CN111461628A - Dynamic inventory prediction method for equipment leasing - Google Patents
Dynamic inventory prediction method for equipment leasing Download PDFInfo
- Publication number
- CN111461628A CN111461628A CN202010358486.1A CN202010358486A CN111461628A CN 111461628 A CN111461628 A CN 111461628A CN 202010358486 A CN202010358486 A CN 202010358486A CN 111461628 A CN111461628 A CN 111461628A
- Authority
- CN
- China
- Prior art keywords
- inventory
- data
- day
- current
- period
- 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
Links
Images
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
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- 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
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0645—Rental transactions; Leasing transactions
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Human Resources & Organizations (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a dynamic inventory prediction method for equipment lease, which comprises the following steps of 1, collecting all influence factors influencing inventory in a system in real time, wherein the influence factors include intention data, order data, various service work orders, inventory data, renting data and the like, 2, calculating and analyzing the influence factors in real time by utilizing the Apache flight and Spark SQ L technology in the prior art, 3, rolling inventory data of the next year according to the currently known inventory data and the influence factors of the current day, pushing the calculated data to an ElasticSearch for a user to inquire, 4, inquiring the latest inventory prediction data by an analyst to generate a transfer and replenishment plan, and 5, recycling to the step 1, calibrating in real time to ensure the timeliness and accuracy of the inventory in the future.
Description
Technical Field
The invention relates to a prediction method, in particular to a dynamic inventory prediction method for equipment leasing, relates to a method for predicting future real-time inventory through a big data technology, and belongs to the technical field related to real-time inventory in leasing industry.
Background
In the leasing industry, due to various uncertainties such as customer requirements, leasing periods, service capacities of different sites and the like, the surplus of the unused inventory is unknown, so that a salesperson can determine whether the supply can be carried out only for leasing requirements of nearly 1-3 days, the salesperson cannot make a commitment for the leasing requirements exceeding the time period, and the system cannot determine whether the supply can be ensured during online booking.
Background analysts can only analyze inventory conditions of each site in a future period by regularly collecting data such as latest customer demand information of front-line salesmen and latest returning date of renting, so as to make allocation and purchase plans of each site. However, this method has obvious disadvantages, such as low offline statistical efficiency and poor real-time performance. There are cases where the impact factor has changed significantly just since the analysis report was made, resulting in the analysis report not being of reference value. Therefore, a new solution to solve the above technical problems is urgently needed.
Disclosure of Invention
The invention provides a dynamic inventory prediction method for equipment leasing, aiming at the problems in the prior art.
In order to achieve the above object, a technical solution of the present invention is a method for predicting dynamic inventory of equipment rentals, the method comprising the steps of:
step 1, acquiring all influence factors influencing inventory in a system in real time, wherein the influence factors include intention data, order data, various service work orders, inventory data, on-lease data and the like;
step 2, calculating and analyzing the influence factors in real time by utilizing Apache Flink and Spark SQ L technology to obtain a work order to be generated in a future time period and a work order completion period calculated based on a service personnel human model, and distributing all factors to a specific future day according to start and stop time;
step 3, according to the currently known inventory data and the influence factor of the current day, the inventory data of the next year is rolled, and the calculated data is pushed to an elastic search for a user to inquire;
step 4, an analyst inquires the latest inventory forecast data to generate a transfer and replenishment plan; the online booking and booking system inquires the latest inventory forecast data to realize an inventory real-time calendar for the online booking client to inquire and order;
and 5, recycling to the step 1, and carrying out real-time calibration to ensure the timeliness and accuracy of the future inventory.
As an improvement of the present invention, the step 1 specifically includes the following steps:
for the condition that the service data is not physically deleted, incremental acquisition is adopted, incremental updating and newly added data are acquired according to the updating time of the data, and the acquisition efficiency is improved;
when the service data is physically deleted, the full collection of the service data is not considered much, otherwise, the data collection is realized through the binlog of the database and the message notification;
and finally storing the data in the ODS layer of the big data as analysis source data.
As an improvement of the present invention, in the step 2, the influence factor is calculated and analyzed, and the step 2 specifically includes the following processes:
performing stream calculation on each influence factor by utilizing a Flink and Spark SQ L distributed mode, calculating the returning time of the leased objects through the required quantity and the service period in all the existing orders, and calculating the time for restacking the leased objects after returning and finishing according to the returning time and the service manpower model of the site, wherein the service manpower model processes the number of the finished work orders every day in one month according to the service manpower change condition of each site and the number of the finished work orders processed every day in one month in history, and the model recalculates once every day to ensure the accuracy of the model;
and finally, distributing each influence factor to a certain day or a certain period of time in the future and storing the influence factors in a Hive temporary table.
As an improvement of the present invention, in the step 3, rolling calculation is performed on the future inventory, and the step 3 specifically includes the following processes:
the influence factors of the current inventory of the site on the second day are accumulated to obtain the forecast inventory on the second day, the influence factors of the forecast inventory on the third day are added to the forecast inventory on the third day to obtain the forecast inventory on the third day, and the inventory data of the next year are calculated in the same way;
relating to part of the formula:
adding the current newly added demand to the current intention demand and the current order demand;
the current lease waiting is equal to the stock allowance (the real-time stock allowance on the current day);
the current available inventory is the current waiting lease + the influence factor which currently causes the inventory increase;
measuring actual inventory in the rent-current period according to the actual inventory measurement in the rent-upper period, and measuring the inventory reduction influence factor in the rent-current period and the inventory increase influence factor in the current period;
measuring and calculating actual inventory in the rent-up period according to the maximum demand in the current period, and measuring and calculating newly increased demand in the rent and the current period;
the current inventory allowance is the current available inventory-the current newly increased demand;
measuring the total equipment number in the period according to the maximum demand in the period;
measuring the total equipment number in the period according to the actual inventory renting rate as the actual inventory in the period;
and after the rolling is finished, storing the rolling result into the Hive library for persistence, and pushing the rolling result to an elastic search for a user to inquire.
As an improvement of the present invention, in the step 4, a corresponding business decision is made on the generated prediction data, and the step 4 specifically includes the following processes:
according to the inventory forecast data, key data such as the time, day, week, month and year dimensions of the next year to be rented, inventory allowance and the like can be checked, and a transfer plan, a purchase plan and the like before different sites are formulated through the key data;
the online booking system displays daily inventory calendars according to daily real-time forecast data to help clients to place orders to the inventory.
Compared with the prior art, the invention has the following advantages:
1) the concept of calculating and predicting future inventory by adding a large amount of existing business data is put forward for the first time, the inventory allowance in the future is unknown and uncertain due to various uncertain factors in the equipment leasing industry, and great trouble is brought to decision making and plan making of an enterprise management layer;
2) the invention ensures the accuracy and the real-time performance of future inventory data through real-time acquisition and calculation, compares the qualitative leap of the offline manual statistical data quality and saves a large amount of manpower, thereby reducing the cost and improving the efficiency of enterprises;
3) the invention utilizes the strong data processing and analyzing capability of big data to predict the future inventory in real time for the leasing business, guides the interior of an enterprise to make a more reasonable allocation and purchase plan, and avoids the conditions that the future inventory shortage causes the loss of orders or the excess inventory influences the cash flow of the company and the like to the maximum extent;
4) by means of the technology, the C-side client can be helped to check real-time inventory in the next year, make an order and lock the inventory, and loss of orders caused by inventory change and the like is reduced.
5) The scheme realizes real-time analysis of inventory influence factors, relevant calculation and rolling calculation of inventory forecast and other formulas by utilizing a big data technology, guides a transfer and purchase plan by utilizing an inventory forecast result, realizes an on-line reserved future inventory calendar by utilizing the inventory forecast result and helps a client to place orders and occupy the inventory.
Drawings
Fig. 1 is a schematic view of the overall structure of the present invention.
The specific implementation mode is as follows:
for the purpose of enhancing an understanding of the present invention, the present embodiment will be described in detail below with reference to the accompanying drawings.
Example 1: referring to fig. 1, a method for dynamic inventory forecast for equipment rental, the method comprising the steps of:
step 1, acquiring all influence factors influencing inventory in a system in real time, wherein the influence factors include intention data, order data, various service work orders, inventory data, on-lease data and the like;
step 2, calculating and analyzing the influence factors in real time by utilizing the Apache Flink and Spark SQ L technology in the prior art to obtain a work order to be generated in a future time period and a work order completion period calculated based on a human model of a service worker, and distributing all the factors to a specific future day according to the starting time and the ending time;
step 3, according to the currently known inventory data and the influence factor of the current day, the inventory data of the next year is rolled, and the calculated data is pushed to an ElasticSearch for a user to inquire;
step 4, an analyst inquires the latest inventory forecast data to generate a transfer and replenishment plan; the online booking and booking system inquires the latest inventory forecast data to realize an inventory real-time calendar for the online booking client to inquire and order;
and 5, recycling to the step 1, and carrying out real-time calibration to ensure the timeliness and accuracy of the future inventory.
The step 1 specifically comprises the following steps:
collecting business data (taking a lease, a part of sites, and a part of influence factors as an example), let us define the number of site service staff as SP, inventory as S, intention lease number as I, order lease number as O, lease number as R, lease number as OR, and reserve number as P, for example, the business data collected at a certain time is as shown in table 1 below:
in the step 2, the influence factors are calculated and analyzed, and the step 2 specifically comprises the following processes:
calculating a refund distribution model (ORM) by calculating and analyzing the distribution condition from the initiation of refund to the return of leases to the warehouse of a client within one historical year of each site, wherein the fixed period refreshing of the model ensures the model readiness, as shown in the following table 2
TABLE 2 Back-lease distribution model
A human model (PM for short) of the service personnel of each site for the provision of the leases is calculated by analyzing the time length for processing and maintaining the leases by the service personnel of the site in the last week, and the model is refreshed at a fixed period to ensure the model readiness, as shown in the following Table 3
| Manpower model | The preparation amount of each person per day |
| Station A | 2 |
| Station B | 3 |
| Site C | 2 |
TABLE 3 self-service manpower model
Calculating the real number of returned places (ROR) per day according to the returned number and the returned number model, wherein the formula is as follows, the number in brackets represents the Nth day (the rule is followed, no special explanation is provided)
The number of actual field returns ROR (0) ═ OR (0) × ORM (0) on the same day
The actual field-returning quantity ROR (1) ═ OR (0) × ORM (1) + OR (1) × ORM (0) for 1 day in the future
Actual field return quantity ROR (2) ═ OR (0) × ORM (2) + OR (1) × ORM (1) +for 2 days in the future
OR(1)*ORM(0)
Take site A as an example
The number ROR (0) of the actual field-returning quantity of the station A on the same day is 10 x 50 percent to 5 percent
The number of real retired fields ROR (1) from 1 day before station a is 10 × 20% +10 × 50% ═ 7
The real field-returning quantity ROR (2) of 2 days before the station A is 10 × 20% +12 × 50% -10, the remaining servicing quantity (short for: L P) of each day of the real finished servicing quantity (short for: RP) is calculated according to the real lease-returning quantity plus the current servicing quantity and the servicing manpower model, and the formula is as follows
The number of real setups completed on the day RP (0) ═ SP (0) × PM > P (0) + ROR (0)? P (0) + ROR (0):
SP(0)*PM
remaining stock quantity on the day L P (0) ═ P (0) + ROR (0) -RP (0)
The number of real repairs completed in 1 day in the future RP (1) ═ SP (1) × PM > L P (0) + ROR (1)?
LP(0)+ROR(1):SP(1)*PM
Remaining stock quantity 1 day in the future L P (1) ═ L P (0) + ROR (1) -RP (1)
The number of real preparations completed RP (2) ═ SP (2) × PM > L P (1) + ROR (2)?
LP(1)+ROR(2):SP(2)*PM
Remaining stock quantity 2 days in the future L P (2) ═ L P (1) + ROR (2) -RP (2)
Take site A as an example
The number of real complete setups RP (0) on the day for site a is 5 x 2>20+ 5? 20+5:5 x 2 ═ 10
The remaining stock quantity L P (0) of station a on the day is 20+ 5-10-15
The number of real preparations completed by site a 1 day in the future RP (1) is 5 x 2>15+ 5? 15+5:5 x 2 ═ 10
The remaining stock amount L P (1) for station a 1 day in the future is 15+7-10 ═ 12
The number RP (2) of actually completed preparations 2 days ago for site a is 5 x 2>12+ 10? 12+10:5 x 2-10
The remaining stock amount L P (2) ═ 12+10-10 ═ 12 in the future 2 days
Each influence factor is distributed in a future day or a period according to the formula and is stored in a Hive temporary table.
In the step 3, rolling calculation is performed on the future inventory, and the step 3 specifically includes the following processes:
the influence factors of the current inventory of the site on the second day are accumulated to obtain the forecast inventory on the second day, the influence factors of the forecast inventory on the third day are added to the forecast inventory on the third day to obtain the forecast inventory on the third day, and the inventory data of the next year are calculated in the same way;
the rolling future inventory and on-lease formula is as follows:
the current day stock RS (0) ═ S (0) -I (0) -O (0) + RP (0)
Stock RS (1) ═ RS (0) -I (1) -O (1) + RP (1) 1 day in the future
Stock RS (2) ═ RS (1) -I (2) -O (2) + RP (2) for 2 days in the future
The maximum demand in the day is R (0) + I (0) + O (0) -ROR (0)
The maximum demand in 1 day in the future is RR (1) ═ RR (0) + I (1) + O (1) -ROR (1)
Maximum demand in the future of 2 days in the lease RR (2) ═ RR (1) + I (2) + O (2) -ROR (2)
Take site A as an example
The stock RS (0) of the station A on the day is 1000-12-15+10 ═ 983
The stock RS (1) of the site A in 1 day in the future is 983-20-40+10 ═ 933
The stock RS (2) of site A in the future 2 days is 933-15-10+10 ═ 918
The maximum demand of site A on the day is 800+12+ 15-5-822 in the RR (0) lease
Station a will have a maximum demand of 822+20+40-7 ═ 889 in lease RR (1) for 1 day in the future
Site a has a maximum demand of 889+15+10-10 ═ 904 in lease RR (2) in the future of 2 days
After the rolling is completed, the formula of the related statistical part is as follows:
adding the current newly added demand to the current intention demand and the current order demand;
the current lease waiting is equal to the stock allowance (the real-time stock allowance on the current day);
the current available inventory is the current waiting lease + the influence factor which currently causes the inventory increase;
measuring actual inventory in the rent-current period according to the actual inventory measurement in the rent-upper period, and measuring the inventory reduction influence factor in the rent-current period and the inventory increase influence factor in the current period;
measuring and calculating actual inventory in the rent-up period according to the maximum demand in the current period, and measuring and calculating newly increased demand in the rent and the current period;
the current inventory allowance is the current available inventory-the current newly increased demand;
measuring the total equipment number in the period according to the maximum demand in the period;
measuring the total equipment number in the period according to the actual inventory renting rate as the actual inventory in the period;
and after the rolling is finished, storing the rolling result into the Hive library for persistence, and pushing the rolling result to an elastic search for a user to inquire.
In the step 4, a corresponding service decision is made on the generated prediction data, and the step 4 specifically includes the following processes:
according to the inventory forecast data, key data such as the time, day, week, month and year dimensions of the next year to be rented, inventory allowance and the like can be checked, and a transfer plan, a purchase plan and the like before different sites are formulated through the key data;
the online booking system displays daily inventory calendars according to daily real-time forecast data to help clients to place orders to the inventory.
It should be noted that the above-mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, and all equivalent substitutions or substitutions made on the above-mentioned technical solutions belong to the scope of the present invention.
Claims (5)
1. A method for dynamic inventory forecast for equipment rental, the method comprising the steps of:
step 1, acquiring all influence factors influencing inventory in a system in real time, wherein the influence factors include intention data, order data, various service work orders, inventory data, on-lease data and the like;
step 2, calculating and analyzing the influence factors in real time by utilizing the Apache Flink and Spark SQ L technology in the prior art to obtain a work order to be generated in a future time period and a work order completion period calculated based on a human model of a service worker, and distributing all factors to a specific future day according to the starting time and the ending time;
step 3, according to the currently known inventory data and the influence factor of the current day, the inventory data of the next year is rolled, and the calculated data is pushed to an ElasticSearch for a user to inquire;
step 4, an analyst inquires the latest inventory forecast data to generate a transfer and replenishment plan; the online booking and booking system inquires the latest inventory forecast data to realize an inventory real-time calendar for the online booking client to inquire and order;
and 5, recycling to the step 1, and carrying out real-time calibration to ensure the timeliness and accuracy of the future inventory.
2. The method for dynamic inventory forecast of equipment lease according to claim 1, characterized in that said step 1 specifically includes the following procedures:
for the condition that the service data is not physically deleted, incremental acquisition is adopted, incremental updating and newly added data are acquired according to the updating time of the data, and the acquisition efficiency is improved;
when the service data is physically deleted, the full collection of the service data is not considered much, otherwise, the data collection is realized through the binlog of the database and the message notification;
and finally storing the data in the ODS layer of the big data as analysis source data.
3. The method for predicting the dynamic inventory of equipment lease according to claim 1, wherein the step 2 includes the following steps for calculating and analyzing the influence factors:
performing stream calculation on each influence factor by utilizing a Flink and Spark SQ L distributed mode, calculating the returning time of the leased objects through the required quantity and the service period in all the existing orders, and calculating the time for restacking the leased objects after returning and finishing according to the returning time and the service manpower model of the site, wherein the service manpower model processes the number of the finishing work orders every day in one month according to the service manpower change condition of each site and the number of the finishing work orders processed every day in one month in history, and the model recalculates once every day to ensure the accuracy of the model;
and finally, distributing each influence factor to a certain day or a certain period of time in the future and storing the influence factors in a Hive temporary table.
4. The method for predicting the dynamic inventory of the equipment rental according to claim 1, wherein the step 3 performs rolling calculation on the future inventory, and the step 3 specifically includes the following steps:
accumulating the influence factors of the current inventory of the site for the second day to obtain predicted inventory of the second day, and adding the influence factors of the third day to obtain predicted inventory of the third day on the third day according to the predicted inventory of the second day and the influence factors of the third day, so that inventory data of the next year are calculated;
relating to part of the formula:
adding the current newly added demand to the current intention demand and the current order demand;
the current lease waiting is equal to the stock allowance (the real-time stock allowance on the current day);
the current available inventory is the current waiting lease + the influence factor which currently causes the inventory increase;
measuring actual inventory in the rent-current period according to the actual inventory measurement in the rent-upper period, and measuring the inventory reduction influence factor in the rent-current period and the inventory increase influence factor in the current period;
measuring and calculating actual inventory in the rent-up period according to the maximum demand in the current period, and measuring and calculating newly increased demand in the rent and the current period;
the current inventory allowance is the current available inventory-the current newly increased demand;
measuring the total equipment number in the period according to the maximum demand in the period;
measuring the total equipment number in the period according to the actual inventory renting rate as the actual inventory in the period;
and after the rolling is finished, storing the rolling result into the Hive library for persistence, and pushing the rolling result to an elastic search for a user to inquire.
5. The method for predicting the dynamic inventory of the equipment lease according to claim 2, characterized in that, in the step 4, a corresponding business decision is made on the generated forecast data, and the step 4 specifically includes the following processes:
according to the inventory forecast data, key data such as the time, day, week, month and year dimensions of the next year to be rented, inventory allowance and the like can be checked, and a transfer plan, a purchase plan and the like before different sites are formulated through the key data;
the online booking system displays daily inventory calendars according to daily real-time forecast data to help clients to place orders to the inventory.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010358486.1A CN111461628B (en) | 2020-04-29 | 2020-04-29 | Dynamic inventory prediction method for equipment leasing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010358486.1A CN111461628B (en) | 2020-04-29 | 2020-04-29 | Dynamic inventory prediction method for equipment leasing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111461628A true CN111461628A (en) | 2020-07-28 |
| CN111461628B CN111461628B (en) | 2022-05-03 |
Family
ID=71684106
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010358486.1A Active CN111461628B (en) | 2020-04-29 | 2020-04-29 | Dynamic inventory prediction method for equipment leasing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111461628B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112734082A (en) * | 2020-12-17 | 2021-04-30 | 北京中智软创信息技术有限公司 | Inventory prediction method |
| CN117455351A (en) * | 2023-09-06 | 2024-01-26 | 广州箭头信息科技有限公司 | Intelligent management method and system for equipment leasing warehouse based on Saas platform |
| CN117608865A (en) * | 2024-01-23 | 2024-02-27 | 江西科技学院 | Mathematical model service method and system of take-away meal delivery platform based on cloud computing |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106529869A (en) * | 2016-10-27 | 2017-03-22 | 国网天津市电力公司 | Material inventory item dynamic characteristic analysis platform and analysis method thereof |
| CN107609747A (en) * | 2017-08-18 | 2018-01-19 | 刘英学 | Based on the drugstore chain operation system and method for predicting and subscribing pattern |
| CN110750549A (en) * | 2019-10-17 | 2020-02-04 | 青岛鲁诺电子科技有限公司 | Vehicle inventory management system based on big data |
| CN110781242A (en) * | 2019-09-29 | 2020-02-11 | 江苏华泽微福科技发展有限公司 | Welfare information processing system and method based on big data analysis |
-
2020
- 2020-04-29 CN CN202010358486.1A patent/CN111461628B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106529869A (en) * | 2016-10-27 | 2017-03-22 | 国网天津市电力公司 | Material inventory item dynamic characteristic analysis platform and analysis method thereof |
| CN107609747A (en) * | 2017-08-18 | 2018-01-19 | 刘英学 | Based on the drugstore chain operation system and method for predicting and subscribing pattern |
| CN110781242A (en) * | 2019-09-29 | 2020-02-11 | 江苏华泽微福科技发展有限公司 | Welfare information processing system and method based on big data analysis |
| CN110750549A (en) * | 2019-10-17 | 2020-02-04 | 青岛鲁诺电子科技有限公司 | Vehicle inventory management system based on big data |
Non-Patent Citations (2)
| Title |
|---|
| 徐欢: "需求预测与库存决策的集成研究", 《上海管理科学》 * |
| 杨亚璪: "基于顾客选择行为的租赁车辆存量动态控制稳健模型", 《工业工程》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112734082A (en) * | 2020-12-17 | 2021-04-30 | 北京中智软创信息技术有限公司 | Inventory prediction method |
| CN117455351A (en) * | 2023-09-06 | 2024-01-26 | 广州箭头信息科技有限公司 | Intelligent management method and system for equipment leasing warehouse based on Saas platform |
| CN117455351B (en) * | 2023-09-06 | 2024-05-17 | 广州箭头信息科技有限公司 | Intelligent management method and system for equipment leasing warehouse based on Saas platform |
| CN117608865A (en) * | 2024-01-23 | 2024-02-27 | 江西科技学院 | Mathematical model service method and system of take-away meal delivery platform based on cloud computing |
| CN117608865B (en) * | 2024-01-23 | 2024-04-05 | 江西科技学院 | Mathematical model service method and system of take-away meal delivery platform based on cloud computing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111461628B (en) | 2022-05-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111461628B (en) | Dynamic inventory prediction method for equipment leasing | |
| US5799286A (en) | Automated activity-based management system | |
| US5970476A (en) | Method and apparatus for industrial data acquisition and product costing | |
| CN106296087A (en) | A kind of Spare Parts Inventory Management System of Facing to Manufacturing industry | |
| CN112488612A (en) | Visualization-based full inventory resource monitoring and displaying method | |
| CN109583806A (en) | A kind of vehicle scheduling pickup method and system based on after single weight under adjustment | |
| CN115630945A (en) | Low-voltage situation perception command system | |
| CN115456217A (en) | Intelligent ship Internet of things data asset management method and system | |
| CN115345564A (en) | Inventory management method based on automatic prediction | |
| US20020019762A1 (en) | Electric power demand prediction method and system therefor | |
| CN111340536A (en) | Model training method, passenger seat rate progress prediction method, system, device and medium | |
| CN113409028A (en) | Power grid material examination system | |
| CN111882693A (en) | Information processing method and device for employee outwork, electronic equipment and medium | |
| Cramer et al. | Performance measurements on mass transit: case study of New York City transit authority | |
| CN116227872A (en) | Enterprise equipment management method and system based on project equipment actual use analysis | |
| CN110659882A (en) | Human resource comprehensive management big data supervision service system | |
| DE102011112411A1 (en) | Method for cost-benefit optimized scheduling and execution of service-benefits at electric cars of pool, involves dynamically creating and adjusting working plans for service task of motor car of pool based on computed efficiency | |
| CN112508390B (en) | Collaborative manufacturing support system and method | |
| CN114971582A (en) | Material demand tracking system based on supply chain management system | |
| CN111445051B (en) | Express mail traffic prediction method, prediction system and express mail employee scheduling method | |
| CN113076302A (en) | Power grid data management method, device, equipment and medium | |
| Ali et al. | The applicability of just-in-time in United Arab Emirates construction projects | |
| CN109165745A (en) | A kind of overhaul of train-set cost information automatic management method and system | |
| CN118313561A (en) | Intelligent engineering cost management system based on big data | |
| US20180075497A1 (en) | Database management system |
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 |