Claims:1. A processor implemented method for demand planning for Made-To-Order manufactur-ing in a computing device, the method comprising:
systematically organizing a product configuration information associated with a product in a repository associated with the computing device, by one or more hardware processors, the product comprises a plurality of customizable components and the product configuration information indicative of one or more user requirements associated with customization of the product;
mapping the product configuration information associated with the product with a plurality of valid product configurations to obtain one or more matching product configurations, by the one or more hardware processors, the plurality of valid product configurations pre-stored in the repository associated with the computing device;
constructing a hierarchical product-component tree for the one or more matching product configurations, by the one or more hardware processors, wherein the hierarchical product-component tree represents a plurality of components and a plurality of sub-components of the one or more matching product configurations arranged hierarchically in a plurality of levels;
identifying one or more new components and sub-components based on a comparison of the product configuration information with the one or more matching product configurations, by the one or more hardware processors;
updating the one or more matching product configurations with the one or more new components and sub-components to obtain a plurality of alternate product configurations associated with the product, by the one or more hardware processors; and
obtaining an optimized product configuration from the plurality of alternate product configurations based on application of one or more optimization parameters to the plurality of alternate product configurations, by the one or more hardware processors.

2. The method as claimed in claim 1, wherein systematically organizing comprises storing the product configuration information in the repository in form of an Entity Attribute Value (EAV) model.
3. The method as claimed in claim 1, wherein mapping the product configuration infor-mation with each of the plurality of valid product configurations comprises:
one to one mapping of the product configuration information with each of the plurality of valid product configurations to obtain a first set of matching product configurations;
clustering the first set of matching product configurations into a plurality of clusters based on a plurality of product attributes; and
concurrently mapping the product configuration information with each of the plurality of the clusters to obtain the one or more matching product configurations.
4. The method as claimed in claim 1, wherein constructing the hierarchical product-component tree comprises:
accessing a matching product configuration associated with the product from the plurality of matching product configurations; and
representing the plurality of components and the plurality of sub-components of the product in a plurality of levels arranged in a hierarchical structure.
5. The method as claimed in claim 1, wherein updating the one or more valid configura-tions comprises at least one of:
replacing the one or more components and sub-components in the one or more matching product configurations with the identified one or more new components and sub-components; and
changing the hierarchical arrangement of the components and sub-components in the one or more matching product configurations.
6. The method as claimed in claim 1, wherein the optimization parameters comprises time and cost.
7. The method as claimed in claim 3, wherein the product attributes for clustering com-prises price of the product, model of the product and feature set.
8. A system executed at a computing device for demand planning for Made-To-Order manufacturing comprising:
one or more memories comprising programmed instructions and repository for storing product configuration information associated with a product; and
one or more hardware processors operatively coupled to the one or more memories, wherein the one or more hardware processors are capable of executing the programmed instructions stored in the one or more memories to:
systematically organize a product configuration information associated with a product in a repository associated with the computing device, the product comprises a plurality of customizable components and the product configuration information indicative of one or more user requirements associated with customization of the product;
map the product configuration information associated with the product with a plurality of valid product configurations to obtain one or more matching product configurations, the plurality of valid product configurations pre-stored in the repository associated with the computing device;
construct a hierarchical product-component tree for the one or more matching product configurations, wherein the hierarchical product-component tree represents a plurality of components and a plurality of sub-components of the one or more matching product configurations arranged hierarchically in a plurality of levels;
identify one or more new components and sub-components based on a comparison of the product configuration information with the one or more matching product configurations;
update the one or more matching product configurations with the one or more new components and sub-components to obtain a plurality of alternate product configurations associated with the product; and
obtain an optimized product configuration from the plurality of alternate product configurations based on application of one or more optimization parameters to the plurality of alternate product configurations.
9. The system as claimed in claim 8, wherein the one or more hardware processors are fur-ther configured by the instructions to systematically organize and store the product con-figuration information in the repository in form of an Entity Attribute Value (EAV) model.
10. The system as claimed in claim 8, wherein to map the product configuration information with each of the plurality of valid product configurations, the one or more hardware processors are further configured by the instructions to:
perform one to one mapping of the product configuration information with each of the plurality of valid product configurations to obtain a first set of matching product configurations;
cluster the first set of matching product configurations into a plurality of clusters based on a plurality of product attributes; and
concurrently map the product configuration information with each of the plurality of the clusters to obtain the one or more matching product configurations.
