JP2018073200A - Safety inventory determination device, method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To enable adequate determination on a safety inventory that each delivery destination should have even if a lead time from the order to delivery of an object to be delivered to a plurality of delivery destinations from one or a plurality of delivery sources is long and even under an unstable circumstance.SOLUTION: An input unit 101 acquires, for an object for which the safety inventory is desired to be determined, ship entry time information for each delivery destination and use amount information for each delivery destination/day. On the assumption that a plurality of delivery destinations included in the ship entry time information for each delivery destination are one delivery destination (hereinafter referred to as all delivery destinations), a first probability distribution calculation unit 103 determines a probability distribution of the intervals of arrival of a ship at all delivery destinations. A second probability distribution calculation unit 104 determines a probability distribution of the intervals of arrival of the ship at each delivery destination on the basis of the probability distribution of the intervals of arrival of the ship at all delivery destinations. A safety inventory calculation unit 105 calculates a safety inventory to be held at each delivery destination on te basis of the probability distribution of the intervals of arrival of the ship at each delivery destination.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an object (raw material, material, product, product, etc.) delivered to a plurality of delivery destinations (factories, commercial facilities, inventory bases, etc.) from one or a plurality of shipping sources (production bases, etc.). The present invention relates to a safety stock determination apparatus, method, and program suitable for use in determining a safety stock to have in advance.

Steel manufacturers, chemical manufacturers, petroleum manufacturers, electric power companies, etc. import raw materials, materials, products, products, etc. from overseas by ship and store them in stock. Too much inventory leads to a slowdown in cash flow and an increase in management costs, so it is desirable that the inventory held be small. On the other hand, if the inventory is too small, it will not be possible to sufficiently absorb the fluctuations in demand and supply, which may lead to production stoppage due to out-of-stock items. Therefore, in order to keep the target shortage rate below a certain level, it can be said that it is essential to set an average inventory (proper inventory) as a management target by predicting fluctuations in demand and supply.
However, when importing objects from around the world by ship, the lead time from ordering to delivery is very long, and is susceptible to fluctuations in demand and supply. For this reason, inventory is likely to fall into an excess or under condition, and the loss once it falls into that state is large. Furthermore, the supply of objects varies greatly due to the occurrence of sudden disruptions due to unseasonable weather, equipment troubles, etc., and frequent fluctuations in the operation of ships that transport huge volumes. Therefore, it was difficult to mathematically derive the appropriate stock.

As a proper inventory setting technique for predicting fluctuations in demand and supply, Non-Patent Document 1 presents a technique for providing an appropriate inventory by adding a cycle inventory and a safety inventory. FIG. 15 shows the transition of inventory with the horizontal axis representing time and the vertical axis representing inventory quantity, and shows the relationship between cycle inventory, safety inventory, and appropriate inventory. The cycle stock is a running stock that has a constant fluctuation of the stock, and the safety stock is a stock that does not cause a shortage even if the supply and demand vary. In Non-Patent Document 1, the cycle stock is given by half of the average value of the received lots, and the safety stock is calculated by “safety factor × maximum lead time ^1/2 × standard deviation of demand”. Here, the maximum lead time is given by “average lead time + safety factor × lead time standard deviation”. The safety factor is a parameter that determines how much delay is allowed, and is determined by the practitioner according to the situation.

  In Patent Document 1, the supply lead time fluctuates such that the order interval is an exponential distribution as demand information, the order delivery date is a normal distribution, the order quantity follows a Γ distribution, and the supply lead time follows a Γ distribution as supply information. Discloses a method for formulating an inventory plan that takes into account the trade-off relationship between improved delivery date compliance and reduced inventory costs.

JP 2014-119768 A

By Takao Suguro, “Appropriate Inventory Approach / How to Find”, published on September 10, 2003, Nikkan Kogyo Shimbun Emery N. Brown et al., The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis, Neural Computation 14, 325-346, 2001 Yosihiko Ogata, On Lewis' Simulation Method for Point Processes, IEEE Transactions On Information Theory, Vol. 27, No.1, January 1981

By the way, since the objects supplied by sea transportation are delivered from various regions outside the country, the lead time is different for each order. However, Non-Patent Document 1 examines only when the lead time is uniform, and when the lead time is different for each order, when the arrival (port arrival) of the ship is greatly delayed from the set lead time, the long time is reached. Cause an out of stock situation.
Furthermore, since the object is transported by ship from a distant overseas, the lead time from ordering to delivery is very long, from one week to several months. On the other hand, the departure time varies greatly due to unseasonable weather, equipment breakdown, interchange with other ships. In addition, at the landing site, the supply timing of the inventory and the departure time vary due to the impact of the port unloading capacity, the offshore vessel due to the warehouse capacity neck, and the unloading equipment failure.
For this reason, when the originally scheduled departure / entry time of a ship has changed significantly, in order to prevent over / under-stocking at multiple delivery destinations, review of delivery destinations including renewal of delivery times, etc. The delivery plan is constantly reviewed and updated. As described above, when the ordering source and the delivery destination are frequently changed after the order is placed once, the relationship between the ordering point and the delivery time necessary for calculating the lead time cannot be understood. That is, the lead time determined using the relationship between the order point and the delivery time cannot be calculated because the order point itself is unstable.
In addition, it is possible to compare the plan with a certain period of time and the actual result and calculate the lead time from the difference. However, the variation in the lead time calculated by this method is unstable because it varies depending on how the scheduled period is selected, and the lead time from ordering to delivery disclosed in Non-Patent Document 1 and Patent Document 1 was used. The safety stock calculation method leads to excess / under stock.
Furthermore, both Non-Patent Document 1 and Patent Document 1 are only considering replenishment of objects and inventory from one production base to one base or a plurality of bases. It is not described when an object is refilled somewhere in one site or multiple sites.

  The present invention has been made in view of the above points, and has a long lead time from ordering to delivery for an object delivered from one or a plurality of shipping sources to a plurality of delivery destinations, and is unstable. The purpose is to enable appropriate determination of the safety stock that each delivery destination should have even under difficult circumstances.

