CN116739655A - Intelligent supply chain management method and system based on big data - Google Patents

Intelligent supply chain management method and system based on big data Download PDF

Info

Publication number
CN116739655A
CN116739655A CN202310865331.0A CN202310865331A CN116739655A CN 116739655 A CN116739655 A CN 116739655A CN 202310865331 A CN202310865331 A CN 202310865331A CN 116739655 A CN116739655 A CN 116739655A
Authority
CN
China
Prior art keywords
commodity
store
target
target store
sales
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202310865331.0A
Other languages
Chinese (zh)
Other versions
CN116739655B (en
Inventor
李强
陈臻
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Langhui Huike Technology Co ltd
Original Assignee
Shanghai Langhui Huike Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Langhui Huike Technology Co ltd filed Critical Shanghai Langhui Huike Technology Co ltd
Priority to CN202310865331.0A priority Critical patent/CN116739655B/en
Publication of CN116739655A publication Critical patent/CN116739655A/en
Application granted granted Critical
Publication of CN116739655B publication Critical patent/CN116739655B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0203—Market surveys; Market polls
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G06Q30/0224—Discounts or incentives, e.g. coupons or rebates based on user history
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Finance (AREA)
  • Accounting & Taxation (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Game Theory and Decision Science (AREA)
  • Theoretical Computer Science (AREA)
  • Educational Administration (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Data Mining & Analysis (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to the technical field of network security management, in particular to an intelligent supply chain management method and system based on big data, comprising the steps of acquiring historical data of a target store, and extracting data information corresponding to each historical period of the target store from the historical data; evaluating sales of the current period of the target store; based on the store influence value and the predicted commodity sales of each target store in the area, evaluating the influence degree of each backorder commodity on the commodity sales of the target store to obtain backorder commodity with the lowest influence degree on the target store sales in the area; adjusting a merchant in an area commodity supply chain based on the target commodity number of the area and the predicted commodity sales of each target store in the area; and transporting each commodity of the merchant to a corresponding distribution center, and transporting the commodity to a corresponding target store by the distribution center.

