CN114648270A - Method and system for calculating safety stock of live parts - Google Patents

Method and system for calculating safety stock of live parts Download PDF

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CN114648270A
CN114648270A CN202210299881.6A CN202210299881A CN114648270A CN 114648270 A CN114648270 A CN 114648270A CN 202210299881 A CN202210299881 A CN 202210299881A CN 114648270 A CN114648270 A CN 114648270A
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郑兴
李汶一
杨文锦
覃聪
杨星凯
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Chengdu Aircraft Industrial Group Co Ltd
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Abstract

The invention discloses a method and a system for calculating a safety stock of a live part, which belong to the technical field of production control of the live part and comprise the following steps: collecting basic data of the service life parts according to the product models to obtain a sequence of the basic data, wherein the basic data comprises: ordering frequency, ordering quantity, manufacturing cycle and delivery time; respectively carrying out normalization processing on four dimensions in the basic data; performing clustering operation on all the life parts of the product, and screening out a cluster of life parts with the largest dimension and the largest dimension; calculating the required quantity of the service life parts to determine a safety stock according to the using condition of the product by the customer; by adopting the mode of establishing the safe stock, the client requirement can be quickly responded, the influence on the parts produced in batches by the host factory is reduced, the guarantee level on the client is improved, and the purchasing requirement of the corresponding client on the long-life parts is quickly improved under the condition of reducing the influence on the parts produced in batches by the host factory.

Description

Method and system for calculating safety stock of live parts
Technical Field
The invention relates to the technical field of production control of a long-life article, in particular to a method and a system for calculating safety stock of the long-life article.
Background
The service life of the parts can be predicted, and the service life guarantee of the parts for the customers is always a key focus of a host factory. At present, order-oriented production (Make To Orher MTO) mode is adopted for production of the long-lived articles, feeding production is carried out after customer orders are received, the mode belongs To a passive corresponding mode, and under the condition that the contract delivery cycle is shorter and shorter, the customer requirements are difficult To meet. In order to quickly correspond to the customer requirements and reduce the influence on the parts produced in batches by a host factory, the guarantee level of customers is improved by adopting a mode of establishing a safe stock.
Aiming at the problem of establishing the quantity of the life piece safety libraries, the order frequency, the order quantity, the manufacturing cycle and the delivery time data of the life piece are collected according to the model of the product and serve as basic data, the data are normalized to obtain the influence of dimension on calculation, all life pieces under the model product are clustered to select parts needing important attention, the parts needing important attention are dimensions and the largest life piece cluster, the quantity of the life piece requirements is calculated by combining actual use conditions to determine safety stock, and the life piece purchasing requirements of corresponding clients are quickly and efficiently ensured under the condition that the influence on parts produced in batches in a host factory is reduced.
Disclosure of Invention
The invention aims To overcome the defects that the existing production of the long-life parts in the prior art adopts an Order-oriented production (Make To Order MTO) mode, the batch production is carried out after receiving a customer Order, the passive response mode is adopted, and the customer requirements are increasingly difficult To meet under the condition that the contract delivery cycle is shorter and shorter, and provides a calculation method for the safe inventory of the long-life parts.
In order to achieve the above purpose, the invention provides the following technical scheme:
a life piece safety stock calculation method comprises the following steps:
s1: collecting basic data of all the service-life parts according to the product models to obtain a sequence of the basic data, wherein the basic data is as follows: ordering frequency, ordering quantity, manufacturing cycle and delivery time;
s2: respectively carrying out normalization processing on four dimensions in the basic data;
s3: performing clustering operation on all the life parts of the product, and screening out a cluster of life parts with the largest dimension and the largest dimension;
s4: and calculating the required quantity of the service life parts according to the using condition of the product of the client to determine the safety stock.
By adopting the technical scheme and the mode of establishing the safety stock, the client demand can be quickly responded, the influence on the parts produced in batches by the host factory is reduced, the guarantee level on the client is improved, and the purchasing demand of the corresponding client on the long-life parts is quickly improved under the condition of reducing the influence on the parts produced in batches by the host factory.