The gist of the present invention for solving the above problems is as follows.
[1] A safety stock determination device for determining a safety stock to be held at each delivery destination for an object delivered from one or a plurality of shipping sources to a plurality of delivery destinations,
For an object for which safety stock is to be determined, input means for fetching usage time information that is delivery time information and usage amount information of the object for each delivery destination as past performance information;
Based on the delivery time information captured by the input means, a plurality of delivery destinations included in the delivery time information are assumed to be one delivery destination (hereinafter referred to as all delivery destinations), and delivery intervals to all delivery destinations First probability distribution calculating means for obtaining a probability distribution of
Based on the usage information captured by the input means and the probability distribution of delivery intervals to all delivery destinations obtained by the first probability distribution calculation means, the delivery destination information included in the delivery time information is sent to each delivery destination. A second probability distribution calculating means for obtaining a probability distribution of delivery intervals;
Based on the usage information fetched by the input means and the probability distribution of the delivery interval to each delivery destination obtained by the second probability distribution calculation means, the safety stock to be possessed by each delivery destination is calculated. A safety stock determination device comprising a safety stock calculation means.
[2] The second probability distribution calculation means uses the object for each delivery destination for delivery to the delivery destination i (i is a symbol indicating the delivery destination) out of the number of deliveries to all delivery destinations. depending on the amount, by assuming that there once in N _i times, to provide a delivery interval of the delivery destination i, the probability distribution of the probability distribution of the delivery interval and convolving N _i times to all delivery destination The safety stock determination device according to [1], characterized by:
[3] the second probability distribution calculating means, delivery times q _i to delivery destination i, as delivery times Q to all delivery destination, and characterized in providing a number N _i of the convolution with Q / q _i The safety stock determination device according to [2].
[4] Provided with a delivery delay allowable value setting means for setting a delivery delay allowable value α%,
The safety stock calculation means further covers 1-α% of the probability distribution of delivery intervals to each delivery destination based on the delivery delay allowable value α% set by the delivery delay allowable value setting means, The difference from the average of the probability distribution of the delivery interval to each delivery destination is the safety stock days, and the safety stock that each delivery destination should have is calculated by multiplying this safety stock days by the average usage for each delivery destination. The safety stock determination device according to any one of [1] to [3], wherein:
[5] Any one of [1] to [4], wherein the delivery to all delivery destinations includes delivery directly from a shipping source and delivery via other delivery destinations. Safety stock determination device described in one.
[6] The first probability distribution calculating means assumes that a probability distribution of delivery intervals to all delivery destinations follows an exponential distribution or a gamma distribution,
The safety probability determination according to any one of [1] to [5], wherein the second probability distribution calculating means is configured such that a probability distribution of a delivery interval to each delivery destination follows a gamma distribution. apparatus.
[7] The first probability distribution calculating means uses a probability distribution of a delivery interval to all the delivery destinations using an average number of deliveries per unit time to the all delivery destinations (hereinafter referred to as an average delivery rate). Represented by an exponential distribution or a gamma distribution,
The safety stock determination apparatus according to [6], wherein the average delivery rate is treated as constant.
[8] The first probability distribution calculating means uses a probability distribution of a delivery interval to all the delivery destinations using an average number of deliveries per unit time to the all delivery destinations (hereinafter referred to as an average delivery rate). Represented by an exponential distribution or a gamma distribution,
The safety stock determination apparatus according to [6], wherein the average delivery rate is modeled as a function changing with time.
[9] The safety stock determination device according to [8], wherein the first probability distribution calculation unit determines the parameter of the function based on a predetermined information criterion.
[10] The safety stock determination apparatus according to [8] or [9], wherein the function is represented by an exponential Fourier series.
[11] The first probability distribution calculating means calculates a future average delivery rate, which is a target period for determining safety stock, as a constant multiple of a past average delivery rate. [8] to [10] ] The safety stock determination apparatus as described in any one of.
[12] The first probability distribution calculating means obtains a constant to be the constant multiple as a value obtained by dividing an average of future average delivery rates by an average of past average delivery rates,
The safety stock determination according to [11], wherein the average of the future average delivery rate is estimated by a single regression analysis using past performance information, assuming that the average of the average delivery rate of the object is proportional apparatus.
[13] The object is delivered from the shipper to the delivery destination by sea transport by ship,
Using the arrival time information as the delivery time information,
Using the probability distribution of the arrival interval to the all delivery destinations of the ship as the probability distribution of the delivery interval to the all delivery destinations,
The safety stock determination according to any one of [1] to [12], wherein a probability distribution of an arrival interval of a ship to each delivery destination is used as a probability distribution of a delivery interval to each delivery destination. apparatus.
[14] A safety stock determination method for determining a safety stock to be held at each delivery destination with respect to an object delivered from one or a plurality of shipping sources to a plurality of delivery destinations,
The input means, for the object for which safety stock is to be determined, fetching, as past performance information, delivery time information, and usage information that is information on the usage of the object for each delivery destination;
The first probability distribution calculation means assumes a plurality of delivery destinations included in the delivery time information as one delivery destination based on the delivery time information captured by the input means (hereinafter referred to as all delivery destinations). Obtaining a probability distribution of delivery intervals to all delivery destinations;
Based on the usage information captured by the input means by the second probability distribution calculating means and the probability distribution of delivery intervals to all the delivery destinations obtained by the first probability distribution calculating means, the delivery time Obtaining a probability distribution of delivery intervals to each delivery destination included in the information;
The safety stock calculation means has at each delivery destination based on the usage amount information fetched by the input means and the probability distribution of delivery intervals to each delivery destination obtained by the second probability distribution calculation means. And calculating a safety stock to be calculated.
[15] A program for determining a safety stock to be held at each delivery destination with respect to an object delivered from one or more shipping sources to a plurality of delivery destinations,
For an object for which safety stock is to be determined, input means for fetching usage time information that is delivery time information and usage amount information of the object for each delivery destination as past performance information;
Based on the delivery time information captured by the input means, a plurality of delivery destinations included in the delivery time information are assumed to be one delivery destination (hereinafter referred to as all delivery destinations), and delivery intervals to all delivery destinations First probability distribution calculating means for obtaining a probability distribution of
Based on the usage information captured by the input means and the probability distribution of delivery intervals to all delivery destinations obtained by the first probability distribution calculation means, the delivery destination information included in the delivery time information is sent to each delivery destination. A second probability distribution calculating means for obtaining a probability distribution of delivery intervals;
Based on the usage information fetched by the input means and the probability distribution of the delivery interval to each delivery destination obtained by the second probability distribution calculation means, the safety stock to be possessed by each delivery destination is calculated. A program for causing a computer to function as a safety stock calculation means.

  According to the present invention, for an object delivered from one or a plurality of shipping sources to a plurality of delivery destinations, the lead time from ordering to delivery is long, and each delivery destination has even an unstable situation. The appropriate safety stock can be determined appropriately.