Description

Intelligent supply chain management method and system based on big data
Technical Field
The invention relates to the technical field of network security management, in particular to an intelligent supply chain management method and system based on big data.
Background
Big data is data which can be assembled into huge and complex data and cannot be processed by general traditional data processing tools of the data, the big data has four characteristics of massive data scale, rapid data circulation, various data types and low value density, the big data mainly has the following advantages that 1, more accurate decision support can be provided, the big data can help enterprises or organizations collect and analyze a large amount of data, thereby obtaining more accurate relations existing between insights and providing data support for executing things decision, 2, hidden relations and modes in a plurality of events can be detected, the large data can be used for analyzing the large data, thereby predicting the hidden relations existing between the data and the development trend of the exposed things in the data, 3, the cost can be effectively reduced and the benefit can be improved, through the analysis of the big data, the problems existing in the aspects of finding the enterprises and the institutions can be better obtained, the problems related to the enterprises and the institutions can be solved from the corresponding plan, the cost can be reduced and the benefit can be improved, when various commodities are sold, the conditions of the commodities can be hidden in the relation between the insights and the commodities can not be estimated to be carried out on the corresponding commodities, but the traders can not cause the brand to be lost, and the market can not cause the market profits to be damaged.
Disclosure of Invention
The invention aims to provide an intelligent supply chain management method and system based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an intelligent supply chain management method based on big data, the method comprises the following steps:
step S100: acquiring historical data of a target store, and extracting data information corresponding to each historical period of the target store from the historical data; based on the data information corresponding to each historical period of the target store, evaluating the sales volume of the target store in the current period to obtain the predicted commodity sales volume of the target store in the current period;
step S200: setting an influence area corresponding to a target store; acquiring commodity types of sold commodities of all shops in the influence area; calculating a store influence value corresponding to the target store in the current period based on the commodity type corresponding to each store in the influence area where the target store is located;
step S300: acquiring an area where a target store is located; acquiring out-of-stock goods in each target store in the area; based on the store influence value and the predicted commodity sales of each target store in the area, evaluating the influence degree of each backorder commodity on the commodity sales of the target store to obtain backorder commodity with the lowest influence degree on the target store sales in the area; acquiring the commodity number of the backorder commodity, and marking the commodity number as the target commodity number of the area;
Step S400: acquiring a distribution center of a merchant in an area; adjusting a merchant in an area commodity supply chain based on the target commodity number of the area and the predicted commodity sales of each target store in the area; and transporting each commodity of the merchant to a corresponding distribution center, and transporting the commodity to a corresponding target store by the distribution center.
Further, step S100 includes:
step S101: corresponding commodity sales in each historical period of the target store are collected; the commodity sales volume is the commodity sales volume corresponding to the target store; obtaining a commodity sales volume set V= { V of a target store 1 、V 2 、...、V n -a }; wherein V is 1 、V 2 、...、V n Commodity sales for the target store in the 1 st, 2 nd, n history periods, respectively;
step S102: recording a history period containing holidays as a special history period; obtaining the sales volume V of the target store in any special history period a The method comprises the steps of carrying out a first treatment on the surface of the Obtaining commodity sales volume V a Commodity sales V of target store in last history period b The method comprises the steps of carrying out a first treatment on the surface of the Calculating commodity sales volume V a Corresponding sales volume V of replacement commodity oa ：
Wherein alpha is x A commodity sales increasing proportion of a target store in an xth history period in the commodity sales set V;
step S103: acquiring a commodity sales volume set Combining each special history period in V; acquiring the sales volume of the replacement commodity corresponding to the sales volume of the commodity of the target store in each special history period; when the current period does not contain holidays, the value of the commodity sales in each special history period in the commodity sales set V is replaced by the corresponding value of the replacement commodity sales; marking the replaced commodity sales volume set V as the commodity sales volume set V o The history periods and the special history periods in the commodity sales volume set V are collectively called as normal history periods; wherein, the commodity sales volume set V o ＝{V o1 、V o2 、...、V on -a }; wherein V is o1 、V o2 、...、V on Commodity sales for the target store in the 1 st, 2 nd, n normal history periods, respectively;
step S104: calculating the change proportion of the commodity sales of the target store in the e-th normal history period Wherein V is oe The sales amount of the commodity in the e normal history period for the target store; v (V) o(e-1) The sales amount of the commodity in the e-1 th normal history period for the target store; recording and collecting the commodity sales volume change proportion of the target store in each normal history period to obtain a commodity sales volume change proportion set beta= { beta of the target store in each normal history period 1 、β 2 、...、β n -a }; wherein beta is 1 、β 2 、...、β n Commodity sales change ratios of the target store in the n normal history periods, respectively 1, 2; obtaining commodity sales variation ratio beta with the largest value in commodity sales variation ratio set beta a And a commodity sales quantity ratio beta of minimum value b The method comprises the steps of carrying out a first treatment on the surface of the Acquiring a history period which is closest to the current period from time, and recording the history period as a sample history period; calculating predicted commodity sales V of a target store in a current period f ：
Wherein beta is y The commodity sales quantity change proportion of the target store in the y normal history period is as follows; n is the number of normal history periods in the commodity sales volume change proportion set beta; v (V) ou Sales commodity quantity of a target store in a sample history period;
step S105: when the current period contains holidays, calculating the commodity sales change proportion gamma of the target store in the special history period:
wherein V is s The commodity sales of a target store in a special history period; v (V) τ The commodity sales of the target store in the last history period of the special history period;
acquiring commodity sales variation ratios of target stores in each special history period in the commodity sales set V; averaging the commodity sales ratios of target stores in each special history period
Calculating predicted commodity sales V of a target store in a current period f ：
Wherein V is ou Sales commodity quantity of a target store in a sample history period;
in the step, if the current period contains holidays, people often have more leisure time and more consumption desire in the holidays, so the sales volume of the store in the holidays is generally increased compared with usual, meanwhile, if whether the current period contains the holidays or not is not considered, larger errors can be generated on the predicted commodity sales volume of the target store in the current period later, if the current period does not contain the holidays, commodity sales volume data of the target store in the historical period without the holidays is used as reference data of the predicted commodity sales volume of the target store in the current period, if the current period contains the holidays, commodity sales volume data of the target store in the historical period with the holidays is used as predicted reference data of the target store in the current period, and the consideration is more scientific, so that the final calculation result is more accurate and practical.
Further, step S200 includes:
step S201: acquiring all shops in the target shop influence area; acquiring commodity types of commodities sold by a target store; respectively acquiring commodity types of commodities sold by all shops in the target shop influence area; when at least one commodity type corresponding to the target store exists in the commodity types corresponding to the stores, marking the stores as comparison stores corresponding to the target stores; acquiring commodity information corresponding to a target store in each history period; acquiring commodity information of each comparison store in the influence area; the commodity information comprises commodity types and commodity numbers corresponding to the commodities under the commodity types; calculating a first store influence value P of a target store on a target store:
