JP2025146331A - Replenishment planning system, replenishment planning method, and program - Google Patents

Abstract

A replenishment planning system, method, and program are provided that create a replenishment plan for replenishment of an item based on whether or not the item has been recently shipped from a warehouse.
[Solution] The replenishment planning system 100 includes a number of days before shipment calculation unit that calculates the number of days that have passed since the most recent shipping date, a shipping interval expected value calculation unit that calculates the shipping interval expected value from the time-series change in the shipping interval, a shipping interval variance calculation unit that calculates the shipping interval variance from the time-series change in the shipping interval, a shipping quantity expected value calculation unit that calculates the shipping quantity expected value from the time-series change in the shipping quantity at the time of shipment, a shipping quantity variance calculation unit that calculates the shipping quantity variance at the time of shipment from the time-series change in the shipping quantity at the time of shipment, a shipping occurrence expected value calculation unit that calculates the shipping occurrence expected value from the number of days that have passed, the shipping interval expected value, and the shipping interval variance, and a replenishment planning creation unit that calculates the expected shipping quantity for each item from the shipping occurrence expected value, the shipping quantity expected value at the time of shipment, the shipping quantity variance at the time of shipment, and inventory quantity data.
[Selected Figure] Figure 1

Description

The present invention relates to a technology for creating a replenishment plan for replenishing items in a warehouse.

In logistics warehouses, etc., the daily work of receiving orders from destinations involves picking, packing, etc., before shipping and delivering. It is particularly important to deliver orders promptly in accordance with the delivery date after receiving them. In order to maintain this quality, stock of each item is replenished on the picking shelves in advance before picking begins, such as at the start of work, to ensure smooth picking operations. From here on, this work will be referred to as replenishment work.

In addition, warehouses are large and manage inventory for tens of thousands of different items. In such cases, it is difficult to replenish all items in a single replenishment operation, so items to be replenished are selected in advance and replenishment operations are carried out. Furthermore, unnecessary replenishment of a wide variety of items results in excess inventory, which is undesirable as it increases the amount of space occupied and management costs. On the other hand, neglecting to replenish items that are actually necessary can result in work being interrupted due to inventory shortages after picking operations have begun, and emergency replenishment operations (emergency replenishment) will be required. For these reasons, it is necessary to create replenishment plans in advance, taking into account factors such as shipping trends.

A common method of creating replenishment plans is to determine whether or not replenishment is necessary and the amount of replenishment to be made based on the average past shipping volume. For example, if the inventory level on the picking shelf is less than the average shipping volume multiplied by coefficient α, it is determined that replenishment is necessary, and the amount required to bring the inventory level to at least the average shipping volume multiplied by coefficient β is replenished, and replenishment work is carried out in this manner.

In this case, since it is difficult to set α and β for each of the tens of thousands of different items, it is common to use a uniform value. However, shipping trends on an item-by-item basis are intermittent, and this must be kept in mind. For example, the average shipping volume of item A, which is shipped x units every day, and item B, which is shipped 10x units once every 10 days, is equal to x units. If coefficient β is set to < 10, item B will always be out of stock at the time of shipping, requiring emergency replenishment. On the other hand, if β > 10, item A will end up with excess stock. Therefore, rather than the average shipping volume including days when there are no shipments, it is better to consider the average shipping volume on days when there are shipments and the shipping interval.

Looking at existing patents from this perspective, Patent Document 1 discloses technology aimed at supporting sales activities by sending reminders to sales staff based on order forecasts, and at production management such as reducing out-of-stock items and optimizing inventory. The technology calculates the standard shipping volume per shipment, the shipping interval per unit shipment, and the standard shipping interval, and then calculates the next or subsequent order volume and the predicted shipping date by taking into account the data on the standard shipping volume, the shipping interval per unit shipment, and the standard shipping interval, as well as statistical fluctuations in this data.

JP 2002-73752 Public Relations

Patent Document 1 deals with shipments from a factory, where the shipping interval corresponds to the factory's production capacity by averaging the quantity over a certain period of time, and is of a nature that does not fluctuate greatly. On the other hand, in the case of shipments from a warehouse, the shipping volume and shipping interval change over time depending on trends in the items being shipped and seasonal fluctuations. Therefore, it is necessary to evaluate the time-series trends in shipping volume and shipping intervals and create a replenishment plan that anticipates appropriate shipping timing.

Furthermore, work hours and storage capacity are finite, and if inventory levels are low and emergency replenishment is required, work efficiency drops. Therefore, in order to avoid emergency replenishment, it is necessary to know whether there has been an immediate shipment. However, it is not easy to determine whether there has been an immediate shipment based on the average shipping volume, the average shipping volume on a given day, or the shipping interval. Furthermore, since the shipping interval averaged over a certain period of time does not necessarily reflect recent shipping trends, it is difficult to determine whether there has been an immediate shipment, even using the technology in Patent Document 1.

The present invention aims to provide a replenishment planning system, replenishment planning method, and program that creates a replenishment plan for replenishment of an item based on whether or not the item has been recently shipped from a warehouse.

The present invention comprises a shipping performance data input unit that inputs shipping performance data including items, shipping quantities, and shipping dates; an inventory quantity data input unit that inputs inventory quantity data including the items and the current inventory quantities of the items; a non-shipment elapsed days calculation unit that calculates the number of days that have elapsed since the most recent shipping date from the shipping performance data; a shipping interval expected value calculation unit that calculates a time series change in shipping intervals from the shipping performance data and calculates an expected value of the shipping interval by weighting the most recent change based on the time series change; a shipping interval variance calculation unit that calculates the variance of shipping intervals using the time series change in shipping intervals; and a shipping interval variance calculation unit that calculates a time series change in shipping quantity at the time of shipment from the shipping performance data and calculates the time series change in shipping quantity at the time of shipment from the shipping performance data. The present invention provides a replenishment planning system comprising: a shipment volume expected value calculation unit that calculates an expected value of shipment volume at the time of shipment by weighting the most recent change amount based on the column change amount; a shipment volume variability calculation unit that calculates the variability of shipment volume at the time of shipment using the time-series change amount of shipment volume at the time of shipment; a shipment occurrence expected value calculation unit that calculates an expected value of shipment occurrence from the number of days elapsed since the most recent shipping date, the expected shipping interval, and the variability of shipping intervals; a forecast shipping volume calculation unit that calculates a forecast shipping volume from the expected value of shipment volume at the time of shipment and the variability of shipment volume at the time of shipment; and a replenishment plan creation unit that creates a replenishment plan including the expected value of shipment occurrence and the forecast shipping volume.

According to the present invention, a replenishment plan for replenishing an item can be created based on whether or not the item has been recently shipped from the warehouse.

1 is an overall configuration diagram of a replenishment planning system according to a first embodiment of the present invention; FIG. 10 is a diagram illustrating an example of shipping performance data. FIG. 10 is a diagram illustrating an example of inventory quantity data. FIG. 10 is a diagram illustrating an example of replenishment plan data. 3 is a flowchart of a replenishment plan creation process in the replenishment planning system according to the first embodiment of the present invention. 10 is a detailed flowchart of a non-shipment elapsed day number calculation process according to the first embodiment of the present invention. 10 is a detailed flowchart of a shipping interval expected value calculation process according to the first embodiment of the present invention. 10 is a detailed flowchart of a shipping interval variation calculation process according to the first embodiment of the present invention. 10 is a detailed flowchart of a shipment occurrence expected value calculation process according to the first embodiment of the present invention. FIG. 10 is a diagram for explaining a method for calculating a shipment occurrence expected value table p[i](x). 10 is a detailed flowchart of a shipping volume expected value calculation process according to the first embodiment of the present invention. 10 is a detailed flowchart of a shipping volume variation calculation process according to the first embodiment of the present invention. 10 is a detailed flowchart of an expected shipping volume calculation process according to the first embodiment of the present invention. 10 is a detailed flowchart of a replenishment plan creation process according to the first embodiment of the present invention. 1 is an example of a display screen of the replenishment planning system according to the first embodiment of the present invention. FIG. 10 is an overall schematic diagram of a replenishment planning system according to a second embodiment of the present invention. 10 is a flowchart of a multi-day replenishment plan creation process in a replenishment planning system according to a second embodiment of the present invention. 10 is a detailed flowchart of a shipment record data update process in a shipment record data update unit of a replenishment planning system according to a second embodiment of the present invention. 10 is a detailed flowchart of an inventory quantity data update process in an inventory quantity data update unit of a replenishment planning system according to a second embodiment of the present invention. FIG. 10 is an overall schematic diagram of a replenishment planning system according to a third embodiment of the present invention. FIG. 10 is a diagram illustrating an example of location master data. 10 is a flowchart of an order arrangement plan creation process in a replenishment planning system according to a third embodiment of the present invention. FIG. 10 is an overall schematic diagram of a replenishment planning system according to a fourth embodiment of the present invention. FIG. 10 is a diagram illustrating an example of item master data. 10 is a flowchart of a vehicle dispatch plan creation process in a replenishment planning system according to a fourth embodiment of the present invention.