It is a figure which shows the function structure of the safety stock determination apparatus which concerns on 1st Embodiment. It is a figure which shows typically the concept of the shipment and delivery of the target object in 1st Embodiment. It is a flowchart which shows the safety stock determination process by the safety stock determination apparatus which concerns on 1st Embodiment. It is a figure which shows the example of the arrival time information according to the delivery destination about a certain target object. It is a figure which shows the example of the usage-amount information according to the delivery destination about a product name X, and a day. It is a figure which shows the example of the histogram of the arrival interval to all the delivery destinations of a ship, and the approximate curve by exponential distribution. It is a figure which shows the example of the histogram of the arrival interval to the delivery destination B of a ship, and the approximated curve by gamma distribution. It is a characteristic view which shows the example of the result of having given initial stock with the safe stock determination method concerning a 1st embodiment, and calculating stock transition. It is a figure which shows the function structure of the safety stock determination apparatus which concerns on 2nd Embodiment. It is a flowchart which shows the safety stock determination process by the safety stock determination apparatus which concerns on 2nd Embodiment. It is a figure which shows the example of the arrival time information according to the delivery destination about a certain target object. It is a characteristic view which shows the example of the graph which displayed the line segment in the estimation result of average arrival rate (lambda) (s), and the port arrival time to all the delivery destinations. It is a figure which shows the example of the graph which compared the arrival interval to each delivery destination of a ship, and the sample produced | generated at random by the dilution algorithm. It is a figure which shows the example of the result of having calculated the safety stock days for every delivery destination. It is a figure for demonstrating the view of stock.

Preferred embodiments of the present invention will be described below with reference to the accompanying drawings.
[First Embodiment]
First, an outline of a safety stock determination method to which the present invention is applied will be described.
In order to suppress the out of stock of the objects (raw materials, materials, products, goods, etc.) supplied by sea transportation, the present inventor has safety stock corresponding to the variation in arrival intervals of ships that supply the objects. I thought I should have. In other words, the safety stock should be given from the difference between the average arrival interval of the ship and the maximum arrival interval of the ship, modeling the arrival interval to the ship destination with a probability distribution, not from the variation in lead time. Thought. If the safety stock is set in this way, even if the ship arrives with a variation from the average arrival interval, the inventory can be managed so as to prevent the target from being out of stock at the delivery destination (factory, commercial facility, inventory base, etc.).

For the above calculation, it is necessary to model the probability distribution of the arrival interval at the ship delivery destination. Therefore, as will be described in detail below, we attempted to construct a probability distribution of ship arrival intervals for each delivery destination by analyzing ship allocation operations and statistically modeling ship allocation.
First, the present inventor analyzed the arrival interval of each ship to each delivery destination, and found that the arrival interval was a probability distribution close to a gamma distribution. The gamma distribution is a distribution that represents a time interval until an arrival event that occurs in accordance with an exponential distribution occurs multiple times. In real problems, the gamma distribution is often used to calculate the life time distribution of equipment and the failure rate.

Based on this knowledge, the present inventor has thought that the arrival phenomenon of each ship at each delivery destination may also be caused by a phenomenon that follows a specific probability distribution such as an exponential distribution. Therefore, assuming a plurality of delivery destinations as one delivery destination (hereinafter referred to as all delivery destinations), the arrival interval of the ship is focused on and calculated. As a result, we found that the arrival interval of ships to all destinations tends to follow a highly random probability distribution such as exponential distribution and gamma distribution. In this case, not only the arrival interval of the ship to all delivery destinations, but also the arrival at the first delivery destination from the shipper, as well as in the case of multi-port unloading that ships arriving in the country visit multiple delivery destinations. Obtained by treating arrival at the port as one arrival at all destinations.
We thought that the reason why the ship randomly arrived at all delivery destinations was that the ship was affected by various influences in the process of arrival at all delivery destinations. For example, the departure time of a ship that is transported from a remote location varies depending on the interchange with other ships, waiting for loading, equipment troubles, and the like. In addition, ships have multiple lead times and vary under the influence of bad weather and ocean currents. In addition, after a ship arrives at a nearby base in the country, it is not only affected by the weather, but also due to congestion at the port and stocking conditions at the unloading destination, the ship will depart / enter the port. Time varies. In this way, if ships affected by various variations are mixed in the process of arriving at all delivery destinations, the randomness will eventually become stronger, and it will be close to a probability distribution with a strong randomness.

  In consideration of the above, the present inventor considered that the ship dispatching operation of the object is not a single transportation between two bases but a random distribution such as an exponential distribution or a gamma distribution as shown in FIG. We thought that ships that arrived at all delivery destinations according to a highly probable probability distribution could be regarded as a task of allocating to each delivery destination according to their usage. Based on this idea, the arrival interval of a ship is divided into two stages: “arrival at all ship destinations” and “arrival at each ship destination”. The probability distribution of the ship is modeled by the convolution integral of the probability distribution of arrival intervals at all ship destinations, and a method to calculate the safety stock that each ship destination should have is established.

Hereinafter, the safety stock determination method according to the first embodiment to which the present invention is applied will be described.
FIG. 1 shows a functional configuration of the safety stock determination apparatus 100 according to the first embodiment. The safety stock determination device 100 is used to determine a safety stock to be held at each delivery destination for an object delivered from one or a plurality of shipping sources to a plurality of delivery destinations. For example, iron ore and coal in steel manufacturers, crude oil in chemical manufacturers and oil manufacturers, and LNG and coal in electric power companies are delivered from overseas shipping sources to domestic destinations using ships. In such a case, the present invention is widely applicable in order to calculate the safety stock that each delivery destination should have.

  Reference numeral 300 denotes a database in which performance information is accumulated and stored. The track record information includes arrival time information for each object and each delivery destination, and usage information for each object, each delivery destination, and each day.

101 is an input unit that is an input means, and from the database 300, for an object (product name X) for which safety stock is desired to be determined, arrival time information by delivery destination and usage information by delivery destination / day And capture. In addition, you may make it capture the performance information of the whole period preserve | saved at the database 300, and may capture the performance information of the analysis object period designated by the user.
In FIG. 4, the example of the port arrival time information according to the delivery destination about the product name X is shown. The arrival time information for each delivery destination is given as a matrix of the name of the delivery destination that has been unloaded, the arrival time at the delivery destination, and the arrival amount [t]. The port entry time information is an example of “delivery time information” in the present invention.
FIG. 5 shows an example of usage amount information for each delivery destination and each day for the product name X. The usage amount information for each delivery destination and each day is given as a matrix of the name of the delivery destination used, the date used, and the usage [t].

  Reference numeral 102 denotes a delivery date delay allowable value setting unit, which is a delivery date delay allowable value setting means, and sets a delivery date delay allowable value. The delivery delay allowable value is set, for example, as a delivery delay rate [%]. The delivery delay allowable value can be appropriately set by the user via the input device 107, and may be set for each object and each delivery destination. A default value may be used.

  Reference numeral 103 denotes a first probability distribution calculation unit which is a first probability distribution calculation unit, and a plurality of items included in the port arrival time information for each delivery destination based on the port arrival time information for each delivery destination captured by the input unit 101. Is assumed to be one delivery destination (hereinafter referred to as all delivery destinations), and the probability distribution of the arrival interval to all delivery destinations of the ship is obtained. In the example of FIG. 4, the delivery destinations are C, C, B,... In time series order, but the delivery destination is not identified, and the ship sends all the delivery destinations to “2015/3/100. 0:00 ”,“ 2015/3/19 14:27 ”,“ 2015/3/20 5:20 ”,... The probability distribution of the arrival interval to all delivery destinations of the ship is an example of the “probability distribution of delivery interval to all delivery destinations” in the present invention.

