Abstract:
Management software for increasing of return on assets (ROA) and, more particularly, to software-enabled systems, methods and apparatus using the metric profit per asset-hour (PPAH) for measuring and increasing profit generated by asset utilization to increase return on assets (ROA) and likewise return on equity (ROE).

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
       [0001]    This patent application claims priority from U.S. provisional patent application Ser. No. 61/698,729, filed Sep. 10, 2012, the entirety of which is incorporated herein by this reference thereto. 
     
    
     BACKGROUND OF THE INVENTION 
       [0002]    1. Technical Field 
         [0003]    This invention relates generally to the field of management software for increasing return on equity (ROE) and more particularly to software enabled systems, methods and apparatus for measuring and increasing profit generated by asset utilization to increase return on assets (ROA). 
         [0004]    2. Description of the Related Art 
         [0005]    Return on equity (ROE) is the highest summary level metric by which the historical financial performance of companies and management teams are judged by investors and the greater financial community. Return on equity measures the rate of growth of the shareholder equity in a business as profit produced in each new time period is added to cumulative past profits and equity investments made in prior times. ROE is the ultimate goal in financial performance, because the higher the ROE ratio, the faster the equity of shareholders is growing, and hence the faster the company&#39;s share price tends to rise. 
         [0006]    Referring to  FIG. 1 , the widely taught DuPont™ (“DuPont”) “profit formula”  100  is often used to explain the factors driving ROE. ROE  111  is comprised of three interacting financial ratios: assets/equity (leverage)  112 , profit/units (margin)  113 , and units/assets (asset turnover)  114  (shown in various algebraic representations (a)-(d). Algebraically, ROE=leverage×margin×turnover, which reveals how effectively a company&#39;s management used investors&#39; equity over a past period (typically a year or a quarter). 
         [0007]    The leverage ratio (assets per equity)  112  is largely determined by conditions in external financial markets and is not under the direct control of a company&#39;s management. Holding leverage to a constant, “f”  122  then, the remaining ratios that management can influence are margin  113  (profit per units) and asset turnover (units per assets)  114 . Taken together margin  113 ×asset turnover  114 =ROA  110 . Return on assets (ROA)  110  is another summary level financial indicator which tends to be monitored on an annual or semi-annual or quarterly basis. The ROA ratio indicates how effective management&#39;s decisions have been in a prior time period in generating profit from all the assets under their control. 
         [0008]    The assets arid unit components are reported in aggregate amounts over the entire period reported, which does not afford the information required for an analysis of the past interplay that existed between the various underlying factors that determined each nor a forward looking analysis of how those factors will influence the future performance. 
         [0009]    Although ROA is the vital high-level indicator of management&#39;s past performance, this backward-looking, historical summary level indicator of financial performance is of minimal usefulness to operating managers and executives who must make detailed, forward-looking, hour-to-hour, day-to-day, month-to-month decisions and plans regarding the most profitable use of assets. In short, improving ROA is a vital goal of managers and executives, but ROA does not serve as a useful metric in business operations. 
         [0010]    Instead of relying on the summary level metric of ROA to evaluate options for improving financial performance, managements rely on the detailed measurement of margin (profit per unit). A wide variety of profit analysis and product costing systems, ERP systems, and others calculate margin in great detail. However, controlling margin alone is not sufficient to drive up ROA. Again, ROA=Margin×Asset Turnover (units per assets). To increase ROA, management must be able to proactively manage both Margin and Asset Turnover together in as much detail as possible, for each transaction, order, production batch, customer, etc. 
