JPH03102496A - Work scheduling system - Google Patents

Abstract

PURPOSE:To reduce the overproduction loss of goods and the loss of sale opportunity by deciding the volume to be worked of the goods for a plan target day based on a sale plan and the stock of the goods, and planning the work schedule of individual employee for the plan target day based on an obtained result. CONSTITUTION:A working schedule planning means 103 decides the volume to be worked of the goods for the plan target day from the sale planned volume of the goods in a sale schedule file 108 and the stock of the goods stored in a stock data file 115, and stores the result in a working schedule file 109. A work schedule planning means 104 plans the work schedule of each employee for the plan target day from the volume to be worked of the goods classified by every time zone in the working schedule file 109 and the work schedule and working capability of each employee in an employee data file 116, and stores the result in a work schedule file 110. Thereby, it is possible to reduce the overproduction loss of the goods and the sale opportunity loss.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a sales planning method, a processing amount planning method for each product, and a work planning method for each employee in the fresh produce section of a mass retailer such as a supermarket.

[Conventional technology]

The raw processing department at Super Market L-L is one of the earning departments among the various departments staffed by store clerks, and this department aims to reduce losses due to overproduction and out-of-stock items and realize low-cost operations. , is an important management issue. In contrast, the literature “New POS Course NHK Commercial Seminar, 1
987,1. (POS System Introduction Case, Supermarket 1 - Sunny Co., Ltd.)'' introduces the introduction of a perishable work system that utilizes POS data.

This system is aimed at the meat department, where there are many products that need to be made in advance for the next day (hereinafter referred to as "shukoshi"), and is designed to handle everything from weekly sales plans to individual work schedules (5) as follows. Create with the flow.

First, the past P O S (P oj.nt of Sa
The basic sales plan quantity (expected quantity) for each day of the week determined from the LOS) data is output. The chief makes slight adjustments to the basic sales plan numbers, taking into account special sales, weather, etc., and creates the final weekly sales plan. On the planned day, from 8 a.m. until the store opens, we will price and organize the products that can be shipped the previous day, and we will price and organize the items that cannot be shipped. , we will process the quantity (40% of the day's sales plan) so that we can have a 1,00% assortment at the time of store opening.

In addition, this system uses 1-4 o'clock as the reference time, and sets the sales ratio for the meat department as a whole to be 40% of 111's sales from opening to 2 p.m., and 60% from 2 p.m. to closing. . From the time the store opens until 2:00 p.m., 60% of the day's sales plan will be processed, including both products that can be shipped and products that cannot be shipped.

After 2:00 p.m., for products that can be shipped, 40% of the next day's sales plan is processed, and for products that cannot be shipped, the amount (6) is processed to accommodate changes on the day.

1. As in Ichito, sales from 1 store to 2:00 p.m. [According to 1, sales from 1 store to 2:00 p.m. are handled with the stock left over from the previous day or processed before the store opens, and sales from 2:00 p.m. until closing are handled. Therefore, we formulate a processing plan based on a system that allows for processing on the same day. Finally, the amount of processing for each product, the job type and working hours of each employee,
It creates individual work schedules based on work skills and other factors, and outputs the results as a daily store plan.

[Problem to be solved by the invention]

1. In the conventional technology, the weekly sales plan presented by the system 11 does not take into account causal data such as weather and special sales caused by fluctuations in sales, so the chief corrects the sales plan by taking into account the causal data. This has led to problems such as discrepancies between sales plans and sales results, resulting in overproduction of products and loss of sales opportunities.

SUMMARY OF THE INVENTION An object of the present invention is to provide a highly accurate sales forecasting method in planning sales pitches in order to reduce the loss of overproduced products and the loss of sales opportunities.

(7) In addition, in the above-mentioned conventional technology, when formulating the weekly sales plan, no consideration is given to whether the budget for the entire department and the corner to which each product belongs is achieved. First, the chief needs to revise the basic sales plan presented by the system by taking into account the number of sales points between products, the balance of sales amounts, etc., so it takes a lot of time to formulate the final sales plan. There was a problem that it was necessary.

A second object of the present invention is to provide a man-machine interface that can present the budget achievement status of the entire department or corner and easily modify the sales plan for each item.

In addition, in the above conventional technology, when planning the processing plan, the amount of processing from store opening to 2:00 p.m. is the same for all products, which is 60% of the sales plan for the day. There were problems such as loss of sales opportunities and loss of sales opportunities.

A third object of the present invention is to provide a processing planning method that takes into consideration the sales characteristics of each product in order to reduce overproduction losses and sales opportunity losses.

