CN111815348B - Regional commodity production planning method based on commodity similarity clustering of stores - Google Patents
Regional commodity production planning method based on commodity similarity clustering of stores Download PDFInfo
- Publication number
- CN111815348B CN111815348B CN202010467295.9A CN202010467295A CN111815348B CN 111815348 B CN111815348 B CN 111815348B CN 202010467295 A CN202010467295 A CN 202010467295A CN 111815348 B CN111815348 B CN 111815348B
- Authority
- CN
- China
- Prior art keywords
- store
- class
- distance
- commodity
- sales
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Biology (AREA)
- Probability & Statistics with Applications (AREA)
- Game Theory and Decision Science (AREA)
- Economics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a regional commodity production planning method based on commodity similarity clustering of stores. The method specifically comprises the following steps: firstly, calculating a similarity matrix of each commodity on time sequence periodicity according to historical sales record data of all commodities of each store in the region every day; calculating the variation of commodity sales under different states according to external factors to obtain a sensitivity degree distance matrix between commodities; and clustering the commodities at a store level by combining a time sequence period similarity and a sensitivity degree distance matrix based on a clustering algorithm, selecting the optimal K class according to an elbow rule and a contour coefficient, predicting future sales on the clustered level, and finally summarizing K class requirements to give a regional production plan. The method is beneficial to reducing the loss of stock and the risk of backlog loss reporting, and plays an important role in improving the accuracy of the regional production plan.
Description
Technical Field
The invention belongs to the technical field of information, and particularly relates to a regional commodity production planning method based on commodity similarity clustering of stores.
Background
With the development of computer technology, computer networks and management systems are being applied to almost all aspects of the retail industry, where regional factory production is machine-critical. In the production plan of the regional commodity, enterprises relate to different sales conditions of each single commodity of each store, different degrees of change of external factors, different production periods of the commodity, different degrees of grasp of each store on the commodity, and the decision maker is difficult to make optimal prediction and decision on the production plan of the regional commodity. Many industries often estimate the total amount of the whole area, collect the production plan amount of the area after the demand amount is set by experience of each store, but because the external change degree of store commodity is different, the store length level is different, and a good production plan is difficult to make; thus, it is a better way to aggregate similar stores and then predict sales in the aggregated categories and give a production plan for the population of the area.
In recent years, more and more industries pay attention to the importance of regional production prediction, and most of industry prediction methods are based on a moving average model of single products in a single store and then are adjusted according to service experience of a decision maker, but the phenomena of demand loss, commodity backlog loss and the like caused by inaccurate prediction still exist. Therefore, the invention provides a regional commodity production planning method based on the commodity similarity clustering of each store, so as to guide enterprises to make a decision of regional commodity production planning more reasonably.
Disclosure of Invention
The invention aims to overcome the defects in the existing regional commodity production plans and provides a regional commodity production planning method based on commodity similarity clustering of stores.
The invention comprises the following steps:
Step 1: firstly, acquiring a transaction detail data set D in a specified time period of the histories of all store commodities, removing activity information and holiday information in the transaction detail data set D, and then counting according to the granularity of the days to obtain a daily sales quantity set S of the store commodities;
Step 2: based on the daily sales quantity set S of the commodities, calculating a time demand mode T of each commodity on periodicity:
Wherein, For the average sales of the commodity in week i, n i represents the number of days in week i within a specified period of time; d is E [1, n i ];
Step 3: according to a daily sales data set S and a weather factor data set X in a specified time period, taking the weather factor data set X as a model input X and taking the daily sales data set as a model output y, and training a linear regression model; then, based on the change of each external weather factor data set X, obtaining a return coefficient, namely a change rate matrix E of daily sales;
step 4: based on the historical store daily sales data set S, the time demand pattern distance between the store and the commodity is calculated from sales data in the period of one month close to the daily sales data set S, and the formula is as follows:
Where i, j represent two different stores, T i k represents the kth element in store i time demand pattern T;
step 5: based on the change rate matrix E of each commodity of each store, the change rate distance of the external factors among the store levels is calculated, and the formula is as follows:
where i, j represent two different stores, A kth element in a change rate matrix E of external factors of store i is represented;
Step 6: based on the time demand mode distance Dis T and the external factor change rate distance Dis E, the distance calculation method of the commodity at the store level is calculated, and the formula is as follows:
DIS(i，j)＝DisT(i,j)+DisE(i，j) (5)
Dis T (i, j) represents the distance of store i, j in the time demand mode, dis E (i, j) represents the distance of store i, j in the rate of change of the external factor, and DIS (i, j) represents the overall distance between stores i, j;
step 7: clustering similar stores according to a defined inter-store distance formula (5);
the similar stores are defined as the distance between stores;
step 8: obtaining optimal class evaluation Score according to the minimum intra-class distance and the contour coefficient, and selecting the optimal classification number k according to the combination of the optimal class evaluation Score;
Step 9: after obtaining the optimal category number k according to Score, summarizing sales in the category level;
Step 10: predicting a future sales y t using an ARIMA model;
Step 11: and summarizing the future sales y t of each category, and obtaining the total predicted demand which is the regional commodity production plan.
