FIELD 
     The present disclosure generally relates to electronic commerce software applications and, more particularly, to a method and system relating to commodities purchasing over a network of distributed computing devices. 
     BACKGROUND 
     Commodity items such as lumber, agricultural products, metals, and livestock/meat are usually traded in the open market between a number of buyers and sellers. The sales transactions of most commodity items involve a number of parameters. For instance, in the trade of commodity lumber, a buyer usually orders materials by specifying parameters such as lumber species, grade, size (i.e., 2×4, 2×10, etc.), and length, as well as the “tally” or mix of units of various lengths within the shipment, method of transportation (i.e., rail or truck), shipping terms (i.e., FOB or delivered), and desired date of receipt, with each parameter influencing the value of the commodity purchase. Given the multiple possible combinations of factors, a commodity buyer often finds it difficult to objectively compare similar but unequal offerings among competing vendors. 
     For example, in a case where a lumber buyer desires to order a railcar load of spruce (SPF) 2×4&#39;s of #2 &amp; Better grade, the buyer would query vendors offering matching species and grade carloads seeking the best match for the buyer&#39;s need or tally preference at the lowest market price. Lumber carloads are quoted at a price per thousand board feet for all material on the railcar. In a market where the shipping parameters are not identical, it is very difficult for buyers to determine the comparative value of unequal offerings. 
     Typically, a lumber buyer will find multiple vendors each having different offerings available. For example, a railcar of SPF 2×4&#39;s may be quoted at a rate of $300/MBF (thousand board feet) by multiple vendors. Even though the MBF price is equal, one vendor&#39;s carload may represent significantly greater marketplace value because it contains the more desirable lengths of 2×4&#39;s, such as market-preferred 16-foot 2×4&#39;s. When the offering price varies in addition to the mix of lengths, it becomes increasingly difficult to compare quotes from various vendors. Further, because construction projects often require long lead times, the lumber product may need to be priced now, but not delivered until a time in the future. Alternately, another species of lumber (i.e., southern pine) may represent an acceptable substitute. 
     Therefore, from the foregoing, there is a need for a method and system that allows buyers to evaluate the price of commodity offerings possessing varying shipping parameters. 
     SUMMARY 
     Disclosed herein are methods and computer-readable storage media having executable instructions stored thereon for evaluating price information. In response to execution by one or more computing devices, the instructions cause the one or more computing devices to perform certain actions. 
     In at least one embodiment, a computer-implemented method to disaggregate a single combined price of a bundled product allows for discovery and evaluation of the price of component parts therein. The method includes receiving, by a computer, at least one price data set representing a bundled product. The price data set is comprised of component parts that correspond to the component parts of the bundled product, wherein at least one component part of the price data set differs in accordance with at least one parameter value of another component part in the price data set. The price data set includes a single combined price for all component parts in the price data set. The method further includes calculating a price for at least one component part of the price data set by obtaining metric data from at least one source for each component part in the price data set, wherein the metric data includes one or more market reference prices responsive to the component parts; using the metric data, computing a market value for each component part and further computing a market value total for the price data set by summing the computed market values of all the component parts in the price data set; generating a proportional value for at least one component part of the price data set by dividing the market value of the component part by the market value total for the price data set; and calculating a price for the at least one component part by multiplying the price data set&#39;s single combined price by the component part&#39;s proportional value. The calculated price of the component part is communicated to an output. 
     In another embodiment, a computer-implemented method to disaggregate a single combined price of a bundled product includes receiving, by a computer, one or more price data sets, wherein each price data set includes component parts, and wherein at least one component part of each price data set differs in accordance with at least one parameter value of another component part in the price data set or in another price data set. Each price data set includes a single combined price for all component parts in the price data set. For each of the price data sets, the method includes calculating a price of each component part in the price data set by obtaining metric data from at least one source for each component part in the price data set, wherein the metric data includes one or more market reference prices responsive to the component parts in the price data set; using the metric data, computing a market value for each component part in the price data set; multiplying the component part&#39;s computed market value by a quantity value for the component part to produce a market value total for the component part; summing the market value total of all component parts in the price data set to produce a market value total for the price data set; dividing the single combined price of the price data set by the market value total of the price data set to produce a ratio or index value for the price data set; and multiplying the computed market value of each component part in the price data set by the data set&#39;s ratio or index value to thereby calculate a price for each component part in the price data set. 
     In yet another embodiment, instructions stored on a computer-readable storage medium cause one or more computing devices executing the instructions to receive at least one price data set representing a bundled product, wherein the price data set is comprised of component parts that correspond to the component parts of the bundled product, wherein at least one component part of the price data set differs in accordance with at least one parameter value of another component part in the price data set. The price data set includes a single combined price for all component parts in the price data set. The instructions further cause the one or more computing devices to calculate a price for at least one component part of the price data set by obtaining metric data from at least one source for each component part in the price data set, wherein the metric data includes one or more market reference prices responsive to the component parts; using the metric data, computing a market value for each component part and further computing a market value total for the price data set by summing the computed market values of all the component parts in the price data set; generating a proportional value for at least one component part of the price data set by dividing the market value of the component part by the market value total for the price data set; and calculating a price for the at least one component part by multiplying the price data set&#39;s single combined price by the component part&#39;s proportional value. The calculated price of the component part is communicated to an output. 
     In still another embodiment, a computer-readable storage medium has instructions stored thereon that, in response to execution by one or more computing devices, cause the one or more computing devices to receive one or more price data sets. Each price data set includes component parts. At least one component part of each price data set differs in accordance with at least one parameter value of another component part in the price data set or in another price data set, and each price data set includes a single combined price for all component parts in the price data set. For each of the price data sets, the instructions further cause the one or more computing devices to calculate a price of each component part in the price data set by obtaining metric data from at least one source for each component part in the price data set, wherein the metric data includes one or more market reference prices responsive to the component parts in the price data set; using the metric data, computing a market value for each component part in the price data set; multiplying the component part&#39;s computed market value by a quantity value for the component part to produce a market value total for the component part; summing the market value total of all component parts in the price data set to produce a market value total for the price data set; dividing the single combined price of the price data set by the market value total of the price data set to produce a ratio or index value for the price data set; and multiplying the computed market value of each component part in the price data set by the data set&#39;s ratio or index value to thereby calculate a price for each component part in the price data set. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
       The foregoing aspects and many of the attendant advantages of the present disclosure will become more readily appreciated as the same become better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings, wherein: 
         FIG. 1  is a block diagram of a prior art representative portion of the Internet; 
         FIG. 2  is a pictorial diagram of a system of devices connected to the Internet, which depict the travel route of data; 
         FIG. 3  is a block diagram of the several components of the buyer&#39;s computer shown in  FIG. 2  that is used to request information on a particular route; 
         FIG. 4  is a block diagram of the several components of an information server shown in  FIG. 2  that is used to supply information on a particular route; 
         FIG. 5  is a flow diagram illustrating the logic of a routine used by the information server to receive and process the buyer&#39;s actions; 
         FIGS. 6A-6B  are flow diagrams illustrating another embodiment of the logic used by the information server to receive and process the quotes and quote requests of both buyers and vendors; 
         FIG. 7  is a flow diagram illustrating another embodiment of the logic used by the information server to execute the process of a catalog purchase; 
         FIGS. 8A-8D  are images of windows produced by a Web browser application installed on a client computer accessing a server illustrating one embodiment of the present disclosure; and 
         FIG. 9  is a flow diagram illustrating one embodiment of the normalization process described herein. 
     
    
    
     DETAILED DESCRIPTION 
     The term “Internet” refers to the collection of networks and routers that use the Internet Protocol (IP) to communicate with one another. A representative section of the Internet  100  as known in the prior art is shown in  FIG. 1  in which a plurality of local area networks (LANs)  120  and a wide area network (WAN)  110  are interconnected by routers  125 . The routers  125  are generally special-purpose computers used to interface one LAN or WAN to another. Communication links within the LANs may be twisted wire pair, or coaxial cable, while communication links between networks may utilize 56 Kbps analog telephone lines, or 1 Mbps digital T-1 lines, and/or 45 Mbps T-3 lines. Further, computers and other related electronic devices can be remotely connected to either the LANs  120  or the WAN  110  via a modem and temporary telephone link. Such computers and electronic devices  130  are shown in  FIG. 1  as connected to one of the LANs  120  via dotted lines. It will be appreciated that the Internet comprises a vast number of such interconnected networks, computers, and routers and that only a small representative section of the Internet  100  is shown in  FIG. 1 . 
     The World Wide Web (WWW), on the other hand, is a vast collection of interconnected, electronically stored information located on servers connected throughout the Internet  100 . Many companies are now providing services and access to their content over the Internet  100  using the WWW. In accordance with the present disclosure, and as shown in  FIG. 2 , there may be a plurality of buyers operating a plurality of client computing devices  235 .  FIG. 2  generally shows a system  200  of computers and devices to which an information server  230  is connected and to which the buyers&#39; computers  235  are also connected. Also connected to the Internet  100  is a plurality of computing devices  250  associated with a plurality of sellers. The system  200  also includes a communications program, referred to as CEA, which is used on the sellers&#39; computing devices  250  to create a communication means between the sellers&#39; backend office software and the server application. 
     The buyers of a market commodity may, through their computers  235 , request information about a plurality of items or order over the Internet  100  via a Web browser installed on the buyers&#39; computers. Responsive to such requests, the information server  230 , also referred to as a server  230 , may combine the first buyer&#39;s information with information from other buyers on other computing devices  235 . The server  230  then transmits the combined buyer data to the respective computing devices  250  associated with the plurality of buyers. Details of this process are described in more detail below in association with  FIGS. 5-7 . 
     Those of ordinary skill in the art will appreciate that in other embodiments of the present disclosure, the capabilities of the server  230  and/or the client computing devices  235  and  250  may all be embodied in the other configurations. Consequently, it would be appreciated that in these embodiments, the server  230  could be located on any computing device associated with the buyers&#39; or sellers&#39; computing devices. Additionally, those of ordinary skill in the art will recognize that while only four buyer computing devices  235 , four seller computing devices  250 , and one server  230  are depicted in  FIG. 2 , numerous configurations involving a vast number of buyer and seller computing devices and a plurality of servers  230 , equipped with the hardware and software components described below, may be connected to the Internet  100 . 