wherein r is the number of commodity types corresponding to the same commodity type between a certain target store and a certain target store; d, d r The number of commodity numbers corresponding to the commodities in the r commodity type in a certain label store; e, e r The number of commodity numbers corresponding to the commodities in the r commodity type in the target store;
step S202: obtaining the store distance between each target store and each target store; calculating a store influence value W of a target store to a target store:
Wherein P is c A first store influence value for the c-th target store on the target store; z is the number of target stores corresponding to the target stores; s is(s) c The store distance between the c-th target store and the target store.
Further, step S300 includes:
step S301: acquiring a target store and sending out-of-stock commodities to a merchant distribution background; acquiring a commodity number corresponding to the backorder commodity; setting a time threshold corresponding to the backorder commodity in the target store; recording a history period with the time length within a time length threshold range from the current period as an out-of-stock message acquisition period; acquiring the backlog information sent by a target store in the backlog information acquisition period; the backorder information comprises a backorder commodity corresponding commodity number;
step S302: setting a threshold value of the times of sending the backorder information; when the number of times of sending the backorder information corresponding to a certain commodity in the backorder information collection period is larger than the threshold value of the number of times of sending the backorder information, marking the commodity number of the commodity as the commodity number corresponding to the backorder commodity corresponding to the current period of the target store;
step S303: setting a store influence value threshold; when the influence value of the target store is larger than the threshold value of the influence value of the store, acquiring the influence value of each target store in the region in the current period; selecting target stores with the store influence value of the current period larger than the store influence value threshold value from all target stores, and marking the target stores as information acquisition target stores; obtaining predicted commodity sales of each information acquisition target store in the area in the current period; acquiring commodity sales of each information acquisition target store in a region of a history period in a current period; when the value of the rising proportion between the commodity sales quantity of the information acquisition target store in the last historical period of the current period and the predicted commodity sales quantity of the target store in the current period is greater than zero, marking the commodity which is out of stock of the information acquisition target store in the current period as the commodity which is out of stock and has lower influence on the sales of the information acquisition target store; acquiring the out-of-stock commodity with low influence on the sales volume of the information acquisition target store in the area, selecting the most out-of-stock commodity corresponding to the information acquisition target store, marking the most out-of-stock commodity as the out-of-stock commodity with the lowest influence on the sales volume of the area store, acquiring the commodity number of the out-of-stock commodity, and marking the commodity number as the target commodity number corresponding to the area;
Setting a threshold value of the times of sending the out-of-stock information in the steps; when the number of times of sending the backorder information corresponding to a certain commodity in the backorder information collection period is larger than the threshold value of the number of times of sending the backorder information, the commodity number of the commodity is recorded as the commodity number corresponding to the backorder commodity corresponding to the current period of the target store, because the timeliness of the data acquisition is stronger in a certain time in the current period, more accurate data support is provided for judging the backorder condition of the commodity of the target store in the current period, meanwhile, because the commodity backorder condition of each target store in the area is different, under the influence of competing stores in the same area environment, the sales of different target stores can show different situation changes, the corresponding quantity of different commodities in different target stores and the sales of the target store can show different situation changes, and the influence of the backorder on the commodity sales of the target store can be obtained, so that the theoretical basis is provided for adjusting the commodity supply chain.
Further, step S400 includes:
step S401: acquiring target commodity numbers of all areas of a merchant in a current period; selecting the target commodity number with the largest number from the target commodity numbers corresponding to the areas, and marking the target commodity number as the target commodity number corresponding to the merchant in the current period; sending a target commodity number corresponding to a merchant in the current period to a merchant background, and prompting the merchant to adjust a commodity production line corresponding to the target commodity number;
Step S402: obtaining predicted commodity sales of each target store in the area; acquiring a distribution center of a merchant in an area; acquiring the commodity stock quantity of commodities in a distribution center; based on the commodity stock corresponding to the distribution center and the predicted commodity sales corresponding to each target store of the distribution center, sending a relevant message prompt to a merchant to prompt the merchant to adjust a commodity supply chain; and the goods distribution center transmits goods distribution information according to the merchant to transport the goods to the corresponding target store.
In order to better realize the method, an intelligent supply chain management system is also provided, and the management system comprises a commodity sales prediction module, a store influence value module, a target commodity numbering module and a supply chain scheduling module;
the commodity sales prediction module is used for acquiring historical data of a target store; extracting data information corresponding to each history period of the target store from the history data of the target store; evaluating the sales condition of the target store in the current period to obtain the predicted commodity sales quantity of the target store in the current period;
the store influence value module is used for selling commodity types of commodities sold by each store in the influence area where the target store is located; acquiring commodity types of commodities sold by a target store; based on commodity types of commodity sold by the target store, evaluating influence degree of the store on the target store in the influence area to obtain a store influence value corresponding to the target store;
The target commodity numbering module is used for evaluating the influence degree of the shortage of each commodity on the sales volume of the store in the area to obtain the shortage commodity with the lowest influence on the sales volume of the store in the area; acquiring the commodity number of the backorder commodity, and marking the commodity number as a target commodity number corresponding to the area;
the supply chain dispatching module is used for dispatching the supply of each commodity in the merchant, transporting each commodity of the merchant to a distribution center corresponding to the area, and transporting the dispatched commodity to a corresponding target store by the distribution center.
Further, the commodity sales prediction module comprises a replacement commodity sales amount unit and a commodity sales amount prediction unit;
the commodity sales volume replacing unit is used for acquiring commodity sales volume of a target store in a commodity sales volume set in any special history period; acquiring commodity sales corresponding to a target store in a normal history period of the last commodity sales in a commodity sales set; calculating the sales amount of the replacement commodity corresponding to the sales amount of the commodity:
the commodity sales amount prediction unit is used for judging whether the current period contains holidays, when the current period contains holidays, recording the last history period of the special history period as a special adjacent history period, and calculating the commodity sales amount of the target store in the current period; and when the current period does not contain holidays, acquiring the commodity sales volume change proportion of the target store in each special history period in the commodity sales volume set V, and calculating the predicted commodity sales volume of the target store in the current period.
Further, the store influence value module comprises a first store influence value unit and a store influence value unit;
the first store influence value unit is used for acquiring commodity information corresponding to the target store in each history period; acquiring commodity information of each comparison store in the influence area; the commodity information comprises commodity types and commodity numbers corresponding to the commodities under the commodity types; calculating a first store influence value of a target store on a certain target store;
the store influence value unit is used for acquiring the store distance between each target store and each target store; and calculating a store influence value of the target store on the target store.
Further, the target commodity numbering module comprises a stock-out commodity obtaining unit and a target commodity numbering unit;
