Description:FORM 2 THE PATENTS ACT, 1970 (39 of 1970) & THE PATENT RULES, 2003 COMPLETE SPECIFICATION (See Section 10 and Rule 13) Title of invention: METHOD AND SYSTEM FOR PREDICTING MULTI-HOP TURNAROUND TIME OPERATIONS IN AIRCRAFT 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 Preamble to the description: The following specification particularly describes the invention and the manner in which it is to be performed. TECHNICAL FIELD The disclosure herein generally relates to turnaround time, and, more particularly, to methods and systems for predicting multi-hop turnaround time operations in aircrafts. BACKGROUND The Airline industry is focusing on improving aircraft turnaround efficiency by minimizing the time taken to perform turnaround activities during the entire journey of aircraft. Turnaround time has a significant impact in terms of marketability and value creation potential of an aircraft, and for this reason, it is considered as an important driver which reduces cost of an aircraft’s journey. Software applications being utilized in the airline industry are complex . Flight delays are one of the most important factor because they cause a lot of overhead costs to the airline industries. Several complicated turnaround activities are coordinated between airports and the airline operators during the journey of the aircrafts. In such complex scenarios, airline industries demand dynamically adapting continuously changing processes related to compliance and regulatory for delivering operational efficiencies. Current available airline systems lack integrated turnaround monitoring and causality capturing environment which links all departments involved in the turnaround process. Furthermore, lack of visibility due to blind spots in the turnaround process leaves the airlines with a standardized approach to minimize delays and improve their on-time performance. The Airline industry is looking for industry proven solutions that can accelerate its digitalization journey for aircraft turnaround management with a platform that supports continuous improvement of airline processes. SUMMARY Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a system for predicting multi-hop turnaround time operations in aircraft is provided. The system includes receiving an input data comprising one or more flight events scheduled between a source and a destination, wherein each flight event includes one or more hops and determining a scheduled turnaround time (TAT) value from outliers of each flight event. Further, a statistical control chart is constructed construct by analyzing the outliers of each flight event based on at least one of (i) if a standard deviation is lower than absolute difference between the baseline TAT value and a moving central line of the actual TAT value by using a plurality of turnaround parameters actual TAT value by using a plurality of turnaround parameters, and (ii) if the standard deviation exceeds absolute difference between the baseline TAT value and the moving central line of the actual TAT value by using a plurality of baseline turnaround parameters. In one embodiment, a station level TAT outlier data corresponding to each hop of each flight event are determined based on the statistical control chart. Then, a continuous improvement plan is estimated based on (i) the statistical control chart, (ii) a plurality of influencing controllable factors, (iii) a plurality of influencing uncontrollable factors, and (iv) a power transformation of the actual TAT value. In accordance with an embodiment, a performance chart of each flight event is constructed by, determining one or more on-time performance parameters (OTP) of each flight event from the statistical control chart and computing a coefficient of variation (CoV) of the OTP based on a ratio of average OTP and a standard deviation of the OTP. Further, a maximum OTP of each flight event is determined based on (i) the coefficient of variation of the OTP, (ii) an improved OTP, and (iii) the one or more OTP, wherein the improved OTP is a sum of the OTP and the plurality of influencing controllable factors, and a one or more uncontrollable activities are computed by estimating the improved OTP and limits of the plurality of influencing uncontrollable factors based on the maximum OTP and the improved OTP. In accordance with an embodiment a coefficient of association is of each flight event is computed between a previous hop of the OTP and the scheduled turnaround time (TAT) value based on a plurality of attributes. In accordance with an embodiment predicting at every hop turnaround time operations delay of each flight leg movement based on an air traffic delay model trained with a plurality of air traffic TAT delays. In accordance with an embodiment, predicting delays impacting air traffic network at current flight leg based on the turnaround time operations delay and determining scheduling status of each flight event based on a threshold delay for next flight leg execution by estimating (i) an estimated time of departure (ETD) of current flight leg using a current flight leg data, (ii) an estimated time of departure time (ETD) of current flight leg using a previous leg data and the current flight leg data, and (iii) an estimated time of arrival (ETA) of the current leg data. In another aspect, a method for predicting multi-hop turnaround time operations in aircraft is provided. The method includes predicting multi-hop turnaround time operations in aircraft is provided. The system includes receiving an input data comprising one or more flight events scheduled between a source and a destination, wherein each flight event includes one or more hops and determining a scheduled turnaround time (TAT) value from outliers of each flight event. Further, a statistical control chart is constructed construct by analyzing the outliers of each flight event based on at least one of (i) if the standard deviation is lower than absolute difference between the baseline TAT value and a moving central line of the actual TAT value by using a plurality of turnaround parameters actual TAT value by using a plurality of turnaround parameters, and (ii) if the standard deviation exceeds absolute difference between the baseline TAT value and the moving central line of the actual TAT value by using a plurality of baseline turnaround parameters. In one embodiment, a station level TAT outlier data corresponding to each hop of each flight event are determined based on the statistical control chart. Then, a continuous improvement plan is estimated based on (i) the statistical control chart, (ii) a plurality of influencing controllable factors, (iii) a plurality of influencing uncontrollable factors, and (iv) a power transformation of the actual TAT value. In accordance with an embodiment, a performance chart of each flight event is constructed by, determining one or more on-time performance parameters (OTP) of each flight event from the statistical control chart and computing a coefficient of variation (CoV) of the OTP based on a ratio of average OTP and a standard deviation of the OTP. Further, a maximum OTP of each flight event is determined based on (i) the coefficient of variation of the OTP, (ii) an improved OTP, and (iii) the one or more OTP, wherein the improved OTP is a sum of the OTP and the plurality of influencing controllable factors, and a one or more uncontrollable activities are computed by estimating the improved OTP and limits of the plurality of influencing uncontrollable factors based on the maximum OTP and the improved OTP. In accordance with an embodiment a coefficient of association is of each flight event is computed between a previous hop of the OTP and the scheduled turnaround time (TAT) value based on a plurality of attributes. In accordance with an embodiment predicting at every hop turnaround time operations delay of each flight leg movement based on an air traffic delay model trained with a plurality of air traffic TAT delays. In accordance with an embodiment predicting delays impacting air traffic network at current flight leg based on the turnaround time operations delay and determining scheduling status of each flight event based on a threshold delay for next flight leg execution by estimating (i) an estimated time of departure (ETD) of current flight leg using a current flight leg data, (ii) an estimated time of departure time (ETD) of current flight leg using a previous leg data and the current flight leg data, and (iii) an estimated time of arrival (ETA) of the current leg data. In yet another aspect, a non-transitory computer readable medium for the system includes predicting multi-hop turnaround time operations in aircraft is provided. The system includes receiving an input data comprising one or more flight events scheduled between a source and a destination, wherein each flight event includes one or more hops and determining a scheduled turnaround time (TAT) value from outliers of each flight event. Further, a statistical control chart is constructed construct by analyzing the outliers of each flight event based on at least one of (i) if the standard deviation is lower than absolute difference between the baseline TAT value and a moving central line of the actual TAT value by using a plurality of turnaround parameters actual TAT value by using a plurality of turnaround parameters, and (ii) if the standard deviation exceeds absolute difference between the baseline TAT value and the moving central line of the actual TAT value by using a plurality of baseline turnaround parameters. In one embodiment, a station level TAT outlier data corresponding to each hop of each flight event are determined based on the statistical control chart. Then, a continuous improvement plan is estimated based on (i) the statistical control chart, (ii) a plurality of influencing controllable factors, (iii) a plurality of influencing uncontrollable factors, and (iv) a power transformation of the actual TAT value. In accordance with an embodiment, a performance chart of each flight event is constructed by, determining one or more on-time performance parameters (OTP) of each flight event from the statistical control chart and computing a coefficient of variation (CoV) of the OTP based on a ratio of average OTP and a standard deviation of the OTP. Further, a maximum OTP of each flight event is determined based on (i) the coefficient of variation of the OTP, (ii) an improved OTP, and (iii) the one or more OTP, wherein the improved OTP is a sum of the OTP and the plurality of influencing controllable factors, and a one or more uncontrollable activities are computed by estimating the improved OTP and limits of the plurality of influencing uncontrollable factors based on the maximum OTP and the improved OTP. In accordance with an embodiment a coefficient of association is of each flight event is computed between a previous hop of the OTP and the scheduled turnaround time (TAT) value based on a plurality of attributes. In accordance with an embodiment predicting at every hop turnaround time operations delay of each flight leg movement based on an air traffic delay model trained with a plurality of air traffic TAT delays. In accordance with an embodiment predicting delays impacting air traffic network at current flight leg based on the turnaround time operations delay and determining scheduling status of each flight event based on a threshold delay for next flight leg execution by estimating (i) an estimated time of departure (ETD) of current flight leg using a current flight leg data, (ii) an estimated time of departure time (ETD) of current flight leg using a previous leg data and the current flight leg data, and (iii) an estimated time of arrival (ETA) of the current leg data. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles: FIG. 1 illustrates an exemplary block diagram of an example system for predicting multi-hop turnaround time operations in aircraft, in accordance with some embodiments of the present disclosure. FIG. 2 illustrates an architecture of an example of the system predicting multi-hop turnaround time operations in aircraft, in accordance with some embodiments of the present disclosure. FIG. 3A and FIG.3B illustrates an example flow diagram for predicting multi-hop turnaround time operations in aircraft using the system of FIG.1, in accordance with some embodiments of the present disclosure. FIG.4 illustrates an air traffic delay model trained to predict turnaround time operations delay of each flight at every hop in aircraft using the system of FIG.1, in accordance with some embodiments of the present disclosure. FIG.5 illustrates an example method for predicting multi-hop turnaround time operations for one or more flight events scheduled between a source and a destination using the system of FIG.1, in accordance with some embodiments of the present disclosure. FIG.6 illustrates an example method of the system predicting TAT delay over internet protocol (IP) based on machine learning (ML) / artificial intelligence (AI) and impacting air traffic network at current flight leg using the system of FIG.1, in accordance with some embodiments of the present disclosure. FIG.7 illustrates graphical representation of a statistical control chart by analyzing outliers of each flight event using the system of FIG.1, in accordance with some embodiments of the present disclosure. FIG.8 illustrates graphical representation of on-time performance parameters versus a plurality of controllable activities identified in the flight event using the system of FIG.1, in accordance with some embodiments of the present disclosure. FIG.9 illustrates graphical representation of baseline outliers of turnaround time using the system of FIG.1, in accordance with some embodiments of the present disclosure. FIG.10 illustrates graphical representation of power transformation of actual TAT value using the system of FIG.1, in accordance with some embodiments of the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. Embodiments herein provide a method and system for predicting multi-hop turnaround time operations in aircraft. The method disclosed, enables to predict turnaround time (TAT) operations delay of flight leg movement. Aircraft turnaround management is complex and involves cross-functional process(es) with multi-interdependencies. The key to airline efficiency depends on consistent on-time performance (OTP) with fine execution of aircraft turnaround management. Turnaround time (TAT) operations are a set of activities performed at airport, while monitoring the aircraft during the journey before takeoff. The method of the present disclosure identifies delay causalities impacting air traffic network with a systematic continuous improvement plan and a performance chart. Here, the scheduled turnaround time activities include for example an engine start, an aircraft arrival, and thereof. The method of the present disclosure provides a business ecosystem platform to drive the efficiencies with an end-to-end digital aircraft turnaround management solution. The disclosed system is further explained with the method as described in conjunction with FIG.1 to FIG.10 below. Referring now to the drawings, and more particularly to FIG. 1 through FIG.10, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method. FIG. 1 illustrates an exemplary block diagram of an example system for predicting multi-hop turnaround time operations in an aircraft, in accordance with some embodiments of the present disclosure. In an embodiment, the system 100 includes one or more hardware processors 104, communication interface device(s) or input/output (I/O) interface(s) 106 (also referred as interface(s)), and one or more data storage devices or memory 102 operatively coupled to the one or more hardware processors 104. The one or more processors 104 may be one or more software processing components and/or hardware processors. In an embodiment, the hardware processors can 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 processor(s) is/are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud, and the like. The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server. The memory 102 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 102 further comprises (or may further comprise) information pertaining to input(s)/output(s) of each step performed by the systems and methods of the present disclosure. In other words, input(s) fed at each step and output(s) generated at each step are comprised in the memory 102 and can be utilized in further processing and analysis. FIG. 2 illustrates the architecture of an example of the system predicting multi-hop turnaround time operations in aircraft, in accordance with some embodiments of the present disclosure. FIG.2 includes an outlier analysis module 202, a continuous improvement planner module 204, and a predictor module 206. The system 100 receives input data of each flight event scheduled between a source and a destination from an external sources. The received input data comprises one or more flight events for a journey scheduled between a source and destination, wherein each journey has one or more hops in between which are further processed by the outlier analysis module 202. The outlier analysis module 202 analyses each flight events and constructs a statistical control chart. The statistical chart is constructed for two different conditions such as if a standard deviation is lower than absolute difference and exceeding the absolute difference. The continuous improvement planner module 204 estimates the statistical control chart and power transformation of the actual TAT value and a performance chart is constructed for one or more on-time performance parameters (OTP) of each flight event for scheduling. The predictor module 206 predicts turnaround time operation delay of each flight leg movement based on an air traffic delay model trained with a plurality of air traffic TAT delays. The predictor module also predicts delays impacting air traffic network at current flight leg based on the turnaround time operations delay and schedule status are evaluated. Functions of the modules for predicting delays in aircrafts, are explained in conjunction with FIG.3 through FIG.10 providing a flow diagram, architectural overviews, and performance analysis of the system 100. FIG. 3A and FIG.3B illustrates an example flow diagram for predicting multi-hop turnaround time operations in aircraft using the system of FIG.1, in accordance with some embodiments of the present disclosure. In an embodiment, the system 100 comprises one or more data storage devices or the memory 102 operatively coupled to the processor(s) 104 and is configured to store instructions for execution of steps of the method 300 by the processor(s) or one or more hardware processors 104. The steps of the method 300 of the present disclosure will now be explained with reference to the components or blocks of the system 100 as depicted in FIG.2 through FIG.10. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods, and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps to be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously. Referring now to the steps of the method 300, at step 302, the one or more hardware processors 104 receive an input data comprising one or more flight events scheduled between a source and a destination, wherein each flight event includes one or more hops. In accordance with an example of the present disclosure, the system 100 monitors the scheduled turnaround time activities in the airport at every hop during the flight journey between the source and the destination. Each flight data includes a flight identifier relates to the wing of the aircraft, a flight leg identifier, a flight number relates to unique number identification for the definite journey, a flight date, an aircraft registration number, a departure station code, an arrival station code, a planned turnaround time, and an actual turnaround time. As an illustrative example (FIG.5), for the purpose of explanation the system 100 during the journey the flight event includes one or more turnaround activities being performed at every hop in each flight. The one or more flight event includes a water filling, a cargo door open, a catering service, a fueling service, a chocks on, an initial walkaround check, a aerobridge or ladder aligned, a A/C door open, a holds open, a load instruction form (LIFB) initial to captain, a toilet, and water service, a cabin cleaning, a guest deplane, a boarding gate operations, a guest boarding at A/C, a load instruction form final to captain, a holds close, an ARC signed, a A/C door close, a final walkaround check, a aerobridge or ladder removed, chocks off, and the like. Here, each flight event activities are dependent on each other, and the activity is represented as ?(X?_i) which is analytically converted into stochastic function with respect to time. If (X_i) is an independent activity, the scheduled turnaround time (TAT) processing time is represented as ?f(X?_i)= t_i. If (X_i) is a dependent activity, the scheduled TAT processing time is represented as ?f(X?_i)= t_1+t_2+t_3+?+t_j. where, each t_j is the scheduled processing time of activity (X_k), where k=1,2,…j. Every turnaround time (TAT) process is the execution time of activities that are not being performed within the predefined scheduled time at every hop in the airport. Sometimes, the scheduled time and an actual processing time of flight events differ either with early completion of the activity or with delay in execution. Stochastic variation present in the scheduled time and actual time performing each flight event, is represented with the variation ?. Here, ?g(X?_i) is the actual processing time of the flight event activity ?(X?_i), that is scheduled with processing time of X_i+?. In case of dependent activity ?(X?_i) is represented as the actual processing time of ?g(X?_i), Where, ?g(X?_i)=(?t_1+??_1 )+(?t_2+??_2 )+(?t_3+??_3 )+?+(?t_j+??_j) and ?g(X?_i)-?f(X?_i)= ?_1+?_2+?_3+?+?_j which contributes on the crucial parts of the outliers. Referring now to the steps of the method 300, at step 304, the one or more hardware processors 104 determine a scheduled turnaround time (TAT) value from outliers of each flight event. From the above step output, from each flight event the scheduled TAT values are determined from the outliers. Referring now to the steps of the method 300, at step 306, the one or more hardware processors 104 construct, a statistical control chart is by analyzing the outliers of each flight event based on at least one of (i) if a standard deviation is lower than absolute difference between the baseline TAT value and a moving central line of the actual TAT value by using a plurality of turnaround parameters actual TAT value, and (ii) if the standard deviation exceeds absolute difference between the baseline TAT value and the moving central line of the actual TAT value by using a plurality of baseline turnaround parameters. In one embodiment, the plurality of turnaround parameters comprises of a moving central line, an upper control limit (UCL) and a lower control limit (LCL). The upper control limit (UCL) includes a primary UCL_1 and a secondary UCL_2. The lower control limit includes a primary LCL_1 and a secondary LCL_2. In one embodiment, the plurality of baseline turnaround parameters comprises of a baseline upper control limit (UCL) and a baseline lower control limit (LCL). The baseline upper control limit (UCL) includes a first UCL_1 value, a second UCL_2 value, a third UCL_3 value, and a fourth UCL_4 value. The baseline lower control limit (LCL) includes a first LCL_1 value, a second LCL_2 value, a third LCL_3 value, and a fourth LCL_4 value. In one embodiment, the statistical control chart is constructed for the standard deviation lower than absolute difference between the baseline TAT value and a moving central line of the actual TAT value by using a plurality of turnaround parameters by computing the primary upper control limit (UCL) and the secondary upper control limit (UCL) based on the plurality of turnaround parameters. The primary upper control limit (UCL_1) is a mean value of the actual TAT value summed with a ratio of moving range mean value of a predefined value is represented in Equation 1, UCL_1=Mean+(3*Mean?of?MR)\/1.128?------------ Equation 1 The secondary upper control limit (UCL_2) is the mean value summed with a predefined number of times of standard deviation is represented in Equation 2, UCL_2=Mean+3*S tan?d ard?Deviation --------- Equation 2 The primary lower control limit (LCL_1) is the difference between a baseline TAT value with the ratio of moving range mean value of the predefined value is represented in Equation 3, LCL_1=Mean?-?(3*Mean?of?MR)\/1.128?-------- Equation 3 The secondary lower control limit (LCL_2) is the difference between the actual TAT mean value and the predefined number of times of standard deviation is represented in Equation 4, LCL_2=Mean?-?(3*S tan?d ard?Deviation) ---------- Equation 4 Further, the statistical control chart is constructed for the standard deviation exceeding absolute difference between the baseline TAT value and the moving central line of the actual TAT value by using a plurality of baseline turnaround parameters where, the first UCL_1 value based on the actual TAT mean value summed with the ratio of moving range mean value and the predefined value is represented in Equation 5, UCL_1=Mean+(3*Mean?of?MR)\/1.128?--------- Equation 5 The second UCL_2 value is the actual TAT mean value summed with predefined times of standard deviation is represented in Equation 6, UCL_2=Mean+3*S tan?d ard?Deviation?-------- Equation 6 The third UCL_3 value is the baseline TAT value summed with the ratio of baseline and mean of moving range value with the predefined number of times represented in Equation 7, UCL_3=?Baseline+(3*Mean?of MR)?\/1.128?- -------- Equation 7 The fourth UCL_4 value is the baseline value summed with the predefined number of times of the standard deviation is represented in Equation 8, UCL_4=Baseline+3*S tan?d ard?Deviation?-------- Equation 8 Further, the first LCL_(1 )value based on the difference between the actual TAT mean value and the ratio of moving range mean value with the predefined value is represented in Equation 9, LCL_1=Mean?-?(3*Mean?of?MR)\/1.128?------- Equation 9 The second LCL_(2 )value is the difference between the mean value and the predefined number of times of the standard deviation is represented in Equation 10, LCL_2=Mean?-?3*S tan?d ard?Deviation?------- Equation 10 The third LCL_(3 ) value is the difference between the baseline TAT value with the ratio of moving range mean value of the predefined value is represented in Equation 11, LCL_3=Baseline-(3*Mean?of?MR\/1.128)------- Equation 11 The fourth LCL_14 value is the difference between the baseline TAT value and the predefined number of times of standard deviation is represented in Equation 12, LCL_4=Baseline-3*S tan?d ard?Deviation?------- Equation 12 FIG.7 illustrates graphical representation of a statistical control chart by analyzing outliers of each flight event using the system of FIG.1, in accordance with some embodiments of the present disclosure. Here, the baseline TAT data advises the aircraft on its continuous improvement opportunities. Overtime when sufficient data is gathered, the outlier analysis is conducted for turnaround baseline process followed at each station and on different aircraft types. Referring now to the steps of the method 300, at step 308, the one or more hardware processors 104 determine a station level TAT outlier data corresponding to each hop of each flight event based on the statistical control chart. At every station, for each flight based on the scheduled TAT value and the actual TAT value, UCL, LCL, and the moving central line is calculated, and the statistical charts are depicted. The actual TAT value of the flights greater than the UCL value will be considered as outliers. Referring now to the steps of the method 300, at step 310, the one or more hardware processors 104 estimate a continuous improvement plan based on (i) the statistical control chart, (ii) a plurality of influencing controllable factors, (iii) a plurality of influencing uncontrollable factors, and (iv) a transformation function of the actual TAT value. With the help of historical actual TAT value, the UCL, the LCL, and the moving central line values are determined which improves the present TAT value of the aircraft by means of technical resources and controlling CPM activities. Further, the statistical analysis of the plurality of influencing controllable factors and the plurality of influencing uncontrollable factors are determined. Parallelly, simulated TAT value is computed using the power transformation on the actual TAT value for further analysis. The plurality of influencing controllable factors includes for example an airport authority, a handling, a technical, and thereof. The airport authorities’ problems due to runway capacities, occupied parking and thereof. The handling problems delayed ground processes such as late passengers, handling agent disposition. The technical problems such as malfunction of technical systems for example aircraft. The plurality of influencing uncontrollable factors includes an air traffic flow management, weather condition, and other factors. The air traffic flow management factors such as restrictions according to crowed air traffic control sectors, traffic flow restrictions. The weather conditions such as negative weather influences such as rain, snow, wind and thereof. Other factors such as aircraft damage, strike, no delay code and thereof. The continuous improvement planner module 310 estimates the statistical control chart daily or weekly or monthly of each flight event which helps segregating the outliers and identify controllable and uncontrollable activities on the respective outliers as represented in the graphical representation of FIG.9. FIG.9 illustrates the outlier analysis for 25 mins quick turnaround process (baseline), which suggests the process was effectively executed at its best within a control range of 21 to 43 mins. The scheduled turnaround time (TAT) value below the actual TAT value obtains the UCL = 43 mins, LCL=21 mins, Moving Central Line = 32 mins, and standard deviation = 4 mins. The power transformation (FIG.10) of the actual TAT value transforms the actual TAT value into a power transformed TAT value with a predefined threshold and compares the statistical control chart between the actual TAT value and the inverse function of the power transformed TAT value. The power transformation of the actual TAT value is the ratio of data points of the actual TAT value with the geometric mean of the historical actual TAT values which is represented in Equation 13, f(X_i )=(((X_i )^t-k))/t(GM(X_1,?X_2,…,X_i )^(t-k) ) -----Equation?13 Where, f(X_i ) is the transformed value of the actual TAT value (X_i) is the data points of the actual TAT value (k) is number of CPM activities GM=Geometric?mean?of?the?historical?data,?0