       FIG. 3  depicts several of the key components of the buyer&#39;s client computing device  235 . As known in the art, client computing devices  235  are also referred to as “clients” or “devices,” and client computing devices  235  also include other devices such as palm computing devices, cellular telephones, or other like forms of electronics. A client computing device can also be the same computing device as the server  230 . An “agent” can be a person, server, or a client computing device  235  having software configured to assist the buyer in making purchasing decisions based on one or more buyer-determined parameters. Those of ordinary skill in the art will appreciate that the buyer&#39;s computer  235  in actual practice will include many more components than those shown in  FIG. 3 . However, it is not necessary that all of these generally conventional components be shown in order to disclose an illustrative embodiment for practicing the present disclosure. As shown in  FIG. 3 , the buyer&#39;s computer includes a network interface  315  for connecting to the Internet  100 . Those of ordinary skill in the art will appreciate that the network interface  315  includes the necessary circuitry for such a connection and is also constructed for use with TCP/IP protocol. 
     The buyer&#39;s computer  235  also includes a processing unit  305 , a display  310 , and a memory  300 , all interconnected along with the network interface  315  via a bus  360 . The memory  300  generally comprises a random access memory (RAM), a read-only memory (ROM), and a permanent mass storage device, such as a disk drive. The memory  300  stores the program code necessary for requesting and/or depicting a desired route over the Internet  100  in accordance with the present disclosure. More specifically, the memory  300  stores a Web browser  330 , such as Netscape&#39;s NAVIGATOR® or Microsoft&#39;s INTERNET EXPLORER® browsers, used in accordance with the present disclosure for depicting a desired route over the Internet  100 . In addition, memory  300  also stores an operating system  320  and a communications application  325 . It will be appreciated that these software components may be stored on a computer-readable medium and loaded into memory  300  of the buyer&#39;s computer  235  using a drive mechanism associated with the computer-readable medium, such as a floppy, tape, or CD-ROM drive. 
     As will be described in more detail below, the user interface which allows products to be ordered by the buyers are supplied by a remote server, i.e., the information server  230  located elsewhere on the Internet, as illustrated in  FIG. 2 .  FIG. 4  depicts several of the key components of the information server  230 . Those of ordinary skill in the art will appreciate that the information server  230  includes many more components than shown in  FIG. 4 . However, it is not necessary that all of these generally conventional components be shown in order to disclose an illustrative embodiment for practicing the present invention. As shown in  FIG. 4 , the information server  230  is connected to the Internet  100  via a network interface  410 . Those of ordinary skill in the art will appreciate that the network interface  410  includes the necessary circuitry for connecting the information server  230  to the Internet  100 , and is constructed for use with TCP/IP protocol. 
     The information server  230  also includes a processing unit  415 , a display  440 , and a mass memory  450 , all interconnected along with the network interface  410  via a bus  460 . The mass memory  450  generally comprises a random access memory (RAM), read-only memory (ROM), and a permanent mass storage device, such as a hard disk drive, tape drive, optical drive, floppy disk drive, or combination thereof. The mass memory  450  stores the program code and data necessary for incident and route analysis as well as supplying the results of that analysis to consumers in accordance with the present disclosure. More specifically, the mass memory  450  stores a metrics application  425  formed in accordance with the present disclosure for managing the purchase forums of commodities products, and a metric server adapter  435  for managing metric data. In addition, mass memory  450  stores a database  445  of buyer information continuously logged by the information server  230  for statistical market analysis. It will be appreciated by those of ordinary skill in the art that the database  445  of product and buyer information may also be stored on other servers or storage devices connected to either the information server  230  or the Internet  100 . Finally, mass memory  450  stores Web server software  430  for handling requests for stored information received via the Internet  100  and the WWW, and an operating system  420 . It will be appreciated that the aforementioned software components may be stored on a computer-readable medium and loaded into mass memory  450  of the information server  230  using a drive mechanism associated with the computer-readable medium, such as floppy, tape, or CD-ROM drive. In addition, the data stored in the mass memory  450  and other memory can be “exposed” to other computers or persons for purposes of communicating data. Thus, “exposing” data from a computing device could mean transmitting data to another device or person, transferring XML data packets, transferring data within the same computer, or other like forms of data communications. 
     In accordance with one embodiment of the present disclosure,  FIG. 5  is a flow chart illustrating the logic implemented for the creation of a Request for Quote (RFQ) by a singular buyer or a pool of buyers. In process of  FIG. 5 , also referred to as the pooling process  500 , a buyer or a pool of buyers generate an RFQ which is displayed or transmitted to a plurality of sellers. Responsive to receiving the RFQ, the sellers then send quotes to the buyers. 
     In summary, the creation of the RFQ consists of at least one buyer initially entering general user identification information to initiate the process. The buyer would then define a Line Item on a Web page displaying an RFQ form. The Line Item is defined per industry specification and units of product are grouped as a “tally” per industry practice. The pooling process  500  allows buyers to combine RFQ Line Items with other buyers with like needs. In one embodiment, the pool buy feature is created by a graphical user interface where the RFQ Line Items from a plurality of buyers are displayed on a Web page to one of the pool buyers, referred to as the pool administrator. The server  230  also provides a Web-based feature allowing the pool administrator to selectively add each RFQ Line Item to one combined RFQ. The combined RFQ is then sent to at least one vendor or seller. This feature provides a forum for pooling the orders of many buyers, which allows individual entities or divisions of larger companies to advantageously bid for larger orders, thus providing them with more bidding power and the possibility of gaining a lower price. 
     The pooling process  500  begins in step  501  where a buyer initiates the process by providing buyer purchase data. In step  501 , the buyer accesses a Web page transmitted from the server  230  configured to receive the buyer purchase data, also referred to as the product specification data set or the Line Item data. One exemplary Web page for the logic of step  501  is depicted in  FIG. 8A . As shown in  FIG. 8A , the buyer enters the Line Item data specifications in the fields of the Web page. The Line Item data consists of lumber species and grade  803 , number of pieces per unit  804 , quantities of the various units comprising the preferred assortment in the tally  805 A-E, delivery method  806 , delivery date  807 , delivery location  808 , and the overall quantity  809 . In one embodiment, the buyer must define the delivery date as either contemporaneous “on-or-before” delivery date, or specify a delivery date in the future for a “Forward Price” RFQ. In addition, the buyer selects a metric or multiple metrics in a field  810  per RFQ Line Item (tally). As described in more detail below, the metric provides pricing data that is used as a reference point for the buyer to compare the various quotes returned from the sellers. The buyer RFQ Line Item data is then stored in the memory of the server  230 . 
     Returning to  FIG. 5 , at a next step  503 , the server  230  determines if the buyer is going to participate in a pool buy. In the process of decision block  503 , the server  230  provides an option in a Web page that allows the buyer to post their Line Item data to a vendor or post their Line Item data to a buyer pool. The window illustrated in  FIG. 8A  is one exemplary Web page illustrating these options for a buyer. As shown in  FIG. 8A , the links “Post Buyer Pool”  812  and “Post to Vendors”  814  are provided on the RFQ Web page. 
     At step  503 , if the buyer does not elect to participate in a pool buy, the process continues to step  513  where the server  230  generates a request for a quote (RFQ) from the buyer&#39;s Line Item data. A detailed description of how the server  230  generates a request for a quote (RFQ) is summarized below and referred to as the purchase order process  600 A depicted in  FIG. 6A . 
     Alternatively, at decision block  503 , if the buyer elects to participate in a pool buy, the process continues to step  505  where the system notifies other buyers logged into the server  230  that an RFQ is available in a pool, allowing other buyers to add additional Line Items (tallies) to the RFQ. In this part of the process, the Line Items from each buyer are received by and stored in the server memory. The Line Items provided by each buyer in the pool are received by the server  230  using the same process as described above with reference to block  501  and the Web page of  FIG. 8A . All of the Line Items stored on the server  230  are then displayed to a pool administrator via a Web page or an email message. In one embodiment, the pool administrator is one of the buyers in a pool where the pool administrator has the capability to select all of the Line Item data to generate a combined RFQ. The server  230  provides the pool administrator with this capability by the use of any Web-based communicative device, such as email or HTML forms. As part of the process, as shown in steps  507  and  509 , the pool may be left open for a predetermined period of time to allow additional buyers to add purchase data to the current RFQ. 
     At a decision block  509 , the server  230  determines if the pool administrator has closed the pool. The logic of this step  509  is executed when the server  230  receives the combined RFQ data from the pool administrator. The pool administrator can send the combined RFQ data to the server  230  via an HTML form or by other electronic messaging means such as email or URL strings. Once the server  230  has determined that the pool is closed, the process continues to block  510  where the Line Items from each buyer (the combined RFQ) are sent to all of the buyers in the pool. The process then continues to step  513  where the server  230  sends the combined RFQ to the vendors or sellers. 
     Referring now to  FIG. 6A , one embodiment of the purchase-negotiation process  600  is disclosed. The purchase-negotiation process  600  is also referred to as a solicited offer process or the market purchase process. In summary, the purchase-negotiation process  600  allows at least one buyer to submit an RFQ and then view quotes from a plurality of vendors and purchase items from selected vendor(s). The logic of  FIG. 6A  provides buyers with a forum that automatically manages, collects, and normalizes the price of desired commodity items. The purchase-negotiation process  600  calculates a normalized price data set that is based on a predefined metric(s). The calculation of the normalized price data set in combination with the format of the Web pages described herein create an integrated forum where quotes for a plurality of inherently dissimilar products can be easily obtained and compared. 
     The purchase-negotiation process  600  begins at step  601  where the RFQ, as generated by one buyer or a pool of buyers in the process depicted in  FIG. 5 , is sent to a plurality of computing devices  250  associated with a plurality of sellers or vendors. The vendors receive the RFQ via a Web page transmitted by the server  230 . In one embodiment, the vendors receive an email message having a hypertext link to the RFQ Web page to provide notice to the vendor. Responsive to the information in the buyers&#39; RFQ, the process then continues to step  603  where at least one vendor sends their quote information to the server  230 . 
     In the process of step  603 , the vendors respond to the RFQ by sending their price quote to the server  230  for display via a Web page to the buyer or buyer pool. Generally described, the vendors send an HTML form or an email message with a price and description of the order. The description of the order in the quote message contains the same order information as the RFQ. 
       FIG. 8B  illustrates one exemplary Web page of a vendor quote that is displayed to the buyer. As shown in  FIG. 8B , the vendor quote includes the vendor&#39;s price  813 , the lumber species and grade  803 , number of pieces per unit  804 , quantities of the various units comprising the preferred assortment in the tally  805 A-E, delivery method  806 , delivery date  807 , and delivery location  808 . In the quote response message, the vendor has the capability to modify any of the information that was submitted in the RFQ. For example, the vendor may edit the quantity values for the various units comprising the preferred assortment in the tally  805 A-E. This allows the vendor to adjust the buyer&#39;s request according to the vendor&#39;s inventory, best means of transportation, etc. All of the vendor&#39;s quote information is referred to as price data set or the RFQ Line Item (tally) quote. 
     Returning to  FIG. 6A , the process continues to step  605 , where the server  230  normalizes the price of each RFQ Line Item (tally) quote from each vendor. The normalization of the vendor&#39;s price is a computation that adjusts the vendor&#39;s price utilizing data from a metric. The normalization process is carried out because each vendor may respond to the Line Items of an RFQ by quoting products that are different from a buyer&#39;s RFQ and/or have a different tally configuration. The normalization of the pricing allows the buyers to objectively compare the relative value of the different products offered by the plurality of vendors. For example, one vendor may produce a quote for an RFQ of: one unit of 2×4×12, two units of 2×4×12, and three units of 2×4×16. At the same time, another vendor may submit a quote for three units of 2×4×12, one unit of 2×4×12, and two units of 2×4×16. Even though there is some difference between these two offerings, the price normalization process provides a means for the buyer to effectively compare and evaluate the different quotes even though there are variations in the products. The price normalization process  900  is described in more detail below in conjunction with the flow diagram of  FIG. 9 . 
     Returning again to  FIG. 6A , at step  607  the vendor&#39;s quote information is communicated to the buyer&#39;s computer for display. As shown in  FIG. 8B  and described in detail above, the vendor&#39;s quote is displayed via a Web page that communicates the vendor&#39;s quote price  813  and other purchase information. In addition, the vendor&#39;s quote page contains a metric price  815  and a quote price versus metric price ratio  816 . The metric price  815  and the quote price versus metric price ratio  816  are also referred to as a normalized price data value. A ratio higher than one (1) indicates a quote price that is above the metric price, and a lower ratio indicates a quote price that is below the metric price. 
     Next, at step  609 , the buyer or the administrator of the buyer pool compares the various products and prices quoted by the vendors along with the normalized price for each Line Item on the RFQ. In this part of the process, the buyer may decide to purchase one of the products from a particular vendor and sends a notification to the selected vendor indicating the same. The buyer notifies the selected vendor by the use of an electronic means via the server  230 , such as an HTML form, a chat window, email, etc. For example, the quote Web page depicted in  FIG. 8B  shows two different quotes with two different tallies, the first quote price  813  of $360, and the second quote price  813 A of $320. If the buyer determines that they prefer to purchase the materials listed in the first quote, the buyer selects the “Buy!” hyperlink  820  or  820 A associated with the desired tally. 
     If the buyer is not satisfied with any of the listed vendor quotes, the server  230  allows the buyer to further negotiate with one or more of the vendors to obtain a new quote. This step is shown in decision block  611 , where the buyer makes the determination to either accept a quoted price or proceed to step  613  where they negotiate with the vendor to obtain another quote or present a counter offer. Here, the server  230  provides a graphical user interface configured to allow the buyer and one vendor to electronically communicate, using, e.g., a chat window, streaming voice communications, or other standard methods of communication. There are many forms of electronic communications known in the art that can be used to allow the buyer and vendors to communicate. 
     The buyer and seller negotiate various quotes and iterate through several steps  603 - 613  directed by the server  230 , where each quote is normalized, compared, and further negotiated until a quote is accepted by the buyer or negotiations cease. While the buyer and seller negotiate the various quotes, the server  230  stores each quote until the two parties agree on a price. At any step during the negotiation process, the system always presents the buyer with an option to terminate the negotiation if dissatisfied with the quote(s). 
     At decision block  611 , when a buyer agrees on a quoted price, the process then continues to step  615  where the buyer sends a notification message to the vendor indicating they have accepted a quote. As described above with reference to steps  603 - 613 , the buyer notification message of step  615  may be in the form of a message on a chat window, email, by an HTML form, or the like. However, the buyer notification must be transmitted in a format that allows the system to record the transaction. The buyer notification may include all of the information regarding the specifications by RFQ Line Item, such as, but not limited to, the buy price, date and method of shipment, and payment terms. 
