Predictive capacity optimizer

Embodiments of the invention are directed to techniques that include predicting, by a computer system, a number of predicted opportunities and signatures of the predicted opportunities expected in a time window. Based on the signatures of the predicted opportunities, the computer system generates a listing of entities ranked according to signatures of the predicted opportunities. The computer system selects the entities to be assigned to the predicted opportunities based, at least in part, on computing capacity related to sales while accounting for any current opportunities having been assigned to the entities.

BACKGROUND

The present invention generally relates to computer systems, and more specifically, to computer-implemented methods, computer systems, and computer program products configured and arranged for providing a predictive capacity optimizer.

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, which analyze current and historical facts to make predictions about future or otherwise unknown events. In industry, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, thereby helping to guide decision-making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provide a predictive score (probability) for each individual (customer, employee, healthcare patient, product, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes across large number operations.

SUMMARY

Embodiments of the present invention are directed to computer-implemented methods for providing a predictive capacity optimizer. A non-limiting example computer-implemented method includes predicting, by a computer system, a number of predicted opportunities and signatures of the predicted opportunities expected in a time window. Based on the signatures of the predicted opportunities, the computer system generates a listing of entities ranked according to signatures of the predicted opportunities. The computer system selects the entities to be assigned to the predicted opportunities based, at least in part, on computing capacity related to sales while accounting for any current opportunities having been assigned to the entities.

DETAILED DESCRIPTION

One or more embodiments of the present invention provide computer-implemented methods, computer systems, and computer program products for providing predictive capacity optimization and/or a predictive capacity optimizer. According to one or more embodiments of the invention, a computer system is configured to predict the type/signature of new business opportunities that will arise over a specific time window. The type/signature of new business opportunities can include the size of a deal; a business sector such as government, public, and/or private; and a product family such as machine learning software applications, software application templates, computer security (including cybersecurity and information technology security (IT security)), etc. The computer system is configured to assign business entities (e.g., business partners) to current opportunities taking into account the requirements of future opportunities, all while avoiding overcommitting a business entity to a current opportunity, if the computer system predicts that a more demanding opportunity (which could be a more profitable opportunity) will arise soon to utilize more of the business capacity of the business entity.

A business entity (or sales resource) has a business capacity to provide a service and/or product using one or more computer systems. Business capacity and/or computing capacity (which can in turn relates to sales capacity) refers to the number of opportunities and the signatures/types of opportunities that a business entity can work on and/or perform at the same time using one or more computer systems. One or more embodiments of the invention are configured to avoid conditions where there is not full (and/or near full) utilization of computer systems (e.g., computing capacity) for respective business entities based on predicted opportunities.

Turning now toFIG.1, a computer system100is generally shown in accordance with one or more embodiments of the invention. The computer system100can be an electronic, computer framework comprising and/or employing any number and combination of computing devices and networks utilizing various communication technologies, as described herein. The computer system100can be easily scalable, extensible, and modular, with the ability to change to different services or reconfigure some features independently of others. The computer system100can be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer system100can be a cloud computing node. Computer system100can be described in the general context of computer system executable instructions, such as program modules, being executed by a computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system100can be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules can be located in both local and remote computer system storage media including memory storage devices.

As shown inFIG.1, the computer system100has one or more central processing units (CPU(s))101a,101b,101c, etc., (collectively or generically referred to as processor(s)101). The processors101can be a single-core processor, multi-core processor, computing cluster, or any number of other configurations. The processors101, also referred to as processing circuits, are coupled via a system bus102to a system memory103and various other components. The system memory103can include a read only memory (ROM)104and a random access memory (RAM)105. The ROM104is coupled to the system bus102and can include a basic input/output system (BIOS) or its successors like Unified Extensible Firmware Interface (UEFI), which controls certain basic functions of the computer system100. The RAM is read-write memory coupled to the system bus102for use by the processors101. The system memory103provides temporary memory space for operations of said instructions during operation. The system memory103can include random access memory (RAM), read only memory, flash memory, or any other suitable memory systems.

The computer system100comprises an input/output (I/O) adapter106and a communications adapter107coupled to the system bus102. The I/O adapter106can be a small computer system interface (SCSI) adapter that communicates with a hard disk108and/or any other similar component. The I/O adapter106and the hard disk108are collectively referred to herein as a mass storage110.

Software111for execution on the computer system100can be stored in the mass storage110. The mass storage110is an example of a tangible storage medium readable by the processors101, where the software111is stored as instructions for execution by the processors101to cause the computer system100to operate, such as is described herein below with respect to the various Figures. Examples of computer program product and the execution of such instruction is discussed herein in more detail. The communications adapter107interconnects the system bus102with a network112, which can be an outside network, enabling the computer system100to communicate with other such systems. In one embodiment, a portion of the system memory103and the mass storage110collectively store an operating system, which can be any appropriate operating system to coordinate the functions of the various components shown inFIG.1.

Additional input/output devices are shown as connected to the system bus102via a display adapter115and an interface adapter116. In one embodiment, the adapters106,107,115, and116can be connected to one or more I/O buses that are connected to the system bus102via an intermediate bus bridge (not shown). A display119(e.g., a screen or a display monitor) is connected to the system bus102by the display adapter115, which can include a graphics controller to improve the performance of graphics intensive applications and a video controller. A keyboard121, a mouse122, a speaker123, etc., can be interconnected to the system bus102via the interface adapter116, which can include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit. 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 Component Interconnect (PCI) and the Peripheral Component Interconnect Express (PCIe). Thus, as configured inFIG.1, the computer system100includes processing capability in the form of the processors101, and, storage capability including the system memory103and the mass storage110, input means such as the keyboard121and the mouse122, and output capability including the speaker123and the display119.

In some embodiments, the communications adapter107can transmit data using any suitable interface or protocol, such as the internet small computer system interface, among others. The network112can be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, among others. An external computing device can connect to the computer system100through the network112. In some examples, an external computing device can be an external webserver or a cloud computing node.

It is to be understood that the block diagram ofFIG.1is not intended to indicate that the computer system100is to include all of the components shown inFIG.1. Rather, the computer system100can include any appropriate fewer or additional components not illustrated inFIG.1(e.g., additional memory components, embedded controllers, modules, additional network interfaces, etc.). Further, the embodiments described herein with respect to computer system100can be implemented with any appropriate logic, wherein the logic, as referred to herein, can include any suitable hardware (e.g., a processor, an embedded controller, or an application specific integrated circuit, among others), software (e.g., an application, among others), firmware, or any suitable combination of hardware, software, and firmware, in various embodiments.

FIG.2is a block diagram of a system200for providing predictive (business) capacity optimization and/or a predictive (business) capacity optimizer in accordance with one or more embodiments of the present invention.FIG.2depicts example business entities225each having one or more computers systems202coupled to computer systems220. Computer system202can communicate with computer systems220over a wired and/or wireless network. Computer systems202can be representative of numerous computers in a datacenter providing services to various users and/or business entities225. Computer systems220can be representative of numerous computer systems utilized to provide computer related services in accordance with their respective business entities225. Computer system202includes software applications204, and computer systems220include provisioning software applications222. Computer systems220can interface directly with software applications204of computer system202and/or use provisioning software application222to interface with software applications204. Provisioning software application222can allocate/provision utilization of computing resources on computer systems220including memory (including virtual memory), inputs/outputs, processors, software applications, templates for software applications, etc. Provisioning software applications222can also manage computing resources on computer systems220in accordance with assignments made by software applications204as discussed further herein. Software applications204can be implemented as software111executed on one or more processors101, as discussed inFIG.1. Similarly, provisioning software applications222can be implemented using software111configured to execute on one or more processors101. Elements of computer system100can be used in and/or integrated into computers system202and computer systems220.

Business entities225can include, for example, different business entities A, B, C, through N, where N represents the last business entity. Each of the business entities225using respective computer systems220has technological capabilities to provide one or more computer related services. In one or more embodiments, business entities225can represent various sellers and/or business partners associated with computer systems202.

Historical data210stored in a database can be representative of numerous databases. The database can contain hundreds, thousands, and/or millions of documents, also referred to as “big data”. In accordance with one or more embodiments, the enormous size of historical data210in databases requires management, processing, and search by a machine (such as computer system202), for example, using computer-executable instructions, and historical data210in databases could not be practically managed, stored, analyzed, and/or processed as discussed herein within the human mind.

FIG.3is a flowchart of a computer-implemented process300for providing predictive (business) capacity optimization and/or a predictive (business) capacity optimizer for the business entities225in accordance with one or more embodiments of the present invention. The computer-implemented process300inFIG.3can be implemented using system200shown inFIG.2. Accordingly, the computer-implemented process300will now be described with reference to system200shown inFIG.2.

At block302, software applications204on computer system202are configured to predict a number of new opportunities that will arise in a given time window, along with their respective opportunity signatures. The time window is set in advance and looks out a period of time in the future. The time window can be set for one month, two months, three months, six months, and so forth. The opportunity refers to the ability for a business entity225to use its business capacity, such as, for example, computer system220, to provide a technological service to a client needing the service. The opportunity may include a sales opportunity in which technology services are to be rendered by the business entity. A new opportunity is a predicted opportunity which can also be referred to as a future opportunity that is predicted to occur within the given time window (e.g., one month) by software applications204. Software applications204can include, be integrated with, and/or call an opportunity prediction model240to predict the new opportunities. Opportunity prediction model240can be implemented using various predictive analytics models and algorithms. Example predictive analytics models can include classification models, clustering models, forecast models, outliers models, time series models, etc., and combinations thereof. Example predictive analytics algorithms can include random forest algorithms, generalized linear model algorithms, gradient boosted model algorithms, K-means algorithms, the prophet algorithms, decision trees, support vector machines (SVM), regression analysis, Bayesian networks, etc., and combinations thereof. Opportunity prediction model240is trained on historical data210. Software applications204are configured to collect and maintain historical data for each of the business entities225, for example, by communicating with computer systems220. Historical data210can be stored in memory206. Historical data210includes historical opportunity records including historical opportunities, opportunity signatures, business entity information, transaction data (e.g., quote, discounts, service agreements, etc.), business entity (seller) details, client details which is the one for whom computing services are provided, business rules, etc. For use with the predictive algorithm of opportunity prediction module240, the historical data210is split into training and test sets. During feature selection, opportunity prediction module240iteratively selects the significant features using the training set and verifies that opportunity prediction model240using the test set. During model fitting, the parameters are estimated for the selected opportunity prediction model240. During prediction, opportunity prediction model240is fed the newly collected data and generates the corresponding predicted number of new opportunities.

Additionally, software applications204are configured to receive input of time window, time threshold, predicted opportunities threshold, and accuracy threshold. Two distinct percentage values “t” and “f” are used, where “t” is a percentage of the number of predicted opportunities and “f” is a percentage of the size of the time window. Whenever the number of opportunities that arose at the time t0+(Δt*f) is less than “t”, monitoring module248is configured to send a notification to the business entity (which can include a sales manager) as discussed further herein. Historical data210is continuously updated and/or periodically updated. Software applications204(e.g., using opportunity prediction model240) are configured to receive as input historical data210and the length of the time window within which the predictions are to fall. The size of the time window is Δt (e.g., one month). For each new opportunity, software applications204are configured to generate a confidence level for the predicted opportunity to arise within the time window [t0, t0+Δt], where t0is the starting point (e.g., the first day of a given month). The historical data210are used to estimate the variation of the prediction, which is assumed to follow a normal distribution. The confidence level is determined by multiplying the significance value from the normal distribution at the desired confidence level by the estimated variation. Software applications204(e.g., using opportunity prediction model240) are configured to predict (as output) a number of new opportunities and an opportunity signature for each opportunity predicted. The opportunity signature is the type of opportunity. For example, each opportunity signature can include a sector, a deal size, and/or product family. The sector refers to the business space of the opportunity such as, for example, public sector, government sector, private sector, banking sector, educational, etc. The deal size refers to the magnitude of the opportunity and/or the business capacity (e.g., including the computing resources of computer system220) needed to fulfill the opportunity. For example, the deal size can include small deals, medium deals, and large deals. The product family refers to the technological area of the opportunity, and the technological services/products that a business entity is provide for the opportunity. For example, the product family can include machine learning products, computer security products, chatbot products, data storage products, etc.

At block304, software applications204on computer system202are configured to generate a rated list of business entities225according to a given opportunity signature for each of the opportunities. For example, if there are three new opportunities, then software applications204generate three separate ranked lists according to the expertise of each business entity225, such that there is one ranked list for each of the three new opportunities. Each business entity225is ranked according to its ability to fulfill the requirements in the opportunity signature of the new opportunity and/or its potential to win the new opportunity. For example, software applications204ranks each business entity225in the ranked list for a particular opportunity using a confidence score which indicates how each business entity is able to fulfill the requirements in the opportunity signature for the particular opportunity. Software applications204can include, be integrated with, and/or call an expert recommendation module242to rank business entities in the list. In order to generate the ranked list of business entities, software applications204(e.g., using expert recommendation module242) are configured to receive as input historical data210, type of business entity, and the predicted opportunity signature for a new opportunity. In one or more embodiments, software applications204(e.g., using expert recommendation module242) can weigh the input to generate a confidence score based on which business entities have the ability to fulfill the requirements in the opportunity signature based on their respective historical data210. Expert recommendation module242can use one or more algorithms to balance determining the ability to fulfill the requirements in the opportunity signature such as sector, deal size, and/or product family. Example algorithms can include a content based filtering algorithm that checks the similarity between an opportunity signature and experts' previous expertise (for a business entity) calculated from the previous opportunities that the experts dealt with, and cluster modeling where the similar experts (of a business entity) can be clustered based on their expertise and the most similar cluster of experts can be recommended for an opportunity considering the opportunity requirements.

In one or more embodiments, expert recommendation module242can score (e.g., 1-5, where 5 is the highest/best) the ability for each business entity to meet/fulfill each factor (e.g., sector, deal size, product family) of the opportunity signature for a particular opportunity, and the score for each factor in the opportunity can be added to compile a confidence score for a given business entity. One or more components in the opportunity signature can be given greater weight in formulating the confidence score for business entities. For example, the score for product family can be give more weight than other components such as sector and deal size, while deal size can be given more weight than sector, such that sector has the least weight of all components. In one case, as recognized by parsing through historical data210for a business entity, software applications204determines that the given business entity has upgraded its storage capacity in computer systems220. When the product family of a new opportunity requires intensive storage capabilities, software applications204generate a high score for product family for the given business entity because of its upgraded storage capacity.

At block306, software applications204on computer system202are configured to determine the amount of different deal sizes that a business entity225(e.g., computer systems220) can handle/accommodate at the same time, for example, based on capacity of computer systems220. Software applications204can include, be integrated with, and/or call a capacity model244. In one or more embodiments, capacity model244can be modelled as an integer knapsack problem where deals correspond to items, the deal sizes map to item weights, the item profit is equal to the item weight, and weight limit represents the maximum capacity (e.g., maximum (computing) capacity of computer systems202) of a business entity. Although integer knapsack problem is one example, it should be appreciated that various algorithms can be used. Capacity model244receives historical data210as input and outputs for each business entity the different combinations of deal sizes that a given business entity can perform at the same time (i.e., business capacity). As an example combination for a given business entity225, it is assumed that a given business entity225via computer system220can accommodate and/or work on at the same time (a) three opportunities of small deal size or two opportunities of medium deal size; (b) one small deal size and one medium deal size; or (c) a single opportunity of big deal size.

At block308, software applications204on computer system202are configured to assign/schedule business entities225to current opportunities and future opportunities (e.g., assignments250) so as to maximize use of expected business capacity utilization of computer systems202per business entity, thereby maximizing expected sales revenue per business entity. Current opportunities are presently available and known in opportunities database208. New opportunities also referred to as predicted opportunities are the future opportunities predicted by software applications204and which are not presently available but are expected to be available in the time window. Software applications204can include, be integrated with, and/or call a scheduling module246. Scheduling module246is configured to receive as input opportunity signatures of predicted opportunities from opportunity prediction model240, ranked lists of business entities225who are the most qualified for accommodating/handling the predicted opportunities from expert recommendation module242, distribution of the currently open opportunities from opportunities database208, expertise/skills of each business entity225(e.g., certificate of a business entity for providing (or selling) product P and/or number of opportunities a business entity had in the past in sector S (historical data210)), and/or business capacity (computing resources of computer systems220) of each business entity.

In one or more embodiments, the scheduling problem can be modelled as a Team Orienteering Problem (TOP) with stochastic rewards for software applications204(e.g., using scheduling module246). In the model, a complete graph G is defined, with nodes that represent opportunities Oieach associated with an uncertain business capacity (reward/profit) Riwith probability pifrom opportunity prediction model240, and edges that represent the duration required to close the opportunity representing the originating node. The duration describes the time it would likely take for revenue from an opportunity to be realized. The optimization seeks to maximize expected business capacity (which in turn seeks to maximize profit) over all active opportunities, given a business entity profile (e.g., sales team profile) and a time budget (e.g., end-of-quarter). Opportunities can be skipped if filling business capacity (e.g., reward) likelihood is low (i.e., business utilization is, for example, 50%, 40%, 30%, and/or less of computing resources of computer system220for a given business entity). The output is generated by software applications204and sent to respective business entities225, for example, business entities A, B, through N via respective computer systems220. The output includes assignments250of predicted opportunities to business entities225, where assignments250of opportunities to business entities225is so that business entities225will have enough business capacity to handle future opportunities given current opportunities. Current opportunities are sales opportunities the business entity is currently working on using business resources (e.g., computer system220).

At block310, software applications204on computer system202are configured to, after a predetermined amount of time, check whether the predicted opportunities are realized (e.g., appear and are manifested a point in time subsequent to when the opportunities were initially predicted) to meet at threshold. For example, the threshold to be met could be 70% by a predetermined amount of time after their initial prediction. If all predicted assignments are realized, the flow ends because the prediction of future opportunities is 100% correct/accurate in the predetermined amount of time after their initial prediction (i.e., threshold met). Software applications204can include, be integrated with, and/or call a monitoring module248. Monitoring module248(and/or software applications204) can receive as input opportunity signatures of predicted opportunities within the time window [t0,t0+Δt] from opportunity prediction model240, threshold “t” as minimum percentage of opportunities that should have arisen within a fraction “f” of the time window, and distribution of the currently open opportunities from opportunities database208.

Monitoring module248(and/or software applications204) can send output business entities225as an update. Whenever at the time t0+(Δt·f) the number of opportunities arose (i.e., realized) is less than the threshold “t”, software applications204are configured to send a notification to the business entities225. Whenever the distribution of open opportunities differs from the recommendation of assignments250by scheduling module246, scheduling module246is configured to re-assign the predicted opportunities accordingly which can include reassigning the predicted opportunity to the second most qualified business entity if the first most qualified business entity has been assigned to a big size deal which will take all its business capacity for the entire time window. After an assignment is generated, monitoring module248tracks the new opportunities that are entered into the opportunities database208. Once an opportunity is created, the opportunity enters in the opportunities database208with state OPEN. Monitoring module248looks to the open opportunities and checks whether they match the predicted ones for the current time window. Once the predicted opportunities are identified, the monitoring module248check whether these opportunities are assigned to the recommended business entities.

Optionally, at block312, if the predetermined amount of time has passed without the threshold amount of predicted opportunities being realized (i.e., the threshold is not met), software applications204are configured to check if any businesses entities choose not to keep business capacity available (i.e., choose not to keep computing resources on computer systems220available) by waiting for the predicted opportunities. If not, flow proceeds to block310. If yes, at block314, software applications204on computer system202are configured to re-run the assigning/scheduling of business entities225to current and future opportunities so as to maximize use of expected business capacity per business entity thereby maximizing expected sales revenue per business entity, while excluding any business entity choosing not to wait for the predicted opportunities.

Various example scenarios are discussed for explanation purposes and not limitation. The concepts discussed and described in various examples can be combined across scenarios in accordance with one or more embodiments. At the start of the system, opportunity prediction model240, expert recommendation module242, and capacity model244are trained using the historical data210and are updated periodically afterwards. The opportunity prediction model240predicts the arrival of new opportunities in the next month, for example, three new opportunities on data privacy products, two medium size opportunities in the banking sector, and one opportunity of big size on natural language processing (NLP) analytics products. Given the opportunity signature (i.e., sector, deal size, product family) of the predicted opportunities, expert recommendation module242produces a ranked list of business entities225ordered by the confidence score of each business entity to win the specific opportunity based on being able to fulfill the requirements in the opportunity signature. Given the opportunity signature of the predicted opportunities, the ranked list of business entities225for each opportunity, and the business capacity of each business entity provided by capacity model244, scheduling module246produces an assignment of opportunities (e.g., assignment250) to business entities so as to maximize the use of their business capacity (e.g., maximize use and available computing resources of their respective computer systems220) (thereby maximizing expected sales revenue). Monitoring module248checks whether the produced assignment250is applied, and in case it is not applied, monitoring module248requires scheduling module246to generate a new assignment250. Applying the assignment250by business entities225in the assignment250causes configuration of computer systems220to meet the requirements of the new opportunity/opportunities for which the computer systems220are required to run. Memory, processors, bandwidth, security protocols, etc., are performed on computer systems220of business entities in order to meet the new opportunity having the opportunity signature. It is assumed that there is an accuracy threshold of 50%, meaning that at least 50% of the predicted opportunities must be realized in actuality. If less than three of the six predicted opportunities are determined to appear (i.e., be realized) by monitoring module248within the first two weeks of the next month (i.e., it is noted that six is the total number of opportunities mentioned in this scenario, having 3 (new opportunities)+2 (medium size opportunities)+1 (opportunity of big size opportunity)), monitoring module248sends an alert to business entities225because this is less than the 50% threshold. The alert gives the business entities225notification that they may choose not to take the predicted opportunity, and if so, rescheduling by scheduling module246will occur excluding the business entity that opted out.

In another example scenario, opportunity prediction model240is configured to predict possible new opportunities that can arise in the predefined time window. For example, in the next month there is a high probability that the following types opportunities will arise: five opportunities of middle size on machine learning products, one opportunity of large size on computer security products, and one opportunity of large size in the banking sector. Based on output from opportunity prediction model240, expert recommendation module242, and capacity model244, scheduling module246is configured to generate a scheduling (e.g., assignments250) for business entities225by load balancing opportunities among business entities225. An example of assignments250is provided by scheduling module246as the following. Business entity A who has a lot of experience in generating computer security products via computer systems220is assigned to and should be available for working on the large deal size on computer security products. Business entity B who has closed deals and generated software technology (e.g., hosting websites on computer systems, processing secure transactions on processors, etc.) via computer systems220in the banking sector in the past is assigned to and should have enough business capacity for handling the upcoming opportunity in the banking sector. Given that there are two opportunities of small size in the education sector and business entity C has technology specialized on this type of deal (e.g., computer templates, software applications, etc., configured to run on computer systems220of business entity C), and given that business entity D works on a broader range of deals, scheduling module246is configured to assign the two opportunities of small size in the education sector to business entity C so that business entity D will be able (and have computer resources of computer systems220available) to handle some of the upcoming opportunities on machine learning products. After making the assignments250, monitoring module248is configured to monitor whether new opportunities appear and business capacity is compliant with the scheduling. Monitoring module248needs to look to (e.g., parse) the opportunities database208for new opportunities that match the predicted ones. The opportunities database208contains information about the business entity to which the opportunity is assigned. Hence, monitoring module248can verify whether the recommended assignment is actually respected or not. For example, if 65% (example threshold) of the predicted opportunity has not appeared after two weeks, monitoring module248sends an alert to computer systems220of the relative business entities225. After receiving the alert, for business entity A has been assigned on three opportunities, business entity A releases the held business capacity and configuration on its computer systems220thereby not waiting for realization of the three opportunities which it has been assigned and declining the assignment; upon monitoring module248receiving the refusal from computer systems220of business entity A, scheduling module246is configured to re-run the scheduling to finding an alternative business entity (e.g., business entity E) who has the business capacity for handling the predicted new opportunity previously assigned to business entity A.

In an example scenario initially without using system200, a business entity with a lot of experience working with public institutions could miss the chance to work on an opportunity that utilizes a high amount of computing resources (e.g., a highly profitable opportunity) in the public sector because the business entity is currently busy using some portion of it computing resources on one or more smaller opportunities (e.g., working on less profitable opportunities). However, using system200in accordance with one or more embodiments, system200can predict that an opportunity utilizing/requiring a high amount of computing resources of computing systems220(e.g., a highly profitable opportunity) in the public sector is going to arise soon, such that an assigned business entity can optimize the commitment of computing resources on computing systems220by feeding the pipeline of the business entity with small-size opportunities so that the business entity will have enough business capacity (including templates for software applications, memory allocated, etc., on computing systems220) for working on the anticipated deal in the public sector. Accordingly, system200optimizes use and/or preparation of computing capacity on computer systems220for a given business entity based on prediction of new opportunities.

In an example scenario initially without using system200, usually big business entities work on a broad range of deals whereas small business entities are specialized on specific type of deals. For example, a not optimal pipeline could assign a deal of a type T to a big business entity A rather than to a smaller business entity B who specializes on the deal of type T. If opportunities of types different than type T come in, business entity A may have already reached its business capacity and/or have at least some of its capacity utilized/taken by deals of type T such that business entity A will not be able to accept the opportunities, whereas business entity B is not qualified for those types of deals. In accordance with one or more embodiments, system200predicts that new opportunities of types different than type T will arise soon. Accordingly, system200is configured to assign the current opportunity of type T to business entity B so that business entity A will have enough capacity for handling the upcoming opportunities predicted by software applications204.

It should be appreciated that assignments250cause of trigger respective computer systems220of respective business entities225to allocate/provision, in advance (i.e., pre-allocate) of predicted opportunities being realized, utilization of computing resources on computer systems220including memory (including virtual memory), inputs/outputs, processors, software applications, templates for software applications, database structures/creation, etc. This allocation improves the functioning of computer systems220by placing computer systems220in a preconfigured state to quickly execute services needed by the predicted opportunities before the predicted opportunities are realized. This allocations also allow computer systems220to be physically and/or virtually restructured in anticipation of the predicted opportunities.

FIG.4is a flowchart of a computer-implemented method400for predictive business capacity optimizer for the business entities225in accordance with one or more embodiments of the present invention. The computer-implemented method400inFIG.4can be implemented using the system200shown inFIG.2. At block402, software applications204(e.g., via opportunity prediction model240) on computer system202are configured to predict a number of predicted opportunities and signatures of the predicted opportunities expected in a time window. At block404, software applications204(e.g., via expert recommendation module242) on computer system202are configured to, based on the signatures of the predicted opportunities, generate a ranked listing of entities (e.g., business entities225having computer systems220) ranked according to signatures of the predicted opportunities. At block406, software applications204(e.g., via scheduling module246) on computer system202are configured to select the entities (e.g., business entities225having computer systems220) to be assigned (e.g., in assignments250) to the predicted opportunities based, at least in part, on computing capacity related to sales (e.g., determined using capacity model244) while accounting for any current opportunities having previously been assigned to the entities. Computing capacity related to sales capacity includes the number of opportunities that a business entity can handle at the same time via its computer systems220.

Selecting the entities assigned to the predicted opportunities based, at least in part, on the computing capacity (e.g., business capacity) is further based, at least in part, on maximizing utilization of computing capacity (e.g., respective computer systems220of business entities225) of the entities that are selected. The computing capacity is determined for each of the entities in the assignments250. The computing capacity for an entity is based on an amount of total opportunities that the entity can accommodate simultaneously using its computer systems220. One signature of the signatures (e.g., opportunity signatures) includes a sector, a size, and a product type for one predicted opportunity. The predicted opportunities are monitored (e.g., via monitoring module248) for a predetermined time to determine if the number of predicted opportunities that are realized meets a threshold. Responsive to the threshold not being met, the computer system202is configured to re-select the entities to be assigned to the predicted opportunities while one or more of removing any entities choosing not to participate.

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and software applications (e.g., software applications204, provisioning software application222, opportunity prediction model240, expert recommendation module242, capacity model244, scheduling module246, monitoring module248, etc.) implemented in workloads and functions96. Also, software applications can function with and/or be integrated with Resource provisioning81.