JP6198257B2 - Reward prediction calculation device, reward prediction calculation server, reward prediction calculation program, and reward prediction calculation method - Google Patents

Description

  The present invention relates to prediction for setting a reasonable remuneration in a membership system introduction / sales organization.

  There are various forms of membership-based referral and sales organizations, but in the basic form, those who purchased products sold by the headquarters through member referrals joined the organization as new members. It pays a member a bonus (bonus).

  Members do not sell products themselves, but can receive rewards from the headquarters through referrals. In addition, the headquarters company can regard the member organization formed through the introduction as an infrastructure for selling products without stores. For this reason, both are considered to have advantages, and for a period of time it has been widely spread.

  However, in light of these advantages, the headquarters formulates an excessive remuneration plan, and as a result, the management plan is changed frequently in order to avoid a bankruptcy in cases where the bankruptcy or management failure occurs. In many cases, members were worried and the membership organization was dissolved. As a result, it was subject to legal regulations, and the number of headquarters was small.

  The cause of such a situation can be found in a compensation plan that tends to be excessive, but it is not easy for the supervising company to set a compensation plan appropriately. If more members can be quickly organized, the operations of the headquarters can continue even if the remuneration is increased. The more rewards, the more members and hands, but the more rewards, the higher the risk of bankruptcy due to the payment of rewards. On the contrary, if the reward is reduced, the attractiveness of attracting the member is poor, so the number of members tends to decrease.

  Whether the compensation plan of the headquarters is appropriate depends on various factors such as the price and attractiveness of the product itself, the number of members, and economic trends. For this reason, it is difficult to predict how a reward will be determined if an appropriate plan is obtained. There is a strong social demand for the headquarters to formulate an appropriate compensation plan. However, in the past, the actual situation was that the prediction was based on the personal experience of the plan creator, and no prediction with an effective quantitative evaluation was made.

In this situation, the inventors also introduced a new member by one of the members, and these new members introduced a new member and branched into two (branched). In the past, trials and errors have been carried out in a quantitative evaluation of the compensation plan in the member-based introduction sales distribution organization that constitutes the sales organization.
However, the method previously proposed by the inventors introduced a dividend rate (sweeping rate D (n)) in the compensation plan to limit the maximum amount of bonus received in a certain period. It was difficult to create (Patent Document 1).

  Another method is to limit the maximum amount of bonus received at different dividend rates (sweeping rate F (n)), but this proposal also makes it difficult to create a flexible compensation plan (patents). Reference 2).

  Therefore, even if n becomes larger and the hierarchy becomes deeper, the dividend rate always converges, and it has the effectiveness and versatility that contributes to flexibly determining an appropriate and reasonable compensation plan for members from the calculated dividend rate A reward prediction calculation device and prediction method have been desired.

JP 2008-90804 A JP 2013-47959 A

  An object of the present invention is to provide a reward prediction calculation device, a prediction server, a prediction program, and a prediction method for setting an appropriate and reasonable reward plan in a member system introduction sales organization.

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<1> In order to achieve the above object, the present invention provides:
A remuneration prediction calculation server that calculates a remuneration amount for members of a member system introduction sales distribution organization and predicts a dividend rate thereof,
The terminal device is communicable with the terminal device, receives the variable condition, the calculation command, and the determination command for calculating the remuneration amount transmitted from the terminal device, and receives the calculated payout rate and the determination command. A communication unit that transmits a payout rate as determination data when it is made to the terminal device;
A storage unit that records the received variable condition and the reward determination rule for the dividend rate to converge;
A control unit that receives the calculation command received and predicts the dividend rate by repeatedly calculating the remuneration amount based on the reward determination rule according to the received variation condition until the determination command is received;
In response to the received determination command, a providing unit that provides the terminal device with a payout rate based on the prediction and a quantitative evaluation based on the payout rate;
With
The reward determination rule is:
[1] Each member has arrangement information mapped so as to form a hierarchy that is virtually arranged and filled in each node of the binary tree data structure,
[2] Regarding the amount of compensation for each member,
The total number of products purchased (S (i)), which is the total number of products purchased by members belonging to lower tiers branched from the member based on the arrangement information, a given maximum limit (M), Based on the given reward base (g),
Calculate the provisional compensation amount (P (i)) that is discretized and evaluated by the compensation base for the total number of items purchased for each tier, and calculate the compensation amount so that the provisional compensation amount does not exceed the maximum limit Configured to
The provisional remuneration amount is the maximum remuneration amount of M yen, the basic remuneration amount of h yen, the remuneration card number of g, for the remuneration amount of members belonging to the i-th hierarchy of the binary tree data structure having a hierarchy of depth n, Each member purchases one product, the total product purchase is S (i), the provisional reward is P (i) yen, and INT {} takes an integer value of the number in {}. When the operator, * is an integration symbol,
P (i) = h * INT {S (i) / g}
Because
The amount of reward determined is
M ≦ P (i) and k + i = n + 1
, The case is classified according to the m-th layer counted from the n-th layer that is the minimum value of k,
[Case 1]:
For members in the hierarchy of i ≦ n−m + 1, the remuneration amount shall be the maximum limit of M yen,
[Case 2]:
For members in the hierarchy of i> n−m + 1, the reward amount is P (i) = h * INT {(2 ^{n−i + 1} −2) / g} yen which is a provisional reward amount ,
It is configured to calculate a reward amount.

Because it is configured in this way, even if the hierarchy becomes infinitely deep, the payout rate always converges. Therefore, when the variable condition (parameter) is moved, the corresponding payout rate corresponding to this is calculated. It is possible to quantitatively evaluate the compensation plan.
In addition, a parameter called a reward card is introduced, and the total purchase quantity is discretized (quantized) with the reward card to add an evaluation to calculate a provisional reward. As a result, instead of using the total number of products purchased as it is, it can be rounded down to an arbitrary stepped (stepped) value. In addition to introducing restrictions, the change in the reward base allows fine adjustments to the hurdles of the headquarters to determine the amount of compensation. In addition, it is easy for members who receive rewards to make adjustments based on the total number of product purchases expected to be linked to the amount of rewards.
Furthermore, old members who are shallow in the membership organization limit the maximum amount of remuneration, and relatively new members who are deep in the hierarchy are rewarded according to the total number of items purchased. Therefore, members who receive relatively high remuneration are limited to the maximum limit, but there is no reduction factor for members who do not reach the maximum limit and do not have many referrals. Is easy to keep, and can be presented to members in advance, so it is easy to accept. In addition, the headquarters can easily evaluate the dividend rate when the maximum limit and the number of rewards are moved simultaneously, so a flexible compensation plan can be considered.
Here, the dividend rate refers to a ratio between a source of dividends such as a sales amount related to the product received by the membership introduction sales distribution organization and a reward amount (the same applies in this specification). This dividend resource includes not only the sales amount of the product itself, but also income that becomes a dividend resource for the organization, such as a membership fee. Further, not only the total sales amount but also a part of the necessary deductions may be used.
In addition, since the converged payout rate corresponding to the changing condition (parameter) is obtained even when the hierarchy is infinitely deep, it is possible to quantitatively evaluate the reward plan.
In addition, since the basic compensation amount is multiplied by the discretized evaluation value to obtain the provisional compensation amount, the dividend amount can be changed with the three main parameters being the maximum limit, the number of compensation bases, and the basic compensation amount. Since it is possible, the compensation plan can be determined flexibly.

<2> In addition, in order to calculate the amount of remuneration for the members of the member system introduction sales distribution organization and to predict the dividend rate, the computer, the variable condition for calculating the amount of remuneration, a calculation instruction and a determination instruction An input means for inputting
Storage means for recording a reward determination rule for convergence of the input fluctuation condition and the dividend rate;
Control means for repeatedly calculating the remuneration amount based on the remuneration determination rule according to the input variation condition until the determination instruction is input and predicting the dividend rate;
Display means for displaying a dividend rate according to the prediction;
When the determination command is input, output means for outputting the payout rate and the variation condition stored simultaneously with the display as determination data;
A reward prediction calculation program for making it function,
[1] Each member has arrangement information mapped so as to form a hierarchy that is virtually arranged and filled in each node of the binary tree data structure,
[2] Regarding the amount of compensation for each member,
The total number of products purchased (S (i)), which is the total number of products purchased by members belonging to lower tiers branched from the member based on the arrangement information, a given maximum limit (M), Based on the given reward base (g),
Calculate the provisional compensation amount (P (i)) that is discretized and evaluated by the compensation base for the total number of items purchased for each tier, and calculate the compensation amount so that the provisional compensation amount does not exceed the maximum limit Configured as a program to function
The provisional remuneration amount is the maximum remuneration amount of M yen, the basic remuneration amount of h yen, the remuneration card number of g, for the remuneration amount of members belonging to the i-th hierarchy of the binary tree data structure having a hierarchy of depth n, Each member purchases one product, the total product purchase is S (i), the provisional reward is P (i) yen, and INT {} takes an integer value of the number in {}. When the operator, * is an integration symbol,
P (i) = h * INT {S (i) / g}
Because
The amount of reward determined is
M ≦ P (i) and k + i = n + 1
, The case is classified according to the m-th layer counted from the n-th layer that is the minimum value of k,
[Case 1]:
For members in the hierarchy of i ≦ n−m + 1, the remuneration amount shall be the maximum limit of M yen,
[Case 2]:
For members in the hierarchy of i> n−m + 1, the reward amount is P (i) = h * INT {(2 ^{n−i + 1} −2) / g} yen which is a provisional reward amount ,
It is configured to calculate a reward amount.

Because of this structure, the payout rate always converges even when the hierarchy is infinitely deep. Therefore, a computer program for quantitative evaluation under various fluctuation conditions based on the converged payout rate is provided. Can do.
In addition, a parameter called a reward base is introduced to calculate a provisional reward amount obtained by discretizing and evaluating the total number of product purchases by the reward base. As a result, the total number of product purchases can be rounded down to a stepwise rounded value. Therefore, in calculating the remuneration amount and the payout rate, the trial calculation can be simplified by changing the remuneration number.
Furthermore, old members who are shallow in the membership organization limit the maximum amount of remuneration, and relatively new members who are deep in the hierarchy are rewarded according to the total number of items purchased. Therefore, members who receive relatively high remuneration are limited to the maximum limit, but there is no reduction factor for members who do not reach the maximum limit and do not have many referrals. Is easy to keep, and can be presented to members in advance, so it is easy to accept. In addition, the headquarters can easily evaluate the dividend rate when the maximum limit and the number of rewards are moved simultaneously, so a flexible compensation plan can be considered.
In addition, since the converged payout rate corresponding to the changing condition (parameter) is obtained even when the hierarchy is infinitely deep, it is possible to quantitatively evaluate the reward plan.
In addition, since the basic compensation amount is multiplied by the discretized evaluation value to obtain the provisional compensation amount, the dividend amount can be changed with the three main parameters being the maximum limit, the number of compensation bases, and the basic compensation amount. Since it is possible, the compensation plan can be determined flexibly.

<3> In addition, in the reward prediction calculation method for calculating the amount of remuneration for the members of the member system introduction sales distribution organization and predicting the dividend rate,
An input step for inputting a variable condition for calculating the reward amount;
A storage step for recording the input variation condition;
A control step of repeatedly calculating the remuneration amount based on a reward determination rule that converges the dividend rate according to the input fluctuation condition when the calculation command is input, and predicting the dividend rate until the determination command is input When,
A display step for displaying a dividend rate based on the prediction;
An output step of outputting the payout rate and the variation condition stored simultaneously with the display as decision data when the user judgment is a desired payout rate;
A reward prediction calculation method including
The reward determination rule is:
[1] Each member has arrangement information mapped so as to form a hierarchy that is virtually arranged and filled in each node of the binary tree data structure,
[2] Regarding the amount of compensation for each member,
The total number of products purchased (S (i)), which is the total number of products purchased by members belonging to lower tiers branched from the member based on the arrangement information, a given maximum limit (M), Based on the given reward base (g),
Calculate the provisional compensation amount (P (i)) that is discretized and evaluated by the compensation base for the total number of items purchased for each tier, and calculate the compensation amount so that the provisional compensation amount does not exceed the maximum limit Configured to
The provisional remuneration amount is the maximum remuneration amount of M yen, the basic remuneration amount of h yen, the remuneration card number of g, for the remuneration amount of members belonging to the i-th hierarchy of the binary tree data structure having a hierarchy of depth n, Each member purchases one product, the total product purchase is S (i), the provisional reward is P (i) yen, and INT {} takes an integer value of the number in {}. When the operator, * is an integration symbol,
P (i) = h * INT {S (i) / g}
Because
The amount of reward determined is
M ≦ P (i) and k + i = n + 1
, The case is classified according to the m-th layer counted from the n-th layer that is the minimum value of k,
[Case 1]:
For members in the hierarchy of i ≦ n−m + 1, the remuneration amount shall be the maximum limit of M yen,
[Case 2]:
For members in the hierarchy of i> n−m + 1, the reward amount is P (i) = h * INT {(2 ^{n−i + 1} −2) / g} yen which is a provisional reward amount ,
It is configured to calculate a reward amount.

Because it is configured in this way, even if the hierarchy becomes infinitely deep, the payout rate always converges. Therefore, when the variable condition (parameter) is moved, the corresponding payout rate corresponding to this is calculated. It is possible to quantitatively evaluate the compensation plan.
In addition, a parameter called a reward card is introduced, and the total purchase quantity is discretized (quantized) with the reward card to add an evaluation to calculate a provisional reward. As a result, instead of using the total number of products purchased as it is, it can be rounded down to an arbitrary stepped (stepped) value. In addition to introducing restrictions, the change in the reward base allows fine adjustments to the hurdles of the headquarters to determine the amount of compensation. In addition, it is easy for members who receive rewards to make adjustments based on the total number of product purchases expected to be linked to the amount of rewards.
Furthermore, old members who are shallow in the membership organization limit the maximum amount of remuneration, and relatively new members who are deep in the hierarchy are rewarded according to the total number of items purchased. Therefore, members who receive relatively high remuneration are limited to the maximum limit, but there is no reduction factor for members who do not reach the maximum limit and do not have many referrals. Is easy to keep, and can be presented to members in advance, so it is easy to accept. In addition, the headquarters can easily evaluate the dividend rate when the maximum limit and the number of rewards are moved simultaneously, so a flexible compensation plan can be considered.
Here, the dividend rate refers to a ratio between a source of dividends such as a sales amount related to the product received by the membership introduction sales distribution organization and a reward amount (the same applies in this specification). This dividend resource includes not only the sales amount of the product itself, but also income that becomes a dividend resource for the organization, such as a membership fee. Further, not only the total sales amount but also a part of the necessary deductions may be used.
Furthermore, old members who are shallow in the membership organization limit the maximum amount of remuneration, and relatively new members who are deep in the hierarchy are rewarded according to the total number of items purchased. Therefore, members who receive relatively high remuneration are limited to the maximum limit, but there is no reduction factor for members who do not reach the maximum limit and do not have many referrals. Is easy to keep, and can be presented to members in advance, so it is easy to accept. In addition, the headquarters can easily evaluate the dividend rate when the maximum limit and the number of rewards are moved simultaneously, so a flexible compensation plan can be considered.
In addition, since the converged payout rate corresponding to the changing condition (parameter) is obtained even when the hierarchy is infinitely deep, it is possible to quantitatively evaluate the reward plan.
In addition, since the basic compensation amount is multiplied by the discretized evaluation value to obtain the provisional compensation amount, the dividend amount can be changed with the three main parameters being the maximum limit, the number of compensation bases, and the basic compensation amount. Since it is possible, the compensation plan can be determined flexibly.

  Furthermore, old members who are shallow in the membership organization limit the maximum amount of remuneration, and relatively new members who are deep in the hierarchy are rewarded according to the total number of items purchased. Therefore, members who receive relatively high remuneration are limited to the maximum limit, but there is no reduction factor for members who do not reach the maximum limit and do not have many referrals. Is easy to keep, and can be presented to members in advance, so it is easy to accept. In addition, the headquarters can easily evaluate the dividend rate when the maximum limit and the number of rewards are moved simultaneously, so a flexible compensation plan can be considered.

  In the above invention, “output” includes not only display and printing, but also the case of giving to other programs and devices as data.

  Further, “input” refers to that which passes through an interface for giving at least a variation condition to the CPU. “Input unit” and “input means” are not only interfaces with humans such as keyboards, mice, and voice input devices, but also interfaces with other programs such as interface circuits and interface programs, and other computers. Including those that take In the embodiment, the keyboard 10 corresponds to this.

  The “program” includes not only a program that can be directly executed by a computer but also a program that can be executed by being installed on a hard disk or the like. It also includes the case where it is compressed or encrypted.

  In the present invention, the payout rate always converges even if the hierarchy becomes infinitely deep for the reward distribution of the member system introduction sales organization. Therefore, quantitative evaluation under various variable conditions is stably performed based on the converged payout rate. be able to. In addition, since the maximum amount of remuneration is determined according to the depth of each member in the organization, a rational remuneration plan that is easy for members to accept can be considered.

It is a figure which shows the whole structure of the reward prediction calculation apparatus which is 1st Embodiment of this invention. It is a figure which shows the hardware structural example using CPU about the apparatus of FIG. It is a flowchart of a reward prediction program. It is a figure which shows the whole structure of the reward prediction calculation system using the network which is 2nd Embodiment of this invention. It is a figure which shows the process flowchart of the server and terminal of the system of FIG. It is a figure which shows the whole structure of the server of the system of FIG. It is explanatory drawing about the binary tree data structure of the membership system sales organization which concerns on this invention. It is explanatory drawing about calculation of the payout rate based on the structure of FIG.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
(Organizational expansion of member system introduction sales organization, binary tree data structure)
Before explaining each example, a binary tree data structure to which a membership introduction / sale organization for which the reward prediction calculation apparatus of the present invention performs prediction is mapped will be described with reference to FIG. Membership referral sales organizations are mapped to a binary tree data structure for reward calculations. That is, each member is virtually arranged at each node of the binary tree data structure to form a hierarchy, and a new member is virtually arranged at any location in this data structure. Black circles shown in FIG. 7 indicate members arranged at each node of the binary tree data structure. FIG. 7 shows that members are filled from the first layer (root node) through the i-th layer to the n-th layer. This shows the sales organization developed. Some black circles are omitted.

(Operation of a member-based referral sales organization)
In the member-based referral and sales organization, the supervising company sells products in response to referrals from members and pays the introduced members according to the remuneration decision rule.
By purchasing the products handled by the headquarters, the member becomes a member of the sales organization and occupies one node of the binary tree data structure. By chaining such introductions, the purchase of the product is promoted, and more members are placed below the member's node, so that more rewards are received.
In this way, since the member receives a reward through the introduction, the motivation to introduce is generated. Similarly, each member who receives a referral can receive a reward through the referral when introducing a new member, so that the organization is similarly developed in the following manner.

  The member who joined first (also referred to as “top”) is virtually arranged in the “first layer (or“ root node ”)” of the binary tree data structure of this member system introduction sales organization. This state can be expressed as “the first member is in the first layer”. A member who receives an introduction from the first member and newly joins the organization is placed below the “second layer”. Assuming that two people are introduced, the introduced members are arranged on two branches in the second layer. Similarly, the binary tree data structure of this member system introduction / sales organization is divided into two branches at each node of each layer, and is expanded to a lower layer. As shown in FIG. 7, two introduced members are arranged at the second layer node, and four members are arranged at the third layer node.

  Of course, each member is not limited to two referrals. Members may introduce 3 or more new members. However, even in this case, the organizational development of this member system introduction sales organization maintains the “binary tree data structure”, and as shown in FIG. It is arranged at any location in the “data structure” and is expanded so as to form a hierarchy by branching from one node into two.

(Assumption 1)
In the reward prediction handled in the present invention, it is assumed that the member system introduction sales organization is mapped to such a binary tree data structure and is filled with members up to the n-th layer (Premise 1). Hereinafter, for the sake of convenience, with respect to the branches that are branched into two from each node, the left branch or the lower branch is referred to as the left, and the right branch or the upper branch is referred to as the right.

  The “binary tree data structure” described above is branched into two, so it is well suited to binary notation. When “0” is assigned to the left branch and “1” is assigned to the right branch, the position of the node where each member is arranged is uniquely determined by such a binary notation. For example, the binary number 101 corresponds to the second node from the left of the third layer of top (first layer) → left (second layer) → right (third layer). For this reason, the position where each member is arranged can be easily set by a logical operation to be handled in the database, and the prospect is good.

(Assumption 2)
This reward is determined by counting the introduction results (product sales results) over a certain period (premise 2). Based on such “premise”, the amount of remuneration that the member should receive during the period is determined by the following “reward determination rule”, and the dividend rate is calculated.

(First embodiment: apparatus)
(Overview of overall device configuration and operation)
In FIG. 1, the whole structure of the reward prediction calculation apparatus 1 which is one Embodiment of this invention is shown.
The input unit 10 is for inputting a fluctuation condition, a calculation command, and the like for performing the reward prediction calculation to the apparatus 1. The storage unit 14 stores the input “variation conditions” and also stores “reward determination rules”.

  When the variation condition is input from the input unit 10, the control unit 12 stores the variation condition in the variation condition table file in the storage unit 14. Next, when the calculation command is input, the control unit 12 searches the reward determination rule 30 in the storage unit 14. Then, based on the reward determination rule 30, the reward amount and the sales amount corresponding to the variable condition are calculated, and the dividend rate is calculated from this and displayed on the display unit 16. The payout rate displayed at the same time as the display and the variation condition serving as the basis thereof are stored in the calculation result file 38 in the storage unit 14.

  The user sees the displayed payout rate, and if it is not the desired payout rate, the user can change the changing condition and input the calculation command again from the input unit 10. As a result of repeating the above operation, when a desired payout rate is displayed, the user inputs a determination command from the input unit, and the control unit 12 determines the displayed payout rate and the underlying variation condition. The data is output as decision data to the calculation result file 38 in the storage unit 14.

(Hardware configuration)
FIG. 2 shows a hardware configuration of the reward prediction calculation apparatus 1 when the control unit 12 of FIG. 1 is realized using the CPU 20. In FIG. 2, a memory 22, a display 16 as a display unit, a keyboard 10 as an input unit, a hard disk (HDD) 14 as a storage unit, and a DVD / CD-ROM drive 24 are connected to the CPU 20. Note that the DVD / CD-ROM drive 24 is not limited to the DVD / CD-ROM, and may be one that can read other external storage media.
The hard disk 14 stores a reward determination rule 30, a reward prediction program 32, an OS (operating system) 34, a variable condition table file 36 input from the keyboard 10, and the like, and is based on a finally determined dividend rate. A calculation result file 38 is stored. The reward determination rule 30 and the reward prediction program 32 are installed in the storage unit 14 from the DVD or CD-ROM 26 via the DVD / CD-ROM drive 24. Even other media may be stored in the storage unit 14 by another interface.
The hard disk 14 is not limited to this, and any hard disk may be used as long as it can read and write to other storage media connected to the CPU 20 or the like, and may be an optical disk, an SSD, a USB memory, or the like.
Further, although the reward determination rule 30 is a part of the reward prediction program, it is not limited to this and may be different.

(Variation condition table file)
Table 1 shows the configuration of the fluctuation condition table file 36.

(Reward decision rule)
The remuneration decision rule calculates the remuneration using the parameters shown in Table 1, and the product purchase is the total number of products purchased by members belonging to a lower hierarchy that branches from the member based on the member arrangement information. Based on the total number, the given maximum amount, and the given reward base, the product purchase total quantity is discretized and evaluated by the reward base for each hierarchy so that the calculation of the dividend rate converges. A provisional compensation amount is calculated, and the compensation amount is determined so that the provisional compensation amount does not exceed the maximum limit.

(Remuneration decision rule parameters)
The “reward determination rule” has a variable condition represented by the variable condition table of Table 1 as a parameter (variable). As will be described later, as a function having such parameters, the payout rate of members virtually arranged in the i-th layer having a depth of the layer up to the n-th layer is expressed by a function F (i) ( 1 ≦ i ≦ n).

The items (parameters) in the variation condition table of Table 1 will be described. Excluding the aggregation period, each parameter is an integer.
The “number of members branched from each member” (number 1) employs a binary tree data structure, so there is one for each of the left branch and the right branch, for a total of two members.
The “number of products purchased by each member” (number 2) is set to one when one member purchases one product.
“One-sided reward card” (No. 3) determines the number of reward units u by discretizing (quantizing) the total number of items purchased by the member in order to calculate the reward amount as will be described later. It is a parameter to do. “One side” means one side of the left and right branches branching from each member, and since the binary tree data structure is actually adopted, this double is the reward base g used in the calculation. In addition, * indicates that a product is taken (hereinafter the same in this specification). This is a basic parameter that has a great influence on the calculation of the dividend rate.
The “aggregation period” (number 4) is a period for calculating a reward, and the reward is calculated based on the number of items purchased in this period.
“Maximum amount” (number 5) refers to the maximum amount of remuneration received by one member during the “aggregation period”. This is M (yen).
The “basic remuneration amount” (number 6) is the remuneration amount to be paid for each remuneration unit u, and the basic remuneration amount multiplied by the remuneration unit is a provisional remuneration amount described later. This is h (yen).
“Product unit price” (number 7) is the price of one product purchased by a new member, and this is A (yen).
“Admission fee” (No. 8) is paid to the headquarters company when a new member joins. This is b (yen).
The “hierarchy depth” (number 9) refers to the number of the hierarchy counted from the root node (top) when the member-based introduction sales distribution organization is expanded into the binary tree data structure, and this is n.

Next, the outline of the reward determination rule is as follows.
The remuneration decision rule is a condition for a member to receive a remuneration, and divides a total product purchase quantity S (i), which will be described later, by a remuneration base g (= 2 * a), and uses the quotient as a remuneration unit u (i). The basic remuneration amount per unit is set to h yen, and the product of the basic remuneration amount and the remuneration unit u (i) is calculated as the provisional remuneration amount P (i). That is, the total product purchase quantity S (i) is digitized (quantized) by the reward base g (= 2 * a) and calculated in a stepwise manner. In this way, when the first step cannot be reached, that is, when the total product purchase quantity S (i) falls below the reward card number g, the reward amount becomes 0 and no reward is paid. The total product purchase quantity S (i) is not directly proportional to the reward amount, but the total product purchase quantity is discretized (quantized) and rounded down. Become. Such an evaluation is performed at each level.
In each step, the total amount of product purchases S (i) is set to pay a basic remuneration amount h yen for every integral multiple of the remuneration base g, but the size of this step is not an integral multiple of the remuneration base g. Or may be changed at each step.

(Remuneration for i-th layer members)
Furthermore, it demonstrates in detail along a reward determination rule.
Since the binary tree data structure shown in FIG. 7 is a hierarchical structure consisting of n layers, it starts from the first layer, and below it is divided into two layers, two branches to the left and right, and two branches from the second layer to the left and right. The third layer is composed of the i-th layer branched to the left and right from the (i-1) layer, and the n-th layer branched to the left and right from the (n-1) layer. Is done.
Then, as shown in the fluctuation condition table of Table 1, each member purchases one item, so the total number of items purchased by the member group subordinate to the member in the i-th layer (that is, subordinate) If S (i) is not included in the i-layer's own member's own product purchase number), S (i) is equal to the number of introducers arranged at a lower level branching from the member's node. Then, since the total number of members of the binary tree data structure starting from the i-th layer and ending at the n-th layer is subtracted 1 from 2 ^{n- (i-1)} -1, (Equation 1) is obtained. . The total number of members of the binary tree data structure starting from the first layer and ending at the nth layer is 2 ⁰ +2 ¹ +2 ² +... +2 ⁽ⁿ⁻¹⁾ = 2 ⁿ −1.

S (i) = (2 ^{n− (i−1)} −1) −1 (pieces)
= 2 ^{n + 1-i} -2 (pieces) (Formula 1)

As a method of determining the amount of reward, a basic amount of reward is determined in advance, and the amount of reward can be determined so as to have a linear relationship with the total number of product purchases by multiplying the product by the total number of product purchases.
However, if the threshold of remuneration is raised a little and a certain number is set as a threshold and the remuneration is paid when the threshold is exceeded, it is expected that the incentive to introduce up to the threshold number will increase. If the threshold can be made variable, the incentive and reward for introduction can be determined more delicately. For this reason, the “total product purchase quantity” is discretized (quantized) by the reward radix g and converted into reward units u (i) for evaluation.
Then, as shown in (Expression 2), the reward unit u (i) is an integer obtained by dividing S (i) = 2 ^{n + 1−i} −2) (pieces) in (Expression 1) by the reward radix g. Can be represented as a part. Here, if the reward base g is 2 * a which is twice the one-side reward base a, the reward unit u (i) is an integer part obtained by dividing S (i) by 2 * a.

u (i) = INT {S (i) / g} (Formula 2)
= INT {S (i) / (2 * a)} (unit)

Here, INT {} is an operator that extracts an integer part of a numerical value in parentheses (hereinafter the same). By determining the basic reward amount for the reward unit u (i) and taking the product of these, the reward amount can be determined.
Considering this, the remuneration amount P (i) that one member of the i-th layer will receive during this counting period is expressed in the basic remuneration amount h (yen) as shown in (Equation 3). The reward unit u (i) of 2) is multiplied.

        P (i) = h * u (i) (yen) (Formula 3)

(Introduction of maximum limit)
The reward P (i) of one member of the i-th layer is obtained as shown in (Equation 3) as described above. When n is large, the amount of reward increases exponentially, so the dividend rate is There is concern about divergence. Therefore, in order not to diverge, the remuneration amount P (i) is set as a provisional remuneration amount, and the maximum limit amount M (yen) is introduced as the upper limit amount.
Since this maximum limit M limits the reward, it is desirable for the member to be fair and easy to accept. Therefore, first, the provisional remuneration amount P (i) is calculated, and then only the amount exceeding the maximum limit is limited to the maximum limit.
Assuming that the layer where the provisional reward amount P (i) reaches the maximum limit M (the layer that initially exceeds the maximum limit amount) is the mth layer from the nth layer, m is the following inequality (formula The minimum value of k satisfying 4) can be obtained.
However, since i is counted from the first layer and k is counted from the nth layer, there is a relationship of i + k = n + 1 between i and k, and considering (Equation 1), as shown in (Equation 4): The minimum value of k obtained is determined by three parameters M, h, and a (g), and does not depend on n. That is, m is a constant unrelated to n.

M ≦ P (i) = h * u (i)
= H * INT {S (i) / g)}
= H * INT {(2 ^{n + 1-i} -2) / g}
= H * INT {(2 ^k -2) / g)} (Formula 4)

In this way, m, which is the layer that reaches the maximum limit M, is obtained, and the following cases are divided to determine the member remuneration for each level. The members from the first layer to the (n + 1) -m layer limit the reward to the maximum limit M yen.
[Case 1]:
For members with i ≦ n + 1−m, the maximum remuneration is M yen.
[Case 2]:
For members with i> n + 1−m, the reward is a provisional reward amount P (i) = h * INT {(2 ^{n + 1−i} −2) / g} yen.
When the remuneration number g is twice the one-side reimbursement number a, members in both lower layers are placed in a well-balanced manner so that the number of items purchased by members belonging to the left and right branches of the member node in the binary tree data structure is a. Match what is being done. Also, discretization is intuitive and easy to accept.
In accordance with such a case division, the total amount B of remuneration during the aggregation period of the entire organization is calculated separately for case 1 and case 2 below. In case 1, the reward amount is uniformly limited to the maximum amount M regardless of the layer to which the member belongs. In Case 2, the remuneration amount of one member belonging to the i-th layer is the same amount as the provisional remuneration amount P (i), and the remuneration amount differs depending on which layer it belongs to.

(Total remuneration BM in Case 1)
In case 1, as shown in FIG. 8, the reward of members from the (n + 1-m) th layer to the top first layer is a uniform maximum M yen. Therefore, as shown in the following (Formula 5), the total amount BM of case 1 may be obtained by multiplying the M yen by the total number of members from the first layer to the (n + 1-m) th layer.

(Total compensation BR in Case 2)
In case 2, the reward amount P (i) of each member of each layer does not reach the maximum limit M. The highest member of the binary tree data structure of Case 2 belongs to the (n−m + 2) th layer as shown in FIG. Therefore, the total remuneration BP (yen) of all members belonging to this member and its subordinate (subordinate) groups is calculated by adding all the remuneration amounts of each member from the (n−m + 2) layer to the n layer. 8 (each hatched portion in FIG. 8), the same as Equation 5 is considered, and the following Equation 6 is obtained, and the second Equation is derived from Equation 2 and Equation 3. When this is rewritten as nm−2 = 1, n = m−1, n−i + 1 = m−1, and i−1 = i− (n−m + 2).

As shown in FIG. 8, the BP obtained by Equation 6 is the total remuneration of one member belonging to the (n−m + 2) layer and all members belonging to the lower hierarchy branched from the member. Therefore, when this is integrated by the number of members belonging to the (n−m + 2) th layer, 2 ^{n−m + 1} , the total remuneration BR of (Case 2) is obtained as shown in (Formula 7). In FIG. 8, the hatched portions in case 2 are displayed in an overlapping manner, but this does not indicate that there is common data, and each hatched portion is independent in the binary tree data structure (another Wood).

BR = BP * 2 ^{n-m + 1} (Formula 7)

  Then, the total reward B for all members during the counting period is the sum of the total reward BM (case 1) shown in (Formula 5) and the total reward BR (case 2) shown in (Formula 7). The total reward B is expressed by the following (formula 8).

B = BM + BR
= M * (2 ^{n−m + 1} −1) + BP * 2 ^{n−m + 1}
= (M + BP) * 2 ^{n-m + 1} -M (Formula 8)

  Further, the total receipt amount I of the controlling company during the totaling period including the product unit price A and the admission fee (registration fee) b can be expressed by the following (formula 9).

I = A * (2 ⁿ −1) + b * 2 ⁿ⁻¹ = (2 * A + b) * 2 ⁿ⁻¹ −A (Equation 9)

  Accordingly, since (Equation 8) divided by (Equation 9) is the payout rate F (n) of the controlling company, the payout rate F (n) can be calculated as (Equation 10) shown below.

F (n) = B / I
= {(M + BP) * 2 ^{n-m + 1} -M} / {(2 * A + b) * 2 ^n-1 -A} * 100 (%) (Formula 10)

(Convergence of dividend rate F (n))
It shows that the payout rate F (n) calculated in this way always converges. Regarding (Expression 10), the limit value where n is infinite is as shown in the following (Expression 11).

lim F (n) = (M + BP) / {2 ^m−2 * (2 * A + b)} * 100 (%) (n → ∞) (formula 11)

The right side of (Expression 11) indicates that when n becomes infinite, it converges to a constant value unrelated to n. Therefore, if the dividend amount is determined in accordance with the reward determination rules shown in (Case 1) and (Case 2), (Equation 11) will not diverge, so the dividend rate can be determined from such a dividend amount. For example, there is no theoretical failure. Therefore, since the calculated payout rate F (n) is a definite value, it can be seen that it is a highly reliable and extremely powerful index of payout. There have been no proposals for determining such a dividend amount, and it has been shown that reward prediction based on such a reward determination rule is a very powerful method.

(Calculation process)
FIG. 3 is a flowchart showing the reward prediction program 30. Hereinafter, the process flow will be described.

(S310) The fluctuation condition is input from the keyboard 10. The input variation condition is stored in the hard disk (HDD) 14. Similarly, a calculation command is input and calculation of the dividend rate is started.

(S312) The fluctuation condition is read from the HDD 14, and the predicted reward and the predicted total sales are calculated according to the reward determination rule described later. Next, the predicted payout rate is calculated by dividing the predicted reward by the predicted total sales.

(S314) The calculated predicted payout rate is displayed on the display 16. The calculated predicted payout rate and the variation condition in that case are recorded in the calculation result file 38 of the HDD 14.

(S316) If the predicted payout rate is a desired value, the process proceeds to (S318). If not, the process returns to (S310) and recalculates the payout rate again according to the changing condition and the calculation command.

(S318) The payout rate, which is a desired value, and its fluctuation condition are output to the HDD 14 and recorded.

(Second Embodiment)
In the first embodiment, a database in which a plurality of reward determination rules and programs are recorded is stored in a device that performs processing. Not limited to this, it is conceivable that these databases are recorded in another device or accessed via a communication line.
In the second embodiment, the reward determination rule is implemented on the server as a calculation program. In this case, since the variation condition is transmitted from the terminal device to the server and the calculation result is transmitted to the terminal device, the variation condition can be easily changed from the terminal device to obtain the dividend rate.
By quantitatively grasping the difference between the dividend rate obtained in this embodiment and the dividend rate based on actual results, it is possible to provide a more rational compensation plan and manage the compensation plan according to the situation. Will.

(Processing server)
FIG. 4 shows a reward prediction calculation system 160 according to this embodiment. In this embodiment, a server device (processing server) 100 and terminal devices 102, 104, 106,... Are connected via the Internet. The server device 100 records a reward prediction calculation program and a reward determination rule, and functions as a web server (cloud server). The terminal devices 102, 104, 106,... Can use the reward prediction calculation function by accessing the server device 100.

FIG. 6 shows the overall configuration of the reward calculation prediction server 100 according to the second embodiment of the present invention. An input unit 110, a control unit 112, a storage unit 114, a display unit 116, and a communication unit 118 are provided.
The input unit 110 is an interface for giving necessary information to the server from the user of the server. It includes not only those that interface with humans such as a keyboard mouse and voice input device, but also those that interface with other programs and other computers such as interface circuits and interface programs.
The control unit 112 performs control for calculating a payout rate by exchanging information with at least the terminal devices 102, 104, 106,... And the storage unit 114.
The storage unit 114 stores at least a reward prediction program and stores the calculation of the payout rate and the result thereof, and stores user registration information and the like as necessary.
The display unit 116 is for displaying at least the payout rate, and for example, a display device may be used.
The communication unit 118 communicates with the outside of the server apparatus 100 using a communication network or the like.

  The hardware configurations of the terminal devices 102, 104, 106,... Are not shown, but are basically the same as the hardware configuration of the server 100 in FIG. Since the terminal device is a client, it is not necessary to store a reward calculation prediction program or the like or perform a reward prediction calculation. At least the variable condition is transmitted to the server 100 and the result of the reward prediction calculation is obtained. It only needs to be received, recorded and maintained.

(Variation conditions)
The variation conditions are the same as those shown in Table 1 of the first embodiment.

(Processing flow)
FIG. 5 shows a flowchart of the reward prediction program recorded in the server device 100 and a flowchart of the browsing program recorded in the terminal device 102 corresponding to this.

  First, the server device 100 receives access from the terminal devices 102, 104, 106,... Via the communication unit 118, and the user identifier input to the terminal device is transmitted to permit the login (not shown). . At that time, the control unit 112 may allow the user identifier information stored in the storage unit 114 in advance. For example, “0102” may be transmitted as a user identifier, and at that time, a password may be used together to ensure security.

(S520) Next, the terminal device 102 permitted to log in transmits the change condition and the calculation command to the server device 100.

(S510) The server device 100 receives this, records the fluctuation condition in the storage unit 114, and activates the reward determination rule and the reward prediction program stored in the storage unit 114 to perform the reward prediction calculation in the control unit 112. Calculate the dividend rate. Further, calculation results such as the payout rate and the fluctuation condition are recorded in the storage unit 114 and transmitted to the terminal device 102 via the communication unit 118.

(S522) The terminal apparatus 102 receives this, and the browsing program displays a screen on which the calculation result is displayed.

(S524) If the user thinks that the payout rate is desired, the process proceeds to (S526), and a determination command is transmitted to the server apparatus 100 by the user operation. If not, the process jumps to (S520) to transmit another variation condition and a calculation command to the server apparatus 100 to be recalculated. This process is repeated, and the server device 100 performs recalculation and transmits the result to the terminal device 102.

(S512) When a determination command is transmitted in step S526 and the server device 100 receives the determination command, a screen displaying the determined payout rate and the changing condition at that time is transmitted to the terminal device 102.

(S528) When the screen of step 512 is transmitted, the terminal apparatus 102 displays the received determined payout rate and variation condition.

  In this way, the terminal device 102,... Can use the reward prediction program of the server device 100.

Hereinafter, a calculation example in which the dividend rate is obtained in accordance with the reward determination rule of the present invention will be shown. This applies to both devices and servers.
(Calculation Example 1)
In accordance with the remuneration decision rule, the fluctuation condition is extracted from the basic form of the remuneration plan, and the dividend rate F is calculated. The fluctuation conditions are determined and calculated as shown in Table 2.
As shown in Table 2, the main parameters, ie, the reward base g (one-side reward base a), the maximum limit M, and the basic reward h are extracted and shown as follows.
g, a: One-side reward base a is 3 (reward base g is 6)
M: Maximum limit M is 2,400,000 yen
h: Basic compensation amount h is 5,000 yen

When the layer depth n = 15 under the conditions of M, h, a (when the number of members is about 32,000), the layer m reaching the maximum limit is calculated using (Equation 4). The minimum value m of k satisfying Equation 4) is
m = 12
It becomes. Then, in the conditions of m and n, the total reward BM of (Case 1) is as follows from (Equation 5):
BM = M * (2 ⁵ -1)
= 2,400,000 * 15 = 36,000,000 (yen)
It becomes. On the other hand, the BP of (Case 2) is from (Equation 6):
BP = h * 2,845
= 5,000 * 2,845 = 14,225,000 (yen)
From (Equation 7), the total compensation BR of (Case 2) is
BR = BP * 2 ⁴
= 14,225,000 * 16 = 227,600,000 (yen)
It becomes. Therefore, from (Equation 8):
Total compensation B = BM + BR
= 36,000,000 + 227,600,000
= 263,600,000 (yen)
It becomes.
On the other hand, the total receipt I of the headquarters is (Formula 9)
Total received amount I = A * (2 ⁿ −1) + b * 2 ⁿ⁻¹ = (2 * A + b) * 2 ⁿ⁻¹ −A
= (2 * 10,000 + 3,000) * 2 ¹⁴ -10,000
= 376,822,000 (yen)
Therefore, by applying (Equation 10) and calculating the dividend rate F (15),
Dividend rate F (15) = (B / I) * 100 (%)
= (263,600,000 / 376,822,000) * 100 (%)
= 69.95%
It becomes.
Such a dividend can be said to be a firm dividend with reduced risk.

  In general, the payout rate F (n) may not necessarily match the actual payout rate, but this discrepancy is caused by the bias of individual member introducers (probable) Presumed to be caused by bias (multiple). Further analysis of these discrepancies will give the headquarters important business information that can be reflected in how to set up compensation plans. In this way, since the reward plan can be quantitatively evaluated as the dividend rate F (n), it becomes possible to formulate a new reward plan that is even more attractive and less risky.

(Calculation example 2: When a = 2)
Next, a calculation example in the case where the variation condition is changed and the one-side reward radix a is a = 2 is shown.
As shown in Table 3, the main parameters, ie, the reward base g (one-side reward base a), the maximum limit M, and the basic reward h are extracted and shown as follows.
g, a: One side reward base a is 2 (reward base g is 4)
M: Maximum limit M is 2,400,000 yen
h: Basic compensation amount h is 5,000 yen

Except for the case where a = 2 (two left branches and two right branches), it is the same as Calculation Example 1.
Under this condition, when the layer depth n = 15, the following result is obtained by calculating the payout rate by applying (Equation 10). In this case, if calculation is performed in the same manner as in calculation example 1, m = 11.
Dividend rate F (15) = 95.9%
It becomes.
This dividend rate figure is fairly high as a dividend assessment and may be risky for the headquarters. However, if there is a possibility that the dividend rate will be lower (for example, the actual dividend rate can be estimated to be around 50%) due to past trends or actual conditions, there is a risk but attractive appeal with strong appeal. It could be considered as one of the reward plans.

(Calculation Example 3: When a = 4)
Furthermore, an example of calculation when the variation condition is changed and a = 4 is shown. This is a case where a member purchases more products.
As shown in Table 4, the main parameters, ie, the reward base g (one-side reward base a), the maximum limit amount M, and the basic reward amount h are extracted and shown as follows.
g, a: One-side reward base a is 4 (reward base g is 8)
M: Maximum limit M is 2,400,000 yen
h: Basic compensation amount h is 5,000 yen

Except for the case where a = 4 (four left branches and four right branches, respectively), it is the same as calculation examples 1 and 2. In this case, if calculation is performed in the same manner as in calculation examples 1 and 2, m = 12.
Dividend rate F (15) = 47.6%
It becomes.
In this case, the dividend rate is less than 50%, and the risk is small compared with the second embodiment, but it will be evaluated that the compensation plan is not attractive.

  As described above, the payout rate was calculated using (Formula 10) in Calculation Examples 1 to 3. By thus changing the three parameters (variation conditions) h, M, and g (a) that determine the reward plan, it is possible to easily obtain a payout rate that always converges. In addition, since the product unit price A, the admission fee b, and the like can be changed to have different payout rates, a flexible and easy compensation plan can be predicted and evaluated.

Advantages regarding such prediction include the following.
First, by making this prediction before the start of organizational development, it is possible to flexibly determine the compensation plan based on quantitative and reliable indicators, taking into account various circumstances such as economic conditions. .
Secondly, by making this prediction after the start of organizational development, we are not only predicting the dividend rate for the entire controlling company, but also by evaluating the difference between the actual value and the predicted value of each member. It is possible to find an active base organization and to develop an overall organizational development strategy, and to provide guidelines for individual member activities.

In addition to the convergence of the reward prediction calculation in this way, the reward prediction calculation can be performed by manipulating parameters that are easy to appeal to intuition, including product unit price A, admission fee b, etc. The development can be easily considered. In addition, it is easy for the headquarters to consider realistic plans that consider the continuation and stabilization of management and the sedation of overheating of referrals, and is effective in meeting social demands. And versatile reward calculation prediction technology can be provided.

1 reward prediction calculation device 10 input unit (keyboard)
12 Control unit 14 Storage unit (hard disk)
16 Display (display)
20 CPU
22 Memory 24 DVD / CD-ROM drive 26 CD / DVD
30 Reward decision rule 32 Reward prediction program 34 OS
36 Fluctuation condition table file 38 Fluctuation condition determination table file 100 Reward prediction calculation server (server device)
102, 104, 106 Terminal device 110 Internet 112 Control unit 114 Recording unit 116 Display unit 118 Communication unit 132 Reward prediction program 134 Browsing program 150 Internet A Product unit price a One-side reward base B Determined remuneration amount BM Limited to maximum amount The total amount of remuneration at the level (1)
BR Total amount of rewards not reaching the maximum limit (case 2)
BP Total amount of remuneration received by one member and all members belonging to the lower ranks from that member b Membership fee F (n) Dividend rate g Compensation base h Basic remuneration amount I Total amount received by supervising company M Maximum amount P (I) Provisional remuneration amount for the member in the i-th layer S (i) Total product purchase number u (i) for the member in the i-th layer Compensation for the member in the i-th layer whose total product purchase amount is S (i) Number of units

Claims (3)

A remuneration prediction calculation server (100) for calculating a remuneration amount for a member of a member system introduction sales distribution organization and predicting a dividend rate thereof,
  It is possible to communicate with the terminal device (102, 104, 106,...) Via the network (110), and to receive the variable condition, calculation command and determination command for calculating the reward amount transmitted from the terminal device. A communication unit (116) for transmitting the calculated payout rate and the payout rate as determination data when the determination command is received to the terminal device;
  A storage unit (114) for recording the received variable condition and the reward determination rule for the dividend rate to converge;
  A control unit (112) that receives the calculation command received and repeatedly calculates the reward amount based on the reward determination rule according to the received variation condition until the determination command is received and predicts the dividend rate; ,
  In response to the received determination command, a providing unit that provides the terminal device with a payout rate based on the prediction and a quantitative evaluation based on the payout rate;
With
  The reward determination rule is:
[1] Each member has arrangement information mapped so as to form a hierarchy that is virtually arranged and filled in each node of the binary tree data structure,
[2] Regarding the amount of compensation for each member,
  The total number of products purchased (S (i)), which is the total number of products purchased by members belonging to lower tiers branched from the member based on the arrangement information, a given maximum limit (M), Based on the given reward base (g),
  Calculate the provisional compensation amount (P (i)) that is discretized and evaluated by the compensation base for the total number of items purchased for each tier, and calculate the compensation amount so that the provisional compensation amount does not exceed the maximum limit And
  The provisional remuneration amount (P (i)) is the remuneration amount of members belonging to the i-th hierarchy of the binary tree data structure having a hierarchy of depth n.
  The maximum limit (M) is M yen, the basic reward amount (h) is h yen, the reward base (g) is g, each member purchases one product,
  In the case where the total number of items purchased is S (i), the provisional reward amount is P (i) yen, INT {} is an operator that takes an integer value of the number in {}, and * is an integration calculation symbol In addition,
        P (i) = h * INT {S (i) / g},
  The amount of reward determined is
        M ≦ P (i) and k + i = n + 1
The case is classified by the hierarchy (i) that is m-th counting from the n-th layer that is the minimum value of k,
[Case 1]:
        For members in the hierarchy of i ≦ n + 1−m, the remuneration amount shall be the maximum limit of M yen,
[Case 2]:
        For members in the hierarchy of i> n + 1−m, the reward amount is P (i) = h * INT {(2 ^{n-i + 1} -2) Calculate the amount of reward as / g} yen,
Reward prediction calculation server.
In order to calculate the amount of remuneration for members of the member system introduction sales distribution organization and to predict the dividend rate,
  Input means for inputting the variable condition for calculating the remuneration amount, a calculation command and a determination command;
  Storage means for recording a reward determination rule for convergence of the input fluctuation condition and the dividend rate;
  When the calculation command is input, control means for repeatedly calculating the reward amount based on the reward determination rule according to the input variation condition until the determination command is input and predicting the dividend rate;
  Display means for displaying a dividend rate according to the prediction;
  When the determination command is input, output means for outputting the payout rate and the variation condition stored simultaneously with the display as determination data;
A reward prediction calculation program for making it function,
[1] Each member has arrangement information mapped so as to form a hierarchy that is virtually arranged and filled in each node of the binary tree data structure,
[2] Regarding the amount of compensation for each member,
  The total number of products purchased (S (i)), which is the total number of products purchased by members belonging to lower tiers branched from the member based on the arrangement information, a given maximum limit (M), Based on the given reward base (g),
  Calculate the provisional compensation amount (P (i)) that is discretized and evaluated by the compensation base for the total number of items purchased for each tier, and calculate the compensation amount so that the provisional compensation amount does not exceed the maximum limit And
  The provisional remuneration amount (P (i)) is the remuneration amount of members belonging to the i-th hierarchy of the binary tree data structure having a hierarchy of depth n.
  The maximum limit (M) is M yen, the basic reward amount (h) is h yen, the reward base (g) is g, each member purchases one product,
  In the case where the total number of items purchased is S (i), the provisional reward amount is P (i) yen, INT {} is an operator that takes an integer value of the number in {}, and * is an integration calculation symbol In addition,
        P (i) = h * INT {S (i) / g},
  The amount of reward determined is
        M ≦ P (i) and k + i = n + 1
The case is classified by the hierarchy (i) that is m-th counting from the n-th layer that is the minimum value of k,
[Case 1]:
        For members in the hierarchy of i ≦ n + 1−m, the remuneration amount shall be the maximum limit of M yen,
[Case 2]:
        For members in the hierarchy of i> n + 1−m, the reward amount is P (i) = h * INT {(2 ^{n-i + 1} -2) Calculate the amount of reward as / g} yen,
Reward prediction calculation program.
In the reward prediction calculation method that calculates the amount of remuneration for members of the member system introduction sales distribution organization and predicts the dividend rate,
  An input step for inputting a variable condition for calculating the reward amount;
  A storage step for recording the input variation condition;
  A control step of repeatedly calculating the remuneration amount based on a reward determination rule that converges the dividend rate according to the input fluctuation condition when the calculation command is input, and predicting the dividend rate until the determination command is input When,
  A display step for displaying a dividend rate based on the prediction;
  When the determination command is input, an output step of outputting the payout rate and the variation condition stored simultaneously with the display as determination data;
A reward prediction calculation method including
  The reward determination rule is:
[1] Each member has arrangement information mapped so as to form a hierarchy that is virtually arranged and filled in each node of the binary tree data structure,
[2] Regarding the amount of compensation for each member,
  The total number of products purchased (S (i)), which is the total number of products purchased by members belonging to lower tiers branched from the member based on the arrangement information, a given maximum limit (M), Based on the given reward base (g),
  Calculate the provisional compensation amount (P (i)) that is discretized and evaluated by the compensation base for the total number of items purchased for each tier, and calculate the compensation amount so that the provisional compensation amount does not exceed the maximum limit And
  The provisional remuneration amount (P (i)) is the remuneration amount of members belonging to the i-th hierarchy of the binary tree data structure having a hierarchy of depth n.
  The maximum limit (M) is M yen, the basic reward amount (h) is h yen, the reward base (g) is g, each member purchases one product,
  In the case where the total number of items purchased is S (i), the provisional reward amount is P (i) yen, INT {} is an operator that takes an integer value of the number in {}, and * is an integration calculation symbol In addition,
        P (i) = h * INT {S (i) / g},
  The amount of reward determined is
        M ≦ P (i) and k + i = n + 1
The case is classified by the hierarchy (i) that is m-th counting from the n-th layer that is the minimum value of k,
[Case 1]:
        For members in the hierarchy of i ≦ n + 1−m, the remuneration amount shall be the maximum limit of M yen,
[Case 2]:
        For members in the hierarchy of i> n + 1−m, the reward amount is P (i) = h * INT {(2 ^{n-i + 1} -2) Calculate the amount of reward as / g} yen,
Reward forecast calculation method.

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