     The purchase-negotiation process  600  is then finalized when the system, as shown in step  617 , sends a confirmation message to a tracking system. The confirmation message includes all of the information related to the agreed sales transaction. 
     Optionally, the process includes step  619 , where the server  230  stores all of the information related to RFQ, offers, and the final sales transaction in a historical database. This would allow the server  230  to use all of the transaction information in an analysis process for providing an improved method of obtaining a lower market price in future transactions and in identifying optimum purchasing strategy. The analysis process is described in further detail below. Although the illustrated embodiment is configured to store the data related to the sales transactions, the system can also be configured to store all of the iterative quote information exchanged between the buyer and vendor. 
     Referring now to  FIG. 6B , an embodiment of the unsolicited offer process  650  is disclosed. In summary, the unsolicited offer process  650 , also referred to as the unsolicited market purchase process, allows at least one buyer to view unsolicited offers from a plurality of vendors and purchase items from a plurality of vendors from the offers. The logic of  FIG. 6B  provides buyers with a forum that automatically manages, collects, and normalizes price quotes based on metric data. By the price normalization method of  FIG. 6B , the server  230  creates an integrated forum where offers from a plurality of inherently dissimilar products can be obtained and normalized for determination of purchase. 
     The unsolicited offer process  650  begins at step  651  where the plurality of vendors are able to submit offers to the server  230 . This part of the process is executed in a manner similar to step  603  of  FIG. 6A , where the vendor submits a quote to the server  230 . However, in the Web page of step  651 , the server  230  generates a Web page containing several tallies from many different vendors. In addition, at step  651 , the server  230  stores all of the unsolicited offer data provided by the vendors. 
     Next, at step  653 , a buyer views the offers stored on the server  230 . This part of the process is carried out in a manner similar to the process of step  603  or  607  where the server  230  displays a plurality of offers similar to the tallies depicted in  FIG. 8A . 
     Next, at step  655 , the buyer selects a metric for the calculation of the normalized price associated with the selected offer. As described in more detail below, metrics may come from publicly available information, i.e., price of futures contracts traded on the Chicago Mercantile Exchange, subscription services such as Crowes™ or Random Lengths™ accessed via the metric server adapter  435  (shown in  FIG. 4 ), or internally generated metrics derived from the data stored in the server  230 . The normalization calculation, otherwise referred to as the normalization process, occurs each time the buyer views a different offer, and the normalization calculation uses the most current metric data for each calculation. The normalization process is carried out because each vendor will most likely offer products that may vary from products of other vendors, and have a different tally configuration from those supplied by other vendors. The normalization of the pricing allows the buyers to compare the relative value of the different products offered by the number of vendors. The metric price for each selected offer is displayed in a similar manner as the metric price  815  and  816  shown in the Web page of  FIG. 8B . 
     Next, at decision block  657 , the buyer selects at least one offer for purchase. This is similar to the process of  FIG. 6A  in that the buyer selects the “Buy!” hyperlink  820  associated with the desired tally to purchase an order. The process then continues to steps  659 - 663 , where, at step  659 , the process transmits a buy notice to the vendor, then at step  661  sends a purchase confirmation to the tracking system, and then at step  663  saves the transaction data in the server database. The steps  659 - 663  are carried out in the same manner as the steps  615 - 619  of  FIG. 6A . In the above-described process, the buyer notification may include all of the information regarding the specifications by RFQ Line Item, and data such as, but not limited to, the buy price, date, and method of shipment, and the payment terms. 
     Referring now to  FIG. 7 , a flow diagram illustrating yet another embodiment of the present disclosure is shown.  FIG. 7  illustrates the catalog purchase process  700 . This embodiment allows buyers to search for a catalog price of desired commerce items, enter their purchase data based on the pre-negotiated catalog prices, and to compare those catalog prices with a selected metric price and the current market price, wherein the current market price is determined by the purchase-negotiation process  600 . 
     The process starts at step  701  where the buyer selects a program buy catalog  443 . The program buy catalog  443  provides buyers with the published or pre-negotiated price of the desired products. Next, at step  703 , based on the catalog information, the buyer then enters their purchase data. Similar to the step  501  of  FIG. 5  and the tally shown in  FIG. 8A , the buyer sends purchase data to the server  230 , such as the desired quantity of each item and the lumber species, grade, etc. 
     The process then proceeds to decision block  707  where the buyer makes a determination of whether to purchase the items using the catalog price or purchase the desired product in the open market. Here, the server  230  allows the user to make this determination by displaying the metric price of each catalog price. This format is similar to the metric price  815  and  816  displayed in  FIG. 8B . 
     At decision block  707 , if the buyer determines that the catalog price is better than a selected metric price, the process then proceeds to steps  709 ,  711 , and  713 , where a program buy from the catalog is executed, and the buyer&#39;s purchase information is stored on the server  230  and sent to the vendor&#39;s system to confirm the sale. These steps  711 - 713  are carried out in the same manner as the confirmation and save steps  617  and  619  as shown in  FIG. 6A . 
