Cotton spinning mill performance and optimization system

Abstract

A factory performance indicator system is disclosed that includes a mask module for acquiring raw cotton yarn production data, a KPI analysis and reporting module, and a confidentiality scale for identifying KPI optimization targets and confidentially providing comparative industry rankings. KPI performance reporting and comparison are provided with visual reporting elements, including a tachometer display showing relative and absolute KPI rankings with competing entities while maintaining data confidentiality.

Description

Technical Field

[0001] Exemplary embodiments of the present invention generally relate to the field of manufacturing performance measurement, reporting, and improvement, and more specifically, to software and hardware systems for collecting, normalizing, analyzing, presenting, and reporting performance measurement criteria in the field of cotton spinning, and related methods of use, as well as methods for optimizing their operations.

Background Art

[0002] The following description regarding the background of the present invention is provided solely to assist in the understanding of the present invention and is not admitted to explain or constitute the prior art of the present invention.

[0003] Many factories in the cotton spinning industry focus on maintaining and improving internal processes and factors that contribute to productivity, product quality, and profitability. However, it is common for cotton factories to lack an understanding and insight into the competitive advantages and disadvantages of their competitors in the industry, making it difficult to evaluate the competitiveness of their own business compared to competing companies. In order for many factories to improve the main categories of focus (e.g., productivity, product quality / performance, and profitability), visibility into areas of disadvantage (i.e., those that require improvement relative to the performance of competing companies) is necessary. It has been found that many cotton factories want to know their performance compared to competing companies in other groups of industry peers at the macro level up to the product level.

[0004] One difficulty in obtaining useful benchmark data of competing companies is generally to avoid disclosing the operational details between competing companies. For this reason, it has been difficult for individual factories to reliably obtain benchmark data of key performance indicators (KPIs) at the industry level. Therefore, many companies have difficulty quantifying the success of their process improvement efforts due to the lack of information sharing between competing companies. It has been found that there is a need for a method to confidentially share factory operation data between competing companies in the industry without compromising data security.

[0005] Therefore, there is an unmet need in the prior art for a cotton spinning mill performance and optimization system that explains industry-level performance metrics. Known references, either alone or in combination, are not considered to teach or suggest the invention now claimed. [Overview of the project]

[0006] The following provides an overview of specific exemplary embodiments of the disclosed technology. This overview is not a comprehensive overview and is not intended to identify or describe the scope of any significant or important aspects or elements of the disclosed technology. However, it should be understood that the use of indefinite articles in the language used to describe and claim the disclosed technology is not intended in any way to limit the described technology. Rather, the use of "a" or "an" should be interpreted as meaning "at least one" or "one or more."

[0007] One embodiment of the disclosed technology provides a factory performance indicator system. An exemplary embodiment is provided which includes a factory performance indicator computing system, multiple participating factories, and multiple users accessing the factory performance indicator computing system via remote computing devices.

[0008] An exemplary embodiment of the present invention includes a factory performance indicator computing system having a memory unit and a processor. The factory performance indicator computing system includes a data storage device for data, including raw factory data and display data. A web and application server interacts with remote computing devices to facilitate the acquisition of factory performance data and the reporting and analysis of factory KPIs.

[0009] All combinations of the aforementioned and additional concepts described in more detail below (unless such concepts are mutually inconsistent) are considered part of the technology disclosed herein and can be implemented to achieve the advantages described herein. Additional features and aspects of the disclosed systems, apparatus, and methods will become apparent to those skilled in the art upon reading and understanding the following detailed descriptions of exemplary embodiments. Further embodiments are possible without departing from the scope and spirit of what is disclosed herein, as will be understood to those skilled in the art. Accordingly, the descriptions provided herein should be considered illustrative and not restrictive in nature.

