Patent Publication Number: US-8127012-B2

Title: System and methods for efficient and adequate data collection in document production environments

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is related to co-pending U.S. patent application Ser. Nos. 11/779,392; 11/779,437; 11/779,454; 11/779,464; 11/779,494; U.S. patent application Ser. No. 11/779,512 and Ser. No. 10/946,756 filed Sep. 22, 2004. 
     BACKGROUND 
     Document production environments, such as print shops, convert printing orders, such as print jobs, into finished printed material. A print shop may process print jobs using resources such as printers, cutters, collators and other similar equipment. Typically, resources in print shops are organized such that when a print job arrives from a customer at a particular print shop, the print job can be processed by performing one or more production functions. 
     Scheduling architectures that organize print jobs arriving at a document production environment and route the print jobs to autonomous cells are known in the art and are described in, for example, U.S. Pat. No. 7,051,328 to Rai et al. and U.S. Pat. No. 7,065,567 to Squires et al., the disclosures of which are incorporated by reference in their entirety. Methods for distributing jobs to a receiver on a network using devices are known in the art and are described in, for example, U.S. Pat. No. 5,513,126 to Harkins et al., the disclosure of which is incorporated by reference in its entirety. 
     It is common for print shops to receive print jobs having variable job sizes. Problems arise when a wide distribution of job sizes exists. This may be referred to as a heavy-tailed distribution. Heavy-tailed distributions usually require significant data before the mean distribution can be computed with accuracy. Even when large sets of data are collected, however, it can be difficult to compute an accurate average job size for heavy-tailed distributions. 
     Transaction print environments that process jobs having a heavy-tailed job-size distribution tend to have inefficient job flows. This is because these environments typically handle very large and very small jobs that are all part of one job pool. It is likely that several small jobs may be delayed if they are queued behind a very large job. Similarly, large jobs can experience flow interruptions if several small jobs requiring multiple setups are ahead of the large jobs in the queue. 
     SUMMARY 
     Before the present methods are described, it is to be understood that this invention is not limited to the particular systems, methodologies or protocols described, as these may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present disclosure which will be limited only by the appended claims. 
     It must be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural reference unless the context clearly dictates otherwise. Thus, for example, reference to a “job” is a reference to one or more jobs and equivalents thereof known to those skilled in the art, and so forth. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art. As used herein, the term “comprising” means “including, but not limited to.” 
     In an embodiment, a production process performance reporting system may include a plurality of print job processing resources and a computer-readable storage medium containing one or more programming instructions for performing a method of providing a report of performance metrics in a document production environment. The method may include receiving job size information for a plurality of print jobs to be performed by one or more print job processing resources in one or more document production environments and identifying a print job size distribution for the plurality of print jobs. One or more performance metrics such as, job turnaround time, job inter-arrival time, average job size, resource utilization and process efficiency, may be determined. The performance metrics may each relate to at least a portion of the one or more print job processing resources. It may be determined whether the print job size distribution exhibits a heavy-tail characteristic and a performance report may be prepared. The performance report may include the one or more determined performance metrics, and, if the print job size distribution exhibits a heavy-tail characteristic, an indication that the one or more determined performance metrics were calculated using a heavy-tailed job size distribution. The performance report may be printed and if the print job size distribution does not exhibit a heavy-tail characteristic, the plurality of jobs may be processed with the one or more print job processing resources based on the performance report. 
     In an embodiment, a production process performance reporting system may include a plurality of print job processing resources, a data collection system operable to coordinate the flow of print jobs to the print job processing resources and a computer-readable storage medium containing one or more programming instructions for performing a method of providing a report of performance metrics in a document production environment. The method may include receiving, by the data collection system, job size information for a plurality of print jobs to be performed by one or more print job processing resources in one or more document production environments and identifying a print job size distribution for the plurality of print jobs. The data collection system may determine one or more performance metrics such as job turnaround time, job inter-arrival time, average job size, resource utilization and process efficiency. The one or more performance metrics may each relate to at least a portion of the one or more print job processing resources. The data collection system may determine whether the print job size distribution exhibits a heavy-tail characteristic. If so, a performance report of the one or more determined performance metrics may be prepared. The performance report may indicate that the one or more determined performance metrics were determined using a heavy-tailed job size distribution. The performance report may be distributed to a user. 
