Source: http://www.google.com/patents/US7117060?dq=7,053,767
Timestamp: 2015-01-30 05:29:32
Document Index: 666065135

Matched Legal Cases: ['art 10', 'arts 18', 'art 100', 'art 100', 'art 200', 'art 200', 'art 200', 'art 300']

Patent US7117060 - Method of improving production through cost of yield measurement - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsThe present invention provides a method for controlling production or manufacturing costs by obtaining yield measurements of unit manufacturing for a multiplicity of products or production lines having a plurality of processes which includes determining a started units number for the plurality of processes....http://www.google.com/patents/US7117060?utm_source=gb-gplus-sharePatent US7117060 - Method of improving production through cost of yield measurementAdvanced Patent SearchPublication numberUS7117060 B2Publication typeGrantApplication numberUS 09/770,355Publication dateOct 3, 2006Filing dateJan 26, 2001Priority dateJan 26, 2001Fee statusLapsedAlso published asUS20020103560Publication number09770355, 770355, US 7117060 B2, US 7117060B2, US-B2-7117060, US7117060 B2, US7117060B2InventorsThomas A. McPhee, Michael E. Cropp, Donald Diangelo, Alberto H. Gay, Carmella Pemberton, Joseph Saltarelli, Nicholas L. Volkringer, John J. DeMarcoOriginal AssigneeInternational Business Machines CorporationExport CitationBiBTeX, EndNote, RefManPatent Citations (8), Non-Patent Citations (2), Referenced by (2), Classifications (10), Legal Events (4) External Links: USPTO, USPTO Assignment, EspacenetMethod of improving production through cost of yield measurementUS 7117060 B2Abstract The present invention provides a method for controlling production or manufacturing costs by obtaining yield measurements of unit manufacturing for a multiplicity of products or production lines having a plurality of processes which includes determining a started units number for the plurality of processes. The method further includes determining a cost per unit for each unit of the plurality of processes, and calculating an expected approved units number for the plurality of processes. The expected approved units number is calculated by multiplying the started units number by an expected yield measurement. The method next includes calculating an actual approved units number for each of the plurality of processes by multiplying the started units number by an actual yield measurement, and calculating an unapproved units number for each of the plurality of processes by subtracting the expected approved units number from the actual approved units number. The method then includes calculating cost of yield measurements for the plurality of processes by multiplying the unapproved units number by the cost per unit, and providing a comparison of the cost of yield measurements for the plurality of processes.
Thus, each product will have an expected number of parts to be shipped, which will be somewhat less than the number of parts started. The ratio of the number of parts shipped to the number of parts started is the actual yield. A plant will have an expected value for this yield (known as the �expected yield� or the �plan yield�). In a best case scenario, the actual yields are at or above their expected (or plan) values, and the plant is producing what is expected, and is profitable. However, in a typical manufacturing environment, expected yields are high and many products may not attain the expectations. The plant must apply it's limited resources (i.e., manpower, purchasing, research) to resolve whatever problems are preventing the products from achieving their expected yield.
SUMMARY OF THE INVENTION The above and other objects and advantages, which will be apparent to one of skill in the art, are achieved in the present invention which is directed to, in a first aspect, a method for controlling production or manufacturing costs by obtaining measurements of unit manufacturing for a multiplicity of products or production lines and having a started units number for a plurality of processes, which includes determining an approved units number for the plurality of processes. The method further includes determining a unit production cost for each unit in the plurality of processes, calculating an unapproved units number for each process, calculating a cost of yield measurement for each of the plurality of processes by multiplying the unapproved units number by the unit production cost for each unit; and comparing the cost for each unapproved unit for each process.
DESCRIPTION OF THE PREFERRED EMBODIMENT(S) In describing the preferred embodiment of the present invention, reference will be made herein to FIGS. 1�5 of the drawings in which like numerals or letters refer to like features of the invention. Features of the invention are not necessarily shown to scale in the drawings.
The present invention as disclosed herein has advantages over previous methods disclosed in the prior art and discussed above. The present invention takes into account that all products are not the same, but, differ in many ways by computing a cost to manufacture each piece or product, and applying this to a yield delta. A dollar value can be assigned to each product that is below or above its expected yield and resources assigned to improve manufacturing processes using the �cost of yield� measurement. Assigning resource in this way provides better allocation of scarce resources to solve production problems.
