Abstract:
A work content variation control system includes an apparatus having a computer-readable medium encoded with a computer program. The computer program, when executed, receives order data for a family grouping of a plurality of ordered products, converts the order data to work content, groups the order data with like order data with respect to the work content, creates parsing rules with respect to the work content and defines setup rules for use to schedule assembly of the ordered products.

Description:
BACKGROUND 
       [0001]    The present disclosure relates generally to information handling systems, and more particularly to a work content variation control system. 
         [0002]    As the value and use of information continues to increase, individuals and businesses seek additional ways to process and store information. One option is an information handling system (IHS). An IHS generally processes, compiles, stores, and/or communicates information or data for business, personal, or other purposes. Because technology and information handling needs and requirements may vary between different applications, IHSs may also vary regarding what information is handled, how the information is handled, how much information is processed, stored, or communicated, and how quickly and efficiently the information may be processed, stored, or communicated. The variations in IHSs allow for IHSs to be general or configured for a specific user or specific use such as financial transaction processing, airline reservations, enterprise data storage, or global communications. In addition, IHSs may include a variety of hardware and software components that may be configured to process, store, and communicate information and may include one or more computer systems, data storage systems, and networking systems. 
         [0003]    IHSs are typically assembled in an assembly line where parts are added and software is installed in a process that begins with a number of part and ends with a finished product. In an effort to significantly reduce manufacturing costs in a highly configurable build to order environment (e.g., in an IHS build to order environment), a progressive assembly line (e.g., lean lines) may be implemented in the manufacturing facility. Traditionally, an assembly line works best in a low work content variation environment. This may be due to the fact that high work content variation results in assembly line inefficiencies because the slowest assembly station in the assembly line may shift each time a different configuration is assembled. In other words, the production line is as fast as the slowest station and as the configuration changes, the slowest portion of the assembly time or the bottleneck, may move from one station to another station because different parts or different numbers of parts are being assembled at a given station. 
         [0004]    As such, what is needed is work content variation control system to develop rules that production control can use to schedule factory assembly, while minimizing work content variation in the lean lines. The system may minimize work content variation at the platform level within a setup which results in better assembly line efficiencies, improved flow throughout the manufacturing factory and a better rate predictability per setup. 
         [0005]    Accordingly, it would be desirable to provide an improved work content variation control system absent the disadvantages discussed above. 
       SUMMARY 
       [0006]    According to one embodiment, a work content variation control system includes an apparatus having a computer-readable medium encoded with a computer program. The computer program, when executed, receives order data for a family grouping of a plurality of ordered products, converts the order data to work content, groups the order data with like order data with respect to the work content, creates parsing rules with respect to the work content and defines setup rules for use to schedule assembly of the ordered products. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]      FIG. 1  illustrates a block diagram of an embodiment of an information handling system (IHS). 
           [0008]      FIG. 2  illustrates an embodiment of a graph showing % of volume vs. configurations sorted by work content and work content time used in an embodiment of a work content variation control system. 
           [0009]      FIG. 3  illustrates a high-level flow chart of an embodiment of a method for work content variation control. 
           [0010]      FIG. 4  illustrates a detailed flow chart of an embodiment of a method for work content variation control. 
           [0011]      FIG. 5   a  illustrates a chart showing embodiments of different parsing rules for use in the methods provided in  FIGS. 3 and 4 . 
           [0012]      FIG. 6  illustrates embodiment of three balanced bar charts showing work content at each of a number of work stations along an assembly line. 
       
    
    
     DETAILED DESCRIPTION 
       [0013]    For purposes of this disclosure, an IHS  100  includes any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes. For example, an IHS  100  may be a personal computer, a network storage device, or any other suitable device and may vary in size, shape, performance, functionality, and price. The IHS  100  may include random access memory (RAM), one or more processing resources such as a central processing unit (CPU) or hardware or software control logic, read only memory (ROM), and/or other types of nonvolatile memory. Additional components of the IHS  100  may include one or more disk drives, one or more network ports for communicating with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display. The IHS  100  may also include one or more buses operable to transmit communications between the various hardware components. 
         [0014]      FIG. 1  is a block diagram of one IHS  100 . The IHS  100  includes a processor  102  such as an Intel Pentium™ series processor or any other processor available. A memory I/O hub chipset  104  (comprising one or more integrated circuits) connects to processor  102  over a front-side bus  106 . Memory I/O hub  104  provides the processor  102  with access to a variety of resources. Main memory  108  connects to memory I/O hub  104  over a memory or data bus. A graphics processor  110  also connects to memory I/O hub  104 , allowing the graphics processor to communicate, e.g., with processor  102  and main memory  108 . Graphics processor  110 , in turn, provides display signals to a display device  112 . 
         [0015]    Other resources can also be coupled to the system through the memory I/O hub  104  using a data bus, including an optical drive  114  or other removable-media drive, one or more hard disk drives  116 , one or more network interfaces  118 , one or more Universal Serial Bus (USB) ports  120 , and a super I/O controller  122  to provide access to user input devices  124 , etc. The IHS  100  may also include a solid state drive (SSDs)  126  in place of, or in addition to main memory  108 , the optical drive  114 , and/or a hard disk drive  116 . It is understood that any or all of the drive devices  114 ,  116 , and  126  may be located locally with the IHS  100 , located remotely from the IHS  100 , and/or they may be virtual with respect to the IHS  100 . 
         [0016]    Not all IHSs  100  include each of the components shown in  FIG. 1 , and other components not shown may exist. Furthermore, some components shown as separate may exist in an integrated package or be integrated in a common integrated circuit with other components, for example, the processor  102  and the memory I/O hub  104  can be combined together. As can be appreciated, many systems are expandable, and include or can include a variety of components, including redundant or parallel resources. 
         [0017]    In an embodiment, the present disclosure provides a work content variation control system to create rules that production control can use to schedule assembly processes for build-to-order products. One example is to use the work content variation control system of the present disclosure to plan assembly of IHSs by scheduling like systems/processes with like systems/processes on a given assembly line to create similar operation times (e.g., work content) at each of a plurality of work stations along an assembly line. In other words, if, for example, an IHS manufacturing facility has three assembly lines for assembling IHSs, the ordered IHSs that require assembly steps of similar duration in time may be assembled on the same one of the three assembly lines. Thus, the IHSs requiring the lowest operation times may be assembled on line  1 , those requiring the highest operation times may be assembled on line  3  and those in between, may be assembled on line  2 . As such, down-time at each station along the assembly line will be minimized to improve assembly line efficiencies and create an improved assembly product flow. A factor in determining scheduling may be at a platform level of an IHS family to reduce set-up for the assembly lines. 
         [0018]    The work content variation control system of the present disclosure may be used to parse work content variation and create rules that production control can use to schedule manufacturing in an assembly line environment. 
         [0019]    In an embodiment, the system may use historical data and/or market trends to receive order data and converts unique part numbers (PNs) to unique commodities (e.g., Hard Drives, Processors, etc.). Then, based on actual time studies (or estimates for new product platforms) the system assigns an install/assembly cycle time for each commodity at a given work station along the assembly line. At this point total work content may be calculated per system that is to be assembled. Then, based on total work content, the system may investigate what are the main commodities that drive work content cycle time variability within the platform/family. Once the commodities that drive variability are identified, the parsing rules are created and communicated to production control to schedule manufacturing for each available assembly line so that each line is assembling systems having similar work time at each operation station along the assembly line, thereby minimizing down time at any one station along the line. 
         [0020]    It should be understood by a person having ordinary skill in the art that an embodiment of the present disclosure combines actual assembly cycle times per commodity with unique configurations to mathematically predict work content variation within a platform or product family. It should also be understood that an embodiment of the present disclosure provides a way for comparing each individual commodity versus total work content to determine which assembly processes are the main contributors to work content variation. In addition, it should be understood that an embodiment of the present disclosure provides parsing rules that are based on those commodities that drive total work content variation at a product platform level. In an embodiment, a visual system of analyzing a range of configurations within a platform is provided and thus, allows for filtering out the main commodities contributing to work content variation. In addition, once the parsing rules are setup in a factory planner/scheduling tool, the process may be automated. Using automation, minimal intervention is needed from production control. 
         [0021]      FIG. 2  illustrates an embodiment of a graph showing % of volume vs. configurations sorted by work content and work content time (e.g., in seconds) vs. configurations sorted by work content used in an embodiment of a work content variation control system. The % volume is shown as line  136 . The work content is shown as line  138 . Using a work content variation control system, a production control planner can improve efficiency of each of a plurality of assembly lines by scheduling work on the assembly line having a high efficient use of each assembly/work station on each assembly line. Using the graphical depiction of  FIG. 2 , a planner can schedule work for each assembly line based on the work content (e.g., amount of time) for each station along the assembly process for the IHS. In other words, IHSs ordered having a low work content for each assembly step are shown as low work content systems  140 . IHSs ordered having a medium work content for each assembly step are shown as medium work content systems  142 A and  142 B. And, IHSs ordered having a high work content for each assembly step are shown as high work content systems  144 . As should be understood, the system of  FIG. 2  could support  4  assembly lines (e.g.,  140 ,  142 A,  142 B and  144 ). However, any number of assembly lines and any number of work stations on each assembly line may utilize the systems and methods of the present disclosure. 
         [0022]      FIG. 3  illustrates a high-level flow chart of an embodiment of a method  150  for work content variation control. The method  150  starts at  152  where orders have been received. In an embodiment, the orders may be for build-to-order IHSs, such as the IHS  100 . However, the systems of the present disclosure may be utilized on assembly of any type of product. The method  150  then proceeds to block  154  where the method  150  pulls order data to determine family groupings of the orders. By grouping families of orders the method  150  may recognize families such as server IHSs, notebook IHSs, desktop IHSs, or even different product lines within each of these different types of IHSs. Other types of family groupings may be used. The method  150  then proceeds to block  156  where the method  150  reviews the orders, determines what parts or assemblies are required for each order and converts the order to a work content for a particular order. In other words, the method  150  determines how much time will be required to assemble the ordered IHS and how much time will be required at each assembly station for the particular order. The method  150  then proceeds to block  158  where the method groups similar work content orders by creating groups where the orders in each group have similar work content requirements as a whole, and/or in each work station along the assembly line. For example, the method  150  may group orders into groups for low work content systems  140 , medium work content systems  142 A,  142 B and high work content systems  144 , as seen in  FIG. 1 . 
         [0023]    The method  150  then proceeds to block  160  where the method  150  creates parsing rules with respect to work content for the orders. As such, the method  150  creates rules to parse or break-up assembly of the ordered products (e.g., IHSs) into multiple work station operations along the assembly path. For example, assembly of an IHS may be parsed into groupings for adding parts to a chassis or a mother board. The added parts may include a number of processors  102 , a number of memory modules  108 , a number of hard drives  116 , a number of expansion cards/peripherals  128 , such as the graphics processor  110 , the I/O controller  122 , and/or a variety of other devices. The method  150  then proceeds to block  162  where the method defines set-up rules for an IHS (e.g., IHS  100 ) to use to schedule assembly of a plurality of orders along a plurality of assembly lines using the rules parsed in block  160 . The rules may be defined by features such as a volume/number limits for parts to be added. For example, a rule may be that an order requiring ≦1 processors  102 , ≦4 memory modules  108 , ≦2 hard disk drives  116  and ≦5 expansion cards  128  are scheduled to be assembled on the assembly line for low work content systems  140 . See  FIG. 5 . In another example, a rule may be that an order requiring ≦2 processors  102 , ≦4 memory modules  108 , ≦4 hard disk drives  116  and ≦6 expansion cards  128  are scheduled to be assembled on the assembly line for medium work content systems  142 . See  FIG. 5 . In yet another example, a rule may be that an order requiring ≦2 processors  102 , ≦8 memory modules  108 , ≦4 hard disk drives  116  and ≦ 8  expansion cards  128  are scheduled to be assembled on the assembly line for high work content systems  144 . See  FIG. 5 . It is to be understood that other factors may be used to create the rules and other values may be used to create the rules. 
         [0024]    The method  150  then proceeds from block  162  to block  164  where the method  150  communicates the rules defined in block  162  to a scheduling IHS, such as the IHS  100 , so that the scheduling IHS may calculate an assembly schedule. The calculated assembly schedule may then be communicated to a production control group for setting-up the manufacturing/assembly of the ordered products along the respective assembly lines per the schedule and the products may then be assembled. The method then ends at block  166 . 
         [0025]      FIG. 4  illustrates a detailed flow chart of an embodiment of a method  170  for work content variation control. The method  170  is similar to method  150  described above with respect to  FIG. 3 . The method  170  starts at  172  where orders have been received. In an embodiment, the orders may be for build-to-order IHSs, such as the IHS  100 . However, the systems of the present disclosure may be utilized on assembly of any type of product. The method  170  then proceeds to block  174  where the method  170  pulls order data to determine family groupings of the orders. By grouping families of orders the method  170  may recognize families such as server IHSs, notebook IHSs, desktop IHSs, or even different product lines within each of these different types of IHSs. Other types of family groupings may be used. Next, the method  170  proceeds to decision block  176  to determine whether a sample size is relevant to allow for accurate validation. In an embodiment, a sample size may be relevant if it includes more than 1000 samples. However, it is to be understood that any number of samples may be used. If no, the number of samples is not relevant, the method  170  returns to block  174 . If yes, the number of samples is relevant, the method  170  proceeds to block  178  where the method  170  creates a summary of all build part numbers from the sample. The build part numbers may be the part numbers for the parts used to assemble the ordered products. The method  170  then proceeds to block  180  where the method  170  converts the build part numbers to unique commodities. The method  170  then proceeds to block  182  where the method  170  assigns work content time (e.g., the amount of time for a given operation) per commodity, where the assigned time is based on actual historical recorded times for similar work. The method  170  then proceeds to block  184  where the method  170  calculates a cumulative work content value for each of the ordered products. This calculated value may include a sum of the work content values (e.g., work times) for each step in an assembly process for each of the ordered products. 
         [0026]    After calculating the cumulative work content per system at block  184 , the method  170  then proceeds to decision block  186  where the method determines whether the calculated work content is validated by being similar to work content values for similar products previously assembled. If no, the calculated work content is not validated, the method  170  returns to block  180 . However, if yes, the calculated work content is validated, the method  170  proceeds to block  188  where the method  170  sorts the ordered products/systems from least complex (e.g., least added parts) to most complex (e.g., most added parts). The method  170  then proceeds to block  190  where the method creates a total work content  138  and volume curve  136 , such as that shown in  FIG. 2 . The method  170  then proceeds to block  192  where the method  170  determines cutoff points  192 A and  192 B along the curves (e.g.,  136 ,  138 ). The method  170  then proceeds to block  194  where the method  170  checks each commodity work content versus the total work content curve. 
         [0027]    After the method  170  checks each commodity work content versus the total work content curve at block  194 , the method  170  then proceeds to decision block  196  to determine whether commodity work content follows the total work content curve. If no, the commodity work content does not follow the total work curve, the method  170  proceeds to block  198  where the method  170  does not use the commodity to define the rules. On the other hand, if yes, the commodity work contend does follow the total work curve, the method  170  proceeds to block  200  where the method  170  determines quantity rules based on cutoffs defined in the total work content curve (e.g., work content curve  138 ). The quantity rules may relate to a quantity of parts needed to complete assembly of the ordered products. The method  170  then proceeds to block  202  where the method  170  defines setups for the assembly process based on top or most common commodities. The method  170  then proceeds to block  204  where the method  170  applies the rules to historical data from similarly produced products. 
         [0028]    After the method  170  applies the rules to historical data from similarly produced products at block  204 , the method  170  proceeds to decision block  206  to determine whether the setup rules validate the projected order groupings. If no, the setup rules do not validate the projected order groupings, the method  170  returns to block  192 . On the other hand, if yes, the setup rules do validate the projected order groupings, the method  170  proceeds to block  208  where the method  170  groups like-with-like setups and assigns these to specific assembly lines. As such, the assigned ordered products should be assigned to assembly lines where each of the different ordered products has similar assembly times or work content for similar work activities at each work station along the assembly line. The method  170  then proceeds to block  210  where the method  170  communicates the setup rules to a scheduling IHS, such as the IHS  100 . Next, the method  170  proceeds to block  212  where the method  170  applies the setup rules to a factory planner/scheduler system. After applying the setup rules to a factory planner/scheduler system, the method  170  ends at block  214 . 
         [0029]      FIG. 5   a  illustrates a chart showing embodiments of different parsing rules for use in the methods provided in  FIGS. 3 and 4 . As discussed above, the rules may be defined by features such as a volume/number limits for parts to be added. For example, a rule may be that an order requiring ≦1 processors  102 , ≦4 memory modules  108 , ≦2 hard disk drives  116  and ≦5 expansion cards  128  are scheduled to be assembled on the assembly line for low work content systems  140 . In another example, a rule may be that an order requiring ≦2 processors  102 , ≦4 memory modules  108 , ≦4 hard disk drives  116  and ≦6 expansion cards  128  are scheduled to be assembled on the assembly line for medium work content systems  142 . In yet another example, a rule may be that an order requiring ≦2 processors  102 , ≦8 memory modules  108 , ≦4 hard disk drives  116  and ≦8 expansion cards  128  are scheduled to be assembled on the assembly line for high work content systems  144 . It is to be understood that other factors may be used to create the rules and other values may be used to create the rules. In an embodiment, parsing rules may vary depending on platform/family of the ordered products. Also, the rules may relate to actual product outputs as well as expected outputs. Additional features that may factor in to the rules may include software burn-in rate, traditional failure rate, custom factory integration, total work volume, highest work content, number of work stations along the assembly line, units produced per hour, number of parts in the ordered product, type of parts in the ordered product (e.g., type of chassis, and etc.), number of parts used daily, combined units per hour, labeling/packaging, order fulfillment system/factory planner used for scheduling and/or any variety of other factors. 
         [0030]      FIG. 6  illustrates embodiment of three balanced bar charts  220 ,  222 ,  224  showing work content at each of a number of work stations along an assembly line. The steps at each work station K 0 -K 9  may be value added (e.g., install part) or non-value added (e.g., rotate system in conveyer). These charts  220 ,  222 ,  224  show an output for methods  150  and/or  170  after the rules have been created, applied and the work content balanced based on total work content, sequence restrictions and a number of work stations (e.g., K 0 -K 9 ). The X-axis represents each work station (e.g., K 0 -K 9 ) in a progressive assembly line. Any number of stations may be used with the present disclosure. The Y-axis represents the total work content (e.g., in seconds) for each station. The charts  220 ,  222 ,  224  show the work balance per station and as the rules change the balance per station changes due to more or less work content. As should be understood, chart  220  represents the steps of work content for workstations K 0 -K 9  along an assembly line (e.g., low work content systems  140 ) where the work is scheduled using a variation control system of the present disclosure. chart  222  represents the steps of work content for workstations K 0 -K 9  along an assembly line (e.g., low work content systems  142 ) where the work is scheduled using a variation control system of the present disclosure. chart  224  represents the steps of work content for workstations K 0 -K 9  along an assembly line (e.g., low work content systems  144 ) where the work is scheduled using a variation control system of the present disclosure. It should also be understood that the charts  220 ,  222 ,  224  will change with each variation in ordered product as worked through methods  150  and/or  170 . 
         [0031]    Although illustrative embodiments have been shown and described, a wide range of modification, change and substitution is contemplated in the foregoing disclosure and in some instances, some features of the embodiments may be employed without a corresponding use of other features. Accordingly, it is appropriate that the appended claims be construed broadly and in a manner consistent with the scope of the embodiments disclosed herein.