CN112949889A - Classified inventory and secondary distribution method based on Internet of things and big data technology - Google Patents

Classified inventory and secondary distribution method based on Internet of things and big data technology Download PDF

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CN112949889A
CN112949889A CN201911269854.9A CN201911269854A CN112949889A CN 112949889 A CN112949889 A CN 112949889A CN 201911269854 A CN201911269854 A CN 201911269854A CN 112949889 A CN112949889 A CN 112949889A
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陈新圣
胡永焕
张玉鑫
李俊颖
洪芳华
顾逸峰
肖锋
刘钊文
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Shanghai Jiulong Enterprise Management Consulting Co ltd
State Grid Shanghai Electric Power Co Ltd
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Abstract

A classified inventory and secondary distribution method based on the Internet of things and big data technology belongs to the field of storage management. The method comprises dynamic inventory prediction based on a big data technology and an MRP operation mechanism, an intelligent storage network based on a grading inventory theory and secondary delivery and delivery path optimization; the method comprises the steps of designing a level inventory management model and a secondary distribution path optimization model based on the existing demand unit receiving data and a storage network of a power supply company; establishing a dynamic inventory prediction model by analyzing data such as historical purchase lead time, inventory consumption, project material requirements, spare part accessories and the like; designing a stock storage model by combining a level stock management model and a dynamic stock prediction model; and designing a secondary distribution management and control scheme of the inventory materials based on the technology of the Internet of things according to the material demands and the inventory consumption. Can be widely applied to the fields of warehouse material management and logistics distribution management.

Description

Classified inventory and secondary distribution method based on Internet of things and big data technology
Technical Field
The invention belongs to the field of storage, and particularly relates to a method for grading inventory of storage materials, planning logistics and managing the logistics.
Background
Electric power is an indispensable part in the modern society, and is related to the national civilization, and the timely and accurate supply of electric power materials is the basis of the electric power supply guarantee, so that electric power enterprises have severe requirements on material distribution.
With the continuous promotion of a new round of electric power system innovation, the profit mode of the power grid enterprise is changed from the current purchase and sale price difference mode into a permitted cost plus reasonable profit accounting mode, the power grid enterprise needs to keep continuous healthy development, and the important benefit of cost control becomes the key point of enterprise management. For the material management link, the reasonable reduction of logistics cost and the reduction of unnecessary cost waste become important challenges to be faced, and inventory cost control reflected by stock level and distribution mode upgrading aiming at improving logistics distribution efficiency have important significance for the optimization of logistics cost, and the level of stock cost directly influences the operation cost of a power grid.
Disclosure of Invention
The invention aims to provide a grading inventory and secondary distribution method based on the Internet of things and a big data technology. The method comprises the steps of designing a hierarchical inventory management model and an inventory material secondary distribution route optimization scheme suitable for a company by using requirement utilization data accumulated by material speciality and combining a hierarchical warehousing network management concept and adopting a big data analysis technology and an internet of things technology; meanwhile, data such as historical purchase lead time, inventory consumption, project material requirements, spare part accessories and the like are analyzed, a dynamic inventory requirement prediction model is established, and further, an inventory reserve quota model and a secondary distribution control scheme based on a full network hierarchical structure are designed based on a hierarchical inventory management model and the dynamic inventory requirement prediction model, so that a scientific model basis is provided for optimizing inventory cost and logistics cost.