a stock-out commodity acquisition unit configured to set a threshold of the number of times of sending the stock-out information; when the number of times of sending the out-of-stock information corresponding to a certain commodity in the out-of-stock information collection period is larger than the threshold value of the number of times of sending the out-of-stock information, marking the commodity as the out-of-stock commodity corresponding to the current period of the target store;
the target commodity numbering unit is used for acquiring the area where the target store is located; and acquiring the out-of-stock commodity with low influence on the sales volume of the store in each information acquisition target store in the area, selecting the out-of-stock commodity with the lowest influence on the sales volume of the store in the corresponding information acquisition target store, marking the out-of-stock commodity as the out-of-stock commodity with the lowest influence on the sales volume of the store in the area, acquiring the commodity number of the out-of-stock commodity, and marking the commodity number as the target commodity number corresponding to the area.
Further, the supply chain scheduling module comprises a supply chain scheduling unit;
the supply chain dispatching unit is used for acquiring a distribution center of a merchant in the area; acquiring the commodity stock quantity of commodities in a distribution center; based on the commodity stock corresponding to the distribution center and the predicted commodity sales corresponding to each target store of the distribution center, sending a relevant message prompt to a merchant to prompt the merchant to adjust a commodity supply chain; and the goods distribution center transmits goods distribution information according to the merchant to transport the goods to the corresponding target store.
Compared with the prior art, the invention has the following beneficial effects: according to the method and the system, the sales amount of the store commodity can be intelligently predicted according to the period, the shortage condition of the commodity in different areas is evaluated according to different preference degrees of the commodity in different areas, the shortage commodity with smaller influence on the sales amount of the store commodity in each area is obtained, and the commodity number corresponding to the shortage commodity is obtained, so that the supply chain of the merchant is intelligently adjusted based on the commodity number corresponding to each area, and the maximization of the sales amount of the store commodity in each area of the merchant is realized.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a method and system for intelligent supply chain management based on big data according to the present invention;
FIG. 2 is a schematic block diagram of a big data based intelligent supply chain management method and system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: an intelligent supply chain management method based on big data, the method comprises the following steps:
step S100: acquiring historical data of a target store, and extracting data information corresponding to each historical period of the target store from the historical data; based on the data information corresponding to each historical period of the target store, evaluating the sales volume of the target store in the current period to obtain the predicted commodity sales volume of the target store in the current period;
Wherein, step S100 includes:
step S101: corresponding commodity sales in each historical period of the target store are collected; the commodity sales volume is the commodity sales volume corresponding to the target store; obtaining a commodity sales volume set V= { V of a target store 1 、V 2 、...、V n -a }; wherein V is 1 、V 2 、...、V n Commodity sales for the target store in the 1 st, 2 nd, n history periods, respectively;
step S102: recording a history period containing holidays as a special history period; obtaining the sales volume V of the target store in any special history period a The method comprises the steps of carrying out a first treatment on the surface of the Obtaining commodity sales volume V a Commodity sales V of target store in last history period b The method comprises the steps of carrying out a first treatment on the surface of the Calculating commodity sales volume V a Corresponding sales volume V of replacement commodity oa ：
Wherein alpha is x A commodity sales increasing proportion of a target store in an xth history period in the commodity sales set V;
step S103: acquiring individual characteristics in a commodity sales volume set VA history period; acquiring the sales volume of the replacement commodity corresponding to the sales volume of the commodity of the target store in each special history period; when the current period does not contain holidays, the value of the commodity sales in each special history period in the commodity sales set V is replaced by the corresponding value of the replacement commodity sales; marking the replaced commodity sales volume set V as the commodity sales volume set V o The history periods and the special history periods in the commodity sales volume set V are collectively called as normal history periods; wherein, the commodity sales volume set V o ＝{V o1 、V o2 、...、V on -a }; wherein V is o1 、V o2 、...、V on Commodity sales for the target store in the 1 st, 2 nd, n normal history periods, respectively;
step S104: calculating the change proportion of the commodity sales of the target store in the e-th normal history period Wherein V is oe The sales amount of the commodity in the e normal history period for the target store; v (V) o(e-1) The sales amount of the commodity in the e-1 th normal history period for the target store; recording and collecting the commodity sales volume change proportion of the target store in each normal history period to obtain a commodity sales volume change proportion set beta= { beta of the target store in each normal history period 1 、β 2 、...、β n -a }; wherein beta is 1 、β 2 、...、β n Commodity sales change ratios of the target store in the n normal history periods, respectively 1, 2; obtaining commodity sales variation ratio beta with the largest value in commodity sales variation ratio set beta α And a commodity sales quantity ratio beta of minimum value b The method comprises the steps of carrying out a first treatment on the surface of the Acquiring a history period which is closest to the current period from time, and recording the history period as a sample history period; calculating predicted commodity sales V of a target store in a current period f ：
Wherein beta is y The commodity sales quantity change proportion of the target store in the y normal history period is as follows; n is the number of normal history periods in the commodity sales volume change proportion set beta; v (V) ou Sales commodity quantity of a target store in a sample history period;
step S105: when the current period contains holidays, calculating the commodity sales change proportion gamma of the target store in the special history period:
wherein V is s The commodity sales of a target store in a special history period; v (V) τ The commodity sales of the target store in the last history period of the special history period;
acquiring commodity sales variation ratios of target stores in each special history period in the commodity sales set V; averaging the commodity sales ratios of target stores in each special history period
Calculating predicted commodity sales V of a target store in a current period f ：
Wherein V is ou Sales commodity quantity of a target store in a sample history period;
for example, the commodity sales change ratios of the target stores in the respective special history periods are averaged10%; v (V) ou 100000; calculating predicted commodity sales V of a target store in a current period f ＝(100％+10％)×100000＝110000；
Step S200: setting an influence area corresponding to a target store; acquiring commodity types of sold commodities of all shops in the influence area; calculating a store influence value corresponding to the target store in the current period based on the commodity type corresponding to each store in the influence area where the target store is located;
Wherein, step S200 includes:
step S201: acquiring all shops in the target shop influence area; acquiring commodity types of commodities sold by a target store; respectively acquiring commodity types of commodities sold by all shops in the target shop influence area; when at least one commodity type corresponding to the target store exists in the commodity types corresponding to the stores, marking the stores as comparison stores corresponding to the target stores; acquiring commodity information corresponding to a target store in each history period; acquiring commodity information of each comparison store in the influence area; the commodity information comprises commodity types and commodity numbers corresponding to the commodities under the commodity types; calculating a first store influence value P of a target store on a target store:
wherein r is the number of commodity types corresponding to the same commodity type between a certain target store and a certain target store; d, d r The number of commodity numbers corresponding to the commodities in the r commodity type in a certain label store; e, e r The number of commodity numbers corresponding to the commodities in the r commodity type in the target store;
step S202: obtaining the store distance between each target store and each target store; calculating a store influence value W of a target store to a target store:
Wherein P is c A first store influence value for the c-th target store on the target store; z is the number of target stores corresponding to the target stores; s is(s) c A store distance between the c-th target store and the target store;