Further, the clustering in the step 7 adopts k-means clustering, and is implemented as follows:
input:
Data set
And (3) outputting:
Class center point Label C of each point
Initializing:
Randomly selecting k center points mu from the data set S 1,…,μk
Firstly, initializing and randomly selecting k class center points, dividing each sample s (i) into a class mark c (j) nearest to mu j, updating the value of mu j of each class center point according to c (j), and repeating iteration until the class center is unchanged or the change amount is smaller than a certain threshold value; the obtained c is the class of each store and the similar stores.
Further, the step 8 is specifically implemented as follows:
Intra-class distance SSE:
selecting the number k of classes by selecting a way that minimizes the overall distance;
profile coefficient SC:
a (i) is the average distance from the sample i to other samples in the class, b (i) is the average distance from the sample i to all samples in other classes, and the smaller the intra-class distance is, the larger the inter-class distance is, the number of class centers k is selected;
Optimal class evaluation Score:
The method of combining the intra-class distance and the contour coefficient is that the smaller the intra-class distance is, the larger the inter-class distance is, the k number is within the range of reasonable class center number.
Further, after obtaining the optimal category number k according to Score in step 9, the sales volume is summarized in the category level to obtain the summarized sales data, which is specifically implemented as follows:
summarizing store sample commodity sales s (i) belonging to the category c (k);
Further, the future sales prediction using the ARIMA model described in step 10 is specifically implemented as follows:
Taking the collected sales data X as a model input X:
μ is a constant term, ε t is an error term, γ i is an autocorrelation coefficient, and θ i is an error term coefficient.
The invention has the beneficial effects that:
according to the method, the daily sales data and the sensitivity degree of the commodity are used for calculating the similarity of the commodity at the store level, and then the regional production plan is predicted according to the ARIMA model, so that a scientific and referenceable prediction result is provided for the region, decision making of the production plan by enterprises and the region and more reasonable inventory management are facilitated, and the method plays an important role in reducing the loss of stock and backlog loss risk and improving the regional production plan accuracy.
Drawings
FIG. 1 is a specific flow chart of an embodiment of the present invention employing the method.
Fig. 2 is a diagram showing the results of the embodiment of the present invention using this method.
Detailed Description
The objects and effects of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings and tables. According to the method, actual conditions are considered, a commodity time sequence mode and a distance matrix of the sensitivity degree of external factors are adopted according to store historical sales data, the optimal class center K number is selected by using the class inner distance and the profile coefficient, each commodity is clustered at store level by using a K-means algorithm, sales prediction is carried out on future sales according to an ARIMA model, and a production plan decision of regional commodities is realized.
A regional commodity production planning method based on commodity similarity clustering of stores.