The technical scheme of the invention is as follows: the grading inventory and secondary distribution method based on the Internet of things and the big data technology is characterized by comprising the following steps:
the grading inventory and secondary distribution method based on the Internet of things and the big data technology comprises dynamic inventory prediction based on the big data technology and an MRP operation mechanism, an intelligent storage network based on a grading inventory theory and secondary distribution and distribution path optimization;
the dynamic inventory prediction based on the big data technology and the MRP operation mechanism comprises demand pattern analysis, weighted ABC classification and MRP operation mechanism design;
the intelligent warehousing network based on the hierarchical inventory theory comprises an inventory management mode of upstream and downstream enterprise right responsibility balance and risk sharing developed on the basis of VMI; emphasizes that all nodes in the supply chain participate at the same time, and jointly make an inventory plan, so that the expectations of inventory managers among all nodes in the supply chain on the demands are kept consistent, and the phenomenon of variation and amplification of the demands is eliminated;
the secondary distribution and distribution path optimization comprises the following steps:
(1) determining the quantity of materials required by each turnover library;
(2) comparing with the vehicle loading capacity to determine the number of vehicles required;
(3) according to the demand of each turnover warehouse, a mileage conservation method is applied, nearby warehouses are delivered by the same automobile, and meanwhile, the condition of cross transportation is avoided, and a delivery path is formed;
(4) according to the real-time road conditions, the distribution paths are adjusted to a certain degree, and the situation that the distribution cannot be carried out in time due to road congestion in the peak period is avoided.
Specifically, the grading inventory and secondary distribution method based on the internet of things and the big data technology utilizes the requirement utilization data accumulated by material speciality and combines the hierarchy inventory network management concept, and utilizes the big data analysis technology and the internet of things technology to design a hierarchy inventory management model and an inventory material secondary distribution path optimization scheme suitable for a power supply company; meanwhile, data such as historical purchase lead time, inventory consumption, project material requirements, spare part accessories and the like are analyzed, a dynamic inventory requirement prediction model is established, and further, an inventory reserve quota model and a secondary distribution control scheme based on a full network hierarchical structure are designed based on a hierarchical inventory management model and the dynamic inventory requirement prediction model, so that a scientific model basis is provided for optimizing inventory cost and logistics cost.
Further, in the demand pattern analysis, dividing the demand into a continuous demand, an intermittent demand and a sporadic demand according to the continuous interruption degree; for the intermittent demand, dividing the intermittent demand into an intermittent demand A, an intermittent demand B and an intermittent demand C according to the data volume; and determining the upper line range of the initial MRP from the angles of continuous discontinuity and the required fluctuation degree by combining the traditional ABC division.
Further, in the weighted ABC classification, the goods and materials are reclassified from five aspects of the stock shortage influence degree, the customer contribution degree, the acquisition difficulty degree, the prediction difficulty degree and the fund occupation amount for the subsequent MRP mechanism design.
Furthermore, in the MRP operation mechanism design, a material list suitable for MRP planning management is listed by integrating the demand characteristics of the materials, ABC classification and weighted ABC classification, and an operation mechanism based on consumption automatic supply and an operation mechanism based on planning periodic supply are selected from a static inventory control strategy and a dynamic inventory control strategy.
The intelligent warehousing network takes the intensive management of deep material resources as a main line, realizes the management concept of the whole life cycle of assets, further perfects a warehousing network system with two-stage storage of a central warehouse and a turnover warehouse, effectively integrates the existing warehousing resources, and constructs a scientific, reasonable and efficient modern warehousing network layout.
The secondary distribution and distribution path is optimized, the geographical position of the transfer warehouse, the material demand, the carrying capacity of the vehicle, the distribution times and the like are integrated, and a reasonable vehicle distribution path is designed.
The invention relates to a grading inventory and secondary distribution method based on the Internet of things and big data technology, which is characterized in that a level inventory management model and a secondary distribution route optimization model are designed based on the existing demand unit receiving data and a storage network of a power supply company; establishing a dynamic inventory prediction model by analyzing data such as historical purchase lead time, inventory consumption, project material requirements, spare part accessories and the like; designing a stock storage model by combining a level stock management model and a dynamic stock prediction model; and designing a secondary distribution management and control scheme of the inventory materials based on the technology of the Internet of things according to the material demands and the inventory consumption.