step S300: acquiring an area where a target store is located; acquiring out-of-stock goods in each target store in the area; based on the store influence value and the predicted commodity sales of each target store in the area, evaluating the influence degree of each backorder commodity on the commodity sales of the target store to obtain backorder commodity with the lowest influence degree on the target store sales in the area; acquiring the commodity number of the backorder commodity, and marking the commodity number as the target commodity number of the area;
wherein, step S300 includes:
step S301: acquiring a target store and sending out-of-stock commodities to a merchant distribution background; acquiring a commodity number corresponding to the backorder commodity; setting a time threshold corresponding to the backorder commodity in the target store; recording a history period with the time length within a time length threshold range from the current period as an out-of-stock message acquisition period; acquiring the backlog information sent by a target store in the backlog information acquisition period; the backorder information comprises a backorder commodity corresponding commodity number;
Step S302: setting a threshold value of the times of sending the backorder information; when the number of times of sending the backorder information corresponding to a certain commodity in the backorder information collection period is larger than the threshold value of the number of times of sending the backorder information, marking the commodity number of the commodity as the commodity number corresponding to the backorder commodity corresponding to the current period of the target store;
step S303: setting a store influence value threshold; when the influence value of the target store is larger than the threshold value of the influence value of the store, acquiring the influence value of each target store in the region in the current period; selecting target stores with the store influence value of the current period larger than the store influence value threshold value from all target stores, and marking the target stores as information acquisition target stores; obtaining predicted commodity sales of each information acquisition target store in the area in the current period; acquiring commodity sales of each information acquisition target store in a region of a history period in a current period; when the value of the rising proportion between the commodity sales quantity of the information acquisition target store in the last historical period of the current period and the predicted commodity sales quantity of the target store in the current period is greater than zero, marking the commodity which is out of stock of the information acquisition target store in the current period as the commodity which is out of stock and has lower influence on the sales of the information acquisition target store; acquiring the out-of-stock commodity with low influence on the sales volume of the information acquisition target store in the area, selecting the most out-of-stock commodity corresponding to the information acquisition target store, marking the most out-of-stock commodity as the out-of-stock commodity with the lowest influence on the sales volume of the area store, acquiring the commodity number of the out-of-stock commodity, and marking the commodity number as the target commodity number corresponding to the area;
Step S400: acquiring a distribution center of a merchant in an area; adjusting a merchant in an area commodity supply chain based on the target commodity number of the area and the predicted commodity sales of each target store in the area; transporting each commodity of the merchant to a corresponding distribution center, and transporting the commodity to a corresponding target store by the distribution center;
wherein, step S400 includes:
step S401: acquiring target commodity numbers of all areas of a merchant in a current period; selecting the target commodity number with the largest number from the target commodity numbers corresponding to the areas, and marking the target commodity number as the target commodity number corresponding to the merchant in the current period; sending a target commodity number corresponding to a merchant in the current period to a merchant background, and prompting the merchant to adjust a commodity production line corresponding to the target commodity number;
step S402: obtaining predicted commodity sales of each target store in the area; acquiring a distribution center of a merchant in an area; acquiring the commodity stock quantity of commodities in a distribution center; based on the commodity stock corresponding to the distribution center and the predicted commodity sales corresponding to each target store of the distribution center, sending a relevant message prompt to a merchant to prompt the merchant to adjust a commodity supply chain; the goods distribution center transmits goods distribution information according to the merchant and conveys the goods to a corresponding target store;
In order to better realize the method, an intelligent supply chain management system is also provided, and the management system comprises a commodity sales prediction module, a store influence value module, a target commodity numbering module and a supply chain scheduling module;
the commodity sales prediction module is used for acquiring historical data of a target store; extracting data information corresponding to each history period of the target store from the history data of the target store; evaluating the sales condition of the target store in the current period to obtain the predicted commodity sales quantity of the target store in the current period;
the store influence value module is used for selling commodity types of commodities sold by each store in the influence area where the target store is located; acquiring commodity types of commodities sold by a target store; based on commodity types of commodity sold by the target store, evaluating influence degree of the store on the target store in the influence area to obtain a store influence value corresponding to the target store;
the target commodity numbering module is used for evaluating the influence degree of the shortage of each commodity on the sales volume of the store in the area to obtain the shortage commodity with the lowest influence on the sales volume of the store in the area; acquiring the commodity number of the backorder commodity, and marking the commodity number as a target commodity number corresponding to the area;
The supply chain dispatching module is used for dispatching the supply of each commodity in the merchant, transporting each commodity of the merchant to a distribution center corresponding to the area, and transporting the dispatched commodity to a corresponding target store by the distribution center;
the commodity sales prediction module comprises a replacement commodity sales amount unit and a commodity sales amount prediction unit;
the commodity sales volume replacing unit is used for acquiring commodity sales volume of a target store in a commodity sales volume set in any special history period; acquiring commodity sales corresponding to a target store in a normal history period of the last commodity sales in a commodity sales set; calculating the sales amount of the replacement commodity corresponding to the sales amount of the commodity:
the commodity sales amount prediction unit is used for judging whether the current period contains holidays, when the current period contains holidays, recording the last history period of the special history period as a special adjacent history period, and calculating the commodity sales amount of the target store in the current period; when the current period does not contain holidays, acquiring commodity sales quantity change proportion of a target store in each special history period in a commodity sales quantity set V, and calculating predicted commodity sales quantity of the target store in the current period;
The store influence value module comprises a first store influence value unit and a store influence value unit;
the first store influence value unit is used for acquiring commodity information corresponding to the target store in each history period; acquiring commodity information of each comparison store in the influence area; the commodity information comprises commodity types and commodity numbers corresponding to the commodities under the commodity types; calculating a first store influence value of a target store on a certain target store;
the store influence value unit is used for acquiring the store distance between each target store and each target store; calculating a store influence value of the target store on the target store;
the target commodity numbering module comprises a stock-out commodity obtaining unit and a target commodity numbering unit;
a stock-out commodity acquisition unit configured to set a threshold of the number of times of sending the stock-out information; when the number of times of sending the out-of-stock information corresponding to a certain commodity in the out-of-stock information collection period is larger than the threshold value of the number of times of sending the out-of-stock information, marking the commodity as the out-of-stock commodity corresponding to the current period of the target store;
the target commodity numbering unit is used for acquiring the area where the target store is located; acquiring the out-of-stock commodity with low influence on the sales volume of the store in each information acquisition target store in the area, selecting the most out-of-stock commodity of the corresponding information acquisition target store, marking the most out-of-stock commodity as the out-of-stock commodity with the lowest influence on the sales volume of the store in the area, acquiring the commodity number of the out-of-stock commodity, and marking the commodity number as the target commodity number corresponding to the area;
