AUTOMATED SYSTEM AND METHOD FOR MANAGEMENT, JOB SCHEDULING, AND PROCESS OPTIMIZATION USING ARTIFICIAL INTELLIGENCE

Abstract

ABSTRACT AUTOMATED SYSTEM AND METHOD FOR MANAGEMENT, JOB SCHEDULING, AND PROCESS OPTIMIZATION USING ARTIFICIAL INTELLIGENCE A management system (100) comprising an input unit (102) and a processor (104). The input unit (102) receives one of, requests for quotation (RFQ) and purchase orders (PO). The processor (104) is configured to process the RFQ and the PO. The processor (104) is further configured to add inventory based on the one of, the RFQ and the PO, determine inventory levels based on the PO, recommend one or more materials based on one or more requirements, analyse scheduled shipping dates, determine and schedule job assignments based on the inventory, an estimated time required for execution of at least one manufacturing job of one or more manufacturing jobs, workload at a machine facility that is required to execute the at least one manufacturing job, and generate a notification when available time is insufficient for scheduled manufacturing job. FIG. 1 is the reference figure.

Specification

Description:FORM 2
THE PATENT ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10; rule 13)




"AUTOMATED SYSTEM AND METHOD FOR MANAGEMENT, JOB SCHEDULING, AND PROCESS OPTIMIZATION USING ARTIFICIAL INTELLIGENCE"




Akash Abaji Kadam an Indian citizen, address of, Shivaneri Bunglow, Shantisagar, Colony, 100 FT road, Sangli, Maharashtra, 416416, and,
Naga Venkata Madhulatha Chavva an Indian citizen, address of, 3906 223rd PL SE, Bothell, 98021.



THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.


TECHNICAL FIELD

The present disclosure relates generally to manufacturing processes. More particularly, the present disclosure relates to a management system and a method for managing manufacturing operations.

BACKGROUND

In the rapidly evolving manufacturing landscape, efficient management of workloads and inventory has become critical to maintaining operational success and profitability. Traditional manufacturing systems often struggle with the complexities of scheduling, inventory management, and real-time tracking, leading to delays, overstocking, or stockouts that can severely impact productivity and customer satisfaction. Current methodologies tend to rely heavily on manual processes and static estimations, which do not account for the dynamic nature of production demands and resource availability. Consequently, there is a pressing need for an advanced solution that integrates these functionalities into a cohesive management system.

Accordingly, effectively managing workloads and inventory has become essential for achieving operational success and profitability. Traditional manufacturing systems frequently encounter difficulties with scheduling, inventory oversight, and real-time tracking, resulting in delays, excess stock, or shortages that can negatively affect productivity and customer satisfaction. These conventional approaches often depend on manual processes and fixed estimations, which fail to adapt to the dynamic demands of production and resource availability.

The limitations of existing methodologies highlight the urgent requirement for a more sophisticated solution that integrates various functionalities into a unified management system. Manufacturers are seeking innovative tools that can streamline processes, enhance accuracy, and respond effectively to fluctuating production needs. By moving away from outdated practices, the industry can better navigate the complexities of modern manufacturing environments.

Addressing these challenges necessitates the development of an advanced management system that brings together the critical aspects of scheduling, inventory management, and real-time tracking. Such a system would not only improve operational efficiency but also enhance the overall customer experience by minimizing delays and ensuring optimal resource utilization. Ultimately, the integration of these functions is vital for manufacturers aiming to thrive in an increasingly competitive landscape.

Thus, there is a need for a technical solution that overcomes the aforementioned problems of conventional management techniques.

SUMMARY

This system and method for inventory management and job scheduling in manufacturing streamlines key operational processes by integrating multiple specialized modules to automate and optimize tasks is disclosed. At the core, a request processing module receives and handles requests for quotations (RFQs) and purchase orders (POs), initiating inventory checks and scheduling.

An inventory management module tracks incoming materials and assesses stock levels upon PO receipt, suggesting new material orders based on projected requirements. To optimize usage, this module can identify and combine similar materials needed across different jobs, reducing redundancy and waste.