Reference numeral 104 denotes a second probability distribution calculation unit which is a second probability distribution calculation unit, which is obtained by the first probability distribution calculation unit 103 and the usage amount information for each delivery destination, which is captured by the input unit 101, and the first probability distribution calculation unit 103. Based on the probability distribution of arrival intervals at all ship destinations, the probability distribution of arrival intervals at each ship destination (a plurality of destinations included in the port arrival time information for each ship destination) is obtained. The probability distribution of the arrival interval at each delivery destination of the ship is an example of the “probability distribution of the delivery interval to each delivery destination” in the present invention. As shown in FIG. 2, a ship loaded with an object of product name X arrives at all delivery destinations with Q ships (that is, the number of deliveries to all delivery destinations is Q) and depends on the amount used by each delivery destination. q Ships q _A , q _B , and so on. Here, one in N _i = Q / q _i vessels in certain delivery destination i times, assuming that is periodically Haisen (i.e., delivery to a delivery destination i is when there once in N _i times suppose), give the arrival interval of the delivery destination i of the ship, the probability distribution of the arrival interval to all the delivery destination of the ship at the N _i times the convolution integral of the probability distribution. i is a symbol representing a delivery destination, and i = A, B,..., Z in the example of FIG.

  Reference numeral 105 denotes a safety stock calculation unit which is a safety stock calculation means, and includes usage amount information for each delivery destination and date fetched by the input unit 101, a delivery date delay allowable value set by the delivery date delay allowable value setting unit 102, Based on the probability distribution of the arrival interval at each delivery destination of the ship obtained by the probability distribution calculation unit 104 of 2, the safety stock to be possessed by each delivery destination is calculated.

  Reference numeral 106 denotes an output unit serving as an output unit, which outputs the result of the safety stock calculated by the safety stock calculation unit 105. For example, the result is displayed on the display 108 or the result is sent to an external device of the apparatus 100.

  Reference numeral 107 denotes an input device such as a pointing device or a keyboard, and 108 denotes a display.

Next, a safety stock determination method by the safety stock determination device 100 will be described.
FIG. 3 is a flowchart showing the safety stock determination process performed by the safety stock determination apparatus 100 according to the first embodiment.
In step S <b> 1, the input unit 101 determines, from the database 300, port arrival time information (see FIG. 4) for each delivery destination and the delivery destination / day for an object (product name X) for which safety stock is desired to be determined. Usage amount information (see FIG. 5).

  Next, in step S2, the delivery delay allowable value setting unit 102 sets the delivery delay allowable value α%. The delivery delay tolerance α% is set as a delivery delay percentage [%].

Next, in step S3, the first probability distribution calculation unit 103 obtains a probability distribution of arrival intervals at all ship destinations of the ship. Regardless of whether the ship arrives at all destinations, it is a direct arrival from the shipper or an arrival through another port (multi-port freight).
FIG. 6 is a diagram depicting a histogram of arrival intervals at all ship destinations calculated from the port entry time information of FIG. 4 and an approximate curve based on an exponential distribution. As can be seen from FIG. 6, the arrival intervals at all ship destinations follow an exponential distribution. Here, the exponential distribution is a probability distribution in which a probability density function is given by Expression (1), and represents an arrival interval of randomly occurring discrete events. The reason for the random arrival intervals at all ship destinations is that ships that are transported from far away from the country vary greatly due to bad weather, equipment troubles, etc., so ships from various shipping sources come to Japan. When mixed in the process of arriving, it is thought that the randomness eventually became stronger and became closer to the exponential distribution. λ is an average arrival rate to all ship destinations, and represents an average number of ship arrivals per unit time. As a result of estimating λ using the maximum likelihood estimation method for the port arrival time information of FIG. 4, the value was 1.0 [ships / day]. The average arrival rate λ to all delivery destinations of a ship is an example of “average delivery rate to all delivery destinations (average number of deliveries per unit time)” in the present invention.

  Note that the arrival interval of all ships to the delivery destination may follow a gamma distribution. When a ship generated according to an exponential distribution is shared by a plurality of companies, the company-wide arrival interval seen from a certain company may follow a gamma distribution.

Next, in step S4, the second probability distribution calculation unit 104 obtains a probability distribution of arrival intervals at each ship destination of the ship.
As shown in FIG. 2, the ship loaded with objects product names X arrives Q vessels in all delivery destination, q _A vessels in accordance with the delivery destination another usage, q _B vessels, ... and distribution Think of it as a ship. Here, it is assumed that a certain delivery destination i is regularly dispatched once to N _i = Q / q _i ships. Under this assumption, the delivery destination i, is once Haisen each time the ship arrives N _i vessels in all delivery destination. That is, the probability distribution of the arrival interval of the delivery destination i ship, according to an exponential distribution, a ship to arrive to all the delivery destination is given as the time of the distribution of until N _i cough. Time behavior according to the exponential distribution until the predetermined number of times occur is given by N _i times the convolution integral of the exponential distribution, the general gamma distribution. In other words, if the probability distribution of the arrival interval of the ship to the delivery destination i is g _i (t), it can be modeled by a gamma distribution as shown in Equation (2) by using two parameters N _i and λ.

Incidentally, the number N _i of the convolution integral is not limited to the above calculation method. For example, if you want to determine the N _i in proportion to the delivery destination-specific usage, a value obtained by multiplying the ratio of delivery destination-specific usage Irifune number Q of total delivery destination may be given as N _i. Specifically, it is assumed that two delivery destinations A and B are used, and the ratios of the respective usage amounts are 0.4 and 0.6. In this case, the delivery destination A, B each N _i is, Q × 0.4 for delivery destination A, if Q × 0.6 for delivery destination B, corresponding to the ratio of the amount _Ni is determined.

Figure 7 is a histogram of the arrival interval of the delivery destination B ship, lambda respect delivery destination B, the N _i respectively calculated, in view depicting the approximate curve by gamma distribution prepared using the equation (2) is there. The number of times of arrival at each delivery destination q _i and the parameters λ and N _i of the gamma distribution were calculated from the port entry time information of FIG. 4 and given the values shown in Tables 1 to 3. By using only two parameters λ and N _i , a curve close to actual results can be obtained.

  In order to ensure the validity of this approximate curve, it is desirable to perform a statistical test using the Kolmogorov-Smirnov test (KS test) or the like. The KS test is a technique for establishing a null hypothesis that two populations or probability distributions follow the same distribution, and testing whether this null hypothesis holds. In the KS test, the probability p-value that this null hypothesis is established is calculated. If the p-value is 0.05 or less, the null hypothesis is rejected and the two populations or distributions do not follow the same distribution. Can be tested. This time, the KS test was performed on the histogram and the approximate curve of FIG. As a result, the p value was 0.62, showing a value sufficiently larger than 0.05, and the null hypothesis that the histogram and the approximate curve follow the same distribution was not rejected.