     In an embodiment, a computer-implemented method of providing a report of performance metrics in a production process may include receiving, with a computer, job size information for a plurality of jobs to be performed by one or more resources in one or more production environments and identifying a job size distribution for the plurality of jobs. One or more performance metrics, such as job turnaround time, job inter-arrival time, average job size, resource utilization and process efficiency, may be determined with the computer. The performance metrics may each relate to at least a portion of the one or more resources. It may be determined with the computer whether the job size distribution exhibits a heavy-tail characteristic. A performance report may be prepared that includes the one or more determined performance metrics, and, if, the job size distribution exhibits a heavy-tail characteristic, an indication that the one or more determined performance metrics were calculated using a heavy-tailed job size distribution. The performance report may be printed and, if the print job size distribution does not exhibit a heavy-tail characteristic, the plurality of jobs may be processed with the one or more print job processing resources based on the performance report. In an embodiment, a computer-implemented method of providing a report of performance metrics in a production process may include receiving, by a computer, job size information for a plurality of jobs to be performed by one or more resources in one or more production environments and identifying a job size distribution for the plurality of jobs. One or more performance metrics, such as job turnaround time, job inter-arrival time, average job size, resource utilization and process efficiency may be determined with a computer. The performance metrics may each relate to at least a portion of the one or more resources. It may be determined with the computer whether the job size distribution exhibits a heavy-tail characteristic. If so, a performance report of the one or more determined performance metrics may be prepared. The performance report may indicate that the one or more determined performance metrics were determined using a heavy-tailed job size distribution. The performance report may be distributed to a user. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an exemplary print shop production environment according to an embodiment. 
         FIG. 2  depicts a graphical representation of a first job size distribution from a production environment according to an embodiment. 
         FIG. 3  depicts a graphical representation of a second job size distribution from another production environment according to an embodiment. 
         FIG. 4  depicts a plot of ln(CCDF) versus ln(job size) for a thin-tailed job size distribution, such as that illustrated in  FIG. 2  according to an embodiment. 
         FIG. 5  depicts a plot of ln(CCDF) versus ln(job size) for a heavy-tailed job size distribution, such as that illustrated in  FIG. 3  according to an embodiment. 
         FIG. 6  illustrates the behavior of a heavy-tailed distribution as compared to a thin-tailed distribution over a range of job sizes. 
         FIG. 7  depicts an exemplary plot of decay rate of a job size distribution versus certain job size threshold values according to an embodiment. 
         FIG. 8  depicts an exemplary flow chart of providing a report of performance metrics according to an embodiment. 
         FIG. 9  depicts an environment suitable for providing a report of performance metrics in a production process according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     For purposes of the discussion below, a “print shop” refers to an entity that includes a plurality of document production resources, such as printers, cutters, collators and the like. A print shop may be a freestanding entity, including one or more print-related devices, or it may be part of a corporation or other entity. Additionally, the print shop may communicate with one or more servers by way of a local area network, a wide area network, such as the Internet or the World Wide Web or the like. 
     A “job” refers to a logical unit of work that is to be completed for a customer. A job may include one or more print jobs from one or more clients. A production system may include a plurality of jobs. Although the disclosed embodiments pertain to document production systems, the disclosed methods and systems can be applied to production systems in general. 
     A “print job” refers to a job processed in a document production system. For example, a print job may include producing credit card statements corresponding to a certain credit card company, producing bank statements corresponding to a certain bank, printing a document, or the like. Although the disclosed embodiments pertain to print jobs, the disclosed methods and systems can be applied to jobs in general in other production environments, such as automotive manufacturing, semiconductor production and the like. 
       FIG. 1  shows an example of a production environment  50 , in this case, exemplary elements of a print shop. Print jobs may enter the print shop manually or electronically and be collected at an electronic submission system  55  such as a computing device and/or scanner. Jobs are sorted and batched at the submission system or another location before being delivered to one or more print engines such as a color printer  56 , black-and-white printer  57  and/or a continuous feed printer  58 . Jobs may exit the print engine and be delivered to one or more finishing devices or areas such as a collator  60 , cutter  62 , and/or binder  64 . The finishing areas may include automatic or manual areas for such finishing activities and they also may include an automatic or manual inserter  70 . Finally, jobs may move to a postage metering station  72  and/or shipping station  74 . Jobs may move from one location to another in the print shop by automatic delivery or manual delivery such as by hand or by one or more paper carts  81 - 85 . 
     A job size distribution may describe a probability distribution of a real-valued random variable. Examples of types of job size distributions may include normal distributions, exponential distributions, logarithmic distributions, cumulative distributions and the like. 
     A group of jobs having a large job size distribution may be referred to as having a heavy-tailed distribution. A heavy-tailed distribution may be characterized as a job size distribution processing a tail that decays slowly. In other words, as the value of the random variable increases, a probability associated with the random variable decreases. Heavy-tailed distributions may have many small jobs mixed with a few very large jobs. As such, even though the majority of the job sizes are small, a substantial contribution to the mean or variance for the jobs considered in the distribution may come from the few large jobs. Accordingly, the difference between the mean and median may be pronounced for heavy-tailed distributions. 
     In an embodiment, X may be a random variable with a cumulative density function (“CDF”), F(x)=P[X≦x]. The area under the CDF from 0 to X as X approaches infinity may be equal to one. A complementary CDF(“CCDF”) may be represented by F c (x)=P[X&gt;x], where the CCDF=1−CDF. The CDF may be heavy-tailed if the CCDF˜cx −α  where α is between zero and two. As such, 
     
       
         
           
             
               
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     Accordingly, the decay rate of a CDF for large job sizes may be equal to α. The decay rate of the CDF may be represented by the slope of the CDF. 
       FIG. 2  illustrates a graphical representation of a first job size distribution (JSD 1 ) from a production environment.  FIG. 3  illustrates a graphical representation of a second job size distribution (JSD 2 ) from a different production environment.  FIG. 4  illustrates a plot of the natural log (“ln”) of CCDF versus ln(job size) for JSD 1 .  FIG. 5  illustrates a plot of ln(CCDF) versus ln(job size) for JSD 2 . 
     As illustrated by  FIG. 4 , the 
                 lim     x   -&gt;   ∞       ⁢       ln   ⁡     (   CCDF   )         ln   ⁡     (   x   )           ,         
or the slope of the curve  400  as x approaches very large values, where x represents job size, is approximately −6.4 for large job sizes. As illustrated in  FIG. 5 , the slope of the curve  500  is approximately −1.26 for large job sizes. In other words, for JSD 1 , α˜6.4 and for JSD 2 , α˜1.26. As such, JSD 1  may be considered a thin-tailed distribution because α˜6.4 (i.e., outside the range of minimum and maximum decay values, i.e., 0&lt;α&lt;2 range for heavy-tailed distributions). However, JSD 2  may be considered a heavy-tailed distribution, because α˜1.26 (i.e., inside the range of minimum and maximum decay values, i.e., 0&lt;α&lt;2 range for heavy-tailed distributions).
 
     In an embodiment, the job size distribution may be tested for a heavy-tail characteristic by computing a decay rate of a complementary cumulative density function for the job size distribution for very large values of job sizes. 
     A heavy-tailed distribution may require significantly more data than a normal distribution before an accurate mean may be calculated. This is because, for normal distributions, the sample mean converges to the population mean inversely as the square root of the sample size. As such, for large sample sizes, the sample mean may be used as the population mean. The sample mean for heavy-tailed distributions, on the other hand, may converge to the population means inversely as n 1−(t/α) . As α approaches 1, the convergence rate may be very poor and the rages done on heavy-tailed distributions may be inaccurate. 
     This problem is illustrated by  FIG. 6 , which shows a plot of the sample mean for a normal distribution as compared to a heavy-tailed distribution. The index of stability, α, for the heavy-trailed distribution  605  is 0.6. In comparison, the normal distribution  600  has an index of stability α=2. The index of stability may represent the decay rate of a complementary cumulative density function or the like. In an embodiment, the index of stability may be compared to a threshold value. For example, if the index of stability of a CCDF is less than a threshold value, the job size distribution may be identified as a heavy-tailed distribution. Similarly, if the index of stability of a CCDF exceeds the threshold value, the job size distribution may be identified as a thin-tailed distribution. In an embodiment, the threshold may be a value in the range of zero to two. 
       FIG. 6  illustrates the behavior of a heavy-tailed distribution  605  as compared to a thin-tailed distribution  600  (the normal distribution) over a range of job sizes. As the job size increases, the means of the two distributions may start approaching the population means.  FIG. 6  shows a plot of the mean for a normal distribution  600  and a heavy-tailed distribution  605 . As illustrated by  FIG. 6 , the mean quickly converges for the normal distribution  600 . However, the mean of the heavy-tailed distribution  605  takes significantly longer to converge. As illustrated by  FIG. 6 , even after receiving 2000 datapoints  610 , the sample mean does not show convergence. 
     In an embodiment, if a distribution is determined to be a heavy-tailed distribution, the jobs in the distribution may be grouped into a plurality of subgroups such that at least one subgroup is not a heavy-tailed distribution. A job size distribution may be split into two or more subgroups by selecting a threshold job size and calculating the α associated with the distribution to the right of the threshold job size. For example, referring back to  FIG. 3 , if a job size threshold of 20,000 is chosen, then a left-most distribution segment may range from 0 to 20,000 and a right-most distribution segment may range from 20,000 to 2,696,637. Because the left-most distribution segment is bounded, it is not a heavy-tailed distribution. Because, in practice, the random variable is unlikely to assume infinite values, the job set may be finite thus producing a finite distribution. As such, the variability of the right-most distribution may decrease as the threshold value increases, and the right-most distribution segment may be approximated as a thin-tailed distribution. As a job size threshold is increased, a distribution may become less and less similar to a heavy-tailed distribution because the variability associated with the distribution decreases. 
       FIG. 7  illustrates a plot of the decay rate of the job size distribution depicted in  FIG. 3  versus certain job size threshold values. As illustrated by  FIG. 7 , as the job size threshold increases, so does the decay rate. Somewhere above a job size threshold of 5,000  700 , the resulting distribution  705  is no longer heavy-tailed because the decay rate is greater than or equal to two. As such, if 20,000 is selected as the job size threshold, the left-most distribution segment  710  (i.e., from 0 to 20,000) may be thin-tailed because the distribution is bounded while the left-most distribution segment  715  (i.e., from 20,000 to 50,000) may also mimic thin-tailed because the decay rate associated with the segment is greater than two. 
     In an embodiment, a data collection system may prepare a report of performance metrics where each metric may relate to at least a portion of one or more resources in the production environment. The metrics may measure job turnaround time, job inter-arrival time, average job size, resource utilization, process efficiency and/or the like. Job turnaround time may refer to the time required to completely process a job. Job inter-arrival time may refer to the time that has elapsed between job arrivals. Average job size may refer to the mean job size of the plurality of jobs in the job size distribution. Resource utilization may refer to the percentage of time that a resource is performing jobs over a time period. Process efficiency may refer to how efficient a resource is in performing assigned jobs. 
     In an embodiment, the data collection system may determine whether any of the metrics were calculated using an invalid job size distribution. An invalid job size distribution may be one that produces invalid measures. For example, a job size distribution may be invalid if it exhibits a heavy-tail characteristic, has properties similar to a heavy-tail distribution or the like. If a computed metric is determined to be invalid, a report may indicate that the metric was calculated using an invalid job size distribution, such as a heavy-tailed job size distribution. Based on the report, subsequent decisions affecting the scheduling and routing of jobs may be made. For example, if a job size distribution exhibits a heavy-tail characteristic, it may be processed using an autonomous cell that is designed to process specific ranges of job sizes. 
     In an embodiment, a report may only be generated for those job size distributions that produce one or more invalid metrics. For example, if a job size distribution is determined to be thin-tailed or normally distributed, a report may not be generated. 
     In another embodiment, performance metrics may be collected from a plurality of production environments. Accordingly, the data collection system may determine whether the metrics calculated using an aggregate job size distribution that includes jobs from one or more environments among the plurality of production environments is invalid. 
     In an embodiment, if a report includes one or more metrics that were determined using an underlying heavy-tailed distribution, a user may utilize the report information in various ways. For example, a heavy-tailed inter-arrival time distribution may alert a user that performance characteristics determined by queuing network models that utilize the distribution may lead to inaccurate results. A queuing network model may approximate real queuing situations or systems so that queuing behavior may be analyzed. Often, queuing network models utilize a coefficient of variation of inter-arrival time. A coefficient of variation may be the ratio of the standard deviation of the distribution to the mean of the distribution. For heavy-tailed distributions, if the coefficient of variation is invalid, performance metric computations utilizing the metrics may also be invalid. A G/G/m queuing model may be used as an example, where the first ‘G’ represents a general inter-arrival time distribution, the second ‘G’ represents a general processing time distribution and the ‘m’ represents identical servers. A mean waiting time of a G/G/m queuing model, may be represented by: 
                 [         c   a   2     +     c   e   2       2     ]     [         u   ⁢       2   ⁢     (     m   +   1     )           -   1       m   ⁡     (     1   -   u     )         ]     ⁢     t   e           
where c a  is the coefficient of variation of inter-arrival time, c e  is the coefficient of variation of production time on a printer, u is the average utilization of the printer, m is the number of printers and t e  is the average processing time on a printer.
 
     Because heavy-tailed distributions may have very high variances that do not converge as the sample size grows, if the underlying distribution used for calculating c a  and c e  is heavy-tailed, then these coefficient of variation values are likely invalid. As such, the estimate of mean queue waiting time and optimization studies based on invalid performance metrics is likely invalid. Optimization studies based on invalid performance metric computations may also be invalid. As such, providing alerts to users of invalid data may be beneficial. 
     In another embodiment, a user may be alerted to the existence of a heavy-tailed distribution in performing hypothesis testing. Hypothesis testing may refer to a method of determining whether two job size distributions are statistically different. A metric derived from a heavy-tailed distribution that is used in a hypothesis test may not provide a statistically accurate result. For example, mean process cycle efficiency measures may be determined using a heavy-tailed distribution both before and after process changes are made. The two distributions may then be subjected to a hypothesis test to determine whether the process changes affected any process cycle efficiency metrics. Because the measures were calculated using a heavy-tailed distribution, however, the results of the hypothesis test may yield invalid conclusions. 
     In another embodiment, a user may be alerted to the existence of a heavy-tailed distribution in the determination of process capability. Process capability refers to the ability of a process to operate within one or more defined parameters, such as an upper limit, a lower limit or the like. For example, a turnaround time distribution may exhibit a heavy-tailed characteristic. If a service level agreement provides that only a certain percentage of jobs can have job processing times that exceed an upper specification limit, then achieving this percentage may be unlikely using a heavy-tailed turnaround time distribution because such a distribution has a significant probability of having an actual turnaround time that exceeds the percentage. As such, a user may be alerted to use alternative scheduling policies that take into account the heavy-tailed characteristics or to use service level contracts that are more flexible. 
     In an embodiment, once generated, the report may be distributed to one or more users. The data collection system may distribute a performance report to a user indicating that the present job size distribution exhibits a heavy-tail characteristic. The performance report may be distributed to users by printing, emailing, faxing, scanning or the like. In an embodiment, the performance report may be distributed to a remote user by a communications network or the like. 
       FIG. 8  depicts an exemplary flow chart of providing a report of performance metrics according to an embodiment. Job size information for a plurality of print jobs may be received  800  and a job size distribution may be identified  805  for the plurality of print jobs. The data collection system may determine  810  performance metrics relating to at least a portion of the resources. The performance metrics may include job turnaround time, average job size, resource utilization, process efficiency and the like. The data system may determine  815  whether the job size distribution exhibits a heavy-tail characteristic. If so, a performance report containing the determined performance metrics and an indication that the metrics were determined using a heavy-tailed distribution may be prepared  820 . The performance report may then be distributed  825  to a user. 
       FIG. 9  depicts an environment suitable for practicing the illustrative embodiments. A data collection system  910  may be in communication with a non-transitory computer-readable storage medium  940  and one or more production environments  900  via a network  920 . The production environment  900  may include resources  930   a -N such as a printer, a copier, a binder, a hole-punch, a collator, a sealer or any other equipment used to process jobs. The collection system  910  may be implemented on a stand-alone computer system or may be integrated into the resources. The data collection system  910  may also be implemented by distributed components such as separate electronic devices. A network  920  may interconnect the resources  930   a -N with the data collection system  910 , as illustrated in  FIG. 9 . The network  920  may include a local area network (LAN) or a wide area network (WAN), such as the Internet, the World Wide Web or the like. The network may also be formed by communication links that interconnect the data collection system  910  and the resources  930   a -N. Alternatively, the disclosed embodiments may be practiced in environments where there is no network connection. 
     It will be appreciated that various of the above-disclosed and other features and functions, or alternatives thereof, may be desirably combined into many other different systems or applications. Also that various presently unforeseen or unanticipated alternatives, modifications, variations or improvements therein may be subsequently made by those skilled in the art which are also intended to be encompassed by the following claims.