JP2001266048A - Supply and demand adjustment simulation method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a simulation method which supports decision making, based on an objective index of the loss cost at decision making time, related to supply and demand adjustment of a supply chain. SOLUTION: A supply and demand adjustment simulation program 100 accepts the reception planned quantity and the shipping planned quantity, which are inputted for each planned period and generates plural numerical values, in the case of variance of the shipping planned quantity due to the variance in the supply and demand as a supply and demand variance scenario in one planned period and multiplies the stock quantity or the shortage quantity in the planned period, by the loss cost part unit quantity, to calculate the loss cost in each supply and demand variance scenario, and loss costs of individual supply and demand variance scenarios are displayed in parallel so that the reception quantity in the pertinent planned period can be determined by a user.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for supporting a decision on the quantity of goods received in supply chain management in the manufacturing industry. The present invention relates to a simulation method for supporting a decision on supply-demand adjustment based on an opportunity loss cost resulting from loss of a product.

[0002]

2. Description of the Related Art In supply chain management, a general manufacturing company performs prediction and risk analysis using decision support software. For example, in the United States
Crystal Ball, a risk analysis software developed by Decisioneering, Inc. and used worldwide, simulates the results using a Monte Carlo simulation approach based on inputs of risk parameters and their probability distributions. The Monte Carlo simulation is a technique for simulating a possible result by stochastically varying parameters affecting the result. Crystal Ba
If the function of ll is used, the numerical value of the result also fluctuates depending on the degree of fluctuation of the risk parameter, so that, for example, the numerical value range of the result and the probability of entering the numerical value range can be seen. In addition, the sensitivity analysis function allows the degree of influence on the output result to be viewed numerically. Such risk analysis software can be used as a decision support tool in a company's supply chain management because it can visualize and provide a variety of information on risks.

[0003] In addition to Crystal Ball, Fi, which is an application module of control system design software MATLAB,
As in the nancial ToolBox, risk simulation often uses Monte Carlo simulation.

[0004]

When making a decision on supply-demand adjustment in the supply chain, Monte Carlo simulation can show the range of results stochastically, but the information displayed in such a way does not necessarily make the decision. It was not information that would make it easier. For example, when determining the incoming quantity in the supply and demand adjustment, the fluctuation in demand can be represented by a probability distribution by Monte Carlo simulation, thereby simulating the incoming quantity that achieves a predetermined profit and loss. However, the decision on the quantity to be received is left to the decision maker. That is, the simulation of the received quantity does not directly lead to the objective determination of the received quantity, and there is a large room for the subjective decision of the decision maker. It is necessary to prepare a more rational index as an index indicating risk, and to display information directly linked to a decision in a decision-making scene.

An object of the present invention is to provide a simulation method that supports more rational decision-making on supply and demand adjustment based on an objective index called loss cost.

[0006]

SUMMARY OF THE INVENTION The present invention receives a planned shipment quantity and a planned shipment quantity input at each planning time, and receives a plurality of planned shipment quantities when the planned shipment quantity fluctuates due to demand fluctuation at one planning time. Numerical values are created as demand fluctuation scenarios, and for each of these demand fluctuation scenarios, either the inventory quantity or the shortage quantity at the planning time is multiplied by the loss cost per unit quantity to calculate the loss cost. The demand and supply adjustment simulation method is characterized in that the loss costs for each demand fluctuation scenario are displayed in parallel so that the quantity of goods received can be determined.

[0007]

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

FIG. 1 is a diagram showing a schematic processing procedure of a supply and demand adjustment simulation program 100 of the present embodiment. The supply and demand adjustment simulation program 100 first receives initial data input from the input device 130 (step 110). Here, the initial data is the received quantity, the shipped quantity, the selling price, and the product cost of a certain product at each time T. The timing T is expressed as the first, second, third,... The entire initial data may be the planned figures,
Actual values may be included such as actual values up to March and planned values from the fourth month onward. Next, the simulation target time T
Is set to the first planned time (step 112). First
If the simulation is to be performed from the timing, T = 1 is set.

Next, a loss cost is calculated based on the input shipment plan quantity and the fluctuation range of demand, and the calculation result is displayed on a display device in the form of a graph (step 114). The loss cost calculation includes a loss cost calculation for checking the loss cost sensitivity and a calculation of an expected loss cost value. Next, the user receives the arrival data at the time T inputted from the input device 130 by the user's judgment with reference to the displayed loss cost (step 116). Incoming data is input as a coefficient (arrival coefficient) with the shipping planned quantity being 1.0. Next, an arrival scenario determined by the arrival data is created (step 118). The receiving scenario is the receiving quantity calculated by the determined receiving coefficient × the planned shipping quantity. In step 120, the simulation target time T is incremented by one to proceed to the next simulation time (step 120). Next, it is determined whether or not the simulation target time T is the end time (step 122). If it is not the end time, the processing from step 114 to step 122 is repeated.

If it is the end time, the profit and loss amount is calculated and displayed from the time series data of the receiving scenario and the shipping scenario (planned shipping quantity) created according to the user's instruction (step 124). Finally, the calculated profit / loss and loss cost are stored in the storage device as the arrival scenario storage data 140 (step 126).

FIG. 2 is a hardware configuration diagram of the present embodiment. The computer includes a main processing unit 200, an input device 130 connected to the main processing unit 200, and a display device 135.
And a floppy (registered trademark) disk drive 204. The supply and demand adjustment simulation program 100 read from the floppy disk 206 via the floppy disk drive 204 is read into the memory 210 of the main processing unit 200 and
Performed by 0. The main body processing device 200 includes a storage device such as a fixed disk drive for storing the arrival scenario storage data 140. In place of the floppy disk drive 204 and the floppy disk 206, C
The data may be read into the main processing unit 200 from a storage medium such as D or MO via a dedicated drive.