Compared with the prior art, the invention has the advantages that:
1. according to the technical scheme, a hierarchical inventory management model and a secondary distribution path optimization model are designed based on the existing demand unit receiving data and a storage network of a power supply company;
2. according to the technical scheme, a dynamic inventory prediction model is established by analyzing data such as historical purchase lead time, inventory consumption, project material requirements, spare part accessories and the like;
3. according to the technical scheme, a level inventory management model and a dynamic inventory prediction model are combined to design an inventory storage model;
4. according to the technical scheme, the secondary distribution management and control scheme of the inventory materials is designed based on the internet of things technology according to the material demands and the inventory consumption.
Drawings
FIG. 1 is a schematic diagram of the present invention for determining the upper range of initial MRP;
FIG. 2 is a schematic diagram of the MRP operation mechanism design of the present invention;
FIG. 3 is a schematic diagram of the delivery path of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The technical scheme of the invention mainly comprises the following parts:
dynamic inventory prediction based on big data technology and MRP operation mechanism:
the dynamic inventory forecast based on big data technology and MRP (Material Requirement Planning) operation mechanism is based on the Requirement mode of comprehensive analysis materials, reclassification is carried out on the materials by combining with weighted ABC, a Material list for MRP Planning management is obtained, and then the inventory quota is forecasted from an inventory control strategy.
(1) Analysis of the demand pattern:
dividing the demand into continuous demand, intermittent demand and sporadic demand according to the continuous interruption degree. The quantity of the discontinuous demand materials is large, and the discontinuous demand materials are divided into a discontinuous demand A, a discontinuous demand B and a discontinuous demand C according to the data volume. And determining the upper line range of the initial MRP from the perspective of continuous discontinuity and the fluctuation degree of the demand by combining the traditional ABC division.
And (4) specific pattern analysis. The parameters are valued and the requirements are classified and summed, as shown in fig. 1.
(2) Weighted ABC classification:
and (4) reclassifying the materials from five aspects of the influence degree of the shortage of goods, the contribution degree of the customers, the difficulty degree of acquisition, the difficulty degree of prediction and the fund occupation amount for the subsequent MRP mechanism design. Wherein the class a supplies require significant attention.
(3) MRP operation mechanism design:
the method comprises the steps of listing a material list suitable for MRP to carry out planning management by integrating the demand characteristics of the materials, ABC classification and weighted ABC classification, and selecting a static inventory control strategy, a dynamic inventory control strategy, an operation mechanism based on consumption automatic supply and an operation mechanism based on plan periodic supply.
Net demand-gross demand + in-transit order + in-check order + in-process order-plan prospect (stock requisites, project orders, reserve orders) -max (safety stock, inventory-on-hand) -expected consumption of long lead product in probabilistic big order
MRP operation method:
firstly, setting materials of a re-ordering point to run once every week;
secondly, the residual materials are all set to run MRP (Time-phased) monthly;
since the electric power supplies are not involved in production, each calculation of MRP is performed to overwrite the original MRP data and generate a completely new MRP, i.e., a periodic MRP. It is recommended to initially set up to run the MRP monthly.
The MRP strategy incorporating the power supply subclass is as follows:
the strategy is as follows: MRP planning based on historical consumption
Wool demand (average consumption in past 3-4 months) (automatic order change)
Wool requisition (automatic order transfer) fixed order lot
Partially set the lower limit of stock, partially set the lower limit of stock (monthly order)
Strategy two: MRP planning based on inventory quota setting
Setting the upper limit of stock, the lower limit of stock and the safety stock
Wool demand being the upper limit of inventory
Setting a lower inventory limit, wherein the lower inventory limit comprises safety inventory
Strategy (c): future prediction based MRP planning
Static (or dynamic) safety stock giving 3-month rolling demand forecasts
And (4) combining the gross demand with the next month predicted value or combining the gross demand with the next month operation and maintenance demand and the project rolling week material plan into a month.
A specific MRP operation mechanism design can be seen in fig. 2.
Secondly, an intelligent warehousing network based on a grading inventory theory:
the overall goal of the hierarchical inventory is: the method has the advantages that intensive management of material resources is taken as a main line, the full-life-cycle management concept of the assets is implemented, a storage network system with two-stage storage of a central warehouse and a turnover warehouse is further perfected, existing storage resources are effectively integrated, and scientific, reasonable and efficient modern storage network layout is constructed.