Next, in step S5, the safety stock calculation unit 105 calculates the safety stock that each delivery destination should have.
In order to keep the delivery rate achievement rate at 1-α% or more under the probability distribution of the arrival interval of the ship to the delivery destination i given by the equation (2), the probability distribution g _i (t) is set to 1-α. It is only necessary to give the difference between the points covering% and the average value of the probability distribution g _i (t) as the safety stock days. The safety distribution days are Z [days], the cumulative distribution function of the probability distribution g _i (t) of the arrival interval at the ship destination i is set as G _i (·), and the inverse function of the cumulative distribution function is G ⁻¹ ( -)), The safety stock days to keep the delivery rate achievement rate 1-α% or more is given by equation (3).

  However, the safety stock days calculated by the equation (3) is a calculation equation that keeps the delivery delay at α% or less, and the stockout does not occur with this probability. This is because the safety stock days calculated by the equation (3) assume a constant usage amount of stock. In practice, if it is known in advance that the inventory of a certain object is small, the production is adjusted so that the planned arrival time of the ship is anticipated and the use is transferred to another object in advance so that it will not run out of stock. it can.

Table 4 shows the result of calculating the safety stock days [days] for each delivery destination by setting the delivery delay allowable value α% as 5%.
The safety stock is obtained by calculating the average usage amount [t / day] for each delivery destination from the usage amount information of FIG. 5 and multiplying it by the safety stock days [day] obtained in Table 4. Table 5 shows the calculation result of the average usage [t / day]. Table 6 shows the result of calculating the safety stock [t] by multiplying the safety stock days [days] by the average usage [t / day].

As described above, the safety stock can be calculated using the calculation of the convolution integral by giving the probability distribution of the arrival intervals to all the delivery destinations of the ship and the number of ships allocated to each delivery destination of the ship.
FIG. 8 shows the result of calculating the stock transition by giving the initial stock at the delivery destination B by the safety stock determination method according to the present embodiment. If the safety stock calculated this time is used, it can be understood that there is no out of stock. Table 7 shows the result of calculating the safety stock for a plurality of objects. As described above, by presenting the safety stock to the user for each delivery destination for various objects, the stock can be appropriately maintained.

  Next, in step S6, the output unit 106 outputs the safety stock result calculated in step S5.

As described above, the lead time from ordering to delivery is long and unstable for objects delivered from one or more shippers from overseas or multi-port to multiple delivery destinations by sea transport. Even under circumstances, it is possible to appropriately determine the safety stock that each delivery destination should have so as to keep the delivery date achievement rate above a certain level. As a result, for example, by comparing the determined safety stock with the current safety stock, it becomes possible to determine the amount of inventory for each delivery destination, and a large inventory reduction is expected.
Also, when determining the safety stock, the arrival of the ship is divided into two stages, “arrival at all ship destinations” and “arrival at each ship destination”. The probability distribution of arrival intervals is modeled by the convolution integral of the probability distribution of arrival intervals to all ship destinations. For example, a method of directly obtaining the probability distribution of arrival intervals at each ship destination from the actual information may be considered, but the accuracy is inferior compared to obtaining the probability distribution of arrival intervals at all ship destinations. . On the other hand, in the method to which the present invention is applied, the arrival of the ship is divided into two stages of “arrival at all ship destinations” and “arrival at each ship destination”, and the number of N is large. Since the probability distribution of the arrival interval to each delivery destination of the ship is obtained with high accuracy after obtaining the probability distribution of the arrival interval to all delivery destinations of the ship, the accuracy of determining the safety stock can be improved.

[Second Embodiment]
With the safety stock determination method described in the first embodiment, it is possible to determine the safety stock to be held in each delivery destination in a desired analysis target period for each target.
Here, the occurrence probability of the supply interruption of the object may vary depending on the season. Since the object is transported from a distant overseas, the supply amount is known to fluctuate due to seasonal influences such as floods, typhoons, and cold waves. For example, an object shipped from Australia has a risk of stopping the shipment of the object due to a break in the supply chain on land due to a natural disaster such as a flood in January to March when Australia is in the rainy season. Therefore, it is necessary to anticipate a supply disruption in advance before the period of 1 to 3 months, and to secure more safety stock than usual.

In the safety stock determination method described in the first embodiment, the safety stock is not determined in consideration of such seasonal variation.
In this case, it is conceivable that the analysis target period is subdivided in accordance with a mesh to be considered for seasonal variation, and the safety stock is determined for each of the subdivided periods. However, in order to determine the safety stock with high accuracy, a sufficiently large amount of performance information is required even during the subdivision period. That is, it cannot be applied to an object with a low delivery frequency. In particular, an object for which safety inventory is calculated by applying the present invention is assumed to be transported from a distant overseas, so it is often delivered in large lots and low frequency delivery. In many cases, results information is not available enough to determine the

In order to solve the above problem, the present inventor made improvements to the probability distributions of “arrival intervals at all ship destinations” and “arrival intervals at each ship destination” described in the first embodiment. In consideration of the seasonal variation, we tried to model the probability distribution of the arrival interval at each ship destination.
In order to add a seasonal variation factor to the probability distribution of the arrival interval of each ship's delivery destination, we came up with the idea that this probability distribution should change by time, but the probability of occurrence of seasonal variation is It varies depending on the object and the year, and the generation factors vary widely, so it is very difficult to set seasonal variations quantitatively for each object. Furthermore, since the number of ship arrival times by number of delivery destinations is small, more track record information was needed to estimate seasonal variations with high accuracy.

Therefore, paying attention to the arrival of the ship to all the delivery destinations, the probability distribution of the arrival interval to all the delivery destinations of the ship, including seasonal variations, is calculated using the time of arrival of the ship to all the delivery destinations with a lot of track record information. I came up with the idea of creating a probability distribution of arrival intervals at ship destinations using the modeled probability distribution.
Specifically, the average delivery rate λ, which has been treated as constant in the first embodiment, is modeled as a function λ (s) that changes with time at time s. Create a probability distribution of arrival intervals. As in the first embodiment, if the probability distribution of the arrival interval to each ship destination of the ship is given by the convolution integral of the probability distribution of the arrival interval to all ship destinations, the seasonal variation is taken into consideration. It came to a conclusion that it was possible to model the interval of arrival at each ship destination.

The arrival process taking into account non-uniformity of the average arrival rate λ (s) is called a non-uniform Poisson process. The non-uniform Poisson process is a process that represents a phenomenon in which individual events arrive independently but the average arrival rate changes with time.
Next, the flow for determining λ (s) and safety stock when the arrival intervals at all ship destinations follow a non-uniform Poisson process will be described. However, the method shown below is not limited to the case where the arrival interval to all ship destinations follows a non-uniform Poisson process. For example, it may be a counting process (non-uniform renewal process) in which the probability distribution of the arrival intervals to all ship destinations follows an independent same distribution such as a gamma distribution and the average arrival rate changes with time. The non-uniform renewal process can be handled in the same way as the non-uniform Poisson process if the time-stretching theory shown in Non-Patent Document 2 is used.

Since the average arrival rate λ (s) has a different shape and frequency component for each object, the average arrival rate λ (s) must be determined based on performance information. A method for estimating the average arrival rate λ (s) from the performance information will be described below.
First, the average arrival rate λ (s) is modeled by an exponential Fourier series shown in Equation (4). Next, parameters suitable for each object are determined by determining the parameters of the exponential Fourier series based on a statistical information criterion.

In the exponential Fourier series of Equation (4), A _i and B _i are variables that give a model shape, and P is a period. In the exponential Fourier series, by setting the period P, fluctuations in various periods can be modeled. For example, if P = 1 year, it is possible to model seasonal variation in a year cycle. Further, the variables A _i and B _i giving the model shape can model various frequency components depending on the number of parameters.
In the present embodiment, the parameter of the average arrival rate λ (s) is determined using Akaike's Information Criterion (AIC) as shown in Equation (5). AIC is one of the evaluation indexes of the model, and the first term of the equation (5) represents the log likelihood function of the model, and the second term represents the number of parameters. AIC is a standard in which the number of parameters representing the complexity of a model is added as an evaluation index in addition to the maximum likelihood estimation method that determines a parameter by maximizing a likelihood function. In AIC, a plurality of models can be compared with each other, and a model having the best balance between goodness of model fit and complexity can be determined.

Here, the likelihood function of the time series {s _i } ⁿ _{i = 1} of port arrival times to all delivery destinations according to the non-uniform Poisson process is that the ship arrival event does not occur from time s _i-1 to s _i , and the time The probability of a ship arrival event occurring at s _i is expressed by equation (6).

  Assuming that each ship arrival event is independent, the likelihood function can be expressed in the form of a product as shown in Equation (7).

  As described above, the likelihood function is created by substituting the average arrival rate λ (s) shown in Expression (4) into Expression (7), and the parameter that maximizes the likelihood function is determined by the maximum likelihood estimation method. If the obtained log likelihood and the number of parameters of the model are substituted into the equation (5) and AIC is obtained and the models are compared with each other, it is possible to determine the best fit model from a plurality of models.

Hereinafter, the safety stock determination method according to the second embodiment to which the present invention is applied will be described. In addition, it demonstrates centering around difference with the safety stock determination method which concerns on 1st Embodiment, and abbreviate | omits detailed description about a common point with 1st Embodiment.
FIG. 9 shows a functional configuration of the safety stock determination apparatus 200 according to the second embodiment. The input unit 201, the due date delay allowable value setting unit 202, the second probability distribution calculation unit 204, the safety stock calculation unit 205, the output unit 206, the input device 207, and the display 208 are the input unit 101, the delivery date in the first embodiment. This is the same as the allowable delay value setting unit 102, the second probability distribution calculation unit 104, the safety stock calculation unit 105, the output unit 106, the input device 107, and the display 108, and a description thereof will be omitted.
As a difference from the first embodiment, the first probability distribution calculation unit 203 includes an estimation unit 203a that estimates the average arrival rate λ (s).
In addition, a cycle setting unit 209 that sets the cycle P is provided. The period P can be appropriately set by the user via the input device 207. A default value may be used. From the viewpoint of considering the seasonal variation, in most cases, it is considered that P = 1 year is set in order to model the seasonal variation of the annual cycle.

Next, a safety stock determination method by the safety stock determination device 200 will be described.
FIG. 10 is a flowchart showing a safety stock determination process performed by the safety stock determination device 200 according to the second embodiment.
Steps S11 and S12 are the same as steps S1 and S2 in the first embodiment, and a description thereof is omitted here.

In step S13, the estimation unit 203a of the first probability distribution calculation unit 203 estimates the average arrival rate λ (s) based on the port arrival time information for each delivery destination captured in step S11. In the present embodiment, it is assumed that the period P is one year and the seasonal variation of the maximum one-year period is modeled.
In FIG. 11, the example of the port arrival time information according to the delivery destination about a certain target object is shown. As described in the first embodiment, the delivery destinations are A, C, A,... In chronological order. However, if the delivery destinations are not identified, the ship will send all the delivery destinations to “2015/1. / 6 15:00 ”,“ 2015/1/12 21:00 ”,“ 2015/1/14 6:00 ”, and so on.

FIG. 12 and Table 8 show the results of determining a model using AIC from the arrival times at all delivery destinations obtained from the arrival time information of FIG.
FIG. 12A is a graph showing the estimation result of the average arrival rate λ (s), and shows the estimation result when the number of parameters having the lowest AIC is five. FIG. 12B shows line segments at the time of arrival at all delivery destinations. As can be seen from FIG. 12, it can be seen that the arrival frequency of the ship was lowest between May 2015 and July 2015.
Table 8 shows the relationship between the number of parameters of equation (4), parameter values, and AIC. As can be seen from Table 8, it can be seen from the port entry time information of FIG. 11 that the AIC is the lowest when the number of parameters is 5. Thus, by determining a model using AIC, it was possible to determine a model that considered the balance between the degree of model fit and the number of parameters.

  Next, in step S14, the first probability distribution calculation unit 203 obtains a probability distribution of arrival intervals at all ship destinations of the ship. Step S14 is the same as step S3 except that the average arrival rate treated as constant in the first embodiment is a function λ (s) that changes with time at time s, and the description thereof is omitted here.

Next, in step S15, the second probability distribution calculation unit 204 obtains a probability distribution of arrival intervals at each ship destination of the ship. Step S15 is the same as Step S4 except that the average arrival rate, which has been treated as constant in the first embodiment, is a function λ (s) that changes with time at time s, and arrives at the ship destination i. When the probability distribution of the interval is g _i (s, t), it is possible to model with a gamma distribution as shown in Expression (8) by using two parameters N _i and λ (s).

The number of times of arrival at each delivery destination q _i and the parameter N _{i of the} gamma distribution were calculated from the arrival time information of FIG. 11 and given the values shown in Tables 9 and 10.

As described above, the average arrival rate estimating the AIC as a reference lambda (s), by decided parameter N _i of the gamma distribution, the ship in consideration to the non-uniformity of the arrival time of each delivery destination The probability distribution could be modeled.

As with the first embodiment, the validity of the estimation result is desirably a statistical test using a KS test or the like. However, since the probability distribution of Expression (8) is a function that depends on the time s, it is difficult to directly compare with the port arrival time information for each delivery destination as in the first embodiment.
Therefore, a KS test is performed by generating a sufficiently large number of random numbers according to the probability distribution of equation (8) within the safety stock calculation period and comparing the obtained sample with the port entry time information for each delivery destination. For random numbers, first, the probability distribution of equation (8) is converted into a non-uniform Poisson process by the time expansion / contraction theory described in Non-Patent Document 2. Thereafter, random numbers that follow a non-uniform Poisson process were generated according to the algorithm described in Non-Patent Document 3 (dilution algorithm).
FIG. 13 is a graph comparing the arrival interval of a ship to each delivery destination and a sample randomly generated by a dilution algorithm. Table 11 shows the results of performing two-sided KS tests at a significance level of 5% for both samples under the null hypothesis that there is no difference between the actual value and the population representative value of the model sample. As a result of generating models 1685, 746, and 665 times for delivery destinations A, B, and C, and performing KS test, the P value of KS test exceeded the significance level of 5% at all sites. The hypothesis was not rejected. That is, it cannot be said that there is a difference between the actual value and the mother representative value of the model sample.

Next, in step S16, the safety stock calculation unit 205 calculates the safety stock that each delivery destination should have.
In order to keep the delivery rate achievement rate 1-α% or more under the probability distribution of the arrival interval of the ship to the delivery destination i given by the equation (8), the probability distribution g _i (s, t) is set to 1 The difference between the points covered by -α% and the average value of the probability distribution g _i (s, t) may be given as the number of safety stock days. The safety distribution days are set to Z (s) [days], the probability distribution g _i (s, t) of the arrival interval at the ship destination i is set to G _i (·), and the inverse function of the cumulative distribution function Is set to G ⁻¹ (·), the safety stock days to keep the delivery rate 1-α% or more is given by the equation (9).

  However, as described in the first embodiment, the safety stock days calculated by the formula (9) is a calculation formula that keeps the delay in delivery to α% or less, and the stockout does not occur with this probability. . This is because the safety stock days calculated by Equation (9) assume a constant usage amount of stock. In practice, if it is known in advance that the inventory of a certain object is small, the production is adjusted so that the planned arrival time of the ship is anticipated and the use is transferred to another object in advance so that it will not run out of stock. it can.

  FIG. 14 shows the result of calculating the safety stock days [days] for each delivery destination by setting the delivery delay allowable value α% as 5%. As shown in FIG. 14, it is understood that the safety stock days can be calculated according to the season by modeling the average arrival rate as a function λ (s) that changes with time at time s. In particular, as shown in FIG. 12, in the port entry time information of FIG. 11, it was confirmed that the supply amount decreased from May to July, but as shown in FIG. 14, the above period is larger than usual. Results with safety stock days were obtained.

  The safety stock is obtained by calculating the average usage [t / day] for each delivery destination and multiplying the obtained safety stock days [days]. Table 12 shows the calculation result of the average usage [t / day]. Table 13 shows the result of calculating the safety stock [t] by multiplying the safety stock days [days] by the average usage [t / day]. In this way, by using the non-uniform Poisson process to model the probability distribution of arrival intervals at all ship destinations, we were able to calculate the safety stock considering seasonal variations.

  In step S17, the output unit 206 outputs the safety stock result calculated in step S16.

By the way, the safety stock obtained in the present embodiment is a value based on past results, and it is considered that the safety stock is determined in anticipation of a future supply schedule.
Here, how to appear in the seasonal variation of the average arrival rate of the future, not the same as appear the way of seasonal variation of the average arrival rate, which was estimated from past experience λ (s), also unchanged from the past further convolution number N _i of the integration Assume that Then, the future average arrival rate λ future (s) is obtained by multiplying the average arrival rate λ past (s) determined using the actual information by a constant so as to match the future supply amount. ) Is predicted. Specifically, the average arrival rate estimated using the actual information is set with the average value of the average arrival rate determined using the actual information as λ past average and the average value of the expected average arrival rate as the λ future average. If λ past (s) is set, the average arrival rate λ future (s) representing a seasonal variation expected in the future can be determined by the equation (10).

  The average arrival rate is proportional to the cumulative value of the arrival amount of the target object during the analysis target period (hereinafter, the cumulative arrival amount). Therefore, the average value λ future average of the expected average arrival rate can be modeled by the equation (11) if the future predicted value of the accumulated arrival amount is known. In Equation (11), a and b are constants, and may be estimated from the record information using single regression analysis or the like.

Thus, even when the future supply conditions are different from the past, the safety stock can be determined by providing a prediction model of the probability distribution of arrival intervals at all ship destinations in advance. If the number N _i is also changed future convolution is anticipated, first, as shown in equation (12), all the delivery destination by multiplying the future average analysis period λ obtained from Equation (11) Calculate the expected number of ships entering Q '.

Next, as shown in equation (13), by obtaining the N _i is multiplied by the ratio of the object-specific, delivery destination another scheduled usage expected Irifune number Q'to all delivery destination, the predicted value of N _i Can be requested.

Calculated from the above, by using the average N _i and λ future, it is possible to determine a safety stock in anticipation to supply schedule in the future.

  Further, the analysis period of the average arrival rate λ past (s) estimated using past performance information may be a single year or a plurality of years. For example, if the practitioner wants to model the average seasonal variation of the past multiple years, the average arrival rate λ past (s) may be estimated for the performance information of the multiple years. If you want to set a safety stock to prevent the delivery rate compliance rate from exceeding a certain level even in the year with the greatest seasonal variation effect, the average arrival rate λ past (s) for the data for that single year. It may be estimated. As described above, by modeling the seasonal variation by changing the analysis target period, it is possible to determine the safe stock amount that more closely matches the actual requirement.

In this embodiment, an exponential Fourier series is used for the average arrival rate λ (s). However, the present invention is not limited to this, and for example, a Fourier series or a polynomial may be used.
In addition, although AIC is used as the information criterion, the present invention is not limited to this, and other information criterion regarding the determination of the model can be used. For example, an information criterion such as a Bayesian information criterion (BIC) may be used.

Although the present invention has been described together with the embodiments, the above-described embodiments are merely examples of implementation in carrying out the present invention, and the technical scope of the present invention is interpreted in a limited manner by these. It must not be. That is, the present invention can be implemented in various forms without departing from the technical idea or the main features thereof.
The safety stock determination device to which the present invention is applied is realized by a computer device including a CPU, a ROM, a RAM, and the like, for example. 1 and 9, the safety stock determination devices 100 and 200 are illustrated as one device, but may be configured by a plurality of devices, for example.
The present invention also provides software (program) that implements the functions of the present invention to a system or apparatus via a network or various storage media, and the system or apparatus computer reads out and executes the program. It is feasible.

