Spare part demand prediction method based on intermittent fitting index

Abstract

The invention discloses a spare part demand prediction method based on an intermittent fitting index, and the method comprises the following steps: 1), obtaining a spare part data set, and carrying out the data cleaning of the number of spare parts; 2) acquiring a spare part demand data set, and converting the spare part demand data set into a spare part demand sequence; 3) performing data cleaning on the spare part demand sequence; 4) obtaining an intermittent index of each spare part demand sequence; 5) classifying each spare part demand sequence according to the average demand interval and the demand variation degree; 6) constructing an intermittent fitting index; 7) clustering each spare part demand sequence based on an intermittent fitting index; 8) establishing a BP neural network spare part demand prediction model, and performing model training by using the spare part demand sequence in the step 7) as input data of the BP neural network prediction model; and 9) carrying out spare part demand prediction by using the model obtained by training. The prediction model can better capture the characteristics of the sequence, so that the prediction effect of the model is improved.

Description

Technical Field

[0001] The invention relates to inventory management technology, and in particular to a spare parts demand forecasting method based on intermittent fitting indicators. Background Art

[0002] Maritime transport has become the main mode of transport in international trade due to its large capacity, low cost and wide application range, which has promoted a substantial increase in the number of new ship orders. However, due to the obvious increase in demand for ship supporting equipment, it is difficult for ship companies to quickly increase their production capacity in a short period of time. The inventory management costs of shipping companies are too high, and the inventory in warehouses is piling up. It is necessary to improve the current management of shipyard spare parts inventory from the perspective of optimizing spare parts demand forecasting. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a spare parts demand prediction method based on intermittent fitting indicators in view of the defects in the prior art.

[0004] The technical solution adopted by the present invention to solve its technical problem is:

[0005] A spare parts demand forecasting method based on intermittent fitting index comprises the following steps:

[0006] 1) Obtain a spare parts data set, wherein the spare parts data includes spare parts numbers and corresponding spare parts quantities, and perform data cleaning on the spare parts quantities;

[0007] 2) Obtaining a spare parts demand data set, and converting the spare parts demand data set into a spare parts demand sequence;

[0008] The spare parts demand data set includes the time and quantity of each spare part being shipped out of the warehouse;

[0009] According to the spare part number, the same spare parts are integrated into a sequence according to the delivery time. The time interval of the values ​​in the sequence is equal to the fixed delivery cycle time interval. For a certain delivery, if there is no delivery demand in the spare part demand sequence, the sequence is filled with 0;

[0010] A spare parts demand data set is converted into a spare parts demand sequence, and the sequence of the spare parts is:

[0011] T = {x1, x2, ..., x i , ..., x n}, x i ∈{1,2,3,...,m}

[0012] In the formula, x i is the i-th outbound demand quantity in the spare parts demand data;

[0013] 3) Perform data cleaning on spare parts demand sequence;

[0014] Use 3σ criteria to clean the spare parts demand sequence data;

[0015] 4) Obtain the intermittent index of each spare parts demand sequence;

[0016] 4.1) Calculate the average demand interval of each spare parts demand sequence;

[0017] ADI=n i / z i ;

[0018] Where n i Indicates that the i-th sequence has n i cycles, z i represents the number of non-zero cycles in the ith sequence, and ADI represents the average demand interval in a sequence;

[0019] 4.2) Calculate the demand variation degree CV of each spare parts demand sequence 2 ;

[0020]

[0021]

[0022] In the formula, S i represents the standard deviation of non-zero demand cycles, l represents the number of non-zero demand cycles, represents the demand in the ith non-zero demand period, represents the average demand during the non-zero demand period;

[0023] 5) Classify each spare parts demand sequence according to the average demand interval and demand variation;

[0024] The classification is based on the following:

[0025] ADI≤1.32 and CV 2 ≤0.49, stable demand category; it means that the demand is relatively stable and there are fewer zero-demand periods.

[0026] ADI≤1.32 and CV 2 >0.49, unstable demand category; it means that the demand is unstable, has high variability, and occurs frequently.

[0027] ADI>1.32 and CV 2 ≤0.49, intermittent demand type; it means that the demand is irregular and sporadic, and the demand is relatively stable.

[0028] ADI>1.32 and CV 2>0.49, blocky demand type; it means that the demand pattern appears randomly, there are a lot of time periods without demand and the demand in each period varies greatly, accompanied by a large number of zero demand stages.