Next, a form for implementing the present invention (hereinafter referred to as an "embodiment") will be described in detail, with reference to the drawings as appropriate. In the following figures, the same elements are designated by the same numbers or symbols throughout the description of the embodiment.

First Embodiment
FIG. 1 is a diagram showing the overall configuration of a replenishment planning system according to a first embodiment of the present invention.
The replenishment planning system 100 is a system that generates replenishment plan data 40, which is a replenishment plan for items in a warehouse, based on actual shipment data 10 and inventory quantity data 20, and includes an actual shipment data input unit 101, a number of days before shipment calculation unit 102, an expected shipping interval calculation unit 103, a shipping interval variation calculation unit 104, an expected shipping quantity calculation unit 105, an expected shipping quantity calculation unit 106, an expected shipment occurrence calculation unit 107, a predicted shipping quantity calculation unit 108, an inventory quantity data input unit 109, and a replenishment plan creation unit 110.

The shipping performance data input unit 101 accepts input of shipping performance data 10 output from a warehouse management system (WMS) or the like.

Figure 2 shows an example of shipping performance data 10. Each row of shipping performance data 10 consists of an item, shipping destination, shipping quantity, and shipping date.

The unshipped days calculation unit 102 calculates the number of days 30 (shown in Figures 5 and 6) that have elapsed since the most recent shipping date to the present for each item based on the shipping performance data 10.

The shipping interval expected value calculation unit 103 calculates the time series change in shipping interval for each item based on the shipping performance data 10, and uses this time series change to calculate the shipping interval expected value 31 (shown in Figures 5 and 7), which indicates the expected value of the shipping interval.

The shipping interval variation calculation unit 104 calculates the amount of change in the shipping interval over time for each item based on the shipping performance data 10, and uses this amount of change over time to calculate the shipping interval variation 32 (shown in Figures 5 and 8), which indicates the variation in the shipping interval.

The shipment volume expected value calculation unit 105 calculates the time-series change in shipment volume for each item based on the actual shipment data 10, and uses this time-series change to calculate the shipment volume expected value 33 (shown in Figures 5 and 11) at the time of shipment, which indicates the expected value of the shipment volume at the time of shipment.

The shipment volume variation calculation unit 106 calculates the time-series change in shipment volume for each item based on the actual shipment data 10, and uses this time-series change to calculate the shipment volume variation 34 (shown in Figures 5 and 12) at the time of shipment, which indicates the variation in shipment volume at the time of shipment.

The shipment occurrence expected value calculation unit 107 calculates the shipment occurrence expected value 35 (shown in Figures 5 and 9) indicating the probability that a shipment will occur on the current date for each item based on the number of elapsed days 30 calculated by the number of days before shipment calculation unit 102, the shipping interval expected value 31 calculated by the shipping interval expected value calculation unit 103, and the shipping interval variance 32 calculated by the shipping interval variance calculation unit 104.

The expected shipment volume calculation unit 108 calculates the expected shipment volume 36 (shown in Figures 5 and 13) for each item based on the expected shipment volume 33 at the time of shipment calculated by the expected shipment volume calculation unit 105 and the shipment volume variance 34 at the time of shipment calculated by the shipment volume variance calculation unit 106.

The inventory quantity data input unit 109 accepts input of inventory quantity data 20 output from a warehouse management system (WMS) or the like.

Figure 3 shows an example of inventory quantity data 20. Each row of inventory quantity data 20 consists of an item, a location ID that identifies the storage location of the item, and the current inventory quantity of the item.

The replenishment plan creation unit 110 creates replenishment plan data for each item, including the shipment occurrence expected value 35 calculated by the shipment occurrence expected value calculation unit 107 and the expected shipping quantity 36 calculated by the expected shipping quantity calculation unit 108. The replenishment plan creation unit 110 may also include, for each item, the feasibility of replenishment determined from the results of comparing the expected shipping quantity 36 with the inventory quantity data 20, and the required replenishment quantity calculated based on the expected shipping quantity 36 and the inventory quantity data 20, in the replenishment plan data.

Figure 4 shows an example of replenishment plan data 40. Each row of replenishment plan data 40 consists of an item, a replenishment necessity flag, a predicted shipping amount, and a required replenishment amount. The replenishment plan data 40 is stored in the multiday replenishment planning system 200 or an external storage unit (not shown).

Next, a process for generating a replenishment plan in the replenishment planning system 100 according to the first embodiment of the present invention will be described with reference to FIGS.
FIG. 5 is a flowchart of a replenishment plan creation process in the replenishment planning system 100 according to the first embodiment of the present invention.

First, the number of days before shipment calculation unit 102 executes the number of days before shipment calculation process shown in FIG. 6 (to be described later) for each item based on the actual shipment data 10 acquired via the actual shipment data input unit 101, and calculates the number of days before shipment 30 (S1).
The shipping interval expected value calculation unit 103 executes the shipping interval expected value calculation process shown in FIG. 7 (to be described later) for each item based on the shipping performance data 10 acquired via the shipping performance data input unit 101, and calculates the shipping interval expected value 31 (S2).

The shipping interval variation calculation unit 104 executes the shipping interval variation calculation process shown in FIG. 8 (to be described later) for each item based on the shipping performance data 10 acquired via the shipping performance data input unit 101, and calculates the shipping interval variation 32 (S3).
Next, the shipment occurrence expected value calculation unit 107 executes the shipment occurrence expected value calculation process shown in Fig. 9 (described later) for each item based on the number of elapsed days 30 calculated in S1, the shipment interval expected value 31 calculated in S2, and the shipment interval variation 32 calculated in S3, and calculates the shipment occurrence expected value 35 (S4). Note that S1 to S3 may be executed simultaneously or in any order.

The shipment volume expected value calculation unit 105 executes the shipment volume expected value calculation process shown in FIG. 11 described later for each item based on the shipment performance data 10 acquired via the shipment performance data input unit 101, and calculates the shipment volume expected value 33 at the time of shipment (S5).
The shipment volume variation calculation unit 106 executes a shipment volume variation calculation process shown in FIG. 12 (described later) for each item based on the shipment record data 10 acquired via the shipment record data input unit 101, and calculates the shipment volume variation 34 (S6). Note that S5 and S6 may be executed simultaneously or in any order. Furthermore, S5 and S6 may be executed simultaneously with S1 to S4 or in any order.

Next, the estimated shipment volume calculation unit 108 executes the estimated shipment volume calculation process shown in Fig. 13 (described later) for each item based on the expected shipment volume value 33 at the time of shipment calculated in S5 and the shipment volume variation 34 at the time of shipment calculated in S6, and calculates the estimated shipment volume 36 (S7). Note that S7 may be executed simultaneously with S1 to S4, or may be executed in any order.
Finally, the replenishment plan creation unit 109 executes the replenishment plan creation process shown in FIG. 13 (to be described later) based on the inventory quantity data 20 acquired via the inventory quantity data input unit 109, the expected shipment occurrence value 35 calculated in S4, and the forecast shipment quantity 36 calculated in S7 for each item, and generates replenishment plan data 40 (S8).

Figure 6 is a detailed flowchart of the number of days since shipment calculation process (S1 in Figure 5) performed by the number of days since shipment calculation unit 102. The number of days since shipment calculation unit 102 starts processing when actual shipping data 10 is input. Hereinafter, the total number of items included in actual shipping data 10 will be assumed to be n, and each item will be represented as item [i] (i = 1, 2, ..., n).

First, the number of days since shipment calculation unit 102 sets the initial value 1 to i (S11).
Next, the number of days before shipment calculation unit 102 starts a loop for extracting the number of days before shipment T[i] for all items included in the shipment record data 10 (S12).
Next, the number of days since shipment calculation unit 102 reads all row data for item [i] from the actual shipment data 10 (S13).
Next, the number of days since shipment calculation unit 102 extracts the most recent shipping date from the row data of the item [i] read in S13 (S14).