[0010] The accompanying drawings incorporated herein and forming part thereof schematically illustrate one or more exemplary embodiments of the disclosed technology and, together with the general description above and the detailed description below, help to illustrate the principles of the disclosed subject matter. The drawings are described below. [Brief explanation of the drawing]

[0011] [Figure 1] This is a schematic diagram of an exemplary embodiment of the Cotton Spinning Mill Performance Indicator (MPI) system. [Figure 2] This is a schematic diagram of an exemplary embodiment of an MPI system, showing the data path for a single factory utilizing a factory performance data acquisition module. [Figure 3] This is a simplified flowchart illustrating the data acquisition and reporting process in one embodiment of an MPI system. [Figure 4] This is a simplified flowchart illustrating the data acquisition and reporting process in a further exemplary embodiment of the MPI system, showing the data acquisition step. [Figure 5] This flowchart illustrates an exemplary embodiment of such initial factory configuration steps. [Figure 6] This is a flowchart illustrating an exemplary embodiment of a simplified use of the reporting module. [Figure 7]This figure shows an illustrative depiction of a factory performance indicators report. [Figure 8] This figure shows an exemplary embodiment of the visualization of raw material yield display KPIs. [Figure 9] This figure shows an exemplary tabular representation of a single KPI as depicted through a factory KPI reporting and analysis module on a remote computing device. [Figure 10] This figure shows an exemplary embodiment of a module shown in relation to illustrative display for product-level machine productivity reports. [Figure 11] This is an exemplary embodiment of a labor productivity report displayed alongside a tachometer reading. [Figure 12] This is a diagram illustrating the exemplary use of a factory-level KPI report, shown in relation to the report. [Figure 13] This is a schematic diagram of a preferred embodiment of the MPI system. [Modes for carrying out the invention]

[0012] Next, exemplary embodiments will be described with reference to the drawings. Reference numerals will be used throughout the detailed description to refer to various elements and structures. The following detailed description includes many details for illustrative purposes, but those skilled in the art will understand that many variations and modifications to the following details are within the scope of the disclosed art. Thus, the following embodiments are described without loss of generality to the claimed subject matter and without imposing limitations on the claimed subject matter.

[0013] The examples described herein are for illustrative purposes only and are provided to aid in the description of the apparatus, devices, systems, and methods described herein. None of the features or components shown in the drawings or described below should be construed as required for any particular embodiment of these apparatus, devices, systems, or methods unless otherwise specified. For readability and clarity, specific components, modules, or methods may be described only in relation to specific drawings. The failure to specifically describe combinations or partial combinations of components should not be understood as indicating that any combination or partial combination is impossible. Furthermore, for any method described herein, whether or not the method is described in conjunction with a flowchart, any explicit or implicit ordering of steps performed in the execution of the method should be understood as not meaning that those steps must be performed in the order presented, but rather that they may be performed in a different order or in parallel, unless otherwise specified or required by the context.

[0014] The present invention is described more fully below with reference to the accompanying drawings illustrating exemplary embodiments of the invention. However, the invention may be embodied in many different forms and should not be construed as being limited to the exemplary embodiments described herein. Rather, these embodiments are provided to ensure that this disclosure is thorough and complete and to fully convey the scope of the invention to those skilled in the art. In the drawings, the sizes and relative sizes of layers and areas may be exaggerated for clarity.

[0015] The terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the invention. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural form unless the context otherwise expressly indicates otherwise. Similarly, the use of the word “or” is intended to be nonexclusive unless the context expressly indicates otherwise. Where used herein, “comprises” or “comprising” identifies the presence of a described feature, integer, step, operation, element, or component, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or groups thereof.

[0016] Embodiments of the present invention are described herein with reference to drawings that are schematic diagrams of idealized embodiments (and intermediate structures) of the present invention. Therefore, deformations from the diagrammed shapes are expected, for example, as a result of manufacturing techniques or tolerances. Accordingly, embodiments of the present invention should not be construed as being limited to specific shapes in the region shown herein, but should include, for example, shape deviations resulting from manufacturing.