In the warehouse level design, although the single-level warehouse is simple to manage, the single-level warehouse has a 'bull whip effect'. The bullwhip effect is a demand variation amplification phenomenon on a supply chain, and is that when information flow is transmitted from a final client to an original supplier, the information is distorted and amplified step by step, so that the demand information fluctuates more and more. The influence on the inventory management is that the inventory is high. While a multi-stage warehouse can effectively reduce the total inventory of the warehouse, the management is relatively complex.
In warehouse management, a certain power supply company adopts a two-level structure according to the regional characteristics and the material supply characteristics of the area where the power supply company is located: the two-stage warehouse management is taken as the main part, and the three-stage warehousing points are taken as the supplement, so that a warehousing network structure with 1 central warehouse and 12 turnover warehouses is formed.
The inventory management mode design based on the combined inventory management of a certain power supply company is an inventory management mode which is developed on the basis of VMI and has balanced and risk-shared right responsibility of upstream and downstream enterprises. The simultaneous participation of all nodes in the supply chain is emphasized, and the inventory plan is jointly made, so that the expectation of the inventory manager among all the nodes of the supply chain on the demand is kept consistent, and the phenomenon of amplification of variation of the demand is eliminated.
Thirdly, secondary distribution and distribution path optimization:
a certain power company has twelve turnover storehouses, and when the quantity of certain materials in the turnover storehouses is lower than that of safety stocks, the central storehouses provide the materials for replenishing the storehouses. Because the requirement of the power engineering project on the response speed is high, when a plurality of turnover libraries need to be supplemented, the geographic positions of the turnover libraries, the material demand, the carrying capacity of vehicles, the distribution times and the like need to be integrated, and a reasonable vehicle distribution path is designed.
The central warehouse and twelve turnover warehouses constitute 13 nodes connected in pairs, and there are 78 combinations of C132, i.e. 78 paths between any two warehouses in the 13 warehouses. The 78 routes and the travel distance between the two warehouses are searched by using electronic map software such as Google, Baidu and the like by taking the two warehouses as a starting point and an end point respectively. The central bin is denoted by the letter O and the twelve turnover bins are denoted by the letters a to L. When a plurality of turnover libraries need to be supplemented, the distribution path determining step is as follows:
(1) determining the quantity of materials required by each turnover library;
(2) comparing with the vehicle loading capacity to determine the number of vehicles required;
(3) according to the demand of each turnover warehouse, a mileage conservation method is applied, nearby warehouses are delivered by the same automobile, and meanwhile, the condition of cross transportation is avoided, and a delivery path is formed;
(4) according to the real-time road conditions, the distribution paths are adjusted to a certain degree, and the situation that the distribution cannot be carried out in time due to road congestion in the peak period is avoided.
In order to reduce the waiting time of the last distributed warehouses, the warehouse is divided into two areas according to the geographical position in 12 turnover warehouses, 7 warehouses near a suburb ring are one distribution area, and 4 warehouses and a chongming area inside the suburb ring line are one distribution area.
Taking the delivery of 7 warehouses near the suburb loop as an example, as shown in fig. 3, the load capacity of each automobile is 5 tons, 7 turnover warehouses from a to G require the central warehouse 0 to deliver materials, the numbers on the straight line are distances, and the quantity in the brackets is the material demand of the corresponding turnover warehouse.