11. The system as claimed in claim 8, wherein to construct the hierarchical product-component tree, the one or more hardware processors are further configured by the in-structions to:
access a matching product configuration associated with the product from the plurality of matching product configurations; and
represent the plurality of components and the plurality of sub-components of the product in a plurality of levels arranged in a hierarchical structure.
12. The system as claimed in claim 8, wherein to update the one or more valid configura-tions, the one or more hardware processors are further configured by the instructions to perform at least one of:
replace the one or more components and sub-components in the one or more matching product configurations with the identified one or more new components and new sub-components; and
change the hierarchical arrangement of the components and sub-components in the one or more matching product configurations.
13. The system as claimed in claim 8, wherein the optimization parameters comprises time and cost.
14. The system as claimed in claim 10, wherein the product attributes for clustering com-prises price of the product, model of the product and feature set.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See section 10 and rule 13)

Title of invention:
DEMAND PLANNING FOR MADE-TO-ORDER MANUFACTURING

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.
TECHNICAL FIELD
[001] The present subject matter relates, in general, to demand planning and, in par-ticular, to a demand planning for Made-To-Order manufacturing.

BACKGROUND
[002] Made-To-Order manufacturing is a pull-type supply chain management used by companies to manufacture products according to product configuration specifications of users. The pull-type supply chain management synchronizes demand with manufacture of a business unit to increase throughput and to eliminate wastage of business processes. The pull-type manufacturing is applicable to constructions, construction of plants, aircrafts, vessels, bridges, automobiles, electrical and electronic equipment and the like. Here, the manufactur-ing is performed after confirming a demand and the production volume is driven by demand of the users. Hence, the companies are able to supply products with exact configuration speci-fication as specified by the user.
[003] Typically, the Made-to-Order manufacturing approach poses certain challenges in meeting the user requirements. For instance, as the Made-To-Order manufacturing involves flexibility of selection of features, challenges due to compatibility of selected features, utiliza-tion of inventory, timely procurement of required accessories and the like may be posed in meeting demands of the user. Also, requirements of the users are typically dynamic in nature. As an example, a configuration requirement of one user may be different from another user. Also, the configuration requirement of the users may change over time. Hence, due to the dy-namic and challenging nature of the Made-To-Order manufacturing approach, the conven-tional demand planning tools may be inefficient in providing an optimal product plan configu-ration for product manufacturing.

SUMMARY
[004] The following presents a simplified summary of some embodiments of the dis-closure in order to provide a basic understanding of the embodiments. This summary is not an extensive overview of the embodiments. It is not intended to identify key/critical elements of the embodiments or to delineate the scope of the embodiments. Its sole purpose is to present some embodiments in a simplified form as a prelude to the more detailed description that is presented below. In view of the foregoing, an embodiment herein provides a method and sys-tem for demand planning for Made-To-Order manufacturing.
[005] In one aspect, a processor-implemented method for demand planning for Made-To-Order manufacturing, is provided. The method includes systematically organizing a product configuration information associated with a product in a repository associated with the computing device, by one or more hardware processors. The product includes a plurality of customizable components and the product configuration information is indicative of one or more user requirements associated with customization of the product. Further, the method includes mapping the product configuration information associated with the product with a plurality of valid product configurations to obtain one or more matching product configura-tions, by the one or more hardware processors. The plurality of valid product configurations are pre-stored in a repository associated with the computing device. Furthermore, the method includes constructing hierarchical product-component tree for the one or more matching product configurations, by the one or more hardware processors. The hierarchical product-component tree represents a plurality of components and a plurality of sub-components of the one or more matching product configurations in a plurality of levels arranged hierarchically. Moreover, the method includes identifying one or more new components and sub-components based on a comparison of the product configuration information with the one or more match-ing product configurations, by the one or more hardware processors. Also, the method in-cludes updating the one or more matching product configurations with the one or more new components to obtain a plurality of alternate product configurations associated with the prod-uct, by the one or more hardware processors. In addition, the method includes obtaining an optimized product configuration from the plurality of alternate configurations based on appli-cation of one or more optimization parameters to the plurality of alternate product configura-tions, by the one or more hardware processors.
