JPH0816950B2 - Production system for over-the-counter products - Google Patents

Description

TECHNICAL FIELD The present invention relates to a production system for over-the-counter products, and more specifically, to quickly and accurately grasp sales information at a store, and to respond to this information promptly and flexibly. The present invention relates to a production system capable of producing goods.

[Conventional technology]

When a manufacturer produces a product, it is common to first determine the production quantity based on the sales plan, give a production instruction to the production factory, and then perform additional production as needed while observing the situation. Target.

However, it is extremely dangerous for a maker that deals with highly fashionable products, which are required to bring new products to the market every year and whose sales period is extremely short, simply by adopting the above production system. In other words, if you make a sales plan, produce and sell products based on this, and it sells at a stretch because it is doing better than planned, naturally it will run out of stock and lead to loss of sales opportunities, If they feared this and made an overly large plan, on the other hand, if the sales were sluggish, the unsold stock would be excessive, leading to a loss of bad inventory.

[Problems to be Solved by the Invention]

In order to solve such problems, many manufacturers have been studying various demand forecasting methods. That is, the above problem is solved by producing a product after predicting the demand by using the past sales record of similar products as reference data. However, regarding new products, it is extremely difficult to grasp the evaluation of consumers and trends of other manufacturers in the same industry, and to predict the advertising effect. Furthermore, the weather and other factors have a great impact on sales. However, the reality is that it has been admitted that there is a limit to demand forecasting, and therefore that relying on it is extremely dangerous.

The present invention has been made in view of such a situation, and quickly and accurately grasps sales information at a store,
By making it possible to flexibly produce products in response to this information, the objective is to provide an ideal production system that produces what can be sold when it sells, without holding inventory. It is a thing.

[Means for solving the problem]

In order to achieve the above object, the system for producing over-the-counter products according to the present invention has the following configuration. That is, an over-the-counter sales information collection means for collecting sales volume data for each product from a plurality of sample stores selected from a large number of stores, and for each product, product classification, price range classification, target age classification, sales method A product characteristic data table in which product-related characteristics such as classification are stored, and the actual results of sales of each of the plurality of sample stores are stored as a composition ratio to the entire store, and the product in each of the plurality of sample stores is stored. A sample store characteristic data table in which related characteristics are stored, and based on the data obtained from the product characteristic data table and the sample store characteristic data table, the sales quantity data for each product collected from the plurality of sample stores Expansion estimation means for calculating the expansion estimation value and a predetermined period based on the transition of past sales results A plurality of types of sales transition patterns that have been created and a demand forecast data table that stores final sales forecast values for each of the sales transition patterns are provided. From the plurality of types of sales transition patterns in this demand forecast data table, A demand forecasting method that selects the sales transition pattern that most closely approximates the transition pattern of the estimated expansion of sales volume data for each product, and uses the final sales forecast value for the selected sales transition pattern as the final sales forecast value for the product. And an inventory information data table that stores the inventory quantity of each product, etc., and the production that determines the required production quantity of each product from the data obtained from this inventory information data table and the final sales forecast value of each product It has a quantity determination means and a raw material data table that stores the raw material composition, quantity, etc. for each product. The raw material data is prepared from the data obtained from the raw material data table and the required production quantity determined above, and the raw material preparation means for preparing the required quantity of the required raw material and the production instruction based on the determined required production quantity are executed. The main point is that the production means is

[Action]

In the above configuration, the required production quantity for each product is set in accordance with the sales quantity data collected from the store, and the raw materials required to produce this required quantity of goods are prepared. , The required quantity of products are produced. Therefore, it is possible to realize an ideal production system that "when you sell, produce what you can sell as much as you can", and when you sell, you run out of products and lose sales opportunities, or conversely you have bad inventory. It becomes possible to completely solve such a problem.

Also, the sales quantity data for each product is collected from a sample shop selected from a large number of sales shops, and this sales quantity data is converted into each of the above data obtained from the product characteristic data table and the sample shop characteristic data table. Since the enlargement estimation is performed based on this, it is possible to obtain an accurate and useful enlargement estimation value by reflecting the bias of the product-related characteristics in the sample store.

Furthermore, the final sales forecast value for the product is selected by selecting the one that most closely approximates the transition pattern of the estimated expansion of sales volume data for each product from among multiple types of past sales transition patterns for each predetermined period. Since the required production quantity is determined based on this final sales forecast value, it is possible to review the final sales forecast value for each prescribed period and change the required production quantity to an appropriate value each time. It will be possible to carry out raw material preparation and production without excess or deficiency.

Thus, the present invention is a system in which a store and a production site are directly connected to each other as described above, and is flexible and rational in response to information from the front line of sales without any human labor. It is an extremely useful over-the-counter product production system that allows production to proceed.

〔Example〕

 Embodiments of the present invention will be described below with reference to the drawings.

FIG. 1 is an overall configuration diagram of a production system showing an embodiment of the present invention. As shown in the figure, this system comprises an over-the-counter sales information collecting means (1), a production quantity setting means (2),
It is composed of an instruction means (3) for instructing raw material preparation and production and a production means (4).

The over-the-counter sales information collection means (1) is provided for each of a plurality of stores, and is a POS terminal (1a), (1b), (1c) ... And these POS terminals (1a), (1b), (1c) ... Is connected to a host computer (H) and a public line network (11). The POS terminals (1a), (1b), (1c) ... store the product name and quantity each time a product is sold,
The stored information is transmitted to the host computer (H) at a fixed time every day.

The above-mentioned POS terminals (1a), (1b), (1c) ... are not placed at all stores, but some percentages of them are selected and placed as sample stores. This sample store can efficiently grasp sales information by selecting from a group of stores with high sales in the whole.

The production quantity setting means (2) includes an expansion estimation section (5), a demand prediction section (6), and a production quantity determination section (7).

The sales information sent from a plurality of POS terminals (1a), (1b), (1c), etc. via the public line network (11) is input to the expansion estimating unit (5) as input information at any time. Has become. Then, in the expansion estimation unit (5), the expansion estimation value can be obtained by the following equation (1).

Further, how to obtain this will be described below with a detailed example.

FIG. 2 shows an example of the product characteristic data table (51) connected to the expansion estimating unit (5), and the product groups A, B, C are shown.
... The product category, the price range category, the target age category, and the selling category are stored in each X. Figure 3 shows
These four categories are shown respectively, and the product categories are basic cosmetics, makeup cosmetics, shampoo and conditioner,
The 6 categories of perfumes, colognes, cosmetics, and men's cosmetics are represented by the numbers 1 to 6, and the price range classification is low, medium or
5 categories of low, middle, middle / high, and high price range, target age categories are low, low-average middle, average, average-high middle, and high-age group 5 categories, sales category , Display, a little display, neither can be determined, a little 5 counseling and 5 categories of counseling sales respectively
It is composed of numbers.

Such product characteristic data is determined at the stage of planning a product, and the above-mentioned numbers are input and stored for each item of each classification as shown in the column of product X in FIG. ing. That is, the product X belongs to the product category 1 ... Basic cosmetics, the price range category 2, the medium / low price range, the target age category 5, the older age group, the sales category 2 ... somewhat display sales, and is “slightly close to the display sales”. It is a basic cosmetic product for middle- and low-priced elderly people who sell it.

Further, FIGS. 4A to 4E are views showing an example of the sample store characteristic data table (52).
S _1, S _2, S ₃ respectively for the input data which has been determined by comparing the purchase contents and amount of last year and all branches, is adapted to store. That is, FIG. 4 (a) shows sample stores S ₁ , S ₂ , S ₃
It is the data table (52a) which showed the sales composition ratio for every, Comprising: (b)-(e) of FIG.
Data table (52b), (52c), (52d) showing the target age characteristics and selling characteristics for each sample store S ₁ , S ₂ , S ₃ .
(52e). These data tables (52b), (52
The above four characteristics in c), (52d), and (52e) are indexed (1 if the national average is the same) corresponding to the four types of classification described above based on FIG. Product category 1 at store S ₁ ... Index of basic cosmetics is a ₁ ,
Price range 1 ... The low price range index is g ₁ .

However, assuming that the sales numbers of the products X in the sample stores S ₁ , S ₂ , S ₃ are y ₁ , y ₂ , y ₃ , respectively, the product characteristic data table (51) and the sample store characteristic data table (52) described above are Using the data, the estimated expansion value of the product X can be obtained by the following equation (2).

That is, as shown in FIG. 2, the product characteristics of the product X are the product classification 1, the price range classification 2, the target age classification 5, and the selling style classification.
Since it is set to 2, the index in each characteristic of FIGS. 4 (b) to (e) corresponds to the classification set as described above, that is, a ₁ in the product characteristic (52b). ,
a _2, a _3, in the price range characteristics _{(52c) h 1, h 2} , h 3, in the Age characteristics _{(52d) p 1, p 2} , p 3, sales methods characteristic (52e)
In, r ₁ , r ₂ and r ₃ are selected.

The demand estimation unit (6) is connected to the expansion estimation unit (5) that calculates the expansion estimation value as described above, and in this demand prediction unit (6), each product sent from the expansion estimation unit (5) Demand forecasts are being made based on the expanded estimates.

This demand forecasting unit (6) is shown in the flowchart of FIG. 6 using the data stored in the demand forecast data tables (61a), (61b) as shown in FIGS. 5 (a) and 5 (b). The demand is forecasted in steps. That is, the demand forecast data table (61
In a), dozens of sales transition patterns for five days after the product is released (three types in this embodiment are mentioned) are stored, and the final sales prospect value for each pattern is also stored. The same data table (61b) shown in (b) has the same sales transition pattern and final sales forecast value for 5 days after the release, that is, for 6 days after the release of the product judged as the pattern in the table (61a). It is stored in.

However, step: The daily sales transition data of the new product X after the sale is received from the expansion estimating part (5), and the target demand forecasting part data table (61) is determined from the elapsed days after the step. That is, if it is 5 days after the release, the above table (61
a), and the table (61b) above after 6 days, and so on. Then, step; the data table (61) determined in the step is searched to find the sales transition pattern that is the closest to the transition pattern of the received sales transition data, and step; the final sales forecast value of the product is obtained, and step; This value is passed to the production quantity determination unit (7) described later.

That is, assuming that the sales transition pattern of the new product X for 5 days is as shown in FIG. 7, now, FIG.
The demand forecast data table (61a) shown in (1) is determined as the target table, the table (61a) is searched, and pattern matching is performed. As a result, it is judged that the sales transition pattern of the new product X is similar to the pattern stored in the table (61a), and it is predicted that at least a _{2 of} this product X will be finally sold and the product will be produced. The quantity determination unit (7) is acted upon. Further, since this final sales forecast value is the lowest value in the past sales record, when the pattern is selected in the table (61a), the sales of a ₂ pieces are guaranteed.

The final sales forecast value thus obtained by the demand forecasting section (6) is sent to the production quantity determining section (7) connected to the demand forecasting section (6), as shown in FIG. The production amount is determined by the steps shown in the flowchart of FIG. 9 by using the stock information data table (71). That is, the inventory information data table (7
In 1), the stock quantity of each of the new products X, Y, Z ... and the already instructed quantity of additional production are input and stored.

Then, step: obtaining the final sales forecast value x of the new product X from the demand forecasting section (6), and step: obtaining the inventory quantity z of this product X from the inventory information data table (71) and instructing additional production. Obtain the quantity u and step; if the new product X has passed n days since its release, sales results by expansion estimation during this period (Yi is the sales record by the expansion estimation on the i-th day after the release) is calculated. Then, the step: how many additional productions of the new product X need to be calculated by the following formula (3), Step: As a result, that is, the required additional production quantity S is transferred to the raw material ordering section (8) and the control section (4a) of the production means (4) described later, and is stored in the inventory information data table (71). The above-mentioned additional production already instructed quantity data, which has been instructed, is updated based on the above-mentioned calculation result and made into new data.

The required additional production quantity determined by the production quantity determination unit (7) as described above is supplied to the raw material ordering unit (8) and the raw material ordering unit (8) via the instruction means (instruction unit) (3) connected to the determination unit (7). It is sent to the control section (4a) of the production means (4) at the same time, and immediately starts the production system. Then, every time production is completed in the production means (4), the inventory information data table (71) is appropriately
The stock quantity data and the instructed quantity data for additional production are updated and used as new data. That is, although the stock quantity of the new product X is currently Z, when the information that the production of the required additional quantity S of the product X is completed is received from the control unit (4a) of the production means (4). , Product X
The inventory quantity data of is updated to Z + S pieces.

Further, the currently instructed quantity of the product X is U, but similarly to the above, when the production end information for the required additional production quantity S is received, the already instructed quantity data of the product X is U-. S
The individual.

That is, the raw material ordering unit (8) is shown in FIGS. 10 (a) to (d).
The raw material data table (81) shown in FIG. 11 is used to calculate the order quantity of the raw material according to the steps of the flowchart shown in FIG. 11, and to order the raw material to the raw material procurement section (9). This raw material data table (81) is a raw material composition data table (81a) by product shown in FIG. 10 (a), and a required quantity data table by raw material (81) shown in FIG. 10 (b).
b), raw material inventory data table (8
1c), and a raw material-based storage schedule data table (81d) shown in FIG. In the raw material composition data table for each product (81a), the required quantity per unit of each constituent raw material α, β ... X for each product A, B ... X is, for example, to make one product X, p _It is input and stored such that _one piece is required, while the required quantity data table for raw materials (81b) is required (used) for each day (1 to n days). Raw material quantity a ₁ , a
₂ ... a _n has been entered. Further, the stock data table (81c) classified by raw material shows that the stock quantity zα, z of the raw materials α, β ...
β ... is stored, and a scheduled receipt data table for each raw material (81
In d), scheduled storage quantities of raw materials α, β ... (1 to n days) are stored respectively.

However, step: The additional production required quantity S of the new product X is received from the production quantity determining unit (7), and step: The planned production date j is received from the production means (4) described later on a relative date with today as 0. . And step; Raw material composition data table by product (81a)
Are obtained to obtain the constituent raw materials of this product X and the required quantities p ₁ , p ₂ ...

Step; Required quantity per unit of this constituent raw material p ₁ , p ₂
... is multiplied by the additional production required quantity S sent from the production quantity determination unit (7) to obtain the raw material addition required quantity sp ₁ , sp ₂ ..., and the step; this raw material addition required quantity sp ₁ , sp ₂ ... It is added to the required quantity by raw material aj, bj ... Required on the planned production date j in the separately required quantity data table (81b). Then, step: Check the stock quantity by raw material as of today in the stock data table by raw material (81c), and calculate the daily excess and deficiency number for each raw material by the following formula (4) by the following formula (4). The day it occurred,
That is, the delivery date is designated when the number of excesses and deficiencies becomes negative. The order quantity at this time is determined by the following formula (5), and the order quantity is ordered to the raw material procurement section (9).

That is, in order to produce three new products X, the raw material α is 3p.
It was confirmed from the raw material composition data table for each product (81a) that _one piece is required, and this required quantity 3p ₁ is the planned production date j in the required quantity data table for each raw material (81b).
It is added to the required (planned) quantity aj of the raw material α originally required. Therefore, on the jth day, the raw material α
+ It comes to 3p ₁ or necessary. Let's call this new aj. However, if the stock of the raw material α is zα pieces today, the raw material α is k It is calculated that there are remaining pieces. If this value becomes negative for the first time on the kth day, the kth becomes the delivery date of the raw material α.

Further, the production means (4) for receiving the production instruction from the instruction unit (3) and performing the production is a production unit (4b) having a hardware configuration.
And a control unit (4a) for controlling the production unit (4b) according to the production instruction.

The production unit (4b) includes a drive unit (4c) for setting and outputting the action range, speed, power, etc. of each machine unit, and this drive unit (4c) is controlled by the control unit (4a). It has become. However, when the production instruction is received as shown in the flowchart of FIG. 12, each drive unit (4c) is automatically switched by searching the drive unit output value data table (41) as shown in FIG. . The drive unit output value data table (41) includes drive units for the products A, B, ... Z.
Output values of 1, 2 ... Are input and stored in advance.

However, step: when the production instruction of the product X is received from the instruction section (3), step: the output value stored in each drive section (4c) is once cleared, and step: the drive section output value for this product X After searching the data table (41) and determining the output value of each drive unit (4c), step: transmitting the output value to each drive unit, temporarily storing the output value, and starting production of the product X. To do.

Further, the production means (4) has a system capable of immediately responding to the production instruction from the instruction section (3). That is, as shown in FIG. 14, in the production line (production department) (4b) to which the additional production instruction for the new product X is to be sent, the standard products A, B, C (preliminary production are usually made in advance. Products that do not remain unsold) and have a relatively large delivery time, and the standard product when the additional production instruction for the new product X is input from the instruction unit (3).
This makes it possible to produce the product X preferentially by gradually moving A, B, and C forward, that is, to immediately start the movement from the steps described with reference to FIG. 12 for the product X.

〔The invention's effect〕

As described above, the present invention is a system in which the production quantity is set immediately in response to the over-the-counter sales information at the store, the raw material preparation instruction and the production instruction for producing this quantity are given, and the production is performed. Thus, when it sells, it is possible to produce only what can be sold.

Therefore, it is possible to solve all the conventional problems such as lack of products at stores and loss of sales opportunities, and conversely having a lot of defective inventory, and it is possible to establish an ideal manufacturing system. is there.

[Brief description of drawings]

FIG. 1 is an overall block diagram showing an embodiment of a system according to the present invention, FIG. 2 is a diagram showing a data structure of a product characteristic data table, and FIG. 3 is an explanation showing an example of product characteristic classification in FIG. FIGS. 4 (a) to 4 (e) are views showing the data structure of the sample store characteristic data table, and FIG. 5 (a).
And (b) are diagrams showing the data structure of the demand forecast data table, FIG. 6 is a flow chart showing the operation of the demand forecast unit, FIG. 7 is an explanatory diagram of the sales transition pattern of products, and FIG. 8 is an inventory information data table. 9 shows a data structure of
Fig. 10 is a flow chart showing the operation of the production quantity determination unit.
(A)-(d) is a figure which shows the data structure of a raw material data table, FIG. 11 is a flowchart which shows operation | movement of a raw material ordering part, FIG. 12 is a flowchart which shows operation | movement of the control part of a production means, FIG. FIG. 14 is a diagram showing a data structure of a drive unit output value data table, and FIG. 14 is an explanatory diagram of a production system in the production unit. (1) …… In-store sales information collection means, (2) …… Production quantity setting means, (3) …… Instruction means (instruction section), (4) ……
Production means, (5) …… Expansion estimation section, (6) …… Demand forecasting section, (7) …… Production quantity determination section, (8) …… Raw material ordering section, (9) …… Raw material procurement section.

Continuation of front page (56) References JP 62-278604 (JP, A) JP 1-224864 (JP, A) Actually opened 62-71756 (JP, U)

Claims (1)

[Claims] 1. An over-the-counter sales information collection means for collecting sales volume data for each product from a plurality of sample stores selected from a large number of stores, and a product classification, price range classification, and target age classification for each product. , A product characteristic data table that stores product-related characteristics such as sales category, and the results of sales of each of the plurality of sample stores are stored as a composition ratio with respect to the entire store, and each of the plurality of sample stores is stored. And a sample store characteristic data table in which the product-related characteristics in the above are stored, and based on data obtained from the product characteristic data table and the sample store characteristic data table, sales for each product collected from the plurality of sample stores Expansion estimation means for calculating expansion estimates of quantity data, and predetermined based on past sales performance trends It is equipped with a demand forecast data table that stores multiple types of sales transition patterns created for each period and the final sales forecast value for each sales transition pattern. Among the multiple types of sales transition patterns in this demand forecast data table From the above, select the sales transition pattern that most closely approximates the transition pattern of the estimated expansion of sales volume data for each product, and use the final sales forecast value for the selected sales transition pattern as the final sales forecast value for that product. Equipped with a forecasting means and an inventory information data table that stores the inventory quantity of each product, etc., and determines the required quantity of production for each product from the data obtained from this inventory information data table and the final sales forecast value for each product. Raw material data table that stores the raw material composition, quantity, etc. for each product A raw material preparation means for preparing a required quantity of the required raw material from the data obtained from the raw material data table and the determined required production quantity, and a production instruction based on the determined required production quantity A production system for over-the-counter products, which comprises: