Patent Publication Number: US-8126765-B2

Title: Market demand estimation method, system, and apparatus

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     This invention relates to market demand estimation, and particularly to estimation of demand for information technology (IT) services. 
     2. Description of Background 
     In IT services markets, demand for a service offering is generally governed by a price and quality of the service offering. Customers have different profiles and respond differently to price and quality or levels of service offerings. The level of service offering that a firm can offer depends upon its capacity and the demand. Demand variability due to changing market conditions can be high and firms often need to act quickly to maintain a level of capacity sufficient to provide a level of service offering to which they have committed. Therefore, there is an interdependence between demand, price, quality of service, and capacity. Accordingly, decisions regarding service, pricing and capacity planning are inter-twined. 
     An objective of pricing decisions is to set a price associated with a service level to achieve business objectives or goals, such as revenue increase and customer base expansion for example. An objective of capacity planning decisions is to maintain a level of capacity to meet service level agreements. Both capacity planning and pricing decisions require knowledge of demand. 
     In an on-demand market paradigm, the demand for a specific service level is uncertain because customers have the flexibility to select and pay for services as and when needed, without any long-term fixed cost obligation. IT services markets are particularly uncertain because firms are constantly exploring markets for new services and experimenting with different service offerings. 
     Present approaches estimate demand as a function only of price and can be classified as exploratory and non-exploratory. The exploratory and non-exploratory approaches can be further classified as parametric or non-parametric. Parametric approaches model demand distribution using some known family of probability distribution function and estimate the unknown parameters of the demand distribution. Non-parametric approaches estimate the empirical distribution of demand. Both parametric and non parametric approaches can update the estimated quantities in a static or dynamic manner. 
     Non-exploratory approaches for demand estimation are typically suited for products that are well established in the markets and have demand that is predictable from historical data. For products having demand with volatility, such as subject to fluctuations of changing market conditions for example, non-exploratory approaches may fail to perform well. 
     Exploratory approaches include firm experimentation with different service offerings to simultaneously learn the demand while doing business. A typical exploratory approach includes setting a price, and observing customer response to this price. The observation is used to update knowledge about the unknown demand. However, this requires offering each of the different price levels a sufficient number of times so as to get a close estimate of a curve that describes the demand. The drawback of such approaches is that the demand learning is at the expense of foregone profits. Further, these approaches lack interaction of service quality, are not scalable, and are time consuming. Accordingly, there is a need in the art for a demand estimation arrangement that overcomes these drawbacks. 
     SUMMARY OF THE INVENTION 
     An embodiment of the invention includes a method to estimate demand of a market for a service. The method includes selecting a set of feasible service offerings to offer for sale to the market from a set of candidate service offerings, observing a response of the market to the set of feasible service offerings offered for sale, and estimating a demand of the market for the service based upon the observed response. Each service offering of the set of feasible service offerings and the set of candidate service offerings is defined by a price and a service level. 
     A further embodiment of the invention includes a system for estimating demand of a market for a service. The system includes processing, display, storage, input and output resources for executing machine readable instructions stored in the storage; the machine readable instructions for selecting a set of feasible service offerings to offer for sale to the market from a set of candidate service offerings, observing a response of the market to the offered set of feasible service offerings, and estimating a demand of the market for the service based upon the observed response. Each service offering of the set of feasible service offerings and the set of candidate service offerings is defined by a price and a service level. 
     System and computer program products corresponding to the above-summarized methods are also described and claimed herein. 
     Additional features and advantages are realized through the techniques of the present invention. Other embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed invention. For a better understanding of the invention with advantages and features, refer to the description and to the drawings. 
     TECHNICAL EFFECTS 
     As a result of the summarized invention, technically we have achieved a solution which provides an estimate including service price and quality of market demand thereby allowing enhanced price and capacity planning decisions to increase business objectives, such as revenue growth and customer expansion, for example. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The subject matter which is regarded as the invention is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other objects, features, and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which: 
         FIG. 1  illustrates one example of an infrastructure for operation of a demand estimation system in accordance with an embodiment of the invention. 
         FIG. 2  illustrates one example of a collaboration diagram representing a demand estimation process in accordance with an embodiment of the invention. 
         FIG. 3  illustrates one example of an expanded view of an integrated demand learning, pricing, and resource planning framework in accordance with an embodiment of the invention. 
         FIG. 4  illustrates one example of a flowchart of process steps of a decision point in accordance with an embodiment of the invention. 
         FIG. 5  illustrates one example of a flowchart of process steps of a method to estimate demand of a market for a service in accordance with an embodiment of the invention. 
     