The supply chain scheduling module comprises a supply chain scheduling unit;
the supply chain dispatching unit is used for acquiring a distribution center of a merchant in the area; acquiring the commodity stock quantity of commodities in a distribution center; based on the commodity stock corresponding to the distribution center and the predicted commodity sales corresponding to each target store of the distribution center, sending a relevant message prompt to a merchant to prompt the merchant to adjust a commodity supply chain; and the goods distribution center transmits goods distribution information according to the merchant to transport the goods to the corresponding target store.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. An intelligent supply chain management method based on big data, the method comprising:
step S100: acquiring historical data of a target store, and extracting data information corresponding to each historical period of the target store from the historical data; based on the data information corresponding to each historical period of the target store, evaluating the sales volume of the target store in the current period to obtain the predicted commodity sales volume of the target store in the current period;
step S200: setting an influence area corresponding to a target store; acquiring commodity types of sold commodities of all shops in the influence area; calculating a store influence value corresponding to the target store in the current period based on the commodity type corresponding to each store in the influence area where the target store is located;
Step S300: acquiring an area where a target store is located; acquiring out-of-stock goods in each target store in the area; based on the store influence value and the predicted commodity sales of each target store in the area, evaluating the influence degree of each backorder commodity on the commodity sales of the target store to obtain backorder commodities with the lowest influence degree on the target store sales in the area; acquiring the commodity number of the backorder commodity, and marking the commodity number as the target commodity number of the area;
step S400: acquiring a distribution center of a merchant in the area; adjusting a commodity supply chain of a merchant in the area based on the target commodity number of the area and the predicted commodity sales of each target store in the area; and transporting each commodity of the merchant to a corresponding distribution center, and transporting the commodity to a corresponding target store by the distribution center.
2. The intelligent supply chain management method based on big data according to claim 1, wherein the step S100 comprises:
step S101: corresponding commodity sales in each historical period of the target store are collected; the commodity sales volume is the commodity sales volume corresponding to the target store; obtaining a commodity sales volume set V=V of a target store 1 、V 2 、...、V n The method comprises the steps of carrying out a first treatment on the surface of the Wherein V is 1 、V 2 、...、V n Commodity sales for the target store in the 1 st, 2 nd, n history periods, respectively;
step S102: recording a history period containing holidays as a special history period; obtaining the sales volume V of the target store in any special history period a The method comprises the steps of carrying out a first treatment on the surface of the Obtaining commodity sales volume V a Commodity sales V of target store in last history period b The method comprises the steps of carrying out a first treatment on the surface of the Calculating commodity sales volume V a Corresponding sales volume V of replacement commodity oa ：
Wherein alpha is x A commodity sales increasing proportion of a target store in an xth history period in the commodity sales set V;
step S103: acquiring each special history period in the commodity sales volume set V; acquiring the sales volume of the replacement commodity corresponding to the sales volume of the commodity of the target store in each special history period; when the current period does not contain holidays, the value of the commodity sales in each special history period in the commodity sales set V is replaced by the corresponding value of the replacement commodity sales; marking the replaced commodity sales volume set V as the commodity sales volume set V o The history periods and the special history periods in the commodity sales volume set V are collectively called as normal history periods; wherein, the commodity sales volume set V o ＝{V o1 、V o2 、...、V on -a }; wherein V is o1 、V o2 、...、V on Commodity sales for the target store in the 1 st, 2 nd, n normal history periods, respectively;
step S104: calculating the change proportion of the commodity sales of the target store in the e-th normal history period Wherein V is oe The sales amount of the commodity in the e normal history period for the target store; v (V) o(e-1) The sales amount of the commodity in the e-1 th normal history period for the target store; recording and collecting the commodity sales volume change proportion of the target store in each normal history period to obtain a commodity sales volume change proportion set beta= { beta of the target store in each normal history period 1 、β 2 、...、β n -a }; wherein beta is 1 、β 2 、...、β n Respectively 1 st, 2 nd,.., the rate of change of sales of the target store in n normal history periods; obtaining commodity sales variation ratio beta with the largest value in commodity sales variation ratio set beta a And a commodity sales quantity ratio beta of minimum value b The method comprises the steps of carrying out a first treatment on the surface of the Acquiring a history period which is closest to the current period from time, and recording the history period as a sample history period; calculating predicted commodity sales V of a target store in a current period f ：
Wherein beta is y The commodity sales quantity change proportion of the target store in the y normal history period is as follows; n is the number of normal history periods in the commodity sales volume change proportion set beta; v (V) ou Sales commodity quantity of a target store in a sample history period;
step S105: when the current period contains holidays, calculating the commodity sales change proportion gamma of the target store in the special history period:
wherein V is s The commodity sales of a target store in a special history period; v (V) τ The commodity sales of the target store in the last history period of the special history period;
acquiring commodity sales variation ratios of target stores in each special history period in the commodity sales set V; averaging the commodity sales ratios of target stores in each special history period
Calculating predicted commodity sales V of a target store in a current period f ：
Wherein V is ou The amount of merchandise sold for the target store during the sample history period.
3. The intelligent supply chain management method based on big data according to claim 2, wherein the step S200 includes:
step S201: acquiring all shops in the target shop influence area; acquiring commodity types of commodities sold by a target store; respectively acquiring commodity types of commodities sold by all shops in the target shop influence area; when at least one commodity type corresponding to a target store exists in all commodity types corresponding to the store, marking the store as a comparison store corresponding to the target store; acquiring commodity information corresponding to a target store in each history period; acquiring commodity information of each comparison store in the influence area; the commodity information comprises commodity types and commodity numbers corresponding to the commodities under the commodity types; calculating a first store influence value P of a target store on a target store:
Wherein r is the number of commodity types corresponding to the same commodity type between a certain target store and a certain target store; d, d r The number of commodity numbers corresponding to the commodities in the r commodity type in a certain label store; e, e r The number of commodity numbers corresponding to the commodities in the r commodity type in the target store;
step S202: obtaining the store distance between each target store and each target store; calculating a store influence value W of a target store to a target store:
wherein, the liquid crystal display device comprises a liquid crystal display device,P c a first store influence value for the c-th target store on the target store; z is the number of target stores corresponding to the target stores; s is(s) c The store distance between the c-th target store and the target store.
4. The intelligent supply chain management method based on big data according to claim 1, wherein the step S300 comprises:
step S301: acquiring a target store and sending out-of-stock commodities to a merchant distribution background; acquiring a commodity number corresponding to the backorder commodity; setting a time threshold corresponding to the backorder commodity in the target store; recording a history period with the time length within a time length threshold range from the current period as an out-of-stock message acquisition period; acquiring the backlog information sent by a target store in the backlog information acquisition period; the information of the shortage comprises a commodity number corresponding to the shortage commodity;
Step S302: setting a threshold value of the times of sending the backorder information; when the number of times of sending the backorder information corresponding to a certain commodity in the backorder information collection period is larger than the threshold value of the number of times of sending the backorder information, marking the commodity number of the commodity as the commodity number corresponding to the backorder commodity corresponding to the current period of the target store;