FIG. 3 is a diagram showing a processing procedure for calculating the loss cost and displaying the sensitivity of the loss cost calculation and display processing in step 114. First, at the simulation target time T, the demand-supply adjustment simulation program 100 reads the fluctuation range of the demand with respect to the planned shipment quantity from the display screen on the display device 135 to which data has been input (step 310). The fluctuation range is expressed by a minimum fluctuation rate (%) and a maximum fluctuation rate (%) based on the shipment quantity. The process of step 310 may be performed together with the initial data input process of step 110. Next, N demand fluctuation scenarios are created based on the input fluctuation width (step 312). Next, a loss cost is calculated for each of the created demand fluctuation scenarios (step 314). The method of calculating the loss cost will be described later. Next, the loss costs of each demand fluctuation scenario are compared and displayed in parallel on the display device 135 (step 316). This display is the loss cost sensitivity display due to the fluctuation scenario.

FIG. 4 is a diagram showing a processing procedure of processing for calculating an expected loss cost value and displaying the result of the loss cost calculation and display processing in step 114. First, at the simulation target time T, the supply and demand adjustment simulation program 100 receives the fluctuation probability data input from the input device 130 with respect to the initial data (step 410).

The variation probability data 422 shows an example of probability data input corresponding to five variation scenarios. Here, the demand fluctuation probability data 424, the arrival fluctuation probability data 426, the price fluctuation probability data 428, and the cost fluctuation probability data 430 are listed, but at least one or more of the four probability data is used. It is not necessary to input all the fluctuation probability data listed. In the present embodiment, the following description will be made based on probability data of demand fluctuation (shipment fluctuation). Shipment fluctuation (50% ~
150%) corresponds to each demand fluctuation scenario and indicates a demand coefficient with respect to the planned shipment quantity. Hereinafter, the demand coefficient is represented by a numerical value with the shipment plan quantity being 1.0. The fluctuation probability is the probability that each demand fluctuation scenario will occur. The change probability is obtained by a method such as analyzing business data.

The probability input process in step 410 is composed of steps 416 to 420. Step 4
At 16, the fluctuation probability data 422 input by the user via the input device 130 is obtained. In step 418, it is determined whether or not the obtained variation probability data 422 is correct in light of predetermined restrictions. If not, a message indicating that the fluctuation probability data 422 obtained in step 420 is invalid is displayed, and the process returns to step 416.

If the fluctuation probability data 422 conforms to the restrictions, the expected loss cost value is calculated using the input fluctuation probability data 422 (step 412). Next, the calculation result of the expected loss cost is displayed on the display device 135 (step 414).

FIG. 5 is a diagram showing calculation formulas used in loss cost sensitivity calculation processing and loss cost expected value calculation processing. As the loss cost (T) at the time T, either the inventory loss cost (T) or the opportunity loss cost (T) is applied depending on the demand fluctuation scenario. The inventory loss cost is a loss cost caused by having a surplus inventory quantity when the cost falls at the target time T. The opportunity loss loss cost is a loss cost caused by losing a sales opportunity when the demand quantity exceeds the stock quantity.

The inventory loss cost (T) is the inventory quantity (T)
X Calculated by cost reduction (T). Stock quantity (T)
Is the stock quantity at the end of the target time T, and basically T
When> 0, it is calculated by the following equation.

Inventory quantity (T) = Inventory quantity (T-1) + Incoming scenario (T) -Shipping scenario (T) Here, Inventory quantity (0) = Initial value of Inventory quantity (T)
Set to 0. The arrival scenario (T) indicates the quantity at the time T according to the receiving scenario, and the shipping scenario (T) indicates the quantity at the time T according to the shipping scenario. In the case of the arrival simulation described later, this basic formula is used as the stock quantity (T).

In the case of the loss cost sensitivity calculation, the formula of the stock quantity (T) is embodied as follows.

Stock quantity (i, T) = Stock quantity (T-1)
+ Receiving scenario (T)-Demand quantity (i, T) based on demand fluctuation scenario Here, the inventory quantity (i, T) and the demand quantity (i, T) are:
The inventory quantity and the demand quantity when the i-th demand fluctuation scenario is taken at the time T. The receiving scenario (T) does not depend on i.

In the calculation of the expected value of the loss cost, the formula of the stock quantity (T) is embodied as follows.

Stock quantity (j, i, T) = Stock quantity (T−
1) + Incoming quantity (j, T) according to the incoming fluctuation scenario-Demand quantity (i, T) according to the demand fluctuation scenario Here, the inventory quantity (j, i, T) takes the j-th incoming scenario at time T, This is the inventory quantity when the i-th demand fluctuation scenario is taken.

The cost drop (T) is when the product cost (T + 1) is input, and the product cost (T) ≧ the product cost (T)
+1), product cost (T) -product cost (T +
1). When the product cost (T) <the product cost (T + 1), the cost increases from the target time T to T + 1, and the cost decrease (T) is 0. Therefore, in this case, the inventory loss cost (T) becomes zero.

The opportunity loss loss cost (T) is calculated by a shortage quantity (T) × a profit (T). The shortage quantity (T) is calculated as [demand quantity (T) based on demand fluctuation scenario of interest]-[arrival planned quantity (T)]-[time (T-1)
Until stock is up to]. The profit amount (T) is calculated as follows: if the selling price (T) ≧ the product cost (T), the selling price (T) −
Product cost (T). Sales Price (T) <Product Cost (T
In the case of +1), the profit (T) is set to 0. Therefore, in this case, the opportunity loss cost (T) is zero.

When the above-mentioned cost reduction (T) and profit (T) are summarized, it can be said that the cost per unit quantity is loss cost.