Referring to FIG. 1, a flow chart 10 generally describing the steps of a preferred embodiment of the present invention is shown. The method is started 12 by identifying the process or processes used to make each of a plurality of products 14. Then, identify the incremental cost added to each product by each process identifying the cost per unit of the product produced 16. Next, identify the expected number of good parts 18 generated for a given number of started parts for each process of each product. This number is the �expected yield� for each product through each process. Then, identify the quantity of each product started in a given time period (e.g. Daily, Weekly, Month to Date, etc.) in each process for that particular product 20. Next, identify the quantity of goods or approved finished units of each product in each process in the same time period 22. Then, compute the actual yield of each product in each process by dividing the number of good or approved finished units by the total number of units started 24. After that, determine the �Yield Delta� by subtracting the actual yield for each product in each process from the expected yield for each product in each process 26.
Next, multiply the �Yield Delta� by the number of parts started for each product for each process 28. This represents the number of additional parts that would have to be started to get the expected number of good parts out of each process, for each product. This assumes that the actual yield is below the expected one. If the actual yield is above the expected yield, this number is negative, and represents the number of extra good parts finished above the number expected.
Subsequently, multiply the unit costs (16) by the result of multiplying the yield data by the number of parts started for each product and process (28). The result is a dollar value representing the �Cost of Yield� for each process step for each product. This information can be used to effectively prioritize which products in which process steps are impacting the plant the most, and which should be addressed first.
The �Cost of Yield� measurement includes the cost of the product as well as the quantity approved or determined to be good. Thus, a conversion of a straightforward yield delta measurement to a cost in dollars is employed using the preferred embodiment of the present invention. The ability to compute a dollar cost, which can then be compared across all products, is one of the advantages of the present invention.
In operation, the preferred embodiment for this invention includes a software program running on a computer system with the following characteristics. The computer collects yield information on all products for all processes in the factory on a �regular� basis. A �regular� basis is frequently enough to take action against any new problems found, for example, daily, so that the computer system updates information recording the parts started, and approved parts once a day. Also, it is possible to have �real time� measurements, and more frequent reports if desired.
Number of parts started*target yield=number of parts expected to be shipped.Number of parts started*actual yield=number of parts actually shippedNumber of parts actually shipped−number of parts expected to be shipped=quantity of parts lost due to yield.Quantity of parts lost due to yield*out of pocket cost per part=�COST OF YIELD�.
Performing the calculation to measure �Cost of Yield� across all the products in the manufacturing plant will show which products are costing (or saving) the company the most money. Limited resources are then allocated to work on the products costing the company the most money.
Referring to FIGS. 2 and 3, embodiments are shown of charts depicting the benefits of the present invention. The chart shown in FIG. 2 displays a yield management chart 100 before implementing cost of yield measurements. Column �A� lists the five products �A�E� which processes will be monitored. Column �B� indicates for each product how many parts are started, beginning with product �A� having 100 parts, and ending with product �E� starting with 75 parts. Then, all the parts are processed 102, going through their individual processing in the production plant. Next, column �C� shows the amount of parts that have been processed and are still good, starting with product �A� having 89 good parts, and ending with product �E� having 64 good parts. Column �D� shows the yield of each product and column �E� shows the expected yield or the plan for what yield would be acceptable. Column �F� shows the difference of the expected yield verses the actual yield shown in columns �D� and �E�.
Concluding from the chart 100, the product �E� with a delta difference of −0.05 is the largest delta. This largest difference between the expected yield and the actual yield, from this analysis, would result in the company spending more resources on this product to try to increase the efficiency and increase its yield.
However, referring to FIG. 3, a chart 200 shows tabulation of yield management after implementing a cost of yield measurement of the present invention as explained herein. The chart 200 has a column �A� showing five products �A�E�. Column �B� shows how many parts are started for each product. Then each product is processed 202. Column �C� shows how many parts are good or approved after their processing. Column �D� depicts the yield for each product �A�E �. Column �E� discloses the planned yield or expected yield for each product, in this case 90% for all. Column �F� discloses the difference between the actual yield and the expected yield, shown in columns �D� and �E� respectively. Column �G� shows the parts delta, which is the amount of parts that were rejected for each process. Column �H� depicts the cost of each of those products. Column �I� gives the cost of yield in dollars which shows the number of parts multiplied by their dollar value to give a total cost for the total number of parts that are rejected for each process.
As can be seen, referring to FIGS. 2 and 3, the cost of yield is very different from the lowest yield product. The lowest yield product calculation does not show how much value that product possesses. Where value is the total cost of the product's delta between yield �D� and plan �E�. Whereas, in FIG. 3, the chart 200 shows the products costing the business the most money may not be the lowest yielding products. Thus, even though product �B� has a higher yield (0.88), than product �E� (0.85), product �B� has a higher loss in dollars, at $5.00 than product �E� at, $2.00. Thus, the company using these calculations would concentrate resources on the production lines �B� and �D� since these are costing the company the most money in lost yield, and improving the yield for these products adds the most value in dollars to the company's profit.
