CN111461628B - Dynamic inventory prediction method for equipment leasing - Google Patents
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Abstract
The invention relates to a dynamic inventory prediction method for equipment leasing, which comprises the following steps: 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 SQL technology in the prior art; 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; and 5, recycling to the step 1, and carrying out real-time calibration to ensure the timeliness and accuracy of the future inventory. The scheme predicts the real-time inventory condition of each station every day in the next year in real time based on the strong data processing and analyzing capability of big data.
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 the Apache Flink and Spark SQL 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 the factors to a specific future day according to the start-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 SQL distribution mode, calculating the returning time of the leases according to the required quantity and the service cycle of all the existing orders, and calculating the time for restocking the leases to be rented after the returning and the preparation of the leases are finished according to the returning time and the service human model of the site; the service manpower model calculates the number of the preparation work orders processed by each site every day according to the service manpower change condition of each site and the number of the preparation work orders processed by each site 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 and calculating the actual inventory in the rent-current period according to the actual inventory in the current period, wherein the actual inventory in the rent-current period causes inventory reduction influence factors and inventory increase influence factors 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 rental rate 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 SQL 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 actual field return quantity ROR (0) of the station A on the day is 10 x 50% ═ 5
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 (abbreviated as LP) of the real servicing quantity (abbreviated as RP) per day 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
the remaining stock quantity LP (0) ═ P (0) + ROR (0) -RP (0) on the day
The number of real repairs completed in 1 day in the future RP (1) ═ SP (1) × PM > LP (0) + ROR (1)?
LP(0)+ROR(1):SP(1)*PM
The remaining stock quantity LP (1) ═ LP (0) + ROR (1) -RP (1) for 1 day in the future
The number of real preparations completed in 2 days in the future RP (2) ═ SP (2) × PM > LP (1) + ROR (2)?
LP(1)+ROR(2):SP(2)*PM
The remaining stock quantity LP (2) ═ LP (1) + ROR (2) -RP (2) for 2 days in the future
Take site A as an example
The number RP (0) of actually completed preparations on the day for station a is 5 x 2>20+520+5:5 x 2 ═ 10
The remaining stock quantity LP (0) of station a on the day is 20+5-10 ═ 15
The number RP (1) of actually completed preparations 1 day in the future for station a is 5 × 2>15+515+5:5 × 2 ═ 10
The remaining stock quantity LP (1) of station a 1 day in the future is 15+7-10 ═ 12
The number RP (2) of sites a that actually completed the staging in 2 days is 5 x 2>12+1012+10:5 x 2-10
The remaining stock quantity LP (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 in the upper period (the real-time stock allowance in the current day);
the current available inventory is the current waiting lease + the influence factor which currently causes the inventory increase;
measuring and calculating the actual inventory in the rent-current period according to the actual inventory in the current period, wherein the actual inventory in the rent-current period causes inventory reduction influence factors and inventory increase influence factors 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 (3)
1. A method for dynamic inventory forecast for equipment rental, the method comprising the steps of:
step 1, collecting all influence factors influencing inventory in a system in real time, namely intention data, order data, various service work orders, inventory data and on-lease data;
step 2, calculating and analyzing the influence factors in real time by utilizing the Apache Flink and Spark SQL 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 the influence factors to each specific future day according to start-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 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;
step 5, recycling to the step 1, and calibrating in real time to ensure the timeliness and accuracy of future inventory;
the step 1 specifically comprises 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;
for the condition that the service data is physically deleted, when the service data is not much, the full-quantity collection is considered, otherwise, the data collection is realized through the binlog of the database and the message notification;
finally, storing the data in the ODS layer of the big data as analysis source data;
in the step 2, the influence factors are calculated and analyzed, and the step 2 specifically comprises the following processes:
carrying out stream calculation on each influence factor by utilizing Apache Flink and Spark SQL in a distributed mode, calculating the returning time of the leases according to the required quantity and the service cycle of all the existing orders, and calculating the time for restocking the leases to be rented after returning and servicing are finished according to the returning time and the service human model of the site; the service manpower model calculates the number of the preparation work orders processed by each site every day according to the service manpower change condition of each site and the number of the preparation work orders processed by each site 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 in each day or a period of time in the future and storing the influence factors in a Hive temporary table.
2. 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;
the formula is referred to:
the current new demand = current intention demand + current order demand;
the current waiting lease = the current inventory allowance is the current real-time inventory allowance;
current available inventory = current pending lease + impact factor currently causing inventory increase;
the current-term actual inventory is measured according to the actual inventory in the rent = upper term, and the inventory reduction influence factor caused in the rent-current term and the inventory increase influence factor caused in the current term are measured;
measuring and calculating actual inventory in a lease = upper period according to the maximum demand in the current period, and measuring and calculating newly increased demand in the lease + the current period;
current inventory balance = current available inventory-current new demand;
calculating the total equipment number in the period according to the maximum demand rental rate = the maximum demand in the period;
measuring the total equipment number in the period according to the actual inventory renting rate = 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.
3. 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 of the dimensions of the time, day, week, month and year to be rented and inventory allowance of the next year can be checked, and a transfer plan and a purchase plan among 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.
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