At the simulation target time T, the expected loss cost (j, T) when the j-th incoming scenario of interest is taken is calculated by the calculation formula in FIG. j corresponds to the 1, 2,... Nth receiving scenario. The demand coefficient (i, T) is a demand coefficient when the i-th demand fluctuation scenario is taken at the time T. The arrival coefficient (j, T) is the arrival coefficient when the j-th arrival scenario is taken at the time T, and is the demand coefficient (j, T) when the j-th demand fluctuation scenario is taken at the time T. be equivalent to. The demand probability (i, T) is a change probability at which the i-th demand change scenario occurs at the time T, and is a numerical value in a range of 0 to 1.0. The loss cost (i, T) is a loss cost when the i-th demand fluctuation scenario is taken at the time T, and is either the inventory loss cost (T) or the opportunity loss loss cost (T) described above. Σ _i indicates that the calculated values on the right side are summed from 1 to N for i.

FIG. 6 is an example of a display screen showing an example of the initial data input in step 110, an example of the fluctuation range data input in step 114, and an example of the loss cost graph displayed in step 114. The initial data includes the arrival plan quantity data 612, the shipment plan quantity data 614, the sales price data 616, and the product cost data 6 for each time T.
18 has been entered. Here, the initial data is time-series data from the time T of 1 to 9, and when the time T reaches 10, the product loses its commercial value and is planned to be disposed of. The fluctuation width data includes the minimum demand fluctuation data 620 and the maximum demand fluctuation data 622 at each time T.
Is entered.

The loss cost sensitivity graph 630 shows the loss cost at the time T = i calculated based on the input initial data and the fluctuation range data. Here, the loss costs calculated for each of the demand coefficients set for the five demand fluctuation scenarios are displayed in parallel. The expected loss cost graph 640 includes the input initial data,
The expected loss cost value for each arrival scenario at time T = i is shown based on the input fluctuation probability data 422 and the calculated loss cost. The value of each receiving coefficient is equal to the value of the demand coefficient.

Hereinafter, a specific example of the processing of the supply and demand adjustment simulation program 100 will be described using the data shown in FIG. 6 as an example. First, in step 110, an initial data input process is performed. At this time, each data of the arrival plan quantity data 612 to the product cost data 618 is input. Then step 1
The process proceeds to step S12, where the simulation target time T is set to 1, and the process proceeds to step S114. In step 114, the minimum demand fluctuation data 620 and the maximum demand fluctuation data 622 when the simulation target time T is 1 are input, loss cost calculation and graph display processing are performed, and the process proceeds to step 116. Here, in the loss cost graph display processing in step 114, there are a case where a loss cost sensitivity graph is displayed and a case where a loss cost expected value graph is displayed, which are performed by the processing of steps 310 to 316 and steps 410 to 414, respectively. First, a case where a loss cost sensitivity graph is displayed in the loss cost graph display processing in step 114 will be described.

In step 310, the range of fluctuation when the simulation target time T is 1 is read. Here, the minimum demand fluctuation data 6 when the simulation target time T is 1
The minimum fluctuation 50% and the maximum fluctuation 150% of the shipment demand quantity are read from the values of 20 and the maximum demand fluctuation data 622.

Next, at step 312, N
Create change scenarios. Here, for example, N = 5, (1) a scenario in which the minimum fluctuation is 50%, (2) a scenario in which an intermediate value between the minimum fluctuation of 50% and the planned shipment quantity is taken, (3) a scenario according to the planned shipment quantity, It is assumed that five scenarios are set, namely, (4) a scenario in which an intermediate value between the maximum fluctuation of 150% and the planned shipment quantity is taken, and (5) a scenario in which the maximum fluctuation is 150%. Each fluctuation scenario created at this time is (1) 0.5, (2) 0.75, (3) 1, (4) with 1 based on the planned shipment quantity.
1.25, (5) 1.5.

Next, at step 314, a loss cost is calculated. Here, a loss cost is calculated for each of the fluctuation scenarios (1) to (5). The calculation formula shown in FIG. 5 is used as the loss cost calculation formula. When applied to the inventory loss cost formula and the opportunity loss cost formula, when the simulation target time T is 1, the planned arrival quantity is 200 pieces, the planned shipment quantity is 150 pieces, the selling price is 3500 yen, and the product cost is 280 pieces.
In the case where the simulation target time T is 2 and the product cost is 2800 yen, the cost decrease (1) is 0 yen.
Yen and profit (1) are 700 yen. The inventory quantity (1) and shortage quantity (1) change depending on the demand fluctuation scenario,
Thereby, the loss cost of each fluctuation scenario can be calculated. The fluctuation scenario (1) is a scenario in which demand is only 50% of the planned shipment quantity, and the inventory quantity (1) is 150-1.
50 ÷ 2 = 75 pieces, change scenario (2) is a scenario in which demand is only 75% of the planned shipment quantity, and inventory quantity (1) is 150-150 × 3 ÷ 4 ≒ 38 pieces, change scenario (3)
Is a scenario in which the demand is as per the shipping plan, the inventory quantity (1) is 150-150 = 0, and the fluctuation scenario (4) is a scenario in which the demand is 125% of the planned shipping quantity, and the inventory quantity (1) is 150 × 5 ÷ 4−150 ≒ 38 pieces, the fluctuation scenario (5) is a scenario in which the demand is 150% of the planned shipment quantity, and the shortage quantity (1) is 150 × 3 ÷ 2−150 =
It becomes 75 pieces. Since the cost decrease (1) is 0 yen and the profit amount (1) is 700 yen, the loss cost of the fluctuation scenarios (1) to (3) is 0 yen, and the loss cost of the fluctuation scenario (4) is 38 × the opportunity loss loss cost. 700 = 26600 yen, the loss cost of the fluctuation scenario (5) is the opportunity loss loss cost of 7
It can be calculated that there is a loss cost of 5 × 700 = 52,500 yen.

Next, at step 316, the loss cost sensitivity is displayed. Here, a loss cost sensitivity graph is displayed in which the loss costs calculated in step 314 are compared and displayed for the five demand coefficients, and the process proceeds to step 116.

Next, a case will be described in which a loss cost expected value graph is displayed subsequent to the display of the loss cost sensitivity graph in the loss cost graph display processing in step 114.

First, at step 410, a probability input process is performed. Step 410 is further performed by the processing of steps 416 to 420. First, in step 416, a probability input value acquisition processing is performed. Here, the input value of the shipping demand probability that can occur in each of the above fluctuation scenarios (1) to (5) is obtained. Assuming that the input data is represented by the shipping fluctuation probability data 424 in FIG. 4 and this data is obtained when the simulation target time T is 1,
In the case of the fluctuation scenario (1), the probability is 0.2, that is, 20.
%, The probability for the variation scenario (2) is 0.2 or 20%, the probability for the variation scenario (3) is 0.5 or 50%, and the probability for the variation scenario (4) is The probability is 0.05 or 5%, and the probability for the variation scenario (5) is 0.05 or 5%.
Next, the process proceeds to step 418, in which the acquired input value is determined. If the total value of the input probabilities is 1, step 4
Proceed to step 12; if not 1, proceed to step 420. Here, the total value of the change probabilities of the demand change probability data 424 is 1
Therefore, the process proceeds to step 412.

Next, in step 412, loss cost expected value calculation processing is performed. Here, the expected value of the loss cost is calculated for each of the receiving operation scenarios (1) to (5) described below. The formula for calculating the expected loss cost at this time is shown in FIG.
Is used. Applying to the calculation formula of FIG. 5, when the simulation target time T is 1, the planned arrival quantity is 200 pieces, the planned shipment quantity is 150 pieces, the selling price is 3500 yen, and the product cost is 2800 yen. Is 2800 yen, the cost decrease (1) is 0 yen and the profit (1) is 70
It is 0 yen. The receiving operation scenario (1) is a scenario in which only 50% of the planned shipping quantity is received. Similarly, the receiving operation scenario (2) is 75% of the planned shipping quantity, and the receiving operation scenario (3) is the same as the planned shipping quantity. The operation scenario (4) is a scenario for receiving 125% of the planned shipping quantity, and the receiving operation scenario (5) is a scenario for receiving 150% of the planned shipping quantity. The receiving quantity of each receiving operation scenario (1) to (5) is 75 pieces, 113 pieces, 150 pieces, 188 pieces, respectively.
And 225. Looking at the receiving operation scenario (1), the demand fluctuation scenario (1) is a case where there is a demand of 50% of the planned shipment quantity, that is, 75 pieces of demand, the stock quantity (1) is 0 pieces, and the occurrence probability is 0. .2. Similarly, the demand fluctuation scenario (2) has a shipment plan quantity of 7
When there is a demand of 5%, that is, 113 pieces, the shortage quantity (2) is 38 pieces, and the occurrence probability is 0.2. Demand fluctuation scenario (3) is as planned for shipment, ie, 15
When there is no demand, the shortage quantity (3) is 75 pieces, and the occurrence probability is 0.2. The demand fluctuation scenario (4) is a case where there is a demand of 125% of the planned shipment quantity, that is, 188 pieces. The shortage quantity (4) is 113 pieces and the occurrence probability is 0.2. The demand fluctuation scenario (5) is a case where there is a demand of 150% of the planned shipment quantity, that is, 225 pieces of demand. The shortage quantity (5) is 150 pieces, and the occurrence probability is 0.2. From the above, it is calculated by applying to the calculation formula of expected loss cost for the receiving operation scenario (1),
The expected loss cost (1,1) = 52640.

Next, at step 414, display processing of the expected loss cost value is performed. Here, a loss cost expected value graph is displayed in which the expected loss cost values of the respective fluctuation scenarios calculated in step 412 are compared and displayed for five receiving coefficients.

When the process of step 114 is completed, the process proceeds to step 116. In step 116, an incoming data reading process is performed. Here, for example, since the user has the highest loss cost in the case of the fluctuation scenario (5) and the expected value of the loss cost in the case of the receiving operation scenario (5) is 0, the loss cost can be minimized and the risk is minimized. The delivery quantity is 1.
If it is determined to be "5 times," the user inputs information "1.5 times the planned shipping quantity" as the arrival data, and this information is read as the arrival data.

Next, in step 118, a receiving scenario creation process is performed. Using the arrival data read in step 116, 150 × 1.5 = 225 items are adopted as the arrival scenario.

Next, at step 120, the simulation target time T is advanced by one, and then at step 122, an end determination process is performed. If not completed, steps 114 to 122 are repeated, and if completed, the process proceeds to step 124. In step 120, the simulation target time T is 2
In the end determination processing in step 122, the end is determined when the time T reaches 10 because the initial data is data from the time T = 1 to T9.

At step 124, a profit / loss graph display process for displaying a profit / loss graph based on the receiving scenario created by the processing of steps 110 to 122 is performed.
The profit and loss amount due to the arrival scenario, the loss cost when the loss cost is 0, that is, the ideal supply and demand adjustment is performed, and the loss cost are displayed in a comparative manner.

At step 126, a receiving scenario storing process is performed. When the user wants to save the created receiving scenario, the user can save the receiving scenario and the data displayed on the profit / loss graph in a file.

Next, with respect to the supply and demand adjustment simulation program 100 having a GUI (graphical user interface), the user's operation and the supply and demand adjustment simulation program 100 will be described while exemplifying the display screen.
The relationship between the processes will be described.

FIG. 7 is a diagram showing an example of an initial input screen displayed on the display device 135 by the supply and demand adjustment simulation program 100. The initial input screen 700 is a screen for inputting input data when executing the supply and demand adjustment simulation of the present invention, and is an input screen for inputting initial data. An input area 702 is an input area for a supply and demand plan value, and an input area 704 is an input area for an expected fluctuation range of a shipment demand quantity. In FIG. 7, an input area 702 has received receipt plan quantity data 612, shipment plan quantity data 614, sales price data 616, and product cost data 618, and an input area 704 has minimum fluctuation range data of the shipment demand quantity. 620 and the maximum fluctuation width data 6 of the shipment demand quantity
22 has been entered. Also, a “data read” button 70
Reference numeral 6 denotes a button for reading a supply and demand plan value from an external file.
A button for clearing the supply and demand plan value in 2.
A “data clear” button 710 is a button for clearing the expected fluctuation range data of the shipment demand quantity. Further, a “price fluctuation graph” button 724 is displayed in the input and output plan value input area 70.
A button for instructing to display a graph of price fluctuations based on the data input to 2. "Receipt fluctuation graph"
The button 726 is a button for instructing to perform a graph display of receipt / shipment fluctuation based on the data input to the input / output plan value input area 702. “Profit and Loss Graph” button 728
Is a button for instructing to display the profit and loss amount according to the supply and demand plan value. “Simulation” button 730
Is a button for instructing to execute a receipt simulation using the data input to the input area 702 of the supply and demand plan value and the input area 704 of the expected fluctuation width data of the shipment demand quantity. The end button 732 is a button for giving an instruction to end the processing of the supply and demand adjustment simulation program 100.

Supply and demand adjustment simulation program 10
0 displays the initial input screen 700 on the display device 135 and waits for data input by the user. In step 110, an initial data input process is performed. The user enters the initial input screen 70
The initial data and the fluctuation range data of the initial data are input via the “0”. The data to be input here is the data entered in the input and output plan value input area 702 and the data entered in the expected fluctuation range of the shipping demand quantity. When the user presses the “arrival simulation” button 730, the initial data and its fluctuation range data are read, and the supply and demand adjustment simulation program 100 reads the arrival simulation screen 8 shown in FIG.
00 is displayed.

FIG. 8 is a diagram showing an example of a start screen of the arrival quantity determination simulation. In FIG. 8, a received quantity determination simulation start screen 800 is a start screen displayed when the “receiving simulation” button 730 shown in FIG. 7 is pressed. The loss cost sensitivity graph display area 802 is a display area of a graph that displays the sensitivity of loss cost caused by a change in the shipment demand quantity. The expected loss cost graph display area 804 is a display area of a graph that displays the expected loss cost calculated from the change probability when the received quantity is changed according to the change in the shipping demand quantity. The commentary display area 806 is a display area for displaying information on the arrival simulation.
The “start simulation of arrival” button 808 is a button for instructing to start the simulation and display the loss cost sensitivity. The receiving scenario input area 810 is an area for the user to input a receiving scenario. The shipping scenario display area 812 is an area for displaying a scenario of the shipping quantity. Here, shipping scenario display area 8
Although the shipment scenario shown in FIG. 12 uses the shipment plan quantity data 614, the shipment quantity scenario to be displayed is a shipment quantity scenario that is simulated in consideration of a demand scenario and an occurrence probability. It doesn't matter.
The “return to input screen” button 814 is a button for instructing to return to the initial input screen of FIG.

The description of the processing of the supply and demand adjustment simulation program 100 will be continued with reference to FIG. 8. In step 112, a simulation target time T is set. The user can set the simulation target time T using the “start simulation of arrival” button 808 on the arrival simulation screen 800 and simultaneously proceed to the next step, step 114. The simulation target time T is set to 1 at the time when the user presses the “start arrival simulation” button 808, and the supply and demand adjustment simulation tool proceeds to an arrival simulation screen 900 shown in FIG. In this example, the simulation target time T at the start of the simulation is set to 1, but the simulation target time at the start is 1
It does not need to be specified, and can be specified.

FIG. 9 shows that the simulation target time T is 1
It is a figure showing an example of an arrival simulation screen at the time of. The arrival simulation screen 900 is an example of the arrival simulation screen displayed when the “start arrival simulation” button 808 shown in FIG. 8 is pressed. At this time, in the loss cost sensitivity graph display area 802, a loss cost sensitivity graph corresponding to a demand coefficient due to a change in shipment demand quantity when the simulation target time T is 1 is displayed. A demand scenario display area 904 displays a demand fluctuation value of each fluctuation scenario when a fluctuation scenario is created by dividing the fluctuation range due to the maximum fluctuation and the minimum fluctuation of the demand quantity at the simulation target time T into N pieces. It is. The demand change probability data input area 906 is an input area for inputting the probability that the demand quantity becomes the demand change value displayed in the demand scenario display area 904.
The “expected loss cost” button 908 indicates that
This is a button for instructing to calculate the expected loss cost value at the simulation target time T using the data input to the demand fluctuation probability data input area 906 and the demand fluctuation data 622 and to display the graph as a graph. The receiving data input area 910 is an input area for receiving data that is input when a user creates a receiving scenario. The “to next period” button 912 is a button for instructing the simulation target time T to proceed to the next time after the arrival of the simulation scenario at the simulation target time T.

Here, the receipt data input area 910 uses a coefficient based on the scheduled shipment quantity as the receipt data, but the receipt data may be specified by the receipt quantity, or the receipt quantity and the receipt coefficient are switched. By providing a method, both can be used in combination.

The description of the processing of the supply and demand adjustment simulation program 100 will be continued with reference to FIG. Step 1
At 14, loss cost graph display processing is performed. The processing of step 114 is configured by the processing of steps 310 to 316 and the processing of steps 410 to 414. Here, the processing from step 310 to step 316 and the processing from step 410 to step 414
It is not necessary to perform both of the processes, and only one of the processes may be performed, or the process may be omitted by the user's selection. Here, the processing from step 310 to step 316 will be described.

First, in step 310, the fluctuation range input is obtained. From the minimum demand fluctuation data 620 and the maximum demand fluctuation data 622, the simulation target time T is 1
The data of 50% and 150% are acquired as the demand fluctuation width at the time of. In step 312, a change scenario is created. In the example shown in FIG. 9, the value of the numerical value N indicating the number of change scenarios is 5, and as shown in the demand scenario display area 904, the change value is 50%, 75
Five fluctuation scenarios of%, 100%, 125%, and 150% are created. In step 314, loss cost calculation processing for each variation scenario is performed. At step 316, a loss cost sensitivity display graph is displayed. When the “start arrival simulation” button 808 is pressed, the processing of steps 310 to 316 is performed, and the arrival simulation screen 90 is displayed.
At 0, a loss cost sensitivity graph is displayed in the loss cost sensitivity graph display area 802. After step 314, proceed to step 116.

Next, at step 116, an incoming data reading process is performed. Here, loss cost sensitivity graph display area 8
With reference to the loss cost sensitivity graph displayed in 02, the user who has considered the quantity received when the simulation target time T is 1 reads the numerical value input to the arrival data input area 910. In the case of FIG. 9, the data “1.5” input to the receiving data input area 910 is a receiving coefficient based on the value of the shipping scenario when the simulation target time T is 1, and this is the receiving data. And proceeds to the next step 118.

At step 118, a receiving scenario creation process is performed. Here, the data of 150 pieces, which is the shipping scenario when the simulation target time T displayed in the shipping scenario display area 812 is 1, is multiplied by the incoming data “1.5” read in step 116, which is 225. The receiving scenario of an individual is created, and the process proceeds to the next step 120.

In step 120, the simulation target time is advanced by one. Here, the simulation target time T is advanced from 1 to 2, and the process proceeds to the next step 122.

In step 122, an end determination process is performed.
If the simulation target time T is not the last time of the arrival simulation, the process proceeds to step 114. If the simulation target time T is the last time of the arrival simulation, the process proceeds to step. Here, since the simulation target time T is 2, the process proceeds to step 114.

On the arrival simulation screen 900, when the user presses the "next" button 912, the arrival data "1.5" is read (step 11).
6) In the receiving scenario input area 810, data of 225 is input by automatic calculation in the area where the simulation target time T is 1 (step 118), and the simulation target time T is advanced to 2 (step 120), and the end determination is made. The process proceeds to Step 114 (Step 122). At this time, the supply / demand adjustment simulation program 100 proceeds to an arrival simulation screen 1000 shown in FIG.

FIG. 10 is a diagram showing an example of the arrival simulation screen when the simulation target time T is 2. The arrival simulation screen 1000 is an example of the arrival simulation screen displayed when the “next term” button 912 shown in FIG. 9 is pressed. The “to previous period” button 1002 is a button for instructing to return the simulation target time T to the previous time after the arrival scenario is created at the simulation target time T. Here, in the loss cost sensitivity graph display area 802, a loss cost sensitivity graph due to a change in the shipping demand quantity when the simulation target time T is 2 is displayed. In the explanation display area 806, the profit and loss due to the arrival scenario and the shipping scenario at the simulation target time T-1 and the total profit and loss up to the simulation target time T-1 are displayed. Here, the profit and loss amount when the simulation target time T is 1 and the total profit and loss amount until the simulation target time T is 1 are displayed. Also, the “To the previous period” button 1002
These buttons are displayed on the arrival simulation screen in the above case. When the simulation target time T is 2, by pressing the “previous period” button 1002, it is possible to return to the reception simulation screen when the simulation target time T is 1, that is, the reception simulation screen 900.

Next, a case where both the loss cost sensitivity graph and the loss cost expected value graph are displayed will be described.

FIG. 11 is a diagram showing an example of a screen on which expected loss cost values are displayed in the simulation for determining the quantity of goods when the simulation target time T is 2. FIG.
In FIG. 1, the arrival simulation screen 1100 is shown in FIG.
This is an example of the arrival simulation screen when the fluctuation probability data is input to the demand fluctuation probability data input area 906 at 0 and the expected loss cost display button 908 is pressed.

FIG. 12 is a diagram showing an example of a display screen when the input of the demand change probability is incorrect in the arrival simulation when the simulation target time T is 2. The arrival simulation screen 1200 is an example of the arrival simulation screen when the fluctuation probability data is input to the demand fluctuation probability data input area 906 in FIG. 10 and the “loss cost expected value” button 908 is pressed. Here, the total value of the fluctuation probability data input to the demand fluctuation probability data input area 906 is 0.9. The message display box 1202 displays a message prompting correction of the probability data input to the demand change probability data input area 906 so that the total value is set to 1, and the “expected loss cost value” button 908 is pressed. When the total probability value is not 1, it is displayed.

In FIGS. 11 and 12, the arrival simulation screen 1100 and the arrival simulation screen 1
In the case of 200, the processing of step 110 to step 122 and the processing of step 310 to step 316
To FIG. 10. Step 410 for calculating the expected loss cost and displaying the graph below
Step 414 will be described.

First, in step 410, demand fluctuation probability input processing is performed. Here, the arrival simulation screen 110
At 0, the demand change probability data is input to the demand change probability data input area 906. The detailed processing of step 410 is as follows.
First, in step 416, a probability input value acquisition process is performed. Here, the demand change probability data input to the demand change probability data input area 906 is obtained. Next, in step 418, input value determination processing is performed, and the process proceeds to the next step based on the result of the determination processing. If the total probability value of the demand change probability data input to the demand change probability data input area 906 is 1, it is determined that the input is correct and the process proceeds to step 412. If not, the input value is determined to be incorrect and the step 420 is performed. Proceed to. Here, since the total probability value of the demand fluctuation probability data is 1 in FIG. In step 412, loss cost expected value calculation processing is performed. When the demand change probability data is input to the demand change probability data input area 906 and the “loss cost expected value” button 908 is pressed, steps 416 to 418 and step 4 are performed.
12 is performed and the arrival simulation screen 110
0 is displayed. In FIG. 12, since the total probability value of the demand change probability data is 0.9, the process proceeds to step 420. In step 420, a re-input instruction message display process is performed, and the process returns to step 416. When the “expected loss cost” button 908 is pressed, the processing of steps 416 to 420 is performed, and the arrival simulation screen 1
At 200, a message display box 1202 is displayed.

With reference to FIG. 13, the end determination processing when the simulation target time T is 9 will be described. FIG. 13 is a diagram illustrating an example of the arrival simulation screen in a state where the creation of the arrival scenario has been completed. The arrival simulation screen example 1300 is a screen in which the creation of the arrival scenario up to the simulation target time T of 9 has been completed and the arrival scenario can be corrected. The “Profit / Loss Graph” button 1302 is a button for determining the time-series receiving scenario input in the receiving scenario input area 810 and instructing to display the profit / loss obtained by the scenario. Here, if the “to previous period” button 1002 is pressed, the receiving scenario input in the receiving scenario input area 810 can be corrected.

The description of the supply and demand adjustment simulation program 100 will be continued with reference to FIG. Step 12
In 2, a termination determination process is performed. If it is determined that the scenario creation has been completed, the process proceeds to step 124 to perform profit / loss graph display processing. When a “Profit and Loss Graph” button 1302 is pressed on the receipt simulation screen 1300, the receipt scenario input in the receipt scenario input area 810 is determined, and it is determined that the scenario creation has been completed, and the process proceeds to the next step, step. . "Profit and loss graph" button 13
When “02” is pressed, the supply and demand adjustment simulation program 100 displays the arrival simulation screen 1 shown in FIG.
Proceed to 400.

FIG. 14 is a diagram showing an example of a receipt simulation screen displaying a profit / loss graph of the created receipt scenario. In FIG. 14, the arrival simulation screen 1
Reference numeral 400 denotes a screen displayed when the “Profit and Loss Graph” button 1302 in FIG. 13 is pressed. The profit / loss graph display area 1402 is a profit / loss graph display area that displays the profit / loss amount and the loss cost obtained by the created receiving scenario input to the receiving scenario input area 810. The “return” button 1404 is a button for instructing the display of the profit / loss graph to return to the receiving scenario creation screen with the simulation target time T of 9. The “save” button 1406 is a button for instructing the storage device to store the created arrival scenario and the profit / loss and loss cost caused by the arrival scenario as the arrival scenario storage data 140.

The description of the supply and demand adjustment simulation program 100 will be continued with reference to FIG. Step 124
Then, a profit / loss graph display process is performed. Here, a profit / loss graph display area 1402 of the arrival simulation screen 1400 is shown.
To display the profit and loss graph. This profit / loss graph displays a profit / loss amount, a loss cost, and an ideal profit / loss amount in the case where there is no loss cost, which are generated by the data input to the receiving scenario input area 810 and the data displayed in the shipping scenario display area 812.

Next, the routine proceeds to step 126, where a receiving scenario storing process is performed. Here, when the “Save” button 1406 is pressed, the processing of step 126 is started, and the data input to the receiving scenario input area 810, the data input to the shipping scenario input area 812, and the profit / loss graph display area 1402 are displayed. Process to save the profit and loss information of the profit and loss graph. Incoming simulation screen 14
At 00, by pressing the “return” button 1404, the user can return to the receiving scenario creation screen without performing the saving process.

Next, a “price fluctuation graph” button 724,
The display screen when the “shipment / shipment fluctuation graph” button 726 or the “gain graph” 728 is pressed will be described.

FIG. 15 is a diagram showing an example of a price fluctuation graph display screen. In FIG. 15, a price change graph display screen 1500 includes a “price change graph” button 72 shown in FIG.
It is an example of a price fluctuation graph display screen displayed when 4 is pressed. The price fluctuation graph display area 1506 is a display area for displaying a price fluctuation graph that displays the sales price data 616 and the product cost data 618 two-dimensionally. A “return to input screen” button 814 is a button provided for transitioning to the input screen shown in FIG. 7 for changing or confirming initial data.

FIG. 16 is a diagram showing an example of a receipt / shipment fluctuation graph display screen. In FIG. 16, a receipt / shipment fluctuation graph display screen 1600 is an example of a receipt / shipment fluctuation graph display screen displayed when the “receipt / shipment fluctuation graph” button 726 shown in FIG. 7 is pressed. Receipt / shipment fluctuation graph display area 16
Reference numeral 06 denotes a display area of a receipt / shipment fluctuation graph that two-dimensionally displays the receipt plan quantity data 612 and the shipment plan quantity data 614.

FIG. 17 is a diagram showing an example of a profit / loss graph display screen. In FIG. 17, the profit / loss graph display screen 17
00 is an example of a profit / loss graph display screen displayed when the “Profit / Loss Graph” button 728 shown in FIG. 7 is pressed, and the profit / loss graph display area 1706 includes the arrival plan quantity data 612 and the shipping plan quantity. This is a display area of a profit / loss graph for two-dimensionally displaying a result of calculating a profit / loss amount using data 614, sales price data 616, and product cost data 618.

When the “price fluctuation graph” button 724 is pressed, the supply-demand adjustment simulation program 100
Displays a price fluctuation graph using the sales price data 616 and the product cost data 618. At this time, a screen 1500 shown in FIG. 15 is displayed. When the “receipt / shipment fluctuation graph” button 726 is pressed, the arrival schedule quantity data 612
And a shipment / shipment fluctuation graph is displayed using the shipment plan quantity data 614. At this time, a screen 1600 shown in FIG. 16 is displayed. When the “Profit / Loss Graph” button 728 is pressed, the arrival plan quantity data 612 to the product cost data 618
Is used to display the profit / loss graph. At this time, a screen 1700 shown in FIG. 17 is displayed.

Although the displayed graph is two-dimensional in FIGS. 11, 15 and 16, it may be three-dimensionally displayed. When the loss cost sensitivity graph is three-dimensionally displayed in FIG. 11, each variation scenario is displayed on two axes for two types of time-series data among the initial data, and the loss cost is displayed on the remaining one axis. Further, when the expected loss cost graph is three-dimensionally displayed in FIG. 11, two types of operation scenarios corresponding to two types of fluctuation scenarios in the initial data are displayed on two axes, and the remaining one is displayed.
Display loss cost on two axes. For example, two types of operation scenarios corresponding to changes in the planned shipment quantity and the product cost indicate a receiving operation scenario and a sales price adjustment operation scenario.

In the above embodiment, the demand fluctuation and the corresponding arrival simulation have been described. However, other patterns such as the sales price fluctuation and the corresponding cost adjustment simulation using the price fluctuation probability data 428 may be used. It is equally possible.

[0076]

The present invention can provide information directly linked to decision making by using the concept of loss cost as an index indicating a risk in a situation where the quantity to be received is determined in a supply and demand adjustment operation. Therefore, a more rational decision can be made as compared with the prior art.
In addition, since it is possible to provide information directly linked to decision making, a decision maker without specialized knowledge can make more correct decision.

Further, according to the present invention, it is possible to simulate the determination of the incoming quantity in the supply and demand adjustment business in a time series, and to reflect the incoming simulation scenario at the determined time T in the simulation at the next time T + 1.

Further, according to the present invention, since the loss cost can be displayed not only in the magnitude of the loss cost at the time of demand fluctuation but also in the expected value of the loss cost due to the demand fluctuation in consideration of the demand fluctuation probability, the risk quantification can be performed. In addition, it is possible to present probabilistic risks.

[Brief description of the drawings]

FIG. 1 is a diagram showing a schematic processing procedure of a supply and demand adjustment simulation program 100 according to an embodiment.

FIG. 2 is a diagram illustrating a hardware configuration of the embodiment.

FIG. 3 is a diagram illustrating a processing procedure of processing for calculating a loss cost and displaying the sensitivity according to the embodiment.

FIG. 4 is a diagram illustrating a processing procedure of a process of calculating an expected loss cost value and displaying a result according to the embodiment;

FIG. 5 is a diagram showing calculation formulas used in a loss cost sensitivity calculation process and a loss cost expected value calculation process of the embodiment.

FIG. 6 is a diagram illustrating an example of a graph of input initial data, fluctuation range data, and displayed loss cost.

FIG. 7 is a diagram illustrating an example of an initial input screen.

FIG. 8 is a diagram illustrating an example of a start screen of a simulation for determining the quantity of received goods.

FIG. 9 is a diagram showing an example of an arrival simulation screen when a simulation target time T is 1;

FIG. 10 is a diagram showing an example of an arrival simulation screen when the simulation target time T is 2;

FIG. 11 is a diagram showing an example of a screen displaying expected loss cost values in an arrival simulation when the simulation target time T is 2;

FIG. 12 is a diagram illustrating an example of a display screen when an input of a demand change probability is incorrect in an arrival simulation when the simulation target time T is 2;

FIG. 13 is a diagram illustrating an example of an arrival simulation screen in a state where creation of an arrival scenario has been completed.

FIG. 14 is a diagram showing an example of an arrival simulation screen displaying a profit / loss graph of the created arrival scenario.

FIG. 15 is a diagram showing an example of a price fluctuation graph display screen.

FIG. 16 is a diagram showing an example of a receipt / shipment fluctuation graph display screen.

FIG. 17 is a diagram showing an example of a profit / loss graph display screen.

[Explanation of symbols]

100: supply and demand adjustment simulation program, 110
... Initial data input processing block, 114 ... Loss cost calculation and display processing block, 116 ... Incoming data reading processing block, 118 ... Incoming scenario creation processing block, 1
22: End determination processing block, 124: Profit and loss graph display processing block, 126: Receiving scenario storage processing block

 ──────────────────────────────────────────────────続 き Continuing from the front page (72) Inventor Hirotaka Mizuno 1099 Ozenji Temple, Aso-ku, Kawasaki City, Kanagawa Prefecture F-term in System Development Laboratory, Hitachi, Ltd. (Reference) 5B049 AA02 AA06 CC27 EE41 FF01

Claims (5)

[Claims] 1. A receiving plan quantity and a shipping planning quantity inputted for each planning time are received, and a plurality of numerical values when the shipping planning quantity fluctuates due to a demand fluctuation at one planning time are respectively created as demand fluctuation scenarios. Then, for each of the demand fluctuation scenarios, one of the inventory quantity and the shortage quantity at the planning time is multiplied by the loss cost per unit quantity to calculate the loss cost, so that the user can determine the receiving quantity at the planning time. And a parallel display of the loss costs for each of the demand fluctuation scenarios. 2. The demand quantity of the planned time according to a demand fluctuation scenario in which the stock quantity and the shortage quantity are obtained by adding the stock plan quantity of the plan time to the stock remaining until the previous plan time. 2. The supply and demand adjustment simulation method according to claim 1, wherein 3. The method according to claim 1, wherein the loss cost is a loss cost due to a decrease in the cost of the product when multiplied by the stock quantity, and is a profit amount due to the sale when the shortage quantity is multiplied. Supply and demand adjustment simulation method described. 4. A receiving scenario corresponding to each demand fluctuation scenario is created based on the occurrence probability set for each of said demand fluctuation scenarios, and said receiving scenario and each demand fluctuation scenario are used to generate said receiving scenario. Calculating the loss cost, multiplying the calculated loss cost by the occurrence probability to calculate a cumulative expected loss cost value over the entire occurrence probability, and displaying the expected loss cost value for each arrival scenario in parallel. The supply and demand adjustment simulation method according to claim 1. 5. A program stored in a computer-readable storage medium, the program having the following functions: a storage medium storing a program: an arrival plan input at each planning time A function of receiving a quantity and a shipment planning quantity, a function of creating a plurality of numerical values when the shipment planning quantity fluctuates due to demand fluctuations for one planning time as a demand fluctuation scenario,
For each of the demand fluctuation scenarios, a function of calculating a loss cost by multiplying one of the inventory quantity and the shortage quantity at the planning time by the loss cost per unit quantity, and enabling the user to determine the arrival quantity at the planning time. A function of displaying the loss costs for each of the demand fluctuation scenarios in parallel.