Job scheduling is handled by a dedicated job scheduling module that coordinates schedules by evaluating due dates, available materials, estimated production times, and machine workload. If time is insufficient to complete tasks as scheduled, the system sends a notification to relevant personnel.

Additionally, an operation tracking module leverages input devices at each machine, enabling operators to log task start and stop times, which helps the system record actual job times. This real-time tracking supports accurate assessments of production efficiency.

An AI optimization module continually refines scheduling and estimation by analyzing the gap between estimated and actual times. Using historical data on job size, machine performance, and operation type, it updates estimation parameters, learns from past data, and suggests improved estimation times. This module also identifies bottlenecks by flagging significant discrepancies between planned and actual timings, enabling proactive adjustments.

The system includes a user interface that displays AI-driven suggestions for estimations, helping estimators make data-informed decisions. Through these interconnected components, the system effectively improves inventory management, job scheduling accuracy, and production efficiency in a manufacturing environment.
In an aspect, a management system that has an input unit and a processor is disclosed. The input unit is configured to receive one of, requests for quotation (RFQ) and purchase orders (PO). The processor is coupled to the input unit. The processor includes a request processing module, an inventory management module, and a scheduling module. The request processing module is configured to process the RFQ and the PO. The inventory management module is configured to add inventory based on the one of, the RFQ and the PO. The inventory management module is configured to determine inventory levels based on the PO. The inventory management module is further configured to recommend one or more materials based one or more requirements. The scheduling module is configured to analyse scheduled shipping dates. The scheduling module is further configured to determine and schedule job assignments based on the inventory, an estimated time required for execution of at least one manufacturing job of one or more manufacturing jobs, and workload at a machine facility that is required to execute the at least one manufacturing job. The scheduling module is further configured to generate a notification when available time is insufficient for scheduled manufacturing job.

In this aspect, the processor further has an operation tracking module that is configured to facilitate an operator to start and stop the at least one manufacturing job. The operation tracking module is further configured to determine an actual time taken for the at least one manufacturing job.

In this aspect, the processor further includes an Artificial Intelligence (AI) module that is configured to compare the estimated time and an actual time required for execution of the at least one manufacturing job. The AI module is further configured to update estimation data based on historical working times. The AI module is further configured to analyse the size of an article manufactured by execution of the at least one manufacturing job. The AI module is further configured to identify bottlenecks associated with the management system based on discrepancies between the actual time and the estimated time.

In this aspect, the inventory management module is further configured to identify and combine similar materials required for at least two different manufacturing jobs.
In this aspect, the AI module is further configured to learn from historical data associated with the size of the article, operation performed, and a plurality of characteristics associated with the machine facility. The AI module is further configured to dynamically adjust forecast estimation times based on observed trends.
In this aspect, the operation tracking module further includes a tablet and an input device that is disposed at the machine facility.
In this aspect, the management system further includes a user interface configured to display one or more suggestions for estimation times to an estimator based on one of, a historical data and a real-time data.

In this aspect, the processor is further configured to identify bottlenecks in the management system based on discrepancies between estimated times and actual times.

In another aspect a method for managing operations is disclosed. The method includes a step of receiving, by way of an input unit, requests for quotation (RFQ) and purchase orders (PO). The method further includes a step of processing, by way of a request processing module of a processor coupled to the input unit, the RFQ and the PO. The method further includes a step of adding, by way of an inventory management module of the processor, inventory based on the one of, the RFQ and the PO. The method further includes a step of determining, by way of an inventory management module, inventory levels based on the PO. The method further includes a step of recommending, by way of the inventory management module, one or more materials based on one or more requirements. The method further includes a step of analysing, by way of a scheduling module of the processor, shipping dates. The method further includes a step of determining and scheduling, by way of the scheduling module, job assignments based on the inventory, an estimated time required for execution of at least one manufacturing job of one or more manufacturing jobs, and workload at a machine facility that is required to execute the at least one manufacturing job. The method further includes a step of generating , by way of the scheduling module, a notification when available time is insufficient for scheduled manufacturing job.