The invention comprises the following steps:
Step 1: firstly, acquiring a transaction detail data set D in a specified time period of the histories of all store commodities, removing activity information and holiday information in the transaction detail data set D, and then counting according to the granularity of the days to obtain a daily sales quantity set S of the store commodities;
Step 2: based on the daily sales quantity set S of the commodities, calculating a time demand mode T of each commodity on periodicity:
Wherein, For the average sales of the commodity in week i, n i represents the number of days in week i within a specified period of time; d is E [1, n i ];
Step 3: according to a daily sales data set S and a weather factor data set X in a specified time period, taking the weather factor data set X as a model input X and taking the daily sales data set as a model output y, and training a linear regression model; then, based on the change of each external weather factor data set X, obtaining a return coefficient, namely a change rate matrix E of daily sales;
step 4: based on the historical store daily sales data set S, the time demand pattern distance between the store and the commodity is calculated from sales data in the period of one month close to the daily sales data set S, and the formula is as follows:
Where i, j represent two different stores, T i k represents the kth element in store i time demand pattern T;
step 5: based on the change rate matrix E of each commodity of each store, the change rate distance of the external factors among the store levels is calculated, and the formula is as follows:
where i, j represent two different stores, A kth element in a change rate matrix E of external factors of store i is represented;
Step 6: based on the time demand mode distance Dis T and the external factor change rate distance Dis E, the distance calculation method of the commodity at the store level is calculated, and the formula is as follows:
DiS(i，j)＝DisT(i,j)+DisE(i，j) (5)
Dis T (i, j) represents the distance of store i, j in the time demand mode, dis E (i, j) represents the distance of store i, j in the rate of change of the external factor, diS (i, j) represents the overall distance between stores i, j;
step 7: clustering similar stores according to a defined inter-store distance formula (5);
the similar stores are defined as the distance between stores;
step 8: obtaining optimal class evaluation Score according to the minimum intra-class distance and the contour coefficient, and selecting the optimal classification number k according to the combination of the optimal class evaluation Score;
Step 9: after obtaining the optimal category number k according to Score, summarizing sales in the category level;
Step 10: predicting a future sales y t using an ARIMA model;
Step 11: and summarizing the future sales y t of each category, and obtaining the total predicted demand which is the regional commodity production plan.
Further, the clustering in the step 7 adopts k-means clustering, and is implemented as follows:
input:
Data set
And (3) outputting:
Class center point Label C of each point
Initializing:
Randomly selecting k center points mu from the data set S 1,…,μk
Firstly, initializing and randomly selecting k class center points, dividing each sample s (i) into a class mark c (j) nearest to mu j, updating the value of mu j of each class center point according to c (j), and repeating iteration until the class center is unchanged or the change amount is smaller than a certain threshold value; the obtained c is the class of each store and the similar stores.
Further, the step 8 is specifically implemented as follows:
Intra-class distance SSE:
selecting the number k of classes by selecting a way that minimizes the overall distance;
profile coefficient SC:
a (i) is the average distance from the sample i to other samples in the class, b (i) is the average distance from the sample i to all samples in other classes, and the smaller the intra-class distance is, the larger the inter-class distance is, the number of class centers k is selected;
Optimal class evaluation Score:
The method of combining the intra-class distance and the contour coefficient is that the smaller the intra-class distance is, the larger the inter-class distance is, the k number is within the range of reasonable class center number.
Further, after obtaining the optimal category number k according to Score in step 9, the sales volume is summarized in the category level to obtain the summarized sales data, which is specifically implemented as follows:
summarizing store sample commodity sales s (i) belonging to the category c (k);
Further, the future sales prediction using the ARIMA model described in step 10 is specifically implemented as follows:
Taking the collected sales data X as a model input X:
μ is a constant term, ε t is an error term, γ i is an autocorrelation coefficient, and θ i is an error term coefficient.
FIG. 2 is an example of the results of a regional production plan for a target commodity obtained according to the present invention for 3 days in the future, showing a comparison of predicted throughput and true sales.
The present invention is not limited to the above embodiments, and those skilled in the art can practice the present invention using other various embodiments in light of the present disclosure. Therefore, the design structure and thought of the invention are adopted, and some simple changes or modified designs are made, which fall into the protection scope of the invention.