[006] In another aspect, a system for demand planning for Made-To-Order manufac-turing is provided. The system includes one or more memories comprising programmed in-structions and repository for storing product configuration information associated with a product; and one or more hardware processors operatively coupled to the one or more memo-ries, wherein the one or more hardware processors are capable of executing the programmed instructions stored in the one or more memories to systematically organize a product configu-ration information associated with the product in a repository associated with the computing device. The product includes a plurality of customizable components and the product config-uration information indicative of one or more user requirements associated with customization of the product. Further, the one or more hardware processors are capable of executing the programmed instructions to map the product configuration information associated with the product with a plurality of valid product configurations to obtain one or more matching prod-uct configurations. The plurality of valid product configurations pre-stored in a repository as-sociated with the computing device. Furthermore, the one or more hardware processors are capable of executing the programmed instructions to construct hierarchical product-component tree for the one or more matching product configurations. Moreover, the one or more hardware processors are capable of executing the programmed instructions to identify one or more new components and sub-components based on a comparison of the product con-figuration information with the one or more matching product configurations. Also, the one or more hardware processors are capable of executing the programmed instructions to update the one or more matching valid product configurations with the one or more new components to obtain a plurality of alternate product configurations associated with the product. In addition, the one or more hardware processors are capable of executing the programmed instructions to obtain an optimized product configuration from the plurality of alternate product configura-tions based on application of one or more optimization parameters to the plurality of alternate product configurations.
[007] In yet another aspect, a computer program product comprising a non-transitory computer-readable medium having embodied therein a computer program for demand plan-ning for Made-To-Order manufacturing, is provided. The computer readable program, when executed on a computing device, causes the computing device to: systematically organize a product configuration information associated with a product in a repository associated with the computing device. The product includes a plurality of customizable components and the product configuration information indicative of one or more user requirements associated with customization of the product. Further, the one or more hardware processors are capable of executing the programmed instructions to map the product configuration information asso-ciated with the product with a plurality of valid product configurations to obtain one or more matching product configurations. The plurality of valid product configurations pre-stored in a repository associated with the computing device. Furthermore, the one or more hardware pro-cessors are capable of executing the programmed instructions to construct hierarchical prod-uct-component tree for the one or more matching product configurations. Moreover, the one or more hardware processors are capable of executing the programmed instructions to identify one or more new components and sub-components based on a comparison of the product con-figuration information with the one or more matching product configurations. Also, the one or more hardware processors are capable of executing the programmed instructions to update the one or more matching valid product configurations with the one or more new components to obtain a plurality of alternate product configurations associated with the product. In addition, the one or more hardware processors are capable of executing the programmed instructions to obtain an optimized product configuration from the plurality of alternate product configura-tions based on application of one or more optimization parameters to the plurality of alternate product configurations.

BRIEF DESCRIPTION OF THE FIGURES
[008] The detailed description is described with reference to the accompanying fig-ures. 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 reference like features and modules.
[009] FIG. 1 illustrates a network environment implementing a demand planning for Made-To-Order manufacturing, in accordance with an example embodiment of the present disclosure;
[0010] FIG. 2 illustrates a block diagram of a system for the demand planning for Made-To-Order manufacturing, in accordance with an example embodiment of the present disclosure;
[0011] FIG. 3A depicts a product-component tree with parts and sub parts associated with a product for demand planning for Made-To-Order manufacturing, in accordance with an example embodiment of the present disclosure;
[0012] FIG. 3B depicts the product-component tree with new components to be sub-stituted, new sub-components to be included and the new sub-components to be substituted, in accordance with an example embodiment of the present disclosure;
[0013] FIG. 3C depicts the product-component tree with substituted new component, substituted new sub-component and included new sub-component, in accordance with an ex-ample embodiment of the present disclosure;
[0014] FIG. 4 depicts the system performance trend during the demand planning for Made-To-Order manufacturing, in accordance with an example embodiment of the present disclosure;
[0015] FIG. 5 illustrates a flow diagram for demand planning for Made-To-Order manufacturing, in accordance with an example embodiment of the present disclosure; and
[0016] FIG. 6 illustrates a detailed flow diagram for demand planning for Made-To-Order manufacturing, in accordance with an example embodiment of the present disclosure.
[0017] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems and devices embodying the princi-ples of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

DETAILED DESCRIPTION
[0018] Companies generally use Made-To-Order (MTO) manufacturing approach to satisfy the exact requirements of the user. The MTO strategy facilitate in manufacturing the products once the user places the order and allows more flexible customization rather than purchasing directly from the retailer. Further, MTO approach eliminates the problem of exces-sive inventory associated with Made-To-Stock (MTS), a traditional strategy for production. The MTS strategy requires an accurate demand forecast to predict inventory requirement and effectiveness, and the order is based on the prediction. But, due to unpredictable nature of business environment, predicting the inventory requirement is challenging.
[0019] Customization of products broadens the product selection of the user and pro-vides a broad utilization of inventory for the manufacturer. Due to the difference in customi-zation desired by different users, the MTO manufacture of products is challenging. Conven-tionally used methods and tools are ineffective in delivering customizable cost effective and time effective products to the users.
[0020] The present subject matter overcomes the limitations of conventional demand planning systems. For example, various embodiments of the present disclosure allow demand planning for MTO manufacturing. The system performs demand planning based on the under-lying technology of the target application. In an embodiment, the system receives a product configuration information given by the user. The product configuration information is com-pared with a plurality of valid product configurations stored in the repository to obtain one or more matching product configurations. The matching product configurations are represented as a hierarchical product-component tree. The hierarchical product-component tree is updated with new product-components if any. The updating results in a plurality of alternate product configurations. The plurality of alternate product configurations are optimized based on one or more optimization parameters and a product configuration is suggested to the user. An im-plementation of the demand planning system for Made-To-Order manufacturing is described further in detail with reference to FIGS. 1 through 6.
[0021] A product-component includes parts and-sub parts of a product. As an exem-plary the product-components of a car includes bonnet, bumper, radiator, rocker, engine and the like. The sub parts of an engine-part includes spark plug, piston, piston rings, crankshaft, sump and the like. Accordingly, hereinafter, the term product-component and product-part shall be used interchangeably. Also, the term sub-component and sub-part shall be used inter-changeably.
[0022] FIG. 1 illustrates a network environment 100 implementing a system 102 for demand planning for Made-To-Order manufacturing, according to an embodiment of the pre-sent subject matter. The demand planning system for Made-To-Order manufacturing 102, hereinafter referred to as the system 102, is configured for demand planning for Made-To-Order manufacturing. The system 102 may be embodied in a computing device, for instance a computing device 104.
[0023] Although the present disclosure is explained considering that the system 102 is implemented on a server, it may be understood that the system 102 may also be implement-ed in a variety of computing systems, such as a laptop computer, a desktop computer, a note-book, a workstation, a cloud-based computing environment and the like. In one implementa-tion, 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 106-1, 106-2... 106-N, collectively referred to as user devices 106 hereinafter, or applications re-siding on the user devices 106. Examples of the user devices 106 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, a Smartphone, a Tablet Computer, a workstation and the like. The user devices 106 are communicatively coupled to the system 102 through a network 108.
[0024] In an embodiment, the network 108 may be a wireless or a wired network, or a combination thereof. In an example, the network 108 can be implemented as a computer net-work, as one of the different types of networks, such as virtual private network (VPN), intra-net, local area network (LAN), wide area network (WAN), the internet, and such. The net-work 106 may either be a dedicated network or a shared network, which represents an associ-ation of the different types of networks that use a variety of protocols, for example, Hyper-text Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), and Wireless Application Protocol (WAP), to communicate with each other. Further, the network 108 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices. The network devices within the network 108 may interact with the system 102 through communication links.
[0025] As discussed above, the system 102 may be implemented in a computing de-vice 104, such as a hand-held device, a laptop or other portable computer, a tablet computer, a mobile phone, a PDA, a smartphone, and a desktop computer. The system 102 may also be implemented in a workstation, a mainframe computer, a server, and a network server. In an embodiment, the system 102 may be coupled to a data repository, for example, a repository 112. The repository 112 may store data processed, received, and generated by the system 102. In an alternate embodiment, the system 102 may include the data repository 112. The compo-nents and functionalities of the system 102 are described further in detail with reference to FIG. 2.
[0026] FIG. 2 illustrates a block diagram of the demand planning system 200 for Made-To-Order manufacturing, in accordance with an example embodiment. The demand planning system 200 (hereinafter referred to as system 200) may be an example of the system 102 (FIG. 1). In an example embodiment, the system 200 may be embodied in, or is in direct communication with the system, for example the system 102 (FIG. 1). The system 200 in-cludes or is otherwise in communication with one or more hardware processors such as a pro-cessor 202, at least one memory such as a memory 204, and an I/O interface 206. The proces-sor 202, memory 204, and the I/O interface 206 may be coupled by a system bus such as a system bus 208 or a similar mechanism.
[0027] The I/O interface 206 may include a variety of software and hardware inter-faces, for example, a web interface, a graphical user interface, and the like The interfaces 206 may include a variety of software and hardware interfaces, for example, interfaces for periph-eral device(s), such as a keyboard, a mouse, an external memory, a camera device, and a print-er. Further, the interfaces 206 may enable the system 102 to communicate with other devices, such as web servers and external databases. The interfaces 206 can facilitate multiple commu-nications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the interfaces 206 may include one or more ports for connecting a number of computing systems with one another or to another server computer. The I/O interface 206 may include one or more ports for connecting a number of devices to one another or to another server.
[0028] The hardware processor 202 may be implemented as one or more microproces-sors, 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 hardware processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204.
[0029] The memory 204 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. In an embodiment, the memory 204 includes a plurality of modules 220 and a repository 240 for storing data processed, received, and generated by one or more of the modules 220. The modules 220 may include routines, programs, objects, components, data structures, and so on, which perform particular tasks or implement particular abstract data types.
[0030] The memory 204 also includes module(s) 220 and a data repository 240. The module(s) 220 include, for example, a product configuration receiving module, a comparison module, a hierarchical product-component tree constructing module, a module to update hier-archical product-component tree, and an optimization module. The other modules may in-clude programs or coded instructions that supplement applications or functions performed by the demand planning system 200. The modules 220, amongst other things, can include rou-tines, programs, objects, components, and data structures, which perform particular tasks or implement particular abstract data types. The modules 220 may also be used as, signal proces-sor(s), state machine(s), logic circuitries, and/or any other device or component that manipu-lates signals based on operational instructions. Further, the modules 220 can be used by hard-ware, by computer-readable instructions executed by a processing unit, or by a combination thereof. The modules 220 can include various sub-modules (not shown). The module 220 may include computer-readable instructions that supplement applications or functions performed by the system 200.
[0031] The data repository 240 may include valid product configurations 242, re-ceived product configuration information 244, hierarchical product-component tree 246 and other data 248. Further, the other data 248 amongst other things, may serve as a repository for storing data that is processed, received, or generated as a result of the execution of one or more modules in the module(s) 220.The repository 240 is further configured to maintain a plu-rality of valid product configurations. Herein, the valid product configuration may refer to product customization information previously designed to meet needs of a plurality of exist-ing users. The information present in the valid product configuration may include, but not lim-ited to the categories such as, size, color, volume, smell, taste, quantity, material composition, functionality, features and the like. As an example, the product configuration information of a car may include, size, color, material composition, body style, model, features and the like. The features of a car may include cruise control, seat heater, tow hitch, automatic transmission, Digital Versatile Disc (DVD) video system, third row seat, sunroof, navigation system, leather seats and the like.
[0032] Although the data repository 240 is shown internal to the demand planning system 200, it will be noted that, in alternate embodiments, the data repository 240 can also be implemented external to the demand planning system 200, where the data repository 240 may be stored within a database communicatively coupled to the demand planning system 200. The data contained within such external database may be periodically updated. For ex-ample, new data may be added into the database and/or existing data may be modified and/or non-useful data may be deleted from the database. In one example, the data may be stored in an external system, such as a Lightweight Directory Access Protocol (LDAP) directory and a Relational Database Management System (RDBMS). In another embodiment, the data stored in the data repository 240 may be distributed between the demand planning system 200 and the external database.
[0033] In an embodiment, the system 200 receives a product configuration infor-mation associated with a product through I/O interface 206. The system 200 may also receive the product configuration information through electronic data transfer. The electronic data transfer includes e-mail, Short Message Service (SMS) and the like. In an embodiment, the product configuration information may be a Sales and Operations Planning (S & OP) sheet. Further, the system 200 systematically organizes the received product configuration infor-mation in the repository 240 associated with the computing device 104. The product includes a plurality of customizable components.
[0034] The system 200 is further configured to systematically organize the received product configuration information. Also, the system 200 performs structural and temporal da-ta validations on the systematically organized product configuration information. The struc-tural data validation includes data type validation, range and constraint validation, code and cross reference validation, conditional constraints validation and the like. The temporal data validation includes valid time validation, transaction time validation and the like. The valid time is the time period when a fact is true in the real world and the transaction time is the time period when a fact stored in the database was known. In an embodiment, the product config-uration information may be stored in the repository 240 as an Entity Attribute Value (EAV) Model. In EAV model, the information is stored in the form of a table. The columns in the table includes Entity (E), Value (V) and Attributes (A). An example of the EAV model for the product ‘car’ is presented below in Table 1.
[0035] Referring now to Table 1, the EAV model for a ‘car’ represents an ‘Entity’ as a car, the ‘Attributes’ associated with the car includes model, colour and body style. The ‘Val-ue’ for the attribute model is luxury car, the value of the attribute colour is black and the value of the attribute body style is hatchback.
[0036] Herein, it will be noted that the above EAV model for the car is an example and is presented for the sake of understanding of said model, however the example should not be construed as limiting to the embodiments. The scope of the embodiments extends be-yond the example presented herein.
Table 1
Entity Attribute Value
Car Model Luxury Car
Color Black
Body Style Hatchback

[0037] In an embodiment, the system 200 performs one-to-one mapping of each of the plurality of valid product configurations with the received product configuration infor-mation associated with the product to obtain a first set of matching product configurations. In an embodiment, the system 200 performs mapping of each of the plurality of valid product configurations with the received product configuration information based on a pattern match-ing of the valid and received product configuration information. In an embodiment, system 200 performs the pattern matching of the valid and received product configuration infor-mation by using RETE (Latin word for ‘net’ or ‘comb’) or Lazy Evaluation Algorithm for Production System (LEAPS) pattern matching models. The RETE and LEAPS pattern matching models provides a generalized logical description to the functionality and responsi-ble for matching data tuples against rules in a pattern matching production system. The data tuple is an ordered set of data constituting a record. In an embodiment, said mapping of the valid and the received product configuration information can be performed by the comparison module.
[0038] The system 200 is caused to cluster the first set of matching product configu-rations into a plurality of clusters based on a plurality of product attributes. In an embodiment, the plurality of product attributes used for clustering may include price of the product, model of the product and feature set associated with the product. The product configuration infor-mation associated with the product is mapped with each of the plurality of the clusters in a concurrent manner to obtain one or more matching product configurations. For example, as-suming that the valid product configurations are arranged in 10 clusters. Now if a product configuration received at the system 200, the system 200 is caused to match the received product configuration with all the 10 clusters concurrently, or in parallel. Herein, matching the ‘perceived product configuration with the valid product configurations in 10 clusters’ refers to simultaneously performing a matching operation by the processor 202. It will be noted that due to the concurrent matching of the valid product information with all the 10 clusters, a speed of matching is increased, thereby enhancing system performance with respect to the processing time. A graph indicating an enhancement of system performance due to the con-current mapping of the valid and received product configurations is described further with reference to FIG 4.
[0039] In an embodiment, the system 200 constructs hierarchical product-component tree for the one or more matching product configurations. The “hierarchical product-component tree” construction refers to a representation of a plurality of components and a plurality of sub-components of the one or more matching product configurations in a plurality of levels arranged hierarchically. An example depicting a hierarchical product-component tree with components and sub-components associated with a product is represented and described further with reference to FIG. 3A. The system 200 compares the one or more matching prod-uct configurations represented in the form of hierarchical product-component tree with the product configuration information associated with a product to identify one or more new parts and sub-components of the product. In an embodiment, the comparison module com-pares the one or more matching product configurations represented in the form of hierarchical product-component tree with the product configuration information associated with the product to identify one or more new components and sub-components of the product. An ex-ample describing product-component tree with new components to be substituted, the new sub-components to be included and the new sub-components to be substituted, is depicted and explained further with reference to FIG. 3B.
[0040] In an embodiment, the system 200 updates the hierarchical product-component tree with the identified new-components and sub-components to obtain a plurality of alter-nate product configurations associated with the product. The updating may replace the one or more components and sub-components in the one or more matching product configurations. The updating may alter the hierarchy of components and sub-components in the one or more matching product configurations. An example explaining the product-component tree with substituted component, substituted sub-component and included sub-component, is depicted and described further in detail with reference to FIG. 3C.
[0041] In an embodiment, the system 200 applies a set of Constraint Handling Rules (CHR) to the set of alternate product configurations to reduce the number of alternate prod-uct configurations. Here, the system 200 applies the CHR rules by running a combination of pattern matching models and alternate product configurations not satisfy the CHR rules are eliminated. The CHR rules may fulfill a set of business defined constraints. The business de-fined constraints may include the constraints associated with configuration, inventory, price, weight, compatibility, supply and the like. As an example, the business defined constraints includes: (i) exhausting one or more parts from inventory (ii) time to supply a specific part (iii) power consumption under 15W (iv) having 12V flat socket and the like. However, in various scenarios, many other business defined constraints may be included for the purpose of apply-ing CHR to the set of alternate product configurations, without limiting the scope of the dis-closed embodiments.
[0042] In certain scenario, the user requirement may be driven by certain constraints. For instance, the user requirement may include a time constraint, and may wish to obtain the MTO product within a specific time frame. In such a scenario, the time frame specified by the user may be translated into an optimization constraint or an optimization parameter, and the system 200 is caused to optimize the plurality of alternate product configurations based on a plurality of optimization parameters to obtain an optimized product configuration. In another example scenario, the user requirement may include cost constraint and may wish to obtain MTO product within a specific cost. Herein, it will be noted that for the brevity of descrip-tion, the optimization parameters are assumed to include ‘cost’ and ‘time’ only. In an embod-iment, the constraints may be aggregate rules applicable at the overall product. The aggregate rules includes ‘total cost of the product less than a particular amount’, ‘total weight of the product less than a particular weight’, ‘number of miles for full tank of fuel more than certain miles’ and the like. However, in various scenarios, many other optimization parameters may be included for the purpose of optimization of product configurations, without limiting the scope of the disclosed embodiments.
[0043] FIGS. 3A, 3B and 3C illustrate process of altering hierarchical product-component tree associated with a product 310 for demand planning for Made-To-Order man-ufacturing of the product 310, in accordance with an example embodiment. In particular, FIG. 3A illustrates an example depicting a hierarchical product-component tree 340 with compo-nents and sub- components associated with a product for demand planning for Made-To-order manufacturing of the product. For example, as illustrated in FIG. 3A, the hierarchical product-component tree 340 of the product 310 is shown to include parts such as component 312 and component 314. The component 312 is shown to include sub-components such as a sub-component 316 and sub-component 318. The component 314 is shown to include sub-component 320. Taking example of the product as a laptop, the Product 310 may be assumed to be laptop, the component 312 and components 314 may be assumed to be Memory and Processor, respectively. Also, the sub-components 316 and318 may be assumed to be hard disk and Random Access Memory (RAM), and the sub-component 320 may be assumed to be a processor core. Herein, it will be understood that the example of hierarchical product-component tree is presented for the purpose of understanding. For various different product configuration, the hierarchical product-component tree can be represented in a more elaborate and complex forms. It will also be understood that the example hierarchical product-component tree for the car explained above is merely for the purpose of understanding, and should not be construed as limiting to the embodiments.
[0044] As described with reference to FIG. 2, the system for example the system 200 compares the valid product configuration and the received product configuration to obtain the new components and sub-components. An example of identified new components and sub-components for the product 310 of FIG. 3A is illustrated further with reference to FIG. 3B. Referring to FIG. 3B, the new component 322 to be substituted, sub-component 324 to be substituted and sub-component 326 to be included are represented in rectangular boxes with dashed boundary. Pre-existing components and sub-components are represented in the rectangular boxes with solid boundary. The new components and sub-components may be included in the hierarchical product-component tree 340 to obtain an updated hierarchical product-component tree 350. For example, the updated hierarchical product-component tree 350 is illustrated in FIG. 3C. Referring to FIG. 3C, the updated hierarchical product-component tree 350 is shown to have the identified new component 322, of the FIG. 3B is substituted to the component 312. The new sub-component 324 is substituted to the sub-component 316 and the new sub-component 326 is included to the component 314. Taking example of the product as a laptop, the component 312 may be the RAM. The standard con-figurations for a RAM are 2 Giga Bytes RAM 316 and 4 Giga Bytes RAM 318 and the user requests for 10 GB RAM, the system 200 may substitute the 2 GB RAM 316 with 10 GB RAM 324 and substitute RAM 312 with the component RAM slot 322.
[0045] FIG. 4 illustrates system performance trend during the demand planning for Made-To-Order manufacturing, in accordance with an example embodiment. In particular, the processing at the computing device (for instance the computing device 102 of FIG. 1) may lead to utilization of resources, such as memory utilization and processing power. During the process of demand planning for Made-To-Order manufacturing, the utilization of the re-sources may vary with every step. The disclosed demand planning process includes stages such as a validation stage, a matching stage, a substitution stage, a constraint application stage and an optimization stage. At each of the said stages, the utilization of the resources may vary, as illustrated in FIG. 4. The validation stage, the matching stage, the constraints application stage and the optimization stages are CPU bound stages and the memory overhead is less. The substitution stage is a memory bound stage and the CPU overhead is less. Thus the re-source utilization is done is a balanced manner. Also, the overhead on CPU is reduced by var-ious methods. For example, as shown in FIG. 4, the CPU overhead on the matching stage is initially reduced by applying validation on the system inputs like S&OP sheets, product con-figurations. Also, the CPU overhead during the matching stage is further reduced by the pro-cess of clustering and parallel matching. Moreover, the CPU overhead during the further pro-cessing is reduced by exclusion and best-fit methods to avoid asymptotic computations. In an embodiment, the alternate product configurations not satisfying certain constraints are elimi-nated by applying CHR and the optimized alternate product configuration is obtained by ap-plying best-fit methods. Here, the best-fit methods includes constraints based optimization.
[0046] FIG. 5 illustrates a flow diagram of a method 500 for demand planning for Made-To-Order manufacturing in accordance with an example embodiment of the present disclosure. The method 500 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, ob-jects, components, data structures, procedures, modules, functions, etc., that perform particu-lar functions or implement particular abstract data types. The method 500 may also be prac-ticed in a distributed computing environment where functions are performed by remote pro-cessing devices that are linked through a communication network. The order in which the method 500 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 500, or an alternative method. Furthermore, the method 500 can be implemented in any suitable hardware, software, firmware, or combination thereof.
[0047] At 502, the system 200 receives a product configuration information associat-ed with a product. At 504, the system 200 matches the received product configuration infor-mation associated with a product with a plurality of valid product configurations stored in the repository 240 to obtain one or more matching product configurations. At 506, the system 200 constructs a hierarchical product-component tree. At 508, the system 200 identifies new components by comparing product configuration information associated with a product with one or more matching product configurations. At 510, the system 200 updates the hierarchical product-component tree with the new components identified to obtain a plurality of alternate product configurations. At 512, the system 200 optimizes the plurality of valid alternate product configurations and suggest the user.
[0048] FIG. 6 illustrates a detailed flow diagram 600 for demand planning for Made-To-Order manufacturing, in accordance with an example embodiment of the present disclo-sure. At 602, the system 200 receives a product configuration information associated with a product. At 604, the system 200 matches the received product configuration information as-sociated with the product with a plurality of valid product configurations stored in the reposi-tory 240 to obtain first set of matching product configurations. At 606, the system 200 clus-ters the first set of matching product configurations based on a plurality of product attributes to obtain a cluster of valid product configuration variants. At 608, the system 200 concurrent-ly map the product configuration information with clusters of valid product configuration var-iants to obtain one or more matching product configurations. At 610, the system 200 con-structs a hierarchical product-component tree for the one or more matching product configura-tions. At 612, the system 200 identifies new components by comparing product configuration information associated with the product with one or more matching product configurations. At 614, the system 200 updates the hierarchical product-component tree with the new com-ponents identified to obtain a plurality of alternate product configurations and CHR con-straints are applied to reduce the number of alternate product configurations. At 616, the sys-tem 200 optimizes the plurality of alternate product configurations and suggests the user.
[0049] The written description describes the subject matter herein to enable any per-son skilled in the art to make and use the embodiments. The scope of the subject matter em-bodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[0050] Various embodiments disclose methods and system for demand planning for Made-To-Order manufacturing are able to provide optimized product configuration to the us-er and increases customer satisfaction. Moreover, the demand planning for Made-To-Order manufacturing helps in efficient usage of inventory. Also, due to the parallel matching and efficient usage of memory, the performance of the system is increased.
[0051] It is, however to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be pro-grammed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodi-ments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[0052] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resi-dent software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in con-nection with the instruction execution system, apparatus, or device.
[0053] The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), Compact Disk-Read/Write (CD-R/W) and Digital Versa-tile Disc (DVD).