[0017] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having the same meaning as their meanings in the context of the relevant art, and it will be further understood that they should not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0018] Exemplary embodiments of the disclosed systems and methods are provided in part to advance the development of manufacturing facilities and improve the performance of the processes carried out by those facilities. With particular attention to the manufacture of cotton yarn, producers are engaged in the continuous development and improvement of internal manufacturing processes to improve product quality and yield, as well as the improvement of margins resulting from efficiency in areas such as energy use and labor productivity. Many yarn manufacturers are skilled and knowledgeable with respect to their internal systems and processes, but lack information regarding competitiveness comparisons across the industry.

[0019] In a preferred embodiment, comparative metrics are provided in a manner such that a manufacturer can focus improvement efforts on specific process areas. Additionally, such metrics are also useful for making an objective description of the characteristics of a particular manufacturer's products to the market without sacrificing confidentiality or competitive advantage, while at the same time limiting the risk of liability for wrongful acts resulting from advertising.

[0020] Referring initially to FIG. 1, a schematic diagram of an exemplary embodiment of a cotton spinning mill performance indicator (MPI) system is shown. In this embodiment, a plurality of cotton spinning mills 100 participate in the implementation of the indicator system, whereby performance data is collected on several key performance indicators (KPIs). Each mill 100 preferably represents the location of a single physical cotton spinning mill and can be organized into related groups of mills such as Group 1, Group 2, ... Group X, as shown at 102. These mill groups 102 can include any number of mills 100, such as a single mill or two or more mills (i.e., up to a certain number of mills M). In a preferred embodiment, the mill groups 102 are used, for example, to maintain visibility of mill-level KPIs across mills that are physically separated but owned and operated. Similarly, a mill group may, for example, include only a single mill. Generally, the mill groups 102 are used at the permission / access level when utilizing the system, as will be described in more detail below herein.

[0021] In a preferred embodiment, each factory 100 participating in the MPI system has a unique factory identification number, a set of factory-level parameters, and one or more units (e.g., Unit 1, Unit 2, ... up to Unit N). Factory units are defined to separate machinery, production personnel, and associated products during the reporting period. In a preferred embodiment, the MPI system is configured to convert raw performance data into KPI data reported at three main levels: (1) factory level, (2) unit level, and (3) product level.

[0022] An exemplary embodiment of the MPI system further comprises a factory performance indicator computing system 120 including at least one memory unit 122 and at least one processor 124. Once factory-level parameters are defined for a factory, the MPI computing system 120 is used to collect raw performance data from the factory for storage in a raw factory data database 126. In some embodiments, the raw factory data database 126 is used to store all data entries received from a factory or factory group user, but is not used to store calculated or derived KPI results. In preferred embodiments, a separate application or display database 128 may be used to store data such as master factory data, customer data, user data, and KPI data. Certain embodiments of the disclosed MPI computing system 120 may include components such as a web server 130, an application server 132, or a combination thereof, which are used to provide communication between the MPI computing system and the user, preferably via an application programming interface (API).

[0023] Preferred embodiments of the present invention are intended to include system access via one or more remote computing devices 140, such as a personal user device or a specially-purpose remote terminal. In these embodiments, a user 144 is provided with authenticated access 142 to the system to perform various tasks. The scope of interactions and access available to a given user 144 may depend on the user's account settings or role. In some cases, user 144 may have data entry or submission privileges, in which case the user is permitted to submit raw factory data to the computing system 120. In other cases, user 144 may have reporting access, in which case certain information, including KPI data, factory data, and comparative data, is available via the display 150.

[0024] A further description of exemplary use of the MPI system is shown in schematic form in relation to Figure 2, where the data path is shown for a single factory 200 utilizing the Plant Performance Data Acquisition (MPDA) module 246. In some embodiments, the participating factory 200 can use one of several spinning mill reporting systems 210 available in the industry. The spinning mill reporting system 210 is an internal system that provides several manufacturing management benefits, including, but is not limited to, machine and sensor networking and the collection, management, and storage of process data. In a preferred embodiment, for example, a participating factory 200 having the functionality of the spinning mill reporting system 210 can be optionally provided with the opportunity to output raw reporting data directly from the spinning mill reporting system 210 to the MPI computing system 220. This reporting structure may be periodic, continuous, or any combination thereof, depending on the specific application and the capabilities of the reporting system 210.

[0025] The MPI computing system 220 shown in relation to Figure 2 is similar to that shown in relation to Figure 1 and includes at least one memory unit 222, at least one processor 224, a raw factory data database 226, a display database 228, and communication / access servers such as a web server 230 and an application server 232 (which may be conceptually or physically separated from the memory units / processors). Figure 2 also shows an embodiment in which an MPDA module 246 is deployed to facilitate factory-level or group-level account configuration and the acquisition of standardized, accurate, and timely raw factory data. In a preferred embodiment, the MPDA module provides guided data input to the user configuring the factory account. An initial configuration data filter or template 247 is used to define factory parameters for factory 200 and to set up N units and P products related to the factory's manufacturing activities. An operation data filter or template 248 is provided to facilitate the ingestion of raw factory data during factory participation in the use of the MPI system.

[0026] In one exemplary embodiment, the operation data mask 248 can be embodied as a digital file (e.g., a Microsoft Excel file) containing ordered raw factory data corresponding to a defined operation period. In a preferred embodiment, the operation data mask 248 includes a series of logical checks performed to ensure accuracy, completeness, and standardization of the raw data. Preferably, the operation data mask 248 of the MPDA module 246 further includes features to facilitate and promote data entry, thereby facilitating timely raw data collection.

[0027] In another exemplary embodiment, the operational data mask 248 is embodied as an automated data acquisition function that receives data from the spinning mill reporting system 210 and performs validation on the imported data before writing it to the raw factory data database 226. Several embodiments can further provide the operational data mask 248 in the form of a web application interface that combines automated data import, manual data entry, and data validation. In a preferred embodiment, all of the aforementioned methods of acquiring raw factory data via the MPDA module 246 are provided to multiple participating factories.

[0028] As will be described in more detail below in relation to Figure 1, the embodiment shown in relation to Figure 2 also provides a KPI reporting and analysis module 260 accessible to an authenticated user 244 via a remote computing device. In a preferred embodiment, the comparative aggregated manufacturing KPI data is stored in a display database 228, possibly separately from a raw database 226, to reduce the risk of accidental exposure of confidential information between competing companies.

[0029] In some embodiments, the factory KPI reporting and analysis module 260 is configured to implement one or more artificial intelligence (AI) or machine learning (ML) analysis submodules. The collection of industry-level data for factory production metrics and KPI visibility facilitates advanced analysis of said data to provide desirable additional KPI links and insights for participating factories. In these embodiments, it is considered advantageous to include such feedback in the reporting module 260. Using AI / ML models, for example, relationships between raw factory data inputs and key KPI data can be determined and modeled to derive unique insights into product quality, factory profitability, and product performance.

[0030] As will be understood by those skilled in the art, AI-based classification techniques may be modified according to desired embodiments without departing from the disclosed techniques. For example, an AI classification scheme may utilize one or more of the following, individually or in combination: Hidden Markov models, recurrent neural networks (RNNs), convolutional neural networks (CNNs), deep learning, Bayesian notation, generalized adversarial networks (GANs), support vector machines, image alignment methods, or applicable rule-based systems. Where regression algorithms are used, they may include, but are not limited to, stochastic gradient descent regressors, passive positive regressors, or other such algorithms.

[0031] Machine learning classification models can also be based on clustering algorithms (e.g., minibatch K-means clustering algorithm), recommendation algorithms (e.g., miniwise hashing algorithm or Euclidean locality-sensitive hashing (LSH) algorithm), or anomaly detection algorithms such as local outlier factors. Furthermore, machine learning models can use dimensionality reduction techniques such as minibatch dictionary learning algorithm, incremental principal component analysis (PCA) algorithm, latent Dirichlet allocation algorithm, minibatch K-means algorithm, or one or more of such equivalent methods.