In this aspect, the method further includes identifying, by way of the processor, bottlenecks in a management system based on discrepancies between estimated times and actual operation times.
BRIEF DESCRIPTION OF DRAWINGS

The above and still further features and advantages of aspects of the present disclosure become apparent upon consideration of the following detailed description of aspects thereof, especially when taken in conjunction with the accompanying drawings, and wherein:

FIG. 1A illustrates a block diagram of a management system, in accordance with an embodiment of the present disclosure;
FIG. 1B illustrates a block diagram of a processor of the management system of FIG. 1A, in accordance with an embodiment of the present disclosure;
FIG. 2 illustrates a flowchart of a method for managing manufacturing operations, in accordance with an embodiment of the present disclosure; and
FIG. 2B illustrates a flowchart of a method for managing inventory, in accordance with an exemplary embodiment of the present disclosure.
To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures.

DETAILED DESCRIPTION

Various aspects of the present disclosure provide a management system and a method for managing manufacturing operations. The following description provides specific details of certain aspects of the disclosure illustrated in the drawings to provide a thorough understanding of those aspects. It should be recognized, however, that the present disclosure can be reflected in additional aspects and the disclosure may be practiced without some of the details in the following description.

The various aspects including the example aspects are now described more fully with reference to the accompanying drawings, in which the various aspects of the disclosure are shown. The disclosure may, however, be embodied in different forms and should not be construed as limited to the aspects set forth herein. Rather, these aspects are provided so that this disclosure is thorough and complete, and fully conveys the scope of the disclosure to those skilled in the art. In the drawings, the sizes of components may be exaggerated for clarity.

It is understood that when an element or layer is referred to as being "on," "connected to," or "coupled to" another element or layer, it can be directly on, connected to, or coupled to the other element or layer or intervening elements or layers that may be present. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The subject matter of example aspects, as disclosed herein, is described with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventor/inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different features or combinations of features similar to the ones described in this document, in conjunction with other technologies. Generally, the various aspects including the example aspects relate to a management system and a method for managing manufacturing operations.

As mentioned there remains a need to provide techniques that solves the aforementioned problems. Accordingly, the present disclosure provides a system and a method that solves the above problems. The proposed management system addresses these challenges by leveraging artificial intelligence (AI) to enhance the accuracy and efficiency of manufacturing scheduling and inventory management. By employing sophisticated algorithms and real-time data analysis, the system optimizes workload scheduling based on precise time estimations and resource availability. This innovative approach not only allows for better allocation of resources but also enables manufacturers to adapt swiftly to changing production requirements. The inclusion of machine learning techniques ensures that the system continuously refines its estimations and scheduling processes, effectively addressing common pain points in manufacturing operations. Moreover, the management system introduces a novel inventory management module that integrates with its scheduling capabilities. This module is designed to maintain accurate stock levels, reducing the likelihood of overstocking or running out of critical materials. By analysing historical data, production trends, and supply chain variables, the system provides actionable insights and automated inventory adjustments. This proactive strategy helps manufacturers streamline their operations, minimize downtime, and ensure timely access to the necessary materials for production, ultimately enhancing overall operational efficiency. In summary, the management system represents a significant advancement in manufacturing technology, combining advanced predictive analytics with practical scheduling and inventory solutions. Its ability to merge AI-driven insights with real-time operational data offers manufacturers a robust tool for optimizing workload management and inventory accuracy. The system's adaptability to various manufacturing environments and its user-friendly interface further position it as a transformative solution for modern manufacturing challenges, setting a new standard for efficiency and productivity in the industry.

FIG. 1 illustrates a block diagram of a management system 100, in accordance with an embodiment of the present disclosure. The management system 100 (hereinafter referred to and designated as "the system 100") may effectively manage manufacturing workloads and inventory is essential for operational success and profitability in the manufacturing industry. The system 100 employs an advanced technique of artificial intelligence (AI) to enhance the precision and efficiency of manufacturing scheduling and inventory management. By integrating powerful AI algorithms with real-time data analysis, the system 100 optimizes workload scheduling with accurate time estimations and aligns resources to meet production demands. Using machine learning techniques, the system continuously adjusts schedules and improves forecasting accuracy to address common challenges in manufacturing processes. Beyond scheduling optimization, the system 100 employs AI-driven inventory analysis to maintain accurate stock levels, helping manufacturers avoid overstocking or stockouts. This intelligent inventory system analyses historical data, identifies production trends, and accounts for supply chain dynamics to generate actionable insights and facilitate automated inventory adjustments. Through this proactive approach, manufacturers can reduce delays, streamline operations, and ensure the timely availability of materials, contributing to a smoother production flow and increased operational efficiency.A key innovation of the system 100 is its ability to combine advanced predictive analytics with practical scheduling and inventory solutions. The system 100 provides a comprehensive tool that adapts to the unique needs of different manufacturing environments, catering to variations in production requirements, machine capabilities, and resource availability. By merging AI with real-time analysis, the system not only enhances production planning but also provides flexibility in responding to fluctuating demands and operational constraints. Overall, the system 100 marks a significant advancement in manufacturing technology by offering a robust solution for optimizing workload management and inventory accuracy. The system 100 provides manufacturers with the ability to make data-driven decisions, improve scheduling precision, and maintain optimal stock levels, ultimately driving operational efficiency and enhancing profitability. As a versatile and adaptive tool, it supports both immediate needs and long-term productivity, setting a new standard in manufacturing process management.

The system 100 may include an input unit 102, a processor 104, and a user interface 106. The input unit 102, the processor 104, and the user interface 106 may be communicatively coupled to each other. Specifically, the input unit 102, the processor 104, and the user interface 106 may be communicatively coupled to each other by way of a first communication channel 108. The first communication channel 108 may be configured to facilitate the input unit 102, the processor 104, and the user interface 106 to exchange information associated with the system 100 with each other. The first communication channel 108 may be a wired channel or a wireless channel. Embodiments of the present disclosure are intended to include and/or otherwise cover any type of the first communication channel, without deviating from the scope of the present disclosure.

The input unit 102 may be configured to receive one of, requests for quotation (RFQ) and purchase orders (PO). The input unit 102 may be a mobile phone, a tablet, a personal digital assistance (PDA) device, and the like. Embodiments of the present disclosure are intended to include and/or otherwise cover any type of the input unit 102, without deviating from the scope of the present disclosure.

FIG. 1B illustrates a block diagram of the processor 104 of the management system of FIG. 1A, in accordance with an embodiment of the present disclosure. The processor 104 may include one or more components that may be coupled to each other. Specifically, the processor 104 may include the one or more components that may facilitate exchange of information associated with the system 100 with each other.

The processor 104 may be coupled to the input unit 102. The processor 104 may include a request processing module 104a, an inventory management module 104b, a scheduling module 104c, an operation tracking module 104d, and an Artificial Intelligence (AI) module 104e. The request processing module 104a, the inventory management module 104b, the scheduling module 104c, the operation tracking module 104d, and the AI module 104e may be communicatively coupled to each other. Specifically, the request processing module 104a, the inventory management module 104b, the scheduling module 104c, the operation tracking module 104d, and the AI module 104e may be communicatively coupled to each other by way of a second communication channel 110. The second communication channel 110 may be configured to facilitate the request processing module 104a, the inventory management module 104b, the scheduling module 104c, the operation tracking module 104d, and the AI module 104e to exchange information of the processor 104 with each other. The second communication channel 110 may be a wired communication channel or the wireless communication channel. Embodiments of the present disclosure are intended to include and/or otherwise cover any type of the second communication channel, without deviating from the scope of the present disclosure.

The request processing module 104a may be configured to process the RFQ and the PO. The inventory management module 104b may be configured to add inventory based on the one of, the RFQ and the PO. The inventory management module 104b may be further configured to determine inventory levels based on the PO. The inventory management module 104b may be further configured to recommend one or more materials based on one or more requirements. The schedule module 104c may be configured to analyze scheduled shipping dates. The schedule module 104c may be further configured to determine and schedule job assignments based on the inventory, an estimated time required for execution of at least one manufacturing job of one or more manufacturing jobs, and workload at a machine facility that is required to execute the at least one manufacturing job. The schedule module 104c may be further configured to generate a notification when available time is insufficient for scheduled manufacturing job.

In operation, the system 100 integrates multiple aspects of manufacturing operations-inventory management, job scheduling, and real-time optimization-into a cohesive system 100 that enables a data-driven approach to production. In a typical scenario, a customer initiates the manufacturing process by sending a Request for Quotation (RFQ). Upon receiving this RFQ, the system 100 automatically assesses the inventory to check material availability and performs an initial estimation. This preliminary estimation includes the required material quantities, anticipated time for each task, and current machine workload. By automating this initial stage, the system 100 quickly generates a reliable quote, streamlining communication with the customer and reducing the time and resources typically needed for manual estimations. Once the customer places a Purchase Order (PO), the system 100 registers it and checks the inventory to confirm that all necessary materials are in stock. If any items are missing, the system 100 can suggest ordering additional stock or identify compatible substitute materials to avoid delays. The system 100's ability to recognize similar material requirements across different jobs also allows for combined orders, reducing procurement costs and optimizing resource allocation. This efficient approach to inventory management prevents both over-ordering and stockouts, ensuring a smoother production flow and reducing operational disruptions due to material shortages. With a confirmed order and set ship date, the processor 104includes the scheduling module 104c that allocates jobs based on various factors such as material availability, machine workload, operator availability, and estimated operation times. By dynamically adjusting job assignments, the system 100 prioritizes tasks to ensure that they are completed by the required dates. In cases where the calculated production time is insufficient to meet the deadline, the system 100 proactively alerts the scheduling manager, providing an analysis of the shortfall and indicating whether additional resources or adjustments in workload are necessary. This proactive scheduling capability minimizes the risk of delays, keeping production on track even in complex, high-demand environments.

To facilitate accurate tracking of job progress, operators at each machine use tablet-based interfaces to start and stop tasks, allowing the system 100 to record the actual time spent on each job. This real-time tracking eliminates the need for manual data entry and enhances the accuracy of production data. By comparing actual completion times with initial estimates, the system 100 constantly updates its data, enabling it to improve future time predictions. This continuous data collection and analysis contribute to more precise job estimations over time, reducing the gap between estimated and actual production times. The AI module 104e further enhances the system 100's optimization capabilities. By analysing historical data, including factors such as part size, material type, and specific machine performance, the AI module 104e refines its time and resource predictions. For example, if certain tasks consistently require more time on a particular machine, the system 100 learns from this pattern and adjusts future estimates accordingly. This adaptive learning capability ensures that job estimates reflect real-world conditions, ultimately improving resource allocation and minimizing scheduling errors.

Additionally, the system 100 identifies and addresses potential bottlenecks by analysing discrepancies between estimated and actual times. By recognizing areas where tasks are consistently delayed, whether due to machine limitations, material shortages, or specific operational steps, the system 100 provides actionable insights to production managers. These recommendations might include shifting tasks to alternative machines, adjusting shifts, or prioritizing resources for specific jobs. This ability to manage bottlenecks in real time enables the system 100 to maintain an optimized workflow, increasing throughput, reducing delays, and ensuring a steady, efficient production process.

In some embodiments of the present disclosure, the operation tracking module 104d may be configured to facilitate an operator to start and stop the at least one manufacturing job. The operation tracking module 104d may be further configured to determine actual time taken for the at least one manufacturing job.

In some embodiments of the present disclosure, the AI module 104e may be further configured to compare the estimated time and an actual time required for execution of the at least one manufacturing job. The AI module 104e may be further configured to update an estimation data based on historical working times. The AI module 104e may be further configured to analyze of an article manufactured by execution of the at least one manufacturing job. The AI module 104e may be further configured to identify bottlenecks associated with the management system 100 based on discrepancies between the actual time and the estimated time.

In some embodiments of the present disclosure, the inventory management module 104b is further configured to identify and combine similar materials required for at least two different manufacturing jobs.

In some embodiments of the present disclosure, the AI module 104e may be further configured to learn from historical data associated with the size of the article, operation performed, and a plurality of characteristics associated with the machine facility. The AI module 104e may be further configured to dynamically adjust forecast estimation times based on the observed trends.

In some embodiments of the present disclosure, the operation tracking module 104d further includes tablet and input device that is disposed at the machine facility.
In some embodiments of the present disclosure, the system 100 may further include a user interface 106 configured to display one or more suggestions for estimation times to an estimator based on one of, a historical data and a real-time data.

In some embodiments of the present disclosure, the user interface 106 may be one of, a screen, a Light Emitting Diode (LED) display, a Liquid Crystalline Display (LCD), and a user device.

In some exemplary embodiments of the present disclosure, the system 100 may be configured to exhibit setup time, running time for a particular order quantity. For example, the system 100 may be configured to exhibit the setup time and the running time for shear operation, the laser operation, the deburr operation, the break press operation, the assembly operation, the paint operation, and the handling operation. Accordingly, table 1 as provided herein below shows various values for the setup time and the running time for multiple operations as mentioned hereinabove.

Table 1
In some exemplary embodiments of the present disclosure, the system 100 may be configured to perform an analysis for the job number 1 that may be created in the system 100. The system 100 may be configured to perform material and hardware check. The buyer may order the required material and the hardware for requisite manufacturing process. Once everything is available, the code may be schedule the workload as per available time left to shipping for each job. The system 100 may be configured to facilitate to assign the machine facility when the operator is having only one job. The time may be estimated based on estimator time calculation. The system 100 may be configured to facilitate to schedule next jobs as per ship time left on hand for each job. During each operation, the actual time of each operation may be reported by each machine operator and the time may be used by the application to replace the estimated time. The AI module 104c of the system 100 may be configured to learn part specifications and time required for each operation and help the estimator use the precise time for each operation.

FIG. 2 illustrates a flowchart of a method 200 for managing manufacturing operations, in accordance with an embodiment of the present disclosure. The method 200 may include following steps for managing the manufacturing operations.

At step 202, the system 100 may be configured to receive the requests for quotation (RFQ) and the purchase orders (PO). Specifically, the system 100, by way of an input unit 102, may be configured to receive the requests for quotation (RFQ) and the purchase orders (PO).

At step 204, the system 100 may be configured to process the RFQ and the PO. Specifically, the system 100, by way of the request processing module 104a of the processor 104, may be configured to process the RFQ and the PO.

At step 206, the system 100 may be configured to add the inventory based on the one of, the RFQ and the PO. Specifically, the system 100, by way of the inventory management module 104b of the processor 104, may be configured to add the inventory based on the one of, the RFQ and the PO.

At step 208, the system 100 may be configured to determine the inventory levels based on the PO. Specifically, the system 100, by way of the inventory management module 104b, may be configured to determine the inventory levels based on the PO.
At step 210, the system 100 may be configured to recommend one or more materials based on the one or more requirements. Specifically, the system 100, by way of the inventory management module 104b, may be configured to recommend the one or more materials based on the one or more requirements.

At step 212, the system 100 may be configured to analyse the shipping dates. Specifically, the system 100, by way of the scheduling module 104c of the processor 104 may be configured to analyse the shipping dates.

At step 214, the system 100 may be configured to determine and schedule the estimated time required for execution of the at least one manufacturing job of the one or more manufacturing jobs, and workload at a machine facility that is required to execute the at least one manufacturing job.

At step 216, the system 100 may be configured to generate the notification when available time is insufficient for scheduled manufacturing job. In some embodiments of the present disclosure, the method 200 further includes a step of identifying, by way of the processor, bottlenecks in the management system 100 based on discrepancies between estimated times and actual operation times.

FIG. 2B illustrates a flowchart of a method 201 for managing inventory, in accordance with an exemplary embodiment of the present disclosure. The management of manufacturing processes begins with the receipt of a Request for Quotation (RFQ), which initiates a chain of actions within the system. Once the RFQ is received, the system allows for the addition of inventory, ensuring that materials are readily available for the production process. Following the RFQ, an estimation phase occurs, wherein the necessary calculations and projections are made. Upon receiving a Purchase Order (PO), the system checks the available inventory to confirm that the required materials are in stock and ready for use.As the production timeline approaches, the system logs into the details pertaining to the specific ship date. It actively seeks out similar materials that can be combined to optimize inventory usage and minimize procurement costs. When an order is placed by the buyer, the system automatically suggests any additional materials needed, streamlining the purchasing process. Once materials are received, they are marked in the system to ensure accurate tracking and inventory management. With a clear view of the days remaining until shipment and the existing workload, the system schedules each job across the machines according to the estimated time required for completion. If the calculations indicate that there is insufficient time to meet the deadlines, the system notifies the user of the required overtime necessary to ensure timely delivery. Each machine is equipped with a tablet that allows operators to start and stop jobs, recording the actual time spent on each task, which feeds back into the system for accurate time tracking. Artificial Intelligence (AI) plays a pivotal role in enhancing the efficiency of the system 100. It compares the estimated time with the actual time taken for each job, allowing the estimation sheet to be updated based on real working conditions. Additionally, the AI learns from various parameters, such as part size and operations performed, to suggest the most accurate estimation times for future jobs. By analysing the discrepancies between estimated and actual times, the AI can identify bottlenecks within the system, helping managers make informed decisions to improve overall productivity and streamline operations.

The system 100 may advantageously eliminate need of human interaction that manually calculate job times and schedule a workload. The system 100 may advantageously facilitate to provide more accurate estimation. The system 100 may advantageously facilitate to automate inventory check.

To summarize, a management system (100) includes an input unit (102) designed to receive requests for quotations (RFQs) or purchase orders (POs). The system also includes a processor (104) connected to the input unit (102). This processor is responsible for processing RFQs and POs to determine material or production requirements and for updating inventory based on these identified needs. It then assesses current inventory levels to ensure there are adequate materials on hand and suggests additional materials for purchase if needed. Additionally, the processor analyzes scheduled shipping dates for incoming materials and organizes job assignments based on factors such as scheduled shipping dates, current inventory levels, estimated time required to complete each manufacturing job, and the workload at the machine facility that will perform the job. Finally, the system generates a notification when available time is insufficient to meet a scheduled job's timing requirements.

In a real-world manufacturing scenario, a furniture production company receives an order for custom office desks. The management system receives a purchase order (PO) for this order through its input unit. The processor then processes this PO to identify the required materials, such as wood, metal frames, and varnish. Based on current inventory levels, the system updates stock levels to reflect these requirements and checks if there's enough material available for the job. Upon finding a shortage in wood supply, it recommends ordering additional wood.

Once materials are confirmed, the system examines scheduled shipping dates for the wood delivery and organizes the job assignments across available workstations, accounting for each station's workload, estimated completion times, and the arrival date of materials. If a station is overbooked and unlikely to meet the desk order deadline, the system notifies the production manager, highlighting the time deficit so adjustments can be made. This proactive approach helps ensure that the company meets customer deadlines efficiently while optimizing resources and minimizing delays.

The foregoing discussion of the present disclosure has been presented for purposes of illustration and description. It is not intended to limit the present disclosure to the form or forms disclosed herein. In the foregoing Detailed Description, for example, various features of the present disclosure are grouped together in one or more aspects, configurations, or aspects for the purpose of streamlining the disclosure. The features of the aspects, configurations, or aspects may be combined in alternate aspects, configurations, or aspects other than those discussed above. This method of disclosure is not to be interpreted as reflecting an intention the present disclosure requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed aspect, configuration, or aspect. Thus, the following claims are hereby incorporated into this Detailed Description, with each claim standing on its own as a separate aspect of the present disclosure.

Moreover, though the description of the present disclosure has included description of one or more aspects, configurations, or aspects and certain variations and modifications, other variations, combinations, and modifications are within the scope of the present disclosure, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights which include alternative aspects, configurations, or aspects to the extent permitted, including alternate, interchangeable and/or equivalent structures, functions, ranges or steps to those claimed, whether or not such alternate, interchangeable and/or equivalent structures, functions, ranges or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter.
, Claims:We Claim:

1. A management system (100) comprising:
an input unit (102) configured to receive one of requests for quotation (RFQ) and purchase orders (PO);
a processor (104) coupled to the input unit (102), and the processor (104) configured to:
process the RFQ and PO to obtain one or more requirements;
add inventory based on the one or more obtained requirements;
determine inventory levels based on the added inventory;
recommend one or more materials based on the determined inventory levels;
analyse scheduled shipping dates for receipt of the one or more materials;
determine and schedule job assignments based on the analysed scheduled shipping dates, the determined inventory levels, an estimated time required for execution of at least one manufacturing job of one or more manufacturing jobs, and workload at a machine facility that is required to execute the at least one manufacturing job; and
generate a notification when available time is insufficient for scheduled manufacturing job.

2. The management system (100) as claimed in claim 1, wherein the processor (104) configured to:
facilitate an operator to start and stop the at least one manufacturing job; and
determine actual time taken for the at least one manufacturing job.

3. The management system (100) as claimed in claim 1, wherein the system further comprising an Artificial Intelligence (AI) module (104e) configured to:
compare the estimated time and an actual time required for execution of the at least one manufacturing job;
update an estimation data based on historical working times;
analyse size of an article manufactured by execution of the at least one manufacturing job; and
identify bottlenecks associated with the management system (100) based on discrepancies between the actual time and the estimated time.

4. The management system (100) as claimed in claim 1, wherein the processor is further configured to identify and combine similar materials required for at least two different manufacturing jobs.

5. The management system (100) as claimed in claim 3, wherein the AI module (104e) is further configured to:
learn from historical data associated with the size of the article, operation performed, and a plurality of characteristics associated with the machine facility; and
dynamically adjust forecast estimation times based on observed trends.

6. The management system (100) as claimed in claim 2, wherein the processor further comprising tablet and input device that is disposed at the machine facility.

7. The management system (100) as claimed in claim 1, the system further comprising a user interface (106) configured to display one or more suggestions for estimation times to an estimator based on one of, a historical data and a real-time data.

8. The management system (100) as claimed in claim 7, wherein the user interface (106) is one of, a screen, a Light Emitting Diode (LED) display, a Liquid Crystalline Display (LCD), and a user device.

9. A method (200) for managing manufacturing operations, the method (200) comprising:
receiving (202), by an input unit (102), requests for quotation (RFQ) and purchase orders (PO);
processing (204), by a processor (104) coupled to the input unit (102), the RFQ and PO to obtain one or more requirements;
adding (206), by the processor (104), inventory based on the one or more obtained requirements;
determining (208), by the processor (104), inventory levels based on the added inventory;
recommending (210), by the processor (104), one or more materials based on the determined inventory levels;
analysing (212), by the processor (104), scheduled shipping dates for receipt of the one or more materials;
determining and scheduling (214), by the processor (104), assignments based on the analysed scheduled shipping dates, the determined inventory levels, an estimated time required for execution of at least one manufacturing job of one or more manufacturing jobs, and workload at a machine facility that is required to execute the at least one manufacturing job; and
generating (216), by the processor (104), a notification when available time is insufficient for scheduled manufacturing job.

10. The method (200) as claimed in claim 9, further comprising: identifying, by the processor (104), bottlenecks in a management system (100) based on discrepancies between estimated times and actual operation times.

Documents