Replenishment planning system, replenishment planning method, and program

Abstract

To provide a replenishment planning system, method, and program for creating a replenishment plan for replenishment of an item based on whether or not the item has been shipped recently from a warehouse.SOLUTION: A replenishment planning system 100 comprises: an unshipped days calculation unit that calculates the number of days elapsed since the most recent shipping date; a shipping interval expected value calculation unit that calculates a shipping interval expected value from the time-series change amount of a shipping interval; a shipping interval variation calculation unit that calculates a shipping interval variation from the time-series change amount of the shipping interval; a shipment-time expected shipping quantity calculation unit that calculates a shipment-time shipping quantity expected value from the time-series change amount of a shipment-time shipping quantity; a shipment-time shipping quantity variation calculation unit that calculates a shipment-time shipping quantity variation from the time-series change amount of the shipment-time shipping quantity; a shipping occurrence expected value calculation unit that calculates a shipping occurrence expected value from the number of days elapsed, the shipping interval expected value, and the shipping interval variation; and a replenishment planning unit that calculates a forecasted shipping quantity for each item based on the shipping occurrence expected value, the shipping quantity expected value at the time of shipment, the shipping quantity variation at the time of shipment, and inventory amount data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a technique for creating a replenishment plan for replenishing items in a warehouse. [Background technology]

[0002] In logistics warehouses, etc., when orders are received from the destination, they are picked, packed, etc., and then shipped and delivered daily. It is especially important to deliver the order promptly according to the delivery date after receiving it. 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 work. From here on, this work will be called replenishment work.

[0003] Furthermore, large warehouses 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 is undesirable, as it results in excess inventory, increases the amount of space occupied, and increases 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.

[0004] A common method of creating a replenishment plan is to determine whether or not replenishment is necessary and the amount of replenishment based on the average shipping volume in the past.For example, if the inventory level on the picking shelf is less than the average shipping volume x coefficient α, it is determined that replenishment is necessary, and the replenishment work is carried out in such a way that the amount of inventory is equal to or greater than the average shipping volume x coefficient β.

[0005] In this case, since it is difficult to set α and β for each of the tens of thousands of items, it is common to use a uniform value. However, shipping trends on an item-by-item basis are intermittent, and this must be taken into consideration. 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 inventory. Therefore, rather than the average shipping volume including days without shipments, it is possible to consider the average shipping volume on days with shipments and the shipping interval.

[0006] Looking at existing patents from this perspective, Patent Document 1 discloses a technology for the purpose of supporting sales activities by sending reminders to sales staff in accordance with order forecasts, and for production management such as reducing out-of-stock items and optimizing inventory, and for calculating the standard shipping volume per shipment, the shipping interval per unit shipment, and the standard shipping interval, and then calculating 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 these data. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] JP2002-73752 Public Relations Summary of the Invention [Problem to be solved by the invention]

[0008] 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 does not fluctuate significantly. On the other hand, in the case of shipments from a warehouse, the shipping volume and shipping interval change over time depending on the trends and seasonal fluctuations of the items being shipped. Therefore, it is necessary to evaluate the time-series trends in the shipping volume and shipping interval and create a replenishment plan based on the appropriate shipping timing.

[0009] Furthermore, since work man-hours and storage capacity are finite, and if inventory is insufficient 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 shipment volume, the average shipment volume on a certain day, or the shipping interval. Furthermore, since the shipping interval averaged over a certain period does not necessarily reflect the most recent shipping trend, it is difficult to determine whether there has been an immediate shipment, even using the technology of Patent Document 1.

[0010] Therefore, an object of the present invention is to provide a replenishment planning system, a replenishment planning method, and a program that create a replenishment plan for replenishment of an item based on whether or not the item has been shipped recently from a warehouse. [Means for solving the problem]

[0011] The present invention includes a shipping performance data input unit for inputting shipping performance data including items, shipping quantities, and shipping dates, an inventory quantity data input unit for inputting inventory quantity data including the items and the current inventory quantities of the items, an unshipped elapsed days calculation unit for calculating the number of days elapsed since the most recent shipping date from the shipping performance data, a shipping interval expected value calculation unit for calculating a time-series change in shipping intervals from the shipping performance data and calculating 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 for calculating variance in shipping intervals using the time-series change in shipping intervals, and a shipping interval calculation unit for calculating a time-series change in shipping quantity at the time of shipping from the shipping performance data and calculating the time-series change in shipping intervals based on the time-series change. The present invention provides a replenishment planning system comprising: a shipment volume expected value calculation unit for calculating an expected value of shipment volume at time of shipment by weighting the most recent change amount based on column change amount; a shipment volume variability calculation unit for calculating variability in shipment volume at time of shipment using time-series change amount of shipment volume at time of shipment; a shipment occurrence expected value calculation unit for calculating 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 in shipping interval; a forecast shipment volume calculation unit for calculating a forecast shipment volume from the expected value of shipment volume at time of shipment and the variability in shipment volume at time of shipment; and a replenishment plan creation unit for creating a replenishment plan including the expected value of shipment occurrence and the forecast shipment volume. [Effects of the Invention]

[0012] According to the present invention, a replenishment plan for replenishing an item can be created based on whether or not the item has been shipped recently from the warehouse. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is an overall configuration diagram of a replenishment planning system according to a first embodiment of the present invention. [Figure 2] FIG. 10 is a diagram illustrating an example of shipping performance data. [Figure 3] FIG. 10 is a diagram illustrating an example of inventory quantity data. [Figure 4] FIG. 10 is a diagram illustrating an example of replenishment plan data. [Figure 5]3 is a flowchart of a replenishment plan creation process in the replenishment planning system according to the first embodiment of the present invention. [Figure 6] 10 is a detailed flowchart of a non-shipment elapsed day number calculation process according to the first embodiment of the present invention. [Figure 7] 10 is a detailed flowchart of a shipping interval expected value calculation process according to the first embodiment of the present invention. [Figure 8] 4 is a detailed flowchart of a shipping interval variation calculation process according to the first embodiment of the present invention. [Figure 9] 10 is a detailed flowchart of a shipment occurrence expected value calculation process according to the first embodiment of the present invention. [Figure 10] FIG. 10 is a diagram for explaining a method for calculating the shipment occurrence expected value table p[i](x). [Figure 11] 10 is a detailed flowchart of a shipping volume expected value calculation process according to the first embodiment of the present invention. [Figure 12] 4 is a detailed flowchart of a shipping volume variation calculation process at the time of shipping according to the first embodiment of the present invention. [Figure 13] 4 is a detailed flowchart of an expected shipping volume calculation process according to the first embodiment of the present invention. [Figure 14] 4 is a detailed flowchart of a replenishment plan creation process according to the first embodiment of the present invention. [Figure 15] 1 is an example of a display screen of the replenishment planning system according to the first embodiment of the present invention. [Figure 16] FIG. 10 is an overall schematic diagram of a replenishment planning system according to a second embodiment of the present invention. [Figure 17] 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. [Figure 18] 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. [Figure 19] 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. [Figure 20] FIG. 10 is an overall schematic diagram of a replenishment planning system according to a third embodiment of the present invention. [Figure 21] FIG. 10 is a diagram illustrating an example of location master data. [Figure 22] 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. [Figure 23] FIG. 10 is an overall schematic diagram of a replenishment planning system according to a fourth embodiment of the present invention. [Figure 24] FIG. 10 is a diagram illustrating an example of item master data. [Figure 25] 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. DETAILED DESCRIPTION OF THE INVENTION

[0014] Next, a mode for carrying out the present invention (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.

[0015] 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 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.

[0016] The shipping performance data input unit 101 receives input of shipping performance data 10 output from a warehouse management system (WMS) or the like.

[0017] 2 is a diagram showing an example of the shipping performance data 10. Each row of the shipping performance data 10 includes an item, a shipping destination, a shipping amount, and a shipping date.

[0018] The non-shipment elapsed days calculation unit 102 calculates the number of days 30 (shown in FIGS. 5 and 6) that have elapsed from the most recent shipping date to the present for each item based on the shipping performance data 10.

[0019] The shipping interval expected value calculation unit 103 calculates the time series change in the shipping interval for each item based on the shipping performance data 10, and calculates the shipping interval expected value 31 (shown in Figures 5 and 7) indicating the expected value of the shipping interval using the time series change.

[0020] The shipping interval variation calculation unit 104 calculates the time series change in shipping interval for each item based on the shipping performance data 10, and uses the time series change to calculate shipping interval variation 32 (shown in Figures 5 and 8) which indicates the variation in shipping interval.

[0021] 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 calculates the time-series change in shipment volume expected value 33 (shown in Figures 5 and 11) indicating the expected value of the shipment volume at the time of shipment using the time-series change.

[0022] The shipment volume variation calculation unit 106 calculates the time-series change in shipment volume for each item based on the shipping performance data 10, and calculates 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, using the time-series change.

[0023] The shipment occurrence expected value calculation unit 107 calculates, for each item, a shipment occurrence expected value 35 (shown in Figures 5 and 9) indicating the probability that a shipment will occur on the current date, based on the number of elapsed days 30 calculated by the number of elapsed days before shipment calculation unit 102, the shipment interval expected value 31 calculated by the shipment interval expected value calculation unit 103, and the shipment interval variance 32 calculated by the shipment interval variance calculation unit 104.

[0024] The expected shipment quantity calculation unit 108 calculates the expected shipment quantity 36 (shown in Figures 5 and 13) for each item based on the expected shipment quantity 33 at the time of shipment calculated by the expected shipment quantity at the time of shipment calculation unit 105 and the shipment quantity variation 34 at the time of shipment calculated by the shipment quantity variation calculation unit 106.

[0025] The inventory data input unit 109 receives input of inventory data 20 output from a warehouse management system (WMS) or the like.

[0026] 3 is a diagram showing an example of the inventory data 20. Each row of the inventory data 20 consists of an item, a location ID that identifies the storage location of the item, and the current inventory amount of the item.

[0027] 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. Furthermore, the replenishment plan creation unit 110 may include, for each item, the possibility of replenishment determined from the comparison result between the expected shipping quantity 36 and 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.

[0028] 4 is a diagram showing an example of replenishment plan data 40. Each row of the 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).

[0029] Next, a process for generating a replenishment plan in the replenishment plan 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.

[0030] First, the number of days since shipment calculation unit 102 executes the number of days since shipment calculation process shown in FIG. 6, which will 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 since shipment 30 (S1). The shipping interval expected value calculation unit 103 executes the shipping interval expected value calculation process shown in FIG. 7, which will 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).

[0031] The shipping interval variation calculation unit 104 executes the shipping interval variation calculation process shown in FIG. 8 (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 (to be 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.

[0032] The shipment volume expected value calculation unit 105 executes the shipment volume expected value calculation process shown in FIG. 11, which will be 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 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.

[0033] 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 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) for each item 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 expected shipment quantity 36 calculated in S7, and generates replenishment plan data 40 (S8).

[0034] 6 is a detailed flowchart of the number of elapsed days before shipment calculation process (S1 in FIG. 5) in the number of elapsed days before shipment calculation unit 102. The number of elapsed days before shipment calculation unit 102 starts processing when actual shipment data 10 is input. Hereinafter, the total number of items included in actual shipment data 10 is assumed to be n, and each item is represented as item [i] (i = 1, 2, n).

[0035] 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 to extract the number of days before shipment T[i] for all items included in the shipping record data 10 (S12). Next, the number of days since shipment calculation unit 102 reads all row data of 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 item [i] read in S13 (S14).

[0036] 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). 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.

[0037] 7 is a detailed flowchart of the shipping interval expected value calculation process (S2 in FIG. 5) in the shipping interval expected value calculation unit 103. The shipping interval expected value calculation unit 103 starts processing when the shipping performance data 10 is input. Note that the same processes as those shown in FIG. 6 are denoted by the same reference numerals, and explanations thereof will be omitted.

[0038] 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 amount is set to "0". Furthermore, following S11, a loop is started to calculate the shipping interval expected value TE[i] (S22).

[0039] 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, arranges them in chronological order, and calculates 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).

[0040] Here, the calculation of the forecast series LT[i] of shipping intervals that takes into account the most recent time when Simple Exponential Smoothing (SES) is used 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 that takes into account the most recent time can be done using any method that can take into account the most recent time, and is not limited to SES, and may also be the Holt method, Holt-Winters method, variable response smoothing, or the like.

[0041]

number

[0042] Returning to FIG. 7, the shipment interval expected value calculation unit 103 acquires the last value of the forecast series LT[i] of the shipment interval of the item[i] calculated in S23 as the shipment interval expected value TE[i] (S25). Then, after processing in S16, if i>n, it is determined that the shipping interval expectation 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 TE[i] is output as the shipping interval expectation 31.

[0043] 8 is a detailed flowchart of the shipping interval variation calculation process (S3 in FIG. 5) in shipping interval variation calculation unit 104. Shipping interval variation calculation unit 104 starts processing when actual 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 explanations thereof will be omitted. First, the shipping interval variation calculation unit 104 performs interpolation of missing dates in the shipping performance data 10 (S21). After the process of S11, the 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.

[0044] 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 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.

[0045] 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 shipment interval expected value 31, and the shipment 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 to calculate 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.

[0046] 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 expectation TE[i] from the shipping interval expectation 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. Then, the shipment occurrence expected value calculation unit 107 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 expectation TE[i] is "0" in the standard normal distribution.

[0047] By assuming that the probability of 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 the expected shipment occurrence value p[i] that is less affected by shipping history. Details will be explained later in Figure 10.

[0048] 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 the shipment occurrence expected value distribution table p[i](x). 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.

[0049]

number

[0050] The shipment occurrence expected value distribution table p[i](x) should be created to cover at least the range from the standardized number of days before 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 to another item, even if the items are different, as long as the number of days before shipment T[i], shipping interval expected value TE[i], and shipping interval variation σT[i] are the same.

[0051] 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 the value can be obtained, the obtained value of p[i](TN[i]) is set as p[i] (S45). On the other hand, if the value cannot be obtained, the process proceeds to S46 to calculate the value of p(TN[i]) by interpolation.

[0052] 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]).

[0053] Next, the shipment occurrence expected value calculation unit 107 calculates p[i](x1), p[i](x) from the shipment occurrence expected value table p[i](x). S ) is obtained (S46). S is the input value to the shipment occurrence expected value table p[i](x), and x1≦TN[i]≦x S , x1 <x S , x1,x S All of these have values ​​in the standard normal distribution integral table p[i](x). Next, the shipment occurrence expected value calculation unit 107 calculates the standardized number of days before shipment TN[i] and the input values ​​x1, x2 to the shipment occurrence expected value table p[i](x). S , p[i](x1), p[i](x S ), the value of p[i](TN[i]) is interpolated. Then, this interpolated value p[i](TN[i]) is set as p[i] (S47). Note that for linear interpolation, only two input values ​​(s=1, 2) are required, and for quadratic polynomial interpolation, only three input values ​​(s=1, 2, 3) are required.

[0054] After processing 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 shipment 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.

[0055] 10 is a diagram explaining the calculation method of 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 standard deviation σT of the shipping interval variance, which are input to the shipment occurrence expected value calculation unit 107, are represented as condition C.

[0056] The expected value of shipment occurrence for item [i] under condition C can be expressed as 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 days or more). If the number of days since the previous shipping date is 4 days, the most recent possible number of days without shipment is 4 days or more, and since the expected shipping interval TE under condition C is 5 days, the event of shipment occurring is associated with the event of the most recent number of days without shipment being 4 to 5 days or more.

[0057] If we assume that the number of days without shipment T can be approximated to a normal distribution with expected shipping interval TE and standard deviation σT, 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 days or more) can be calculated by integral formula 1001 / integral formula 1002. However, condition C, which is a parameter in integral formulas 1001 and 1002, changes depending on the shipping record of the item, so it is desirable to reduce the number of parameters in the integral formula as much as possible.

[0058] Therefore, by converting the normal distribution of the number of days T before shipment into a standard normal distribution, p[i] (the most recent number of days before shipment is 4 to less than 5 days / the most recent number of days before shipment is 4 days or more) can be calculated using integral formula 1003 with fewer parameters divided by integral formula 1004. In this way, the shipment occurrence expected value table p[i](x) is calculated using integral formula 1003 divided by integral formula 1004, which has fewer parameters and reduces the influence of the item's shipping record.

[0059] 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 expected value calculation unit 105 starts a loop process of calculating the shipping volume expected value VE[i] at the time of shipment (S51).

[0060] 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 the item [i] read in S13 (S52). Next, the shipment volume expected value calculation unit 105 calculates the expected series LV[i] of shipping intervals taking the most recent shipment into consideration, using the shipment volume series DV[i] calculated in S52 (S53).

[0061] Here, the calculation of the forecast series LV[i] of shipment volume at time of shipment that takes into account the most recent data when Simple Exponential Smoothing (SES) is used 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 can take into account the most recent data, and is not limited to SES, and may also be the Holt method, Holt-Winters method, variable response smoothing method, or the like.

[0062]

number

[0063] Returning to FIG. 11, the shipment volume expectation calculation unit 105 acquires the last value of the forecast series LV[i] of the shipment volume at the time of shipment of item[i] calculated in S53 as the shipment volume expectation VE[i] (S54). Next, after processing 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 shipping 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.

[0064] 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 process of S11, the shipping quantity variation calculation unit 106 starts a loop for calculating the shipping quantity variation σV[i] (S61). Next, the processes of S13, S52, and S53 are executed in order to calculate the forecast series LV[i] of the shipping volume at the time of shipment, taking into account the most recent data, for item [i].

[0065] 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 to obtain 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] 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] as the shipment volume variation 34.

[0066] 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 value of 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 estimated shipment volume calculation unit 108 starts a loop for calculating the estimated shipment volume V[i] (S71).

[0067] Next, the expected shipment volume calculation unit 108 calculates the expected shipment volume V[i] based on the expected shipment volume VE[i] at the time of shipment of the expected shipment volume 33 and the shipment volume variation σV[i] at the time of shipment of the variation 34 (S72). The expected shipment 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 value VE[i] itself, since the shipment occurrence expected value p[i] has already taken into account the variations in the shipping interval and the shipment volume at the time of shipment.

[0068] Then, after processing S16, if i>n, the expected shipment volume calculation unit 108 determines that the expected shipment volume V[i] has been obtained for all items with the expected shipment volume at time of shipment 33 and the shipment volume variation at time of shipment 34, terminates the loop processing (S73), and outputs the pair of item [i] and the expected shipment volume V[i] as the expected shipment volume 36.

[0069] FIG. 14 is a detailed flowchart of the replenishment plan creation process (S8 in FIG. 5) in the replenishment plan creation unit 110. The replenishment plan creation unit 110 starts the process when the inventory quantity data 20, the expected shipment occurrence value 35, the expected shipment quantity 36, and the shipment occurrence expected value threshold Pthres are input. Here, the shipment occurrence expected value threshold Pthres is a fixed parameter determined in advance. Also, 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 FIG. 6 are denoted by the same reference numerals, and explanations thereof will be omitted.

[0070] First, after the process of S11, the replenishment plan creation unit 110 starts a loop process for determining a replenishment necessity flag (S81).

[0071] 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 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.

[0072] Next, if the shipment occurrence expected value p[i] is equal to or greater than the shipment occurrence expected value threshold Pthres and the predicted shipment volume V[i] is greater than the inventory quantity data S[i], the replenishment plan creation unit 110 sets the replenishment necessity flag Frep[i] to "1" which means replenishment is "necessary" (S83).

[0073] Next, the replenishment plan creation unit 110 calculates the required replenishment amount Vin[i] based on the inventory data S[i] and the expected 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.

[0074] 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 volume 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). If 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."

[0075] Next, after the processing of S16, if i>n holds, the replenishment plan creation unit 110 determines that the replenishment necessity flag Frep[i] has been determined for all items included in the actual shipping data 10, terminates the loop processing (S87), and outputs a set of item[i], replenishment necessity flag Frep[i], estimated shipping quantity V[i], and required replenishment quantity Vin[i] as replenishment plan data 40. Note that the replenishment plan data 40 only needs to include at least one of the replenishment necessity flag Frep[i], estimated shipping quantity V[i], and required replenishment quantity Vin[i].

[0076] 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 consists of a set of items [i], corresponding replenishment necessity flags Frep[i], expected shipment occurrence values ​​p[i], inventory amounts S[i], expected shipment amounts V[i], and required replenishment amounts Vin[i].

[0077] As shown in the figure, the display screen displays the current date, which is the current day, and a replenishment plan based on replenishment plan data 40. The replenishment plan consists of items (item names or IDs), whether replenishment is required, expected shipment occurrence values, inventory amounts, expected shipment amounts, and required replenishment amounts.

[0078] For replenishment necessity, if the replenishment necessity flag Frep[i] in the replenishment plan data 40 is "1", "Needed" is displayed, and if it is "0", a blank is displayed. For expected shipment occurrence value, the expected shipment occurrence value p[i] in the replenishment plan data 40 is displayed as a percentage. For inventory quantity, inventory quantity data S[i] is displayed. For expected shipment quantity, the expected shipment quantity V[i] in the replenishment plan data 40 is displayed. For required replenishment quantity, the required replenishment quantity Vin[i] in the replenishment plan data 40 is displayed.

[0079] The display screen may display only items that require 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, among the items that require replenishment, only a preset number (number of replenishable items) of items may be displayed in descending order of expected shipment occurrence value, expected shipment volume, or required replenishment volume. Furthermore, only items whose expected shipment occurrence value, expected shipment volume, or required replenishment volume are equal to or greater than a preset threshold value may be displayed.

[0080] 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. Furthermore, it is possible to calculate the expected shipping volume and required replenishment volume and include them in the replenishment plan, making it possible to grasp the replenishment priority and the personnel and time required for replenishment. Furthermore, it is possible to create a replenishment work plan, such as personnel allocation and time schedule.

[0081] <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 / processing according to the second embodiment will be referred to as a multi-day replenishment planning system / processing. In the second embodiment, a configuration different from the first embodiment will be described.

[0082] FIG. 16 is an overall schematic diagram of a 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, 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 predicted shipping quantity calculation unit 108, an inventory quantity data input unit 109, a replenishment plan creation unit 110, a shipping performance data update unit 210, an inventory quantity data update unit 211, and a planned days management unit 212.

[0083] 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 number of planned days.

[0084] 17 is a flowchart of the multi-day replenishment plan creation process in the multi-day replenishment planning system 200 of the present invention. The planned days management unit 212 collects the shipping performance data 10, the inventory quantity data 20, the replenishment plan data 40, and the m The process starts when the planning period N, which indicates the number of days for which the plan is to be created, and the shipment occurrence expected value threshold Pthres are entered.

[0085] 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).

[0086] Next, the same functional units as those in the replenishment planning system 100 described in the first embodiment are used to generate the shipment performance data 10 m days later. m , inventory data after m days 20 mBased on the fixed number of planning days N and the fixed threshold value Pthres for the expected value of shipment occurrence for determining whether or not shipment is made, the replenishment plan creation process is performed, and the replenishment plan data 40 after m days is generated. m is generated and stored 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.

[0087] Next, the shipping performance data update unit 210 executes the shipping performance data update process shown in FIG. 18 to update the replenishment plan data 40 generated in S103. m Based on this, the shipping performance data after m days 10 m The shipping data after m+1 days 10 m+1 (S104). In addition, the inventory data update unit 211 executes the inventory data update process shown in FIG. 19 to update the replenishment plan data 40 generated in S103. m Based on this, the inventory data after m days 20 m The inventory data after m+1 days 20 m+1 (S105) Although S104 and S105 are executed in parallel, they may be executed in any order.

[0088] 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≧N, the planned days management unit 212 determines that the replenishment plan data 40m for each day for N days has been generated, ends the loop processing (S107), and stores the replenishment plan data 40m for each day for N days from the current day to N−1 days later, which was generated and stored in S103. m The replenishment plan data 40 including (m=0 to N-1) is output.

[0089] 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).

[0090] The shipping performance data update unit 210 updates the shipping occurrence expectation value p m It is determined whether [i] is equal to or greater than the shipment occurrence expected value threshold Pthres, that is, whether shipment of item [i] will occur in m days (S112). m If [i] is equal to or greater than the shipment occurrence expected value threshold Pthres, that is, if the shipment of item [i] occurs after m days, the process proceeds to S113. m If [i] is less than the shipment occurrence expected value threshold Pthres, that is, if shipment of item [i] will not occur in m days, the process proceeds to S16.

[0091] The shipping performance data update unit 210 updates the replenishment plan data 40 m days later. m Based on this, the shipping performance data after m days 10 m The shipping data after m+1 days 10 m+1 Specifically, the shipping performance data after m days is updated to m , Item [i], shipment quantity is expected shipment quantity V m+1 [i], add a row that corresponds to the date m days later (today's date) as the shipping date, and create shipping record data 10. m Shipment performance data 10 m+1 Update to.

[0092] After the process of S16, if i>n, the shipping performance data update unit 210 updates the shipping performance data 10 m It is determined that updating has been completed for all items included in the data (S114), and the loop processing is terminated. m+1 Output.

[0093] 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).

[0094] Next, the inventory data update unit 211 calculates the expected shipment occurrence value p m It is determined whether [i] is equal to or greater than the shipment occurrence expected value threshold Pthres (S122). m If [i] is equal to or greater than the shipment occurrence expected value threshold Pthres, the process proceeds to S123. m If [i] is less than the shipment occurrence expected value threshold Pthres, the process proceeds to S124.

[0095] Next, the inventory quantity data update unit 211 updates the inventory decrease amount Vout m [i] is the expected shipment volume V after m days m [i] is set (S123). Alternatively, the inventory data update unit 211 may update the inventory decrease amount Vout m [i] is set to "0" (S124).

[0096] Next, the inventory data update unit 211 updates the replenishment need flag Frep m It is determined whether [i] is "1" or "0", that is, whether replenishment of item [i] is required after m days (S125). m If [i] is "1", that is, if replenishment of item i is required after m days, the process proceeds to S126. On the other hand, the replenishment necessity flag Frep m+1 If [i] is "0", that is, if replenishment of item i is not required after m days, the process proceeds to S127.

[0097] Next, the inventory data update unit 211 calculates the required replenishment amount Vin m [i] with V m [i]-S m [i] is set (S126). 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 Vm [i]-S m If [i] is 20, the required replenishment amount is Vin m [i] will be 24 units, which is the equivalent of two boxes. Also, when replenishing, it can be set to replenish up to the maximum capacity of the shelf / row where item [i] is stored.

[0098] Alternatively, the inventory data update unit 211 may update the required replenishment amount Vin m [i] is set to "0" (S127). The inventory data update unit 211 updates the inventory amount S m [i] is the inventory quantity S after m days m [i] + S126 / S127 required replenishment amount Vin m [i] - Inventory reduction amount Vout calculated in S123 / S124 m Inventory quantity S after m+1 days calculated in [i] m+1 Update to [i] (S128). After the process of S16, if i>n, the inventory data update unit 211 updates the inventory data 20 m The loop process is terminated (S129) when it is determined that the update has been completed for all items included in the inventory data 20 after m+1 days. m+1 Output.

[0099] According to this embodiment, it is possible to generate a replenishment plan for multiple days, from the day of plan creation to several days later. By planning replenishment work not only for the current day but also for the future, it becomes possible to carry out replenishment ahead of schedule 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.

[0100] <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 a replenishment plan. Hereinafter, in the replenishment planning system according to the third embodiment, the process of creating an order arrangement plan is 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.

[0101] 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 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, a planned number of days management unit 212, a location master data input unit 313, and an order placement plan creation unit 314.

[0102] The location master data input unit 313 receives input of location master data 50 output from a warehouse management system (WMS) or the like.

[0103] 21 is a diagram showing an example of location master data 50. Each row of the location master data 50 consists of an item, a replenishment source location, a lead time, a shelf number, a row, and a row. Note that for items ordered from a factory, the shelf number, row, and row associated with the shelf may be empty.

[0104] The order arrangement plan creation unit 314 creates order arrangement plan data 60 based on the location master data 50 and the N-day worth of replenishment plan data 40 created by the replenishment plan creation unit 110.

[0105] 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 shipment performance data 10, the inventory quantity data 20, the location master data 50, the number of planned days N, and the shipment occurrence expected value threshold Pthres, and generates replenishment plan data 40 for each day for N days. m (m=0 to N-1) is generated (S201).

[0106] Next, after the processing of S11, the order arrangement plan creation unit 314 starts a loop process for generating order arrangement plan data (S202). Next, the order placement plan creation unit 314 creates the replenishment plan data 40 for each day for N days. m Based on this, the replenishment plan data 40 for which the replenishment necessity flag Frep[i] for item [i] is "1" m Identify all the replenishment plan data 40 m For each item, the date on which replenishment will be performed (replenishment date) is identified, and it is determined whether or not it has been identified (S203). m If the replenishment plan data 40 cannot be identified, there will be no replenishment until N days after the replenishment plan is created, so the process proceeds to S16. m If one or more items can be identified, the replenishment plan data 40 m The replenishment date is identified for each item, and the process proceeds to S204. m Since this is a replenishment plan m days after the date of plan creation, the replenishment date is calculated by adding m to the date of plan creation.

[0107] Next, the order arrangement planning 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 occurs for replenishment (S205). If the lead time Ldt[i] is greater than "0", i.e., a lead time occurs 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 occurs for replenishment, the process proceeds to S16.

[0108] Next, the order arrangement plan creation unit 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 date AT[i] is calculated for each of them. Next, the order placement plan creation unit 314 creates the replenishment plan data 40 m The required replenishment amount Vin[i] of item[i] on the replenishment date identified in S203 is obtained from (S207). If there are multiple replenishment dates, the required replenishment amount Vin[i] is calculated for each replenishment date, just like the order date AT[i].

[0109] Next, the order arrangement plan creation unit 314 stores the arrangement date AT[i] calculated in S206, the replenishment source location Loc[i] acquired in S204, and the required replenishment amount Vin[i] acquired in S207 in association with the item[i] 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 arrangement date AT[i] and required replenishment amount Vin[i] are associated with the item[i] and replenishment source location Loc[i].

[0110] After the process of S16, if i>n, the order placement plan creation unit 314 creates the replenishment plan data 40 for each day for N days. m The order arrangement plan data 60 is an order arrangement plan for N days from 0 to N-1 days later.

[0111] According to this embodiment, in addition to the replenishment plan, an order arrangement plan can be created for items that require transportation from ordering to the warehouse, such as procuring replenishment items from an external warehouse or ordering replenishment items from a factory, and that incur lead times for replenishment. 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.

[0112] <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 arrangement, in addition to a replenishment plan and an order arrangement 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.

[0113] 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 predicted 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 shipping performance data input unit 101, the number of days before shipment calculation unit 102, the shipping interval expected value calculation unit 103, and the shipping interval variation calculation unit 104 are shown in one block, and the shipping quantity expected value calculation unit 105 and the shipping quantity variation calculation unit 106 are shown in another block. However, as in Figures 1, 15, and 19, each has an independent function.

[0114] The item master data input unit 415 receives input of item master data 70 output from a warehouse management system (WMS) or the like.

[0115] 24 is a diagram showing an example of the item master data 70. Each row of the item master data 70 consists of an item, a unit volume, a unit weight, and a classification. Information on the replenishment unit (e.g., one unit, 12 units, etc.) may also be included.

[0116] 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 .

[0117] 25 is a flowchart of a vehicle distribution 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 procedure based on the actual shipment data 10, the inventory quantity data 20, the location master data 50, the item master data 70, the number of planned days N, and the shipment occurrence expected value threshold value Pthres, and creates order arrangement plan data 60 for N days (S301).

[0118] Next, the vehicle dispatch plan creation unit 416 creates delivery data SD that associates the identified item [i] with the total required replenishment amount of the identified item [i] for each combination COM(AT, Loc) of dispatch date AT and replenishment source location Loc based on the order dispatch 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 as an array of COM[j], and the internal variable j (j=1, 2, ..., p) is initialized to j=1. j starts from the initial value 1 and an increment value of 1 is added each time a single process is completed, and the process is repeated until it exceeds the final value p, where p is the number of COM(AT, Loc).

[0119] 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, first, for each item to be transported from the replenishment source location Loc on the dispatch date AT, the total required replenishment amount is 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.

[0120] Next, the vehicle dispatch plan creation unit 416 determines whether transportation is necessary based on whether the total volume calculated in S304 is greater than 0 (S305). Note that this determination may also be made based on whether the total weight calculated in S304 is greater than 0. If the total volume is greater than 0, that is, if transportation is necessary, the process proceeds to S306. On the other hand, if the total volume is 0, that is, if transportation is not necessary, the process proceeds to S308. Note that in S304, only one of the total volume or total weight may be calculated. In this case, in S305, it is sufficient to determine whether the value calculated in S304 is greater than 0.

[0121] 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, a list is set that contains the volume and load capacity for each vehicle type and the number of vehicles available for operation by vehicle type and by day, and one or more vehicles are selected 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 may be used. 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).

[0122] Then, when j>p holds, 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.

[0123] According to this embodiment, in addition to the replenishment plan and the order placement plan, a vehicle placement plan for transporting items ordered from external warehouses or factories for replenishment can be created. This allows for the creation of a complex replenishment plan that includes the timing and amount of replenishment, ordering arrangements for the replenishment items, and vehicle arrangements for transporting the replenishment items.

[0124] It should be noted that the present invention is not limited to the above-described embodiment, and includes various modifications. For example, the above-described embodiment has been described in detail to provide a better understanding of the present invention, and the present invention is not necessarily limited to an embodiment having all of the configurations described.

[0125] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-described configurations, functions, etc. may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function can be stored in storage devices such as nonvolatile 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.

[0126] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and not all control lines and information lines on the product are necessarily shown. In reality, it can be considered that almost all components on the drawings are interconnected. [Explanation of symbols]

[0127] 10. Shipment performance data 20 Inventory data 30 days passed 31 Expected shipping interval 32 Variation in shipping intervals 33 Expected shipment volume 34 Variation in shipping volume 35 Expected shipment occurrence 36 Estimated shipment volume 40 Replenishment Planning Data 50 Location master data 60 Order Planning Data 70 Item Master Data 80 Vehicle Arrangement Plan Data 100 Replenishment Planning System 101 Shipping performance data entry section 102 Unshipped days calculation unit 103 Shipping Interval Expected Value Calculation Unit 104 Shipping interval variation calculation unit 105 Expected shipping quantity calculation unit at time of shipment 106 Shipping quantity variation calculation unit 107 Shipment occurrence expected value calculation unit 108 Forecast shipping volume calculation unit 109 Inventory quantity data input section 110 Replenishment Planning Department 200 Multi-day replenishment planning system 210 Shipping performance data update section 211 Inventory data update unit 212 Planning Days Management Department 300 Replenishment Planning System 313 Location Master Data Entry Section 314 Order Planning Department 400 Vehicle Arrangement Planning System 415 Item Master Data Entry Section 416 Vehicle Arrangement Planning Department

Claims

1. 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:

2. 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 necessity in the replenishment plan.

3. 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.

4. 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.

5. 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. 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. 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:

8. 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. 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.

10. 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. 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. 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:

13. 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

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