CN116777349A

CN116777349A - Spare part inventory risk management method for information system

Info

Publication number: CN116777349A
Application number: CN202310747992.3A
Authority: CN
Inventors: 杜莹; 王申; 王力; 宋仕平; 蒋源; 高宏伟
Original assignee: Chinese People's Liberation Army 66389 Unit 70 Division
Current assignee: Chinese People's Liberation Army 66389 Unit 70 Division
Priority date: 2023-06-25
Filing date: 2023-06-25
Publication date: 2023-09-19

Abstract

An information system spare part inventory risk management method, comprising the steps of: s1: analysis of LEC risk assessment method: improving an LEC traditional risk evaluation method, and analyzing a fault rate value in the LEC risk evaluation method; s2: constructing an evaluation index system: constructing an evaluation index system on the improved LEC traditional risk evaluation method; s3: designing a risk evaluation model: designing an improved LEC spare part inventory risk evaluation model; constructing an LEC evaluation system: constructing a traditional LEC evaluation system model, wherein the mathematical model is as follows: d=l×e×c, where L is the probability of accident occurrence, and the invention is beneficial to the warehouse manager to actively and effectively manage risk of spare part inventory, and has the characteristics of realizing controllable, controllable and in-control of spare part inventory.

Description

Spare part inventory risk management method for information system

Technical Field

The invention belongs to the technical field of information spare part risk management, and particularly relates to an information system spare part inventory risk management method.

Background

Spare parts are parts for equipment maintenance, accident replacement, consumption and storage, such as spare reels, spare cables, etc. required for a fiber optic communications transport network. Which spare parts are stored frequently depends on the service life of the spare parts, and how much is stored, and then on the consumption of the spare parts and the repair capacity and supply cycle of the area repair center. The spare part stock and the demand have a dynamic association relation, if the spare part stock is too much, the stock of the material is backlogged, and meanwhile, the expense cost is increased; if spare parts are not in sufficient inventory, normal equipment operation may be affected. The stock of spare parts needs to have certain safety reserve, can be determined according to the equipment condition and data such as daily equipment spare part consumption, the spare parts of key equipment, the spare parts which are not easy to purchase and the special spare parts with ordering points, can properly increase the reserve quota, and the stock management of the specific equipment spare parts is a complex and detailed work and is an important component of spare part management work.

Currently, in terms of spare part demand management, estimation is mainly performed empirically, and this method is difficult to be consistent with demand when determining the required amount of spare parts.

Therefore, how to perform scientific spare part inventory risk management is a significant real-world topic.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention aims to provide an information system spare part inventory risk management method, which solves the technical problem of how to perform scientific spare part inventory risk management and has the characteristics of realizing the controllability, the energy control and the on-control of spare part inventory.

In order to achieve the above purpose, the invention adopts the following technical scheme:

an information system spare part inventory risk management method, comprising the steps of:

s1: analysis of LEC risk assessment method: improving an LEC traditional risk evaluation method, and analyzing a fault rate value in the LEC risk evaluation method;

s2: constructing an evaluation index system: constructing an evaluation index system on the improved LEC traditional risk evaluation method;

s3: designing a risk evaluation model: an improved LEC spare part inventory risk assessment model is designed.

The S1, analysis LEC risk evaluation method comprises the following specific steps:

s11: constructing an LEC evaluation system: constructing a traditional LEC evaluation system model, wherein the mathematical model is as follows: d=l×e×c, where L is the probability of accident occurrence, E is the frequency of exposure of the operator to the dangerous environment, C is the possible consequences of the accident once it occurs, and D is the magnitude of the risk for evaluating the working conditions;

s12: improved LEC evaluation system: improving the values of L and E in an LEC evaluation system, wherein E is the influence degree of failure rate, and C is the influence degree of failure occurrence results, namely the importance degree of spare parts;

s13: dividing a fault period: according to the fault, the definition that the parts of the equipment lose the specified functions due to a certain reason in the using process of the equipment is taken as a basis, and the equipment is divided according to different fault types;

s14: and (3) fault rate setting: shaping the failure rate, wherein the failure rate refers to the probability of failure in unit time when a product works to a certain moment, is called a failure rate function, and is expressed by f (t);

s15: predicting a failure rate: and searching a plurality of simple models to accurately predict the failure rate of the equipment.

And S13, dividing the fault period into sudden faults and progressive faults, and dividing the service life of the equipment into three stages of early fault period, accidental fault period and wear fault period on the basis of the progressive faults.

S15, predicting the failure rate comprises the following specific steps:

s151: latent fault prediction analysis: the spare part has a degradation period from the potential fault to the functional fault, and the potential fault is subjected to predictive analysis during the degradation period;

s152: setting a failure rate prediction analysis method: and setting a least square method multi-element trend index model as a fault rate prediction analysis model to predict.

The analysis method of the latent fault prediction analysis in the S151 is as follows: assuming that the probability of occurrence of a functional failure at time t is f (t) during a loss failure period, a common failure distribution is: an exponential distribution, a normal distribution, a lognormal distribution, a weibull distribution, a gamma distribution, and the like, wherein the probability density function of the exponential distribution is: αeβt, wherein: f (t) represents the failure rate of the device in%/year; t represents the working use time of the equipment, and the unit is year; alpha and beta are constants, and are based on a fault rate prediction function: f (t) = 0.1723e ^0.4213(t-13) And predicting the failure rate.

S2, constructing an evaluation index system, which comprises the following specific steps:

s21: and (3) data collection: mastering basic data of the number, consumption and functional attribute types of the inventory items;

s22: grading: scoring according to importance and classifying into three grades;

s23: drawing an ABC analysis chart: and drawing an ABC analysis chart to form an ABC classification management standard table.

S22, dividing the grade into: class a is very important, with the highest level of importance; class B is important, with a degree of importance between class a and class C; class C is general.

S3, designing a risk evaluation model comprises the following specific steps:

s31: constructing a hierarchical analysis model: constructing an analytic hierarchy process model by adopting an AHP analysis method;

s32: ABC classification: classifying the first layer five modules and the second layer parts by using an ABC classification method;

s33: model spare part: according to the characteristics of the equipment, the influence factors on the inventory risk of the telephone network spare parts are equipment service life L, failure rate E and importance degree C.

The beneficial effects of the invention are as follows:

compared with the prior art, the invention constructs a spare part inventory LEC evaluation index system by analyzing the basic thought of L, E, C in the traditional LEC method and the influence degree of equipment service life, failure rate and importance, designs an LEC spare part inventory risk evaluation model and is applied to spare part inventory management, so that the model has universal applicability, has the spare part inventory risk management which can be expanded to other communication equipment and other industry fields, is beneficial to the positive and effective risk management of the spare part inventory by warehouse managers, and realizes the controllable, energy-controllable and in-control advantages of the spare part inventory; the invention adopts the least square method multi-element trend index model to predict the failure rate prediction analysis model, strengthens the fineness and accuracy of the prediction, and has the advantages of more accurate failure rate prediction and more scientific basis for reserve of spare parts; the invention adopts the ABC classification method to classify, and can derive a plurality of classification categories to realize the detail of classification, so the invention has the advantage of being convenient for the corresponding risk degree of the branch office to take corresponding spare part inventory management measures.

Drawings

Fig. 1 is a schematic overall flow chart of an inventory risk management method for spare parts of an information system according to the present invention.

Fig. 2 is a schematic flow chart of an analysis LEC risk evaluation method of the information system spare part inventory risk management method according to the present invention.

Fig. 3 is a schematic diagram of a failure rate prediction flow of an information system spare part inventory risk management method according to the present invention.

Fig. 4 is a schematic flow chart of an evaluation index system for constructing an information system spare part inventory risk management method according to the present invention.

Fig. 5 is a schematic flow chart of a design risk evaluation model of an information system spare part inventory risk management method according to the present invention.

Fig. 6 is a schematic diagram illustrating a level of influence of equipment life in an information system inventory risk management method according to the present invention.

Fig. 7 is a histogram of the degree of influence of failure rates of the service life of the telephone network device in different periods in the information system spare part inventory risk management method according to the present invention.

