DESC:
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
TRANSFORMING SERVICE ENGAGEMENT FROM AS-IS STATE TO TO-BE STATE

Applicant
Tata Consultancy Services Limited
A Company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.
CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application claims priority to Indian Provisional Patent Application No. 3237/MUM/2013, filed on October 15, 2013, the entirety of which is hereby incorporated by reference.
TECHNICAL FIELD
[002] The present subject matter described herein, in general, relates to transforming a service engagement, and more particularly to transforming the service engagement from an as-is state model to a to-be state model.
BACKGROUND
[003] A service engagement defines terms of an engagement between a client and a service provider. The terms of the engagement comprises scope, deliverables and acceptance criteria. A change in the terms of the engagement necessitates transformation of the service engagement. By and large, the transformation of the service engagement may be executed by changing the service engagement from an as-is state to a to-be state.
[004] The purpose of the transformation is to increase value realization from the service engagement while minimizing costs, risks, and side-effects of the transformation. The purpose of the transformation may comprise ‘improved efficiency’, ‘cost reduction’, ‘vendor exploitation’, ‘improved service availability and quality’. In order to achieve the purpose, it is essential to implement a suitable strategy. Existing transformation methods fail to provide any suitable strategy for achieving said purpose.
SUMMARY
[005] This summary is provided to introduce aspects related to systems and methods for transforming a service engagement from an as-is state model to a to-be state model and the aspects are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[006] In one implementation, a method for transforming a service engagement from an as-is state model to a to-be state model is disclosed. The method comprises, selecting, based on a user input, the to-be state model from a plurality of to-be state models. The to-be state model defines objectives and value parameters for the service engagement. Further, the value parameters impact a realization of the objectives for the service engagement. The method further comprises receiving, by a processor, contextual parameters for the service engagement. The contextual parameters represent constraints and facts for the service engagement. The method also comprises identifying, by the processor, a set of strategies for the realization of the objectives. A strategy of the set of strategies comprises one or more dynamic parameters influencing the value parameters. The set of strategies are identified based on the contextual parameters, the objectives, and the value parameters. The method further comprises configuring, by the processor, quantitative values for the one or more dynamic parameters based on quantitative values of targets and tolerances for the objectives and the value parameters. The method also comprises computing, by the processor, quantitative values for the objectives based on the quantitative values of the one or more dynamic parameters, thereby transforming the service engagement from the as-is state model to the to-be state model.
[007] In one implementation, a system for transforming a service engagement from an as-is state model to a to-be state model is disclosed. The system comprises a processor and a memory coupled to the processor for executing a plurality of modules present in the memory. The plurality of modules comprises a selecting module, a receiving module, an identifying module, a configuring module, and a computing module. The selecting module selects, based on a user input, the to-be state model from a plurality of to-be state models. The to-be state model defines objectives and value parameters for the service engagement. The value parameters impact a realization of the objectives for the service engagement. The receiving module receives contextual parameters for the service engagement. The contextual parameters represent constraints and facts for the service engagement. The identifying module identifies a set of strategies for the realization of the objectives. The strategy of the set of strategies comprises one or more dynamic parameters influencing the value parameters. The set of strategies are identified based on the contextual parameters, the objectives, and the value parameters. The configuring module configures quantitative values for the one or more dynamic parameters based on quantitative values of targets and tolerances for the objectives and the value parameters. The computing module computes quantitative values for the objectives based on the quantitative values of the one or more dynamic parameters, thereby transforming the service engagement from the as-is state model to the to-be state model.
[008] In one implementation, a non-transitory computer readable medium embodying a program executable in a computing device for transforming a service engagement from an as-is state model to a to-be state model is disclosed. The program comprises a program code for selecting, based on a user input, the to-be state model from a plurality of to-be state models. The to-be state model defines objectives and value parameters for the service engagement. The value parameters impact a realization of the objectives for the service engagement. The program comprises a program code for receiving contextual parameters for the service engagement. The contextual parameters represent constraints and facts for the service engagement. The program further comprises a program code for identifying a set of strategies for the realization of the objectives. A strategy of the set of strategies comprises one or more dynamic parameters influencing the value parameters. The set of strategies are identified based on the contextual parameters, the objectives, and the value parameters. The program further comprises a program code for configuring quantitative values for the one or more dynamic parameters based on quantitative values of targets and tolerances for the objectives and the value parameters. The program further comprises a program code for computing quantitative values for the objectives based on the quantitative values of the one or more dynamic parameters, thereby transforming the service engagement from the as-is state model to the to-be state model.
BRIEF DESCRIPTION OF THE DRAWINGS
[009] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components.
[0010] Figure 1 illustrates a network implementation of a system for transforming a service engagement from an as-is state model to a to-be state model, in accordance with an embodiment of the present subject matter.
[0011] Figure 2 illustrates the system, in accordance with an embodiment of the present subject matter.
[0012] Figure 3 shows a flowchart for illustrating a method for transforming a service engagement from an as-is state model to a to-be state model, in accordance with an embodiment of the present subject matter.
[0013] Figure 4 shows a flowchart for illustrating a method for identifying a set of strategies, in accordance with an embodiment of the present subject matter.
[0014] Figure 5 shows a flowchart for illustrating a method for computing an impact factor matrix, in accordance with an embodiment of the present subject matter.
[0015] Figure 6 shows a flowchart for illustrating a method for transforming a service engagement from an as-is state model to a to-be state model, in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION
[0016] Systems and methods for transforming a service engagement are described. The present subject matter discloses a mechanism for transforming the service engagement from an as-is state model to a to-be state model. In order to transform the service engagement, the to-be state model may be selected from a plurality of to-be state models. Alternatively, the to-be state model may be created by a user. The to-be state model so selected may define objectives and value parameters for the service engagement. The value parameters influence a realization of the objectives for the service engagement.
[0017] The service engagement may further be associated with contextual parameters defined by stakeholders involved in the service engagement. The contextual parameters may represent constraints or facts for the service engagement. Further, the contextual parameters may be used as a basis to identify a set of strategies for the realization of the objectives. The contextual parameters may influence the identification of the set of strategies. Further, the identification of the set of strategies may be based on the objectives, and the value parameters. A strategy of the set of strategies may comprise one or more dynamic parameters influencing the value parameters. The set of strategies may be identified using an impact factor matrix and a causal map. The impact factor matrix may define an impact of the one or more dynamic parameters on the objectives and on the value parameters.
[0018] In order to implement the strategy for realizing the objectives, the one or more dynamic parameters may be configured. The one or more dynamic parameters may be configured using quantitative values of targets and tolerances assigned to the objectives and value parameters, to provide quantitative values of the one or more dynamic parameters. Further, the quantitative values of the one or more dynamic parameters may be used to compute quantitative values for the objectives, and cost parameters and risk parameters associated with the realization of the objectives. The quantitative values for the objectives, the cost parameters, and the risk parameters may be represented using a time-series. The time series may enable a user to analyze optimality of the set of strategies identified.
[0019] While aspects of described system and method for transforming the service engagement from the as-is state model to the to-be state model may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system.
[0020] Referring now to Figure 1, a network implementation 100 of a system 102 for transforming a service engagement is illustrated, in accordance with an embodiment of the present subject matter. In one embodiment, the system 102 may transform the service engagement from the as-is state model to the to-be state model. At first, the to-be state model may be selected from a plurality of to-be state models. The to-be state model may define objectives and value parameters for the service engagement. Further, the system 102 may associate the service engagement with contextual parameters. The system 102 may further use the contextual parameters as a basis to identify a set of strategies. The set of strategies may be identified based on the objectives and the value parameters. A strategy of the set of strategies may comprise one or more dynamic parameters influencing the value parameters. Additionally, the one or more dynamic parameters may be configured using quantitative values of targets and tolerances assigned to the objectives and value parameters, to provide quantitative values of the one or more dynamic parameters. The quantitative values of the one or more dynamic parameters may be used to compute quantitative values for the objectives.
[0021] Although the present subject matter is explained considering that the system 102 is implemented on a server, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, and the like. In one implementation, the system 102 may be implemented in a cloud-based environment. It will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2…104-N, collectively referred to as user devices 104 hereinafter, or applications residing on the user devices 104. Examples of the user devices 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices 104 are communicatively coupled to the system 102 through a network 106.
[0022] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
[0023] Referring now to Figure 2, the system 102 is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 206.
[0024] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with a user directly or through the client devices 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[0025] The memory 206 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 206 may include modules 208 and data 210.
[0026] The modules 208 include routines, programs, objects, components, data structures, etc., which perform particular tasks, functions or implement particular abstract data types. In one implementation, the modules 208 may include a selecting module 212, a receiving module 214, an identifying module 216, a configuring module 218, a computing module 220, a displaying module 222, and other modules 224. The other modules 224 may include programs or coded instructions that supplement applications and functions of the system 102.
[0027] The data 210, amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the modules 208. The data 210 may also include a system database 226, and other data 228. The other data 228 may include data generated as a result of the execution of one or more modules in the other modules 224.
[0028] In one implementation, at first, a user may use the client device 104 to access the system 102 via the I/O interface 204. Alternatively, the system 102 may be access by a user directly with the help of the I/O interface 204. The user may register themselves using the I/O interface 204 in order to use the system 102. The working of the system 102 may be explained in detail in Figures 3, 4, and 5 explained below. The system 102 may be used for transforming a service engagement from an as-is state model to a to-be state model. The as-is state model may represent a current state of the service engagement. Since the as-is state model may represent the current state, the as-is state model may be definitive. For example, consider that the service engagement is an Information Technology (IT) outsourcing service engagement between a service provider and a client. The as-is state model for the IT outsourcing service engagement may be a staff-augmentation model.
[0029] The to-be state model may represent a target state of the service engagement. Therefore, a plurality of to-be state models may exist. The plurality of to-be state models may be based on a user’s context and goals to be achieved for the service engagement. Referring to the example of the IT outsourcing service engagement, the to-be state model may be a managed services model. The managed services model may comprise multiple configurations based on – ‘roles and responsibilities definition’, ‘pricing model definition’, ‘service provisioned, ‘definition of Service Level Agreement (SLA) and Key Performance Indicators (KPI)’, and ‘knowledge management’. The IT outsourcing service engagement may be transformed from the staff-augmentation model to the managed services model.
[0030] Referring now to the Figure 3, a method for transforming the service engagement from the as-is state model to the to-be state model is disclosed. In order to transform the service engagement, the system 102, at step 302, may select the to-be state model from the plurality of to-be state models. The plurality of to-be state models may be stored in the system database 226. Specifically, in the present implementation, the to-be state model may be selected by the selecting module 212. In one embodiment, the selecting module 212 may select the to-be state model based on a user input. The to-be state model may be accessed by the user from the plurality of to-be state models stored in the system database 224. Alternatively, the to-be state model may be created by the user and added to the system database 226.
[0031] Still referring to the Figure 3, at step 304, the to-be state model may define objectives for the service engagement. The objectives may comprise the goals for the service engagement. For example, consider the IT outsourcing service engagement between the service provider and the client. The objectives for the IT outsourcing service engagement may be ‘improved efficiency’, ‘cost reduction’, ‘vendor exploitation’, ‘improved service availability and quality’. Further, the objectives may be defined by the service provider or the client for the service engagement.
[0032] In the Figure 3, at step 306, the to-be state model may define value parameters for the service engagement. The value parameters may impact a realization of the objectives. For example, consider that the objectives defined for the IT outsourcing service engagement may be ‘reduction in time to market a product’ and ‘production cost benefits’. The realization of the objectives ‘reduction in time to market a product’ and ‘production cost benefits’ may be impacted by productivity of the employees. For example, when the productivity of the employees may be only ‘10%’, the time to market the product may increase owing to lesser productivity. Thus, for the objectives ‘reduction in time to market a product’ and ‘production cost benefits’, the value parameter may be ‘productivity of the employees’.
[0033] Further, the value parameter for the objectives ‘reduction in time to market a product’ and ‘production cost benefits’ may be ‘amount of rework’. The amount of rework may impact the realization of the objectives. As the amount of rework increases, the time to market the product may be affected due to decrease in the productivity.
[0034] After selecting the to-be state model, the system 102 may employ the receiving module 214. The receiving module 214, at step 308, may receive, for the service engagement, contextual parameters defined by the service provider and the client. The contextual parameters may define a context, facts, or constraints for the service engagement. Still referring to the example of the IT outsourcing service engagement, the contextual parameters associated with the IT outsourcing service engagement may comprise ‘available resources’, ‘off-shoring constraints’, ‘service level agreements’, and ‘initial state of service delivery processes’. More particularly, the contextual parameters may be defined as ‘minimum 10 % of resources required at off-shore’, ‘maximum time permitted to hire a resource is 100 days’, and ‘percent automation permitted is 20%’. The contextual parameters for the service engagement may be identified and defined by the service provider and the client before initiating transformation of the service engagement from the as-is state model to the to-be state model.
[0035] The receiving module 214, at step 310, may further receive quantitative values of targets and tolerances for the objectives and the value parameters. The quantitative values of the targets and the tolerances may be set by the user. For example, a quantitative value of a target for the objective ‘cost reduction’ may be ‘20 %’. Similarly, the quantitative value of the target for the objective ‘improved efficiency’ may be ‘30 %’. Further, a quantitative value of tolerance for an objective ‘time to market - 11 months’ may be ’12 months’. When the time to market may exceed 12 months, a penalty may be levied. Thus, for a quantitative value of the objective defined as 11 months, the quantitative value of the tolerance may be 12 months.
[0036] After receiving the contextual parameters, and the quantitative values of the targets and the tolerances for the objectives and the value parameters, the system 102 may employ the identifying module 216. The identifying module 216, at step 312, may use the contextual parameters as a basis for identifying a set of strategies. The identifying module 216 may identify the set of strategies valid only for the contextual parameters defined for the service engagement. The set of strategies identified may change when the contextual parameters defined for the service engagement change. The identifying module 216 may identify the set of strategies in order to realize the objectives defined by the to-be state model. Therefore, the set of strategies may be identified based upon the objectives and the value parameters.
[0037] The system database 226 may store a mapping of the objectives and the value parameters with the set of strategies. The system database 226 may comprise of multiple mappings from the objectives and the value parameters to the set of strategies for different contextual parameters. Thus, the identifying module 216 may identify the set of strategies based on the mapping of the objectives and the value parameters to the set of strategies. The mapping may vary with respect to the contextual parameters defined for the service engagement. For example, consider the to-be state model defining the objective as ‘reduction in time to market’. The target set for reducing the time to market may be 600 days. The value parameter for the objective may be ‘productivity of the employees’ as the time to market depends on the productivity of the employees. Also, consider that the contextual parameter may be ‘minimum 10 % of resources required at off-shore’. For the value parameter, ‘productivity of the employees’, and the contextual parameter ‘minimum 10 % of resources required at off-shore’, the identifying module 216 may identify the set of strategies comprising, ‘Delivery Management’, ‘Resource Management’, ‘Competency Management’, and ‘Governance and Communication’. The strategy ‘Delivery Management’ may further comprise a strategy ‘Process Automation’. Similarly, the strategy ‘Resource Management’ may comprise a strategy ‘Resource Bench Management’.
[0038] Further, a strategy of the set of strategies may comprise one or more dynamic parameters influencing the value parameters. The one or more dynamic parameters may be changeable parameters for the service engagement. More particularly, the one or more dynamic parameters may be configured in order to implement the strategy. Referring to above example of the set of strategies identified, the strategy ‘Process Automation’ may comprise the one or more dynamic parameters. The one or more dynamic parameters may be ‘fixed time required for process automation’, ‘increment in process automation’, ‘fixed number of resources required for automation’, and ‘percentage of team resources required for automation.’ The strategy ‘Resource Bench Management’ may further comprise one or more dynamic parameters - ‘maximum number of resources on bench’, and ‘percentage of resources on bench’.
[0039] In one embodiment, the identifying module 216 may identify the set of strategies using an impact factor matrix for the one or more dynamic parameters. The impact factor matrix may define an impact of the one or more dynamic parameters on the objectives and the value parameters. Referring to the Figure 4, a method for identifying the set of strategies is disclosed. At step 402, the objectives to be realized may be selected by the user. At step 404, the value parameters may be identified from a set of value parameters by using a causal map. The causal map may be a map linking a set of parameters. The causal map may be pre-defined for the service engagement. The set of parameters may comprise the contextual parameters, the value parameters, the objectives, and the one or more dynamic parameters. The causal map may depict the impact of a parameter of the set of parameters on other parameters of the set of parameters. For example, resources in a team may impact a rate of work being done. The rate of work being done may further impact a time required to complete the work. The time required to complete the work may further impact the time to market. In addition, the resources in the team may impact resource costs, the resource costs may impact total costs, and the total costs may further impact cost benefits from the service engagement. Thus, the causal map may be backward traced to identify the value parameters impacting the realization of the objectives. Further, at step 406, the set of strategies may be identified from a database of strategies using the causal map. The causal map may be backward traced in order to identify the set of strategies impacting the realization of the objectives.
[0040] Still referring to the Figure 4, at step 408, for the set of strategies identified impact models may be retrieved. The impact models may be retrieved based on the contextual parameters defined for the service engagement. The impact model may change with a change in the contextual parameters defined for the service engagement. Therefore, the set of strategies identified may change based on the contextual parameters. For example, for a contextual parameter ‘Minimum number of off-shore resources required to be 10’, the strategy may be ‘management of off-shore resources.’ Further, the impact models for the set of strategies may be retrieved from a strategy impact database. The strategy impact database may store the impact factor matrix for the one or more dynamic parameters. In order to identify the set of strategies, at step 410, it may be checked if the impact factor matrix exists for the one or more dynamic parameters. In case the impact factor matrix exists, the set of strategies may be ordered based on a decreasing impact of the one or more dynamic parameters on the objectives and the value parameters. In case the impact matrix doesn’t exist, the impact factor matrix may be computed (at step 412).
[0041] Referring now to the Figure 5, a method for computing the impact factor matrix is disclosed. In order to compute the impact factor matrix, at step 502, the set of strategies may be selected. At step 504, the contextual parameters may be received for the service engagement. Further, at step 506, the strategy may be associated with constraints. As the strategy comprises the one or more dynamic parameters, the constraints may be set for the one or more dynamic parameters. The constraints may be set in terms of a base value, a minimum value, and a maximum value. For example, the base value for a dynamic parameter ‘number of resources on bench’ may be ‘50’, the minimum value may be ‘10’ and the maximum value may be ‘70’. Further, at step 508, the objectives and the value parameters may be selected. At step 510, in order to compute the impact factor matrix an experimental design may be selected. The experimental design may define a plurality of combinations of the one or more dynamic parameters. For example, the experimental design may be a full factorial design or a box design. At step 512, the experimental design may be implemented to run experiments for the plurality of combinations of the one or more dynamic parameters. The experiments may provide as an output (at step 514), an impact of the plurality of combinations of the one or more dynamic parameters on the objectives and the value parameters. The impact of the plurality of combinations of the one or more dynamic parameters on the objectives and the value parameters may be represented using the impact factor matrix.
[0042] After identifying the set of strategies, the set of strategies may be displayed to the user in a hierarchical manner. The strategy and the one or more dynamic parameters having a higher impact on the objectives and the value parameters may be displayed first. Further, at step 314, the strategy to be implemented may be selected by the user. The strategy selected by the user may also be associated with cost parameters and risk parameters. The cost parameters may represent a cost associated with implementing the strategy selected. For example, the cost parameters may comprise labor costs, knowledge management costs, training costs, process improvement costs, over head costs, and total project execution costs. The risk parameters may be associated with a risk accompanying the implementation of the strategy selected. The risk parameters may comprise low quality of service, low resource utilization, excessive resource utilization, and impact on productivity and cycle time thereby causing schedule over-run.
[0043] In order to implement the strategy selected, the one or more dynamic parameters present in the strategy may be configured using the configuring module 218. In one embodiment, the one or more dynamic parameters may be configured using a simulation software. For example, the simulation software ‘Vensim’ may be used for configuring the one or more dynamic parameters. The configuration of the one or more dynamic parameters may provide quantitative values for the one or more dynamic parameters. The quantitative values obtained after the configuration may be optimized quantitative values. At first, for the objectives and the value parameters quantitative values of targets and tolerances may be assigned. The quantitative values of the targets and the tolerances for the objectives and the value parameters may be set or assigned by the user. For example, the quantitative value of target for the objective ‘time to market’ may be 600 days and the quantitative value of tolerance for the objective may be 700 days. Similarly, the quantitative value of the target for the objective ‘total costs’ may be $ 2 million and the quantitative value of the tolerance for the objective may be $ 2.25 million. Further, the one or more dynamic parameters, selected by the user for configuration, may be assigned a range. The quantitative values of the targets and the tolerances for the objectives and the value parameters, and the range for the one or more dynamic parameters may be given as input to the simulation software. The simulation software may configure the one or more dynamic parameters based on the input. Table 1 illustrates the configuration of the one or more dynamic parameters using the simulation software.

Strategy- Process Automation Iteration -0 Iteration -1 Iteration -2 Iteration -3 Iteration -4 (Optimized using the simulation software)
Dynamic Parameters Increment in Process Automation 0 0.3 0.3 05 0.348
Percentage of team resources used for automation 0 1 1 0 1
Strategy- Resource Management
Dynamic Parameters Percentage of resources on bench 0 0 0.2 0.1 0.21309
Maximum number of resources on bench 100 100 100 100 100
Minimum number of resources on bench 0 0 5 5 5
Mean interval (days) between resource level reviews. 5 5 5 5 10
Senior to Junior Employees ratio 0.2 0.2 0.2 0.15 0.2
On-Site to Off-site ratio 0.2 0.2 0.1 0.1 0.1
Strategy – Competency Development
Dynamic Parameters Improvement in team competency 0 0 4 10 10
Number of subject matter experts in team 3 3 5 3 2
Percentage time spent on training 0 0 100% 100% 100%
Table 1
[0044] The table 1 illustrates the configuration of the one or more dynamic parameters for Iteration 0, Iteration 1, Iteration 2, Iteration 3, and Iteration 4. The quantitative values for the Iteration 4 may be the optimized quantitative values configured based on the quantitative values configured for the Iteration 0, Iteration 1, Iteration 2, and Iteration 3.
[0045] After configuring the one or more dynamic parameters, the system 102 may employ the computing module 220. The computing module 220 may compute the quantitative values for the objectives based on the quantitative values of the one or more dynamic parameters. The computing module 220, at step 318, may implement the simulation software to compute the quantitative values for the objectives.
[0046] The computing module 220 may also compute the quantitative values for the cost parameters, and the risk parameters based on the configured values of the one or more dynamic parameters. Table 2 illustrates the quantitative values for the objectives, the cost parameters, and the risk parameters using the configured quantitative values of the one or more dynamic parameters for the Iteration-0, Iteration -1, Iteration-2, Iteration-3, and Iteration-4.

Quantitative Values Iteration -0 Iteration -1 Iteration -2 Iteration -3 Iteration - 4
Time to Market 700 days 690 days 660 days 671 days 640 days
Production Cost Benefits 15% 21% 21% 9% 22%
Cost Parameters (in million units) (in million units) (in million units) (in million units) (in million units)
Labor costs 7.336 6.757 6 5.7 6.1
Knowledge Management Costs 0 0 1 1.08 1.08
Training Costs 0 0 0.12 0.12 0.1
Process Improvement Costs 0 0.3 0.8 1.1 0.41
Overhead Costs 0.035 0.03 0.03 0.1 0.04
Total Project Execution Costs 7.37 6.87 7.3 8.1 7.8
Risk Parameters
Quality of Service 0.75 0.8 0.8 0.8 0.82
Resource Utilization 88% 88% 90% 90% 83%
Productivity (Work Orders/hour) 0.88 0.92 0.9 1 1
Cycle Time 1.847 days 1.587 days 1.6 days 1.5 days 1.8 days
Schedule over-run (%) 17% 15% 10% 12% 7%
Table 2
[0047] The cost parameters for the one or more dynamic parameters may be mapped in the system database 226. The computing module 220 may retrieve the cost parameters associated with the dynamic parameter from the system database 226. The risk parameters may be defined as an outcome of negative effects of the set of strategies and the one or more dynamic parameters on the objectives and the value parameters. In order to compute the risk parameters a sensitivity analysis is performed. For example, consider that the dynamic parameter may be ‘number of training sessions for resources’. The ‘number of training sessions for the resources’ may have a negative impact on the value parameter ‘productivity’. The negative impact may be quantified in terms of a risk parameter. The negative impact may be obtained by performing the sensitivity analysis. In the sensitivity analysis, a number of experiments may be run by the simulation software for a range of values of the dynamic parameter. For example, the range of values of the ‘number of training sessions for the resources’ may be 1-10. The sensitivity analysis may provide a graph depicting the impact of the ‘number of training sessions for the resources’ on the productivity. The negative impact of the ‘number of training sessions for resources’ on the ‘productivity’ may be indicated by a negative slope of the graph. Thus, the negative slope of the graph may be the quantitative value obtained for the risk parameter.
[0048] In another embodiment, the system 102 may employ the displaying module 222 to display a time-series of the objectives, the risk parameters, and the cost parameters. The time-series of the objectives, the risk parameters, and the cost parameters may be used by the user to analyze optimality of the set of strategies.
[0049] In one embodiment, the optimality of the set of strategies may be analyzed by computing a net value of the objective. In order to compute the net value of the objective, each dynamic parameter of the one or more dynamic parameters may be assigned a cost function. In a next step, the system 102 may employ the cost function to compute a cost for each dynamic parameter. The cost function may be defined using a set of parameters the user wishes to optimize. For example, for the objective ‘Reduce time to market at reduced costs’, the cost function may be defined as,
Cost function = -0.000001 * Total costs (in currency units) – 1* Time to market (in days)
The system 102 may further maximize the cost function iteratively to arrive at maximize value for the cost function. The maximum value for the cost function may represent lowest costs and time to market. The set of parameters to be optimized may be assigned weights. The weights may be assigned to the set of parameters in order to ensure that the values of the set of parameters are of same magnitude. For example, value of the total costs may be millions of dollars and the value of the time to market may be in hundreds of days. As the time to market may have a higher importance that total costs, the magnitude of the total costs may be reduced by multiplying the value of the total costs with a factor. The factor in the cost function illustrated above may be -0.000001. The magnitude of the time to market may be kept unchanged by multiplying the value of the time to market with 1.
[0050] Alternatively, the system 102 may compute the cost using the causal map. The causal map may be forward traced in order to compute the cost. The net value of the objective may be computed by subtracting value of the risk parameter and the cost from the quantitative value of the objective.
[0051] Still referring to the Figure 3, at step 320, the optimality of the set of strategies may be used to identify if the objectives are realized. When the objectives may be realized the user may check if the objectives may be optimized further (step 322). When the objectives may be optimized, and when the objectives are not realized the user may choose to re-configure the one or more dynamic parameters (step 324), modify the strategy selected (step 326), revise the targets and tolerances set for the objectives and the value parameters (step 328), or select another to-be state model (step 330).
[0052] Referring now to Figure 6, a method 600 for transforming a service engagement from an as-is state model to a to-be state model is shown, in accordance with an embodiment of the present subject matter. The method 600 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 600 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
[0053] The order in which the method 600 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 600 or alternate methods. Additionally, individual blocks may be deleted from the method 600 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 600 may be considered to be implemented in the above described system 102.
[0054] At block 602, the to-be state model may be selected from a plurality of to-be state models. The to-be state model may define objectives and value parameters for the service engagement. The value parameters may impact a realization of the objectives for the service engagement. In one implementation, the to-be state model may be selected from the plurality of to-be state models by the selecting module 212.
[0055] At block 604, for the service engagement, contextual parameters may be received. The contextual parameters may represent constraints and facts for the service engagement. In one implementation, the contextual parameters for the service engagement may be received by the receiving module 214.
[0056] At block 606, a set of strategies may be identified for the realization of the objectives. A strategy of the set of strategies may comprise one or more dynamic parameters influencing the value parameters. The set of strategies may be identified based on the contextual parameters, the objectives, and the value parameters. In one implementation, the set of strategies may be identified by the identifying module 216.
[0057] At block 608, quantitative values may be configured for the one or more dynamic parameters. The quantitative values for the one or more dynamic parameters may be configured based on quantitative values of targets and tolerances for the objectives and the value parameters. In one implementation, the quantitative values may be configured by the configuring module 218.
[0058] At block 610, quantitative values for the objectives may be computed. The quantitative values for the objectives may be computed based on the quantitative values of the one or more dynamic parameters. In one implementation, the quantitative values for the objectives may be computed by the computing module 220.
[0059] Although implementations for methods and systems for transforming the service engagement from the as-is state model to the to-be state model have been described in language specific to structural features and/or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for transforming the service engagement from the as-is state model to the to-be state model. ,CLAIMS:1. A method for transforming a service engagement from an as-is state model to a to-be state model, the method comprising:
selecting, based on a user input, the to-be state model from a plurality of to-be state models, wherein the to-be state model defines objectives and value parameters for the service engagement, and wherein the value parameters impact a realization of the objectives for the service engagement;
receiving, by a processor, contextual parameters for the service engagement, wherein the contextual parameters represent constraints and facts for the service engagement;
identifying, by the processor, a set of strategies for the realization of the objectives, wherein a strategy of the set of strategies comprises one or more dynamic parameters influencing the value parameters, and wherein the set of strategies are identified based on the contextual parameters, the objectives, and the value parameters;
configuring, by the processor, quantitative values for the one or more dynamic parameters based on quantitative values of targets and tolerances for the objectives and the value parameters; and
computing, by the processor, quantitative values for the objectives based on the quantitative values of the one or more dynamic parameters, thereby transforming the service engagement from the as-is state model to the to-be state model.

2. The method of claim 1, wherein the quantitative values of the targets and the tolerances for the objectives and the value parameters are set by the user.

3. The method of claim 1, further comprising computing quantitative values for risk parameters and cost parameters based on the quantitative values of the one or more dynamic parameters.
4. The method of claim 1, wherein the identification of the set of strategies for the realization of the objectives is influenced by a change in the contextual parameters.

5. The method of claim 1, wherein the set of strategies, and the one or more dynamic parameters are identified using an impact factor matrix for the one or more dynamic parameters.
6. The method of claim 5, wherein the impact factor matrix defines an impact of the one or more dynamic parameters on the objectives and the value parameters.

7. The method of claim 1, further comprising displaying a time-series of the objectives, the risk parameters, and the cost parameters to analyze optimality of the set of strategies identified.

8. The method of claim 1, wherein the to-be state model is created by a user.

9. A system for transforming a service engagement from an as-is state model to a to-be state model, the system comprising:
a processor; and
a memory coupled to the processor, wherein the processor is capable of executing a plurality of modules stored in the memory, and wherein the plurality of modules comprising:
a selecting module, to select, based on a user input, the to-be state model from a plurality of to-be state models, wherein the to-be state model defines objectives and value parameters for the service engagement, and wherein the value parameters impact a realization of the objectives for the service engagement;
a receiving module to receive contextual parameters for the service engagement, wherein the contextual parameters represent constraints and facts for the service engagement;
an identifying module to identify a set of strategies for the realization of the objectives , wherein a strategy of the set of strategies comprises one or more dynamic parameters influencing the value parameters, and wherein the set of strategies are identified based on the contextual parameters, the objectives, and the value parameters;
a configuring module to configure quantitative values for the one or more dynamic parameters based on quantitative values of targets and tolerances for the objectives and the value parameters; and
a computing module to compute quantitative values for the objectives based on the quantitative values of the one or more dynamic parameters, thereby transforming the service engagement from the as-is state model to the to-be state model.
10. The system of claim 9, wherein the quantitative values of the targets and the tolerances for the objectives and the value parameters are set by a user.

11. The system of claim 9, wherein the computing module further computes quantitative values for the risk parameters and the cost parameters based on the quantitative values of the one or more dynamic parameters.

12. The system of claim 9, wherein the identification of the set of strategies for the realization of the objectives is influenced by a change in the contextual parameters.

13. The system of claim 9, wherein the set of strategies, and the one or more dynamic parameters are identified using an impact factor matrix for the one or more dynamic parameters.

14. The system of claim 13, wherein the impact factor matrix defines an impact of the one or more dynamic parameters on the objectives and the value parameters.

15. The system of claim 9, wherein the plurality of modules further comprises a displaying module to display a time-series of the objectives, the risk parameters, and the cost parameters to analyze optimality of the set of strategies identified.

16. The system of claim 9, wherein the to-be state model is created by a user.

17. A non-transitory computer readable medium embodying a program executable in a computing device for transforming a service engagement from an as-is state model to a to-be state model, the program comprising:
a program code for selecting, based on a user input, the to-be state model from a plurality of to-be state models, wherein the to-be state model defines objectives and value parameters for the service engagement, and wherein the value parameters impact a realization of the objectives for the service engagement;
a program code for receiving contextual parameters for the service engagement, wherein the contextual parameters represent constraints and facts for the service engagement;
a program code for identifying a set of strategies for the realization of the objectives, wherein a strategy of the set of strategies comprises one or more dynamic parameters influencing the value parameters, and wherein the set of strategies are identified based on the contextual parameters, the objectives, and the value parameters;
a program code for configuring quantitative values for the one or more dynamic parameters based on quantitative values of targets and tolerances for the objectives and the value parameters; and
a program code for computing quantitative values for the objectives based on the quantitative values of the one or more dynamic parameters, thereby transforming the service engagement from the as-is state model to the to-be state model.