CN107491922A - A kind of quantity in stock Forecasting Methodology of unmanned supermarket - Google Patents
A kind of quantity in stock Forecasting Methodology of unmanned supermarket Download PDFInfo
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- CN107491922A CN107491922A CN201710795612.8A CN201710795612A CN107491922A CN 107491922 A CN107491922 A CN 107491922A CN 201710795612 A CN201710795612 A CN 201710795612A CN 107491922 A CN107491922 A CN 107491922A
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- 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
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
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Abstract
The invention discloses a kind of quantity in stock Forecasting Methodology of unmanned supermarket, the commodity output cycle is determined；Method for Sales Forecast model is established according to the commodity output cycle and prediction sales volume factor of influence；The Method for Sales Forecast value in preset time period is determined based on the Method for Sales Forecast model；Quantity in stock in the preset time period is controlled based on the Method for Sales Forecast value；In this way, can reasonably be predicted stock, and then dynamic stock is reasonably controlled, so as to realize maximum revenue.
Description
Technical field
The present invention relates to unmanned supermarket's technical field, specially a kind of quantity in stock Forecasting Methodology of unmanned supermarket.
Background technology
With the progress of Technology Times, the raising of people's lives quality, unmanned supermarket enters the visual field of people.Unmanned supermarket
Commodity are peddled, control stock is an important ring for maximum revenue.For example too small stock once meets activity and may taken off
Pin, influences maximum revenue；The sales volume that excessive stock has been overly dependent upon, once sales volume has change to cause kinds of goods to be hoarded, due to
The life cycle of some commodity is shorter, can also influence income.So how reasonably to control dynamic stock most important.
The content of the invention
It is an object of the invention to provide a kind of quantity in stock Forecasting Methodology of unmanned supermarket, to solve in above-mentioned background technology
The problem of proposition.
To achieve the above object, the present invention provides following technical scheme：A kind of quantity in stock Forecasting Methodology of unmanned supermarket, its
It is characterised by, methods described includes：
Determine the commodity output cycle；Wherein, the commodity output cycle refers at least one commodity from operation is started to production
The All Time gone out；
Method for Sales Forecast model is established according to the commodity output cycle and prediction sales volume factor of influence；
The Method for Sales Forecast value in preset time period is determined based on the Method for Sales Forecast model；
Quantity in stock in the preset time period is controlled based on the Method for Sales Forecast value.
Preferably, the determination commodity output cycle, including：
The cycle influences factor in commodity output cycle is obtained, the cycle influences factor comprises at least the production of each product
Cycle, commodity rate；
The commodity output cycle is determined with reference to the cycle influences factor.
Preferably, Method for Sales Forecast model is established according to the commodity output cycle and prediction sales volume factor of influence, including：
Using the commodity output cycle as time reference, when N that acquisition is adapted with the commodity output cycle are default
Between history sales volume data in section；The N is the positive integer more than or equal to 1；
The first Method for Sales Forecast model is established based on the history sales volume data；
The second Method for Sales Forecast model in the preset time period is determined based on prediction sales volume factor of influence；
Method for Sales Forecast model is established according to the first Method for Sales Forecast model and the second Method for Sales Forecast model.
Preferably, it is described to establish the first Method for Sales Forecast model based on history sales volume data, including：
Search the history sales volume factor of influence in N number of preset time period；
Determine the weight shared by each history sales volume factor of influence；
With reference to shared by the history sales volume data, each history sales volume factor of influence and the history sales volume factor of influence
Weight, establish the first Method for Sales Forecast model.
Preferably, the second Method for Sales Forecast mould determined based on prediction sales volume factor of influence in the preset time period
Type, including：
Using the commodity output cycle as time reference, the prediction sales volume factor of influence in the preset time period is determined；
The prediction sales volume factor of influence is used to characterize the element that will have an impact the sales volume in the preset time；
Determine the weight shared by each prediction sales volume factor of influence；
With reference to the weight shared by prediction sales volume factor of influence and each prediction sales volume factor of influence, the preset time is determined
The second Method for Sales Forecast model in section.
