CN104850898A - Prediction logic order generation method in manufacturing industry - Google Patents
Prediction logic order generation method in manufacturing industry Download PDFInfo
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- CN104850898A CN104850898A CN201510105509.7A CN201510105509A CN104850898A CN 104850898 A CN104850898 A CN 104850898A CN 201510105509 A CN201510105509 A CN 201510105509A CN 104850898 A CN104850898 A CN 104850898A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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- Y02P90/30—Computing systems specially adapted for manufacturing
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Abstract
The invention discloses a prediction logic order generation method in the manufacturing industry. The prediction logic order generation method comprises creating an E-business marketing database, database key field information comprises sorts and types of sales products, sales time nodes, sales quantity, sales unit price, client regions, and client attributes, and the method further comprises creating sales trend theory model steps, creating a search engine, and outputting prediction logic order steps. The prediction logic order generation method is advantageous in that an enterprise can arrange production plans and organize product production and processing without receiving actual orders; the enterprise changes passive production to active production, the purchase of raw materials is increased, and the planning of labor force management is improved; the enterprise can grasp the preference and tendency of the product demand of clients via the analysis of the prediction logic order, and the development strategy formulation of the enterprise is facilitated.
Description
Technical field
The present invention relates to a kind of manufacturing industry logic order generation method, be that enterprise passes through to excavate the large data analysis of marketing, build enterprise product future sales trend model, thus export the method for the virtual order of various type product.
Background technology
Traditional manufacture relies on order existence, and order derives from frequent customer mostly.Along with manufacturing industry progressively sets foot in E-commerce Marketing, rely on the transformation that pattern that traditional order arranges production there occurs essence, a lot of enterprise can not adapt to new business model, causes the product of certain model in short supply, and the product stock of other models piles up, very large on asset turnover impact.In order to catch up with goods, recruit producers, due to without strict training, cause crudy to go wrong, even waste of raw materials is huge temporarily.Discharge workman again after having caught up with goods, make employee lack sense of ownership.Cause the main cause of this passive situation to be that enterprise cannot grasp the sales trend of this enterprise, scheduling by rule of thumb, pin pain for the treatment of the head when the head aches cures pin, and enterprise can not come out of from Order pattern.
Summary of the invention
The object of the invention is to provide a kind of manufacturing industry logic order generation method, is that enterprise passes through to excavate the large data analysis of marketing, builds enterprise product future sales trend model, thus export the method for the virtual order of various type product.
To achieve these goals, the solution of the present invention is: a kind of manufacturing industry prediction logic order generation method, comprise and create electric business's marketing database, described database key field information comprises: the kind model of sell goods, selling time node, sales volume, sales price, client region, client properties, wherein, described method comprises establishment sales trend theoretical model step further and builds search engine, prediction of output logic order step;
Described establishment sales trend theoretical model step is:
The first step: by
formula (1)
Obtain the average sale speed V of selected product
,
Wherein,
sfor total sales volume,
mfor the time in units of the moon,
m n -M 1 market sale for this product total moon number;
Second step: by
formula (2)
The sales volume seasonal variety degree value X of selected product is obtained with average sale speed V,
Wherein S
1be the sales volume of first month, Sn is the sales volume of n-th month;
3rd step: by
formula (3)
Obtain the change general trend value T of selected product sales volume,
Wherein
s n -S n-1 for the difference of adjacent bimestrial sales volume,
dfor the time in units of sky,
d n -D n-1 be the difference (number of days) of the time in twice adjacent month,
be actually two adjacent month sales volume change slope;
4th step: obtain sales trend theoretical sales volume value model P by average selling speed V, the sales volume seasonal variety degree value X of product and the change general trend value T of product sales volume,
P=f (V, X, T) formula (4);
Described structure search engine, prediction of output logic order step are:
The first step: obtain the selected average sale speed V of product and the sales volume seasonal variety degree value X of selected product in the past period by formula (1) and formula (2);
Second step: determine that search time, spacer was " just N number of moon in past " or " N number of moon of the same period in former years " by the X value of the first step;
3rd step: the formula (3) of the time interval section determined containing second step is set as search engine obtains the change general trend value T of selected product past sales volume;
4th step: by the search engine of the 3rd step by sales volume value P=(1+T) × Sm formula (5)
Export the prediction logic order that selected product will estimate N number of month sales volume value P future,
Wherein, Sm is the total sales volume of this type product within time interval section N number of moon.
Scheme is further: the described X value by the first step determines that search time, spacer was " just in the past N number of
Month " or " N number of moon of the same period in former years " in, when X value is greater than threshold value, described search time, spacer was decided to be " N number of moon of the same period in former years ", otherwise described search time, spacer was decided to be " N number of moon of just having pass by "; Wherein said threshold value is 0.3 to 0.6.