    
    
     The detailed description explains the preferred embodiments of the invention, together with advantages and features, by way of example with reference to the drawings. 
     DETAILED DESCRIPTION OF THE INVENTION 
     An embodiment of the invention provides a demand estimation arrangement for IT services as a function of both price and quality of service. Such demand estimation is an integral component of capacity planning and pricing for IT services to obtain business objectives under uncertain demand. A further embodiment includes the interdependence between demand, price, quality of service and capacity. 
     An embodiment includes a demand learning phase and a decision phase. The demand learning phase quantifies demand as a function of both price and quality (service level) by experimenting with different service offerings and using analytical models to link the capacity with price, service level and demand. Each service offering is identified by a tuple or vector of factors to evaluate, such as a price and service level grouping for example. The demand learning phase includes a series of decision points, at each of which the firm decides on the set of service offerings to experiment with in the subsequent interval. At the end of each interval there is another decision point. 
     The decision phase occurs when the firm decides to stop experimenting with different service offerings and select the price and quality offering to best meet the business objectives based upon the demand learned from the demand learning phase. Accordingly, the decision phase follows the demand learning phase that has enough iteration to provide sufficient confidence about demand for different feasible service offerings. The firm may also make appropriate capacity planning decisions at this phase. In an embodiment, the demand learning phase and decision phase can be implemented in an iterative manner resulting in dynamic pricing and capacity planning. 
     Referring to  FIG. 1 , there is shown an embodiment of a processing system  100  for implementing the teachings herein. In this embodiment, the system  100  has one or more central processing units (processors)  101   a ,  101   b ,  101   c , etc. (collectively or generically referred to as processor(s)  101 ). In one embodiment, each processor  101  may include a reduced instruction set computer (RISC) microprocessor. Processors  101  are coupled to system memory  114  and various other components via a system bus  113 . Read only memory (ROM)  102  is coupled to the system bus  113  and may include a basic input/output system (BIOS), which controls certain basic functions of system  100 . 
       FIG. 1  further depicts an input/output (I/O) adapter  107  and a network adapter  106  coupled to the system bus  113 . I/O adapter  107  may be a small computer system interface (SCSI) adapter that communicates with a hard disk  103  and/or tape storage drive  105  or any other similar component. I/O adapter  107 , hard disk  103 , and tape storage device  105  are collectively referred to herein as mass storage  104 . A network adapter  106  interconnects bus  113  with an outside network  116  enabling data processing system  100  to communicate with other such systems. A screen (e.g., a display monitor)  115  is connected to system bus  113  by display adapter  112 , which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller. In one embodiment, adapters  107 ,  106 , and  112  may be connected to one or more I/O busses that are connected to system bus  113  via an intermediate bus bridge (not shown). Suitable I/O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Components Interface (PCI). Additional input/output devices are shown as connected to system bus  113  via user interface adapter  108  and display adapter  112 . A keyboard  109 , mouse  110 , and speaker  111  all interconnected to bus  113  via user interface adapter  108 , which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit. 
     Thus, as configured in  FIG. 1 , the system  100  includes processing means in the form of processors  101 , storage means including system memory  114  and mass storage  104 , input means such as keyboard  109  and mouse  110 , and output means including speaker  111  and display  115 . In one embodiment, a portion of system memory  114  and mass storage  104  collectively store an operating system such as the AIX® operating system from IBM Corporation to coordinate the functions of the various components shown in  FIG. 1 . 
     It will be appreciated that the system  100  can be any suitable computer or computing platform, and may include a terminal, wireless device, information appliance, device, workstation, mini-computer, mainframe computer, personal digital assistant (PDA) or other computing device. 
     Examples of operating systems that may be supported by the system  100  include Windows 95, Windows 98, Windows NT 4.0, Windows XP, Windows 2000, Windows CE, Windows Vista, Macintosh, Java, LINUX, and UNIX, or any other suitable operating system. The system  100  also includes the network adapter or interface  106  for communicating over the network  116 . The network  116  can be a local-area network (LAN), a metro-area network (MAN), or wide-area network (WAN), such as the Internet or World Wide Web. 
     Users of the system  100  can connect to the network  116  through any suitable network interface  106  connection, such as standard telephone lines, digital subscriber line, LAN or WAN links (e.g., T1, T3), broadband connections (Frame Relay, ATM), and wireless connections (e.g., 802.11(a), 802.11(b), 802.11(g)). 
     As disclosed herein, the system  100  includes machine readable instructions stored on machine readable media (for example, the hard disk  103 ) for capture and interactive display of information shown on the screen  115  of a user. As discussed herein, the instructions are referred to as “software”  120 . The software  120  may be produced using software development tools as are known in the art. Also discussed herein, the software  120  may also be referred to as a “demand estimation tool”  120  or other similar terms. The software  120  may include various tools and features for providing user interaction capabilities as are known in the art. 
       FIG. 2  depicts a collaboration diagram representing an embodiment of a demand estimation process  200 . 
     Inputs  205  include a candidate set of offerings  210 , prior beliefs about demand  215  and a service model  220 . 
     The candidate set of service offerings  210  is reduced, or pruned to a subset of feasible service offerings  310  ( FIG. 3 ) for a market in which the company wants to operate, as will be described further below. In an embodiment, the candidate set of service offerings  210  has elements of the form (price, service level). A service level  225  represents the set of all factors, apart from a price  230  of service, characterizing the quality of the offered service, which can affect a user decision to take service from one of a plurality of different service providers. Examples of factors that influence the service level  225  include business goals of the company  235 , available resources  237  owned by the company, market determinants  240 , and competitors&#39; service offerings  245 . 
     Prior beliefs  215  or knowledge about demand corresponding to different feasible service offerings are estimated. This can be done using historical data  250  corresponding to past observed demand. Further, processing of the historical data  250  may be accomplished using one or both of demand forecasting techniques  255  and expert opinions  260 . 
     The service model  220  utilizes statistical and analytical techniques to describe interdependency between demand, the set of candidate service offerings  210 , competitors&#39; service offerings  245  and available resources  237 . Examples of appropriate statistical and analytical techniques include regression analysis  262 , and queuing models  263 . 
     The inputs  205  are provided to an integrated demand-learning, pricing and resource planning framework  265 . A broad illustration of the framework  265  includes offering for sale  270  to the market a service-class tuple (one of the price and service-level(s) groupings) from the feasible set of service offerings  310 , observing a customer response  275  to the offering, and updating beliefs  280  about market share. At decision block  285  the process determines whether additional iterations of offering  270 , observing  275 , and updating  280  are required. If so, the process continues. Alternatively, if following the updating  280 , the process determines that the additional learning is not required, the decision block  285  represents a stopping condition, and the information gained by the learning may be used to select an appropriate service class to further the business objectives. 
       FIG. 3  depicts an expanded view of the framework  265 . The framework  265  broadly includes a demand learning phase  290 , a decision phase  295 , and a monitoring phase  300 . 
     An objective of the demand learning phase  290  is to efficiently explore demand associated with different service-offering tuples from the candidate set of service offerings  210 . The planning framework  265  begins with the candidate set of service offerings  210  and corresponding models  220 . The demand learning phase  290  includes one or more decision points  305 . 
       FIG. 4  depicts a flowchart of process steps of the decision point  305 . At an initial decision point  305 , the feasible set of service offerings  310  (see  FIG. 3 ) is selected and includes the candidate set of service offerings  210 . At subsequent decision points  305 , the company prunes, or removes service offerings from the feasible set of service offerings  310  (that initially incorporates the candidate set of offerings  210 ) thereby redefining the feasible set of service offerings  310  as a subset of the candidate set of offerings  210 , absent the pruned service offerings. 
     Providing each service offering of the feasible set of service offerings  310  requires company resources, for which there is invariably a cost. Accordingly, it may occur that the company&#39;s present resources are insufficient to support the selected feasible set of service offerings  310 . Decision block  315  utilizes a resource prediction algorithm (e.g., model-based prediction algorithm) to determine if there are enough resources available internally to support the selected feasible set of service offerings  310 . If there are sufficient resources available, the decision point  305  proceeds to a learning interval  320 , as will be described further below. If there are not sufficient resources available, the selected feasible set of service offerings  310  requires additional resources, and the process continues to decision block  325 . 
     Decision block  325  depicts a cost-benefit analysis to determine if it is beneficial for the company to invest in the additional resources for the selected feasible set of service offerings  310  to learn more about the corresponding demand. Because the framework  265  is still within the learning phase  290 , (with the desired output of an enhanced demand estimate), any cost-benefit analyses are based on estimates of demand learned up to this point, which may require further refinement. Therefore, the cost-benefit analysis at decision block  325  shall be robust enough to account for these discrepancies or uncertainties between true and estimated demand. 
     If the company decides to offer this selected feasible set of service offerings  310 , the decision point  305  proceeds to step  330 , in which it purchases the resources needed for the selected feasible set of service offerings  310  to proceed to the learning interval  320 . This may be achieved by short term leasing of those resources or by purchasing them on-demand for the next learning interval  320 . Such short term leasing or on-demand purchase prevents a need for long term investments in resources, which may prove to be futile following subsequently refined demand estimates and corresponding appropriate selected sets of service offerings  310  to optimize business objectives. 
     Alternatively, if the company decides not to invest in resources and thus not to offer the full selected feasible set of service offerings  310  (for which it lacks required resources), the process continues to step  335 . At step  335  the selected feasible set of service offerings  310  is pruned by removing at least one service offering from the set  310 , and redefining the selected feasible set of service offerings  310  to be absent the removed service offering(s). Stated alternatively, the selected feasible set of service offerings  310  is pruned or reduced and redefined as being absent the pruned service offering, which thereby requires fewer resources. The process described above is repeated until the pruned, redefined feasible set of service offerings  310  matches the present resources as evaluated at decision block  315  or is determined to be worth investment by the cost-benefit analysis at decision block  325 . 
     Referring back now to  FIG. 3 , following decision point  305 , the demand learning phase  290  proceeds to the learning interval  320 . At the learning interval  320 , the company offers for sale to the market the selected feasible set of service offerings  310  and observes the market response, such as the purchasing actions of the market, to the selected feasible set of service offering(s)  310  offered for sale by the company. Each service offering is at a given price and service level. The process then proceeds to a demand update step  340 . Observations made during the learning interval  320  are utilized to update beliefs about demand for different service offerings and the model  220  to be used in subsequent iterations. 
     Decision block  345  evaluates the differences between the observations made during the learning interval  320  and those predicted by the model  220 . Stated alternatively, decision block  345  evaluates a value associated with the prior iteration of the demand learning phase  290  in terms of refinement of the model  220 . 
     If it is determined that the difference between predictions made by the model  220  and observations made during the learning interval  320  of the prior iteration were significant, and that a subsequent iteration is likely to provide significant changes, decision block  345  continues the demand learning phase  290 , and the process returns to decision point  305 , using the updated beliefs from model  220  for another iteration of the demand learning phase  290 . 
     Alternatively, if it is determined by decision block  345  that differences between predictions of the model  220  and observations made during the learning interval  320  of the prior iteration were insignificant relative to the resources utilized, and that a subsequent iteration is unlikely to provide significant changes, decision block  345  stops the demand learning phase  290 , and the process continues to the decision phase  295 . 
     At the conclusion of the demand learning phase  290 , the company obtains useful estimates of demand associated with different feasible service-offerings. As described above, the set of feasible service offerings  310  is a subset of the set of candidate service offerings  210  that are identified before the demand learning phase  290  begins. 
     The decision phase  295  includes selection by the company of a preferred or desired set of service offering(s)  347  and offering for sale, to the market, the desired set of service offering(s) based on the learned estimates of demand resulting from the demand learning phase  290  and corresponding business objective estimates. The decision phase also includes long term investments, such as purchasing resources to support offering the desired set of service offerings  347 . 
     The monitoring phase  300  includes observation of the market for factors that can potentially affect demand. At decision block  350 , if such factors are not observed, the process returns to the decision phase  295  in which the company continues to offer the desired set of service offering(s)  347  previously selected. Alternatively, if such factors are observed, the process continues to block  355 , in which the candidate set of service offerings  210  and prior beliefs about demand  215  are provided to the decision point  305 , thereby initiating another iterative loop of the demand learning phase  290 . Accordingly, the method can be adaptive and be implemented as an apparatus for dynamic demand learning, pricing and resource planning. 
     With reference to  FIGS. 2 through 4 , an example of implementation of an embodiment of the demand estimation process  200  is described below. 
     Consider an IT services company that is interested in estimating a demand of the market for IT services. The company intends to offer a new service, is interested in offering only one service offering at any given time. Because it is a new service, there is no competition in the market, and therefore competitor&#39;s offerings  245  are not considered. The company wants to explore all of the service offerings from the candidate set of service offerings  210 , and therefore proceeds to invest in any necessary lacking resources via the resource procurements at Step  330  of the decision point  305  during the demand learning phase  290 . Therefore, the feasible set of service offerings at the end of the demand learning phase  290  is same as the candidate set of service offerings  210  identified at the beginning of the demand learning phase  290 . Stated alternatively, the process does not proceed from decision block  325  to step  335  and none of the service offerings of the selected set of service offerings  310  are pruned at Step  335  from the candidate set of service offerings  210 . 
     Candidate Set of Service Offerings 
     The service level, or quality associated with the service is a delay experienced by the user in getting this service. For example, M is a vector representing the candidate set of service offerings  210  and is defined by:
 
 M ={( p   1   ,d   1 ), . . . ( p   M   ,d   M )}  (equation-1)
 
where p represents the price of the specific offering of the candidate set of service offerings  210  having M service offerings, and d represents a delay, or level of quality associated with the specific offering of the candidate set of service offerings  210  having M service offerings. Each service offering (p m ,d m ) of the candidate set of service offerings  210 , where m=1 . . . M has an associated actual demand Π m . Π m  represents a fraction of customers that will take the associated service offering (p m ,d m ) if it is offered in the market. Therefore, a value of Π m  is between 0 and 1.
 
     Prior Beliefs about Demand 
     Because the true value of Π m  is not known a priori, prior beliefs about demand are obtained using techniques such as historical data from other related services, market surveys, and expert opinions, for example. The prior belief about Π m  is characterized as a probability distribution over the set [0,1]. For example, f 0   Π     m   (x) is the belief that the true value of Π m  is x at the beginning of the demand learning phase  290 . 
     Service Model 
     The service model relating service level (delay) as a function of demand and capacity is described by
 
 d   m =1/(μ c   m −Π m )  (equation-2)
 
where c m  is the capacity required by the company to support service offering (p m ,d m ) and 1/μ represents the average resource requirement of a service request. It will be appreciated that Equation-2 follows from modeling the IT services as a queue and using a standard queuing formula for the expected delay. Equation-2 can be mathematically rearranged to yield:
 
     
       
         
           
             
               
                 
                   
                     
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     Demand Learning Phase 
     One possible method to efficiently explore demand in this phase is to model the problem of selecting the service offerings to offer at decision points  305  as a dynamic programming with Bayesian learning. 
     For example, a state is associated with each service offering of the candidate set of service offerings  210 . Accordingly, the state of the system is the set of states of all these states. For example, let {s 0   m , m=1, . . . , M} be the state at the beginning of the demand learning phase  290  and it is decided to offer service offering m in the learning interval  320  between decision point 0 and 1. In response to this choice, the new state at decision point 1 is {s 1   m , m=1, . . . , M}. Based on the observations of the learning interval  320  and the new state, the beliefs about Π m , m=1, . . . , M are updated by the demand update at block  340  to f 1   Π     m   (x). These therefore become the new estimates for demand. It will be appreciated that an exemplary manner to update the beliefs about demand at each decision point  305  is through the use of Bayesian learning approach. 
     A reward, such as a factor of the business objectives for example, is associated with each choice made for a learning interval. The reward depends upon the state of the system at the start of learning interval and the service offering choice made for the interval. For example, if at the start of the learning interval between decision points n and n+1 for some n=1, 2, . . . , the system state is {s n   m , m=1, . . . , M} and the decision made at decision point n is to offer service offering m in this interval then the reward is described by: 
                     r   ⁡     (     s   ,   m     )       =       ∫   0   1     ⁢       (         ∏   m     ⁢           ⁢     p   m       -       α   m     ⁢     c   m         )     ⁢       f   n       ∏   m     ⁢               (     ∏   m     ⁢           )     ⁢           ⁢     ⅆ     ∏   m                   (     equation   ⁢     -     ⁢   4     )               
where α m  is the per unit capacity cost to the company and c m  is given by equation-3. Accordingly, the reward is a measure of the expected revenue obtained if the company decides to offer service offering m. It will be appreciated that this is not the same as the true value of the expected revenue with service offering m as the demand is not known exactly. Different techniques can be applied to solve this dynamic programming problem including, but not limited to, value iteration, policy iteration, reinforcement learning, and Gittin&#39;s index.
 
     After a sufficiently good estimate of demand associated with a service offering is obtained, the company can decide at decision block  345  to stop the demand learning phase  290  for this particular service offering. The decision at block  345  can be based on a stopping criterion, which can be same or different for all or a subset of service offerings. An example criterion can be to stop the demand learning phase  290  for a service offering after that service offering is offered some N number of times. 
     Decision Phase 
     {  f   Π     m   , m=1, . . . , M} describes the set of learned estimates of demand for the feasible set of service offerings at the conclusion of the demand learning phase  290 . The company chooses a preferred service offering based upon the estimated demand {  f   Π     m   , m=1, . . . , M}, to achieve business objectives such as to maximize revenue. The preferred service offering is determined by solution of the following optimization problem: 
     
       
         
           
             
               
                 
                   
                     
                       
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     wherein: 
     m represents an index value of a specific service offering of the set of feasible service offerings; 
     Π m  represents an actual demand corresponding to the specific service offering; 
     p m  represents a price corresponding to the specific service offering; 
     α m  represents a per unit capacity cost corresponding to the specific service offering; 
     d m  represents a service level corresponding to the specific service offering; 
     1/μ represents an average resource requirement of a service request; and 
       f   Π     m    represents an estimated demand corresponding to the specific service offering. 
     If m* is the preferred service offering which is the solution to the above optimization problem, the company can decide to maintain a capacity described by: 
     
       
         
           
             
               
                 
                   
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     wherein: 
     m* represents an index value of the preferred service offering; 
     c m*  represents a service capacity corresponding to the preferred service offering; 
     d m*  represents a service level corresponding to the preferred service offering; 
     Π m*  represents an actual demand corresponding to the preferred service offering; 
       f   Π     m   * represents an estimated demand corresponding to the preferred service level; and 
     1/μ represents an average resource requirement of a service request. 
     With reference to  FIGS. 2-4 ,  FIG. 5  depicts a flowchart  400  of process steps of an embodiment of a method for estimating demand of a market for a service. 
     The method begins at step  405  including selecting the set of feasible service offerings  310  to offer for sale to the market from the set of candidate service offerings  210 , each service offering of the set of feasible service offerings  310  and the set of candidate service offerings  210  defined by price  230  and service level  225 . The method proceeds to step  410  including observing a response of the market to the set feasible service offerings  310  offered for sale. Step  415  includes estimating a demand of the market for the service based upon the observed response. In one embodiment, the method concludes with recording the estimated display upon a storage device, such as the mass storage  104 . In another embodiment, the method further includes displaying the estimated demand via the display screen  115  ( FIG. 1 ). 
     In an embodiment, the method further includes updating the model  200  linking a service offering of the set of feasible service offerings  310  to the estimated demand of the market based upon response observed at step  410 . 
     In another embodiment, the selecting the set of feasible service offerings  310  at step  405  includes initially defining the set of feasible service offerings  310  as the set of candidate service offerings  210  and determining whether to invest resources to offer the set of feasible service offerings to the market for sale at decision block  325 . In response to determining not to invest resources, process block  335  includes removing at least one service offering from the set of feasible service offerings  310 , thereby redefining the set of feasible service offerings  310  as being absent the removed at least one service offering. 
     The capabilities of the present invention can be implemented in software, firmware, hardware or some combination thereof. 
     As one example, one or more aspects of the present invention can be included in an article of manufacture (e.g., one or more computer program products) having, for instance, computer usable media. The media has embodied therein, for instance, computer readable program code means for providing and facilitating the capabilities of the present invention. The article of manufacture can be included as a part of a computer system or sold separately. 
     Additionally, at least one program storage device readable by a machine, tangibly embodying at least one program of instructions executable by the machine to perform the capabilities of the present invention can be provided. 
     The flow diagrams depicted herein are just examples. There may be many variations to these diagrams or the steps (or operations) described therein without departing from the spirit of the invention. For instance, the steps may be performed in a differing order, or steps may be added, deleted or modified. All of these variations are considered a part of the claimed invention. 
     While the preferred embodiment to the invention has been described, it will be understood that those skilled in the art, both now and in the future, may make various improvements and enhancements which fall within the scope of the claims which follow. These claims should be construed to maintain the proper protection for the invention first described.