(8) In addition, in the above-mentioned conventional technology, when formulating a work plan for each employee, work instructions are given for processing work before the opening of the store that is 40% of the sales plan for the same day for products that cannot be processed.

Therefore, if the employees have extra energy, there is a problem that the employees have idle time, the setup time for product processing increases, and the work efficiency decreases.

A fourth object of the present invention is to provide a work plan that shortens the setup time due to changing the type of product to be processed and allows employees with idle time to perform processing work ahead of schedule.

In addition, the prior art described in L does not take into account the method of correcting the processing plan according to the sales situation of each product on the target day of the plan, resulting in problems such as overproduction of products and loss of sales opportunities. was there.

A fifth object of the present invention is to provide a same-day processing planning system that takes into account the sales characteristics of each product, in order to reduce the loss of overproduction of products that cannot be shipped and the loss of sales opportunities.

[Means to solve the problem]

(9) In order to achieve the above purpose, a normal number of customers prediction means that predicts the number of customers on the planned day using a quantification type I analysis method from actual customer number data, causal data, and expected causal data on the planned day. If a special causal that occurs only once a year (such as Obon, Christmas, etc.) becomes the expected causal on the planning target date, the actual number of customers from last year when the causal occurred is used as the predicted number of guests for the planning target. A means for predicting the number of customers on a special day, and a means for calculating a growth rate in the number of customers for one year from the total number of customers in the last few weeks of the planning target date and the total number of customers in the previous year corresponding to the last few weeks. A planning target that predicts the final number of customers on the planning target day by multiplying the predicted number of customers obtained by the normal daily number of customers prediction means or the special daily number of customers prediction means by the customer number growth rate obtained by the customer number growth rate calculation means. The average sales points per unit number of customers calculated from the daily customer number prediction means, sales point performance data, and customer number performance data are multiplied by the predicted number of customers on the planning target day obtained by the planning target daily customer number prediction means. A sales point prediction means for predicting the number of sales points for each product on a day, and a sales point prediction means for predicting the number of sales points for each product on a day. In order to achieve the second objective, we also need to calculate the amount of product sales when displaying each product on the planning date. A corner-by-corner sales planning means that totals the predicted sales amount for each product for each corner, which is a grouping of categories, a budget achievement status tracking means that captures the budget achievement status for the entire department and all corners, and a budget A means for supporting the formulation of a sales plan for each item is provided, which supports the revision of sales forecast results for each item by both parties while referring to the achievement status of the sales forecast for each item.

Furthermore, in order to achieve the third objective, for products that cannot be shipped, the amount of processing that is predicted to be the minimum amount that will be sold by the closing time is set as the amount of processing from the opening time to the standard time. A processing planning means will be provided.

In order to achieve the fourth and eleventh objective of the present invention, in the pre-opening processing work for products that cannot be processed, if there is enough work to do, products that are scheduled to be processed in a later processing time are prioritized for processing. A work planning means is provided to gradually increase the number of processing lots for each product, starting from the highest ranking order, and increasing the number of processing lots before the store opens.

In order to achieve the fifth and final purpose, for each product.
Calculate the ratio of the sales volume up to the base time to the total sales volume of ``, calculate the sales points after the base time from that ratio and the actual sales points up to the base increase time on the planned engraving date, and then A day-day machining planning means is provided for determining the amount of machining after the reference time based on the reference time.

[Effect]

In the 8111 means of predicting the number of passengers at the entrance, using the quantification type I analysis method from the actual number of customers data, the causal data, and the expected causal data for the planning target date, using the planning target 1 for which no special causal occurrence is expected as an example. Predict the number of customers for the planning target [1. The special all customer number prediction means targets a planning day on which a special causal is expected to occur, and determines the actual number of customers from last year when the special causal occurred as the predicted number of customers on the planning day. The customer growth rate calculation method calculates the annual growth rate of the number of customers from the total number of customers for the most recent month of the target FJ and the total number of customers for last year corresponding to the most recent month (12). . The planning target daily number of passengers forecasting means multiplies the total number of passengers obtained by the normal daily number of passengers forecasting means or the special daily number of passengers forecasting means by the number of passengers growth rate obtained by the number of customers growth rate calculation means, and calculates the final number of passengers for the planning target day. Predict the number of customers. The sales points prediction means calculates the total by multiplying the average sales points per unit number of customers calculated from the sales points performance data and the customer number performance data by the predicted number of customers on the planning target day obtained by the planning target day customer number prediction means. Predict the number of sales for each product of Taisho H. The sales amount estimation II means calculates the sales amount for the planning date by multiplying the estimated sales number for each simple product obtained by the sales amount prediction means by the selling price of each product.

As a result, it is possible to predict with high accuracy the number of units sold and the sales amount of each product, and it is possible to reduce the loss of overproduction of products and the loss of sales opportunities.

The corner-by-corner sales planning means calculates the predicted sales amount of each product obtained by the sales amount prediction means for each corner, which is a grouping of products by category when each product is displayed. The budget achievement status presentation means presents the budget achievement status for the entire department (13) and corner as a whole. The single-item sales planning support means provides a man-machine function that allows the planner to easily change the predicted sales number and selling price for each product in the corner selected in consideration of the budget achievement status. This makes it possible to quickly formulate a sales plan that takes into consideration the budget performance of the entire department and corner.

The processing plan planning means for products that cannot be shipped creates a processing plan based on the products that cannot be shipped, with the minimum amount expected to be sold by the closing time as the amount of processing from the store opening time to the standard time. do. This makes it possible to particularly reduce losses caused by overproduction of products that cannot be shipped.

In the work planning means, in the pre-opening processing work of products that cannot be processed, if there is work margin, the products scheduled for processing in later processing time slots are processed in order of processing priority before the store opens. A work plan is drawn up to gradually increase the number of processing lots 1- of each product to reduce the number of work boxes. As a result, setup time for product addition and idle time for employees can be shortened (14), and work efficiency can be improved.

In the same-day processing plan, the sales up to the standard time for each product are 11" total sales. Calculate the ratio of the quantity, calculate the number of sales points after the standard time from that ratio and the actual number of sales points up to the base increase time of the plan, and then calculate the number of sales points after the standard time based on that. Determine the amount of processing. As a result, the amount of processing is adjusted according to the current sales situation of each product, so it is possible to reduce the loss of overproduction of products that cannot be shipped and the loss of sales opportunities.

〔Example〕

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

1 is an overall configuration diagram of a work scheduling system showing an embodiment of the present invention.

In the sales forecasting means 101, the controller 11-
POS data file managed by 8] - Predict the sales of each product on the planning date from the sales performance data, customer number data, etc. registered in 2) and use the results as sales 1 - prediction. Store in result file 107 (King 5).

In the sales planning means 102, the sales forecast result file 1
．． The planner modifies the predicted sales of each product in 0.7 while considering the budget achievement status, formulates a sales ratio for the planning date, and stores the results in the sales plan file 108.

In “Processing Planning Means”-03, sales plan file 10
Sales plan amount of each product in 8 and inventory amount data file 11
The amount of processing for each product on the planning date is determined from the inventory amount of each product stored in 5, and the result is stored in the processing plan file]-09.

In the work planning means 104, the improvement plan file 109
Processing amount of each product by time zone and employee data file 1. Based on the work schedule and work ability of each employee in 1 and 6, create a work plan for each employee on the planned day,
The results are saved in work plan file 1-1. Store at 0.

In the same-day processing plan planning means Co-05, POS terminal 119
Collected from 1 haircon 1 roller 1 ]. PoS data file 1 of the day managed in 8 (Work 6) of the day's sales and number of customers and processing plan file 10
Based on the planned processing amount of each product in 9, a processing plan up to the standard time of the day is planned and stored in the day's processing plan file 111.

The overall control means 106 controls the control means 1 to 1 from 01 to 1. The workstation 12 performs execution control of each means of 0.05.
0 or the printer 1-21 to display the output results from each means.

Hereinafter, sales forecast A111 means 1, which is the main part of this embodiment.
-01, Sales planning means 102, Processing planning means 1
03, Work Planning Means 104, Part 1 A specific example of the structure of the processing planning means 105 will be described in detail.

First, the current structure of the sales forecasting means]-01 is shown in the 21st column.

In the basic customer number forecasting department 20, POS data file 1
The customer count data registered in the King 2, the causal (factors caused by fluctuations in customer numbers such as weather and other store conditions) performance data, and the forecast of the planned engraving date registered in the forecast causal data file 113 ( 17) Predict the basic number of customers for the planning date using the causal-based quantification type 1 analysis method, and store the result in the basic number of customers table 208 shown in FIG.

In the customer number growth rate calculation unit 202, the POS data file 1
The growth rate of the number of customers per year is calculated from the actual capacity data registered in 12, and the result is stored in the growth rate table 209 of the number of customers in format 1- shown in FIG.

The number of customers prediction unit 203 predicts the number of customers on the planned day from the basic capacity in the basic number of customers table 208 and the growth rate of the number of customers in the number of customers growth rate table 209, and if necessary, the planner can modify the number of customers [1 The results are then converted into a predicted number of customers table 2 in the format shown in Figure 5]. Store in O.

The unit number of customers and sales points calculation unit 204 calculates the POS data file 1. 1 Calculate the average sales -4-points per 10th customer (hereinafter referred to as PI value) for each product from the customer count performance data and perfect 10 points performance data registered in 2, and use the result as the PI value. 6 is stored in the PI value table 211 in the format shown in FIG.

(18) The sales score prediction unit 205 predicts the sales score of each product of the planning target ['' from the predicted number of customers on the planning target date in the predicted customer number table 210 and the PI value of each product in the PT value table 211, and calculates the result. It is stored in the sales score table 21-2 in the format shown in FIG.

In the sales amount prediction unit 206, the jlt value data file 2
The sales amount of each product on the planning target date is predicted from the unit price of each product in 13 and the predicted sales number of each product in the sales number table 212, and the result is stored in the sales amount table 2↓4 in the format shown in FIG. do.

The execution control unit 207 controls the execution of steps 201 to 206.

Next, the processing method of each processing unit 201 to 206 will be explained.

First, the 3+p method of the basic customer number prediction unit 201 will be described using flowcharts 1 to 1 shown in FIG.

<Step 9 0 1>: Setting the planning target ['' Set the planning target BY year M month D date. This information is taken into this processing unit in the form of an interaction with the planner.

(19) <Step 9 0 2>: Judgment of occurrence of special causal Causes due to fluctuations in the number of customers are determined by constant 1 every day.
Causals are classified into causals that occur during - (normal causals) and causals that occur only in a specific period or period (special causals). If the predicted causal of the planning target date registered in the predicted causal data file 208 is only the normal causal, proceed to step 903. If a special causal is included, it will be released in ep905.

<Step 9 0 3>: Extraction of actual customer number data and causal performance data From among the actual customer number data and causal performance data registered in POS data file 1-12, actual customer number data for the last few months of the planning target date. , Extract causal performance data. In this example, the data for the last one to two months of the planning target date is stored as the number of customers in the format shown in Figure 10.
Store it in the causal results table.

<Step 9 0 4>: Estimation of the basic number of customers on a normal day sl: Ep 9 0 3 customer number, customer number actual data in (20) in the causal performance table, causal performance data, and registered in the expected causal data file 208. Based on the predicted causal, quantification type 1 analysis is performed, and the number of prefectural main customers y on the target day of splitting (day-): f-guchi is estimated using the following formula.

Here, : Number of factors (items) B. Flood. 1: Item X. + Number of categories (categories) 11/il I as itel I1 due to 19J
'l. Weather and other store conditions are set, and the day of the week items are divided into seven categories from day 1 to day 1, and weather is u*, i... There are four categories: rain and snow, and other store status is divided into two categories: closed and closed. Furthermore, as the external standard (actual number of customers), one month's worth of data on the planning target date, that is, the actual data for month Y (M1 King) is adopted. Based on these data, quantification I
When performing class analysis, the number scene for each category is the 12th
It will look like the figure. Using these quantities, if the plan is 1,000 J, (the causal is 1 (1 day is 1 fire), weather equipment, etc., if the store is closed, then, ノ, (The number of regular customers 7 is calculated using the following formula.

y = X 12+ X 2Z+ X ss=Soil 3 6
0 - 7 4 + 1-○O-1386 (persons) (s
step 905>: J, (determining the number of regular customers for the special account) For a planning quadrant in which a special causal is expected to occur, the actual number of customers from last year when the special causal occurred is determined as the basic number of customers for the planned day.For example, , as shown in Figure 13, if the predicted causal (22) for the planning date includes item 11, 'Christmas Eve', and the causal 'Christmas Eve' is included, the POS Data file”-1
In 2, from the actual customer number data stored in the same manner as in 1-4, the actual number of customers 2 in the year 12J124 (Y-1)
500 people is determined as the Ml image subject ['', that is, the basic number of customers on December 24, Y year.

Next, a method for calculating the growth rate in the number of customers by the customer growth rate calculation unit 202 will be explained. First, POS data file 112
From this, we extract the data on the number of fire introductions for the most recent few weeks for the planned 1:l period, and the actual capacity data from last year for the last few weeks. Then, the next person calculates the growth rate ρ in the number of customers.

, ] = 1 where, n : Total of the last few weeks of the planning target date "1 number Y 2 Western calendar of the planning target date (23) K(Y) : Actual number of customers K+y- in the latest planning target El of Y year.j1" xl, (y] -) Actual number of customers in the last 5] days of the planning target date In this embodiment, the actual number of customers data for the most recent 4a of the planning target date is as shown in 151st woman + (a), and The most recent 4
If last year's actual number of customers corresponding to a week is given as shown in Figure 15 (})), the capacity growth rate ρ is calculated as follows.

The customer number prediction unit 203 calculates the basic number of customers obtained by the basic customer number prediction unit. From the customer number growth rate ρ obtained by the customer number growth rate calculation unit, the final predicted number of customers Y for the planned day is calculated using the following formula.

Y = y, neρ In this embodiment, the predicted number of customers Y is as follows.

? =]386m1.035=#1d35 (person) Single A~
．． The number of customers and sales points calculation unit 204 calculates the actual number of customers data registered in the POS data (24) data file 1 and 2.
From the sales point performance data, the average number of sales points (PI value) D0' per unit number of customers for the product is calculated using the following formula.

N Where, N: Total number of weeks most recent to the planning target date] Number UJ:
ii Image most recent target date. ] [] Actual sales of item l [1 item Kj: Planned target date n' [Actual number of customers a on recent j day
2 unit number of customers conversion coefficient In this example, the actual number of customers for the planning target "I" in the last four weeks is as shown in Figure 15 (a), and the actual number of customers for each product is as shown in Figure 15 (a). For example, if the number of customers is 1000, 244 y), the average f+N sales of 3 saury [1 item per item) (saury)
is calculated as follows.

The sales score prediction unit 205 calculates the sales score of product i on the planning target day from the predicted number of customers Y obtained by the customer number prediction unit 203 and the PI value D of product 1 obtained by the unit customer sales score calculation unit 204. is calculated using the following formula.

■(1): 7 YuD(f》/a) In this example, if the predicted number of customers Y and the PI value of 3 saury fish are calculated as described above, then the sales of 3 saury fish on the planned day The score 'y (san!) is calculated as follows.

■ (Sanma) =1386 * 5.3/10
0 0 L: 8 (ke) The sales amount prediction unit 206 calculates the unit price c (iゝ
and the sales score of product j obtained by the sales score prediction unit 205■
From this, the sales amount W of the product on the planning date is calculated using the following formula.

W 2v *c”ゝIn this example, if the unit price data for three Pacific saury fish is given as shown in Figures (25), (26), and 17, and the number of sold items is given as shown above, then the planned harvest is The sales amount W(SA,!) of 3 saury fish in 1I is calculated as follows.

W (saury) = 8 x 400 = 3200 (yen) According to the sales forecasting means 01, which has the following configuration, the number of sales points, sales of each product of the planning target [''. Pre-hardening can be performed at the same time, reducing losses from overproduction and out-of-stock losses for 1+'tj products.

Next, the detailed processing flow of the sales plan J<7 plan means 1-02 will be explained according to the flowchart shown in FIG.

(Step 1 8 0 1 >: In the SL draft processing department of the sales plan by corner, sales forecasting means 1.
The predicted sales minus the amount of each product obtained in step 1 are totaled.

An example of a sales plan by corner is shown in No. 19.

In the same figure, in the item rI1901, the results of the overall classification of the predicted sales amount of each product are presented. Also, item 19
In o2, the total predicted sales amount (27) for each corner is presented. Item soil 903 has
The budget and actual results up to the planned date are presented.

Diagram 1904 shows the total of the previous week's budget and actual results, and the achievement rate calculated by actual Mt/budget.
05, the total budget and actual results from the beginning of the month to the target date of the plan, as well as the achievement rate are presented.

(Step 1 8 0 2 >: Judgment of completion of sales plan planning The results obtained by the sales forecasting means 1-01-, that is,
If the draft of the sales plan has achieved 110% of the budget and the creation of the sales plan has been completed, the process ends; otherwise, the process proceeds to step 803.

<Step 1 8 0 3>: Selection of corner to be modified for sales plan Based on the sales plan for each corner, select the corner to which the single item belongs in order to modify the sales plan for each item.

(Step 1 8 0 4 >: Modify the sales analysis by item. For each product in the corner selected in step 1 8 0 3, modify the sales number and selling price to achieve the budget < 41i, sales plan by item Make a plan.

FIG. 20 shows an example of a sales plan for individual items. (28) In the figure, when modifying the sales number and unit price, enter the desired number in the 'After modification' column.

<Step 805>: Judgment of completion of sales plan planning Once the planning of the sales plan for each item has been completed and the budget has been achieved for all corners, proceed! end.

If there is a corner for which the individual product sales plan has not yet been completed, the process returns to stop 803.

According to this embodiment, the budget for the entire department can be presented when creating a sales plan, and the predicted number of items to be sold and selling prices can be easily changed. With Kozai L, you can quickly formulate a sales plan that takes into account the budget achievement of the entire department and corner.

Next, the machining plan 3'7 planning means 103 will be explained. In this means, the processing amount PI from the work start time to the store opening time, the processing amount from the store opening time to the standard time -tPz,
Addition from arrival time to closing time ft'c P s
, determine the amount of processing for each product for each of the three time periods.

This method consists of a processing planning department for 1'6 products that can be processed for next day's sale, and a processing plan for products that cannot be shipped (771 steps). 29) There is a planning department for the processing of products that cannot be shipped.

First, P' of product j that can be sent to the processing planning department for products that can be shipped.
The formula for calculating %, p'; , PT will be explained. Let the amount of product j- on display at the time of store opening be o (1ゝ), and the amount of product j- from previous [j] be Z.

・Z(11 ≧OL′′, then P'i' =
0. If I) < o (1ゝ, then ding, (j)
10 (11 -7.411 The sales plan (number of sales) for product l is ■ (11. The ratio of the ten sales volume up to the standard time to the daily sales volume is α 3゛1.

pi : l = v ( l l −ZI l l1
α3
0, the next day's sales plan (number of sales) for product j is R+.

Pa=R'! 'α Next, the formula for calculating P 1 1 P 2H P 3 of product 1 in the square product processing planning department will be explained.

Pf:) = ON+ Actual results of each product (3
0) Sell-1. Let the set of points be <K(.iゝ)(,j
-1,2+N), then from the latest data, product]
The number of points 13L'' for which - can be sold at least on both calculation days is calculated as follows.

B ” = m-in ({k':' ))・B(
1) If 〉Of1+, then P5ゝ1B(+ゝ1O
L'・B(++≦030, then I)(muゝ=○P(
;l is determined by the current LI processing plan <f plan means 1 0 5.

According to this embodiment, it is possible to suppress the loss of overproduction of irreproducible products.

Next, the work plan Sl draft means 104 will be explained. In this method, employees are assigned to different types of processing work (for example, in the fresh fish department, there are various processing lines such as sashimi processing lines, fillet processing lines, etc.).

Hereinafter, details of the process will be explained using flowchart 1 to 211'R1.

(Step 2 1 0 1 > : Tentative assignment of processed pears to employees (31) Processing tasks are selected in order of priority, and are provisionally assigned to employees with high assignment priorities.

For example, as shown in Figure 22, if a processing work assignment priority table and a work schedule table for each employee are given and IF+ is given, sashimi, which is the processing work with the highest horizontal priority, will be assigned priority priority. Assign it to Mr. A who has the highest rank.

Similarly, fillet work is assigned to Mr. 13.

<st. ep 2 1 0 1 > : Load calculation for ensuring minimum display amount Step 2 Determine whether or not the employee assigned in 1 0 1 can process the minimum display amount. First, the negative amount xJ for processing the minimum display amount is calculated using the following formula.

x1-Σ(n:'lj:) i=1 where, j: processing operation P: total number of items i: item number] 1 2 processing operations,] To ensure the minimum display amount of product Fl i Processing scene t1: Processing work, 1-(32) of item j in f j′1 +! Jj (step 2 1 0 3 > : Assigned employee decision judgment qtop 2102 calculated negative xJ is compared with the assigned employee's work time until the delivery date for securing the minimum display amount, and if it can be done, the assigned employee is / It is determined that the work is possible, and the work is carried out on st.ep2 1 0 4. If it is determined that the processing work is not suitable, the next work assignment customer face (11゜l of 1'i, the employee or , employees who come to work early (to extract 1, steep 2 1.. 0 1
Return to

In the specific example of [Second] *T(a), Mr. A's working days are:
7:30-16:00 (J, load x' is 1
This indicates that the process was completed before 1:00.

(Step 2 1. 0 4 > : Adjustment of processing power to ensure minimum display quantity) If the work is completed before the store opens, a certain increase (products with low quantity, products with good sales, etc.) ) to assess whether it is possible to process the stocked items for each item at once.If possible, make the loft larger so that it can be made within the time required for the minimum display amount.

In the specific example of Section 23X (b), after the opening of the store,
l:3) One piece of product a, two pieces of product b, which were to be sold.
Respectively, opening 1) 1f processing l1, 'flllI Q:
It shows that it has moved to F.

(sl; c door 2 1. 0 5 > : Determination of the processing area for the store in Shif-eup, including the processing total for products that could not be manufactured by step 2 1.-0 4). Determine the processing failure of the shioki and shikoshi portions.

The specific example in FIG. 23(c) shows the result of a work plan that also takes into consideration the 8: service that is to be completed after the opening of the store.

According to this embodiment, setup time for product processing and idle time of employees can be shortened, and the number of workers can be reduced and work efficiency can be improved.

Next, the processing plan planning means]-05 will be explained. In this method, 1] measurement target 1 collected from the OS
Sales of the day -.・From the same-day sales-L prediction department, which predicts sales after the standard time based on customer number data, etc., and the same-day processing amount planning department, which determines the amount of processing for each product after the standard time based on the prediction results. Some.

The same-day sales forecasting department uses the actual sales volume data up to the reference time (34) for the most recent weeks and the actual sales data of 1]-1 to calculate the total sales volume of 1-unit of product i. The sales ratio α(j) (hereinafter referred to as 1. selling) until the time of sale is calculated using the following formula' (1, N Total number of weeks [■ number uJ
: Sales volume of R'H product j up to the reference time in "Planning target 1j latest, ]1" UT: Total sales volume of product j in the latest j day of planning target date α(1) calculated above, opening of the store on that day Sales volume record Um of product j from time T"S to a certain time Tp ('I"8~
From time 'J', to closing time Te using
The sales amount U'' (Tp~go1→) is calculated using the following formula.

(35) For example, as shown in Figure 24, if 6 units of product X have been sold by the reference time Tp, then product
Sales of -. Using the set of ■, the quantity U becomes as follows (assuming α = 0.2).

In the same-day processing amount planning department, the t. J
”' Based on (T p − T e ), time T
Determine the processing amount Pto of product j after p. This machining amount P(1) corresponds to the machining amount P3 explained in the machining plan planning means 103 mentioned above. Here, the inventory amount of product i is assumed to be 7°'.

・U"' (Tp-Te)) 7." then P0ゝ=
'Kan'"' (Tp ~ Go.) 1 Z0ゝ・U"
(“If I'P~T(・)≦ZLIl, then P = O In the above case, if product 1 cannot be shipped, (36) measures such as discounts will be taken, and if it can be shipped, The amount will be transferred to the next day.

The following variations can be considered as the method I1 of "Jukoku No To 1] Sales Forecasting Department." First, "II+, 1l'7"
11; Accumulate the number of sold I items and the number of customers for each item from 'l' to the reference time. Next, the cumulative sales points are divided by the cumulative number of customers to calculate 141 the day's P I {ii for each product. And sales-. The number of customers after the base KO time is calculated by subtracting the raw total number of customers for "To 1" from the predicted number of customers obtained by the customer number prediction section of the prediction means 101. Finally, the number of customers is
Multiply by I {lα to calculate the number of sold items for each product per hour.

According to the present invention, the amount of processing can be adjusted depending on the sales of the product on the day, and the loss of overproduction of the product and the loss of sales opportunities can be reduced at the same time.

[Effect of departure]

According to the present invention, sales of each product on the planning date -.
Score, selling F agreement is i'+; It is possible to contradict the degree of spoilage, and it is possible to reduce losses due to overproduction of products and sales opportunity losses.

(37) Furthermore, according to the present invention, the budget for the entire department can be presented when formulating a sales plan, and the predicted number of sold L items and selling price can be easily changed. As a result, it is possible to quickly create a sales plan that takes into consideration the budget achievement status of the entire department and corner.

Further, according to the present invention, for products that cannot be shipped, the planning contingency L+>! Since the amount of processing is adjusted according to the sales of ``S +'', it is particularly effective in reducing losses caused by overproduction of products that cannot be shipped.

Further, according to the present invention, setup time for product processing and idle time of employees can be shortened, and the number of workers can be reduced and work efficiency can be improved.

4. ．． Brief explanation of the fourth aspect of IS Figure 1 is a block diagram showing the overall structure of a work scheduling system that is an embodiment of the present invention, Figure 2 is a block diagram showing the structure of a sales forecasting means, and the 31st threshold - Figure 8,
Figures 10 to 7 are diagrams showing the tables of each insert used in the example, Figure 9 is a flowchart 1- showing the execution of the basic customer number forecast i111 of the example, and No. 18 is a flowchart of the example. Flowchart 1-, which shows the processing of the sales planning method (38), Figure 19 is the corner of the sales plan example)
Figure 1 shows an example of a sales plan, Figure 20 shows an example of a sales plan for each item according to the embodiment, Figure 21 shows a flowchart for planning the work plan of the embodiment, Figure 22 Figure 23 shows an example of provisional preparation (1) for employees in the work of the embodiment;
The figure is an explanatory diagram of the processing of the xj1j complete prediction unit of the length example.

1. 0 1.Sales/Thousand Means, 102 edition sales pitch-1
Image EF plan means, 103・Processing plan Q plan means, 1 0 4
・Work planning means, 105・Processing plan planning means, 113 ・Forecast causal data file, 11-6
・(39) Haya 3 Zusu 4 Figure 5 Figure Otsu Figure 7 Zukka 6 Figure /ρ Figure No./3 Figure I /4 Figure /OtsuE Tomi No 7 Figure abusel5 Figure (0-) ( b) Nest/8 Figure

Claims (1)

[Scope of Claims] 1. Sales forecasting means for predicting the sales of each product on the planning date; and sales that formulates a sales plan for each product on the planning date based on the results obtained by the sales forecasting unit. a planning means;
A processing planning means for determining the amount of processing for each product on the planning date based on the sales plan and the inventory amount of each product; A work scheduling system consisting of a work planning means for creating work plans for employees. 2. For planning target dates that do not require consideration of causal events (hereinafter referred to as special causal events) that occur infrequently throughout the year, such as year-end, New Year's holidays, and Obon holidays (hereinafter referred to as special causal events), actual customer number data, A sales forecasting means comprising a normal daily number of customers forecasting means for predicting the number of customers on a planned day using a quantification type I analysis method from causal data and expected causal data on the planned day. 3. For a planned day on which a patent causal is expected to occur, the system is characterized by having a means for predicting the number of customers on a special day, which uses the actual number of customers from last year when the special causal occurred as the predicted number of customers on the planned day. Sales forecasting means. 4. It is characterized by the provision of a means for calculating the growth rate of the number of customers for one year from the total number of customers for the last few weeks of the planning target date and the total number of customers for last year corresponding to the last few weeks. The sales forecasting means according to claim 2 or 3, wherein: 5. Multiplying the predicted number of customers obtained by the means for predicting the number of daily customers according to claim 2 or the means for predicting the number of special customers per day according to claim 3 by the growth rate of customers obtained by the means for calculating the growth rate of customers according to claim 4. A sales forecasting means comprising a means for predicting the number of customers on a planning day for predicting the final number of customers on a planning day. 6. The average sales points per unit number of customers calculated from the sales point performance data and the customer number performance data is multiplied by the predicted number of customers on the planning day obtained by the means for predicting the number of customers on the planning day according to claim 5. A sales prediction means comprising a sales number prediction means for predicting the sales number of each product on a target date. 7. A sales amount prediction means is provided for calculating the sales amount on the planning target day by multiplying the predicted sales points for each product obtained by the sales point prediction means according to claim 6 by the selling price of each product. sales forecasting method. 8. Corner-specific sales planning means for summing up the predicted sales amount of each product obtained by the sales amount prediction means according to claim 5 for each corner, which is a grouping of product types when each product is displayed; A budget achievement status presentation means that presents the budget achievement status for the entire department and corner, and for each product in the corner selected by the planner in consideration of the budget achievement status.
A sales plan planning means is provided with a sales plan planning support means for each item having a man-machine function that can change predicted sales points and selling prices. 9. Divide the daily processing time into three periods: from work start time to store opening time, from store opening time to an arbitrarily determined standard time, and from standard time to closing time, and process each product for each time period. 2. The work scheduling system according to claim 1, wherein the workpiece scheduling system determines the processing amount of the workpiece. 10. For products that cannot be sold in advance for the next day's sale (hereinafter referred to as stocking), the minimum amount expected to be sold by the store opening time is calculated as the amount of processing from the closing time to the standard time. 10. The processing planning means according to claim 9. 11. When processing products that cannot be shipped before the store opens, if there is time to do so, the products that are scheduled to be processed in a later processing time will be processed in order of priority before the store opens. 2. The work scheduling system according to claim 1, wherein the work scheduling system formulates a work plan. 12. Calculate the ratio of the sales volume up to the standard time to the total sales volume for each product in a day, and determine the amount of processing after the standard time based on that ratio and the actual number of sales items up to the standard time on the planning target date. 2. The work scheduling system according to claim 1, further comprising a same-day processing planning means.