Next, the number of days since shipment calculation unit 102 calculates the number of days since shipment T[i] based on the most recent shipping date extracted in S14. Specifically, the number of days since shipment T[i] is calculated by subtracting the most recent shipping date extracted in S14 from the date of plan creation (hereinafter simply referred to as the "today") (S15).
Next, the number of days since shipment calculation unit 102 sets i+1=i (S16).
Then, when i>n, the number of days without shipment calculation unit 102 determines that the number of days without shipment T[i] has been extracted for all items included in the shipping performance data 10, terminates the loop processing (S17), and outputs the pair of item[i] and the number of days without shipment T[i] for all items as the number of days elapsed since the previous shipping date, 30.

Figure 7 is a detailed flowchart of the shipping interval expected value calculation process (S2 in Figure 5) in the shipping interval expected value calculation unit 103. The shipping interval expected value calculation unit 103 starts processing when actual shipping data 10 is input. Note that the same processes as those shown in Figure 6 are assigned the same reference numerals and descriptions are omitted.

First, the shipping interval expected value calculation unit 103 complements missing dates in the shipping performance data 10 (S21). Specifically, a row is added to the shipping performance data 10 in which the missing date for each item is set as the shipping date, the shipping destination is left blank, and the shipping quantity is set to "0". Furthermore, following S11, a loop is started to calculate the shipping interval expected value TE[i] (S22).

Next, following S13, the shipping interval expected value calculation unit 103 calculates the number of days without shipment from the k (k=1, 2, ...)th shipping date to the k+1th shipping date based on the row data of item [i] read in S13, and arranges them in chronological order to calculate the shipping interval series DT[i] (S23). Note that the number of days without shipment after the kth shipping date is calculated as the k+1th shipping date - the kth shipping date - 1.
Next, the shipping interval expected value calculation unit 103 calculates the shipping interval expected series LT[i] taking the most recent shipping interval into consideration, using the shipping interval series DT[i] calculated in S22 (S24).

The calculation of the forecast series LT[i] of shipping intervals taking into account the most recent time when using Simple Exponential Smoothing (SES) is shown in equation (1). The shipping interval expectation calculation unit 103 recursively creates forecast series LT[i] for multiple α using equation (1) and selects the forecast series that minimizes the error with the shipping interval series DT[i]. Note that the calculation of the forecast series LT[i] of shipping intervals taking into account the most recent time can be done using any method that takes into account the most recent time, and is not limited to SES; it can also be the Holt method, Holt-Winters method, variable response smoothing, etc.

Returning to FIG. 7, the shipping interval expected value calculation unit 103 acquires the last value of the shipping interval forecast series LT[i] for the item [i] calculated in S23 as the shipping interval expected value TE[i] (S25).
Then, after processing in S16, if i>n, it is determined that the shipping interval expectation value TE[i] has been obtained for all items included in the shipping performance data 10, and the loop processing is terminated (S26), and the pair of item [i] and its shipping interval expectation value TE[i] is output as the shipping interval expectation value 31.

8 is a detailed flowchart of the shipping interval variation calculation process (S3 in FIG. 5) in the shipping interval variation calculation unit 104. The shipping interval variation calculation unit 104 starts the process when the shipping performance data 10 is input. Note that the same processes as those shown in FIGS. 6 and 7 are denoted by the same reference numerals and their explanations are omitted.
First, the shipping interval variation calculation unit 104 performs (S21) interpolation of missing dates in the shipping performance data 10. After the process of S11, the shipping interval variation calculation unit 104 starts a loop for calculating the shipping interval variation σT[i] (S31).
Next, the processes of S13, S23, and S24 are executed in order to calculate the predicted series LT[i] of shipping intervals for item [i], taking the most recent interval into consideration.

Next, the shipping interval variation calculation unit 104 calculates the standard deviation of the forecast series LT[i] of shipping intervals for item[i] calculated in S24 to obtain the shipping interval variation σT[i] (S32).
Then, after processing S16, when i>n, the shipping interval variation calculation unit 104 determines that the shipping interval variation σT[i] has been obtained for all items included in the shipping performance data 10, terminates the loop processing (S33), and outputs the pair of item [i] and its shipping interval variation σT[i] as shipping interval variation 32.

9 is a detailed flowchart of the shipment occurrence expected value calculation process (S4 in FIG. 5) in the shipment occurrence expected value calculation unit 107. The shipment occurrence expected value calculation unit 107 starts the process when the number of elapsed days 30, the expected shipping interval 31, and the shipping interval variation 32 are input. Note that the same processes as those shown in FIGS. 6 to 8 are denoted by the same reference numerals, and the description thereof will be omitted.
First, after the process of S11, the shipment occurrence expected value calculation unit 107 starts a loop process of calculating the shipment occurrence expected value p[i] (S41). Here, the shipment occurrence expected value p[i] is the probability that shipment of item i will occur on the day.

Next, the shipment occurrence expected value calculation unit 107 obtains, for item [i], the number of days before shipment T[i] from the number of elapsed days 30, the shipping interval expected value TE[i] from the shipping interval expected value 31, and the shipping interval variance σT[i] from the shipping interval variance 32, and converts the normal distribution of the number of days before shipment T[i] represented by these values into a standard normal distribution. The shipment occurrence expected value calculation unit 107 then converts the number of days before shipment T[i] into the standardized number of days before shipment TN[i] in the standard normal distribution (S42). Specifically, the standardized number of days before shipment TN[i] is calculated using (T[i] - TE[i]) / σT[i]. Note that the shipping interval expected value TE[i] is "0" in the standard normal distribution.

By assuming that the probability of a shipment occurring during the number of days prior to shipment T[i] approximates a normal distribution with a shipping interval expectation TE[i] and standard deviation σT[i], the expected shipment occurrence value p[i] can be calculated by dividing the probability on the current day by the probability after that day in the normal distribution. Then, by converting the distribution of the number of days prior to shipment T[i] into a standard normal distribution with a mean of 0 and a standard deviation of 1, the impact of the shipment occurrence expectation value distribution table p[i](x) on the shipping history of item [i] can be reduced, making it possible to obtain an expected shipment occurrence value p[i] that is less affected by shipping history. Details will be explained later in Figure 10.

Next, the shipment occurrence expected value calculation unit 107 creates a shipment occurrence expected value distribution table p[i](x) (S43). Specifically, the shipment occurrence expected value calculation unit 107 calculates p[i](a) using equation (2) for multiple input values (random variables) a, and creates a shipment occurrence expected value distribution table p[i](x). Note that the shipment occurrence expected value distribution table p[i](x) created in this way is a table that returns a numerical result for each discrete input value a.

The shipment occurrence expected value distribution table p[i](x) should at least cover the range from the standardized number of days pending shipment TN[i] to "0" in a standard normal distribution. Furthermore, the shipment occurrence expected value calculation unit 107 can reference the shipment occurrence expected value distribution table p[i](x) created for one item for other items, even if the items are different, as long as the number of days pending shipment T[i], shipping interval expected value TE[i], and shipping interval variance σT[i] are the same.

Next, the shipment occurrence expected value calculation unit 107 determines whether the value of p[i](TN[i]) can be obtained from the shipment occurrence expected value distribution table p[i](x) created in S43 (S44). If it can be obtained, the obtained value of p[i](TN[i]) is set as p[i] (S45). On the other hand, if it cannot be obtained, the process proceeds to S46 to calculate the value of p(TN[i]) by interpolation.

Ideally, it would be guaranteed that the value of p[i] (TN[i]) can be referenced from the shipment occurrence expected value table p[i](x). However, in reality, the input values of the shipment occurrence expected value table p[i](x) are discrete, and the shipment occurrence expected value table p[i](x) does not necessarily have the value of p[i](TN[i]) corresponding to TN[i]. Therefore, if the value of p[i](TN[i]) cannot be obtained from the shipment occurrence expected value table p[i](x), the interpolation calculation shown in S46 and S47 is performed to obtain the value of p[i](TN[i]).

Next, the shipment occurrence expected value calculation unit 107 obtains the values of p[i]( _x1 ) and p[i](xS) from the shipment occurrence expected value table p[i]( _x ) (S46). Here, _x1 and _xS are input values to the shipment occurrence expected value table p[i](x), and _x1 ≦TN[i]≦ _xS , _x1 < _xS , _x1 and _xS all have values in the standard normal distribution integral table p[i](x).
Next, the shipment occurrence expected value calculation unit 107 interpolates the value of p[i](TN[i]) based on the standardized number of days before shipment TN[i], the input values _x1 , _xS to the shipment occurrence expected value table p[i](x), and p[i]( _x1 ) and p[i]( _xS ) obtained in S46. Then, this interpolated value p[i](TN[i]) is set to p[i] (S47). Note that linear interpolation requires two input values (s=1, 2), and quadratic polynomial interpolation requires three input values (s=1, 2, 3).

