CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application does not claim priority from any patent application.
TECHNICAL FIELD
[002] The present disclosure in general relates to the field of product lifecycle management. More particularly, the present invention relates to a system and method for predicting slippage or failure in a partner ecosystem.
BACKGROUND
[003] In Manufacturing Industry, OEMs are the central focus of any analysis. OEMs deal with partners for various purposes. For procurement, OEMs interact with strategic and transactional suppliers. For manufacturing OEMs interact with a contract manufacturer. For logistics, OEMs interact with 3PL and 4PL. For planning, interaction takes place between OEM and all sorts of partners. OEMs also interact with all sorts of service providers like Rating Agencies, Certification Agencies, ISV, Consulting Partners etc. These interactions are based on a symbiotic approach where OEM and partner both get benefited.
[004] In the new paradigm, partners & OEMs form a network and interact with each other. The fundamental difference between the traditional & new paradigm is that in the new paradigm partners interact among themselves and also with the OEMs, while in the traditional paradigm each partner used to interact with OEM alone. In the new approach, all of them use a common digital platform for mutual interaction for sharing selected pieces of information and transactions. This Network of partners is enriched when Partner’s Partner (I.e. Level 2 Partners) and few facilitating agencies also join the network to create a bigger ecosystem. This ecosystem encourages co-creation and innovation and helps the OEM get an edge over its competitors.
[005] A platform enabled Eco System of partners where interaction takes place in multiple ways and value creation is done through collaboration and co-creation is the ideal means to foster a relationship. The positive impact generated by such an ecosystem leads to higher KPI values & results in higher dollar value generation and saving.
3
[006] As the size the eco system becomes larger, the chance of slippage in any corner of the eco system also gets higher. Slippage may be like - wrong issue of certification, delivery delay, wrong part delivery, ISV product performance, asset breakdown, incomplete order, payment delay, pilferage etc. It is for the interest of all the partners and for the entire eco system that number of slippage or failures should be reduced. Any solution concept which can reduce the number of slippage or failure in the partner eco system is well desired.
SUMMARY
[007] Before the present systems and method for predicting slippage or failure in a partner ecosystem is illustrated. It is to be understood that this application is not limited to the particular systems, and methodologies described, as there can be multiple possible embodiments that are not expressly illustrated in the present disclosure. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only, and is not intended to limit the scope of the present application. This summary is provided to introduce concepts related to systems and method for predicting slippage or failure in a partner ecosystem. 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.
[008] In one implementation, a method for predicting slippage or failure in a partner ecosystem is illustrated. The method may comprise receiving historical transaction data corresponding to each partner from a set of partners associated with a partner ecosystem. The historical transaction data may be associated with a set of activities corresponding to the partner ecosystem. The method may further comprise identifying interdependency between the set of partners based on analysis of historical transaction data and receiving demand forecast data associated with a user selected activity from the set of activities. The method may further comprise identifying a first subset of partners associated with the user selected activity. The method may further comprise predicting a probability of slippage or failure, associated with each partner from the first subset of partners. In one embodiment, the probability of slippage or failure is determined based on analysis of demand forecast data with respect to the historical transaction data associated with the first set of partners. The method may further comprise identifying one or more weak partners, from the first subset of partners, with high probability of failure. The method
4
may further comprise suggesting one or more alternate partners from the set of partners to replace the one or more weak partners, thereby predicting slippage or failure in a partner ecosystem.
[009] In another implementation, a system for predicting slippage or failure in a partner ecosystem is illustrated. The system comprises a memory and a processor coupled to the memory, further the processor is configured to execute programmed instructions stored in the memory. In one embodiment, the processor may execute programmed instructions stored in the memory for receiving historical transaction data corresponding to each partner from a set of partners associated with a partner ecosystem. The historical transaction data is associated with a set of activities corresponding to the partner ecosystem. Further, the processor may execute programmed instructions stored in the memory for identifying interdependency between the set of partners based on analysis of historical transaction data associated with a user selected activity from the set of activities, Further, the processor may execute programmed instructions stored in the memory for identifying a first subset of partners associated with the user selected activity. Further, the processor may execute programmed instructions stored in the memory for predicting a probability of slippage or failure, associated with each partner from the first subset of partners. In one embodiment, the probability of slippage or failure is determined based on analysis of demand forecast data with respect to the historical transaction data associated with the first set of partners. Further, the processor may execute programmed instructions stored in the memory for identifying one or more weak partners, from the first subset of partners, with high probability of slippage or failure. Further, the processor may execute programmed instructions stored in the memory for suggesting one or more alternate partners from the set of partners to replace the one or more weak partners based on the demand forecast data and historical transaction data, thereby predicting slippage or failure in a partner ecosystem.
[0010] In yet another implementation, a computer program product having embodied computer program for predicting slippage or failure in a partner ecosystem is disclosed. The program may comprise a program code for receiving historical transaction data corresponding to each partner from a set of partners associated with the partner ecosystem. The historical transaction data may be associated with a set of activities corresponding to the partner ecosystem. The program may comprise a program code for
5
identifying interdependency between the set of partners based on analysis of historical transaction data associated with a user selected activity from the set of activities. The program may comprise a program code for identifying a first subset of partners associated with the user selected activity. The program may comprise a program code for predicting a probability of slippage or failure, associated with each partner from the first subset of partners. In one embodiment, the probability of slippage or failure is determined based on analysis of demand forecast data with respect to the historical transaction data associated with the first set of partners. The program may comprise a program code for identifying one or more weak partners, from the first subset of partners, with high probability of slippage or failures. The program may comprise a program code for suggesting one or more alternate partners from the set of partners to replace the one or more weak partners based on the demand forecast data and historical transaction data, thereby predicting slippage or failure in a partner ecosystem.
BRIEF DESCRIPTION OF DRAWINGS
[0011] 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.
[0012] Figure 1 illustrates a network implementation of a system configured for predicting slippage or failure in a partner ecosystem, in accordance with an embodiment of the present subject matter.
[0013] Figure 2 illustrates the system configured for predicting slippage or failure in a partner ecosystem, in accordance with an embodiment of the present subject matter.
[0014] Figure 3 illustrates a method for predicting slippage or failure in a partner ecosystem, in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION
[0015] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. The words “comparing”, “analysing”, “determining”, “identifying”, and other forms thereof, are intended to be equivalent in meaning and be
6
open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in predicting slippage or failure in a partner ecosystem, the exemplary, systems and method to predict failure in a partner ecosystem testing is now described. The disclosed embodiments of the system and method for predicting slippage or failure in a partner ecosystem are merely exemplary of the disclosure, which may be embodied in various forms.
[0016] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure to predict failure in a partner ecosystem is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.
[0017] In a partner ecosystem, predictive and prescriptive analytics based solution helps partners take correct decision based on analytics based outcome. This enables business users to reconfigure document or alter any action based on probability of failures and forecasted slippages and take proactive decisions to improve reliability and performance. The present system enables an interactive and intuitive visual interface to iteratively ask and answer questions and discover new insights. The system enables a set of pre-built reports and dashboards for rapid onboarding. In one embodiment, after a natural disaster like flood and earthquake also the system enables qualitative situational analysis post disasters and recommend alternate production, sourcing or logistics options.
[0018] The system is configured to analyse historical transaction data and can predicted chance of failure if they want to do transaction with other partners in the eco system for specific type of transactions. In one embodiment, the system can also predict details of type of failure. Based on the analysis, the partners can choose their right counterpart for specific work. For example, an OEM should know for placing a purchase order for a specific SKU, chances of slippage for each supplier/ partner. Accordingly, they can decide. Any partner in the eco system should know what is the chance of getting
7
the correct result from a certification agency for a specific purpose of certification. Partners like OEM, customers and suppliers should collectively know what is the predicted chance of slippage or failure for any 3PL for carrying any specific SKU for a specific ship to address. This way partners can collectively know each other’s strength and accordingly they can take the right decision in the eco system. The system can be developed once and can be deployed in any part of the partner eco system and can be accommodated in most of the hardware platform. It is cloud enabled and capable to collect historical transactional data of partners automatically. The system is accompanied with cloud enabled visibility platform which ensures visualization of partner action before prediction and after actionable intelligence. Further, the network implementation of system configured for predicting slippage or failure in a partner ecosystem is illustrated with Figure 1.
[0019] Referring now to Figure 1, a network implementation 100 of a system 102 for predicting failure in a partner ecosystem is disclosed. 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 over a server. Further, the system 102 may be implemented in a cloud network. The system 102 may further be configured to communicate with a partner ecosystem 108. The partner ecosystem 108 may be configured to manage transactions between different partners. The partner ecosystem 108 may comprise a set of partners comprising OEM, Suppliers, Customers, Rating Agencies, Certification Agencies, ISV Vendors, System Integrators, Managed Services Providers, Retailers, Platform Providers, Platform Extenders, Contract Manufacturers, 3PL, 4PL etc.
[0020] Further, 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 device 104 hereinafter, or applications residing on the user device 104. Examples of the user device 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user device 104 may be communicatively coupled to the system 102 through a network 106.
8
[0021] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 may 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.
[0022] In one embodiment, the system 102 may be configured to capture data from the partner ecosystem 108. Process the data based on different predefined parameters and user inputs to determine point of failure or slippage associated with the different partners and suggest alternate partners to carry out business with. Further, the system 102 for predicting failure in a partner ecosystem is elaborated with respect to figure 2.
[0023] Referring now to figure 2, the system 102 configured for predicting failure in a partner ecosystem 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, at least one processor 202 may be 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 the user directly or through the user device 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 may 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
9
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 may include routines, programs, objects, components, data structures, and the like, which perform particular tasks, functions or implement particular abstract data types. In one implementation, the module 208 may include a data collection module 212, a data processing module 214, a prediction module 216, a data analysis module 218, and other modules 220. The other modules 220 may include programs or coded instructions that supplement applications and functions of the system 102.
[0027] The data 210, amongst other things, serve 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 central data 228, and other data 230. In one embodiment, the other data 230 may include data generated as a result of the execution of one or more modules in the other modules 220.
[0028] In one implementation, a user may access the system 102 via the I/O interface 204. The user may be registered using the I/O interface 204 in order to use the system 102. In one aspect, the user may access the I/O interface 204 of the system 102 for obtaining information, providing input information or configuring the system 102.
DATA COLLECTION MODULE 212
[0029] In one embodiment, the data collection module 212 may be configured for receiving historical transaction data corresponding to each partner from a set of partners associated with the partner ecosystem 108. The historical transaction data may be associated with a set of activities corresponding to the partner ecosystem 108. In one embodiment, the set of activities may comprise product manufacturing, raw material supply, logistics, marketing, labour management, and a like. Further, the set of partners
10
may comprise Suppliers, Customers, Rating Agencies, Certification Agencies, ISV Vendors, System Integrators, Managed Services Providers, Retailers, Platform Providers, Platform Extenders, Contract Manufacturers, 3PL, and 4PL. In one embodiment, the historical transaction data associated with each partner may comprise critical information corresponding to timeliness, quality, quantity of products processed by the partner. The historical transaction data may also comprise user reviews and social media reviews of the partner. Further, the historical data may also comprise historical product demand trends associated with the partner ecosystem 108.
[0030] Further, the data collection module 212 is configured for receiving demand forecast data associated with a user selected activity from the set of activities. The demand forecast data may be determined based on analysis of historical transaction data. In one embodiment, the historical product demand trends may be analysed in order to determine demand forecast data. The demand forecast data may also be generated based on prediction of future natural disasters such as floods, earthquakes, famines, etc.
DATA PROCESSING MODULE 214
[0031] In one embodiment, the data processing module 214 is configured for identifying interdependency between the set of partners based on analysis of historical transaction data. For example, the set of partners may be classified into first level partners that are directly dealing with the OEM and a second level of partners who are associated with the first level of partners. The complex interdependency between partners may be analysed in order to determine the interdependency between the set of partners. The data processing may be performed external to the system 102 or in the system 102.
[0032] Further, the data processing module 214 is configured for identifying a first subset of partners associated with the user selected activity. For example, if the user selected activity is logistics. Then the data processing module 214 is configured to identify all a first subset of partners that are associated with the logistics process of the OEM. The first subset of partners may comprise the first level of logistics partners as well as second level of logistics partners.
PREDICTION MODULE 216
[0033] Further, the prediction module 216 is configured for predicting a probability of slippage or failure, associated with each partner from the first subset of partners. The
11
probability of slippage or failure may be determined based on analysis of demand forecast data with respect to the historical transaction data associated with the first set of partners. In one embodiment, the probability of failure corresponds to one of a Schedule Production Delay, Wrong Part Delivery, Delivery Not on the Time, Delivery Not in Full Quantity, Payment Delay, Incorrect Order Quantity, High Cash to Cash Cycle, High delivery Lead Time, High Managed Services Cost, Failure in SLAs, Product Failure Function Wise, Platform Troubles Function Wise, and Certification Error.
[0034] Further, the prediction module 216 is configured for identifying one or more weak partners, from the first subset of partners, with high probability of slippage or failure. For example, if there are 10 logistics partners associated with an OEM, the prediction module 216 is configured to analyse demand forecast data with respect to historical data associated with each of the logistics partners to determine which logistics partners have a high chance of failure to fulfil the demand forecasted by the system 102.
DATA ANALYSIS MODULE 218
[0035] Further, the data analysis module 218 is configured for suggesting one or more alternate partners from the set of partners to replace the one or more weak partners based on the demand forecast data and historical transaction data. In other words, the data analysis module is configured to process the demand forecast data and the historical transaction data associated with each and every logistics partner in order to find replacement/ alternate logistics partner who can fill the deficit of the one or more inefficient partners. Further, method to predicting slippage or failure in the partner ecosystem 108 is illustrated with respect to figure 3.
[0036] Referring now to figure 3, a method 300 for predicting failure in a partner ecosystem, is disclosed in accordance with an embodiment of the present subject matter. The method 300 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, and the like, that perform particular functions or implement particular abstract data types. The method 300 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
12
be located in both local and remote computer storage media, including memory storage devices.
[0037] The order in which the method 300 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 300 or alternate methods. Additionally, individual blocks may be deleted from the method 300 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 300 can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 300 may be considered to be implemented in the above described system 102.
[0038] At block 302, the data collection module 212 may be configured for receiving historical transaction data corresponding to each partner from a set of partners associated with an the partner ecosystem 108. The historical transaction data may be associated with a set of activities corresponding to the partner ecosystem 108. In one embodiment, the set of activities may comprise product manufacturing, raw material supply, logistics, marketing, labour management, and a like. Further, the set of partners may comprise Suppliers, Customers, Rating Agencies, Certification Agencies, ISV Vendors, System Integrators, Managed Services Providers, Retailers, Platform Providers, Platform Extenders, Contract Manufacturers, 3PL, and 4PL. In one embodiment, the historical transaction data associated with each partner may comprise critical information corresponding to timeliness, quality, quantity of products processed by the partner. The historical transaction data may also comprise user reviews and social media reviews of the partner. Further, the historical data may also comprise historical product demand trends associated with the partner ecosystem 108.
[0039] At block 304, the data collection module 212 is configured for receiving demand forecast data associated with a user selected activity from the set of activities. The demand forecast data may be determined based on analysis of historical transaction data. In one embodiment, the historical product demand trends may be analysed in order to determine demand forecast data. The demand forecast data may also be generated based on prediction of future natural disasters such as floods, earthquakes, famines, etc.
13
[0040] At block 306, the data processing module 214 is configured for identifying interdependency between the set of partners based on analysis of historical transaction data. For example, the set of partners may be classified into first level partners that are directly dealing with the OEM and a second level of partners who are associated with the first level of partners. The complex interdependency between partners may be analysed in order to determine the interdependency between the set of partners. The data processing may be performed external to the system 102 or in the system 102.
[0041] At block 308, the data processing module 214 is configured for identifying a first subset of partners associated with the user selected activity. For example, if the user selected activity is logistics. Then the data processing module 214 is configured to identify all a first subset of partners that are associated with the logistics process of the OEM. The first subset of partners may comprise the first level of logistics partners as well as second level of logistics partners.
[0042] At block 310, the prediction module 216 is configured for predicting a probability of failure, associated with each partner from the first subset of partners. The probability of slippage or failure may be determined based on analysis of demand forecast data with respect to the historical transaction data associated with the first set of partners. In one embodiment, the probability of slippage or failure corresponds to one of a Schedule Production Delay, Wrong Part Delivery, Delivery Not on the Time, Delivery Not in Full Quantity, Payment Delay, Incorrect Order Quantity, High Cash to Cash Cycle, High delivery Lead Time, High Managed Services Cost, Failure in SLAs, Product Failure Function Wise, Platform Troubles Function Wise, and Certification Error.
[0043] At block 312, the prediction module 216 is configured for identifying one or more weak partners, from the first subset of partners, with high probability of slippage or failure. For example, if there are 10 logistics partners associated with an OEM, the prediction module 216 is configured to analyse demand forecast data with respect to historical data associated with each of the logistics partners to determine which logistics partners have a high chance of slippage or failure to fulfil the demand forecasted by the system 102.
[0044] At block 314, the data analysis module 218 is configured for suggesting one or more alternate partners from the set of partners to replace the one or more weak partners
14
based on the demand forecast data and historical transaction data. In other words, the data analysis module is configured to process the demand forecast data and the historical transaction data associated with each and every logistics partner in order to find replacement/ alternate logistics partner who can fill the deficit of the one or more inefficient partners. The data analysis module 218 may further be configured to predicted value of failure and slippage for any transaction for any partner in the Eco System. Based on the prediction, the data analysis module 218 may also enable selection of alternate Partners for the specific tasks and detect chance of failure and slippage for those Partners as well for a specific task. The data analysis module 218 may further enable selection of the right Partner for the right tasks. The data analysis module 218 may enable actionable intelligence based on cause & effect analyses, cost analyses for different activities. The data analysis module 218 may also enable situational analysis post natural disaster and enablement of alternate arrangement for Production, Sourcing and Logistics. The system 102 once built can be deployed for different partner eco systems members like OEM, Suppliers, Customers, Rating Agencies, Certification Agencies, ISV Vendors, System Integrators, Managed Services Providers, Retailers, Platform Providers, Platform Extenders, Contract Manufacturers, 3PL, 4PL etc.
[0045] Although implementations for systems and methods for predicting slippage or failure in a partner ecosystem has been described, 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 predicting failure in a partner ecosystem.
15
WE CLAIM:
1. A system for predicting slippage or failure in a partner ecosystem, the system comprising:
a memory;
a processor coupled to the memory, wherein the processor is configured to execute programmed instructions sored in the memory for:
receiving historical transaction data corresponding to each partner from a set of partners associated with a partner ecosystem, wherein the historical transaction data is associated with a set of activities corresponding to the partner ecosystem;
receiving demand forecast data associated with a user selected activity from the set of activities;
identifying interdependency between the set of partners based on analysis of historical transaction data;
identifying a first subset of partners associated with the user selected activity;
predicting a probability of slippage or failure, associated with each partner from the first subset of partners, wherein the probability of slippage or failure is determined based on analysis of demand forecast data with respect to the historical transaction data associated with the first set of partners;
identifying one or more weak partners, from the first subset of partners, with high probability of slippage or failure;
suggesting one or more alternate partners from the set of partners to replace the one or more weak partners based on the demand forecast data and historical transaction data, thereby predicting slippage or failure in a partner ecosystem.
2. The system of claim 1, wherein the set of activities comprise product manufacturing, raw material supply, logistics, marketing, labour management.
3. The system of claim 1, wherein the set of partners comprise OEMs, Suppliers, Customers, Rating Agencies, Certification Agencies, ISV Vendors, System
16
Integrators, Managed Services Providers, Retailers, Platform Providers, Platform Extenders, Contract Manufacturers, 3PL, and 4PL.
4. The system of claim 1, wherein the demand forecast data is determined based on analysis of historical transaction data.
5. The system of claim 1, wherein the probability of slippage or failure corresponds to one of a Schedule Production Delay, Wrong Part Delivery, Delivery Not on the Time, Delivery Not in Full Quantity, Payment Delay, Incorrect Order Quantity, High Cash to Cash Cycle, High delivery Lead Time, High Managed Services Cost, Failure in SLAs, Product Failure Function Wise, Platform Troubles Function Wise, and Certification Error.
6. A method for predicting slippage or failure in a partner ecosystem, the method comprising steps for:
receiving, by a processor, historical transaction data corresponding to each partner from a set of partners associated with a partner ecosystem, wherein the historical transaction data is associated with a set of activities corresponding to the partner ecosystem;
receiving, by the processor, demand forecast data associated with a user selected activity from the set of activities;
identifying, by the processor, interdependency between the set of partners based on analysis of historical transaction data;
identifying, by the processor, a first subset of partners associated with the user selected activity;
predicting, by the processor, a probability of slippage or failure, associated with each partner from the first subset of partners, wherein the probability of slippage or failure is determined based on analysis of demand forecast data with respect to the historical transaction data associated with the first set of partners;
identifying, by the processor, one or more weak partners, from the first subset of partners, with high probability of slippage or failure;
suggesting, by the processor, one or more alternate partners from the set of partners to replace the one or more weak partners based on the demand
17
forecast data and historical transaction data, thereby predicting slippage or failure in a partner ecosystem.
7. The method of claim 6, wherein the set of activities comprise product manufacturing, raw material supply, logistics, marketing, labour management.
8. The method of claim 6, wherein the set of partners comprise Suppliers, Customers, Rating Agencies, Certification Agencies, ISV Vendors, System Integrators, Managed Services Providers, Retailers, Platform Providers, Platform Extenders, Contract Manufacturers, 3PL, and 4PL.
9. The method of claim 6, wherein the demand forecast data is determined based on analysis of historical transaction data.
10. The method of claim 6, wherein the probability of failure corresponds to one of a Schedule Production Delay, Wrong Part Delivery, Delivery Not on the Time, Delivery Not in Full Quantity, Payment Delay, Incorrect Order Quantity, High Cash to Cash Cycle, High delivery Lead Time, High Managed Services Cost, Failure in SLAs, Product Failure Function Wise, Platform Troubles Function Wise, and Certification Error.
11. A computer program product having embodied thereon a computer program to generate a test suite for predicting slippage or failure in a partner ecosystem, the computer program product comprises:
a program code for receiving historical transaction data corresponding to each partner from a set of partners associated with a partner ecosystem, wherein the historical transaction data is associated with a set of activities corresponding to the partner ecosystem;
a program code for receiving demand forecast data associated with a user selected activity from the set of activities;
a program code for identifying interdependency between the set of partners based on analysis of historical transaction data;
a program code for identifying a first subset of partners associated with the user selected activity;
18
a program code for predicting a probability of slippage or failure, associated with each partner from the first subset of partners, wherein the probability of slippage or failure is determined based on analysis of demand forecast data with respect to the historical transaction data associated with the first set of partners;
a program code for identifying one or more weak partners, from the first subset of partners, with high probability of slippage or failure;
a program code for suggesting one or more alternate partners from the set of partners to replace the one or more weak partners based on the demand forecast data and historical transaction data, thereby predicting slippage or failure in a partner ecosystem.