[0032] Referring to Figure 3, a simplified flowchart illustrates the data acquisition and reporting process in one embodiment of the MPI system. In this embodiment, the data acquisition step 302 is performed periodically via the use of discrete digital file versions of the operational data mask 304. In a preferred embodiment, the reporting period may be set to, for example, a 3-month interval. The appropriate factory user receives a digital file once per reporting period, along with a request to provide the raw factory performance data. In a preferred embodiment, the template 303 is a Microsoft Excel file with predetermined data input fields and internal data validation checks. This request can be made via an MPI system portal accessible only with appropriate user authentication credentials and access rights. Preferably, the completed template is returned before a predetermined date.

[0033] Next, the data acquired in 302 is stored in 306 and processed for system-wide reporting. In one embodiment, in reporting step 308, an MPI report 310 is distributed to participating factories. The MPI report 310 can be implemented in a tabular format that includes absolute KPI rankings, thereby allowing each participating factory to recognize only its own factory and unit code. In some embodiments, this is provided via a secure MPI portal.

[0034] Referring here to Figure 4, a simplified flowchart illustrates the data acquisition and reporting process in a further exemplary embodiment of the MPI system, where the data acquisition step 402 is primarily driven by periodic input to a secure, web-accessible operational data mask 404. In a preferred embodiment, the data mask 404 or template is provided as a single-page application accessible from remote networked user devices, such as desktop computer devices, but is not limited to this. Regardless of which user device is used, it is preferable that the data acquisition mask 404 is accessed only by authenticated users.

[0035] The data acquired in 402 is then stored in 406 and processed for system-wide reporting. In one embodiment, in reporting step 408, the MPI report 410 is distributed to participating factories. In a preferred embodiment, the MPI report 410 can be embodied in a display tool module, which is a mobile-first reactive web application accessible to authenticated users on remote computing devices, limited to smartphones, tablets, desktop and laptop personal computers, and other such devices. The MPI report 410 acquires available KPIs and filters from the system and, depending on the user's selection or predetermined filters, acquires only the requested data from the display database necessary to display the requested charts / information.

[0036] The participation of properties and features disclosed herein in an MPI system comprises at least one configuration step. A flowchart illustrating an exemplary embodiment of such an initial plant configuration step is shown in relation to Figure 5. In one embodiment, the spinning mill reporting system 510 is partially utilized to access the MPI computing system 520 to set up participation via an initial configuration data template 547 used for acquiring and reporting MPI data. In the initial configuration data acquisition step (or a subsequent editing step of that data as necessary), a set of data relating to the characteristics of the participating plant and its operating parameters is acquired and stored by the MPI computing system 520.

[0037] In an exemplary embodiment of the MPI computing system 520, the initial configuration data template 547 is provided in a web-accessible format for access by an authenticated user device. In some embodiments, the user device may be a component of the spinning mill reporting system 510, and in other embodiments, the user device may be separate from the system 510 or a combination thereof. In a preferred embodiment, the configuration step includes a series of input prompts designed to guide the user through the configuration step to initialize a participating mill account, thereby obtaining several types of data, such as basic data 560, mill configuration data 562, product portfolio data 564, staff management data 566, energy consumption data 568, efficiency data 570, and other data 572.

[0038] In an exemplary embodiment of the MPI system, the basic data 560 acquired in the configuration step includes, for example, account user information and role assignments, as well as basic information about participating factories such as billing information and physical addresses. In some embodiments, factory configuration data 562 includes information defining the number of units to which the factory should be divided. Each unit should be defined so as to be maintained independently of other units, with no overlap of staff or equipment between any two units. Each unit in the factory is provided with an anonymized code when the factory configuration data 562 is provided during initial setup. In a highly sensitive MPI computing system 520, the actual names of units actually used within the factory may be optionally stored for internal reference, but it is preferable that all factory and unit codes be anonymized. The factory configuration data 562 may also include, but is not limited to, unit-specific information such as spinning technology identification information (e.g., ring spinning), bale openers per unit, carding machines per unit, and the total number and number of carding machines with automatic can changers and automatic can transport systems.