After processing in S16, if i>n, the shipment occurrence expected value calculation unit 107 determines that the shipment occurrence expected value p[i] has been obtained for all items included in the shipping performance data 10, terminates the loop processing (S48), and outputs the pair of item [i] and its shipment occurrence expected value p[i] as the shipment occurrence expected value 35.

Figure 10 explains how to calculate the shipment occurrence expected value table p[i](x) described above. For a certain item [i], the number of days before shipment T, the expected shipping interval TE, and the shipping interval variance (standard deviation σT) input to the shipment occurrence expected value calculation unit 107 are represented as condition C.

The expected value for shipping occurrence for item [i] under condition C can be expressed as p[i] (the most recent number of days without shipping is 4 to 5 days / the most recent number of days without shipping is 4 or more days). If 4 days have passed since the last shipping date, the most recent possible number of days without shipping is 4 or more days, and since the expected shipping interval TE under condition C is 5 days, the event of shipping occurring is associated with the event where the most recent number of days without shipping is 4 to 5 days.

p[i] (the most recent number of days without shipment is 4 to 5 days / the most recent number of days without shipment is 4 or more days) can be calculated using integral formula 1001/integral formula 1002, assuming that the number of days without shipment T can be approximated to a normal distribution with expected shipping interval TE and standard deviation σT. However, because condition C, which is a parameter in integral formulas 1001 and 1002, varies depending on the item's shipping history, it is desirable to reduce the number of parameters in the integral formula as much as possible.

Therefore, by converting the normal distribution of the number of days without shipment T into a standard normal distribution, p[i] (the most recent number of days without shipment is 4 to 5 days / the most recent number of days without shipment is 4 or more days) can be calculated using integral formula 1003/integral formula 1004, which has few parameters. In this way, the shipment occurrence expected value table p[i](x) is calculated using integral formula 1003/integral formula 1004, which has few parameters and reduces the influence of the item's shipping history.

11 is a detailed flowchart of the expected shipment volume calculation process (S5 in FIG. 5) in the expected shipment volume calculation unit 105. The expected shipment volume calculation unit 105 starts the process when the actual shipment data 10 is input. Note that the same processes as those shown in FIG. 6 are denoted by the same reference numerals and descriptions thereof will be omitted.
First, following S11, the shipping volume expectation value calculation unit 105 starts a loop process of calculating the shipping volume expectation value VE[i] at the time of shipping (S51).

Next, following S13, the shipment volume expected value calculation unit 105 calculates the shipment volume series DV[i] at the time of shipment by arranging the shipment volumes in chronological order based on the row data of item [i] read in S13 (S52).
Next, the shipment volume expected value calculation unit 105 calculates the expected shipment interval series LV[i] taking the most recent shipment into consideration using the shipment volume series DV[i] calculated in S52 (S53).

The calculation of the forecast series LV[i] of shipment volume at time of shipment that takes into account the most recent data when using Simple Exponential Smoothing (SES) is shown in equation (3). The shipment volume expectation calculation unit 105 recursively creates the forecast series LV[i] for multiple α using equation (3) and selects the forecast series that minimizes the error with the shipment volume series DV[i] at time of shipment. Note that the calculation of the forecast series LV[i] of shipment volume at time of shipment that takes into account the most recent data can be done using any method that takes into account the most recent data, and is not limited to SES, and may also use the Holt method, Holt-Winters method, variable response smoothing, etc.

Returning to FIG. 11 , the shipment volume expectation calculation unit 105 acquires the last value of the shipment volume forecast series LV[i] for the shipment volume of item [i] calculated in S53 as the shipment volume expectation VE[i] (S54).
Next, after processing in S16, if i>n, the shipment volume expectation calculation unit 105 determines that the shipment volume expectation VE[i] has been obtained for all items included in the shipment performance data 10, terminates the loop processing (S55), and outputs the pair of item [i] and its shipment volume expectation VE[i] as the shipment volume expectation 33.

12 is a detailed flowchart of the shipping volume variation calculation process (S6 in FIG. 5) in the shipping volume variation calculation unit 106. The shipping volume variation calculation unit 106 starts the process when the shipping performance data 10 is input. Note that the same processes as those shown in FIGS. 6 and 11 are denoted by the same reference numerals, and the description thereof will be omitted.
First, after the processing of S11, the shipping quantity variation calculation unit 106 starts a loop for calculating the shipping quantity variation σV[i] at the time of shipping (S61).
Next, the processes of S13, S52, and S53 are executed in order to calculate the forecast series LV[i] of shipping quantities at the time of shipment, taking into account the most recent shipments, for item [i].

Next, the shipment volume variation calculation unit 106 calculates the standard deviation of the forecast series LV[i] of the shipment volume at the time of shipment of the item [i] calculated in S53, and obtains the shipment volume variation σV[i] at the time of shipment (S62).
Then, after processing S16, when i>n, the shipment volume variation calculation unit 106 determines that the shipment volume variation σV[i] at the time of shipment has been obtained for all items included in the shipping performance data 10, terminates the loop processing (S63), and outputs the pair of item [i] and the shipment volume variation σV[i] at the time of shipment as the shipment volume variation 34.

13 is a detailed flowchart of the expected shipment volume calculation process (S7 in FIG. 5) in the expected shipment volume calculation unit 108. The expected shipment volume calculation unit 108 starts the process when the expected shipment volume at time of shipment 33 and the shipment volume variation at time of shipment 34 are input. Note that the same processes as those shown in FIG. 6 are denoted by the same reference numerals, and the description thereof will be omitted.
First, after the process of S11, the forecast shipping volume calculation unit 108 starts a loop for calculating the forecast shipping volume V[i] (S71).

Next, the expected shipping volume calculation unit 108 calculates the expected shipping volume V[i] based on the expected shipping volume VE[i] of the expected shipping volume 33 and the shipping volume variance σV[i] of the shipping volume variance 34 (S72). The expected shipping volume V[i] may be a value that takes into account the standard deviation, such as VE[i] + 3σV[i], or it may be the same value as VE[i], since the shipping occurrence expected value p[i] already takes into account the variances in both the shipping interval and the shipping volume.

Then, after processing S16, if i>n, the forecasted shipping volume calculation unit 108 determines that the forecasted shipping volume V[i] has been obtained for all items with the expected shipping volume value at time of shipment 33 and the shipping volume variance at time of shipment 34, terminates the loop processing (S73), and outputs the pair of item [i] and the forecasted shipping volume V[i] as the forecasted shipping volume 36.

Figure 14 is a detailed flowchart of the replenishment plan creation process (S8 in Figure 5) in the replenishment plan creation unit 110. The replenishment plan creation unit 110 starts processing when the inventory quantity data 20, expected shipment occurrence value 35, expected shipment quantity 36, and expected shipment occurrence value threshold Pthres are input. Here, the expected shipment occurrence value threshold Pthres is a fixed parameter determined in advance. Furthermore, of the inventory quantity data, the inventory quantity data for item [i] is represented as inventory quantity data S[i]. Note that the same processes as those shown in Figure 6 are assigned the same reference numerals and descriptions are omitted.

First, after processing S11, the replenishment plan creation unit 110 starts a loop process to determine the replenishment necessity flag (S81).

Next, the replenishment plan creation unit 110 determines whether the shipment occurrence expected value p[i] of the shipment occurrence expected value 35 is greater than or equal to the shipment occurrence expected value threshold value Pthres, and whether the expected shipment volume V[i] of the expected shipment volume 36 is greater than the inventory quantity data S[i] (S82).
If the shipment occurrence expected value p[i] is equal to or greater than the shipment occurrence expected value threshold Pthres and the forecast shipment volume V[i] is greater than the inventory volume data S[i], the process proceeds to S83. On the other hand, if the shipment occurrence expected value p[i] is less than the shipment occurrence expected value threshold Pthres or the forecast shipment volume V[i] is equal to or less than the inventory volume data S[i], the process proceeds to S85.

Next, if the expected shipment occurrence value p[i] is equal to or greater than the shipment occurrence expected value threshold value Pthres and the expected shipment volume V[i] is greater than the inventory volume data S[i], the replenishment plan creation unit 110 sets the replenishment necessity flag Frep[i] to "1," which means replenishment is "necessary" (S83).

The replenishment plan creation unit 110 then calculates the required replenishment amount Vin[i] based on the inventory data S[i] and the forecasted shipping amount V[i], specifically V[i] - S[i] (S84). If a replenishment unit amount set for each item is defined, the required replenishment amount may be a multiple of the minimum replenishment unit amount that satisfies the required replenishment amount. For example, if one box contains 12 items, the replenishment unit amount is 12 items, and if V[i] - S[i] is 20, the required replenishment amount Vin[i] is 24 items, which is the equivalent of two boxes.

On the other hand, if the expected shipment occurrence value p[i] is less than the threshold value Pthres for the expected shipment occurrence value, or if the expected shipment volume V[i] is less than or equal to the inventory quantity data S[i], the replenishment plan creation unit 110 sets the replenishment necessity flag Frep[i] to "0", which means "no replenishment" (S85).
Next, the replenishment plan creation unit 110 sets the required replenishment amount Vin[i] to "0" (S86). When the replenishment necessity flag Frep[i] is set to "0" in S85, which means "no replenishment," the required replenishment amount Vin[i] also becomes "0," which means "no replenishment."

Next, if i > n after processing S16, the replenishment plan creation unit 110 determines that the replenishment necessity flag Frep[i] has been determined for all items included in the shipping performance data 10, terminates the loop processing (S87), and outputs replenishment plan data 40, each set consisting of item[i], replenishment necessity flag Frep[i], estimated shipping volume V[i], and required replenishment volume Vin[i]. Note that the replenishment plan data 40 only needs to include at least one of the replenishment necessity flag Frep[i], estimated shipping volume V[i], and required replenishment volume Vin[i].

FIG. 15 shows an example of a display screen of replenishment planning data of the replenishment planning system according to the first embodiment of the present invention.
In this display example, the replenishment plan data is composed of a set of items [i], each of which is associated with a replenishment necessity flag Frep[i], an expected shipment occurrence value p[i], an inventory amount S[i], an expected shipment amount V[i], and a required replenishment amount Vin[i].

As shown in the figure, the display screen displays the current date and a replenishment plan based on replenishment plan data 40. The replenishment plan consists of the item (item name or ID), whether replenishment is required, expected shipment occurrence, inventory amount, expected shipment amount, and required replenishment amount.

The replenishment necessity field is based on the replenishment necessity flag Frep[i] in the replenishment plan data 40. If the flag is "1", "Needed" is displayed, and if it is "0", a blank field is displayed. The expected shipment occurrence value is the expected shipment occurrence value p[i] in the replenishment plan data 40 displayed as a percentage. The inventory quantity is displayed as inventory quantity data S[i]. The expected shipping quantity is displayed as the expected shipping quantity V[i] in the replenishment plan data 40. The required replenishment quantity is displayed as the required replenishment quantity Vin[i] in the replenishment plan data 40.

The display screen may display only items requiring replenishment, or may display the expected shipment occurrence value, expected shipment volume, or required replenishment volume in ascending or descending order, or may display only any required items. Furthermore, of the items requiring replenishment, only a preset number (number of replenishable items) of items may be displayed in descending order of expected shipment occurrence value, highest expected shipment volume, or highest required replenishment volume. Furthermore, only items whose expected shipment occurrence value, expected shipment volume, or required replenishment volume are above a preset threshold value may be displayed.

As described above, according to this embodiment, it is possible to determine the need for replenishment for each item based on the expected shipment occurrence value calculated based on the shipping interval taking into account the most recent shipment, and the expected shipping volume at the time of shipment taking into account the most recent shipment. This makes it possible to create a replenishment plan based on the need for replenishment for each item. As a result, it is possible to prioritize replenishment of items that are likely to be shipped, thereby avoiding emergency replenishment. In addition, it is possible to calculate the expected shipping volume and required replenishment volume and include them in the replenishment plan, making it possible to understand replenishment priorities and the personnel and time required for replenishment. It is also possible to create replenishment work plans such as personnel allocation and time schedules.

Second Embodiment
Next, a replenishment planning system according to a second embodiment will be described in detail with reference to FIGS.
The replenishment planning system according to the second embodiment is a system that generates replenishment planning data 40, which is a replenishment plan for multiple days from the current day to several days later, based on actual shipping data 10 and inventory quantity data 20. Hereinafter, the replenishment planning system/process according to the second embodiment will be referred to as a multi-day replenishment planning system/process. In the second embodiment, a configuration different from that of the first embodiment will be described.

FIG. 16 is an overall schematic diagram of the multiday replenishment planning system 200 of the present invention.
The multi-day replenishment planning system 200 includes a shipment performance data input unit 101, a number of days before shipment calculation unit 102, an expected shipping interval calculation unit 103, a shipping interval variation calculation unit 104, an expected shipping quantity calculation unit 105, a shipping quantity variation calculation unit 106, an expected shipment occurrence calculation unit 107, a predicted shipping quantity calculation unit 108, an inventory quantity data input unit 109, a replenishment plan creation unit 110, a shipment performance data update unit 210, an inventory quantity data update unit 211, and a planned number of days management unit 212.

The shipment performance data update unit 210 updates the shipment performance data 10 input to the shipment performance data input unit 101 based on the replenishment plan data 40 obtained in the replenishment plan processing described with reference to FIGS.
The inventory quantity data update unit 211 updates the inventory quantity data 20 input to the inventory quantity data input unit 109 based on the replenishment plan data 40 obtained in the replenishment plan processing described with reference to FIGS.
The planned days management unit 212 manages an internal variable m (m=0, 1, . . . , N−1) that indicates the number of days in order to create a replenishment plan for the planned number of days.

17 is a flowchart of the multiday replenishment plan creation process in the multiday replenishment planning system 200 of the present invention. The planned days management unit 212 starts the process when the shipment performance data 10, inventory quantity data 20, planned days N indicating the number of days for creating the replenishment plan data _40m , and shipment occurrence expected value threshold Pthres are input.

First, the planned days management unit 212 executes a process of initializing an internal variable m, which indicates the number of days from the plan creation date, to 0 (S101). That is, the current day is 0 days later.
Next, the planned days management unit 212 starts a loop process for generating replenishment plan data for N days from the current day (S102).

Next, the same functional units as those of the replenishment planning system 100 described in the first embodiment execute a replenishment plan creation process based on the actual shipment data 10 _m for m days later, the inventory quantity data 20 _m for m days later, the fixed number of planning days N, and the fixed shipment occurrence expected value threshold value Pthres for determining whether or not shipment will occur, to generate replenishment plan data 40 _m for m days later and store it in the replenishment plan data 40 (S103).
When m=0, the actual shipping data m days from now is the actual shipping data 0 days from now, i.e., the actual shipping data used to create the replenishment plan for that day, and is the actual shipping data 10 acquired by the actual shipping data input unit 101. Similarly, when m=0, the inventory quantity data m days from now is the inventory quantity data 0 days from now, i.e., the inventory quantity data used to create the replenishment plan for that day, and is the inventory quantity data 20 acquired by the inventory quantity data input unit 109.

Next, the actual shipment data update unit 210 executes the actual shipment data update process shown in FIG. 18, which will be described later, and updates the actual shipment data 10 m for m days later to actual shipment data 10 _m + ₁ for m+1 days later, based on the replenishment plan data 40 _m generated in S103 (S104).
19, which will be described later, and updates the inventory quantity data 20m for m days from now to the inventory quantity data _20m + ₁ for m+1 days from now based on the replenishment plan data _40m generated in S103 (S105). Note that although S104 and S105 are executed in parallel, they may be executed in any order.

Next, the planned days management unit 212 executes a process of incrementing the internal variable m by 1, that is, a process of setting m+1=m (S106).
Then, when the internal variable m becomes equal to or greater than N, the planned days management unit 212 determines that replenishment plan data 40m for each day for N days has been generated, terminates the loop processing (S107), and outputs replenishment plan data 40 including replenishment plan data _40m (m = 0 to N-1) for each day for N days from the current day to N-1 days later, which was generated and stored in S103.

FIG. 18 is a detailed flowchart of the shipping record data update process in the shipping record data update unit 210.
First, after the process of S11, the shipping performance data update unit 210 starts a loop process for updating the shipping performance data (S111).

The shipment performance data update unit 210 determines whether the shipment occurrence expected value _pm [i] m days from now is equal to or greater than the shipment occurrence expected value threshold Pthres, i.e., whether shipment of item [i] will occur m days from now (S112). If the shipment occurrence expected value _pm [i] is equal to or greater than the shipment occurrence expected value threshold Pthres, i.e., if shipment of item [i] will occur m days from now, the process proceeds to S113. On the other hand, if the shipment occurrence expected value _pm [i] is less than the shipment occurrence expected value threshold Pthres, i.e., if shipment of item [i] will not occur m days from now, the process proceeds to S16.

The actual shipping data update unit 210 updates the actual shipping data 10 _m for m days later to actual shipping data 10 _{m +1 for m+1 days later based on the replenishment plan data 40 m for} m days later (S113). Specifically, a row is added to the actual shipping data 10 _m for m days later, associating the item [i], the expected shipping volume V _m+1 [i] as the shipping volume, and the date m days later (today's date) as the shipping date, thereby updating the actual shipping data 10 _m to actual shipping data 10 _m+1 .