         [0011]    However, until now, no computer-aided system has combined at any level of detail desired, on a forward-looking planning basis, margin and asset turnover data values to report the metric profit per asset-hour (PPAH). Consequently, instead of maximizing what investors actually want, higher ROA (in order to achieve the ultimate goal of higher ROE), management teams have traditionally measured and pursued the improvement of the only useful detailed profit indicator available to them margin. To allow management teams to effectively pursue their shareholders&#39; goal of higher ROA (to yield a higher ROE), management teams need access to a detailed, practical measure of ROA, or margin and asset turnover, or profit per asset-hour. The invention calculates and displays the metric of profit per asset-hour, incorporating both margin  113  and asset turn over  114 , at any level of detailed desired, as part of a forward-planning and decision-support environment which allows management teams to pursue the metric their investors actually want, higher ROA in order to achieve higher ROE and faster share price growth. 
       SUMMARY OF INVENTION 
       [0012]    A primary element of the present invention is a metric that measures the profit produced by an asset over a unit of time (second, minute, hour, etc.). This metric is expressed throughout as “profit per asset-hour hereafter, also “PPAH”). 
         [0013]    While the metric of profit per asset-time is expressed with the unit of time being an hour, the invention is not so limited. An hour may, in most cases, be the most incisive or convenient unit of time to use but in any particular case another different unit of time may prove more useful and could be used without departing from the invention as will be obvious from what follows. Thus, “profit per asset-unit of time” should be considered as having the same meaning as “profit per asset-hour” in describing and understanding the invention. 
         [0014]    Detailed measurement of the speed at which manufacturing assets deliver profit can advantageously guide management decision-making in accurately anticipating, pursuing, and accepting orders and allocating production capacity against those orders to get those assets to make money faster. PPAH also provides information that helps decision-makers consider different futures where they adjust customer, sales, and manufacturing planning in order to improve asset utilization and capital investment activities for increasing ROA. PPAH, when used as described herein by decision-makers to assess manufacturing, sales, and customer combinations, provides a means to better anticipate results in the future and adjust decision-making pertaining to product mix, customer mix, and asset mix to drive the maximization of ROA. 
         [0015]    The software, methods, apparatus and systems of the present invention provide management with powerful insight into what has driven ROA in the past and what are the best decisions moving forward to increase ROA. 
         [0016]    More specifically, in one embodiment, the present invention provides software that causes a computer to: extract selected data from one or more non-transitory databases of transactional processing management systems, such as enterprise resource planning systems, production management systems, other legacy systems, open source systems, proprietary systems, or the like; calculate various values from the extracted data including PPAH; and display the calculated results on a digital display device in an interactive format. 
         [0017]    The invention departs from known systems by calculating and reporting profit over a selected time period factoring in products, customers, margins, productivity and any number of other variables that have an impact on the metric PPAH. Moreover, the metric PPAH is calculated and reported for individual assets, customers, products, customer-product mix, etc. 
         [0018]    Because margin  113  and asset turnover  114  have to be measured and managed jointly to improve ROA  110 , such improvement is not necessarily achieved by simply adjusting these variables separately. The adjustment of margin  113  and asset turnover  114  to increase ROA  110  usually involves making tradeoffs increases and/or decreases in component values within the constrained limits of the components to yield improved ROA. Prior to the implementation of the metric PPAH, as made possible by the present invention, margin  113  has been almost universally used as the primary metric for profitability analysis and management. With the present invention providing management access to the new metric of PPAH, far more refined profit analysis and planning is made possible revealing new opportunities for management to increase ROA. 
         [0019]    Asset turnover  114  is traditionally measured only on a consolidated level for the various products of the company taken together, over all the assets used on an annual, semi-annual or quarterly basis. Although the data necessary to calculate the PPAH metric, at the hourly level and for each transaction, order, asset, each customer, product, etc., are typically captured by production control systems for the various products made by a company, prior to the present invention this data has not been extracted and processed for each transaction, order, asset, customer, product, etc., and integrated with other available data in a form useful for aiding management in analyzing past performance and making prospective marketing, sales, production, asset investment decisions on an hour-to-hour, day-to-day basis with the continuous improvement of ROA  110  as the goal. 
         [0020]    While the present invention has application to all industries, it has the greatest impact on the manufacturing sector where the assets employed (be they natural, man-made, or human) in production are significant. This is especially true for manufacturers who produce a wide variety and volume of products, stock-keeping units (SKUs), for an array of customers, often including multiple production facilities (hereafter, also “High Mix”). In industries such as chemicals, steel, semiconductors, electronic components, packaging, and paper, or the like, a single company may often produce hundreds, if not tens of thousands, of distinct product types and items. While such High Mix product manufacturers attempt to measure and control the unit profit margin of their products, their systems do not enable them to measure and manage PPAH, or the rate of cash contribution or profit flow per hour of asset utilization for a given transaction, order, product, customer, asset or any other variable that contributes to the calculation of ROA  110 . Manufacturers are also unable to discern the sensitive and non-linear relationship between margin  113  and asset turnover  114 . Profit analysis systems are traditionally based on margin per unit rather than profit per asset-hour (PPAH). Production control (PC) or manufacturing execution systems (MES) measure machine time used and physical unit throughput rates, but lack the integration with cost and financial information required to calculate profit per asset-hour which directly drives ROA  110 . With the ability of the present invention to measure, report, and explore the future impact of upcoming business decisions on ROA  110 , decision-makers in marketing, sales, production, operations, finance, and all other functional areas of a complex enterprise, have for the first time the ability to analyze, accurately anticipate, plan, and positively influence the rate of cash contribution or profit per asset per hour. The present invention, for the first time, makes it possible for management teams to see and understand precisely—down to the transaction, or sales order level, and the like where trade-off adjustments to prices, costs, productivity, volume, and product mix speed up the overall flow of profits through the assets and thereby improve ROA  110 . 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0021]      FIG. 1  is a schematic diagram that depicts algebraically the DuPont&#39; formula for return on investment (ROE) and return on assets (ROA), expressed in various algebraic notations (a)-(e) according to the prior art; 
           [0022]      FIG. 2  is a schematic diagram that depicts the formula for profit per asset hour (PPAH) according to the present invention and as useful for individual assets, products, customers etc.; 
           [0023]      FIG. 3  is a flowchart of the PPAH system of an embodiment of the invention including process steps and components; 
           [0024]      FIG. 4  is a block diagram of the integrated profit per asset-hour planning system, (PPAHPS) according to an embodiment of the invention; 
           [0025]      FIG. 5  is a graph depicting an example of a way to chart PPAH for profit maximization, according to an embodiment of the invention; 
           [0026]      FIG. 6  is a block schematic diagram of a system in the exemplary form of a computer system according to an embodiment; and 
           [0027]      FIG. 7  is an exemplary PPAH formatted dataset. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0028]    Referring also to  FIG. 2 , a new metric—Profit Per Asset-Hour (PPAH)  330 , according to the present invention, is the metric of margin  113  multiplied by units per asset hour (UPAH)  202 . Using this metric, as described below, allows the performance character of manufacturing assets to be matched to the specific products they produce, including the product margin returned by the asset over time, by product, by customer, by order, or raw material used, and any other known factor that impacts or influences the metric PPAH. 
         [0029]    PPAH  330  also provides a basis for improving ROA  110  and hence ROE  101 . By extracting and aggregating the output units from the assets for a specified period of time, such as a minute, an hour, or any other measurable time unit, based on the type of products manufactured, and knowing the margin  113  of the products manufactured, PPAH  330  can be anticipated, calculated, evaluated, and adjusted to produce increases in financial returns. 
         [0030]    In a typical High Mix manufacturing company, the necessary data for calculating PPAH  330  is available unsystematically in the company&#39;s databases. These databases include, but are not limited to, the databases underlying transactional processing management systems (TPM Systems) including without limitation: enterprise resource planning systems (ERP), financial reporting systems (FRS), inventory and invoicing systems (IIS), marketing systems (MS), production control (PC), and manufacturing execution systems (MES). Collectively, these and like legacy systems are hereinafter referred to as “TPM Systems”. 
         [0031]    Useful transaction-level data and information that can be extracted from a company&#39;s existing TPM Systems may include but are not limited to: costs such as cost of material and direct labor cost for each product made, as well as indirect costs such as depreciation of equipment and other overheads allocated to individual products. Similarly, other important data that can be extracted includes but are not limited to: pricing details, volume incentives and promotions provided to customers, sales targets, sales forecasts inventory costs invoicing details from asset utilization, asset scheduling details, and production throughput rates. 
         [0032]    Referring to  FIG. 3 , a computer implemented system  300 , having sufficient processing power and storage capability, with a minimum of components, of the present invention generates a PPAH database  307  of saved formatted data variables and calculated results (data set) shown in expanded detail at  340 . Collected data  305  from a company&#39;s TPM System, such as, for example, data on products sold  312 , sales volumes (quantity)  313 , price per unit  314 , costs (of product)  315  (including direct and indirect costs), and asset  316  used in manufacturing of each product, is extracted by method step  301  and consolidated by method step  303  and stored in database  306  (referred to hereafter as “Input Data” database  306 ). Additional qualitative information on customers  311  and products  318  that may be needed to optimize customer and product mix also may be extracted  302  from TPM Systems  305 , consolidated  303  and stored in Input Data database  306 . In addition, some transactional information such as, but not limited to, seasonal material cost variations, changing prices, changing product volumes, that may impact profit per asset-hour  330  are also extracted  302  from TPM Systems  305 , consolidated  303  and stored in Input Data database  306 . The collected information in Input Data database  306  is used by PPAH integrated planning system (PPAHPS)  304  (described in greater detail below in connection with  FIG. 4 ) to make the calculations by method step  310  of the key financial and operational ratios such as but not limited to cost per unit  320 , profit per unit  321 , and units per asset-hour  322 , enabling the computation of a PPAH  330  for each transaction, order, product, asset, customer, etc. The formatted data variables from Input Data database  306  and computed key financial and operational ratios  314 ,  320 ,  321 ,  322  (hereafter also referred to collectively as “F&amp;O ratios”) and computed PPAH  330 , are stored in the PPAH Database Store  307  from which they can be displayed by method step  308  in a useful interactive format on a display device  309  (such as that illustrated in  FIG. 7 ). 
         [0033]    The saved, formatted data variables in PPAH Database Store  307  can be changed, as described more fully below, in which case the F&amp;O ratios and PPAH  330  are recalculated and stored in database  307  from which they can be displayed by method step  308 . 
         [0034]    Referring to  FIGS. 3 ,  4  and  7 , PPAHPS  304  ( FIG. 3 ) implemented on a computer system with peripheral storage systems Input Data database  306  and PPAH Database Store  307 . PPAHPS  304  comprises a computer  401 , having at least one processor for handling the data processing needs, a PPAH Configuration Data Store  404  containing business process flow and data transformational rules and a PPAH software store  402  that stores software that, when implemented by computer  401 , causes the computer  401  to, among other things, read, integrate, and format data from Input Data database  306  and calculate the F&amp;O ratios and PPAH  330  all of which (including the  340  dataset by which the ratios are calculated) are stored in PPAH Database Store  307  in a PPAH format from PPAH Format Store  403 , such as the format of PPAH formatted data-set  700  shown in  FIG. 7  and described below. This PPAH format enables the computation of PPAH  330  from the various input data elements and data variables in PPAH Database Store  307  without additional database searches. 
         [0035]    Transformation rules of PPAH Configuration Data Store  404  enable software from PPAH Software store  402  to cause the computer  401  to calculate the F&amp;O ratios and PPAH  330  using data from existing data stores such as TPM Systems and the like, or manually entering input data, or any combination thereof representing a subset of input data expressing transformational instructions, such as the actual of estimated PPAH for a customer, market segment, or product group during various time ranges, or other highly complex transformation schemes. Such transformational rules define a manner of collecting, organizing, and integrating the different input data elements to enable computer  401  to calculate the F&amp;O ratios and PPAH  330  under various forecasted or planned circumstances, requests, and other influences, and the like. 
         [0036]    In operation, data elements for computing the F&amp;O ratios and PPAH  330  are provided to the PPAH Format used by PPAHPS  304  from the Input Data database  306 . The data variables from Input Data database  306  are used to populate the PPAH format  700 . Computer  401  then runs the PPAHPS  304  a software program from PPAH Software Store  402  on the input data variables to compute the profit ratios,  320  to  322  (F&amp;O ratios) and PPAH  330 . The results are input to the PPAH format to generate the PPAH formatted dataset  700  similar to the exemplary format shown in  FIG. 7 . This resulting dataset  340 , formatted as shown in an exemplary format  700  ( FIG. 7 ) is stored in PPAH Database Store  307 , where it is accessible to and useful for decision-makers. 
         [0037]    PPAH Format Store  403 , PPAH Configuration Store  404 , and PPAH Software Store  402  interact with each other and data from Input Data database  306  whereby computer  401  performs the  310  method step of calculating the F&amp;O ratios and PPAH  330 , and displaying the data and calculated ratios on display device  309  in a format such as that shown in the example of  FIG. 7  in a manner well known to those skilled in the art. 
         [0038]    The interactive PPAH formatted dataset  700  enables values of individual cells to be changed (in a “what if” analysis) causing the computer  401  to recalculate the data, which in most cases will cause the displayed values in other cells to change to the accurately recalculated values. 
         [0039]    The typical variables that may be modified, via manual intervention, for accurately anticipating and forecasting detailed scenarios include, but are not limited to, sales quantities and prices, product costs, business operating expenses, production times and capacity information, and business asset values. Additional quantitative and qualitative information reflecting customer purchases, product volumes, and other transactional information that impact business operations may also be linked to the data inputs within the PPAH formatted dataset  700  to enable decision-makers to understand the factors driving the PPAH of particular transactions, orders, products, customers, and assets. 
         [0040]    Thus, the invention enables decision-makers to simulate and forecast various detailed external (marketplace) and internal (workplace) scenarios by modifying any of several data inputs in the integrated PPAH formatted dataset  700  which when recalculated by method step  310  accurately predicts the financial profit-making impact of current and future conditions and decisions. 
         [0041]    PPAHPS  304  provides the decision-maker with the capability to vary each data input element in the PPAH formatted dataset  700 , individually and as a group within the PPAHPS  304  and simulate for the resultant PPAH  330  value. The results of these simulations enable decision-maker to make better informed decisions on the impact these decisions will have on future detailed PPAH  330  and overall ROA. The decision-makers are able to get a more accurate view of the impact on profitability by correctly anticipating results and observing the outcomes of changes to one or more variables, using PPAHPS  304 , as the various data elements are uniquely interdependent and integrated. 
         [0042]    Some business choices that must be optimized in a multi-product company may include but are not limited to: 1) What product mix should decision-makers give greater influence?; 2) Which customers, according to profit contribution, should be given greater priority?; and, 3) How can decision-makers improve profit within the confines of current capacity utilization, including capital expenditure planning related to expansion, or the reduction of physical production capacity through the elimination of facilities. The scenario modeling activity leading to answers to these questions is provided readily by use of PPAHPS  304  in accordance with embodiments of the invention described herein. 
         [0043]    In accordance with an embodiment of the invention, advantages of PPAHPS  304 , in addition to the capability of extracting profit results, include enabling the user to have control over the following:
       Ability to integrate various sources of data into PPAHPS  304 , based on the PPAH metric. Typical prior art forecasts include quantity requirement projections and price with no related costs data associated with each of the specific products manufactured and without production run time data at key production steps. PPAHPS  304  selective input functionality enables the decision-maker to determine and configure in PPAHPS  304  a criteria enabling search and automatic input for calculating forecast results with the heretofor missing data such as costs and production flow rates. Due to this ability of PPAHPS  304  to look-up, calculate, and selectively input detailed cost and production flow rate modifications or additions to each line item, the user is able to calculate the profitability of their forecast line items results by PPAH  330  and assess their ranking PPAH.   PPAHPS  304  provides information and data that enables decision-makers to anticipate future ROA accurately by providing the ability to simulate and/or forecast various scenarios, but is not limited to such scenarios: PPAHPS  304  allows decision-makers to modify any one or several data input elements or data variables that may impact the profitability of any forecast, such as but not limited to quantity, price, cost, production flow rate, and equipment capacity changes. PPAHPS  304  gives the decision-maker the unique ability to accurately anticipate, modify, and adjust future influential events and their data parameters and see the impact of various combinations of potential events to determine which event(s) and likely results increase ROA thereby providing the decision-makers the opportunity to achieve the profit improvement results which the PPAHPS  304  uniquely makes available.   PPAHPS  304  allows the decision-maker to make adjustments or edits to the input data elements or data variables at any level of aggregation/disaggregation with the capability of assessing the impact of those adjustments across available and pending sales orders ranked by PPAH. This assessment of impact provides decision-makers the opportunity to change the parameters of incoming sales orders in order to improve the profitability of the assets.       
 
         [0047]    It will be obvious to those skilled in the art that not all possible sets of components of PPAH  330  are shown in the exemplary data-set  700 . The exemplary sets of components  700  that are shown provide an understanding of the invention and detail of extraction and compilation of PPAH information using the invention in accordance with an embodiment. 
         [0048]    Referring to  FIG. 5 , is an exemplary and non-limiting graph locates a plurality of products A-F of a company&#39;s manufacturing line relative to their individual PPAH  330 . The left vertical axis represents profit per unit (margin)  113  and the lower horizontal axis represents units per asset-hour  202 . The components of profit per asset-hour  330  for any given product are the two coordinates that locate the product on the graph. Each broken-line contour curve  502  represents all combinations of profit/unit and units per asset-hour that equal one value of profit-per-asset-hour  330 . Each of these contour curves  502  and profit per asset-hour values also reflect an ROA% based on the value of the asset base applicable to that set of data depicted in the chart and calculated using the transformational rules. The broken-line contour curves  502  are a plot of aggregate ROA levels expressed as a percent. By plotting the PPAH of a product it can be immediately seen if that product will meet a ROA target set by the company. For the different products A-F shown, the invention provides decision-makers the ability to understand and adjust the component variables which describe the character of the associated products, orders, manufacturing assets, prices, and the like, which influence their financial return generated, ROA. For example, products A, B, and F display a profit per asset-hour ratio that does not represent achieving, for example, a 10% targeted ROA, inasmuch as they reside below the 10% ROA threshold curve  502 . 
         [0049]    However, under traditional unit and margin analysis, decision-makers would errantly perceive these products as more significant contributors to ROA, because they either display significant unit margin (F) or unit velocity (A and B). Products A and B, by example, present significant unit velocity, but lower unit margin, while products C, D, and F, by example, present higher unit margin but lower unit velocity. Unless decision-makers have integrated and combined access to margin  113  and UPAH  202 , trade-off sensitivities (position relative to a curve  502 ) for each product they will be unable to accurately anticipate the results of different potential futures, make decisions, and take initiatives to move products, orders, and customers toward higher levels of ROA, as depicted by the combination higher margin and UPAH products, in the case of product E, which resides above the 15% ROA curve. Products B, C, and D are shown as having alternate positions B′, C′, and D′ (all above the 10% curve) to illustrate the possibility of moving these products into a higher ROA level by modifying one or more of the variables (see  FIG. 7 ) that determine their PPAH  330 . 
         [0050]    A person skilled in the art would readily appreciate that the invention disclosed herein is described with respect to specific embodiments that are exemplary. However, this should not be considered a limitation on the scope of the invention. Specifically, other implementations of the disclosed invention are envisioned and hence the invention should not be considered to be limited to the specific embodiments discussed herein above. Embodiments may be implemented on other computing capable systems and processors or a combination of the above. Embodiments may also be implemented as a software program stored in a memory module, to be run on an embedded, standalone or distributed processor, or processing system. Embodiments may also be run on a processor, a combination of integrated software and hardware, or as emulation on hardware on a server, a desktop, or a mobile computing device. The invention should not be considered as being limited in scope based on specific implementation details, but should be considered on the basis of current and future envisioned implementation capabilities. 
       An Additional Example Machine Overview 
       [0051]      FIG. 6  is a block schematic diagram of a system in the exemplary form of a computer system  600  within which a set of instructions for causing the system to perform any one of the foregoing methodologies may be executed. In alternative embodiments, the system may comprise a network router, a network switch, a network bridge, personal digital assistant (PDA), a cellular telephone, a Web appliance, or any system capable of executing a sequence of instructions that specify actions to be taken by that system. 
         [0052]    The computer system  600  includes a processor  602 , a main memory  604 , and a static memory  606 , which communicate with each other via a bus  608 . The computer system  600  may further include a display unit  610 , for example, a liquid crystal display (LCD). The computer system  600  also includes an alphanumeric input device  612 , for example, a keyboard; a cursor control device  614 , for example, a mouse; a disk drive unit  616 ; a signal generation device  618 , for example, a speaker; and a network interface device  628 . 
         [0053]    The disk drive unit  616  includes a machine-readable medium  624  on which is stored a set of executable instructions, i.e. software,  626  embodying any one, or all, of the methodologies described herein below. The software  626  is also shown to reside, completely or at least partially, within the main memory  604  and/or within the processor  602 . The software  626  may further be transmitted or received over a network  630  by means of a network interface device  628 . 
         [0054]    In contrast to the system  600  discussed above, a different embodiment uses logic circuitry instead of computer-executed instructions to implement processing entities. Depending upon the particular requirements of the application in the areas of speed, expense, tooling costs, and the like, this logic may be implemented by constructing an application-specific integrated circuit (ASIC) having thousands of tiny integrated transistors. Such an ASIC may be implemented with CMOS (complementary metal oxide semiconductor), TTL (transistor-transistor logic), VLSI (very large systems integration), or another suitable construction. Other alternatives include a digital signal processing chip (DSP), discrete circuitry (such as resistors, capacitors, diodes, inductors, and transistors), field programmable gate array (FPGA), programmable logic array (PLA), programmable logic device (PLD), and the like. 
         [0055]    It is to be understood that embodiments may be used as or to support software programs or software modules executed upon some form of processing core (such as the CPU of a computer) or otherwise implemented or realized upon or within a system or computer readable medium. A machine-readable medium includes any mechanism for storing or transmitting information in a form readable by a machine, e.g. a computer. For example, a machine-readable medium includes read-only memory (ROM); random-access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; electrical, optical, acoustical or other form of propagated signals, for example, carrier waves, infrared signals, digital signals, etc.; or any other type of media suitable for storing or transmitting information. 
         [0056]    Further, it is to be understood that embodiments may include performing operations and using storage with cloud computing. For the purposes of discussion herein, cloud computing may mean executing algorithms on any network that is accessible by internet-enabled or network-enabled devices, servers, or clients and that do not require complex hardware configurations, e.g. requiring cables and complex software configurations, e.g. requiring a consultant to install. For example, embodiments may provide one or more cloud computing solutions that enable users to obtain a profit improvement using a metric of profit per asset hour for improving return on assets (ROA) on such internet-enabled or other network-enabled devices, servers, or clients. It further should be appreciated that one or more cloud computing embodiments may include providing a profit improvement using a metric of profit per asset hour for improving return on assets (ROA) using mobile devices, tablets, and the like, as such devices are becoming standard consumer devices. 
         [0057]    Although the invention is described herein with reference to the preferred embodiment, one skilled in the art may readily appreciate that other applications may be substituted for those set forth herein without departing from the spirit and scope of the present invention. Accordingly, the invention should only be limited by the claims included below.