[0039] In preferred embodiments, particularly in ring spinning embodiments, the number of draw frame passes is also provided to the unit. In one exemplary embodiment, the count of draw frame passes is standardized and independent of the inclusion of a combing step, whether tuned or untuned machines are used, and whether those machines are used for sliver blending. The number of drawing machines assigned to the first of multiple drawing frames is defined, and in a factory utilizing the same drawing machine in multiple passages, those drawing machines should be included in only one passage. Note the total number of single and double-head drawing machines, as well as the connection of those machines to the next process step. In some embodiments, the lap and combing machines are defined per unit, such as the total number of lappers, the number of lappers with automatic lap transport, the total number of combing machines, the number of combing machines with automatic can chargers, and the number of combing machines with automatic can transport systems. Similarly, a preferred embodiment collects roving machine data such as the total number of roving machines, the total number of roving positions, the number (positions) with automatic doffing, and whether the machines have an automatic transport system from roving to spinning machine, a semi-automatic transport system, or a fully automatic transport system (the sum of the aforementioned automatic numbers must equal the total number of roving positions).

[0040] In a preferred embodiment, ring spinning configuration data is also acquired, such as the total number of ring spinning machines, the total number of installed ring spinning positions (spindles), the total number of positions (spindles) equipped with an automatic doffing system, and the total number of positions (spindles) equipped with an automatic yarn splicing system. In some embodiments, the factory configuration data also includes information about winders, such as the total number of installed winders, the number of installed winding positions, the number of positions equipped with circular magazines, the number of positions equipped with filling stations, and the number of positions directly connected to spinning machines (i.e., where human handling is not required). In some embodiments, this data also includes the number of positions maintained by fully automated package removal, including manual package removal without conveyor belts, semi-automatic package removal with conveyor belts, and automatic transport to packaging stations.

[0041] In a preferred embodiment, the factory configuration data may also include, but is not limited to, any factory-level information such as the number of packaging stations, the number of steam rooms, the number of steamers, the number of semi-automatic packaging systems, and the number of fully automatic packaging systems.

[0042] Exemplary embodiments of the present invention further provide the collection of product portfolio data 564. Product portfolio data 564 may include, but is not limited to, information such as the number of different articles manufactured, the amount of cotton used to manufacture combed cotton yarn, the percentage of US cotton used to manufacture combed cotton yarn, the amount of cotton used to manufacture carded cotton yarn, the percentage of US cotton used to manufacture carded cotton yarn, the material amounts of all cellulosic fibers (e.g., viscose, modal, lyocell, cupro, etc.), the input material amounts of all synthetic fibers (e.g., polyester, acrylic, polyamide, polypropylene, etc.), the input material amounts of all other fiber materials (e.g., wool, silk, linen, etc.), the input material amounts of all filament yarns (e.g., mono or multifilament yarns for core or double-core yarn production), and the input material amounts of all recycled materials. In some embodiments, waste management information is also collected to form product portfolio data 564, such as pneumatic waste percentage, hard waste percentage, residual waste percentage, blowroom waste percentage, card waste percentage, and comber noil percentage.