Then, after the processing of S16, when i>n, the shipping performance data update unit 210 determines that updating has been completed for all items included in the shipping performance data 10 _m , terminates the loop processing (S114), and outputs the shipping performance data 10 _m+1 after m+1 days.

FIG. 19 is a detailed flowchart of the inventory data update process in the inventory data update unit 211.
First, after the process of S11, the inventory quantity data update unit 211 starts a loop process for updating inventory quantity data (S121).

Next, the inventory quantity data update unit 211 determines whether the shipment occurrence expected value _pm [i] m days from now is equal to or greater than the shipment occurrence expected value threshold Pthres (S122). If the shipment occurrence expected value _pm [i] is equal to or greater than the shipment occurrence expected value threshold Pthres, the process proceeds to S123. On the other hand, if the shipment occurrence expected value _pm [i] is less than the shipment occurrence expected value threshold Pthres, the process proceeds to S124.

Next, the inventory data update unit 211 sets the inventory decrease amount Vout _m [i] to the expected shipping amount V _m [i] m days later (S123).
Alternatively, the inventory quantity data update unit 211 sets the inventory decrease amount Vout _m [i] to "0" (S124).

Next, the inventory quantity data update unit 211 determines whether the replenishment need flag Frep _m [i] after m days is "1" or "0", i.e., whether replenishment of item [i] is required after m days (S125). If the replenishment need flag Frep _m [i] after m days is "1", i.e., replenishment of item i is required after m days, the process proceeds to S126. On the other hand, if the replenishment need flag Frep _m+1 [i] after m days is "0", i.e., replenishment of item i is not required after m days, the process proceeds to S127.

Next, the inventory quantity data update unit 211 sets the required replenishment amount Vin _m [i] to V _m [i] - _{S m} [i] (S126). Note that if a replenishment unit amount is defined for each item, the required replenishment amount may be a multiple of the minimum replenishment unit amount that satisfies the required replenishment amount. For example, if one box contains 12 items, the replenishment unit amount is 12 items, and if V _m [i] - _{S m} [i] is 20, the required replenishment amount Vin _m [i] is 24 items, which is equivalent to two boxes. Furthermore, when replenishing, it may be set so that the item [i] is replenished to the maximum capacity of the shelf or row where it is stored.

Alternatively, the inventory data update unit 211 sets the required replenishment amount Vin _m [i] to "0" (S127).
The inventory quantity data update unit 211 updates the inventory quantity S _m [i] m days from now to the inventory quantity S _m +1 [i] m+1 days from now, calculated using the inventory quantity S _m [i] m days from now + the required replenishment quantity Vin _m [i] calculated in S126/S127 - the inventory reduction quantity Vout m [i] calculated in _S123/S124 (S128).
Then, after processing S16, when i>n, the inventory quantity data update unit 211 determines that updating has been completed for all items included in the inventory quantity data _20m , terminates the loop processing (S129), and outputs inventory quantity data 20m ₊₁ for m+1 days later.

This embodiment makes it possible to generate replenishment plans for multiple days, from the day the plan is created to several days later. By planning replenishment work not only for the current day but also for the foreseeable future, it becomes possible to advance replenishment when it is predicted that a large amount of replenishment will be required in the future, which is effective from the perspective of load leveling.

Third Embodiment
Next, a replenishment planning system according to a third embodiment will be described in detail with reference to FIGS.
The replenishment planning system according to the third embodiment is a system that creates order arrangement plan data, which is an ordering and arrangement plan, in addition to the replenishment plan. Hereinafter, the process of creating an order arrangement plan in the replenishment planning system according to the third embodiment will be referred to as an order arrangement plan creation process. In the third embodiment, a configuration different from the first and second embodiments will be described.

FIG. 20 is an overall schematic diagram of a replenishment planning system according to a third embodiment of the present invention.
The replenishment planning system 300 includes a shipment performance data input unit 101, a number of days before shipment calculation unit 102, a shipping interval expected value calculation unit 103, a shipping interval variation calculation unit 104, a shipping quantity expected value calculation unit 105, a shipping quantity variation calculation unit 106, a shipment occurrence expected value calculation unit 107, a forecasted shipping quantity calculation unit 108, an inventory quantity data input unit 109, a replenishment plan creation unit 110, a shipment performance data update unit 210, an inventory quantity data update unit 211, a planned number of days management unit 212, a location master data input unit 313, and an order arrangement plan creation unit 314.

The location master data input unit 313 accepts input of location master data 50 output from a warehouse management system (WMS) or the like.

Figure 21 shows an example of location master data 50. Each row of location master data 50 consists of an item, replenishment source location, lead time, shelf number, row, and column. Note that for items ordered from a factory, the shelf number, row, and column associated with the shelf may be empty.

The order arrangement plan creation unit 314 creates order arrangement plan data 60 based on the location master data 50 and the N-day replenishment plan data 40 created by the replenishment plan creation unit 110.

22 is a flowchart of the order arrangement schedule creation process in the replenishment planning system according to the third embodiment of the present invention. This process is executed under the control of the planned days management unit 212.
First, each functional unit identical to that of the multi-day replenishment planning system 200 described in the second embodiment executes a multi-day replenishment plan creation process based on the actual shipment data 10, inventory quantity data 20, location master data 50, number of planned days N, and threshold value Pthres for expected shipment occurrence value, and generates replenishment plan data 40 _m (m = 0 to N-1) for each day for N days (S201).

Next, after the processing of S11, the order arrangement plan creation section 314 starts a loop process for generating order arrangement plan data (S202).
Next, the order arrangement plan creation unit 314 identifies all replenishment plan data 40 _m for which the replenishment necessity flag Frep[i] for item [i] is "1" based on the replenishment plan data 40 _m for each day for N days, identifies the date on which replenishment will occur (replenishment date) for each identified replenishment plan data 40 _m , and determines whether or not the date has been identified (S203). If no replenishment plan data 40 _m has been identified, replenishment will not occur until N days after the replenishment plan is created, so the process proceeds to S16. If one or more replenishment plan data 40 _m have been identified, the replenishment date for each replenishment plan data 40 _m is identified, and the process proceeds to S204. Here, the identified replenishment plan data 40 _m is a replenishment plan m days after the date of plan creation, so the replenishment date is calculated by adding m to the date of the date of plan creation.

Next, the order arrangement plan creation unit 314 acquires the replenishment source location Loc[i] and lead time Ldt[i] of the item [i] from the location master data 50 input to the location master data input unit 313 (S204).
Next, the order arrangement plan creation unit 314 determines whether the lead time Ldt[i] acquired in S204 is greater than "0", i.e., whether a lead time is incurred for replenishment (S205). If the lead time Ldt[i] is greater than "0", i.e., a lead time is incurred for replenishment, the process proceeds to S206. On the other hand, if the lead time Ldt[i] is equal to or less than "0", i.e., no lead time is incurred for replenishment, the process proceeds to S16.

Next, the order arrangement plan creation section 314 calculates the arrangement date AT[i] by subtracting LT[i] from the replenishment date of item[i] (S206). If there are multiple replenishment dates, the order arrangement date AT[i] is calculated for each of them.
Next, the order arrangement plan creation unit 314 acquires the required replenishment amount Vin[i] of the item [i] on the replenishment date specified in S203 from the replenishment plan data _40m (S207). If there are multiple replenishment dates, the required replenishment amount Vin[i] is calculated for each replenishment date, similar to the arrangement date AT[i].

Next, the order arrangement plan creation unit 314 associates the order date AT[i] calculated in S206, the replenishment source location Loc[i] obtained in S204, and the required replenishment amount Vin[i] obtained in S207 with the item [i], and stores them in the order arrangement plan data 60 (S208). Note that if there are multiple replenishment dates, the order arrangement plan data 60 will be data in which multiple pairs of order date AT[i] and required replenishment amount Vin[i] are associated with the item [i] and replenishment source location Loc[i].

Then, after the processing of S16, when i>n, the order arrangement plan creation unit 314 determines that order arrangement plan data has been generated for all items included in the replenishment plan data _40m for each day for N days, terminates the loop processing (S209), and outputs the stored order arrangement plan data 60 (S209). Here, the order arrangement plan data 60 is an order arrangement plan for N days from 0 to N-1 days later.

According to this embodiment, in addition to the replenishment plan, it is possible to create an order arrangement plan for items that require lead time for replenishment, such as procuring replenishment items from an external warehouse or ordering replenishment items from a factory, which requires transportation from ordering and ordering to the warehouse. This makes it possible to create a complex replenishment plan that includes replenishment timing, replenishment amount, and ordering arrangements for the items to be replenished. Furthermore, there is no need for a person to plan and execute order arrangements based on the replenishment plan, and emergency replenishment due to human error can be reduced.

<Fourth embodiment>
Finally, a replenishment planning system according to a fourth embodiment will be described in detail with reference to FIGS.
The replenishment planning system according to the fourth embodiment is a system that generates vehicle dispatch plan data, which is a vehicle dispatch plan for transporting items at the time of order dispatch, in addition to a replenishment plan and an order dispatch plan. Hereinafter, the process of creating a vehicle dispatch plan in the replenishment planning system according to the fourth embodiment will be referred to as a vehicle dispatch plan creation process. In the fourth embodiment, a configuration different from the first, second, and third embodiments will be described.

FIG. 23 is an overall schematic diagram of a replenishment planning system according to a fourth embodiment of the present invention.
The vehicle dispatch planning system 400 includes a shipment performance data input unit 101, a number of days before shipment calculation unit 102, a shipping interval expected value calculation unit 103, a shipping interval variation calculation unit 104, a shipment quantity expected value calculation unit 105, a shipping quantity variation calculation unit 106, a shipment occurrence expected value calculation unit 107, a forecasted shipment quantity calculation unit 108, an inventory quantity data input unit 109, a replenishment plan creation unit 110, a shipment performance data update unit 210, an inventory quantity data update unit 211, a planned number of days management unit 212, a location master data input unit 313, an order dispatch plan creation unit 314, an item master data input unit 415, and a vehicle dispatch plan creation unit 416. In the figure, due to the increase in functional units, the shipment performance data input unit 101, the number of days before shipment calculation unit 102, the shipment interval expected value calculation unit 103, and the shipment interval variation calculation unit 104 are shown in one block, and the shipment quantity expected value calculation unit 105 and the shipment quantity variation calculation unit 106 are shown in another block. However, as in Figures 1, 15, and 19, these are independent functions.

The item master data input unit 415 accepts input of item master data 70 output from a warehouse management system (WMS) or the like.

Figure 24 shows an example of item master data 70. Each row of the item master data 70 consists of an item, unit volume, unit weight, and classification. It may also include information on the replenishment unit (e.g., 1 unit, 12 units, etc.).

The vehicle dispatch plan creation unit 416 creates vehicle dispatch plan data 80 based on the item master data 70 and the order dispatch plan data 60 created by the order dispatch plan creation unit 314.

25 is a flowchart of a vehicle dispatch plan creation process in the replenishment planning system according to the fourth embodiment of the present invention. This process is executed under the control of the planned days management unit 212.
First, each functional unit identical to that of the replenishment planning system 300 described in the third embodiment executes an order arrangement plan creation process based on actual shipment data 10, inventory quantity data 20, location master data 50, item master data 70, planned number of days N, and threshold value for expected shipment occurrence value Pthres, and creates order arrangement plan data 60 for N days (S301).

Next, the vehicle arrangement plan creation unit 416 creates delivery data SD that matches the identified item [i] with the total required replenishment amount of the identified item [i] for each combination COM(AT, Loc) of the arrangement date AT and the replenishment source location Loc, based on the order arrangement plan data 60 for N days generated in S301 (S302).
Next, the vehicle dispatch plan creation unit 416 starts a loop process for creating a vehicle dispatch plan (S303). At this time, COM(AT, Loc) is set to an array of COM[j], and an internal variable j (j = 1, 2, ..., p) is initialized to j = 1. j starts from its initial value of 1 and increments by 1 each time a single process is completed, repeating the process until it exceeds the final value p, where p is the number of COM(AT, Loc).

Next, the vehicle dispatch plan creation unit 416 references the unit weight and unit volume for each item in the item master data 70 and calculates the total volume and weight of the items to be transported from the replenishment source location (Loc) on the dispatch date AT from the delivery data SD created in S302 (S304). Specifically, for each item to be transported from the replenishment source location (Loc) on the dispatch date AT, the total required replenishment amount is first multiplied by the unit volume and unit weight, respectively, to calculate the volume and weight for each item. Then, the volumes and weights of all items to be transported from the replenishment source location (Loc) on the dispatch date AT are calculated.

Next, the vehicle dispatch plan creation unit 416 determines whether transportation is necessary based on whether the total volume tallied in S304 is greater than 0 (S305). Note that this determination may also be made based on whether the total weight tallied in S304 is greater than 0. If the total volume is greater than 0, i.e., transportation is necessary, processing proceeds to S306. On the other hand, if the total volume is 0, i.e., transportation is not necessary, processing proceeds to S308. Note that in S304, only one of the total volume or total weight may be tallied. In this case, in S305, it is sufficient to determine whether the value tallied in S304 is greater than 0.

Next, the vehicle dispatch plan creation unit 416 selects a vehicle Vehicle[j] to be dispatched for delivery from the replenishment source location (Loc) based on the total volume and total weight calculated in S304 (S306). For example, if there is only one type of vehicle (small truck, medium truck, large truck, etc.), the number of vehicles is determined based on the pre-stored vehicle volume and load capacity so that the total volume and total weight are greater than the total volume and total weight. Also, if there are multiple types of vehicles, for example, the vehicle dispatch plan creation unit 416 refers to a list that sets the volume and load capacity for each vehicle type and the number of vehicles available for operation by vehicle type and by day, and selects one or more vehicles from those available on the dispatch date AT based on the total volume and total weight. Note that when multiple vehicles are selected, they may be of the same type or a combination of vehicles of different types.
Next, the vehicle dispatch plan creation unit 416 stores the vehicle selected in S306 in the vehicle dispatch plan data 80 in association with the combination COM[j] of the dispatch date AT and the replenishment source location Loc (S307).

Then, when j > p, the vehicle dispatch plan creation unit 416 ends the loop processing (S308), assuming that vehicle dispatch plan data has been created for all combinations of dispatch date AT and replenishment source location Loc, and outputs the stored vehicle dispatch plan data 80.

According to this embodiment, in addition to the replenishment plan and order placement plan, a vehicle placement plan can be created to transport items ordered from external warehouses or factories for replenishment. This allows for the creation of a comprehensive replenishment plan that includes replenishment timing and amount, ordering arrangements for the items to be replenished, and vehicle arrangements to transport the items to be replenished.

Note that the present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to provide a better understanding of the present invention, and the present invention is not necessarily limited to those having all of the described configurations.

Furthermore, the above-mentioned configurations, functions, processing units, processing means, etc. may be implemented in part or in whole in hardware, for example by designing them as integrated circuits. Furthermore, the above-mentioned configurations, functions, etc. may be implemented in software by a processor interpreting and executing programs that implement the respective functions. Information such as programs, tables, and files that implement the respective functions can be stored in storage devices such as non-volatile semiconductor memory, hard disk drives, and solid-state drives (SSDs), or in computer-readable, non-transitory data storage media such as IC cards, SD cards, and DVDs.

Furthermore, the control lines and information lines shown are those considered necessary for explanation, and do not necessarily represent all control lines and information lines on the product. In reality, it is safe to assume that almost all components on the drawings are interconnected.

10 Actual shipping data 20 Inventory quantity data 30 Number of elapsed days 31 Expected shipping interval 32 Shipping interval variance 33 Expected shipping quantity 34 Shipping quantity variance 35 Expected shipping occurrence value 36 Forecasted shipping quantity 40 Replenishment plan data 50 Location master data 60 Order arrangement plan data 70 Item master data 80 Vehicle arrangement plan data 100 Replenishment planning system 101 Actual shipping data input unit 102 Number of elapsed days before shipment calculation unit 103 Expected shipping interval calculation unit 104 Shipping interval variance calculation unit 105 Expected shipping quantity at time of shipment calculation unit 106 Shipping quantity variance calculation unit 107 Expected shipping occurrence value calculation unit 108 Forecasted shipping quantity calculation unit 109 Inventory quantity data input unit 110 Replenishment plan creation unit 200 Multi-day replenishment planning system 210 Actual shipping data update unit 211 Inventory quantity data update unit 212 Planned days management unit 300 Replenishment planning system 313 Location master data input unit 314 Order arrangement plan creation unit 400 Vehicle arrangement planning system 415 Item master data input unit 416 Vehicle arrangement plan creation unit

Claims (13)

a shipping performance data input section for inputting shipping performance data including items, shipping amounts, and shipping dates;
a non-shipment elapsed days calculation unit that calculates the number of days elapsed since the most recent shipping date from the shipping performance data;
a shipping interval expected value calculation unit that calculates a time-series change amount of the shipping interval from the shipping performance data, and calculates a shipping interval expected value by weighting the most recent change amount based on the time-series change amount;
a shipping interval variation calculation unit that calculates a variation in shipping intervals using the time-series change in the shipping intervals;
a shipping volume expected value calculation unit that calculates a time-series change in the shipping volume from the shipping performance data, and calculates an expected value of the shipping volume at the time of shipping by weighting the most recent change based on the time-series change;
a shipping quantity variation calculation unit that calculates a variation in the shipping quantity at the time of shipping using the time-series change in the shipping quantity at the time of shipping;
a shipment occurrence expected value calculation unit that calculates a shipment occurrence expected value from the number of days elapsed since the most recent shipping date, the shipping interval expected value, and the shipping interval variation;
an expected shipping volume calculation unit that calculates an expected shipping volume from the expected value of the shipping volume at the time of shipping and the shipping volume variation at the time of shipping;
a replenishment plan creation unit that creates a replenishment plan including the expected shipment occurrence value and the expected shipment amount;
A replenishment planning system comprising: 10. The replenishment planning system of claim 1,
an inventory data input unit for inputting inventory data including the items and the current inventory amounts of the items;
a replenishment planning system characterized in that the replenishment plan creation unit compares, for each item, the forecasted shipping quantity with the inventory quantity data and/or the expected shipping occurrence value with a preset threshold value to determine whether replenishment is necessary, and includes the result of the replenishment plan. 3. The replenishment planning system of claim 2,
a replenishment planning system, characterized in that the replenishment plan creation unit calculates a required replenishment amount based on the expected shipping amount and the inventory amount data, and includes the required replenishment amount in the replenishment plan. 3. The replenishment planning system of claim 2,
the replenishment plan creation unit determines whether or not to replenish each item so that the number of replenishable items does not exceed the number of replenishable items, based on the expected shipment occurrence value, the expected shipping volume at the time of shipment, the shipping volume variance at the time of shipment, the inventory amount data, and a preset number of replenishable items. 10. The replenishment planning system of claim 1,
a planning days management unit that manages the number of planning days that is set in advance and for which a replenishment plan is created;
a shipping performance data updating unit that adds the estimated shipping amount to the shipping performance data for each item and updates the shipping performance data;
an inventory quantity data update unit that calculates, for each item, an expected inventory quantity after expected shipment based on the expected shipment quantity and the inventory quantity in the inventory quantity data, adds the expected inventory quantity to the inventory quantity data, and updates the inventory quantity data;
Equipped with
the shipping performance data input unit inputs the shipping performance data updated by the shipping performance data update unit,
the inventory quantity data input unit inputs the inventory quantity data updated by the inventory quantity data update unit;
a planned days management unit that terminates the actual shipping data update unit and the inventory quantity data update unit in response to a replenishment plan creation unit creating a replenishment plan for the planned number of days. 6. The replenishment planning system of claim 5,
the replenishment plan creation unit compares, for each item, the expected shipping amount with the inventory amount data and/or the expected shipping occurrence value with a preset threshold value to determine whether replenishment is necessary, and includes the result of the replenishment plan;
a location master data input unit for inputting location master data including items, replenishment source locations, and lead times;
an order arrangement plan creation unit that determines, for each item, a replenishment date when replenishment is required based on the replenishment need for the planned number of days in the replenishment plan, and creates an order arrangement plan based on the replenishment date and an arrangement date calculated from the lead time in the location master data;
The replenishment planning system further comprises: 7. The replenishment planning system of claim 6,
the replenishment plan creation unit calculates a required replenishment amount for each item based on the expected shipping amount and the inventory amount data, and includes the required replenishment amount in the replenishment plan;
an item master data input section for inputting item master data including items, unit volumes, and unit weights;
a vehicle dispatch planning unit that, for each combination of the dispatch date and the replenishment source location, calculates the total volume and total weight of items having the same combination based on the required replenishment amount in the replenishment plan for the number of planned days and the unit volume and unit weight of the item master data, selects vehicles based on the total volume and total weight, and creates a vehicle dispatch plan based on the selected vehicles;
The replenishment planning system further comprises: inputting shipping performance data including items, shipping amounts, and shipping dates, and inventory amount data including items and current inventory amounts of the items;
calculating the number of days elapsed since the most recent shipping date from the shipping performance data;
a step of calculating a time-series change amount of the shipping interval from the shipping performance data, and calculating an expected value of the shipping interval by weighting the most recent change amount based on the time-series change amount;
calculating a variation in shipping intervals using the time-series change in the shipping intervals;
a step of calculating a time-series change in the shipping volume at the time of shipment from the shipping performance data, and calculating an expected value of the shipping volume at the time of shipment by weighting the most recent change based on the time-series change;
calculating a variation in the shipment amount at the time of shipment using the time-series change in the shipment amount at the time of shipment;
calculating an expected value of shipment occurrence from the number of days elapsed since the most recent shipping date, the expected value of the shipping interval, and the shipping interval variance;
calculating an expected shipping volume from the expected value of the shipping volume at the time of shipping and the shipping volume variation at the time of shipping;
creating a replenishment plan including the expected shipment occurrence value and the expected shipment amount;
A replenishment planning method comprising: 9. The replenishment planning method of claim 8,
inputting inventory data including the item and a current inventory amount of the item;
a step of comparing, for each item, the forecasted shipping quantity with the inventory quantity data and/or the expected shipping occurrence value with a preset threshold value to determine whether replenishment is necessary, and including the determination of whether replenishment is necessary in the replenishment plan. 9. The replenishment planning method of claim 8,
a step of managing a predetermined number of planning days for creating a replenishment plan;
adding the estimated shipping amount to the shipping performance data for each item and updating the shipping performance data;
calculating, for each item, an expected inventory quantity after the expected shipment based on the expected shipment quantity and the inventory quantity in the inventory quantity data, adding the expected inventory quantity to the inventory quantity data, and updating the inventory quantity data;
inputting updated shipping performance data and updated inventory quantity data;
further comprising
A replenishment planning system characterized in that the replenishment planning method is terminated when a replenishment plan for the planned number of days has been created. 11. The replenishment planning system of claim 10,
Entering location master data including items, replenishment source locations, and lead times;
a step of comparing the forecasted shipping amount with the inventory data and/or comparing the expected shipping occurrence value with a preset threshold for each item to determine whether replenishment is necessary, and including the replenishment necessity in the replenishment plan;
determining a replenishment date for each item based on whether replenishment is necessary for the planned number of days in the replenishment plan, and creating an order arrangement plan based on the arrangement date calculated from the replenishment date and the lead time in the location master data;
The replenishment planning method further comprises: 12. The replenishment planning method of claim 11,
entering item master data including item, unit volume, and unit weight;
calculating a required replenishment amount for each item based on the expected shipping amount and the inventory amount data, and including the required replenishment amount in the replenishment plan;
a step of aggregating, for each combination of the arrangement date and the replenishment source location, the total volume and total weight of the items having the same combination based on the required replenishment amount in the replenishment plan for the number of planned days and the unit volume and unit weight of the item master data, selecting vehicles based on the total volume and total weight, and creating a vehicle arrangement plan based on the selected vehicles;
The replenishment planning method further comprises: Replenishment planning system,
a shipping performance data input section for inputting shipping performance data including items, shipping quantities, and shipping dates;
a non-shipment elapsed days calculation unit that calculates the number of days elapsed since the most recent shipping date from the shipping performance data;
a shipping interval expected value calculation unit that calculates a time-series change amount of the shipping interval from the shipping performance data, and calculates a shipping interval expected value by weighting the most recent change amount based on the time-series change amount;
a shipping interval variation calculation unit that calculates a variation in shipping intervals using the time-series change in the shipping intervals;
a shipping volume expected value calculation unit that calculates a time-series change in the shipping volume from the shipping performance data, and calculates an expected value of the shipping volume at the time of shipping by weighting the most recent change based on the time-series change;
a shipping quantity variation calculation unit that calculates a variation in the shipping quantity at the time of shipping using the time-series change in the shipping quantity at the time of shipping;
a shipment occurrence expected value calculation unit that calculates a shipment occurrence expected value from the number of days elapsed since the most recent shipping date, the shipping interval expected value, and the shipping interval variation;
an expected shipping volume calculation unit that calculates an expected shipping volume from the expected value of the shipping volume at the time of shipping and the shipping volume variation at the time of shipping;
a replenishment plan creation unit that creates a replenishment plan including the expected shipment occurrence value and the expected shipment amount;
A program that functions as a