As a preferable embodiment of the present invention, the step S1 further includes:
the ordering frequency is the total number of the class of the service-life parts ordered from the client to the host factory in the last n years, the class of the service-life parts has r parts, 0< k is less than or equal to r, and the calculation formula is as follows:
Figure BDA0003565124210000021
wherein, ImnOrdering the life-bearing piece for the mth time in n years, wherein k is the serial number of the life-bearing piece;
the ordering quantity is the total number of the long-life parts ordered by the client to the host factory in the last n years, the number of the long-life parts is r, k is more than 0 and less than or equal to r, and the calculation formula is as follows:
Figure BDA0003565124210000022
wherein, CmnOrdering the number of the m-th order of the long-life piece in n years, wherein k is the number of the long-life piece;
the manufacturing period is the average manufacturing period of the life parts of the type of the life parts of the last n years, the unit is month, the life parts of the type have r parts, k is more than 0 and less than or equal to r, and the calculation formula is as follows:
Figure BDA0003565124210000031
wherein the content of the first and second substances,
Figure BDA0003565124210000032
the order issue time at the mth order of the nth year,
Figure BDA0003565124210000033
for the time at which the order is completed,
Figure BDA0003565124210000034
producing and manufacturing time for the life-bearing part, wherein the unit is day, and k is the serial number of the life-bearing part
The delivery time is the order delivery cycle of the life parts of the type of the last n years, the unit is month, the life parts of the type have r parts, k is more than 0 and less than or equal to r, and the calculation formula is as follows:
Figure BDA0003565124210000035
wherein
Figure BDA0003565124210000036
The delivery cycle of the mth order of the life-bearing piece in n years is determined, the unit is day, and k is the number of the life-bearing piece;
after the preparation is completed, the basic data of all the life parts are four sequences with the same quantity, which are respectively as follows:
the order frequency is { F }1，F2，F3，F4，F5...Fk...Fr}；
The order quantity is { S1，S2，S3，S4，S5...Sk...Sr}；
Manufacturing cycle time of { T1，T2，T3，T4，T5...Tk...Tr}；
Delivery time of { D1，D2，D3，D4，D5...Dk...Dr}。
As a preferable embodiment of the present invention, the step S2 includes: the method adopts a linear normalization technology mode to centralize the intervals of the basic data in [0,1] intervals, and comprises the following steps:
s21: respectively calculating the maximum value and the minimum value in the sequence;
s22: traversing the sequence, calculating in sequence to obtain a new normalized sequence, wherein the order frequency, the order quantity, the manufacturing cycle and the delivery time are respectively calculated according to the following formulas:
Figure BDA0003565124210000041
Figure BDA0003565124210000042
Figure BDA0003565124210000043
Figure BDA0003565124210000044
wherein, FmaxAnd FminRespectively a maximum value and a minimum value of said order frequency, FnkFor the k item, normalized value of order frequency of the life piece, SmaxAnd SminRespectively, a maximum value and a minimum value of the order quantity, SnkNormalized value for the kth order quantity for a life part, TmaxAnd TminRespectively a maximum and a minimum of said manufacturing cycle, TnkNormalized value for the production cycle of the kth item of a useful part, DmaxAnd DminRespectively a maximum value and a minimum value of the delivery time，DnkThe normalized value of the delivery cycle of the life part of the kth item;
s23: the new sequence after treatment is:
order frequency { Fn1，Fn2，Fn3，Fn4，Fn5...Fnk...Fnr}；
Quantity of orders { Sn1，Sn2，Sn3，Sn4，Sn5...Snk...Snr}；
Manufacturing cycle { Tn1，Tn2，Tn3，Tn4，Tn5...Tnk...Tnr}；
Lead time { Dn1，Dn2，Dn3，Dn4，Dn5...Dnk...Dnr}；
Data set is denoted as D ═ x1，x2，x3...xk...xrThe data sample of the kth item of the life piece is xk＝(Fnk，Snk，Tnk，Dnk)。
By adopting the technical scheme, the influence of dimension on calculation is eliminated, and simultaneously, the values of four dimensions in the basic data of the service life piece have uniform intervals, and the subsequent calculation is convenient to carry out.
As a preferable aspect of the present invention, the step S3 includes performing calculation processing on the normalized basic data by using a K-means clustering method, and includes the following steps:
s31: determining the number of clusters: dividing the system into a high hierarchy, a middle hierarchy and a low hierarchy according to the management requirement of the service life parts and the emergency degree of the service life parts, and determining the number of clustering clusters to be 3 by combining the requirement of a clustering algorithm;
s32: initial value selection: selecting clusters which are scattered as much as possible in a data set as initial points;
firstly, respectively calculating the tie values of four dimensions of ordering frequency, ordering quantity, manufacturing cycle and delivery time,
Figure BDA0003565124210000051
constructing new vectors
Figure BDA0003565124210000052
Then find out D-neutrality
Figure BDA0003565124210000057
Vector x with minimum central-european distancei＝{Fni，5ni，Tni，DniAnd (l is less than or equal to i is less than or equal to k) is the first initial point, and the distance calculation formula is as follows:
Figure BDA0003565124210000053
then select D and xiThe most distant vector is taken as the second initial cluster, denoted xi2(i 2 is more than or equal to 1 and less than or equal to k), and finally selecting the sum x in Di、xi2The vector with the maximum sum of the distances of the two vectors is taken as the third initial cluster and is marked as xi3(1≤i3≤k)，xi、xi2、xi3Namely 3 initial cluster centers of the cluster;
s33: clustering operation: calculating the sum of the anisotropy and x in the data set Di、xi2、xi3The respective vector is classified into corresponding clusters according to the size of the Euclidean distance value, and three clusters are constructed
Figure BDA0003565124210000058
Finishing the first clustering calculation, calculating the mean vector of the three clusters,
Figure BDA0003565124210000054
the calculation formula is as follows:
Figure BDA0003565124210000055
wherein n is the clustering operation of the number, and m is the cluster number (m is more than or equal to 1 and less than or equal to 3); then is followed by
Figure BDA0003565124210000059
Performing a second clustering operation on the cluster centers to obtain three reconstructed clusters
Figure BDA00035651242100000510
And mean vector
Figure BDA00035651242100000511
Figure BDA0003565124210000056
S34: and finishing clustering operation: when the mean vector after two successive calculations is not changed, the clustering operation is ended, i.e. the calculation is finished
Figure BDA0003565124210000061
Then, the operation is finished to obtain the final three clusters
Figure BDA0003565124210000062
Figure BDA0003565124210000063
S35: screening high-risk data: obtaining
Figure BDA0003565124210000068
Mean vector of
Figure BDA0003565124210000069
Is defined as
Figure BDA0003565124210000064
And calculating the dimensionality sum of the 3 mean vectors, wherein the calculation formula is as follows:
Figure BDA0003565124210000065
obtaining SjThe maximum value of the order, namely the service life part with the highest comprehensive requirements of order frequency, order quantity, manufacturing cycle and delivery time, isThe corresponding cluster is the high risk list, wherein SjIs the sum of the dimensional values of the mean vector.
By adopting the technical scheme, the problems of unstable clustering process and large calculated amount caused by randomly selecting the initial cluster points in the traditional K-means clustering method are solved.
As a preferable aspect of the present invention, the step S4 of determining the safety stock means that the order quantity of the life-saving pieces in the next year is calculated according to the number of products in service, the service life, and the flight time, in combination with the high risk list of the life-saving pieces:
Figure BDA0003565124210000066
wherein f (x) is whether the current assembled life piece of the client meets the replacement requirement, the requirement is 1, the non-requirement is 0, and YmIs the service life of the life part after the last replacement, Y'mControlling the age of said item of life, HmIs the flying hour of the product after the last change, H'mFlight hours are controlled for the life piece.
The next year order quantity of the longevity pieces is
Figure BDA0003565124210000067
Wherein p is the number of one type of in-service products, and f is the number of one type of service-life parts assembled on a single product.
By adopting the technical scheme, the demand data of the mainly concerned life-bearing piece at the end of the next year is acquired, the host factory issues a production plan in advance by purchasing raw materials or finished products in advance, the safety stock of the life-bearing piece is established to balance production resources, the impact of a burst order on batch production resources is reduced, and the delivery of the life-bearing piece according to time is guaranteed.
In another aspect, a life piece safety inventory computing system is provided, including at least one processor, and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of the methods described above.
Compared with the prior art, the invention has the beneficial effects that: by adopting the mode of establishing the safe stock, the client requirement can be quickly responded, the influence on the parts produced in batches by the host factory is reduced, the guarantee level on the client is improved, and the purchasing requirement of the corresponding client on the long-lived parts is quickly improved under the condition of reducing the influence on the parts produced in batches by the host factory; by adopting a linear normalization technical mode, the intervals of the basic data are concentrated in the interval of [0,1], the influence of dimension on calculation is eliminated, and simultaneously, the values of four dimensions in the basic data of the service life are uniform and convenient for subsequent calculation; the problems of unstable clustering process and large calculated amount caused by randomly selecting initial cluster points in the traditional K-means clustering method are solved; the method comprises the steps of obtaining demand data of the life parts which are mainly concerned at the end of the next year, issuing a production plan in advance by a host factory through purchasing raw materials or finished products in advance, establishing a safety stock of the life parts to balance production resources, reducing impact of a sudden order on batch resources, and guaranteeing scheduled delivery of the life parts.
Drawings
Fig. 1 is a flowchart of a method for calculating a safety stock of a live part according to embodiment 1 of the present invention;
fig. 2 is a block diagram of a system for calculating a safety stock of a live part according to embodiment 2 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to test examples and specific embodiments. It should be understood that the scope of the above-described subject matter is not limited to the following examples, and any techniques implemented based on the disclosure of the present invention are within the scope of the present invention.
Example 1
A method for calculating a life piece safety stock, as shown in fig. 1, comprising the steps of:
s1: collecting basic data of all the service-life parts according to the product models to obtain a sequence of the basic data, wherein the basic data is as follows: ordering frequency, ordering quantity, manufacturing cycle and delivery time;
the step S1 further includes:
the ordering frequency is the total number of the clients ordering one class of life-bearing pieces from the host factory in recent n years, the class of life-bearing pieces is r pieces, k is more than 0 and less than or equal to r, and the calculation formula is as follows:
Figure BDA0003565124210000081
wherein, ImnOrdering the life-bearing piece for the mth time in n years, wherein k is the serial number of the life-bearing piece;
the ordering quantity is the total number of the long-life parts ordered by the client to the host factory in the last n years, the number of the long-life parts is r, k is more than 0 and less than or equal to r, and the calculation formula is as follows:
Figure BDA0003565124210000082
wherein, CmnOrdering the number of the m-th order of the long-life piece in n years, wherein k is the number of the long-life piece;
the manufacturing period is the average manufacturing period of the life parts of the type of the life parts of the last n years, the unit is month, the life parts of the type have r parts, k is more than 0 and less than or equal to r, and the calculation formula is as follows:
Figure BDA0003565124210000083
wherein the content of the first and second substances,
Figure BDA0003565124210000084
the order issue time when the mth order of the nth year is made,
Figure BDA0003565124210000085
for the time at which the order is completed,
Figure BDA0003565124210000091
producing and manufacturing time for said life-bearing part in days, k being said life-bearing partNumbering of articles
The delivery time is the delivery cycle of ordering the life parts in the last n years, the unit is r parts of the life parts in the month, k is more than 0 and less than or equal to r, and the calculation formula is as follows:
Figure BDA0003565124210000092
wherein
Figure BDA0003565124210000093
The delivery cycle of the mth order of the long-life piece in n years is defined as day, and k is the number of the long-life piece;
after the preparation is completed, the basic data of all the life parts are four sequences with the same quantity, which are respectively as follows:
the order frequency is { F }1，F2，F3，F4，F5...Fk...Fr}；
The order quantity is { S1，S2，S3，S4，S5...Sk...Sr}；
Manufacturing cycle time of { T1，T2，T3，T4，T5...Tk...Tr}；
Delivery time of { D1，D2，D3，D4，D5...Dk...Dr}；
Where r is the number of such life parts.
S2: respectively carrying out normalization processing on four dimensions in the basic data;
the step S2 includes: the method adopts a linear normalization technology mode to centralize the intervals of the basic data in [0,1] intervals, and comprises the following steps:
s21: respectively calculating the maximum value and the minimum value in the sequence;
s22: traversing the sequence, calculating in sequence to obtain a new normalized sequence, wherein the order frequency, the order quantity, the manufacturing cycle and the delivery time are respectively calculated according to the following formulas:
Figure BDA0003565124210000101
Figure BDA0003565124210000102
Figure BDA0003565124210000103
Figure BDA0003565124210000104
wherein, FmaxAnd FminRespectively a maximum value and a minimum value of said order frequency, FnkNormalized value for the kth order frequency of a life part, SmaxAnd SminRespectively, a maximum value and a minimum value of the order quantity, SnkNormalized value for the kth order quantity for a life part, TmaxAnd TminRespectively the maximum and minimum value of said manufacturing cycle, TnkNormalized value for the production cycle of the kth item of a useful part, DmaxAnd DminRespectively a maximum value and a minimum value of said delivery time, DnkThe value of the kth item after the delivery cycle normalization of the life piece;
s23: the new sequence after treatment is:
order frequency { Fn1，Fn2，Fn3，Fn4，Fn5...Fnk...Fnr}；
Quantity of orders { Sn1，Sn2，Sn3，Sn4，Sn5...Snk...Snr}；
Manufacturing cycle { Tn1，Tn2，Tn3，Tn4，Tn5...Tnk...Tnr}；
Lead time { Dn1，Dn2，Dn3，Dn4，Dn5...Dnk…Dnr}；
Data set is denoted as D ═ x1，x2，x3...xk...xrThe data sample of the kth item of the life piece is xk＝(Fnk，Snk，Tnk，Dnk)。
S3: performing clustering operation on all the life parts of the product, and screening out a cluster of life parts with the largest dimension and the largest dimension;
the step S3 includes performing calculation processing on the normalized basic data by using a K-means clustering method, and includes the following steps:
s31: determining the number of clusters: dividing the system into a high hierarchy, a middle hierarchy and a low hierarchy according to the management requirement of the service life parts and the emergency degree of the service life parts, and determining the number of clustering clusters to be 3 by combining the requirement of a clustering algorithm;
s32: initial value selection: selecting clusters which are scattered as much as possible in a data set as initial points;
firstly, respectively calculating the tie values of four dimensions of ordering frequency, ordering quantity, manufacturing cycle and delivery time,
Figure BDA0003565124210000111
constructing new vectors
Figure BDA0003565124210000112
Then find out D-neutrality
Figure BDA0003565124210000113
Vector x with minimum central-european distancei＝{Fni，Sni，Tni，DniAnd (1 ≦ i ≦ k) as the first initial point, and the distance calculation formula is:
Figure BDA0003565124210000114
then select D and xiThe most distant vector is taken as the second initial cluster, denoted xi2(i 2 is more than or equal to 1 and less than or equal to k), and finally selecting the sum x in Di、xi2The vector with the maximum sum of the distances of the two vectors is taken as the third initial cluster and is marked as xi3(1≤i3≤k)，xi、xi2、xi3Namely 3 initial cluster centers of the cluster;
s33: clustering operation: calculating the sum of the anisotropy and x in the data set Di、xi2、xi3The respective vector is classified into corresponding clusters according to the size of the Euclidean distance value, and three clusters are constructed
Figure BDA0003565124210000115
Finishing the first clustering calculation, calculating the mean vector of the three clusters,
Figure BDA0003565124210000116
the calculation formula is as follows:
Figure BDA0003565124210000117
wherein n is the clustering operation of the number, and m is the cluster number (m is more than or equal to 1 and less than or equal to 3); then is followed by
Figure BDA0003565124210000118
Performing a second clustering operation on the cluster centers to obtain three reconstructed clusters
Figure BDA0003565124210000119
And mean vector
Figure BDA00035651242100001110
Figure BDA00035651242100001111
S34: and finishing clustering operation: when the mean vector after two successive calculations is not changed, the clustering operation is ended, i.e. the calculation is finished
Figure BDA00035651242100001112
Then, the operation is finished to obtain the final three clusters
Figure BDA00035651242100001113
Figure BDA00035651242100001114
S35: screening high-risk data: obtaining
Figure BDA0003565124210000125
Mean vector of
Figure BDA0003565124210000126
Is defined as
Figure BDA0003565124210000121
And calculating the dimensionality sum of the 3 mean vectors, wherein the calculation formula is as follows:
Figure BDA0003565124210000122
obtaining SjThe maximum value of the order, namely the service life part with the highest comprehensive requirements on order frequency, order quantity, manufacturing cycle and delivery time is the high risk list, wherein S is the high risk listjIs the sum of the dimensional values of the mean vector.
The step S4 of determining the safety stock is to calculate the order quantity of the life-bearing parts in the next year according to the number of existing service products, the service life, the flight time, and the high risk list of the life-bearing parts:
Figure BDA0003565124210000123
wherein f (x) is whether the current assembled life piece of the client meets the replacement requirement, the requirement is 1, the non-requirement is 0, and YmIs the service life of the life part after the last replacement, Y'mControlling the age of said item of life, HmIs the last post-change product flight hour, H'mFlight hours are controlled for the life piece.
The next year order quantity of the longevity pieces is
Figure BDA0003565124210000124
Wherein p is the number of one type of in-service products, and f is the number of one type of service-life parts assembled on a single product.
S4: and calculating the required quantity of the service life parts according to the using condition of the product of the client to determine the safety stock.
By adopting the technical scheme and the mode of establishing the safe stock, the client requirement can be quickly responded, the influence on the mass production parts of the host factory is reduced, the guarantee level of the client is improved, and the purchasing requirement of the corresponding client on the long-life parts is quickly improved under the condition of reducing the influence on the mass production parts of the host factory; by adopting a linear normalization technical mode, the intervals of the basic data are concentrated in the interval of [0,1], the influence of dimension on calculation is eliminated, and simultaneously, the values of four dimensions in the basic data of the service life are uniform and convenient for subsequent calculation; the problems of unstable clustering process and large calculated amount caused by randomly selecting initial cluster points in the traditional K-means clustering method are solved; the method comprises the steps of acquiring demand data of the mainly concerned life-bearing parts at the end of the next year, issuing a production plan in advance by a host factory through purchasing raw materials or finished products in advance, establishing a safety stock of the life-bearing parts to balance production resources, reducing impact of sudden orders on batch resources, and guaranteeing delivery of the life-bearing parts according to time.
Example 2
A life piece safety inventory computing system, as shown in fig. 2, includes at least one processor, and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of any of embodiment 1.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (6)

1. A method for calculating a safety stock of a live part is characterized by comprising the following steps:
s1: collecting basic data of all the service life pieces according to the product models to obtain a sequence of the basic data, wherein the basic data comprises the following components: ordering frequency, ordering quantity, manufacturing cycle and delivery time;
s2: respectively carrying out normalization processing on four dimensions in the basic data;
s3: performing clustering operation on all the life parts of the product, and screening out a cluster of life parts with the largest dimension and the largest dimension;
s4: and calculating the required quantity of the service life parts according to the using condition of the product of the client to determine the safety stock.
2. A method for calculating a safety stock of live parts according to claim 1, wherein in the step S1:
the ordering frequency is the total number of the class of the service-life parts ordered from the client to the host factory in the last n years, the class of the service-life parts has r parts, 0< k is less than or equal to r, and the calculation formula is as follows:
Figure FDA0003565124200000011
wherein, ImnOrdering the life-bearing piece for the mth time in n years, wherein k is the serial number of the life-bearing piece;
the ordering quantity is the total number of the long-life parts ordered by the client to the host factory in the last n years, the number of the long-life parts is r, k is more than 0 and less than or equal to r, and the calculation formula is as follows:
Figure FDA0003565124200000012
wherein, CmnThe mth order quantity of the long-life piece in n years is defined, and k is the serial number of the long-life piece;
the manufacturing period is the average manufacturing period of the life parts of the type of the life parts of the last n years, the unit is month, the life parts of the type have r parts, k is more than 0 and less than or equal to r, and the calculation formula is as follows:
Figure FDA0003565124200000021
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003565124200000022
the order issue time at the mth order of the nth year,
Figure FDA0003565124200000023
for the time at which the order is completed,
Figure FDA0003565124200000024
producing and manufacturing time for the life-bearing part, wherein the unit is day, and k is the serial number of the life-bearing part
The delivery time is the order delivery cycle of the life-bearing parts in the last n years, the unit is month, the life-bearing parts are r parts, k is more than 0 and less than or equal to r, and the calculation formula is as follows:
Figure FDA0003565124200000025
wherein
Figure FDA0003565124200000026
The delivery cycle of the mth order of the life-bearing piece in n years is determined, the unit is day, and k is the number of the life-bearing piece;
after the preparation is completed, the basic data of all the life parts are four sequences with the same quantity, which are respectively as follows:
the order frequency is { F }1，F2，F3，F4，F5...Fk...Fr}；
The order quantity is { S1，S2，S3，S4，S5...Sk...Sr}；
Manufacturing cycle time of { T1，T2，T3，T4，T5...Tk...Tr}；
Delivery time of { D1，D2，D3，D4，D5...Dk...Dr}。
3. The method for calculating a life piece safety stock according to claim 2, wherein the step S2 includes: the method adopts a linear normalization technology mode to centralize the intervals of the basic data in [0,1] intervals, and comprises the following steps:
s21: respectively calculating the maximum value and the minimum value in the sequence;
s22: traversing the sequence, calculating in sequence to obtain a new normalized sequence, wherein the order frequency, the order quantity, the manufacturing cycle and the delivery time are respectively calculated according to the following formulas:
Figure FDA0003565124200000031
Figure FDA0003565124200000032
Figure FDA0003565124200000033
Figure FDA0003565124200000034
wherein, FmaxAnd FminAre respectively shown asMaximum and minimum values of the order frequency, FnkNormalized value for the kth order frequency of a life part, SmaxAnd SminRespectively, a maximum value and a minimum value of the order quantity, SnkNormalized value for the kth order quantity for a life part, TmaxAnd TminRespectively the maximum and minimum value of said manufacturing cycle, TnkNormalized value for the production cycle of the kth item of a useful part, DmaxAnd DminRespectively a maximum value and a minimum value of said delivery time, DnkThe value of the kth item after the delivery cycle normalization of the life piece;
s23: the new sequence after treatment is:
order frequency { Fn1，Fn2，Fn3，Fn4，Fn5...Fnk...Fnr}；
Quantity of orders { Sn1，Sn2，Sn3，Sn4，Sn5...Snk...Snr}；
Manufacturing cycle { Tn1，Tn2，Tn3，Tn4，Tn5...Tnk...Tnr}；
Lead time { Dn1，Dn2，Dn3，Dn4，Dn5...Dnk...Dnr}；
Data set is denoted as D ═ x1，x2，x3...xk...xrX, wherein the data sample of the kth item of the life piece is xk＝(Fnk，Snk，Tnk，Dnk)。
4. The method for calculating the safety stock of the live parts according to claim 3, wherein the step S3 includes calculating the normalized basic data by a K-means clustering method, and includes the following steps:
s31: determining the number of clusters: dividing the system into a high hierarchy, a middle hierarchy and a low hierarchy according to the management requirement of the service life parts and the emergency degree of the service life parts, and determining the number of clustering clusters to be 3 by combining the requirement of a clustering algorithm;
s32: initial value selection: selecting clusters which are scattered as much as possible in a data set as initial points;
firstly, respectively calculating the tie values of four dimensions of ordering frequency, ordering quantity, manufacturing cycle and delivery time,
Figure FDA0003565124200000041
Figure FDA0003565124200000042
constructing new vectors
Figure FDA0003565124200000043
Then find out D-neutrality
Figure FDA0003565124200000044
Vector x with minimum central-european distancei＝{Fni，Sni，Tni，DniI is more than or equal to 1 and less than or equal to k is the first initial point;
then select D and xiThe most distant vector is taken as the second initial cluster, denoted xi2(i 2 is more than or equal to 1 and less than or equal to k), and finally selecting the sum x in Di、xi2The vector with the maximum sum of the distances of the two vectors is taken as the third initial cluster and is marked as xi3(1≤i3≤k)，xi、xi2、xi3Namely 3 initial cluster centers of the cluster;
s33: clustering operation: calculating the sum of the anisotropy and x in the data set Di、xi2、xi3The respective vector is classified into corresponding clusters according to the size of the Euclidean distance value to construct three clusters
Figure FDA0003565124200000045
Finishing the first clustering calculation, calculating the mean vector of the three clusters,
Figure FDA0003565124200000046
the calculation formula is as follows:
Figure FDA0003565124200000047
wherein n is the clustering operation of the number, and m is the cluster number (m is more than or equal to 1 and less than or equal to 3); then is followed by
Figure FDA0003565124200000048
Performing a second clustering operation on the cluster centers to obtain three reconstructed clusters
Figure FDA0003565124200000049
And mean vector
Figure FDA00035651242000000410
Figure FDA00035651242000000411
S34: and (5) finishing clustering operation: when the mean vector after two successive calculations is not changed, the clustering operation is ended, i.e. the calculation is finished
Figure FDA00035651242000000412
Then, the operation is finished to obtain the final three clusters
Figure FDA00035651242000000413
Figure FDA00035651242000000414
S35: screening high-risk data: obtaining
Figure FDA0003565124200000051
Mean vector of
Figure FDA0003565124200000052
Is defined as
Figure FDA0003565124200000053
And calculating the dimensionality sum of the 3 mean vectors, wherein the calculation formula is as follows:
Figure FDA0003565124200000054
obtaining SjThe maximum value of the order, namely the service life part with the highest comprehensive requirements on order frequency, order quantity, manufacturing cycle and delivery time is the high risk list, wherein S is the high risk listjIs the sum of the dimensional values of the mean vector.
5. The method of claim 4, wherein the step S4 of determining the safety stock is to calculate the order quantity of the life-saving parts in the next year according to the quantity of the existing products in service, the service life, and the flight time, in combination with the high risk list of the life-saving parts:
Figure FDA0003565124200000055
wherein f (x) is whether the current assembled service life piece of the client meets the replacement requirement, the requirement is 1, the requirement is not 0, and YmIs the service life of the life part after the last replacement, Y'mControlling the age of said item of life, HmIs the flying hour of the product after the last change, H'mFlight hours are controlled for the life piece.
The next year of the life piece is ordered as
Figure FDA0003565124200000056
Wherein p is the number of the type of products in service, and f is the number of the type of the service-life parts assembled on a single product.
6. A life piece safety inventory computing system comprising at least one processor, and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 5.
CN202210299881.6A 2022-03-25 2022-03-25 Method and system for calculating safety stock of live parts Pending CN114648270A (en)

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