[0043] In a preferred embodiment, the product portfolio data 564 also includes information such as all articles using a standard factory naming system for yarn articles (e.g., "28 / 1 Combing CO Compact"). The spinning and winding characteristics of the articles enumerated in the product portfolio data 564 may also be collected and defined, but are not limited to, the output quality of the winding process, spinning technology (e.g., ring standard, ring core, ring dual core, ring fancy, ring twist spin, ring melange, ring fancy melange, compact, compact core, compact dual core, compact fancy, compact twist spin, or SIRO), British yarn count (Ne), British twist multiplier, actual spin yield in grams per position (spindle) time, automatically calculated production target, maximum (target) spindle speed, ring diameter in mm, yarn fray level per 1,000 spindle hours, spinning machine pneumatic waste ratio, and winding machine hard waste ratio. In exemplary embodiments, the configuration also provides blowroom-to-card information, such as the waste rate of all remaining waste, the waste rate of the blowroom line, the card sliver count in Ne, the target card production, and the carding machine waste rate. In embodiments that combine configuration information, the product portfolio data 564 may include, for example, the comber sliver count in Ne, the target combing production in kilograms / hour, and the comber noil ratio for all combers. For drawing operations, the data 564 may include the draw frame sliver output count in Ne units, preferably provided for each draw frame count. Roving and finishing data is also provided for articles, such as the roving count in Ne, the target roving production in grams per position time, the roving twist multiplier in Ne, and whether steam treatment is involved in article production. For each article, the article material may also preferably be defined to include the ratio of combed cotton, carded cotton, cellulosic materials, synthetic materials, other fiber materials, recycled materials, US cotton, and filament yarn (e.g., elastane or textured polyester).

[0044] In a preferred embodiment, staff management data 566 is also provided in the configuration. This data 566 is provided, for example, to track and describe the effect of staff performance on production output, quality, and profitability. In some embodiments, the staff management data 566 includes, but is not limited to, information (based on units) such as employees by department and function, blowroom workers, number of employees per shift, maintenance, shift type, contractors, and draw frame assignments. Staff are preferably not double-counted and are divided into units and departments using fractional allocation as needed. This data may include production managers responsible for the entire unit and general maintenance employees assigned to the unit. Preferably, plant-level support staff data is also provided in the configuration, for example, to track staff assignments for overhead areas such as raw material stock, steam processing and packaging, cotton yarn stock, quality control, and supervision (e.g., upper management, purchasing, sales, human resources, accounting, etc.). In an exemplary embodiment, the staff management data 566 further includes information on absenteeism and labor turnover, which can be tracked, for example, at the macro (plant) level, department level, or other levels provided by historical data.

[0045] In exemplary embodiments, the configuration generally includes providing energy consumption data 568 that can be used to define the energy consumption of units, machines, and other such manufacturing units within the factory. In preferred embodiments, energy considerations may include providing total pre-spinning machine energy consumption and total spinning / winding machine energy consumption at the factory level or preferably at the unit level. In exemplary embodiments, the factory-level energy consumption data 568 may include information on items such as air conditioning consumption, air conditioning exchange rate, chiller consumption, compressed air consumption, waste collection (HVAC) energy consumption, and remaining energy consumption.

[0046] In some embodiments, the MPI system is configured to provide efficiency feedback, and the efficiency data 570 includes, but is not limited to, unit-level information such as the actual average downtime of all machines per shift per process step and the actual planned maintenance time of all machines per shift per process step. This is preferably done for each blowroom and card step, also taking the draw frame into consideration. It should be noted that embodiments of the MPI system may also include other configuration data 572 that may relate to considerations related to production output, product quality and operating profit margins and profitability analysis, for example.

[0047] In a preferred embodiment, the MPI computing system 520 is configured to provide a KPI reporting and analysis module 560, which is preferably accessible to an authenticated user 544 via a remote computing device 540. A flowchart of an exemplary embodiment of a simplified use of the reporting module is shown in relation to Figure 6. The reporting module 560 is preferably configured to receive a request for KPI reporting from the user 544 via the remote computing device 540 and to fetch the necessary data from the MPI computing system 520 (see, for example, the display database 228 described in relation to Figure 2).

[0048] Reporting module 560 is adapted to provide reports on multiple KPIs organized into multiple KPI groups or categories. In a preferred embodiment, the KPI groups are divided into one or more categories from, for example, labor productivity, machine productivity, efficiency management, energy management, raw material yield, and other groups up to grouping N. Those skilled in the art will understand, without departing from the scope of the claimed invention, that in certain embodiments of the MPI system of the present invention, more or fewer groupings than those provided may be useful. Furthermore, the multiple individual KPIs can be primarily defined by any metric that provides participating plants with insights into factors affecting production, products, and profitability, for example. Exemplary embodiments of multiple KPIs are shown in relation to Figure 6, but others are also available and depend to some extent on the data provided in the initial plant configuration step.