Preferably, the quantity in stock controlled according to the Method for Sales Forecast value in the preset time period, including：
The difference of quantity in stock in the preset time and the Method for Sales Forecast value is controlled to be more than or equal to 0, and less than the
One predetermined threshold value；Wherein, the quantity in stock represents the number of product with the Method for Sales Forecast value.
Compared with prior art, the beneficial effects of the invention are as follows：The quantity in stock Forecasting Methodology of unmanned supermarket, determine that commodity produce
Go out the cycle；Method for Sales Forecast model is established according to the commodity output cycle and prediction sales volume factor of influence；Based on the sales volume
Forecast model determines the Method for Sales Forecast value in preset time period；Controlled based on the Method for Sales Forecast value in the preset time period
Quantity in stock；In this way, can reasonably be predicted stock, and then dynamic stock is reasonably controlled, so as to realize Income Maximum
Change.
Embodiment
Below in conjunction with the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described,
Obviously, described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.Based in the present invention
Embodiment, the every other embodiment that those of ordinary skill in the art are obtained under the premise of creative work is not made, all
Belong to the scope of protection of the invention.
The present invention provides a kind of technical scheme：A kind of quantity in stock Forecasting Methodology of unmanned supermarket, it is characterised in that the side
Method includes：
Determine the commodity output cycle；Wherein, the commodity output cycle refers at least one commodity from operation is started to production
The All Time gone out；
Method for Sales Forecast model is established according to the commodity output cycle and prediction sales volume factor of influence；
The Method for Sales Forecast value in preset time period is determined based on the Method for Sales Forecast model；
Quantity in stock in the preset time period is controlled based on the Method for Sales Forecast value.
The determination commodity output cycle, including：
The cycle influences factor in commodity output cycle is obtained, the cycle influences factor comprises at least the production of each product
Cycle, commodity rate；
The commodity output cycle is determined with reference to the cycle influences factor.
Method for Sales Forecast model is established according to the commodity output cycle and prediction sales volume factor of influence, including：
Using the commodity output cycle as time reference, when N that acquisition is adapted with the commodity output cycle are default
Between history sales volume data in section；The N is the positive integer more than or equal to 1；
The first Method for Sales Forecast model is established based on the history sales volume data；
The second Method for Sales Forecast model in the preset time period is determined based on prediction sales volume factor of influence；
Method for Sales Forecast model is established according to the first Method for Sales Forecast model and the second Method for Sales Forecast model.
It is described to establish the first Method for Sales Forecast model based on history sales volume data, including：
Search the history sales volume factor of influence in N number of preset time period；
Determine the weight shared by each history sales volume factor of influence；
With reference to shared by the history sales volume data, each history sales volume factor of influence and the history sales volume factor of influence
Weight, establish the first Method for Sales Forecast model.
The quantity in stock Forecasting Methodology of unmanned supermarket, determine the commodity output cycle；According to the commodity output cycle and in advance
Survey sales volume factor of influence and establish Method for Sales Forecast model；The Method for Sales Forecast in preset time period is determined based on the Method for Sales Forecast model
Value；Quantity in stock in the preset time period is controlled based on the Method for Sales Forecast value；In this way, stock can be carried out rational pre-
Survey, and then reasonably control dynamic stock, so as to realize maximum revenue.
Although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of changes, modification can be carried out to these embodiments, replace without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (6)
1. a kind of quantity in stock Forecasting Methodology of unmanned supermarket, it is characterised in that methods described includes：
Determine the commodity output cycle；Wherein, the commodity output cycle refers at least one commodity from operation is started to output
All Time；
Method for Sales Forecast model is established according to the commodity output cycle and prediction sales volume factor of influence；
The Method for Sales Forecast value in preset time period is determined based on the Method for Sales Forecast model；
Quantity in stock in the preset time period is controlled based on the Method for Sales Forecast value.
2. according to the method for claim 1, it is characterised in that the determination commodity output cycle, including：
The cycle influences factor in commodity output cycle is obtained, the cycle influences factor comprises at least the production week of each product
Phase, commodity rate；
The commodity output cycle is determined with reference to the cycle influences factor.
3. according to the method for claim 1, it is characterised in that influenceed according to the commodity output cycle and prediction sales volume
The factor establishes Method for Sales Forecast model, including：
Using the commodity output cycle as time reference, the N number of preset time period being adapted with the commodity output cycle is obtained
Interior history sales volume data；The N is the positive integer more than or equal to 1；
The first Method for Sales Forecast model is established based on the history sales volume data；
The second Method for Sales Forecast model in the preset time period is determined based on prediction sales volume factor of influence；
Method for Sales Forecast model is established according to the first Method for Sales Forecast model and the second Method for Sales Forecast model.
4. according to the method for claim 3, it is characterised in that described to establish the first Method for Sales Forecast based on history sales volume data
Model, including：
Search the history sales volume factor of influence in N number of preset time period；
Determine the weight shared by each history sales volume factor of influence；
With reference to the power shared by the history sales volume data, each history sales volume factor of influence and the history sales volume factor of influence
Weight, establishes the first Method for Sales Forecast model.
5. according to the method for claim 3, it is characterised in that described that described preset is determined based on prediction sales volume factor of influence
The second Method for Sales Forecast model in period, including：
Using the commodity output cycle as time reference, the prediction sales volume factor of influence in the preset time period is determined；It is described
Prediction sales volume factor of influence is used to characterize the element that will have an impact the sales volume in the preset time；
Determine the weight shared by each prediction sales volume factor of influence；
With reference to the weight shared by prediction sales volume factor of influence and each prediction sales volume factor of influence, determine in the preset time period
The second Method for Sales Forecast model.
6. according to the method for claim 1, it is characterised in that it is described according to the Method for Sales Forecast value control it is described default when
Between quantity in stock in section, including：
The difference of the quantity in stock in the preset time and the Method for Sales Forecast value is controlled to be more than or equal to 0, and it is pre- less than first
If threshold value；Wherein, the quantity in stock represents the number of product with the Method for Sales Forecast value.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201710795612.8A CN107491922A (en) | 2017-09-06 | 2017-09-06 | A kind of quantity in stock Forecasting Methodology of unmanned supermarket |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201710795612.8A CN107491922A (en) | 2017-09-06 | 2017-09-06 | A kind of quantity in stock Forecasting Methodology of unmanned supermarket |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110619407A (en) * | 2018-06-19 | 2019-12-27 | 北京京东尚科信息技术有限公司 | Object sales prediction method and system, electronic device, and storage medium |
| CN111242532A (en) * | 2020-01-03 | 2020-06-05 | 秒针信息技术有限公司 | Purchasing management method and device and electronic equipment |
Citations (2)
| 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 |
| CN106971249A (en) * | 2017-05-05 | 2017-07-21 | 北京挖玖电子商务有限公司 | A kind of Method for Sales Forecast and replenishing method |
-
2017
- 2017-09-06 CN CN201710795612.8A patent/CN107491922A/en active Pending
Patent Citations (2)
| 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 |
| CN106971249A (en) * | 2017-05-05 | 2017-07-21 | 北京挖玖电子商务有限公司 | A kind of Method for Sales Forecast and replenishing method |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110619407A (en) * | 2018-06-19 | 2019-12-27 | 北京京东尚科信息技术有限公司 | Object sales prediction method and system, electronic device, and storage medium |
| CN110619407B (en) * | 2018-06-19 | 2024-04-09 | 北京京东尚科信息技术有限公司 | Object sales prediction method and system, electronic device and storage medium |
| CN111242532A (en) * | 2020-01-03 | 2020-06-05 | 秒针信息技术有限公司 | Purchasing management method and device and electronic equipment |
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Application publication date: 20171219 |