Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A supply chain demand forecasting method based on big data

A demand forecasting and supply chain technology, applied in the field of big data forecasting, can solve the problems of lack of disclosure and insufficient precision of demand forecasting methods, and achieve the effect of ensuring differentiation

Active Publication Date: 2020-12-25
博拉网络股份有限公司
View PDF12 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Under the current big data background, the trend of supply chain demand forecasting is becoming more and more important, but the corresponding supply chain demand forecasting method is not disclosed in the prior art; on the other hand, the accuracy of the demand forecasting method in the prior art is not high enough , also needs further improvement

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A supply chain demand forecasting method based on big data
  • A supply chain demand forecasting method based on big data
  • A supply chain demand forecasting method based on big data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] Data source in this aspect: the supply chain forecast target market provided by a cross-border e-commerce company going overseas is the accumulation of historical data in Saudi Arabia. Provide product promotion price data, product sales data, product information data, product performance data among users, and platform activity data information in a historical period of one year.

[0047] As an optional method, the data time span is from January 1, 2017 to December 31, 2017, and the selected time period should not be affected by abnormal sales during holidays such as New Year’s Day, and the longer time period and test The difference between time periods is too large to be included; predict the sales forecast value of different products for each week in the next 35 days (the next 5 weeks from January 1, 2018). An algorithm flow chart of supply chain demand forecasting based on big data is attached figure 1 shown, including the following steps:

[0048] The step 101 uses...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention belongs to the field of big data prediction, and particularly provides a supply chain demand prediction method based on big data. The method comprises the following steps of: fusing a rule model and an algorithm model, Different data partitions and feature projects are constructed by using historical sales data of commodities; and two algorithms of a tree model and a linear model areadopted to construct a model for prediction, so that the difference of the model is ensured, and finally, the rule model and the algorithm model with relatively high difference degree and accurate prediction effect are fused based on a tree structure to obtain a final future sales volume prediction result. According to the method, long-term commodity sales can be accurately predicted, a data basis is provided for the supply chain, and key technical support is provided for establishing a global supply chain scheme for an enterprise.

Description

technical field [0001] The invention belongs to the field of big data forecasting, relates to the field of supply chain demand and sales forecasting, and specifically relates to a big data-based supply chain demand forecasting method. Background technique [0002] In the e-commerce industry chain, in order to improve the user's logistics service experience, the supply chain collaboratively prepares the goods in local warehouses in various markets around the world in advance, which can effectively reduce the logistics time and greatly improve the user experience. At present, product production and sales areas are often globalized, and it takes a long time for the entire product preparation chain to purchase, transport, and customs quality inspection. In the context of the new era of rapid development of big data and artificial intelligence technology, using big data analysis and algorithm technology to accurately predict long-term commodity sales and provide a data basis for ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/02
CPCG06Q30/0202
Inventor 童毅周波依
Owner 博拉网络股份有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com