[0029] 6) Construction of intermittent fitting index;

[0030] The intermittent fitting index is constructed by combining the maximum information coefficient MIC, the average demand interval between spare parts demand sequences and the intermittent index Zero-Dist;

[0031] For a given sequence set T = {T1, T2, ..., T M}, M is the number of sequences, where the i-th sequence T i ={x i (1), x i (2), ..., x i (N)}, N is the number of elements in the sequence; the intermittent fitting index InterSim is expressed as follows:

[0032] InterSim(T i , T j )=(1-MIC(T i , T j ))⊙Zero-Dist(T i , T j )⊙ADI * (T i , T j );

[0033] Among them, MIC(T i , T j ) represents the sequence T i and sequence T j The maximum information coefficient between them, ⊙ represents the Hadamard product, Zero-Dist (T i , T j ) represents the sequence T i and sequence T j The distribution difference of the position of the 0 element, ADI * (T i , T j ) represents the sequence T i and sequence T j The average demand interval between

[0034] 7) Clustering each spare parts demand sequence based on the intermittent fitting index;

[0035] Based on the intermittent fitting index, the similarity index between spare parts demand sequences is defined, and the spare parts demand sequences are clustered;

[0036] 8) Establishing a BP neural network spare parts demand forecasting model, using the spare parts demand sequence in step 7) as input data of the BP neural network forecasting model to perform model training;

[0037] 9) Use the trained model to predict spare parts demand.

[0038] According to the above scheme, in step 3), before data cleaning of the spare parts demand sequence, spare parts demand sequences with non-zero demand values ​​appearing less than 3 times are eliminated.

[0039] According to the above scheme, in step 3), the 3σ criterion is used to clean the spare parts demand sequence data, as follows:

[0040] μ-3σ≤x i ≤μ+3σ

[0041] Where: μ is x i The mean of ; σ is the standard deviation;

[0042] When the measured value x i When the deviation from the mean value exceeds the upper or lower control limit, the measured value is judged to be an outlier and is replaced by the mean of the sequence as a correction.

[0043] According to the above scheme, in step 6), the maximum information coefficient MIC is calculated as follows:

[0044]

[0045] In the formula, a and b represent the number of grids in the x and y directions respectively, which is essentially the grid distribution. The number of grid divisions is equal to a×b, and B represents the subset size of the sample;

[0046] The calculation steps are as follows:

[0047] Step 1) grid the original variables and set the values ​​of a and b;

[0048] Step 2) Calculate the mutual information between each sample, and select the grid with the largest mutual information as the relationship between the variables;

[0049] Step 3) Standardize the mutual information value according to the sample size B and the number of grids a and b to obtain the MIC value.

[0050] According to the above scheme, in step 6), the intermittent index Zero-Dist is calculated as follows:

[0051]

[0052] Where: zero * (T i)={zero(x1), zero(x2),..., zero(x N )},x l ∈{x1,x2,…,x N},

[0053] According to the above scheme, in step 6), the average demand interval ADI between spare parts demand sequences * (T i , T j ) is calculated as follows:

[0054]

[0055] Where: ADI(T i ) represents the sequence T i The average demand interval, k is the variable coefficient; ADI * (T i , T j ) is used to represent the sequence T i and sequence T j The difference in the frequency of occurrence of 0 elements in two sequences.

[0056] According to the above scheme, in step 7), the clustering step is as follows:

[0057] Step 7.1: Set each spare parts demand sequence as a class to obtain a class cluster: CLASS = {class1, class2, ..., class M}, set the final clustering to K categories;

[0058] Step 7.2: Find the two classes with the shortest distance from the class cluster CLASS i , class j ; The distance calculation formula is as follows:

[0059]

[0060] Step 7.3: Class i and class j Merge into a new class and update the class cluster CLASS;

[0061] Step 7.4) If |CLASS| = K, go to step 7.5); otherwise go to step 7.2);

[0062] Step 7.5) Calculate the ADI of each sequence in CLASS separately, and calculate the average ADI value of all sequences in each class;

[0063] Step 7.6) If the average ADI value is greater than the preset threshold, it means that the spare parts contained in this category are not in the predictable category, and its corresponding class is set to the unpredictable class CLASS_1 = {class1, class2, ..., class q}, there are q classes in the unpredictable class; the rest are predictable classes: CLASS_2 = {class1, class2, ..., class p}, where there are p classes in the unpredictable class, and q+p=K.