Referring to FIG. 4, an output chart is shown which is preferably created using a software program which extracts �starts�, �goods�, �unit costs�, and �expected yield� data from a relational database to compute a �cost of yield�. The software program creates reports of the computed cost of yield for each product. The preferred reports are a summary of all products and their cost of yield for all processes. A sample report is shown in FIG. 4. The chart 300 includes one line for each product 302, and one column (C�G ) for each process. There is a summary column at the far right �H� which sums the cost of yield for a given product across all processes. The report 300 is typically sorted with the poorest performing product at the top, which makes it easy to see what product needs resources the most. The summary of all products and their cost of yield for a single process step allows easy viewing of which products are performing poorest.
Referring to FIG. 4, column �A�, �report class� is a product classification. There are two main material types used to make products which are organized in the report by the material type. Column �B�, �product� is the name of each product. Costs and yields are tracked by each product. Each product has it's own line in the report. Column �C�, �PI Cost� is the net cost of yield (in dollars) for the PI processing portion of the manufacturing process. The PI process is broken down into five major processes, each one with its own yield target by product. The �PI Cost� is the difference between the plan and actual yield for the product through PI processing, times the number of parts started into the PI process, times the unit cost of each piece. The result of the calculation is a dollar amount (either positive or negative). Column �D�, �PII Cost� is the net cost of yield (in dollars) for the PII processing portion of the manufacturing process. The costs are expressed in dollars, using the yields, starts, and unit costs associated with the PII portion of the manufacturing process. Column �E�, �PIII Cost� is the net cost of yield (in dollars) for the PIII processing portion of the manufacturing process. The costs are expressed in dollars, using the yields, starts, and unit costs associated with the PIII portion of the manufacturing process. Columns �F� and �G�, �PIV Cost� and �PV Cost�, both are net costs of yield (in dollars) for their respective portions of the manufacturing process. Column �H�, �Total Cost� is the sum of the five major manufacturing process costs described above. Each line has the total for the particular product on that line. At the bottom of the report is a total across all products in the report.
Patent CitationsCited PatentFiling datePublication dateApplicantTitleUS5047947Jul 25, 1990Sep 10, 1991Grumman Aerospace CorporationMethod of modeling the assembly of products to increase production yieldUS5077661May 3, 1989Dec 31, 1991Hewlett-Packard CompanyAssignment-dependent resource allocation methodUS5249120Jan 14, 1991Sep 28, 1993The Charles Stark Draper Laboratory, Inc.Automated manufacturing costing system and methodUS5291397Dec 20, 1991Mar 1, 1994Powell Roger AMethod for resource allocation and project control for the production of a productUS5351195Mar 12, 1993Sep 27, 1994The George GroupMethod for improving manufacturing processesUS5715181Apr 3, 1995Feb 3, 1998Horst; Robert L.Isogrammetric analysis method for high-yield processesUS5946661 *Oct 5, 1995Aug 31, 1999Maxager Technology, Inc.Method and apparatus for identifying and obtaining bottleneck cost informationUS5966694Jun 23, 1997Oct 12, 1999Maxager Technology, Inc.Method and apparatus for cycle time costing* Cited by examinerNon-Patent CitationsReference1"Cost of Yield Process Center Dependence is Key." Example.2"Process Center Definition." Example.Referenced byCiting PatentFiling datePublication dateApplicantTitleUS7218980 *Jul 23, 2001May 15, 2007Esilicon CorporationPrediction based optimization of a semiconductor supply chain using an adaptive real time work-in-progress tracking systemUS7369970 *Jun 27, 2001May 6, 2008Honda Giken Kogyo Kabushiki KaishaMethod and apparatus for estimating product cost* Cited by examinerClassifications U.S. Classification700/110, 700/109, 705/400, 700/121International ClassificationG06Q10/00, G06F19/00Cooperative ClassificationG06Q30/0283, G06Q10/06European ClassificationG06Q10/06, G06Q30/0283Legal EventsDateCodeEventDescriptionNov 23, 2010FPExpired due to failure to pay maintenance feeEffective date: 20101003Oct 3, 2010LAPSLapse for failure to pay maintenance feesMay 10, 2010REMIMaintenance fee reminder mailedJan 26, 2001ASAssignmentOwner name: INTERNATIONAL BUSINESS MACHINES CORPORATION, NEW YFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:MCPHEE, THOMAS A.;CROPP, MICHAEL E.;DIANGELO, DONALD;ANDOTHERS;REEL/FRAME:011498/0511;SIGNING DATES FROM 20001220 TO 20010126RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services