Claims (5)
1. The regional commodity production planning method based on the commodity similarity clustering of each store is characterized by comprising the following steps of:
Step 1: firstly, acquiring a transaction detail data set D in a specified time period of the histories of all store commodities, removing activity information and holiday information in the transaction detail data set D, and then counting according to the granularity of the days to obtain a daily sales quantity set S of the store commodities;
Step 2: based on the daily sales quantity set S of the commodities, calculating a time demand mode T of each commodity on periodicity:
Wherein, For the average sales of the commodity in week i, n i represents the number of days in week i within a specified period of time; d is E [1, n i ];
Step 3: according to a daily sales data set S and a weather factor data set X in a specified time period, taking the weather factor data set X as a model input X and taking the daily sales data set as a model output y, and training a linear regression model; then, based on the change of each external weather factor data set X, obtaining a return coefficient, namely a change rate matrix E of daily sales;
step 4: based on the historical store daily sales data set S, the time demand pattern distance between the store and the commodity is calculated from sales data in the period of one month close to the daily sales data set S, and the formula is as follows:
Where i, j represent two different stores, T i k represents the kth element in store i time demand pattern T;
step 5: based on the change rate matrix E of each commodity of each store, the change rate distance of the external factors among the store levels is calculated, and the formula is as follows:
where i, j represent two different stores, A kth element in a change rate matrix E of external factors of store i is represented;
Step 6: based on the time demand mode distance Dis T and the external factor change rate distance Dis E, the distance calculation method of the commodity at the store level is calculated, and the formula is as follows:
DIS(i,j)＝DisT(i,j)+DisE(i,j) (5)
Dis T (i, j) represents the distance of store i, j in the time demand mode, dis E (i, j) represents the distance of store i, j in the rate of change of the external factor, and DIS (i, j) represents the overall distance between stores i, j;
step 7: clustering similar stores according to a defined inter-store distance formula (5);
the similar stores are defined as the distance between stores;
step 8: obtaining an optimal class evaluation Score according to the minimum intra-class distance and the contour coefficient, and selecting the optimal classification number k according to the optimal class evaluation Score and the intra-class distance and the contour coefficient;
Step 9: after obtaining the optimal classification number k according to Score, summarizing sales in the class level;
Step 10: predicting a future sales y t using an ARIMA model;
Step 11: and summarizing the future sales y t of each category, and obtaining the total predicted demand which is the regional commodity production plan.
2. The regional commodity production planning method based on the commodity similarity clustering of each store according to claim 1, wherein the clustering in the step 7 adopts k-means clustering, and the method is realized as follows:
input:
Data set
And (3) outputting:
Class center point Label C of each point
Initializing:
randomly selecting k center points mu from the data set S 1,…,μk
Firstly, initializing and randomly selecting k class center points, dividing each sample s (i) into a class mark c (j) nearest to mu j, updating the value of mu j of each class center point according to c (j), and repeating iteration until the class center is unchanged or the change amount is smaller than a certain threshold value; the obtained c is the class of each store and the similar stores.
3. The regional commodity production planning method based on the commodity similarity clustering of each store according to claim 1 or 2, wherein the step 8 is specifically implemented as follows:
Intra-class distance SSE:
selecting an optimal classification number k by selecting a mode of minimizing the total distance;
profile coefficient SC:
a (i) is the average distance from the sample i to other samples in the class, b (i) is the average distance from the sample i to all samples in other classes, and the smaller the intra-class distance is, the larger the inter-class distance is, the optimal classification number k is selected;
Optimal class evaluation Score:
and selecting the optimal classification number k with the smaller the intra-class distance and the larger the inter-class distance within a reasonable class center number range by combining the intra-class distance and the contour coefficient.
4. The regional commodity production planning method based on the commodity similarity clustering of each store according to claim 3, wherein in step 9, after obtaining the optimal classification number k according to Score, the sales are summarized in the class level, and the summarized sales data are obtained, which is specifically implemented as follows:
A summary of store sample sales s (i) belonging to the class c (k) is made.
5. The regional commodity production planning method based on the commodity similarity clustering of each store according to claim 4, wherein the future sales prediction in step 10 is implemented by using an ARIMA model as follows:
taking the collected sales data X (k) as a model input X:
μ is a constant term, ε t is an error term, γ i is an autocorrelation coefficient, and θ i is an error term coefficient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010467295.9A CN111815348B (en) | 2020-05-28 | 2020-05-28 | Regional commodity production planning method based on commodity similarity clustering of stores |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010467295.9A CN111815348B (en) | 2020-05-28 | 2020-05-28 | Regional commodity production planning method based on commodity similarity clustering of stores |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111815348A CN111815348A (en) | 2020-10-23 |
| CN111815348B true CN111815348B (en) | 2024-07-12 |
Family
ID=72848100
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010467295.9A Active CN111815348B (en) | 2020-05-28 | 2020-05-28 | Regional commodity production planning method based on commodity similarity clustering of stores |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111815348B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113554455B (en) * | 2021-06-30 | 2024-09-24 | 杭州拼便宜网络科技有限公司 | Store commodity analysis method, device and storage medium based on artificial intelligence |
| CN113554335B (en) * | 2021-08-02 | 2022-05-10 | 南京邮电大学 | Production planning method based on big data |
| CN114186711A (en) * | 2021-10-27 | 2022-03-15 | 中山大学 | Industrial raw material consumption prediction method based on multitask time sequence learning |
| CN114862312A (en) * | 2022-05-05 | 2022-08-05 | 浪潮软件股份有限公司 | Reasonable inventory management method based on time series algorithm |
| CN116739655B (en) * | 2023-07-14 | 2024-04-02 | 上海朗晖慧科技术有限公司 | Intelligent supply chain management method and system based on big data |
| CN117807412B (en) * | 2024-03-01 | 2024-04-30 | 南京满鲜鲜冷链科技有限公司 | Cargo damage prediction system based on vehicle-mounted sensor and big data |
| CN119722172A (en) * | 2025-02-27 | 2025-03-28 | 深圳欧税通技术有限公司 | A cross-border e-commerce enterprise portrait data management method, system, device and medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101819668A (en) * | 2010-04-27 | 2010-09-01 | 浙江大学 | Sales predicting model based on product intrinsic life cycle character |
| CN105556557A (en) * | 2013-09-20 | 2016-05-04 | 日本电气株式会社 | Shipment-volume prediction device, shipment-volume prediction method, recording medium, and shipment-volume prediction system |
Family Cites Families (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001084239A (en) * | 1999-09-13 | 2001-03-30 | Toshiba Corp | Method for analyzing and predicting merchandise sales quantity, method for deciding merchandise order quantity and storage medium with program stored therein |
| US7194420B2 (en) * | 2000-11-13 | 2007-03-20 | Ricoh Company, Ltd. | Method and system for planning supply of commodities |
| JP4296026B2 (en) * | 2003-04-30 | 2009-07-15 | 株式会社野村総合研究所 | Product demand forecasting system, product sales volume adjustment system |
| US20140200992A1 (en) * | 2013-01-14 | 2014-07-17 | Oracle International Corporation | Retail product lagged promotional effect prediction system |
| CN105956699A (en) * | 2016-04-29 | 2016-09-21 | 连云港天马网络发展有限公司 | Commodity classification and delivery and sales prediction method based on e-commerce sales data |
| CN107038190A (en) * | 2016-10-28 | 2017-08-11 | 厦门大学 | A kind of intelligent promotion plan modeling method applied to Taobao |
| US11276033B2 (en) * | 2017-12-28 | 2022-03-15 | Walmart Apollo, Llc | System and method for fine-tuning sales clusters for stores |
| US20190205806A1 (en) * | 2017-12-28 | 2019-07-04 | Walmart Apollo, Llc | System and method for determining and implementing sales clusters for stores |
| CN110751497B (en) * | 2018-07-23 | 2024-06-21 | 北京京东尚科信息技术有限公司 | Commodity replenishment method and device |
| CN109784979B (en) * | 2018-12-19 | 2023-06-16 | 中交(厦门)电子商务有限公司 | Big data driven supply chain demand prediction method |
| CN109784984A (en) * | 2018-12-26 | 2019-05-21 | 广东工业大学 | A kind of fresh shops's Intelligent Ordering System |
| CN109978597A (en) * | 2019-01-22 | 2019-07-05 | 广东工业大学 | A kind of Sales Volume of Commodity prediction technique under festivals or holidays effect |
| CN109978612A (en) * | 2019-03-18 | 2019-07-05 | 北京工业大学 | A kind of convenience store's Method for Sales Forecast method based on deep learning |
| CN110189164B (en) * | 2019-05-09 | 2021-06-01 | 杭州览众数据科技有限公司 | Commodity-store recommendation scheme based on information entropy measurement and random feature sampling |
| CN110163427B (en) * | 2019-05-09 | 2021-12-07 | 杭州览众数据科技有限公司 | Method for optimizing storeroom of storeroom |
| CN110163669B (en) * | 2019-05-09 | 2021-07-27 | 杭州览众数据科技有限公司 | Demand prediction method based on characteristic coefficient likelihood estimation and retail business rule |
| CN110490670A (en) * | 2019-08-27 | 2019-11-22 | 上海欧睿供应链管理有限公司 | A kind of demand forecast system of adaptive merchandise sales rule |
| CN111028016A (en) * | 2019-12-12 | 2020-04-17 | 腾讯科技（深圳）有限公司 | Sales data prediction method and device and related equipment |
-
2020
- 2020-05-28 CN CN202010467295.9A patent/CN111815348B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101819668A (en) * | 2010-04-27 | 2010-09-01 | 浙江大学 | Sales predicting model based on product intrinsic life cycle character |
| CN105556557A (en) * | 2013-09-20 | 2016-05-04 | 日本电气株式会社 | Shipment-volume prediction device, shipment-volume prediction method, recording medium, and shipment-volume prediction system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111815348A (en) | 2020-10-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111815348B (en) | Regional commodity production planning method based on commodity similarity clustering of stores | |
| CN108648023A (en) | A kind of businessman's passenger flow forecast method of fusion history mean value and boosted tree | |
| CN103617459A (en) | Commodity demand information prediction method under multiple influence factors | |
| JP2019049850A (en) | Prediction system and method | |
| CN114118636A (en) | Automobile spare part demand prediction system based on multi-model optimization | |
| CN116777508B (en) | Medical supply analysis management system and method based on big data | |
| CN114037138A (en) | Metro short-term entry passenger flow prediction system and implementation method based on double-layer decomposition and deep learning | |
| CN118350611B (en) | Enterprise resource comprehensive integrated intelligent management system and method | |
| US20210142348A1 (en) | Multi-layered system for heterogeneous pricing decisions by continuously learning market and hotel dynamics | |
| CN120069944A (en) | Chain store operation monitoring management method and system based on cloud platform technology | |
| He | Rain prediction in australia with active learning algorithm | |
| CN116579804A (en) | Holiday commodity sales prediction method, holiday commodity sales prediction device and computer storage medium | |
| CN118313568B (en) | Equipment running state prediction method, electronic equipment and storage medium | |
| CN118840046B (en) | Retail data processing method, device, computer equipment and computer storage medium | |
| CN119443321A (en) | Multi-channel room booking method and system for hotels | |
| CN118013469B (en) | Time-dependent model analysis method for managing multidimensional data by enterprise architecture | |
| CN119250943A (en) | Automobile supply chain order user data management method, system, device and medium | |
| CN111950760A (en) | Labor demand forecasting system and forecasting method based on machine learning algorithm | |
| JP2001243401A (en) | Order receipt prediction system | |
| Kabanova et al. | ABC-XYZ inventory analysis accounting for change points | |
| CN119904108B (en) | A statistical analysis method and system for business operations of enterprises | |
| CN116151861B (en) | Construction method of sales volume prediction model constructed based on intermittent time sequence samples | |
| CN119579064B (en) | A method for forecasting spare parts demand based on intermittent fitting index | |
| CN119941085B (en) | Intelligent logistics matching method based on dynamic data empowerment | |
| CN118733601B (en) | A method for synchronous updating of data governance and highway standards |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |