtained in the step S40, the client requirement data, the drawing design data, the contractor engineering planning data and the overall data after the project is completed into M problems at random to obtain a problem population N;
b. selecting Q individuals from N to form a new population C;
c. performing cross operation on demand data of M individual customers in the population N, drawing design data, contractor engineering planning data and overall data after project completion;
d. combining M individuals in the population C and the population N to form a new population Y;
e. performing environment selection operation based on a multi-level interest area on the M population to select a new population P;
f. judging whether the current end condition is met, if so, executing the step g, otherwise, executing the step b;
g. after iteration is finished, sequencing the final population;
h. outputting the solution scheme in the non-dominated solution set and the corresponding NPV optimization target to obtain optimal data;
I. and finally modifying according to the requirements of customers to obtain a final optimal scheme.
8. The method for engineering supply chain management in big data environment according to claim 7, wherein: in said step b, Q individuals are selected from N using a tournament selection strategy.
9. The method for engineering supply chain management in big data environment according to claim 7, wherein: in the step g, the sequence is as follows: a pareto-based fast non-dominated sorting is performed on the last population.
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| CN104156782A (en) * | 2014-07-22 | 2014-11-19 | 天津大学 | Balancing-optimalizing method, for project time limit, quality and cost, used in concrete faced rockfill dam construction |
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