[0064] According to the above scheme, in step 8), the predictable spare parts demand sequence obtained in step 7) is used as input data of the BP neural network prediction model, and each time the first 80% of the demand data of a type of spare parts demand sequence is selected as input data, and the last 20% is selected as output data.

[0065] The beneficial effects produced by the present invention are:

[0066] The present invention designs a new intermittent similarity index InterSim. Different from the traditional intermittent measurement index, this index can more comprehensively measure the similarity between sequences by comprehensively considering factors such as the sparsity, change trend and spacing distribution of the sequences, thereby achieving accurate quantification of the predictability of intermittent sequences, providing a scientific basis for parts classification and demand forecasting, and has strong practicality and high universality.

[0067] The present invention constructs a BP neural network prediction model by combining the intermittent similarity index and the hierarchical clustering method, thereby realizing effective prediction of the demand for parts and spare parts. The prediction model based on the InterSin index of the present invention has advantages in processing sequences with high similarity and utilizing structured information, and can better capture the characteristics of the sequence, thereby improving the prediction effect of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0069] Figure 1 is a method flow chart of an embodiment of the present invention;

[0070] Figure 2 is a schematic diagram of spare parts consumption classification types according to an embodiment of the present invention;

[0071] Figure 3 is a flow chart of intermittent hierarchical clustering according to an embodiment of the present invention;

[0072] Figure 4 is a hierarchical clustering flow chart of an embodiment of the present invention;

[0073] Figure 5 is a graph showing the proportion of spare parts types in the cases of the embodiments of the present invention;

[0074] Figure 6 is a comparison diagram of the prediction results and expected results of the category one neural network of an embodiment of the present invention;

[0075] Figure 7 is a comparison diagram of the prediction results and expected results of the neural network of category 2 of the embodiment of the present invention;

[0076] Figure 8 is a comparison diagram of the prediction results and expected results of the neural network of category three of the embodiment of the present invention;

[0077] Fig. 9 is a comparison diagram of WMA prediction results of category 1 according to an embodiment of the present invention;

[0078] Fig.10 is a category one error analysis diagram of an embodiment of the present invention;

[0079] Fig.11 is a comparison diagram of WMA prediction results of category 2 according to an embodiment of the present invention;

[0080] Fig.12 is a category 2 error analysis diagram of an embodiment of the present invention;

[0081] Fig.13 is a comparison chart of WMA prediction results of category three according to an embodiment of the present invention;

[0082] Fig.14 is a category three error analysis diagram of an embodiment of the present invention; DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0084] like Figure 1 As shown, a spare parts demand forecasting method based on intermittent fitting index includes the following steps:

[0085] 1) Obtain a spare parts data set, wherein the spare parts data includes spare parts numbers and corresponding spare parts quantities, and perform data cleaning on the spare parts quantities;

[0086] If the spare part quantity is 0, it means that there is no outbound record of the spare part in this time period, which means that there is no demand for the spare part in this time period; the spare part quantity with a negative value is an abnormal value. In order to ensure the effectiveness, accuracy and feasibility of subsequent research, the abnormal data with a quantity of 0 and a negative value are eliminated;

[0087] Based on the spare parts data set, we can obtain the historical data of spare parts outbound records;

[0088] 2) Obtaining a spare parts demand data set, and converting the spare parts demand data set into a spare parts demand sequence;

[0089] The spare parts demand data set includes the time and quantity of each spare part being shipped out of the warehouse;

[0090] According to the spare part number, the same spare parts are integrated into a sequence according to the delivery time. The time interval of the values ​​in the sequence is equal to the fixed delivery cycle time interval. For a certain delivery, if there is no delivery demand in the spare part demand sequence, the sequence is filled with 0;

[0091] A spare parts demand data set is converted into a spare parts demand sequence, and the sequence of the spare parts is:

[0092] T = {x1, x2, ..., x i , ..., x n}, x i ∈{1,2,3,...,m}

[0093] In the formula, x i is the i-th outbound demand quantity in the spare parts demand data;

[0094] In order to further improve the quality of training data and reduce the uncertainty of data collection, if there are few non-zero values ​​in the spare parts demand sequence, it will be difficult to analyze the trend and regularity of the sequence, and it will have little predictive value. Therefore, spare parts demand sequences with non-zero demand values ​​appearing less than 3 times are eliminated.

[0095] 3) Perform data cleaning on spare parts demand sequence;

[0096] The 3σ criterion is used to continue data cleaning of spare parts demand sequence.

[0097] μ-3σ≤x i ≤μ+3σ

[0098] Where: μ is x i The mean of ; σ is the standard deviation;

[0099] When the measured value x i When the deviation from the mean value exceeds the upper or lower control limit, the measured value is judged to be an abnormal value, and the mean value of the sequence is used to replace it as a correction;

[0100] 4) Obtain the intermittent index of each spare parts demand sequence;

[0101] 4.1) Calculate the average demand interval of each spare parts demand sequence;

[0102] ADI=n i / z i ;

[0103] Where n i Indicates that the i-th sequence has n i cycles, z i represents the number of non-zero cycles in the ith sequence, and ADI represents the average demand interval in a sequence;

[0104] 4.2) Calculate the demand variation degree CV of each spare parts demand sequence 2 ;

[0105]

[0106] In the formula, S i represents the standard deviation of non-zero demand cycles, l represents the number of non-zero demand cycles, represents the demand in the ith non-zero demand period, represents the average demand during the non-zero demand period;

[0107] 5) Classify each spare parts demand sequence according to the average demand interval and demand variation; Figure 2 ;

[0108] The classification is based on the following:

[0109] ADI≤1.32 and CV 2 ≤0.49, stable demand category; it means that the demand is relatively stable and there are fewer zero-demand periods.

[0110] ADI≤1.32 and CV 2 >0.49, unstable demand category; it means that the demand is unstable, has high variability, and occurs frequently.

[0111] ADI>1.32 and CV 2 ≤0.49, intermittent demand type; it means that the demand is irregular and sporadic, and the demand is relatively stable.

[0112] ADI>1.32 and CV 2 >0.49, blocky demand; indicating that the demand pattern appears randomly, with a large number of time periods without demand and large differences between the demands of different periods, accompanied by a large number of zero demand periods;

[0113] 6) Construction of intermittent fitting index;

[0114] The intermittent fitting index is constructed by combining the maximum information coefficient MIC, the average demand interval between spare parts demand sequences and the intermittent index Zero-Dist;

[0115] For a given sequence set T = {T1, T2, ..., T M}, M is the number of sequences, where the i-th sequence Ti ={x i (1), x i (2), ..., x i (N)}, N is the number of elements in the sequence; the intermittent fitting index InterSim is expressed as follows:

[0116] InterSim(T i , T j )=(1-MIC(T i , T j ))⊙Zero-Dist(T i , T j )⊙ADI * (T i , T j );

[0117] Among them, MIC(T i , T j ) represents the sequence T i and sequence T j The maximum information coefficient between them, ⊙ represents the Hadamard product, Zero-Dist (T i , T j ) represents the sequence T i and sequence T j The distribution difference of the position of the 0 element, ADI * (T i , T j ) represents the sequence T i and sequence T j The average demand interval between

[0118] Maximum Information Coefficient (MIC)

[0119] The MIC algorithm is a correlation measurement algorithm based on a hierarchical method. It discretizes the values ​​of two variables and calculates the mutual information between them. The mutual information is then regularized to obtain a statistical value between 0 and 1, which can be used to measure the correlation between the two variables. The closer the MIC value is to 1, the closer the correlation between the two variables is. The closer the MIC value is to 0, the higher the independence between the two variables is. The calculation formula is as follows:

[0120]

[0121] Where a and b represent the number of grids in the x and y directions, respectively. It is essentially a grid distribution. The number of grid divisions is equal to a×b. B represents the subset size of the sample, which is usually set to about 0.6 of the total sample size.

[0122] The calculation steps are as follows:

[0123] Step 1: Grid the original variables and set the values ​​of a and b;

[0124] Step 2: Calculate the mutual information between each sample and select the grid with the largest mutual information as the relationship between variables;

[0125] Step 3: Standardize the mutual information value according to the sample size B and the number of grids a and b to obtain the MIC value.

[0126] MIC can better characterize the nonlinear correlation and non-functional association between two sequences, thereby theoretically obtaining the changing rules between the sequences.