Scheme is further: described method comprises further, changes spacer N value number described search time, repeat the 3rd step and the 4th step, export the prediction logic order of different N value number in described structure search engine, prediction of output logic order step.
Scheme is further: described method comprises further, change product type, repeat described establishment sales trend theoretical model step and build search engine, prediction of output logic order step, exporting the different product model prediction logic order of following N number of month.
The invention has the beneficial effects as follows:
Advantage of the present invention has: enterprise is without the need to receiving the plan of just can arranging production of actual order and tissue products production and processing; Enterprise is from passive production to active production, and what increase that starting material enter to purchase with manpower management is planned; Enterprise by the analysis to prediction logic order, can grasp client to the preference of product demand and tendency, is conducive to enterprise's work out development strategy.
Below in conjunction with drawings and Examples, the present invention is described in detail.
Accompanying drawing explanation
Fig. 1 the inventive method process flow diagram.
Embodiment
A kind of manufacturing industry prediction logic order generation method, comprise and create electric business's marketing database, described database key field information comprises: the kind model of sell goods, selling time node, sales volume, sales price, client region, client properties, wherein, described method comprises establishment sales trend theoretical model step further and builds search engine, prediction of output logic order step;
Described establishment sales trend theoretical model step is:
The first step: by
formula (1)
Obtain the average sale speed V of selected product
,
Wherein,
sfor total sales volume, M are time (unit is the moon),
m n -M 1 market sale for this product total moon number;
Second step: by
formula (2)
The sales volume seasonal variety degree value X of selected product is obtained with average sale speed V,
Wherein S
1be the sales volume of first month, Sn is the sales volume of n-th month;
3rd step: by
formula (3)
Obtain the change general trend value T of selected product sales volume,
Wherein
s n -S n-1 for the difference of adjacent bimestrial sales volume,
dfor the time in units of sky,
d n -D n-1 be the difference (number of days) of the time in twice adjacent month,
be actually two adjacent month sales volume change slope;
4th step: obtain sales trend theoretical sales volume value model P by average selling speed V, the sales volume seasonal variety degree value X of product and the change general trend value T of product sales volume,
P=f (V, X, T) formula (4);
Described structure search engine, prediction of output logic order step are:
The first step: obtain the selected average sale speed V of product and the sales volume seasonal variety degree value X of selected product in the past period by formula (1) and formula (2);
Second step: determine that search time, spacer was " just N number of moon in past " or " N number of moon of the same period in former years " by the X value of the first step;
3rd step: the formula (3) of the time interval section determined containing second step is set as search engine obtains the change general trend value T of selected product past sales volume;
4th step: by the search engine of the 3rd step by sales volume value P=(1+T) × Sm formula (5)
Export the prediction logic order that selected product will estimate N number of month sales volume value P future,
Wherein, Sm is the total sales volume of this type product within time interval section N number of moon.
In embodiment: the described X value by the first step determines that search time, spacer was that " just the past is N number of
Month " or " N number of moon of the same period in former years " in, when X value is greater than threshold value, described search time, spacer was decided to be " N number of moon of the same period in former years ", otherwise described search time, spacer was decided to be " N number of moon of just having pass by "; Wherein said threshold value is 0.3 to 0.6.
In embodiment: described method comprises further, in described structure search engine, prediction of output logic order step, change spacer N value number described search time, repeat the 3rd step and the 4th step, export the prediction logic order of different N value number.
In embodiment: described method comprises further, change product type, repeat described establishment sales trend theoretical model step and build search engine, prediction of output logic order step, export the different product model prediction logic order of following N number of month.
The logic order mode of above-described embodiment, do not change enterprise traditionally Order pattern arrange production, just traditional order transition is become logic order.So-called logic order, is exactly that enterprise passes through to excavate the large data analysis of marketing, builds enterprise product future sales trend model, thus export the virtual order of various type product.Logic order is different from real customer order, and it is not for a certain client, but for whole market.Logic order neither for the product of specific model, but the comprehensive description to the following potential sales volume of all type products.Embodiment solves the problem how enterprise formulates production planning and sequencing under electric quotient ring border well, solves the problem of enterprise's manpower management and distribution, makes enterprise realize intelligent elasticity manufacture, reduce stock, guarantee the timely supply of material, improve management efficiency, finally strengthen enterprise competitiveness.
In the above-described embodiments: as shown in Figure 1: first create enterprise electricity business marketing database, database must comprise necessary critical field information, as the kind model, timing node, quantity, unit price, client region, client properties (age, sex etc.) etc. of sell goods, and gather each data of selling as far as possible, be stored among database; Then when data sample is abundant, build sales trend theoretical model, model is described by several characteristic parameters, and characteristic parameter is embodied by weight the contribution degree size describing sales trend; Search engine is built finally by sales trend theoretical model, the form of the result of search logically order is exported, logic order comprises the estimation of expected sales in three months futures, six months, nine months of various type product, the time period of this future anticipation will be determined according to the production and processing cycle of product, the production and processing cycle is longer, the time period of prediction is longer, as following half a year, 1 year, the production and processing cycle is shorter, the time period of prediction is shorter, as one month, two months, three months future etc., output logic order, formulation production planning and sequencing.