From the information given, an odometer is formed, column 2 to 8 numbers indicate the shortest distance between two points, the number in parentheses is the saved mileage compared to the route passing O, the saved mileage between a to B is OA + OB-AB 18+ 11-15-14, and the specific graph is as follows:
mileage saving meter
Demand volume O
1.4 18 A
1.6 11 15(14) B
0.8 10 21(7) 6(15) C
2.5 12 29(1) 14(9) 8(14) D
3 8 26(0) 19(0) 18(0) 14(6) E
1 19 28(9) 30(0) 29(0) 31(0) 17(10) F
1.2 9 16(11) 20(0) 19(0) 21(0) 17(0) 14(14) G
The saved mileage in the table is sorted and sorted from big to small to obtain the following sorting table:
mileage-saving sequencing meter
Figure BDA0002313862180000061
Figure BDA0002313862180000071
According to the sequence of saved mileage and the limitations of the required quantity and the vehicle load capacity, the following distribution routes can be obtained:
an automobile 1: O-A-G-F-O, the driving distance is 67 kilometers;
an automobile 2: 0-B-C-D-O, the driving distance is 37 kilometers;
an automobile 3: O-E-O, the driving distance is 16 kilometers.
In conclusion, the technical scheme of the invention is characterized in that:
1. designing a level inventory management model and a secondary distribution path optimization model based on the existing demand unit receiving data and a storage network of a company;
2. analyzing data such as historical purchase lead time, inventory consumption, project material requirements, spare part accessories and the like, and establishing a dynamic inventory prediction model;
3. designing a stock storage model by combining a level stock management model and a dynamic stock prediction model;
4. according to material demands and inventory consumption, a management and control scheme for secondary distribution of inventory materials is designed based on the technology of the Internet of things.
According to the technical scheme, the hierarchical inventory management model and the secondary distribution route optimization scheme of the inventory materials are designed, wherein the hierarchical inventory management model and the secondary distribution route optimization scheme of the inventory materials are suitable for a company by using the requirement utilization data accumulated by material speciality and combining the hierarchical storage network management concept and adopting the big data analysis technology and the internet of things technology. Meanwhile, a dynamic inventory demand prediction model is established by analyzing data such as historical purchase lead time, inventory consumption, project material demand, spare part accessories and the like, and an inventory reserve quota model and a secondary distribution control scheme based on a full network hierarchical structure are designed based on a hierarchical inventory management model and the dynamic inventory demand prediction model, so that a scientific model basis is provided for optimizing inventory cost and logistics cost.
The invention can be widely applied to the fields of warehouse material management and logistics distribution management.

Claims (8)

1. A grading inventory and secondary distribution method based on the Internet of things and big data technology is characterized in that:
the grading inventory and secondary distribution method based on the Internet of things and the big data technology comprises dynamic inventory prediction based on the big data technology and an MRP operation mechanism, an intelligent storage network based on a grading inventory theory and secondary distribution and distribution path optimization;
the dynamic inventory prediction based on the big data technology and the MRP operation mechanism comprises demand pattern analysis, weighted ABC classification and MRP operation mechanism design;
the intelligent warehousing network based on the hierarchical inventory theory comprises an inventory management mode of upstream and downstream enterprise right responsibility balance and risk sharing developed on the basis of VMI; emphasizes that all nodes in the supply chain participate at the same time, and jointly make an inventory plan, so that the expectations of inventory managers among all nodes in the supply chain on the demands are kept consistent, and the phenomenon of variation and amplification of the demands is eliminated;
the secondary distribution and distribution path optimization comprises the following steps:
(1) determining the quantity of materials required by each turnover library;
(2) comparing with the vehicle loading capacity to determine the number of vehicles required;
(3) according to the demand of each turnover warehouse, a mileage conservation method is applied, nearby warehouses are delivered by the same automobile, and meanwhile, the condition of cross transportation is avoided, and a delivery path is formed;
(4) according to the real-time road conditions, the distribution paths are adjusted to a certain degree, and the situation that the distribution cannot be carried out in time due to road congestion in the peak period is avoided.
2. The grading inventory and secondary distribution method based on the internet of things and the big data technology as claimed in claim 1, wherein the grading inventory and secondary distribution method based on the internet of things and the big data technology is characterized in that a grading inventory management model and a secondary distribution path optimization scheme of inventory materials suitable for a power supply company are designed by using the data collected by the materials professionally according to the demands and combining the grading storage network management concept and by using the big data analysis technology and the internet of things technology; meanwhile, data such as historical purchase lead time, inventory consumption, project material requirements, spare part accessories and the like are analyzed, a dynamic inventory requirement prediction model is established, and further, an inventory reserve quota model and a secondary distribution control scheme based on a full network hierarchical structure are designed based on a hierarchical inventory management model and the dynamic inventory requirement prediction model, so that a scientific model basis is provided for optimizing inventory cost and logistics cost.
3. The internet of things and big data technology-based hierarchical inventory and secondary distribution method according to claim 1, wherein in the demand pattern analysis, demands are divided into continuous demands, intermittent demands and sporadic demands according to the degree of continuous discontinuity; for the intermittent demand, dividing the intermittent demand into an intermittent demand A, an intermittent demand B and an intermittent demand C according to the data volume; and determining the upper line range of the initial MRP from the angles of continuous discontinuity and the required fluctuation degree by combining the traditional ABC division.
4. The internet of things and big data technology-based hierarchical inventory and secondary distribution method as claimed in claim 1, wherein in the weighted ABC classification, materials are reclassified from five aspects of stock shortage influence degree, customer contribution degree, acquisition difficulty degree, prediction difficulty degree and capital occupation amount for subsequent MRP mechanism design.
5. The hierarchical inventory and secondary distribution method based on the internet of things and big data technology as claimed in claim 1, wherein in the MRP operation mechanism design, a material list suitable for the MRP to perform planning management is listed by integrating the demand characteristics of the material and ABC classification and weighted ABC classification, and an operation mechanism based on consumption automatic replenishment and an operation mechanism based on planning regular replenishment are selected from a static inventory control strategy and a dynamic inventory control strategy.
6. The internet of things and big data technology-based hierarchical inventory and secondary distribution method according to claim 1, wherein the smart storage network takes intensive material and resource management as a main line, implements a full-life-cycle management concept of assets, further perfects a storage network system with two-level storage of a central warehouse and a turnover warehouse, effectively integrates existing storage resources, and constructs a scientific, reasonable and efficient modern storage network layout.
7. The internet of things and big data technology-based hierarchical inventory and secondary distribution method according to claim 1, wherein the secondary distribution and distribution route is optimized, and a reasonable vehicle distribution route is designed by integrating the geographic position of a turnover warehouse, the material demand, the carrying capacity of vehicles, the distribution times and the like.
8. The hierarchical inventory and secondary distribution method based on the internet of things and the big data technology as claimed in claim 1, wherein the hierarchical inventory and secondary distribution method based on the internet of things and the big data technology is based on the existing demand unit pickup data and the storage network of a power supply company, and a hierarchical inventory management model and a secondary distribution path optimization model are designed; establishing a dynamic inventory prediction model by analyzing data such as historical purchase lead time, inventory consumption, project material requirements, spare part accessories and the like; designing a stock storage model by combining a level stock management model and a dynamic stock prediction model; and designing a secondary distribution management and control scheme of the inventory materials based on the technology of the Internet of things according to the material demands and the inventory consumption.
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Cited By (2)

* Cited by examiner, † Cited by third party
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CN113592309A (en) * 2021-08-02 2021-11-02 上海华能电子商务有限公司 Multi-level inventory quota making method based on data driving
CN115169658A (en) * 2022-06-24 2022-10-11 南京英诺森软件科技有限公司 Inventory consumption prediction method, system and storage medium based on NPL and knowledge graph

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113592309A (en) * 2021-08-02 2021-11-02 上海华能电子商务有限公司 Multi-level inventory quota making method based on data driving
CN113592309B (en) * 2021-08-02 2024-04-30 上海华能电子商务有限公司 Multilevel inventory quota formulation method based on data driving
CN115169658A (en) * 2022-06-24 2022-10-11 南京英诺森软件科技有限公司 Inventory consumption prediction method, system and storage medium based on NPL and knowledge graph
CN115169658B (en) * 2022-06-24 2023-11-21 南京英诺森软件科技有限公司 Inventory consumption prediction method, system and storage medium based on NPL and knowledge graph

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