step S303: setting a store influence value threshold; when the influence value of the target store is larger than the threshold value of the influence value of the store, acquiring the influence value of each target store in the region in the current period; selecting target stores with the store influence value of the current period larger than the store influence value threshold value from all target stores, and marking the target stores as information acquisition target stores; acquiring predicted commodity sales of each information acquisition target store in the area in the current period; acquiring commodity sales of each information acquisition target store in the area in a history period in the current period; when the value of the rising proportion between the commodity sales quantity of the information acquisition target store in the last historical period of the current period and the predicted commodity sales quantity of the target store in the current period is greater than zero, marking the commodity which is out of stock of the information acquisition target store in the current period as the commodity which is out of stock and has lower sales influence of the information acquisition target store; and acquiring the out-of-stock commodity with lower influence on the sales volume of the information acquisition target store in the area, selecting the out-of-stock commodity with the most influence on the sales volume of the area store corresponding to the information acquisition target store, marking the out-of-stock commodity as the out-of-stock commodity with the least influence on the sales volume of the area store, acquiring the commodity number of the out-of-stock commodity, and marking the commodity number as the target commodity number corresponding to the area.
5. The intelligent supply chain management method based on big data according to claim 4, wherein the step S400 comprises:
step S401: acquiring target commodity numbers of all areas of a merchant in a current period; selecting the target commodity number with the largest number from the target commodity numbers corresponding to the areas, and marking the target commodity number as the target commodity number corresponding to the merchant in the current period; sending a target commodity number corresponding to a merchant in the current period to a merchant background, and prompting the merchant to adjust a commodity production line corresponding to the target commodity number;
step S402: obtaining predicted commodity sales of each target store in the area; acquiring a distribution center of a merchant in the area; acquiring the commodity stock quantity of commodities in a distribution center; based on the commodity stock corresponding to the distribution center and the predicted commodity sales corresponding to each target store of the distribution center, sending a relevant message prompt to a merchant to prompt the merchant to adjust a commodity supply chain; and the goods distribution center transmits goods distribution information according to the merchant to transport the goods to the corresponding target store.
6. An intelligent supply chain management system applied to the intelligent supply chain management method based on big data as set forth in any one of claims 1-5, wherein the management system comprises a commodity sales prediction module, a store influence value module, a target commodity numbering module and a supply chain scheduling module;
The commodity sales prediction module is used for acquiring historical data of a target store; extracting data information corresponding to each history period of the target store from the history data of the target store; evaluating the sales condition of the target store in the current period to obtain the predicted commodity sales quantity of the target store in the current period;
the store influence value module is used for selling commodity types of commodities sold by each store in the influence area where the target store is located; acquiring commodity types of commodities sold by a target store; based on the commodity type of the commodity sold by the target store, evaluating the influence degree of the store on the target store in the influence area to obtain a store influence value corresponding to the target store;
the target commodity numbering module is used for evaluating the influence degree of the shortage of each commodity on the sales volume of the store in the area to obtain the shortage commodity with the lowest influence on the sales volume of the store in the area; acquiring the commodity number of the backorder commodity, and marking the commodity number as a target commodity number corresponding to the area;
the supply chain dispatching module is used for dispatching the supply of each commodity in the merchant, transporting each commodity of the merchant to a distribution center corresponding to the area, and transporting the dispatched commodity to a corresponding target store by the distribution center.
7. The intelligent supply chain management system according to claim 6, wherein the commodity sales prediction module includes a replacement commodity sales amount unit, a commodity sales amount prediction unit;
the commodity sales volume replacing unit is used for acquiring commodity sales volume of a target store in a commodity sales volume set in any special history period; acquiring commodity sales corresponding to a target store in a normal history period of the last commodity sales in a commodity sales set; calculating the sales amount of the replacement commodity corresponding to the sales amount of the commodity:
the commodity sales predicting unit is used for judging whether the current period contains holidays, when the current period contains holidays, recording the last history period of the special history period as a special adjacent history period, and calculating the commodity sales of the target store in the current period; and when the current period does not contain holidays, acquiring the commodity sales volume change proportion of the target store in each special history period in the commodity sales volume set V, and calculating the predicted commodity sales volume of the target store in the current period.
8. The intelligent supply chain management system of claim 6, wherein the store impact value module comprises a first store impact value unit, a store impact value unit;
The first store influence value unit is used for acquiring commodity information corresponding to a target store in each history period; acquiring commodity information of each comparison store in the influence area; the commodity information comprises commodity types and commodity numbers corresponding to the commodities under the commodity types; calculating a first store influence value of a target store on a certain target store;
the store influence value unit is used for acquiring the store distance between each target store and each target store; and calculating a store influence value of the target store on the target store.
9. The intelligent supply chain management system according to claim 6, wherein the target commodity numbering module comprises a stock-out commodity acquisition unit, a target commodity numbering unit;
the system comprises a stock-out commodity acquisition unit, a stock-out information sending unit and a stock-out information sending unit, wherein the stock-out commodity acquisition unit is used for setting a stock-out information sending frequency threshold; when the number of times of sending the out-of-stock information corresponding to a certain commodity in the out-of-stock information collection period is larger than the threshold value of the number of times of sending the out-of-stock information, marking the commodity as the out-of-stock commodity corresponding to the current period of the target store;
the target commodity numbering unit is used for acquiring the area where the target store is located; and acquiring the backorder commodity with lower influence on the sales volume of the backorder in each information acquisition target backorder in the area, selecting the backorder commodity with the most influence on the sales volume of the regional backorder from the corresponding information acquisition target backorder, marking the backorder commodity as the backorder commodity with the lowest influence on the sales volume of the regional backorder, acquiring the commodity number of the backorder commodity, and marking the commodity number as the target commodity number corresponding to the area.
10. The intelligent supply chain management system of claim 6, wherein the supply chain scheduling module comprises a supply chain scheduling unit;
the supply chain dispatching unit is used for acquiring a distribution center of a merchant in the area; acquiring the commodity stock quantity of commodities in a distribution center; based on the commodity stock corresponding to the distribution center and the predicted commodity sales corresponding to each target store of the distribution center, sending a relevant message prompt to a merchant to prompt the merchant to adjust a commodity supply chain; and the goods distribution center transmits goods distribution information according to the merchant to transport the goods to the corresponding target store.
CN202310865331.0A 2023-07-14 2023-07-14 Intelligent supply chain management method and system based on big data Active CN116739655B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310865331.0A CN116739655B (en) 2023-07-14 2023-07-14 Intelligent supply chain management method and system based on big data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310865331.0A CN116739655B (en) 2023-07-14 2023-07-14 Intelligent supply chain management method and system based on big data