(1) create electric business's marketing database.
Embodiment adopts HBase database technology to build electric business's marketing database, the basis of conventional two-dimensional structural data increases " timestamp ", forms dimensional structured data.Database comprises necessary critical field information, as the kind model, timing node, quantity, unit price, client region, client properties etc. of sell goods, and gathers each data of selling as far as possible, is stored among database.
(2) create sales trend theoretical model.
Model is described by several characteristic parameters, and characteristic parameter method for designing is as follows:
Suppose that the time period that a certain type product is sold is
m 1 extremely
m n ,
m n -M 1 market sale for this product total moon number,
sfor total sales volume,
m 1 extremely
m n in selling time, the sales volume in per month is respectively
s 1 extremely
s n ; Then this type product sales trend theoretical model characteristic parameter is described below:
1. on average speed is sold
v: reflect the degree that this type product is totally in great demand (or unsalable).
2. sales volume dispersion variance
x: the sales volume seasonal variety degree reflecting this type product.
3. sales trend degree
t: the change general trend reflecting this type product sales volume.
Wherein
s n -S n-1 for the difference of adjacent bimestrial sales volume,
d n -D n-1 be the difference (number of days) of the time in twice adjacent month,
be actually two adjacent month sales volume change slope, visible sales trend degree
treflection be the variation tendency sum of this type product sales volume,
tbe worth larger, illustrate that this production marketing trend strengthens obviously,
tvalue is negative, illustrates that the sales volume of this product is progressively weakening,
tvalue is 0, and illustrate that in a period of time, this production marketing is basicly stable, change is little.
More than define 3 characteristic parameters of sales trend model, can build propensity to consume characteristic model by these 3 parameters now.It is considered herein that, the sales trend characteristic model of a certain type product is the function of these 3 characteristic parameters, that is:
P=f(V,X,T)
(3) build search engine, output logic order.
1. two trend feature parameters of (metastable enterprise operation phase, possibility 1 year or 2 years) in one period in the past are first calculated: on average sell speed
v,sales volume dispersion variance X
.
2. then determine spacer search time, if you want to predict the expectation sales volume of following N number of moon type product, search time, spacer can be decided to be just N number of moon in the past, or N number of moon of the same period in former years.Or actually N number of moon of the N number of same period former years moon in search firm past, then to determine according to the size of sales volume dispersion variance X, because X value is larger, prove that the seasonality of this type product is stronger.Section search time just should be decided to be " N number of moon of the same period in former years " when being greater than a certain value k by the value of sales volume dispersion variance X.K value needs to determine according to embody rule situation, usually selects 0.3 ~ 0.6 proper.
3. search engine is built, the characteristic parameter in the N number of month time interval that 2. calculation procedure is determined: sales trend degree
t, draw the sales volume value P that certain type product will estimate N number of month future:
P=（1＋T）× Sm
Wherein
s m for the total sales volume of this type product within time interval section N number of moon.
4. change the value of the moon in time interval number N, repeat step 3., calculate new
pvalue.
5. change product type, repeat step 1. extremely 4., the logic order exported in N number of moon in future of each type product of certain enterprise is as follows:
×× enterprise each model Product Logic order is as shown in table 1
Table 1: the prediction moon in future number
| Product | V value | X value | T value | N=3 | N=6 | N=9 | N=12 |
| Model 1 | |||||||
| Model 2 | |||||||
| ... | |||||||
| Model n |
Claims (4)
1. a manufacturing industry prediction logic order generation method, comprise and create electric business's marketing database, described database key field information comprises: the kind model of sell goods, selling time node, sales volume, sales price, client region, client properties, it is characterized in that, described method comprises establishment sales trend theoretical model step further and builds search engine, prediction of output logic order step;
Described establishment sales trend theoretical model step is:
The first step: by
formula (1)
Obtain the average sale speed V of selected product
,
Wherein,
sfor total sales volume,
mfor the time in units of the moon,
m n -M 1 market sale for this product total moon number;
Second step: by
formula (2)
The sales volume seasonal variety degree value X of selected product is obtained with average sale speed V,
Wherein S
1be the sales volume of first month, Sn is the sales volume of n-th month;
3rd step: by
formula (3)
Obtain the change general trend value T of selected product sales volume,
Wherein
s n -S n-1 for the difference of adjacent bimestrial sales volume,
dfor the time in units of sky,
d n -D n-1 be the difference of the time in twice adjacent month,
be two adjacent month sales volume change slope;
4th step: obtain sales trend theoretical sales volume value model P by average selling speed V, the sales volume seasonal variety degree value X of product and the change general trend value T of product sales volume,
P=f (V, X, T) formula (4);
Described structure search engine, prediction of output logic order step are:
The first step: obtain the selected average sale speed V of product and the sales volume seasonal variety degree value X of selected product in the past period by formula (1) and formula (2);
Second step: determine that search time, spacer was " just N number of moon in past " or " N number of moon of the same period in former years " by the X value of the first step;
3rd step: the formula (3) of the time interval section determined containing second step is set as search engine obtains the change general trend value T of selected product past sales volume;
4th step: by the search engine of the 3rd step by sales volume value P=(1+T) × Sm formula (5)
Export the prediction logic order that selected product will estimate N number of month sales volume value P future,
Wherein, Sm is the total sales volume of this type product within time interval section N number of moon.
2. a kind of manufacturing industry prediction logic order generation method according to claim 1, is characterized in that,
The described X value by the first step determines that search time, spacer was in " just N number of moon in past " or " N number of moon of the same period in former years ", when X value is greater than threshold value, described search time, spacer was decided to be " N number of moon of the same period in former years ", otherwise described search time, spacer was decided to be " just N number of moon " in the past; Wherein said threshold value is 0.3 to 0.6.
3. a kind of manufacturing industry prediction logic order generation method according to claim 1 and 2, it is characterized in that, described method comprises further, spacer N value number described search time is changed in described structure search engine, prediction of output logic order step, repeat the 3rd step and the 4th step, export the prediction logic order of different N value number.
4. a kind of manufacturing industry prediction logic order generation method according to claim 1 and 2, it is characterized in that, described method comprises further, change product type, repeat described establishment sales trend theoretical model step and build search engine, prediction of output logic order step, exporting the different product model prediction logic order of following N number of month.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106897795A (en) * | 2017-02-17 | 2017-06-27 | 联想(北京)有限公司 | A kind of inventory forecast method and device |
| CN107506842A (en) * | 2017-06-29 | 2017-12-22 | 广州兴森快捷电路科技有限公司 | PCB orders add throwing rate Forecasting Methodology and device |
| CN108074003A (en) * | 2016-11-09 | 2018-05-25 | 北京京东尚科信息技术有限公司 | Predictive information method for pushing and device |
| CN109658154A (en) * | 2018-12-25 | 2019-04-19 | 广州裕琪凌贸易有限公司 | A kind of order forecast method based on the analysis of regional trade data |
| CN112907068A (en) * | 2021-02-09 | 2021-06-04 | 刘连英 | Method for the batch production of fastener groups |
| CN116862669A (en) * | 2023-09-05 | 2023-10-10 | 深圳市明心数智科技有限公司 | Vehicle loan data analysis method, system and medium |
Citations (1)
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| CN1816819A (en) * | 2003-06-30 | 2006-08-09 | Tdk株式会社 | Order forecast system, order forecast method and order forecast program |
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2015
- 2015-03-11 CN CN201510105509.7A patent/CN104850898A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1816819A (en) * | 2003-06-30 | 2006-08-09 | Tdk株式会社 | Order forecast system, order forecast method and order forecast program |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108074003A (en) * | 2016-11-09 | 2018-05-25 | 北京京东尚科信息技术有限公司 | Predictive information method for pushing and device |
| CN108074003B (en) * | 2016-11-09 | 2021-11-12 | 北京京东尚科信息技术有限公司 | Prediction information pushing method and device |
| CN106897795A (en) * | 2017-02-17 | 2017-06-27 | 联想(北京)有限公司 | A kind of inventory forecast method and device |
| CN107506842A (en) * | 2017-06-29 | 2017-12-22 | 广州兴森快捷电路科技有限公司 | PCB orders add throwing rate Forecasting Methodology and device |
| CN109658154A (en) * | 2018-12-25 | 2019-04-19 | 广州裕琪凌贸易有限公司 | A kind of order forecast method based on the analysis of regional trade data |
| CN109658154B (en) * | 2018-12-25 | 2022-10-14 | 山东浪潮新世纪科技有限公司 | Order prediction method based on regional trade data analysis |
| CN112907068A (en) * | 2021-02-09 | 2021-06-04 | 刘连英 | Method for the batch production of fastener groups |
| CN116862669A (en) * | 2023-09-05 | 2023-10-10 | 深圳市明心数智科技有限公司 | Vehicle loan data analysis method, system and medium |
| CN116862669B (en) * | 2023-09-05 | 2023-12-22 | 深圳市明心数智科技有限公司 | Vehicle loan data analysis method, system and medium |
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Application publication date: 20150819 |