Fig. 8 is a histogram of failure rate analysis data of a switch telephone network device of an information system spare part inventory risk management method according to the present invention.

Fig. 9 is an AHP & ABC classification schematic diagram of a telephone network spare part according to the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.

The invention discloses an information system spare part inventory risk management method which is mainly applied to scenes of information system spare part inventory risk management.

Referring to fig. 1 and 2, an information system spare part inventory risk management method includes the following specific steps:

s3: designing a risk evaluation model: designing an improved LEC spare part inventory risk evaluation model;

s1, analyzing LEC risk evaluation method comprises the following specific steps:

s15: predicting a failure rate: the method is characterized in that a plurality of simple models are searched for accurately predicting the failure rate of equipment, a spare part inventory LEC evaluation index system is constructed by analyzing the basic thought of L, E, C in the traditional LEC method and the influence degree of the service life, failure rate and importance of the equipment, an LEC spare part inventory risk evaluation model is designed and applied to spare part inventory management, so that the model has universal applicability, can be expanded to the spare part inventory risk management in other communication equipment and other industry fields, is beneficial to the positive and effective risk management of the spare part inventory by warehouse managers, and realizes the controllable, energy-controllable and in-control of the spare part inventory.

Referring to fig. 2 and 6, in a preferred embodiment, S13, the fault period in the fault period is divided into a sudden fault and a progressive fault, and the service life of the device is divided into three phases of an early fault period, an accidental fault period and a wear-out fault period on the basis of the progressive fault, wherein the probability of occurrence of the sudden fault is independent of time and cannot be predicted; progressive faults are the gradual deterioration of the functions specified by the device for various reasons, so that the complete loss is caused, the occurrence probability is related to time, the prediction and prevention can be realized, and the comprehensiveness and the accuracy of the analysis of the period data are ensured by carrying out detail division on the fault period and subsequent analysis of different categories.

Referring to fig. 3, in a preferred embodiment, S15, the failure rate prediction includes the following specific steps:

Referring to FIG. 3, in oneIn a preferred embodiment, the analysis method of the latent fault prediction analysis in S151 is as follows: assuming that the probability of occurrence of a functional failure at time t is f (t) during a loss failure period, a common failure distribution is: an exponential distribution, a normal distribution, a lognormal distribution, a weibull distribution, a gamma distribution, and the like, wherein the probability density function of the exponential distribution is: αeβt, wherein: f (t) represents the failure rate of the device in%/year; t represents the working use time of the equipment, and the unit is year; alpha and beta are constants, and are based on a fault rate prediction function: f (t) = 0.1723e ^0.4213(t-13) And predicting the failure rate.

Referring to fig. 4, in a preferred embodiment, S2, constructing an evaluation index system includes the following specific steps:

Referring to fig. 4, in a preferred embodiment, S22 is classified into: the class A is very important, the importance level is the highest, the influence degree on the equipment is the greatest, once the equipment is lost, the equipment cannot normally operate, a safety stock is required to be established, the stock is not allowed to be out of stock, but the stock quantity is not required to be checked in time, and the stock is required to be replenished in time; class B is important, with a degree of importance between class a and class C; once missing, a situation occurs that will result in reduced efficiency of the device, but remedial action can be taken to reduce losses, again with a degree of management between class A and class C; class C is general; once the equipment is lost, the equipment can normally run, the influence is small, quantitative purchasing or batch purchasing is generally adopted, the number of orders can be properly reduced, a small amount of backorders are allowed, the failure rate is predicted by utilizing a least square method multi-element trend index model as a failure rate prediction analysis model, compared with the method for predicting the failure rate of the equipment by utilizing the least square method multi-element trend index model, the defect that the relationship between the failure rate and time is only considered, the influence of other factors such as climate, environment, storage mode and the like is ignored is overcome, the fineness and accuracy of the prediction are enhanced, the failure rate prediction is more accurate, and a more scientific basis is provided for reserve of spare parts.

Referring to fig. 5, in a preferred embodiment, S3, designing a risk assessment model includes the following specific steps:

s33: model spare part: according to the characteristics of the equipment, the influence of the service life influence L, the fault rate influence E, the importance degree influence C and the like on the inventory risk of the telephone network spare parts is fully considered, the telephone network spare parts are classified by an ABC classification method, a plurality of classification categories can be derived, the classification details are realized, and the corresponding spare part inventory management measures are conveniently taken for the corresponding risk degree of the branch office.

Examples:

taking program-controlled switching telephone network equipment with the service life of 20 years as an example, from the beginning of operation to the 11 th year of the service life, the equipment is stable in operation, the fault rate is increased year by year after 12 th years, the fault rate data statistics of the equipment in 12 th year to 17 th year are shown in table 1, the fault rate is predicted by using a least square trend index model, the fault rate prediction curve is shown in fig. 7, and the fault rate prediction function is f (t) = () 1723e ^0.4213(t-13) Wherein α= 0.1723, β= 0.4213;

annual failure rate statistics for telephone network equipment

Device life/year	Failure rate/(%/year)
		12	0.1
13	0.2
		14	0.3
15	0.4
		16	0.7
17	1.0

TABLE 1

Table 1 is a statistical table of annual failure rate of telephone network equipment in the risk management method for spare parts of the information system.

The obtained fault rate function f (t) can be used for predicting the fault rate of the equipment after 18 years of working use, and the data can be used for guiding future maintenance work, ordering spare parts and the like;

wherein, the failure rate analysis data and the failure rate influence degree level of the telephone network device are shown in fig. 8 and table 2;

device life/year	Failure rate/(%/year)
		18	1.3
19	2.2
		20	3.3
21	5.0
		22	7.6
23	11.6
		24	17.7
25	27

TABLE 2

Table 2 is a schematic diagram of failure rate analysis data of a switching telephone network device of an information system spare part inventory risk management method according to the present invention.

The degree of influence level of the failure rate of the service life of the telephone network equipment in different periods is shown in fig. 6 and table 3;

class of degree of influence of equipment lifetime

Equipment life t/year	Index rating	Failure rate variation
			0≤t＜0.5	Level 2	Higher onset
0.5≤t＜1	Grade 4	After a period of operation, decline
			1≤t＜18	Grade 5	Low and stable failure rate
18≤t≤20	3 grade	Increasing with increasing run time
			21＜t	Level 1	Failure rate rises rapidly

TABLE 3 Table 3

Table 3 shows the degree of influence of the failure rate of the service life of the telephone network equipment in different periods in the information system spare part inventory risk management method according to the present invention.

Equipment life t/year	Index rating
		0≤t＜0.5	Grade 4
0.5≤t＜17	Grade 5
		17≤t＜18	Grade 4
18≤t≤20	3 grade
		20≤t＜23	Level 2
T＞23	Level 1

TABLE 4 Table 4

In the method for managing the inventory risk of the spare parts of the information system, as shown in table 4, table 4 is the degree of influence level of the failure rate of telephone network equipment in the method for managing the inventory risk of the spare parts of the information system, the telephone network equipment adopts a modularized structure, and a first layer consists of five parts, namely a switching network module, a switching module, a seventh signaling module, a far-end user module and a telephone. Each type of module of the second layer is composed of different units, and the exchange network module mainly comprises a clock unit and an exchange network unit; the exchange module mainly comprises a main processor unit, an auxiliary memory, a sub-processor unit and a relay unit; the signaling module No. seven is mainly composed of a main processor, an auxiliary memory, a signaling link processor No. seven and a physical link transmission layer; the remote user module mainly comprises a main control unit, a user processor and a user board; the telephone comprises a host and a handle;

when the ABC classification method is adopted to classify the first layer five modules and the second layer parts, according to the importance of the modules in the telephone network, the switching network module, the switching module and the signaling module number seven in the first layer five modules are A type, the far-end user module is B type, and the telephone is C type; according to the importance of the components in the equipment, the second layer of components also performs ABC classification, the classification conditions are shown in fig. 9 and table 5, and fig. 9 is an AHP & ABC classification schematic diagram of a telephone network spare part of the information system spare part inventory risk management method provided by the invention. Table 5 is a two-dimensional ABC classification summary view of a telephone network spare part of an information system spare part inventory risk management method according to the present invention.