     At decision block  707 , if the buyer determines that the metric price is better than the catalog price, the process continues to step  717  where the buyer&#39;s purchase data is entered into an RFQ. At this step, the process carries out the first five steps  601 - 609  of the method of  FIG. 6A  to provide buyers with the price data from the open market, as well as provide the normalized prices for each open market quote. At step  719 , the server  230  then displays a Web page that allows the user to select from a purchase option of a catalog or spot (market) purchase. At decision block  721 , based on the displayed information, the buyer will then have an opportunity to make a determination of whether they will proceed with a catalog purchase or an open market purchase. 
     At decision block  721 , if the buyer proceeds with the catalog purchase, the process continues to step  709  where the catalog purchase is executed. Steps  709 - 713  used to carry out the catalog purchase are the same as if the buyer had selected the catalog purchase in step  707 . However, if at decision block  721  the buyer selects the option to proceed with the market purchase, the process continues to step  723  where the RFQ generated in step  717  is sent to the vendor. Here, the process carries out the steps of  FIG. 6  to complete the open market purchase. More specifically, the process continues to step  609  where the buyer compares the normalized prices from each vendor. Once a vendor is selected, the negotiation process of steps  603 - 613  is carried out until the buyer decides to execute the purchase. Next, the transaction steps  615 - 619  are carried out to confirm the purchase, notify the tracking system, and save the transactional data on the historical database. 
     Optionally, the process can include a step where the server  230  stores all of the information related to program buy and metric comparisons and the final sales transaction in a historical database. This would allow the server  230  to use all of the transaction information in an analysis process for providing an improved method of obtaining the value of the program. Although the illustrated embodiment is configured to store the data related to the sales transactions, the system can also be configured to store all of the iterative quote information exchanged between the buyer and vendor. 
     The analysis process allows the server  230  to utilize the sales history records stored in steps  619  and  711  to generate price reports for various third parties as well as provide a means of calculating current market prices for products sold in the above-described methods. The sales history records are also used as the source for a metric, such as those used in the process of  FIGS. 6A ,  6 B, and  7 . As shown in steps  619 ,  663 , and  711 , the server  230  continually updates the historical database for each sales transaction. The analysis reporting process allows a buyer or manager of buyers to conduct analysis on the historical information. This analysis would include multi-value cross compilation for purposes of determining purchasing strategies, buyer effectiveness, program performance, vendor performance, and measuring effectiveness of forward pricing as a risk management strategy. 
     Referring now to  FIG. 9 , a flow diagram illustrating the logic of the normalization process  900  is shown. The logic of the normalization process  900  resides on the server  230  and processes the quotes received from commodity sellers. The logic begins at step  905  where quote data is obtained from the seller in response to the buyer&#39;s RFQ as described above. 
     Next, at step  910 , routine  900  iteratively calculates the board footage (BF) of each type of lumber. Once all the totals are calculated for each type, routine  900  continues to step  915  where the server  230  calculates the total type price. 
     At step  915 , routine  900  iteratively calculates the total type price for the amount of each type of lumber specified in the quote. This is accomplished by taking the total board footage (BF), calculated in block  910 , and multiplying the total BF by the price per MBF specified in the quote. Once all the prices are calculated for each type, routine  900  continues to step  920  where the server  230  calculates total quoted price. At step  920 , the routine  900  calculates the total price for the quote by summing all of the total type prices calculated at step  915 . 
     At step  925 , routine  900  iteratively retrieves the most current price for each type of lumber specified in the quote from a predefined metric source(s). Metrics may come from publicly available information, i.e., price of futures contracts traded on the Chicago Mercantile Exchange, subscription service publications such as Crowes™ or Random Lengths™ accessed via the metric server adapter  435  (shown in  FIG. 4 ), or internally generated metrics derived from the server database. Once all the prices are retrieved for each type, at step  930 , the routine  900  then iteratively calculates the market price for the quantity of each type of lumber in the quote. Once the totals for all types are calculated, routine  900  continues to step  935  where the routine  900  calculates the total market price for the quote by summing all the most current prices calculated in step  930 . Although this example illustrates that steps  910 - 920  are executed before steps  925 - 935 , these two groups of steps can be executed in any order, or in parallel, so long as they are both executed before a comparison step  940 . 
     At step  940 , routine  900  compares the total quoted to the metric price to arrive at a comparative value. In one exemplary embodiment of the current invention, the comparative value is a “percent of metric” value. A value higher than one hundred (100) percent indicates a price that is above the metric rate, and a lower percent indicates a price that is below the metric rate. 
     The operation of the routine  900  can be further illustrated through an example utilizing specific exemplary data. In the example, a buyer sends out a request for a quote (RFQ) requesting a lot of 2×4 S&amp;B lumber consisting of five units of 2″×4″×8′, two units of 2″×4″×14′, and five units of 2″×4″×16′. The buyer then receives quotes from three sellers. Seller A responds with a tally of six units of 2″×4″×8′, four units of 2″×4″×14′, and three units of 2″×4″×16′ for $287 per thousand board feet. Seller B responds with a lot of five units of 2″×4″×8′, one unit of 2″×4″×14′, and six units of 2″×4″×16′ for $283 per thousand board feet. Seller C responds with a lot of one unit of 2″×4″×8′, five units of 2″×4″×14′, and five units of 2″×4″×16′ for $282 per thousand board feet. Suppose also that the typical unit size is 294 pieces/unit, and the metric or reported market price for 2″×4″×8′s is $287.50, for 2″×4″×14′s it is $278.50, and for 2″×4″×16′ it is $288. 
     Viewing the MBF prices for the respective quotes is not particularly informative, given that certain lengths of lumber are more desirable and priced accordingly in the marketplace. By processing the quote from Seller A using routine  900 , we arrive at a total MBF of 29.792, giving a total quoted price of $8,550.30. The selected metric price for the same types and quantities of lumber would be $8,471.12; therefore, the quoted price would have a percent of market value of 100.93%. Processing the quote from Seller B using routine  900 , we arrive at a total MBF of 29.400, giving a total quoted price of $8,320.20. The selected metric price for the same types and quantities of lumber, however, would be $8,437.21; therefore, the quoted price would have a percent of market value of 98.61%. Finally, processing the quote from Seller C using routine  900 , we arrive at a total MBF of 30.968, giving a total quoted price of $8,732.98. The selected metric price for the same types and quantities of lumber, however, would be $8,767.66; therefore, the quoted price would have a percent of market value of 99.38%. By looking at the percent of selected metric value, it is apparent that the price from Seller B is a better value. As shown in the methods of  FIGS. 5-7 , this price normalization process allows users to compare inherently different offers having different quality and quantity values. 
     In yet another example of an application of the normalization process, additional exemplary data is used to demonstrate the analysis of a transaction having one RFQ from a buyer and two different quotes from a seller, normalized to comparable product of another species. In this example, the buyer produces an RFQ listing the following items: one carload of Eastern SPF (ESPF) lumber having four units of 2″×4″×8′, four units of 2″×4″×10′, six units of 2″×4″×12′, two units of 2″×4″×14′, and six units of 2″×4″×16′. The vendor then responds with two different quotes with two different unit tallies and two different prices. The first response lists a quote price of $320 per thousand board feet and a slight modification of the tally provides four units of 2″×4″×8′, four units of 2″×4″×10′, six units of 2″×4″×12′, three units of 2″×4″×14′, and five units of 2″×4″×16′. The second response quotes per the requested tally at a price of $322 per thousand board feet. Both quotes list the delivery location as “Chicago.” 
     To display the quotes, the server  230  produces a Web page similar to that displayed in  FIG. 8C , where the vendor&#39;s modified tally is displayed in highlighted text. The buyer can then view summary metric comparison or select the hypertext link “View Calculation Detail,” which then invokes the server  230  to produce a Web page as shown in  FIG. 8D . Referring now to the Web page illustrated in  FIG. 8D , the data produced by the server  230  compares the response to a selected metric of a different species, Western SPF (WSPF), for items of the same size, grade, and tally. The market price for the same 2×4 tally of ESPF and WSPF are thus simultaneously compared. In an example, Eastern quoted at $322 per thousand board feet, Western metric (Random Lengths™ Jun. 26, 2000 print price plus freight of $80 as defined in Metric Manager) for the same tally being $331.791. This metric comparison is also represented as Quote/Metric Value or Eastern price representing 0.970490, or 97% of comparable Western product. 
     In review of the normalization process, the buyer must select a metric source for price information for a defined item given a set of attributes, i.e., grade, species, and size. The metric may then be mapped to the RFQ item for comparison and does not have to be equivalent of the item. For instance, as explained in the above-described example, it may be desirable to map the market relationship of one commodity item to another. The most current pricing data for the metric is electronically moved from the selected source to the server  230 . As mentioned above, metrics may come from publicly available information, (i.e., price of futures contracts traded on the Chicago Mercantile Exchange), or subscription services, (i.e., Crowes™ or Random Lengths™ publications), or be an internal metric generated by the server  230 . This metric data is used in the normalization process for all calculations, as described with reference to the above-described methods. 
     While the various embodiments of the invention have been illustrated and described, it will be appreciated that within the scope of the appended claims, various changes can be made therein without departing from the spirit of the invention. For example, in an agricultural commodity, an order for Wheat U.S. #2 HRW could be compared to a selected metric of Wheat U.S. #2 Soft White, similar to how different species are analyzed in the above-described example. 
     The above system and method can be used to purchase other commodity items, such as in the trade of livestock. In such a variation, order information such as a lumber tally would be substituted for a meat type, grade, and cut. Other examples of commodity items include agricultural products, metals, or any other items of commerce having several order parameters.
To disaggregate a single combined price of a bundled product, a computer receives a price data set comprised of component parts that correspond to the component parts of the bundled product. At least one component part differs in accordance with at least one parameter value of another component part. A price for a component part is calculated by obtaining metric data that includes one or more market reference prices responsive to the component part. Using the metric data, a market value is computed for each component part and the computed market values are summed to compute a market value total. A proportional value for at least one component part is generated by dividing the market value of the component part by the market value total. A price for the component part is calculated by multiplying the price data set's single combined price by the component part's proportional value.