[0127] Intermittent indicator Zero-Dist:

[0128] Based on the above analysis, the intermittent index (Zero-Dist) is used to reflect the position of the "0" element in the two intermittent spare parts demand sequences;

[0129]

[0130] Where: zero * (T i )={zero(x1), zero(x2),..., zero(x N )},x l ∈{x1,x2,…,x N},

[0131] From the above formula, we can see that Zero-Dist can well measure the time lag characteristics and the distribution difference of the 0-element interval between two time series. It mainly evaluates the similarity between two time series by calculating the difference degree of the 0-element position interval in the two time series. The smaller the Zero-Dist value, the more similar the two time series are, and the larger the value, the greater the difference.

[0132] Average demand interval between spare parts demand sequences ADI * (T i , T j );

[0133]

[0134] Where: ADI(T i ) represents the sequence T i The average demand interval, k is the variable coefficient, here set to k = 1.2; ADI * (T i , T j ) is used to represent the sequence T iand sequence T j The difference in the frequency of occurrence of 0 elements in the two sequences;

[0135] The intermittent fitting index is more comprehensive, accurate and reliable than the traditional similarity measurement method, and can better reveal the association and similarity between sequences.

[0136] 7) Clustering each spare parts demand sequence based on the intermittent fitting index;

[0137] Based on the intermittent fitting index, the similarity index between spare parts demand sequences is defined, and the spare parts demand sequences are clustered; Figure 3 ;

[0138] By defining the similarity index between sequences, agglomerative hierarchical clustering is implemented for time series, and its clustering results can intuitively reflect the association and similarity between sequences.

[0139] like Figure 4 , the steps to construct hierarchical clustering based on intermittent fitting indicators are as follows:

[0140] Step 1: Set each spare parts demand sequence as a class to obtain a class cluster: CLASS = {class1, class2, ..., class M}, set the final clustering to K categories.

[0141] Step 2: Find the two classes with the shortest distance from the class cluster CLASS i , class j The distance calculation formula is as follows:

[0142]

[0143] Step 3: Add the class i and class j Merge into a new class and update the class cluster CLASS;

[0144] Step 4: If |CLASS|=K, go to step 5; otherwise go to step 2;

[0145] Step 5: Calculate the ADI of each sequence in CLASS separately, and calculate the average ADI value of all sequences in each class;

[0146] Step 6: If the average ADI value is greater than the preset threshold, it means that the spare parts contained in this category are not in the predictable category, and its corresponding class is set to the unpredictable class CLASS_1 = {class1, class2, ..., class q}, there are q classes in the unpredictable class; the rest are predictable classes: CLASS_2 = {class1, class2, ..., classp}, where there are p classes in the unpredictable class, and q+p=K.

[0147] 8) Establishing a BP neural network spare parts demand forecasting model, using the spare parts demand sequence in step 7) as input data of the BP neural network forecasting model to perform model training;

[0148] Step 1: Data Processing

[0149] In order to prevent the skewness of the network model weights due to different eigenvalue ranges, affecting the stability and accuracy of the network model, the data must be normalized. The minimum and maximum value method is used to scale the value of each variable to the interval [-1,1] using the mapmin_max function in MATLAB.

[0150] Step 2: Forecast spare parts demand

[0151] Data set division: The predictable spare parts demand sequence obtained based on S6 is used as the input data of the BP neural network prediction model. Each time, the first 80% of the demand data of a type of spare parts demand sequence is selected as the input data, and the last 20% is selected as the output data. There are a total of p predictable classes.

[0152] Step 3: Build the network model

[0153] (1) Set the training function to trainlm, the adaptation learning function to Learning-GDM, and the objective function to MSE.

[0154] (2) Set the network structure to a single hidden layer. In the BP neural network, the number of nodes in the input layer and the output layer is fixed, while the number of nodes in the hidden layer is uncertain. To select the appropriate number of neurons, we can use the calculation formula of the hidden layer:

[0155]

[0156] Where N is the number of hidden layer nodes, i is the number of input layer nodes, j is the number of output layer nodes, and a is an integer less than 10.

[0157] (3) Set the number of network training steps to 1000, the training target minimum error to 0.001, and the learning rate to 0.01.

[0158] 9) Use the trained model to predict spare parts demand.

[0159] A specific embodiment

[0160] With the goal of reducing inventory costs and improving forecasting accuracy, BP neural network and weighted moving average method (WMA) were selected to predict spare parts demand, and the codes involved were programmed in MATLB 2021a.

[0161] In order to verify the spare parts demand prediction based on the hierarchical clustering method of intermittent fitting index, the spare parts demand data of a 4S store in 2018 was collected. The data set includes the spare parts serial number, spare part number, spare part name, origin, quantity, arrival city, delivery date, delivery order number, delivery warehouse, store order number, store review time, manufacturer review time, order type, and whether it is a special order. The data involved is the daily spare parts delivery data from January 2018 to December 2018.

[0162] Step 1: Clean the data according to the number of spare parts. Remove the outliers of spare parts with a quantity of "0" and negative values;

[0163] Step 2: Taking the month as the time series unit, each spare part is set as a sequence, and the length of each spare part sequence in the data set is 12. In order to ensure the neatness of the sequence length and facilitate the subsequent data processing, the months with no demand for each spare part are filled with "0", and the spare part demand sequences with non-"0" demand values ​​appearing less than 3 times are eliminated.

[0164] Step 3: In the present invention, the 3σ criterion is used to filter out the abnormal values ​​of each spare parts sequence, and the mean value of each spare parts sequence is used to correct the abnormal values ​​of the sequence;

[0165] Step 4: Based on the spare parts obtained in step 3, which include various types of consumable parts, 1938 types of intermittent consumable spare parts data are finally screened out through the SBC classification method;

[0166] Step 5: Based on the intermittent spare parts demand sequence obtained in step 4, the intermittent fitting index is used to quantify the trend information and fluctuation law between spare parts sequences;

[0167] Step 6: Based on the intermittent fitting index, the spare parts demand sequence is divided into predictable and unpredictable categories using the staggered clustering method. Set K = 6, where the predictable spare parts include 3 categories and the unpredictable spare parts include 3 categories.

[0168] According to the above hierarchical clustering method, 1938 spare parts types are divided into 6 categories, including 3 predictable time series and 3 unpredictable time series, and finally divided into 4 categories such as Figure 5 As shown:

[0169] Step 7: Based on the three types of predictable spare parts obtained in step 6, the BP neural network and weighted moving average (WMA) algorithm are used to predict spare parts demand. The prediction results are as follows:

[0170] (1) BP neural network prediction results

[0171] In the training process of category 1 (447 spare parts), 5×4900 of the input data are used to train the neural network, and the remaining 5×937 test data sets are used to test the trained network. The results are as follows Figure 6 As shown:

[0172] In the training process of category 2 (766 spare parts), 5×8257 of the input data are used to train the network, and the remaining 5×1625 test data sets are used to test the trained network. The results are as follows Figure 7 As shown:

[0173] In the training process of category 3 (706 spare parts), 5×7691 data in the input data are used to train the network, and the remaining 5×1496 test data set is used to test the trained network. The results are as follows Figure 8 As shown:

[0174] Comparing the predicted data and expected target data obtained by the neural network in the above three types of predictable sequences, we can see that the distribution of the predicted data is consistent with the expected value, indicating that the prediction effect of the model is good, and the difference between its prediction results and the test target data is small.

[0175] (2) Prediction results based on the WMA algorithm

[0176] WMA forecasts were performed for 447 spare parts in the predictable time series category 1, 766 spare parts in category 2, and 706 spare parts in category 3. The previous three months of each spare part were set as historical data, and the weight coefficients were set to 1 / 6, 2 / 6, and 3 / 6, respectively, for forecast analysis. The average value of the forecast data was compared with the average value of the expected target data, and the error analysis results were obtained as follows Figures 9 to 14 shown.

[0177] The WMA algorithm is used to predict the above three categories of spare parts respectively. The predicted values ​​are compared with the expected target values. It can be seen that the errors shown in the figure have large fluctuations, indicating that the prediction algorithm is not robust and the prediction accuracy is not high.

[0178] (3) Comparative analysis of the results of BP neural network and WMA algorithm

[0179] Since the spare parts demand sample data is intermittent data, it contains many zero demands, and there is a lot of noise and volatility. Due to the existence of the square in the mean square error (MSE), the penalty for larger errors is greater, so MSE is sensitive to some noisy data. The mean absolute error (MAE) gives the same weight to the error of each sample, which is relatively more robust and more robust to larger errors. Therefore, MSE and MAE are used to evaluate the prediction accuracy, and the calculation formula is as follows:

[0180]

[0181] From the definition, we can see that the smaller the mean absolute error and the mean square error, the more consistent the predicted value is with the expected target value, and the higher the prediction accuracy of the demand forecasting method.

[0182] Table 1 Comparison of prediction errors between neural network and WMA (category 1)

[0183]

[0184] Table 2 Comparison of prediction errors between neural network and WMA (category 2)

[0185]

[0186] Table 3 Comparison of prediction errors between neural network and WMA (category three)

[0187]

[0188] It can be seen from the above table that in the three categories of predictable time series, the prediction results of the neural network are better than those of the WMA algorithm.

[0189] In summary, compared with the prior art, the present invention has the following advantages:

[0190] The present invention combines intermittent indicators such as MIC, ADI and Zero-dist to introduce a new intermittent similarity indicator InterSim. Different from traditional intermittent measurement indicators, this indicator can more comprehensively measure the similarity between sequences by comprehensively considering factors such as sequence sparsity, change trend and spacing distribution, thereby achieving accurate quantification of the predictability of intermittent sequences, providing a scientific basis for parts classification and demand forecasting, with strong practicality and high universality.

[0191] The present invention constructs a BP neural network prediction model by combining the intermittent similarity index and the hierarchical clustering method, and realizes the effective prediction of the demand for spare parts. Compared with the traditional intermittent demand prediction method, the prediction model based on the InterSin index has advantages in processing sequences with high similarity and utilizing structured information, and can better capture the characteristics of the sequence, thereby improving the prediction effect of the model.

[0192] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A spare parts demand forecasting method based on intermittent fitting index, characterized in that: The following steps are involved: 1) Obtain a spare parts data set, wherein the spare parts data includes spare parts numbers and corresponding spare parts quantities, and perform data cleaning on the spare parts quantities; 2) Obtaining a spare parts demand data set, and converting the spare parts demand data set into a spare parts demand sequence; The spare parts demand data set includes the time and quantity of each spare part being shipped out of the warehouse; According to the spare part number, the same spare parts are integrated into a sequence according to the delivery time. The time interval of the values ​​in the sequence is equal to the fixed delivery cycle time interval. For a certain delivery, if there is no delivery demand in the spare part demand sequence, the sequence is filled with 0; A spare parts demand data set is converted into a spare parts demand sequence, and the sequence of the spare parts is: T={x1,x2,...,x i ,...,x n }; In the formula, x i is the i-th outbound demand quantity in the spare parts demand data; 3) Perform data cleaning on spare parts demand sequence; Use 3σ criteria to clean the spare parts demand sequence data; 4) Obtain the intermittent index of each spare parts demand sequence; 4.1) Calculate the average demand interval of each spare parts demand sequence; <h2 style=";text-align:left;direction:ltr">ADI=n<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> / z<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> ; Where n i Indicates that the i-th sequence has n i cycles, z i represents the number of non-zero cycles in the ith sequence, and ADI represents the average demand interval in a sequence; 4.2) Calculate the demand variation degree CV of each spare parts demand sequence 2 ; In the formula, S i represents the standard deviation of non-zero demand cycles, l represents the number of non-zero demand cycles, represents the demand in the ith non-zero demand period, represents the average demand during the non-zero demand period; 5) Classify each spare parts demand sequence according to the average demand interval and demand variation; 6) Construction of intermittent fitting index; The intermittent fitting index is constructed by combining the maximum information coefficient MIC, the average demand interval between spare parts demand sequences and the intermittent index Zero-Dist; For a given sequence set T = {T1, T2, ..., T M }, M is the number of sequences, where the i-th sequence T i ={x i (1), x i (2), ..., x i (N)}, N is the number of elements in the sequence; the intermittent fitting index InterSim is expressed as follows: InterSim(T i ,T j )=(1-MIC(T i ,T j ))⊙Zero-Dist(T i ,T j )⊙ADI * (T i ,T j ); Among them, among them, MIC(T i , T j ) represents the sequence T i and sequence T j The maximum information coefficient between them, ⊙ represents the Hadamard product, Zero-Dist (T i , T j ) represents the sequence T i and sequence T j The distribution difference of the position of the 0 element, ADI * (T i , T j ) represents the sequence T i and sequence T j The average demand interval between 7) Clustering each spare parts demand sequence based on the intermittent fitting index; Based on the intermittent fitting index, the similarity index between spare parts demand sequences is defined, and the spare parts demand sequences are clustered; 8) Establishing a BP neural network spare parts demand forecasting model, using the spare parts demand sequence in step 7) as input data of the BP neural network forecasting model to perform model training; 9) Use the trained model to predict spare parts demand.

2. The spare parts demand forecasting method based on intermittent fitting index according to claim 1 is characterized in that: In the step 3), before data cleaning of the spare parts demand sequence is performed, spare parts demand sequences with non-zero demand values ​​appearing less than 3 times are eliminated.

3. The spare parts demand forecasting method based on intermittent fitting index according to claim 1 is characterized in that: In step 3), the 3σ criterion is used to clean the spare parts demand sequence data, as follows: μ-3σ≤x i ≤μ+3σ Where: μ is x i The mean of ; σ is the standard deviation; When the measured value x i When the deviation from the mean value exceeds the upper or lower control limit, the measured value is judged to be an outlier and is replaced by the mean of the sequence as a correction.

4. The spare parts demand forecasting method based on intermittent fitting index according to claim 1 is characterized in that: The classification is based on the following: ADI≤1.32 and CV 2 ≤0.49, stable demand category; indicating that demand is relatively stable and there are fewer zero-demand periods; ADI≤1.32 and CV 2 >0.49, unstable demand category; indicating that the demand is unstable, has high variability, and occurs frequently; ADI>1.32 and CV 2 ≤0.49, intermittent demand; indicating that demand is irregular and sporadic, and demand is relatively stable; ADI>1.32 and CV 2 >0.49, blocky demand type; it means that the demand pattern appears randomly, there are a lot of time periods without demand and the demand in each period varies greatly, accompanied by a large number of zero demand stages.

5. The spare parts demand forecasting method based on intermittent fitting index according to claim 1 is characterized in that: In step 6), the maximum information coefficient MIC is calculated as follows: In the formula, a and b represent the number of grids in the x and y directions respectively, which is essentially the grid distribution. The number of grid divisions is equal to a×b, and B represents the subset size of the sample; The calculation steps are as follows: Step 6.1) Grid the original variables and set the values ​​of a and b; Step 6.2) Calculate the mutual information between each sample and select the grid with the largest mutual information as the relationship between the variables; Step 6.3) Standardize the mutual information value according to the sample size B and the number of grids a and b to obtain the MIC value.

6. The spare parts demand forecasting method based on intermittent fitting index according to claim 1 is characterized in that: In step 6), the intermittent index Zero-Dist is calculated as follows: in:

7. The spare parts demand forecasting method based on intermittent fitting index according to claim 1 is characterized in that: In step 6), the average demand interval ADI between spare parts demand sequences * (T i , T j ) is calculated as follows: Where: ADI(T i ) represents the sequence T i The average demand interval, k is the variable coefficient; ADI * (T i , T j ) is used to represent the sequence T i and sequence T j The difference in the frequency of occurrence of 0 elements in two sequences.

8. The spare parts demand forecasting method based on intermittent fitting index according to claim 1 is characterized in that: In step 7), the clustering steps are as follows: Step 7.1: Set each spare parts demand sequence as a class to obtain a class cluster: CLASS = {class1, class2, ..., class M }, set the final clustering to K categories; Step 7.2: Find the two classes with the shortest distance from the class cluster CLASS i , class j ; The distance calculation formula is as follows: Step 7.3: Class i and class j Merge into a new class and update the class cluster CLASS; Step 7.4) If |CLASS| = K, go to step 7.5); Otherwise, go to step 7.2); Step 7.5) Calculate the ADI of each sequence in CLASS separately, and calculate the average ADI value of all sequences in each class; Step 7.6) If the average ADI value is greater than the preset threshold, it means that the spare parts contained in this category are not in the predictable category, and its corresponding class is set to the unpredictable class CLASS_1 = {class1, class2, ..., class q }, there are q classes in the unpredictable class; the rest are predictable classes: CLASS_2 = {class1, class2, ..., class p }, where there are p classes in the unpredictable class, and q+p=K.

9. The spare parts demand forecasting method based on intermittent fitting index according to claim 8, characterized in that: In the step 8), the predictable spare parts demand sequence obtained in step 7) is used as input data of the BP neural network prediction model, and each time the first 80% of the demand data of a type of spare parts demand sequence is selected as input data, and the last 20% is selected as output data.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to claims 1 to 9 is implemented.

Images