Table 5 two-dimensional ABC classification summary of telephone network spare parts

TABLE 5

Taking a telephone network spare part inventory as an example, referring to the AHP and ABC two-layer classification of telephone network spare parts, the two-dimensional importance of the spare parts is distinguished from the two dimensions of the equipment importance and the spare part importance. The two-dimensional ABC classification of spare parts is as follows: firstly, according to two layers of classification of telephone network devices AHP and ABC, all devices in the first layer are classified into A class, B class and C class according to importance, the A class is very important, the B class is important, and the C class is general. Secondly, according to the ABC classification, all spare parts of the second layer are classified according to importance, namely, very important class A, important class B and general class C. The ABC classification of all the equipment in the first layer and all the spare parts in the second layer is combined, the two-dimensional classification is carried out on the spare parts of the equipment, the spare parts of the equipment are classified into nine classes according to importance, the spare parts are ranked behind according to the importance of the equipment, and the importance of the spare parts is AA, BA, CA, AB, BB, CB, AC, BC, CC respectively. Wherein, AA spare parts are very important to equipment, and spare parts are very important; class AB spare parts are very important for the equipment, spare parts are important; AC-type spare parts are very important for the equipment and spare parts are general. Similarly, CC-type spare parts are common to devices and spare parts are common. The two-factor spare part ABC classification matrix is shown in table 7, and table 7 is an LEC risk two-way standard for designing the information system spare part inventory risk management method according to the present invention.

Designed LEC method risk two-way standard

TABLE 7

Working principle: when the method is used, the research is used for researching the inventory risk management problem of spare parts by referring to and improving methods such as LEC risk evaluation method, AHP analysis method and ABC classification, and the like, and the inventory risk evaluation model of the spare parts is designed by using the improved LEC risk evaluation method through issuing the inventory spare parts, counting and analyzing the dynamic information, finding out the consumption rule of the spare parts during the use period, gradually correcting the reserve quota and reasonably reserving the spare parts; secondly, an L evaluation index is established through analysis of the influence degree of the service life of the equipment, an E evaluation index is established through a failure rate index distribution prediction model, and a two-dimensional importance evaluation index of spare parts is established through AHP and ABC classification; finally, the model is applied to spare part inventory risk management of telephone network equipment, a scientific and reasonable conclusion is obtained, and meanwhile, the model plays an important role in timely processing spare part backlog and decision equipment upgrading and reconstruction.

The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.

Claims

1. An information system spare part inventory risk management method, comprising the steps of:

2. The method for risk management of spare parts of information system according to claim 1, wherein said step S1 of analyzing LEC risk assessment method further comprises the following specific steps:

3. The method for risk management of spare parts of information system according to claim 1, wherein the step S13 is to divide the fault period into a sudden fault and a progressive fault, and to divide the life of the equipment into three phases of early fault period, accidental fault period and wear fault period based on the progressive fault.

4. The method for risk management of spare parts of information system according to claim 1, wherein the step of predicting the failure rate in S15 comprises the following steps:

5. The method for risk management of spare parts of information system according to claim 4, wherein the analyzing method of latent fault prediction analysis in S151 is as follows: assuming that the probability of occurrence of a functional failure at time t is f (t) during a loss failure period, a common failure distribution is: an exponential distribution, a normal distribution, a lognormal distribution, a weibull distribution, a gamma distribution, and the like, wherein the probability density function of the exponential distribution is: alpha e ^βt Wherein: f (t) represents the failure rate of the device in%/year; t represents the working use time of the equipment, and the unit is year; alpha and beta are constants, and are based on a fault rate prediction function:and predicting the failure rate.

6. The method for managing inventory risk of spare parts of information system according to claim 1, wherein said step S2 of constructing an evaluation index system comprises the following specific steps:

7. The method for risk management of spare parts of information system according to claim 6, wherein the step S22 is classified into: class a is very important, with the highest level of importance; class B is important, with a degree of importance between class a and class C; class C is general.

8. The method for risk management of spare parts of information system according to claim 1, wherein said step S3 of designing a risk evaluation model comprises the following specific steps: