Mechanism for optimization of production facilities in the offsite construction sector

Abstract

A computer-implemented system for processing and analysing production data in the UK Modern Methods of Construction (MMC) sector. Production data is identified from MMC manufacturers, including facili

Description

The present specification relates to a mechanism for optimizing production facilities in the offsite construction sector, particularly to a system and method for enhancing the efficiency of Modern Methods of Construction (MMC) by facilitating resource sharing among manufacturers. The MMC sector has the potential to address housing shortages in the UK by utilizing offsite construction methods, producing fully or semi-finished housing units in dedicated manufacturing facilities. However, the sector faces challenges that hinder its full potential, including high initial set-up costs, underutilization of manufacturing facilities, and redundant production capacity due to fluctuating demand or inefficient production planning. These inefficiencies lead to financial burdens, insolvencies, and wasted resources. Currently, the industry lacks a system-driven method for addressing this, preventing the effective outsourcing or renting of surplus capacity. Moreover, no existing software solution systematically facilitates and optimizes this sharing process. The present invention addresses these gaps by introducing a technical framework and recommendation-driven system to enable the real-time allocation of production resources, mitigating financial inefficiencies and reducing insolvency risks. Currently, there is no system-driven method for generating and utilizing a database on MMC manufacturers for outsourcing or renting excess production capacity. There is also no known software or platform that effectively manages and optimizes this sharing process. The object of the present invention is to introduce a technical system and platform that optimizes the use of offsite construction facilities by facilitating resource sharing through a location-based recommendation algorithm, thereby improving cost efficiency, reducing redundancy, and creating additional revenue streams for manufacturers. Summary of the Invention According to the present invention, there is provided a computational system according to any of the independent claims. The invention comprises an interactive platform that enables MMC manufacturers to rent, lease, or share their unused production capacities, machinery, and facilities. The system collects and processes data on manufacturers' location, production capacity, facility availability, machinery type, space requirements, and production timelines while matching these with potential users' needs through an advanced recommendation algorithm. By incorporating algorithm-driven search tools, the system ensures that manufacturers with underutilized resources can generate secondary revenue streams, reducing financial losses and maximizing efficiency in MMC production planning. The system ideally integrates several components, including: A client web application for user interaction. An application server that handles requests and processes data. A centralized database containing user accounts, provider information, and user history. A request handling system that validates and processes user inputs. A recommendation engine that employs basic and advanced filtering algorithms to generate optimized facility matches. An email server that alerts users about facility availability. By utilizing a structured facility optimization system, the invention minimizes resource underutilization, prevents facility redundancies, and facilitates real-time decision-making in the MMC sector. Brief Description of the Drawings The invention will now be described, by way of example, with reference to the drawings, of which: Figure 1 is a graphical representation of the system architecture of the facility optimization platform. Figure 2 is a process flowchart detailing the steps for processing a facility request. Figure 3 is an algorithm flowchart depicting the decision-making and recommendation process. Referring to Figure 1, the system architecture comprises several interrelated components that facilitate communication between hirers and facility providers, ensuring seamless interaction and data processing. At the core of the system is a Client Web Application 21, which serves as the primary interface for user interaction. A User 20 accesses the system through this web interface to submit facility requests, review available resources, and receive recommendations. The web interface is connected to the Network 22, which enables data transmission between the client application and the system's main server infrastructure. Users with provider privileges can input facility details, including facility type, category, location, production availability, and machinery inventory. The system dynamically updates its database through real-time data collection and processing, ensuring that hirers receive up-to-date recommendations based on evolving facility availability. The Main System, which processes user requests and manages database interactions, consists of multiple interconnected subsystems, including the Request Handling System 23, an Email Server 25, and a Recommendation Engine 26. The Request Handling System 23 handles incoming requests and ensures proper validation before processing them. It communicates with the backend database and the recommendation engine to provide users with facility options. The Email Server 25 functions as the system's notification mechanism, ensuring that users receive real-time updates about their facility requests. This component includes an Alert System 25, which automatically sends notifications regarding the success or failure of a request, system alerts, and status updates about ongoing transactions between hirers and facility providers. The Recommendation Engine 26 processes user facility requests and generates optimized recommendations. This engine comprises two distinct filtering mechanisms: a Basic Search Algorithm 26 and an Advanced Search Algorithm 27. The Basic Search Algorithm 26 conducts an initial filtering of available facility data, prioritizing exact matches based on user-defined criteria such as location, machinery type, and production capacity. If the Basic Search fails to yield results, the system employs the Advanced Search Algorithm 27, which extends search parameters using an 80% match threshold and a 10% exceedance allowance for facility attributes such as space, manpower, and production capacity. Additionally, for quantity-based parameters, both the 80% match filter and 10% exceedance filter are applied to generate alternative recommendations. All system components interact with a centralized Database 28, which serves as the primary repository for all stored data. The database maintains User Accounts 29, Provider Information 30, and User History and Preferences 31, enabling efficient data retrieval and seamless operation of the recommendation process. The Request Handling System 23 queries the database 28 to fetch relevant facility information in response to user requests, while the Recommendation Engine 26 analyzes the retrieved data to generate facility suggestions. Referring to Figure 2, the process begins at step 31 where a recommendation request is initiated by the user via the client web application. At step 32, this request is transmitted through the internet or network to the application server. Upon reaching the application server at step 33, the system validates the received request data to ensure completeness and compliance with predefined criteria. If the request is deemed invalid at step 34, it is rejected, and the process is terminated at step 35. If the request is valid, the system proceeds to query the database at step 36 to retrieve relevant facility information corresponding to the user's request. Following the database query, at step 37, the system applies basic filtering algorithms to refine the results. These filters consider parameters such as location, facility size, equipment availability, and production capacity to generate an initial set of potential matches. At step 38, the system determines whether a suitable match has been identified. If a match is found, the relevant results are displayed to the user at step 39, thereby concluding the process. If no match is found at step 38, the system proceeds to an advanced filtering stage at step 40. At this stage, the database is queried again, with the application of additional parameters aimed at expanding the scope of search results. At step 41, the results are transmitted to the advanced filtering engine, which applies an advanced filtering algorithm at step 42 to compute more refined recommendations. This algorithm considers parameters beyond the initial filtering criteria, allowing the system to suggest facilities that may not strictly fit within the original request but are sufficiently close to meet the user's needs. Once the advanced filtering process is completed, at step 43, the optimized recommendations are forwarded to the client web application, where they are presented to the user at step 44. This allows the user to review the available options and proceed with selecting a facility that best meets their requirements. The process is then concluded at step 45. Figure 3 illustrates the process flow for the recommendation algorithm employed by the Facility Optimization FO System. The process begins when a user accesses the system through the client web application by opening the website via a URL 50. The system then determines whether the user has signed into the facility optimization platform 51. If the user has not signed in, they are prompted to input their credentials 52 before being granted access to the search functionalities. Upon successful authentication, the user is presented with an interface to initiate searches for available facilities 53. The search process follows a two-tiered approach, incorporating both a Basic Search Algorithm 54 and an Advanced Search Algorithm 60. In the Basic Search 54 phase, the system processes the user's input parameters and applies filtering constraints. These constraints include location, machinery type, production capacity, and timeline. The system first attempts to provide recommendations strictly within the specified location 55 and within the minimum and maximum values defined for each parameter 56. If matching facilities are identified within these constraints, the results are displayed to the user 57. The system ensures that results remain strictly within the user-defined filter 58. If no facility matches the search criteria, the system returns a "no result found" message 59, ensuring that the output does not extend beyond the user's specified parameters. If the Basic Search does not yield suitable recommendations, the system prompts the user to proceed with an Advanced Search 60. In this phase, the system broadens the search criteria by extending beyond the initially defined location 61 and applying a more flexible filtering mechanism. The Advanced Filtering Algorithm identifies facilities that match at least 80% of the user's specified criteria 62. Additionally, it allows for recommendations that exceed the minimum and maximum values of certain parameters, including production space, machinery availability, and manpower 63. It also permits recommendations that exceed the user-specified maximum values by 10% 64. For parameters with quantity-based constraints, the algorithm applies both the 80% match filter and the 10% above-threshold filter 65 to generate a refined list of alternative facility options. The algorithm continuously updates recommendations based on real-time availability data, ensuring that search results remain accurate and relevant. This dynamic update mechanism prevents multiple users from attempting to book the same facility due to outdated information. Once the Advanced Search process is complete, the system presents the final recommendations to the user, enabling an informed decision-making process regarding facility selection. The present invention provides a novel computational system that addresses key inefficiencies in the offsite construction sector by facilitating the optimization of manufacturing facilities. By enabling manufacturers to share unused production capacities, machinery, and facility spaces, the system significantly reduces operational redundancies and financial inefficiencies that have historically plagued the Modern Methods of Construction (MMC) sector. The system introduces a robust, algorithm-driven approach to resource optimization, allowing manufacturers to generate additional revenue by leasing underutilized assets while ensuring that hirers can efficiently locate suitable production facilities. The scalable design of the system allows it to be deployed across multiple MMC suppliers and users in different geographic regions. Additionally, by maximizing the utilization of existing facilities, the system minimizes resource wastage, reducing unnecessary energy consumption and material waste, aligning with industry-wide sustainability goals. Through its innovative recommendation engine, the system ensures that facility matching is conducted in an optimal manner, utilizing both basic and advanced filtering algorithms to refine search results based on real-time data. The basic filtering algorithm provides precise recommendations that strictly adhere to the hirer's specified parameters, ensuring that only facilities meeting the exact requirements are presented. The advanced filtering algorithm enhances the system's adaptability by broadening the scope of recommendations when necessary, introducing facilities that closely match the hirer's request, even if they fall slightly outside the initial search parameters. This dual-filtering approach ensures that users receive the most relevant options without unnecessary manual intervention, improving efficiency and decision-making. The dynamic nature of the system allows real-time updates on facility availability, preventing conflicts arising from simultaneous booking attempts and ensuring that all users operate with the most current data. By automating the facility-matching process, the platform continuously refines its recommendations over time, improving accuracy and enhancing the overall user experience. In some implementations, machine learning techniques may be used to refine recommendations based on past user interactions and preference. This real-time optimization not only streamlines the search process for hirers but also enables facility providers to maximize their resource utilization, reducing downtime and enhancing profitability. From a financial perspective, the invention provides significant cost-saving benefits by promoting shared use of existing infrastructure rather than requiring new manufacturing setups. By lowering initial capital investment barriers for new manufacturers and mitigating the risks associated with underutilized facilities, the system fosters a more resilient MMC sector. Additionally, by enabling manufacturers to generate secondary revenue streams through facility leasing, the invention contributes to the long-term financial stability of the industry, reducing the likelihood of insolvency and enhancing overall sector sustainability. Beyond financial and operational advantages, the system also contributes to environmental sustainability by promoting more efficient resource allocation. By optimizing the utilization of existing manufacturing facilities, the invention reduces unnecessary waste and energy consumption associated with constructing new production sites. This directly supports sustainability goals within the construction industry, aligning with global efforts to minimize resource wastage and promote eco-friendly manufacturing practices. The present invention provides a transformative solution to the challenges facing the offsite construction sector. By leveraging advanced computational methods, realtime data processing, and intelligent recommendation algorithms, the system facilitates seamless interaction between facility hirers and providers, ensuring optimal resource utilization. This invention not only enhances cost efficiency and operational effectiveness but also fosters a more sustainable and financially viable MMC sector, addressing key industry pain points and driving innovation in offsite construction. Many variations are possible without departing from the scope of the present invention, as defined in the appended claims. Also disclosed herein are the following clauses: Clause 1. A computer-implemented system for facilitating facility demand and provision activities in the Modern Methods of Construction (MMC) sector, the system comprising: a client web application, configured to receive facility provider inputs specifying facility attributes, including type, capacity, location, and availability, and to receive facility requests from hirers specifying required facility attributes; a centralized database, configured to store the facility provider inputs and hirer requests as structured data for efficient querying and retrieval; a main system, comprising a request handling system, a recommendation engine, and an email server, wherein the main system is configured to manage incoming user requests, validate the requests, and provide facility options by interacting with the centralized database and recommendation engine; a request handling system, configured to process incoming hirer requests, query the centralized database, and retrieve facility data that satisfies predefined matching criteria; a recommendation engine, configured to filter available facility data based on user-specified search parameters, including location, capacity, facility type, and availability, and to provide optimized facility matches by applying dynamic search capabilities; a user notification system, comprising an email server configured to send notifications to facility providers and hirers regarding facility availability, successful matches, and transaction updates; and a communication network, configured to enable real-time data transmission and interaction between the client web application, the main system, the centralized database, and external components. Clause 2. The system of clause 1, wherein the recommendation engine is further configured to apply a two-tiered filtering mechanism comprising: a basic search stage, which applies strict matching criteria for location, facility type, and capacity based on user-defined minimum and maximum thresholds; and an advanced search stage, which applies flexible matching criteria with a relaxation mechanism that allows partial matches within minimum and maximum threshold ranges for at least one of location, facility size, production capacity, or machinery availability. Clause 3. The system of either of clauses 1 or 2, wherein the recommendation engine dynamically updates the facility availability data in real time by continuously monitoring and retrieving updates from facility providers. Clause 4. The system of any of clauses 1 to 3, wherein the centralized database stores additional contextual information, including user history, past facility selections, and user preferences, which are periodically utilized by the recommendation engine to refine search results and improve future recommendations. Clause 5. The system of any of clauses 1 to 4, wherein the email server is configured to send notifications regarding updated facility availability, booking confirmations, or changes to facility attributes within a time period defined by minimum and maximum notification intervals. Clause 6. A computer-implemented method for optimizing facility utilization and cost efficiency in the Modern Methods of Construction (MMC) sector, the method comprising: receiving facility data from MMC manufacturers via a client web application, wherein the data includes facility type, production capacity, and availability; storing the received facility data in a centralized database; receiving a facility request from a hirer specifying required facility attributes including location, production capacity, and availability; validating the received request against stored facility data to ensure completeness; processing the request using a recommendation engine to identify optimal facility matches; applying a filtering algorithm to return matches based on the hirer's specified parameters; transmitting the final ranked facility recommendations to the hirer via the client web application; and notifying the facility provider and hirer regarding successful matches and updates on facility availability via an email server. Clause 7. The method of clause 6, wherein the processing means further includes applying a two-tiered filtering mechanism, comprising: (a) a basic filtering algorithm for matching facility requests based on exact or nearexact user-defined criteria, including facility type, location, production availability, and equipment requirements, and (b) an advanced filtering algorithm for expanding the search scope by applying minimum and maximum threshold values for at least one of location, production capacity, or facility attributes. Clause 8. The method of either of clauses 6 or 7, wherein the recommendation engine refines its results by incorporating user history data stored in the centralized database, wherein previous facility usage and feedback are used to adjust the ranking of recommended facilities. Clause 9. The method of any of clauses 6 to 8, wherein the recommendation engine dynamically updates facility availability data in real time. Clause 10. A computer-implemented system for generating optimized recommendations for hirers of Modern Methods of Construction (MMC) production facilities, the system including processing means which carry out the following steps: receiving a facility request from a hirer via a client web application, wherein the request specifies required facility attributes including location, production capacity, and availability; storing the facility request in a centralized database; processing the request using a search algorithm to identify available facility matches based on stored facility data; applying a recommendation engine to rank the facility matches based on real-time availability and computed filtering results; executing a search process, which compares the request against matches in the database; transmitting notifications via an email server to alert hirers and facility providers about facility availability, successful matches, and ongoing transactions. Clause 11. The system of clause 10, wherein the search process is further configured to apply a two-tiered filtering mechanism comprising: a basic search stage, which applies strict matching criteria for location, facility type, and capacity based on user-defined minimum and maximum thresholds; and an advanced search stage, which applies flexible matching criteria with a relaxation mechanism that allows partial matches within minimum and maximum threshold ranges for at least one of location, facility size, production capacity, or machinery availability. Clause 12. The system of either of clauses 10 or 11, wherein the search algorithm 5 dynamically updates its filtering criteria based on real-time changes in facility availability, including updates on booked capacities, operational downtimes, and cancellations, ensuring that search results reflect the most current facility status. Clause 13. A computer-readable medium storing instructions that, when executed by 10 at least one processor, cause the at least one processor to perform the method of any of previous clauses 10 to 12.

Claims

1. A computer-implemented system for processing and analyzing production data in the UK Modern Methods of Construction (MMC) sector, the system including processing means which carry out the following steps:identifying production data from MMC manufacturers, including facility utilization rates, downtime periods, and surplus production capacity;processing the identified production data to extract key attributes related to facility usage;normalizing the extracted data into a standardized format independent of manufacturer-specific parameters;determining patterns of underutilization and production redundancies based on the normalized data;comparing the normalized data to historical utilization records to assess trends in facility efficiency;generating reports based on the comparison, wherein the reports provide insights into surplus capacity and opportunities for facility optimization; and transmitting the analyzed redundancy data to facility providers and hirers via a communication interface.

2. The system of claim 1, wherein the processing means further includes a recommendation engine configured to apply a two-tiered filtering mechanism, comprising:(a) a basic filtering algorithm for matching facility requests based on exact or nearexact user-defined criteria, including facility type, location, production availability, and equipment requirements, and(b) an advanced filtering algorithm for expanding the search scope by applying minimum and maximum threshold values for at least one of location, production capacity, or facility attributes.

3. The system of claim 2, wherein the advanced filtering algorithm includes a dynamic adjustment mechanism that continuously refines the minimum and maximum threshold values for facility recommendations based on evolving facility availability, real-time resource demand, and historical usage patterns.

4. The system of claim 1, wherein the recommendation engine is further configured to analyze both current and historical data to predict future availability of facilities, thereby optimizing the timing of facility bookings and suggesting alternate periods of use when current availability thresholds are not met.

5. The system of claim 1, wherein the database includes additional fields for tracking and storing facility maintenance schedules, and the system is configured to exclude facilities from recommendations if the maintenance schedule overlaps with the requested production period beyond a minimum threshold.

6. A computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform the method of any of previous claims.A

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