Publications (2)

Publication Number Publication Date
CN116739655A true CN116739655A (en) 2023-09-12
CN116739655B CN116739655B (en) 2024-04-02

Family

ID=87913450

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310865331.0A Active CN116739655B (en) 2023-07-14 2023-07-14 Intelligent supply chain management method and system based on big data

Country Status (1)

Country Link
CN (1) CN116739655B (en)

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100138281A1 (en) * 2008-11-12 2010-06-03 Yinying Zhang System and method for retail store shelf stock monitoring, predicting, and reporting
CN111667207A (en) * 2019-03-05 2020-09-15 阿里巴巴集团控股有限公司 Supply chain inventory management method and device, storage medium and processor
CN111815348A (en) * 2020-05-28 2020-10-23 杭州览众数据科技有限公司 Regional commodity production planning method based on commodity similarity clustering of stores
CN113298606A (en) * 2020-12-18 2021-08-24 阿里巴巴（中国）有限公司 Commodity object inventory control method and device and electronic equipment
CN113435541A (en) * 2021-07-22 2021-09-24 创优数字科技(广东)有限公司 Method and device for planning product classes, storage medium and computer equipment
JP2021163485A (en) * 2020-03-31 2021-10-11 株式会社日立製作所 Smart supply chain system
CN113780904A (en) * 2021-11-12 2021-12-10 中国信息通信研究院 Supply chain management system and method
CN114219547A (en) * 2022-02-22 2022-03-22 杭州铭信信息科技有限公司 Method, device, equipment and storage medium for determining store ordering amount
CN115689451A (en) * 2022-11-14 2023-02-03 创优数字科技(广东)有限公司 Method, device, terminal and medium for determining replenishment quantity of off-line retail store
CN115880020A (en) * 2022-11-23 2023-03-31 北京数势云创科技有限公司 Method, device and storage medium for acquiring real demand in out-of-stock state
CN116167791A (en) * 2023-02-24 2023-05-26 多点(深圳)数字科技有限公司 Commodity life cycle management method, commodity life cycle management device, computer equipment and storage medium

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100138281A1 (en) * 2008-11-12 2010-06-03 Yinying Zhang System and method for retail store shelf stock monitoring, predicting, and reporting
CN111667207A (en) * 2019-03-05 2020-09-15 阿里巴巴集团控股有限公司 Supply chain inventory management method and device, storage medium and processor
JP2021163485A (en) * 2020-03-31 2021-10-11 株式会社日立製作所 Smart supply chain system
CN111815348A (en) * 2020-05-28 2020-10-23 杭州览众数据科技有限公司 Regional commodity production planning method based on commodity similarity clustering of stores
CN113298606A (en) * 2020-12-18 2021-08-24 阿里巴巴（中国）有限公司 Commodity object inventory control method and device and electronic equipment
CN113435541A (en) * 2021-07-22 2021-09-24 创优数字科技(广东)有限公司 Method and device for planning product classes, storage medium and computer equipment
CN113780904A (en) * 2021-11-12 2021-12-10 中国信息通信研究院 Supply chain management system and method
CN114219547A (en) * 2022-02-22 2022-03-22 杭州铭信信息科技有限公司 Method, device, equipment and storage medium for determining store ordering amount
CN115689451A (en) * 2022-11-14 2023-02-03 创优数字科技(广东)有限公司 Method, device, terminal and medium for determining replenishment quantity of off-line retail store
CN115880020A (en) * 2022-11-23 2023-03-31 北京数势云创科技有限公司 Method, device and storage medium for acquiring real demand in out-of-stock state
CN116167791A (en) * 2023-02-24 2023-05-26 多点(深圳)数字科技有限公司 Commodity life cycle management method, commodity life cycle management device, computer equipment and storage medium

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
任力;王正成;: "买手与S&OP双重模式下的服装库存协同管理研究", 丝绸, no. 05, 20 May 2015 (2015-05-20) *
成其勇;: "化解缺货难题之门店篇", 中国药店, no. 08, 15 August 2016 (2016-08-15) *
龚国华, 胡佳粒, 张峰: "从商品缺货现象看零售业的供应链管理", 物流科技, no. 05, 30 May 2004 (2004-05-30) *

Also Published As

Publication number Publication date
CN116739655B (en) 2024-04-02

Similar Documents

Publication Publication Date Title
US8073727B2 (en) System and method for hierarchical weighting of model parameters
Kök et al. Evaluation of cycle-count policies for supply chains with inventory inaccuracy and implications on RFID investments
KR100613891B1 (en) Customer Asset Valuation System
US8306845B2 (en) Consumer and shopper analysis system
US20060004624A1 (en) Forecast and replenishment analytics
JP2001084239A (en) Method for analyzing and predicting merchandise sales quantity, method for deciding merchandise order quantity and storage medium with program stored therein
Shabani et al. Inventory record inaccuracy and store-level performance
Rekik et al. Exploring inventory systems sensitive to shrinkage–analysis of a periodic review inventory under a service level constraint
Seitz et al. Data driven supply allocation to individual customers considering forecast bias
Han et al. Information technology investment and manufacturing worker productivity
Herath et al. An investigation into the Relationship between Revenue Management Practices and Financial Performance in Star-Rated Hotels in Sri Lanka
Leung et al. Big-box store expansion and consumer welfare
CN116739655B (en) Intelligent supply chain management method and system based on big data
Tomlin Japanese FDI into US service industries: Exchange rate changes and services tradability
KR100738899B1 (en) System for providing service of inauguration and management consult using credit payment system
Shahrabi A quantitative analysis of inaccuracy inventory reducing in multi-period mode: Comparison between RFID and inventory counting
CN114219606A (en) Power data-based power consumption enterprise credit evaluation method and system
Shen et al. Data Mining the Data Processing Technologies for Inventory Management.
Choudhury Market Demand Forecast Method Selection and Application: A Case Study in Hero MotoCorp Ltd.
JP2001312542A (en) Total stock determination device and total stock determination method
Aguirregabiria et al. Decentralized decision-making in retail chains: Evidence from inventory management
US20240078566A1 (en) Methods, systems, articles of manufacture, and apparatus to detect promotion events
KR100785717B1 (en) Method for providing service of inauguration and management consult using credit payment system
Polat The role of forecasting and its potential for functional management: a review from the value-chain perspective
US20230289629A1 (en) Data-driven predictive recommendations

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant