Patent Publication Number: US-11049352-B2

Title: Management apparatus for managing a receipt printer

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a divisional of U.S. patent application Ser. No. 16/107,298, filed on Aug. 21, 2018, which application is based upon and claims the benefit of priority from Japanese Patent Application No. 2017-162452, filed in Aug. 25, 2017, the entire contents of which are incorporated herein by reference. 
    
    
     FIELD 
     Embodiments described herein relate generally to a management apparatus for managing a receipt printer. 
     BACKGROUND 
     In the related art, a point of sales (POS) system is introduced in a store such as a supermarket or a convenience store to process sales of commodities purchased by customers and to manage sales data of the store. 
     The POS system includes one or a plurality of POS terminals and a server such as a store server. Each POS terminal performs sales registration and payment processing for commodities purchased by customers. The server receives and stores sales data from the one or the plurality of POS terminals and manages the sales data of the store. 
     A coin change machine and a bill change machine (hereinafter, collectively referred to as an “automatic change machine”) that are used for deposit and withdrawal of bills and coins (hereinafter, collectively referred to as “money”) are connected to each POS terminal. Money that is received from customers is deposited and stored in each automatic change machine. Change that is to be given to customers is withdrawn from each automatic change machine. 
     Each POS terminal issues receipts to be given to customers. Commodity information related to commodities purchased by customers, payment information related to payment, and the like are printed on the receipts. A roll of receipt paper is stored in each POS terminal, and the receipts are issued by printing the commodity information, the payment information, and the like on the receipt paper. 
     Money needs to be collected from each automatic change machine filled with money. Also, each automatic change machine having not enough money needs to be refilled with money. In addition, if the receipt paper becomes insufficient by issuing the receipts, the receipt paper needs to be replaced with new receipt paper. 
     If refill or collection of money in an automatic change machine or replacement of receipt paper is performed, operations with respect to the corresponding POS terminal or automatic change machine needs to be stopped. Thus, when the store is congested, refill or collection of money in each automatic change machine or replacement of receipt paper in each POS terminal causes customers performing payment to wait. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic diagram illustrating a POS system according to an embodiment. 
         FIG. 2  is a block diagram illustrating a hardware configuration of a server. 
         FIG. 3  is a diagram illustrating a memory configuration of a statistics section. 
         FIG. 4  is a perspective view illustrating a POS terminal. 
         FIG. 5  is a block diagram illustrating a hardware configuration of the POS terminal. 
         FIG. 6  is a function block diagram illustrating a functional configuration of the server. 
         FIG. 7  is a flowchart illustrating a flow of processing performed by the server. 
         FIG. 8  is a flowchart illustrating a flow of prediction processing performed by the server. 
         FIG. 9  is a flowchart illustrating a flow of correction processing performed by the server. 
         FIG. 10  is a flowchart illustrating a flow of processing performed by the POS terminal. 
         FIG. 11  illustrates an example of a display screen displayed on the POS terminal. 
         FIG. 12  is a flowchart illustrating a flow of processing performed by the server. 
         FIG. 13  is a flowchart illustrating a flow of prediction processing performed by the server. 
         FIG. 14  is a flowchart illustrating a flow of correction processing performed by the server. 
         FIG. 15  is a flowchart illustrating a flow of processing performed by the POS terminal. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments provide a management apparatus and a method that enable prediction of an appropriate time period for money refill or an appropriate time period for money collection, or an appropriate time period for replacing receipt paper in which the number of customers is relatively small. 
     In one embodiment, a receipt printer management apparatus includes a first interface for communicating with a receipt printer in a store, a storage device which stores first statistical information indicating a number of customers in the store during past time periods, and second statistical information indicating an amount of paper used by the receipt printer during past time periods, and a processor programmed to perform a prediction processing including: calculating, based on the first and second statistical information, a replacement time period for replacing paper in the receipt printer, and outputting information indicating the calculated replacement time period. 
     Hereinafter, an embodiment will be described in detail with reference to the drawings. In the embodiment, a server will be described as one example of a management apparatus. The embodiment described below is not for limitation. 
       FIG. 1  is a schematic diagram illustrating a POS system according to the embodiment. The POS system includes a server  1 , a plurality of POS terminals  3 , and an automatic change machine  5  connected to each POS terminal  3  in a store T. In this case, the automatic change machine  5  is indirectly connected to the server  1 . 
     Each POS terminal  3  is connected to the server  1  through a communication line N 1  such as a local area network (LAN). Thus, the server  1  and the POS terminal  3  can transmit and receive data and the like with each other. 
     The POS terminal  3  executes sales registration and payment processing for commodities that are sold in the store T. The sales registration refers to a process of acquiring a commodity code by capturing or optically reading a symbol or an image of a symbol such as a barcode assigned to a sold commodity, displaying commodity information (a commodity name, a price, and the like) related to the commodity read from a commodity master based on the acquired commodity code, and storing the commodity information in a RAM or the like. The payment processing refers to, for example, a process of displaying the total amount of money related to a transaction based on the commodity information stored in the RAM or the like during the sales registration, and calculating and displaying the amount of change based on money (cash) received from a customer. 
     The POS terminal  3  transmits information regarding the calculated amount of change to the automatic change machine  5 . The POS terminal  3  also outputs the commodity information and the payment information to a receipt issuing device. The receipt issuing device issues a receipt on which the input commodity information and payment information are printed. If the receipt issuing device is incorporated in the POS terminal  3 , the receipt issuing device functions as a printing device  40  (refer to  FIG. 5 ) which will be described later. The receipt issuing device may be a printing device that is electrically connected to the POS terminal  3 . In the embodiment, the printing device  40  will be described as the receipt issuing device below. The POS terminal  3  also transmits the commodity information and the payment information to the server  1  through the communication line N 1 . 
     The server  1  tracks and manages sales data from the store T based on the commodity information and the payment information received from each POS terminal  3 . The server predicts a time period for refilling money in the automatic change machine  5  and a time period for collecting money from the automatic change machine  5 . The server  1  also predicts a time period for replacing a roll of receipt paper stored in the POS terminal  3 . 
     The automatic change machine  5  is electrically connected to the POS terminal  3 . Thus, the POS terminal  3  and the automatic change machine  5  can transmit and receive data and the like with each other. The automatic change machine  5  has the function of at least one of a coin change machine that is used for deposit and withdrawal of coins, and a bill change machine that is used for deposit and withdrawal of bills. The embodiment will be described using the automatic change machine  5  that has the function of both deposit and withdrawal of coins, and deposit and withdrawal of bills. 
     The automatic change machine  5  includes a coin deposit unit, a coin withdrawal unit, a bill deposit unit, and a bill withdrawal unit (none illustrated). The coin deposit unit is used for deposit of coins from the customer into the automatic change machine  5 . The coin withdrawal unit is used for withdrawal of coins included in money to be paid to the customer as change. The bill deposit unit is used for deposit of bills received from the customer into the automatic change machine  5 . The bill withdrawal unit is used for withdrawal of bills included in the money to be paid to the customer as change. 
     The automatic change machine  5  includes a storage unit in which coins deposited from the coin deposit unit and bills deposited from the bill deposit unit are stored by type. If change information is input from the POS terminal  3 , the automatic change machine  5  withdraws money corresponding to the amount of change from money stored in the storage unit. If the withdrawn money includes coins, the coins are withdrawn from the coin withdrawal unit. If the withdrawn money includes bills, the bills are withdrawn from the bill withdrawal unit. 
     The server  1  can communicate with a server  7  installed in a security company through a communication line N 2  such as an Internet line. Thus, the server  1  and the server  7  in the security company can transmit and receive data and the like with each other. The server  1  can also communicate with a server  8  installed in a bank through the communication line N 2 . Thus, the server  1  and the server  8  in the bank can transmit and receive data and the like with each other. 
     A company that operates the store T outsources, to the security company, work such as transporting money to be used for change to a safe in the store T from the bank, or transporting collected sales money to the bank. The money for change is prepared as change in the automatic change machine  5 . The store T communicates information related to money between the server  1  and the server  7  and the server in order to request the security company to prepare sufficient money for change or to transport collected money for deposit in the bank promptly. 
     The server  1  can also communicate with a server  9  installed in a weather forecasting company through the communication line N 2 . Thus, the server  1  and the server  9  in the weather forecasting company can transmit and receive data and the like with each other. The server  1  continuously or periodically receives meteorological information such as weather, temperature, and humidity from the server  9 . 
     The server  1  estimates the number of customers visiting the store T based on the acquired meteorological information and statistical information (hereinafter referred to as “first statistical information”) which will be described later. The server  1  predicts the time period for refilling money in the automatic change machine  5  or the time period for collecting money from the automatic change machine  5  based on the first statistical information and statistical information (hereinafter referred to as “second statistical information”) related to the amount of money stored in the automatic change machine  5 . 
     Hereinafter, hardware configurations of the server  1  and the POS terminal  3  will be described. First, a hardware configuration of the server  1  will be described.  FIG. 2  is a block diagram illustrating a hardware configuration of the server  1 . As illustrated in  FIG. 2 , the server  1  includes a central processing unit (CPU)  11 , a read-only memory (ROM)  12 , a random access memory (RAM)  13 , a memory unit  14 , and the like. The CPU  11  is a main controller. The ROM  12  stores various programs. Programs and various types of data are loaded into the RAM  13 . The memory unit  14  stores various programs. The CPU  11 , the ROM  12 , the RAM  13 , and the memory unit  14  are connected to each other through a bus  15 . The CPU  11 , the ROM  12 , and the RAM  13  constitute a control unit  100 . That is, the control unit  100  executes a control, described later, related to the server  1  by operating the CPU  11  in accordance with a control program that is stored in the ROM  12  or the memory unit  14  and loaded into the RAM  13 . 
     The memory unit  14  is a non-volatile memory device such as a hard disc drive (HDD) or a flash memory that retains stored information even if power is off. The memory unit  14  includes a control program  141 , a sales management unit  142 , a statistics section  143 , a change management unit  144 , and a receipt management unit  145 . The control program  141  is used for controlling the server  1 . The sales management unit  142  stores and manages sales information that is acquired by counting the commodity information and the payment information which are related to commodities purchased by customers and received from each POS terminal  3 . 
     The statistics section  143  stores the first statistical information, the second statistical information, and third statistical information related to the amount of use of the receipt paper. Details of the statistics section  143  will be described later in connection with  FIG. 3 . For each automatic change machine  5  (that is, for each POS terminal  3 ), the change management unit  144  stores information related to each type of money stored in the automatic change machine  5  and received from the POS terminal  3 . 
     For each POS terminal  3 , the receipt management unit  145  stores information related to the actual amount of use of the receipt paper and received from the POS terminal  3 . The amount of use of the receipt paper is calculated based on data such as the number of issued receipts handed to customers, the amount of printing of commercial messages or logos on the receipt paper, the number of issued receipts including sales promotion information, the number of issued receipt copies, the ratio of electronic receipts (i.e., the ratio of receipts not issued as paper receipts compared to total receipts issued), and the number of non-transaction receipts issued (reports and the like at the time of inspection and settlement). The amount of use of the receipt paper can be estimated based on statistical data of the average amount of use of the receipt paper in one instance of issuing the receipt, and the number of sheets of issued receipt paper. The amount of use of the receipt paper can also be estimated by, for example, measuring a distance in which the receipt paper is transported. The distance in which the receipt paper is transported can be measured based on the number of rotations of a transport roller that transports the receipt paper. 
     The control unit  100  is connected to an operation unit and a display device  18  through the bus  15  and a controller  16 . The operation unit  17  is a keyboard. The display device  18  displays information to an operating person who operates the server  1 . 
     The control unit  100  is also connected to a communication interface (I/F)  19  through the bus  15 . The communication interface  19  is communicably connected to the plurality of POS terminals  3  through the communication line N 1 . The control unit  100  is also connected to a communication interface (I/F)  20  through the bus  15 . The communication interface  20  is communicably connected to the security company server  7 , the bank server  8 , and the weather forecasting company server  9  outside the store T through the communication line N 2 . 
     Next, the statistics section  143  will be described.  FIG. 3  is a diagram illustrating a memory configuration of the statistics section  143  corresponding to one automatic change machine  5 . The statistics section  143  stores, in a base data section  1431 , statistical data (first statistical information) that represents the number of customers visiting the store T. The statistics section  143  stores, in a stored money statistics section  1432 , statistical data (second statistical information) that represents the number of pieces of each type of money stored in the automatic change machine  5 . The statistics section  143  stores, in a used receipt amount statistics section  1433 , statistical data (third statistical information) that represents the amount of use of the receipt paper stored in the POS terminal  3  during one day. 
     The base data section  1431  includes a daily transaction number statistics section  14311 , a day-of-week specific transaction number statistics section  14312 , a weather specific transaction number statistics section  14313 , a special sale day specific transaction number statistics section  14314 , an event specific transaction number statistics section  14315 , a daily transaction item number statistics section  14316 , a day-of-week specific transaction item number statistics section  14317 , a weather specific transaction item number statistics section  14318 , a special sale day specific transaction item number statistics section  14319 , and an event specific transaction item number statistics section  14320 . 
     The daily transaction number statistics section  14311  stores statistical data that represents a daily number of transactions at each time (for example, the number of transactions per hour). With this statistical data, a time range in which the number of transactions is large, and a time range in which the number of transactions is small during one day can be found. In addition, the number of transactions at each time can be found for each day in a predetermined period (for example, the first to the thirtieth (or the thirty-first) of each month). In addition, characteristics (differences) of a daily change in the number of transactions can be found. 
     The day-of-week specific transaction number statistics section  14312  stores statistical data that represents the number of transactions at each time (for example, the number of transactions per hour) for each day (Sunday to Saturday). With this statistical data, a time range in which the number of transactions is large, and a time range in which the number of transactions is small during one day can be found for each day of the week. In addition, the number of transactions at each time can be found for each day of the week. In addition, differences in the number of transactions can be found for each day of the week compared to other days. 
     The weather specific transaction number statistics section  14313  stores statistical data that represents the number of transactions at each time (for example, the number of transactions per hour) for each type of weather (sunny, cloudy, rainy, snowy, a day of strong wind, a day of light wind, a day of high humidity, a day of low humidity, and the like). With this statistical data, a time range in which the number of transactions is large, and a time range in which the number of transactions is small during one day can be found for each type of weather. In addition, the number of transactions at each time can be found for each type of weather. In addition, differences in the number of transactions can be found for each type of weather compared to other types of weather. 
     The special sale day specific transaction number statistics section  14314  stores statistical data that represents the number of transactions at each time (for example, the number of transactions per hour) if the day is a special sale day when a special sale is held. With this statistical data, a time range in which the number of transactions is large, and a time range in which the number of transactions is small during one special sale day can be found. In addition, the number of transactions at each time on a special sale day can be found. In addition, differences in the number of transactions on a special sale day compared to an ordinary day which is not a special sale day can be found. 
     The event specific transaction number statistics section  14315  stores statistical data that represents the number of transactions at each time (for example, the number of transactions per hour) in the case of holding an event (for example, a morning fair or a limited-time sale) and the case of not holding an event. With this statistical data, a time range in which the number of transactions is large, and a time range in which the number of transactions is small during one day in the case of holding an event and the case of not holding an event can be found. In addition, the number of transactions at each time in the case of holding an event and the case of not holding an event can be found. In addition, differences in the number of transactions in the case of holding an event and the case of not holding an event can be found. 
     The daily transaction item number statistics section  14316  stores statistical data that represents the number of items in transactions at each time (for example, the number of items in transactions per hour). With this statistical data, a time range in which the number of items in transactions is large, and a time range in which the number of items in transactions is small during one day can be found. In addition, the number of items in transactions at each time can be found for each day in a predetermined period (for example, the first to the thirtieth (or the thirty-first) of each month). In addition, differences in the number of items in transactions for each time compared to other times can be found. 
     The day-of-week specific transaction item number statistics section  14317  stores statistical data that represents the number of items in transactions at each time (for example, the number of items in transactions per hour) for each day (Sunday to Saturday). With this statistical data, a time range in which the number of items in transactions is large, and a time range in which the number of items in transactions is small during one day can be found for each day of the week. In addition, the number of items in transactions at each time can be found for each day of the week. In addition, differences in the number of items in transactions can be found for each day of the week compared to other days of the week. 
     The weather specific transaction item number statistics section  14318  stores statistical data that represents the number of items in transactions at each time (for example, the number of items in transactions per hour) for each type of weather (sunny, cloudy, rainy, snowy, strong wind, light wind, high humidity, low humidity, and the like). With this statistical data, a time range in which the number of items in transactions is large, and a time range in which the number of items in transactions is small during one day can be found for each type of weather. In addition, the number of items in transactions at each time can be found for each type of weather. In addition, differences in the number of items in transactions can be found for each type of weather compared to other types of weather. 
     The special sale day specific transaction item number statistics section  14319  stores statistical data that represents the number of items in transactions at each time (for example, the number of items in transactions per hour) if the day is a special sale day when a special sale is held. With this statistical data, a time range in which the number of items in transactions is large, and a time range in which the number of items in transactions is small during one special sale day can be found. In addition, the number of items in transactions at each time on a special sale day can be found. In addition, differences in the number of items in transactions on a special sale day compared to an ordinary day which is not a special sale day can be found. 
     The event specific transaction item number statistics section  14320  stores statistical data that represents the number of items in transactions at each time (for example, the number of items in transactions per hour) in the case of holding an event (for example, a morning fair or a limited-time sale) and the case of not holding an event. With this statistical data, a time range in which the number of items in transactions is large, and a time range in which the number of items in transactions is small during one day in the case of holding an event and the case of not holding an event can be found. In addition, the number of items in transactions at each time in the case of holding an event and the case of not holding an event can be found. In addition, differences in the number of items in transactions in the case of holding an event and the case of not holding an event can be found. 
     The server  1  estimates a change in the number of customers visiting the store T on the current day based on the number of transactions, the number of items in transactions, the year, the month, the date, and the day of the week (for example, the current day) when the number of customers is estimated, the presence of a special sale or an event, and the meteorological information acquired from the weather forecasting company, which are stored in the base data section  1431 . 
     Next, the stored money statistics section  1432  will be described. The stored money statistics section  1432  includes a daily money storage statistics section  14321 , a day-of-week specific money storage statistics section  14322 , a weather specific money storage statistics section  14323 , a special sale day specific money storage statistics section  14324 , and an event specific money storage statistics section  14325 . 
     The daily money storage statistics section  14321  stores statistical data that represents the number of pieces of each type of money (for example, the number of pieces of each type of money per hour) stored in the automatic change machine  5  at each time. With this statistical data, a time range in which the amount of each type of stored money is large, and a time range in which the amount of each type of stored money is small in the automatic change machine  5  during one day can be found. In addition, the amount of each type of money stored in the automatic change machine  5  at each time can be found for each day in a predetermined period (for example, the first to the thirtieth (or the thirty-first) of each month). In addition, differences in each type of stored money for each time compared to other times can be found. 
     The day-of-week specific money storage statistics section  14322  stores statistical data that represents the number of pieces of each type of money at each time (for example, the number of pieces of each type of money per hour) stored in the automatic change machine  5  for each day of the week. With this statistical data, a time range in which the amount of each type of stored money is large, and a time range in which the amount of each type of stored money is small in the automatic change machine  5  during one day can be found for each day of the week. In addition, the amount of each type of money stored in the automatic change machine  5  at each time can be found for each day of the week. In addition, differences in each type of stored money can be found for each day of the week compared to other days of the week. 
     The weather specific money storage statistics section  14323  stores statistical data that represents the number of pieces of each type of money at each time (for example, the number of pieces of each type of money per hour) stored in the automatic change machine  5  for each type of weather. With this statistical data, a time range in which the amount of each type of stored money is large, and a time range in which the amount of each type of stored money is small in the automatic change machine  5  during one day can be found for each type of weather. In addition, each type of money stored in the automatic change machine  5  at each time can be found for each type of weather. In addition, differences in each type of stored money can be found for each type of weather compared to other types of weather. 
     The special sale day specific money storage statistics section  14324  stores statistical data that represents the number of pieces of each type of money at each time (for example, the number of pieces of each type of money per hour) stored in the automatic change machine  5  on a special sale day. With this statistical data, a time range in which the amount of each type of stored money is large, and a time range in which the amount of each type of stored money is small in the automatic change machine  5  during one special sale day can be found. In addition, each type of money stored in the automatic change machine  5  at each time on a special sale day can be found. In addition, differences in each type of stored money on a special sale day compared to an ordinary day which is not a special sale day can be found. 
     The event specific money storage statistics section  14325  stores statistical data that represents the number of pieces of each type of money at each time (for example, the number of pieces of each type of money per hour) stored in the automatic change machine  5  on a day when an event is held. With this statistical data, a time range in which the amount of each type of stored money is large, and a time range in which the amount of each type of stored money is small in the automatic change machine  5  during one day when an event is held can be found. In addition, each type of money stored in the automatic change machine  5  at each time on a day when an event is held can be found. In addition, differences in each type of stored money on a day when an event is held compared an ordinary day when an event is not held can be found. 
     The server  1  predicts the time period for refilling money and the time period for collecting money in the automatic change machine  5  by estimating the number of customers visiting the store T based on the statistical number of transactions and the statistical number of items in transactions stored in the base data section  1431 , and also using the statistical number of pieces of each type of money stored in the automatic change machine  5 , which is stored in the stored money statistics section  1432 . 
     Specifically, statistical data that represents the number of transactions on the same day as the current day is extracted from the daily transaction number statistics section  14311 . Statistical data that represents the number of transactions on the same day of the week as the current day is extracted from the day-of-week specific transaction number statistics section  14312 . Statistical data that represents the number of transactions on a day having the same type of weather as the weather forecast of the current day is extracted from the weather specific transaction number statistics section  14313  based on the meteorological information received from the server  9  installed in the weather forecasting company. If the current day is a special sale day, statistical data that represents the number of transactions on a special sale day is extracted from the special sale day specific transaction number statistics section  14314 . If the current day is not a special sale day, statistical data that represents the number of transactions on an ordinary day which is not a special sale day is extracted from the special sale day specific transaction number statistics section  14314 . If the current day is a day when an event is held, statistical data that represents the number of transactions on a day when an event is held is extracted from the event specific transaction number statistics section  14315 . If the current day is not a day when an event is held, statistical data that represents the number of transactions on a day other than a day when an event is held is extracted from the event specific transaction number statistics section  14315 . 
     Statistical data that represents the number of items in transactions on the same day as the current day is extracted from the daily transaction item number statistics section  14316 . Statistical data that represents the number of items in transactions on the same day of the week as the current day is extracted from the day-of-week specific transaction item number statistics section  14317 . Statistical data that represents the number of items in transactions on a day having the same type of weather as the weather forecast of the current day is extracted from the weather specific transaction item number statistics section  14318  based on the meteorological information received from the server  9 . If the current day is a special sale day, statistical data that represents the number of items in transactions on a special sale day is extracted from the special sale day specific transaction item number statistics section  14319 . If the current day is not a special sale day, statistical data that represents the number of items in transactions on a day other than a special sale day is extracted from the special sale day specific transaction item number statistics section  14319 . If the current day is a day when an event is held, statistical data that represents the number of items in transactions on a day when an event is held is extracted from the event specific transaction item number statistics section  14320 . If the current day is not a day when an event is held, statistical data that represents the number of items in transactions on a day other than a day when an event is held is extracted from the event specific transaction item number statistics section  14320 . 
     A quiet time period and a crowded time period of the current day for customers in the store T are estimated based on the extracted statistical data. Specifically, for example, the number of customers visiting on the current day is estimated as the average value of the extracted statistical data. In addition, for example, priorities are assigned to the statistical data, and the number of customers visiting on the current day is estimated as the average value of the statistical data that is weighted in order of priority. The quiet time period and the crowded time period of the current day for customers in the store T are estimated based on the estimated number of customers visiting on the current day. The quiet time period is estimated as a time period when the number of customers visiting the store is the smallest. The crowded time period is estimated as a time period when the number of customers visiting the store is the largest. 
     Next, a time period of a state where the storage unit is almost filled to capacity with money (hereinafter, referred to as “near full,” i.e., a state where the storage unit is not filled yet but will be filled soon), and a time period of a state where the storage unit in which money is stored is almost empty (hereinafter, referred to as “near empty,” i.e., a state where the storage unit is not empty yet but will become empty soon) are estimated for each type of money based on the statistical number of pieces of each type of money stored in the automatic change machine  5 . 
     Specifically, statistical data that represents a statistical number of pieces of each type of money stored in the automatic change machine  5  on the same day as the current day is extracted from the daily money storage statistics section  14321 . Statistical data that represents a statistical number of pieces of each type of money stored in the automatic change machine  5  on the same day of the week as the current day is extracted from the day-of-week specific money storage statistics section  14322 . Statistical data that represents a statistical number of pieces of each type of money stored in the automatic change machine  5  on a day having the same type of weather as the weather forecast of the current day is extracted from the weather specific money storage statistics section  14323  based on the meteorological information received from the server  9 . If the current day is a special sale day, statistical data that represents a statistical number of pieces of each type of money stored in the automatic change machine  5  on a special sale day is extracted from the special sale day specific money storage statistics section  14324 . If the current day is not a special sale day, statistical data that represents a statistical number of pieces of each type of money stored in the automatic change machine  5  on a day other than a special sale day is extracted from the special sale day specific money storage statistics section  14324 . If the current day is a day when an event is held, statistical data that represents a statistical number of pieces of each type of money stored in the automatic change machine  5  on a day when an event is held is extracted from the event specific money storage statistics section  14325 . If the current day is not a day when an event is held, statistical data that represents a statistical number of pieces of each type of money stored in the automatic change machine  5  on a day other than a day when an event is held is extracted from the event specific money storage statistics section  14325 . 
     The near full time period and the near empty time period of the current day for the storage unit of the automatic change machine  5  are estimated based on the extracted statistical data. Specifically, for example, the near full time period and the near empty time period for the automatic change machine  5  are estimated based on the average value of the extracted statistical data. In addition, for example, priorities are assigned to the statistical data, and the near full time period and the near empty time period for the automatic change machine  5  are estimated based on the average value of the statistical data that is weighted in order of priority. 
     The “near full” is desirably a state slightly before the storage unit actually becomes full. That is, the near full time period is desirably a time period slightly before the storage unit is at maximum capacity. The “near empty” is desirably a state slightly before the storage unit actually becomes empty. That is, the near empty time period is desirably a time period slightly before the storage unit is empty. An appropriate refill time period for refilling money and an appropriate collection time period for collecting money are predicted based on the estimated quiet time period and the near full or near empty time period. 
     For example, if the estimated quiet time period and the near full time period approximately match each other, the matching time period is predicted as the collection time period for money. If the estimated quiet time period and the near empty time period approximately match each other, the matching time period is predicted as the refill time period for money. If the estimated quiet time period and the near full time period or the near empty time period do not match each other, a time period included in the quiet time period, or a time period that is between both time periods and is close to the quiet time period is predicted as the collection time period or the refill time period. 
     The server  1  predicts an appropriate refill time period or an appropriate collection time period in which the number of customers is relatively small, and transmits information in a displayable manner such that the POS terminal  3  can display the time period. The POS terminal  3  displays the appropriate refill time period or the appropriate collection time period based on the received information. By doing so, refill or collection of money in the automatic change machine  5  can be performed in an appropriate time period when the number of customers visiting the store T is relatively small. 
     The server  1  corrects the near empty or near full time period based on the statistical number of pieces of each type of money and the actual number of pieces of each type of money stored in the change management unit  144 . For example, the server  1  compares the statistical number of pieces of each type of money with the actual number of pieces of each type of money stored in the change management unit  144 . Consequently, if the statistical number of pieces of each type of money is larger than the actual number of pieces of each type of money, the server  1  corrects the near empty or near full time period to be later than the current estimated time period. The server  1  corrects the refill time period or the collection time period to be later than the current predicted time period. If the actual number of pieces of each type of money is larger than the statistical number of pieces of each type of money, the server  1  corrects the near empty or near full time period to be sooner than the current estimated time period. The server  1  corrects the refill time period or the collection time period to be sooner than the current predicted time period. 
     The refill time period or the collection time period may also be corrected by estimating the number of visiting customers through comparison of the statistical number of visiting customers with the actual number of visiting customers. 
     Next, the used receipt amount statistics section  1433  will be described. The used receipt amount statistics section  1433  includes a daily used receipt amount statistics section  14331 , a day-of-week specific used receipt amount statistics section  14332 , a weather specific used receipt amount statistics section  14333 , a special sale day specific used receipt amount statistics section  14334 , and an event specific used receipt amount statistics section  14335 . 
     The daily used receipt amount statistics section  14331  stores statistical data that represents the amount of use of the receipt paper at each time (for example, the amount of use of the receipt paper per hour). With this statistical data, a time range in which the amount of use of the receipt paper is large, and a time range in which the amount of use of the receipt paper is small in the POS terminal  3  during one day can be found. In addition, the amount of use of the receipt paper at each time in a predetermined period (for example, the first to the thirtieth (or the thirty-first) of each month) can be found. In addition, differences in the amount of use of the receipt paper for each time compared to other times can be found. 
     The day-of-week specific used receipt amount statistics section  14332  stores statistical data that represents the amount of use of the receipt paper at each time (for example, the amount of use of the receipt paper per hour) for each day of the week. With this statistical data, a time range in which the amount of use of the receipt paper is large, and a time range in which the amount of use of the receipt paper is small in the POS terminal  3  during one day can be found for each day of the week. In addition, the amount of use of the receipt paper at each time can be found for each day of the week. In addition, differences in the amount of use of the receipt paper can be found for each day of the week compared to other days of the week. 
     The weather specific used receipt amount statistics section  14333  stores statistical data that represents the amount of use of the receipt paper at each time (for example, the amount of use of the receipt paper per hour) for each type of weather. With this statistical data, a time range in which the amount of use of the receipt paper is large, and a time range in which the amount of use of the receipt paper is small in the POS terminal  3  during one day can be found for each type of weather. In addition, the amount of use of the receipt paper at each time can be found for each type of weather. In addition, differences in the amount of use of the receipt paper can be found for each type of weather compared to other types of weather. 
     The special sale day specific used receipt amount statistics section  14334  stores statistical data that represents the amount of use of the receipt paper at each time (for example, the amount of use of the receipt paper per hour) on a special sale day. With this statistical data, a time range in which the amount of use of the receipt paper is large, and a time range in which the amount of use of the receipt paper is small in the POS terminal  3  on a special sale day can be found. In addition, the amount of use of the receipt paper at each time on a special sale day can be found. In addition, differences in the amount of use of the receipt paper on a special sale day compared an ordinary day which is not a special sale day can be found. 
     The event specific used receipt amount statistics section  14335  stores statistical data that represents the amount of use of the receipt paper at each time (for example, the amount of use of the receipt paper per hour) on a day when an event is held. With this statistical data, a time range in which the amount of use of the receipt paper is large, and a time range in which the amount of use of the receipt paper is small in the POS terminal  3  on a day when an event is held can be found. In addition, the amount of use of the receipt paper at each time on a day when an event is held can be found. In addition, differences in the amount of use of the receipt paper on a day when an event is held compared an ordinary day when an event is not held can be found. 
     The server  1  predicts the replacement time period for the receipt paper stored in the receipt issuing device by estimating the number of customers visiting the store T based on the statistical number of transactions and the statistical number of items in transactions stored in the base data section  1431 , and also using the statistical amount of use of the receipt paper in the POS terminal  3 , which is stored in the used receipt amount statistics section  1433 . 
     Specifically, statistical data that represents a statistical amount of use of the receipt paper on the same day as the current day is extracted from the daily used receipt amount statistics section  14331 . Statistical data that represents a statistical amount of use of the receipt paper on the same day of the week as the current day is extracted from the day-of-week specific used receipt amount statistics section  14332 . Statistical data that represents a statistical amount of use of the receipt paper on a day having the same type of weather as the weather forecast of the current day is extracted from the weather specific used receipt amount statistics section  14333  based on the meteorological information received from the server  9 . If the current day is a special sale day, statistical data that represents the amount of use of the receipt paper on a special sale day is extracted from the special sale day specific used receipt amount statistics section  14334 . If the current day is a day other than a special sale day, statistical data that represents the amount of use of the receipt paper on a day other than a special sale day is extracted from the special sale day specific used receipt amount statistics section  14334 . If the current day is a day when an event is held, statistical data that represents the amount of use of the receipt paper on a day when an event is held is extracted from the event specific used receipt amount statistics section  14335 . If the current day is a day other than a day when an event is held, statistical data that represents the amount of use of the receipt paper on a day other than a day when an event is held is extracted from the event specific used receipt amount statistics section  14335 . 
     A time period of a state where the receipt paper stored in the receipt issuing device will be used up soon (hereinafter, referred to as “near end” (i.e., a state where the receipt paper is not used up yet but will be used up soon)”) is estimated based on the statistical change in the amount of use of the receipt paper. Specifically, for example, the near end time period of the current day for the receipt paper is estimated based on the average value of the extracted statistical data. In addition, for example, priorities are assigned to the statistical data, and the near end time period of the current day for the receipt paper is estimated based on the average value of the statistical data that is weighted in order of priority. 
     The “near end” is desirably a state slightly before an end of the receipt paper where a red band-shaped line is printed at both ends is reached. That is, the near end time period (time range) is desirably a time period slightly before the end of the receipt paper is reached. An appropriate replacement time period for replacing the receipt paper is predicted based on the estimated quiet time period and the near end time period. 
     For example, if the estimated quiet time period and the near end time period match approximately, the matching time period is predicted as the replacement time period. If the estimated quiet time period and the near end time period do not match, a time period included in the quiet time period, or a time period that is between both time periods and is close to the quiet time period is predicted as the replacement time period. 
     The server  1  predicts an appropriate replacement time period in which the number of customers is relatively small, and transmits information in a displayable manner such that the POS terminal  3  can display the time period. The POS terminal  3  displays) the optimal replacement time period based on the received information. By doing so, the receipt paper in the POS terminal  3  can be replaced in a time period when the number of customers visiting the store T is relatively small. 
     The server  1  corrects the near end time period for the receipt paper based on the statistical amount of use of the receipt paper and the actual amount of use of the receipt paper stored in the receipt management unit  145 . Specifically, the server  1  compares the statistical c amount of use of the receipt paper with the actual amount of use of the receipt paper stored in the receipt management unit  145 . Consequently, if the statistical amount of use of the receipt paper is larger than the actual amount of use of the receipt paper, the server  1  corrects the near end time period to be later than the current estimated time period. The server  1  corrects the replacement time period to be later than the current predicted time period. If the actual amount of use of the receipt paper is larger than the statistical amount of use of the receipt paper, the server  1  corrects the near end time period to be sooner than the current estimated time period. The server  1  corrects the replacement time period to be sooner than the current predicted time period. 
     The replacement time period may also be corrected by estimating the number of visiting customers through comparison of the statistical number of visiting customers with the actual number of visiting customers. 
     Hereinafter, the POS terminal  3  will be described.  FIG. 4  is a perspective view illustrating the POS terminal. In  FIG. 4 , the POS terminal  3  includes a main body  46  and a combination keyboard  45 . An operator-use display device  38 , the printing device  40  as the receipt issuing device, a code reader  42 , and the like are disposed in the main body  46 . In addition, the main body  46  includes a control unit  300 , a customer-use display device  39  (refer to  FIG. 5 ), and the like. The operator-use display device  38  displays information to an operator. The customer-use display device  39  displays information to customers. For example, the printing device  40  is equipped with a thermal head and issues receipts by printing information using the thermal head on the receipt paper that is thermal paper. The code reader  42  reads symbols assigned to commodities. The POS terminal  3  acquires the commodity code specifying each commodity based on the read symbols. The code reader  42  may specify commodities using a general object recognition technology based on captured images of commodities. 
     The combination keyboard  45  can be detached from the main body  46 . The combination keyboard  45  includes an operation unit  37 , a subdisplay device  44 , and a card reader  41 . The operation unit  37  is a keyboard that includes a payment key  371 . The payment key  371  is operated at the time of announcing the payment in a transaction with a customer. The subdisplay device  44  displays information to the operator. The subdisplay device  44  displays supplementary information, communication information regarding the store, and the like that are not displayed on the operator-use display device  38 . The subdisplay device  44  displays the collection time period or the refill time period for collecting or refilling money, or the replacement time period for replacing the receipt paper as a message to the operator. 
     Next, a hardware configuration of the POS terminal  3  will be described.  FIG. 5  is a block diagram illustrating a hardware configuration of the POS terminal  3 . As illustrated in  FIG. 5 , the POS terminal  3  includes a CPU  31 , a ROM  32 , a RAM  33 , a memory unit  34 , and the like. The CPU  31  is a main unit of control. The ROM  32  stores various programs. Programs and various types of data are loaded into the RAM  33 . The memory unit  34  stores various programs. The CPU  31 , the ROM  32 , the RAM  33 , and the memory unit  34  are connected to each other through a bus  35 . The CPU  31 , the ROM  32 , and the RAM  33  constitute the control unit  300 . That is, the control unit  300  executes a control, described later, related to the POS terminal  3  by operating the CPU  31  in accordance with a control program that is stored in the ROM  32  or the memory unit  34  and loaded into the RAM  33 . 
     The RAM  33  includes a commodity information section  331 . The commodity information section  331  stores the commodity information (a commodity name, a price of a commodity, and the like) related to commodities that are subjected to the sales registration based on the commodity code acquired from symbols read by the code reader  42 . 
     The memory unit  34  is a non-volatile memory device such as an HDD or a flash memory that retains stored information even if power is off. The memory unit  34  includes a control program  341 , a commodity master  342 , and the like. The control program section  341  is the control program that is used for controlling the POS terminal  3 . For each commodity code specifying a commodity, the commodity master  342  stores the commodity information related to the commodity. 
     The control unit  300  is connected to the operation unit  37 , the operator-use display device  38 , the customer-use display section  39 , the subdisplay device  44 , the printing device  40 , the card reader  41 , and the code reader  42  through the bus  35  and a controller  36 . 
     The control unit  300  is also connected to a communication interface (I/F)  43  through the bus  35 . The communication interface  43  is communicably connected to the server  1  and the other POS terminals  3  through the communication line N 1 . 
     Hereinafter, control of the server  1  and the POS terminal  3  will be described. First, control of the server  1  will be described.  FIG. 6  is a function block diagram illustrating a functional configuration of the server  1 . In accordance with the control program stored in the ROM  12  or the control program  141  stored in the memory unit  14 , the control unit  100  functions as a prediction unit  101 , a first output unit  102 , a first correction unit  103 , a replacement prediction unit  104 , a second output unit  105 , and a second correction unit  106 . 
     The prediction unit  101  has a function of predicting at least one of an appropriate collection time period for collecting money stored in the automatic change machine  5  or an appropriate refill time period for refilling money in the automatic change machine  5  based on the first statistical information (i.e., the statistical information related to the number of customers visiting the store T at the time, which is stored in the base data section  1431 ), and the second statistical information (i.e., the statistical information related to the amount of money stored in the automatic change machine  5  at the time, which is stored in the stored money statistics section  1432 ). 
     The first output unit  102  has a function of transmitting at least one of the predicted collection time period or the predicted refill time period to the POS terminal  3  in a displayable manner. 
     The first correction unit  103  has a function of correcting at least one of the collection time period for money or the refill time period predicted by the prediction unit  101  based on the actual number of pieces of each type of money stored in the automatic change machine  5 . 
     The replacement prediction unit  104  has a function of predicting an appropriate replacement time period for replacing the stored receipt paper based on the first statistical information (i.e., the statistical change in the number of customers visiting the store T at the time, which is stored in the base data section  1431 ), and the third statistical information (i.e., the statistical change in the amount of use of the receipt paper at the time, which is stored in the used receipt amount statistics section  1433 ). 
     The second output unit  105  has a function of transmitting information related to the predicted replacement time period to the POS terminal  3  in a displayable manner. 
     The second correction unit  106  has a function of correcting the replacement time period predicted by the replacement prediction unit  104  based on the actual amount of use of the stored receipt paper. 
     Next, a control processing performed by the server  1  for predicting the refill time period and the collection time period will be described.  FIG. 7  is a flowchart illustrating the flow of the processing performed by the server  1  for predicting the refill time period and the collection time period in the automatic change machine  5 . In  FIG. 7 , the control unit  100  determines whether or not a predetermined timing is reached (Act  11 ). The determination of whether the predetermined timing is reached in Act  11  is performed at, for example, a time slightly before the opening of the store T. At this timing, the collection time period and the refill time period of the current day in the automatic change machine  5  are predicted. If the control unit  100  determines that the predetermined timing is reached (Yes in Act  11 ), the control unit  100  executes prediction (Act  12 ). The control unit  100  returns to Act  11 . 
       FIG. 8  is a flowchart illustrating the prediction processing performed by the control unit  100  in Act  12 . In  FIG. 8 , the control unit  100  acquires base data related to the statistical number of transactions and number of items in transactions from the base data section  1431  of the statistics section  143  based on the year, the month, the date, and the day of the week of the current day, the presence of a special sale, the presence of an event, the meteorological information, and the like (Act  31 ). The control unit  100  finds the number of transactions on the current day and the number of items in transactions on the current day from the acquired base data. The control unit  100  estimates the number of customers visiting the store T on the current day from the found number of transactions and number of items in transactions. The control unit  100  estimates the quiet time period and the crowded time period of the current day based on the estimated number of visiting customers (Act  32 ). Next, the control unit  100  acquires the statistical information related to money stored in the automatic change machine  5  from the stored money statistics section  1432  based on the year, the month, the date, and the day of the week of the current day, the presence of a special sale, the presence of an event, the meteorological information, and the like (Act  33 ). The control unit  100  finds the number of pieces of money stored in the automatic change machine  5  from the acquired information. The control unit  100  estimates the near empty time period and the near full time period for the automatic change machine  5  based on the estimated number of pieces of money (Act  34 ). The control unit  100  (prediction unit  101 ) predicts the refill time period and the collection time period for the automatic change machine  5  based on the estimated quiet time period and the estimated near empty time period and the near full time period (Act  35 ). 
     Next, the control unit  100  creates image data of a graph that visualizes information related to the estimated change in the number of customers and the predicted refill time period and the predicted collection time period (Act  36 ). The control unit  100  (first output unit  102 ) transmits the created image data of the graph to the POS terminal  3  (Act  37 ). 
     Returning to  FIG. 7  again, if the control unit  100  in Act  11  determines that the predetermined timing is not reached (No in Act  11 ), the control unit  100  determines whether or not a predetermined time period (for example, one hour) elapses from the predetermined timing in Act  11  (or the opening time of the store) (Act  13 ). If the control unit  100  determines that the predetermined time period elapses (Yes in Act  13 ), the control unit  100  executes a correction processing (Act  14 ). The control unit  100  then returns to Act  11 . 
       FIG. 9  is a flowchart illustrating the correction processing performed by the control unit  100  in Act  14 . In  FIG. 9 , the control unit  100  acquires correction data (Act  41 ). The correction data is information that is stored in the change management unit  144  and related to each type of money actually stored in the automatic change machine  5 . Next, the control unit  100  compares the statistical number of pieces of each type of money with the actual number of pieces of each type of money stored in the change management unit  144 . The control unit  100  determines whether or not the refill time period and the collection time period for money need to be corrected based on the result of comparison (Act  43 ). If there is no difference between the statistical number of pieces of each type of money and the actual number of pieces of each type of money stored in the change management unit  144 , the control unit  100  determines that the refill time period and the collection time period do not need to be corrected. If there is a difference between the statistical number of pieces of each type of money and the actual number of pieces of each type of money stored in the change management unit  144 , the control unit  100  determines that the refill time period and the collection time period need to be corrected. 
     If the control unit  100  determines that the refill time period and the collection time period need to be corrected (Yes in Act  43 ), the control unit  100  estimates the near empty time period and the near full time period for the automatic change machine  5  again based on the difference. The control unit  100  (first correction unit  103 ) corrects the refill time period and the collection time period predicted in Act  35  for money in the automatic change machine  5  based on the estimated near empty time period and the near full time period, and information related to the number of visiting customers (Act  44 ). 
     Next, the control unit  100  corrects the image data of the graph created in Act  36  based on the corrected refill time period and collection time period (Act  45 ). The control unit  100  transmits the corrected image data of the graph to the POS terminal  3  (Act  46 ). If the control unit  100  determines that the refill time period and the collection time period do not need to be corrected (No in Act  43 ), the control unit  100  returns to Act  11  without processing Act  44  to Act  46 . 
     Returning to  FIG. 7  again, if the control unit  100  in Act  13  determines that the predetermined time period does not elapse (No in Act  13 ), the control unit  100  determines whether or not the predicted time period for refill or collection predicted in Act  35  or the predicted time period corrected in Act  44  is reached (Act  15 ). If the control unit  100  determines that the predicted time period is reached (Yes in Act  15 ), the control unit  100  transmits message data representing a message to the POS terminal  3  (Act  16 ). If the predicted time period is for refilling money, the control unit  100  transmits message data indicating a message that prompts refilling money. If the predicted time period is for collecting money, the control unit  100  transmits message data indicating a message that prompts collecting money. The control unit  100  returns to Act  11 . 
     If the control unit  100  determines that the predicted time period for refill or collection of money is not reached (No in Act  15 ), the control unit  100  determines whether or not a signal indicating that refill or collection of money is performed is received from the POS terminal  3  (Act  17 ). If the control unit  100  determines that the signal is received (Yes in Act  17 ), the control unit  100  stores the amount and the number of pieces of refilled or collected money in the RAM  13  (Act  18 ). The control unit  100  returns to Act  11 . 
     If the control unit  100  determines that the signal indicating that refill or collection of money is performed is not received (No in Act  17 ), the control unit  100  determines whether or not a predetermined time that is set in advance is reached (Act  20 ). If the control unit  100  determines that the predetermined time is reached (Yes in Act  20 ), the control unit  100  transmits amount information stored in the RAM  13  to the server  7  in the security company (Act  21 ). The control unit  100  returns to Act  11 . If the control unit  100  determines that the predetermined time is not reached (No in Act  20 ), the control unit  100  returns to Act  11 . 
     If money for change and the like are needed again, the security company that receives the amount information withdraws money from the bank and transports the money to the store T. If there is money to be collected from the store T, the security company visits the store T to collect the money. 
     Next, control of the POS terminal  3  will be described.  FIG. 10  is a flowchart illustrating the flow of the processing performed by the POS terminal. In  FIG. 10 , the control unit  300  determines whether or not the commodity code is acquired based on the symbols read by the code reader  42  (Act  51 ). If the control unit  300  determines that the commodity code is acquired (Yes in Act  51 ), the control unit  300  searches the commodity master  342  based on the commodity code, acquires the commodity information related to the commodity specified by the commodity code, and stores the commodity information in the commodity information section  331  (Act  52 ). The control unit  300  returns to Act  51 . 
     If the control unit  300  determines that the commodity code is not acquired (No in Act  51 ), the control unit  300  determines whether or not the payment key  371  is operated (Act  53 ). If the control unit  300  determines that the payment key  371  is operated (Yes in Act  53 ), the control unit  300  executes the payment processing for the transaction based on the commodity information stored in the commodity information section  331  and money received from the customer (Act  54 ). Next, the control unit  300  transmits the change information to the automatic change machine  5 . The control unit  300  receives data of the current inventory of each type of money (reflecting deposits and withdrawals) from the automatic change machine  5  (Act  55 ). The current inventory is information related to money stored in the automatic change machine  5 . The current inventory is a numerical value as data and is calculated based on the amount of preparatory money for change, the amount of deposited money, and the amount of withdrawn money. The current inventory data is stored in the automatic change machine  5  and includes the number of pieces and the amount of each type of money. Each time there is a deposit or a withdrawal of money, the automatic change machine  5  updates the current inventory data to up-to-date data. Next, the control unit  300  transmits the commodity information and the payment information related to the payment, and the current inventory data received from the automatic change machine  5  to the server  1  (Act  56 ). The control unit  300  returns to Act  51 . 
     If the control unit  300  in Act  53  determines that the payment key  371  is not operated (No in Act  53 ), the control unit  300  determines whether or not the image data of the graph transmitted by the server  1  in the process of Act  37  or the process of Act  46  is received (Act  61 ). If the control unit  300  determines that the image data of the graph is received (Yes in Act  61 ), the control unit  300  displays an image graph based on the image data of the graph on the subdisplay device  44  (Act  62 ). The control unit  300  returns to Act  51 . The control unit  300  may display the graph image on the operator-use display device  38  instead of the subdisplay device  44 . 
       FIG. 11  is one example of a graph image G displayed in Act  62 . As illustrated in  FIG. 11 , the graph image G includes a horizontal axis as a time axis from the opening to the closing of the store T, and a vertical axis as the number of visiting customers. A line G 1  illustrated in the graph image G represents a change in the number of visiting customers at each time during one day. In the example in  FIG. 11 , the number of customers continues increasing from the opening time of the store and reaches a first peak (position of G 7 ) at around 12:00. In the afternoon, the number of customers starts to decrease and reaches a bottom (position of G 9 ) at around 15:00. Then, until the evening, the number of customers is increased again and reaches a second peak (position of G 8 ) at around 18:00. Then, the number of customers is decreased until the closing time of the store and reaches a second bottom (position of G 10 ) at a time immediately before the closing of the store. That is, the store T has the crowded time period near times G 7  and G 8 , and has the quiet time period near times G 9  and G 10 . 
     The time range of an event on the current day is also illustrated in  FIG. 11 . In the example in  FIG. 11 , a morning fair G 2  is held in a time range of 09:00 to 10:00, and a limited-time sale G 3  is held in a time range of 17:00 to 18:00. 
     In the example in  FIG. 11 , a time range G 11  that includes a quiet time period G 9  is predicted as a refill time period G 4 . A time range G 12  that includes a quiet time period G 10  is predicted as a collection time period G 5 . The operator or a person in charge can view the graph image G and confirm the refill time period G 4  and the collection time period G 5 . By performing refill and collection of money during the confirmed time period in which the number of customers is relatively small, influence on the customers can be minimized. 
     Returning to  FIG. 10  again, if the control unit  300  in Act  61  determines that the image data of the graph is not received (No in Act  61 ), the control unit  300  determines whether or not the message data transmitted by the server  1  in Act  16  is received (Act  63 ). If the control unit  300  determines that the message data is received (Yes in Act  63 ), the control unit  300  displays the message indicated by the message data on the subdisplay device  44  (Act  64 ). The control unit  300  returns to Act  51 . 
     As illustrated in  FIG. 11 , the control unit  300  displays the message displayed in the process of Act  64  in a lower part G 6  of the graph image G. The operator or the person who performs refill or collection of money can view and remember to comply with the message. In addition, the operator or the person who forgets to perform refill or collection of money can view the message and recall that refill or collection of money is to be performed. 
     Returning to  FIG. 10  again, if the control unit  300  in Act  63  determines that the message data is not received (No in Act  63 ), the control unit  300  determines whether or not refill or collection of money from the automatic change machine  5  is executed (Act  65 ). If the control unit  300  determines that refill or collection of money is executed (Yes in Act  65 ), the control unit  300  removes the message displayed in the lower part G 6  of the graph image G (Act  66 ). The control unit  300  transmits the amount and the number of pieces of money refilled or collected to the server  1  (Act  67 ). The control unit  300  returns to Act  51 . If the control unit  300  determines that the signal indicating that refill or collection of money is executed is not received (No in Act  65 ), the control unit  300  returns to Act  51 . 
     According to the embodiment, the server  1  can predict an appropriate refill time period or a collection time period for the automatic change machine  5  and transmit the refill time period or the collection time period to the POS terminal  3 . 
     Hereinafter, a processing performed by the server  1  for predicting the replacement time period for replacing the receipt paper stored in the POS terminal  3  will be described using  FIG. 12  to  FIG. 14 . In  FIG. 12  to  FIG. 14 , descriptions of parts corresponding to the description of  FIG. 7  to  FIG. 9  will not be repeated or will be simplified. The printing device  40  which is the receipt issuing device is indirectly connected to the server  1 .  FIG. 12  is a flowchart illustrating the flow of processing performed by the server  1  for predicting the replacement time period for replacing the receipt paper. As illustrated in  FIG. 12 , the control unit  100  determines whether or not a predetermined timing is reached (Act  71 ). The predetermined timing in Act  71  is, for example, a time slightly before the opening of the store T. At this timing, the replacement time period for replacing the receipt paper is predicted. If the control unit  100  determines that the predetermined timing is reached (Yes in Act  71 ), the control unit  100  executes prediction (Act  72 ). The control unit  100  returns to Act  71 . 
       FIG. 13  is a flowchart illustrating the prediction processing by the control unit  100  in Act  72 . In  FIG. 13 , the control unit  100  processes Act  91  and Act  92 . Since these processes are the same as Act  31  and Act  32 , such descriptions will not be repeated. Next, the control unit  100  acquires the statistical information related to the amount of use of the receipt paper from the used receipt amount statistics section  1433  based on the year, the month, the date, and the day of the week of the current day, the presence of a special sale, the presence of an event, the meteorological information, and the like (Act  93 ). The control unit  100  finds the amount of use of the receipt paper from the acquired information. The control unit  100  estimates the near end time period for the receipt paper based on the found amount of use of the receipt paper (Act  94 ). The control unit  100  (replacement prediction unit  104 ) predicts the replacement time period for replacing the receipt paper based on the estimated quiet time period and the estimated near end time period (Act  95 ). 
     Next, the control unit  100  creates image data of a graph that visualizes information related to the estimated number of customers and the predicted replacement time period for replacing the receipt paper (Act  96 ). The control unit  100  (second output unit  105 ) transmits the created image data of the graph to the POS terminal  3  (Act  97 ). 
     Returning to  FIG. 12  again, if the control unit  100  in Act  71  determines that the predetermined timing is not reached (No in Act  71 ), the control unit  100  determines whether or not a predetermined time period (for example, one hour) elapses from the predetermined timing in Act  71  (or the opening time of the store) (Act  73 ). If the control unit  100  determines that the predetermined time period elapses (Yes in Act  73 ), the control unit  100  executes correction (Act  74 ). The control unit  100  returns to Act  71 . 
       FIG. 14  is a flowchart illustrating the correction processed by the control unit  100  in Act  74 . In  FIG. 14 , the control unit  100  acquires correction data (Act  101 ). The correction data is information related to the actual amount of use of the receipt paper stored in the receipt management unit  145 . Next, the control unit  100  compares the statistical amount of use of the receipt paper with the actual amount of use of the receipt paper stored in the receipt management unit  145 . The control unit  100  determines whether or not the replacement time period needs to be corrected based on the result of comparison (Act  103 ). If there is no difference between the statistical amount of use of the receipt paper and the actual amount of use of the receipt paper stored in the receipt management unit  145 , the control unit  100  determines that the replacement time period for the receipt paper does not need to be corrected. If there is a difference between the statistical amount of use of the receipt paper and the actual amount of use of the receipt paper stored in the receipt management unit  145 , the control unit  100  determines that the replacement time period for the receipt paper needs to be corrected. 
     If the control unit  100  determines that the replacement time period for the receipt paper needs to be corrected (Yes in Act  103 ), the control unit  100  estimates the near end time period for the receipt paper again based on the difference. The control unit  100  (second correction unit  106 ) corrects the replacement time period predicted in Act  95  based on the near end time period estimated again and information related to the number of visiting customers (Act  104 ). 
     Next, the control unit  100  corrects the image data of the graph created in Act  96  based on the corrected replacement time period (Act  105 ). The control unit  100  transmits the corrected image data of the graph to the POS terminal  3  (Act  106 ). If the control unit  100  determines that the replacement time period does not need to be corrected (No in Act  103 ), the control unit  100  returns to Act  71  without processing Act  104  to Act  106 . 
     Returning to  FIG. 12  again, if the control unit  100  in Act  73  determines that the predetermined time period does not elapse (No in Act  73 ), the control unit  100  determines whether or not the predicted time period for replacement of the receipt paper predicted in Act  95  or the predicted time period corrected in Act  104  is reached (Act  75 ). If the control unit  100  determines that the predicted time period is reached (Yes in Act  75 ), the control unit  100  transmits message data representing a message to the POS terminal  3  (Act  76 ). In this case, the control unit  100  transmits message data indicating a message that prompts replacement of the receipt paper. The control unit  100  returns to Act  71 . 
     If the control unit  100  determines that the predicted time period for replacement of the receipt paper is not reached (No in Act  75 ), the control unit  100  determines whether or not a signal indicating that the receipt paper is replaced is received from the POS terminal  3  (Act  77 ). If the control unit  100  determines that the signal is received (Yes in Act  77 ), the control unit  100  stores the fact that the receipt paper is replaced in the RAM  13  (Act  78 ). The RAM  13  cumulatively stores the number of times the receipt paper is replaced. The control unit  100  returns to Act  71 . 
     If the control unit  100  determines that the signal indicating that the receipt paper is replaced is not received (No in Act  77 ), the control unit  100  determines whether or not the number of sheets or rolls of the receipt paper stored in the store T is less than a predetermined number, and refill of the receipt paper is needed (Act  80 ). If the control unit  100  determines that refill of the receipt paper is needed (Yes in Act  80 ), the control unit  100  orders receipt paper from a supplier (Act  81 ). The control unit  100  returns to Act  71 . If the control unit  100  determines that refill of the receipt paper is not needed (No in Act  80 ), the control unit  100  returns to Act  71 . 
     Next, control of the POS terminal  3  will be described.  FIG. 15  is a flowchart illustrating the processing performed by the POS terminal. In  FIG. 15 , descriptions of parts corresponding to the description of  FIG. 10  will not be repeated or will be simplified. In  FIG. 15 , the control unit  300  processes Act  111  to Act  114 . Since Act  111  to Act  114  are the same as Act  51  to Act  54 , such descriptions will not be repeated. Next, the control unit  300  acquires the amount of receipt paper issued (Act  115 ). For example, if payment is made in cash, one sheet of the receipt paper is issued. If payment is made by credit card, two sheets of the receipt paper, including one sheet to be given to the customer and one store copy, are issued. The amount of receipt paper issued may be increased by issuing coupons using the receipt paper. Next, the control unit  300  transmits the commodity information and the payment information related to the payment, and data of the amount of receipt paper issued to the server  1  (Act  116 ). The control unit  300  returns to Act  111 . 
     While a detailed description is not provided, the amount of receipt paper issued is acquired as needed if the receipt paper is issued at a time other than a transaction with a customer such as issuing an inspection or settlement report. 
     If the control unit  300  in Act  113  determines that the payment key  371  is not operated (No in Act  113 ), the control unit  300  processes Act  121  to Act  124 . Since Act  121  to Act  124  are the same as Act  61  to Act  64 , such descriptions will not be repeated. 
     If the control unit  300  in Act  123  determines that the message data is not received (No in Act  123 ), the control unit  300  determines whether or not the receipt paper is replaced from the POS terminal  3  (Act  125 ). If the control unit  300  determines that the receipt paper is replaced (Yes in Act  125 ), the control unit  300  removes the message that is displayed in the lower part G 6  of the graph image G and prompts replacement of the receipt paper (Act  126 ). The control unit  300  transmits a signal indicating the fact that the receipt paper is replaced to the server  1  (Act  127 ). The control unit  300  returns to Act  111 . If the control unit  300  determines that the receipt paper is not replaced (No in Act  125 ), the control unit  300  returns to Act  111 . 
     In the description of the embodiment, the server  1  predicts the refill time period for refilling money, the collection time period for collecting money, and the replacement time period for replacing the receipt paper for one connected POS terminal  3  for simplification of description. However, in actuality, the server  1  executes the same prediction for the plurality of connected POS terminals  3 . 
     While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions. Indeed, the novel embodiments described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the inventions. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the inventions. 
     For example, in the embodiment, the crowded time period and the quiet time period are estimated based on the number of customers visiting the store. Instead, the crowded time period and the quiet time period may be estimated using the number of deployed POS terminals  3  and the number of customers visiting the store. 
     In the embodiment, the refill time period and the collection time period are predicted based on the base data and the actual number of pieces of money stored in the automatic change machine  5 , which is stored in the change management unit  144 . Instead, for example, the refill time period and the collection time period may be predicted based on the ratio of payment in cash to the number of payments. The ratio of payment in cash to the number of payments tends to be proportional to the number of pieces of money stored in the automatic change machine  5 . For example, if the ratio of payment in cash is higher than statistical data, the number of pieces of money stored in the automatic change machine  5  tends to be increased. Thus, if the ratio of payment in cash is high, the refill time period for money is corrected to be slightly sooner. 
     In the embodiment, the process of refill or collection of money and the process of replacement of the receipt paper are described using different flowcharts. Instead, the process of refill or collection of money may be performed at the same time as the process of replacement of the receipt paper. 
     In the embodiment, the server  1  is described as one example of the prediction and management apparatus. Alternatively, the POS terminal  3  may be the prediction and management apparatus. In this case, the first output unit and the second output unit output information to the subdisplay device  44  in a reportable manner. In a self-checkout POS terminal where customers perform the sales registration and the payment, the first output unit and the second output unit controlled by the POS terminal may output information to an attendant PC and the like managing the self-checkout POS terminal in a reportable manner. If the POS terminal  3  is the prediction and management apparatus, the automatic change machine  5  and the printing device  40  are directly connected to the POS terminal  3 . Even if the receipt issuing device is a printing device that is electrically connected to the POS terminal  3 , the printing device is directly connected to the POS terminal  3 . 
     In the embodiment, the server  1  creates the image data of the graph that visualizes the predicted refill time period, the predicted collection time period, and the predicted replacement time period, and transmits the image data of the graph to the POS terminal  3 . Alternatively, the refill time period and the collection time period for money and the replacement time period for the receipt paper may be transmitted to the POS terminal  3  using a method other than the image of the graph. 
     In the embodiment, the first output unit  102  has a function of outputting information related to at least one of the predicted collection time period for collecting money or the predicted refill time period for refilling money to the POS terminal  3  in a displayable manner, and the second output unit  105  has a function of outputting information related to the predicted replacement time period for replacing the receipt paper to the POS terminal  3  in a displayable manner. However, one output unit that provides output to the POS terminal  3  from the server  1  may have the function of outputting information related to at least one of the predicted collection time period for collecting money or the predicted refill time period for refilling money to the POS terminal  3  in a displayable manner, and the function of outputting information related to the predicted replacement time period for replacing the receipt paper to the POS terminal  3  in a displayable manner. In this case, this one output unit functions as the first output unit  102  and also functions as the second output unit  105 . 
     The program executed in the server  1  of the embodiment is provided as a recording of an installable or executable file on a computer-readable recording medium such as a CD-ROM, a flexible disk (FD), a CD-R, or a digital versatile disk (DVD). 
     The program executed in the server  1  of the embodiment may be configured to be stored in a computer that is connected to a network such as the Internet, and provided as a download through the network. The program executed in the server  1  of the embodiment may be configured to be provided or distributed through a network such as the Internet. 
     The program executed in the server  1  of the embodiment may be configured to be provided by embedding the program in a ROM or the like.