DESCRIPTION OF SYMBOLS 100, 200: Safety stock determination apparatus 101, 201: Input part 102, 202: Delivery date delay allowable value setting part 103, 203: 1st probability distribution calculation part 203a: Estimation part 104, 204: 2nd probability distribution calculation part 105, 205: Safety stock calculation unit 106, 206: Output unit 209: Period setting unit 300: Database

Claims (15)

A safety stock determination device for determining a safety stock to be held at each delivery destination with respect to an object delivered from one or a plurality of delivery sources to a plurality of delivery destinations,
For an object for which safety stock is to be determined, input means for fetching usage time information that is delivery time information and usage amount information of the object for each delivery destination as past performance information;
Based on the delivery time information captured by the input means, a plurality of delivery destinations included in the delivery time information are assumed to be one delivery destination (hereinafter referred to as all delivery destinations), and delivery intervals to all delivery destinations First probability distribution calculating means for obtaining a probability distribution of
Based on the usage information captured by the input means and the probability distribution of delivery intervals to all delivery destinations obtained by the first probability distribution calculation means, the delivery destination information included in the delivery time information is sent to each delivery destination. A second probability distribution calculating means for obtaining a probability distribution of delivery intervals;
Based on the usage information fetched by the input means and the probability distribution of the delivery interval to each delivery destination obtained by the second probability distribution calculation means, the safety stock to be possessed by each delivery destination is calculated. A safety stock determination device comprising a safety stock calculation means. The second probability distribution calculating means determines whether the delivery to the delivery destination i (i is a symbol indicating the delivery destination) out of the number of deliveries to all the delivery destinations depends on the usage amount of the object for each delivery destination. Te, by assuming that there once in N _i times, and characterized in providing a delivery interval of the delivery destination i, the probability distribution of the probability distribution of the delivery interval and convolving N _i times to all delivery destination The safety stock determination apparatus according to claim 1. Claim wherein the second probability distribution calculating means, delivery times q _i to delivery destination i, as delivery times Q to all delivery destination, characterized by providing a number N _i of the convolution with Q / q _i 2. The safety stock determination device according to 2. Provided with a delivery delay tolerance value setting means for setting a delivery delay tolerance value α%,
The safety stock calculation means further covers 1-α% of the probability distribution of delivery intervals to each delivery destination based on the delivery delay allowable value α% set by the delivery delay allowable value setting means, The difference from the average of the probability distribution of the delivery interval to each delivery destination is the safety stock days, and the safety stock that each delivery destination should have is calculated by multiplying this safety stock days by the average usage for each delivery destination. The safety stock determination apparatus according to any one of claims 1 to 3, wherein   5. The safety according to any one of claims 1 to 4, wherein the delivery to all delivery destinations includes delivery directly from a shipping source and delivery via another delivery destination. Inventory determination device. The first probability distribution calculating means assumes that a probability distribution of delivery intervals to all delivery destinations follows an exponential distribution or a gamma distribution,
The safety stock determination apparatus according to any one of claims 1 to 5, wherein the second probability distribution calculation unit assumes that a probability distribution of a delivery interval to each delivery destination follows a gamma distribution. The first probability distribution calculating means uses a probability distribution of delivery intervals to all delivery destinations as an exponential distribution using an average delivery count per unit time (hereinafter referred to as an average delivery rate) to all delivery destinations. Or expressed as a gamma distribution,
The safety stock determination device according to claim 6, wherein the average delivery rate is treated as constant. The first probability distribution calculating means uses a probability distribution of delivery intervals to all delivery destinations as an exponential distribution using an average delivery count per unit time (hereinafter referred to as an average delivery rate) to all delivery destinations. Or expressed as a gamma distribution,
7. The safety stock determination apparatus according to claim 6, wherein the average delivery rate is modeled as a function changing with time.   9. The safety stock determination apparatus according to claim 8, wherein the first probability distribution calculation unit determines the parameter of the function based on a predetermined information criterion.   10. The safety stock determination apparatus according to claim 8, wherein the function is expressed by an exponential Fourier series.   The said 1st probability distribution calculation means calculates | requires the future average delivery rate which is the object period which determines safety stock as a constant multiple of the past average delivery rate, The any one of Claims 8 thru | or 10 characterized by the above-mentioned. The safety stock determination device according to the item. The first probability distribution calculation means obtains a constant to be the constant multiple as a value obtained by dividing an average of future average delivery rates by an average of past average delivery rates,
12. The safety stock determination according to claim 11, wherein an average of the future average delivery rate is estimated by a single regression analysis using past performance information, assuming that the average of the average delivery rate of the target is proportional to the cumulative amount of goods received. apparatus. The object is delivered from the shipper to the delivery destination by sea transport by ship,
Using the arrival time information as the delivery time information,
Using the probability distribution of the arrival interval to the all delivery destinations of the ship as the probability distribution of the delivery interval to the all delivery destinations,
The safety stock determination apparatus according to any one of claims 1 to 12, wherein a probability distribution of an arrival interval of a ship to each delivery destination is used as a probability distribution of a delivery interval to each delivery destination. A safety stock determination method for determining a safety stock to be possessed by each delivery destination for an object delivered from one or a plurality of delivery sources to a plurality of delivery destinations,
The input means, for the object for which safety stock is to be determined, fetching, as past performance information, delivery time information, and usage information that is information on the usage of the object for each delivery destination;
The first probability distribution calculation means assumes a plurality of delivery destinations included in the delivery time information as one delivery destination based on the delivery time information captured by the input means (hereinafter referred to as all delivery destinations). Obtaining a probability distribution of delivery intervals to all delivery destinations;
Based on the usage information captured by the input means by the second probability distribution calculating means and the probability distribution of delivery intervals to all the delivery destinations obtained by the first probability distribution calculating means, the delivery time Obtaining a probability distribution of delivery intervals to each delivery destination included in the information;
The safety stock calculation means has at each delivery destination based on the usage amount information fetched by the input means and the probability distribution of delivery intervals to each delivery destination obtained by the second probability distribution calculation means. And calculating a safety stock to be calculated. A program for determining a safety stock to be held at each delivery destination for an object delivered from one or more delivery sources to multiple delivery destinations,
For an object for which safety stock is to be determined, input means for fetching usage time information that is delivery time information and usage amount information of the object for each delivery destination as past performance information;
Based on the delivery time information captured by the input means, a plurality of delivery destinations included in the delivery time information are assumed to be one delivery destination (hereinafter referred to as all delivery destinations), and delivery intervals to all delivery destinations First probability distribution calculating means for obtaining a probability distribution of
Based on the usage information captured by the input means and the probability distribution of delivery intervals to all delivery destinations obtained by the first probability distribution calculation means, the delivery destination information included in the delivery time information is sent to each delivery destination. A second probability distribution calculating means for obtaining a probability distribution of delivery intervals;
Based on the usage information fetched by the input means and the probability distribution of the delivery interval to each delivery destination obtained by the second probability distribution calculation means, the safety stock to be possessed by each delivery destination is calculated. A program for causing a computer to function as a safety stock calculation means.

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