[0049] The reporting module is preferably configured to display optimally on the user device in a mobile-first and adaptable manner. In relation to Figure 7, an exemplary depiction of a factory performance indicator report 670 is shown, which is displayed on a remote computing device 640 via a KPI reporting and analysis module 660. Generally, the MPI report preferably provides the user with visual displays and optional tabular data that partially convey the main cost factors of yarn production at participating factories using the system. In an exemplary embodiment, a cost distribution chart (embodied here as a pie chart) is displayed for a specific item of interest selected by the user via the reporting computer device 640, for example, the cost distribution of 100% combed Ne 30 cotton ring yarn.

[0050] Links to detailed analyses across multiple cost areas are preferably provided for each item tracked by the participating plant. In exemplary embodiments, the user is presented with up to five or more key cost areas for which KPIs and associated analyses are calculated and presented to the user, including, but not limited to, (1) raw material yield, (2) machine productivity, (3) labor productivity, (4) efficiency management, and (5) energy management. In exemplary embodiments shown in relation to Figure 7, button links are provided on the MPI report display 670 to areas such as raw material yield analysis 674, machine productivity analysis 678, efficiency management analysis 678, energy management analysis 680, and labor productivity analysis 682.

[0051] In a preferred embodiment, activating the hyperlink button 674 for raw material yield analysis loads a visualization of at least one raw material yield KPI, such as the raw material yield display 674 shown in relation to Figure 8. Individual reporting KPIs may be selectable for further display on a provisional selection screen (not shown), or KPI categories may be presented holistically or partially in visual or tabular format, or a combination thereof. In an exemplary embodiment, the raw material yield KPI categories are presented with multiple reporting KPIs presented in a tachometer visual format as shown in Figure 8. Here, the KPIs of the raw material yield KPI categories are visually displayed, such as total raw material yield (carded cotton quality (%)) 890, comber noil level 892, and hard waste level 894. In an exemplary embodiment, additional category KPIs can be accessed via an expansion button 896 for display convenience. In a preferred embodiment, industry rankings (or ranking percentiles) are calculated and presented adjacent to the absolute gauge representation of the metric values.

[0052] Referring to Figure 9, an exemplary tabular representation 675 of a single KPI (e.g., KPI 104 Total Raw Material Yield Card Cotton Quality (%) shown in relation to Figure 6) is shown via the factory KPI reporting and analysis module 660 on a remote computing device 640. Tabular representations such as those shown in relation to Figure 9 can optionally display rankings and metrics for multiple items in a more compact space, for example, if necessary.

[0053] If necessary, machine-level productivity reports can be requested via the factory KPI reporting and analysis module 660 through the remote computing device 640. An exemplary embodiment of module 660 is shown in relation to an exemplary display of a product-level machine productivity report 676, shown in Figure 10. Here, module 660 is configured to display a tachometer feature section 1090 that visually shows the relative ranking of participating factory machines for a particular product on an industry-wide basis. The display can preferably be adapted to display, for example, the overall ranking 1092 and percentile ranking information 1094 for selected machines and products.

[0054] Similarly, many other industry-wide KPIs can be reported to participating factories so that the total rank and percentile rankings of multiple related KPIs are selectable and viewable. Figure 11 shows an exemplary embodiment of a labor productivity report 682, in which the tachometer display 1190 shows a rank of 6 out of 81 percentile ranks for the overall factory-level ranking across all units and production items. This can be very useful for participating factories when evaluating the allocation of labor at the factory level to matters such as management, maintenance, and sales.

[0055] Another exemplary use of the factory-level KPI report is shown in relation to report 678, which is shown in Figure 12. This exemplary embodiment of the use of reporting module 660 demonstrates an assessment of the efficiency management of participating factories in exemplary KPI grouping 4 (shown in relation to Figure 6). These and many other industry comparisons are made possible by the anonymized reporting of participating factory data in a secure and useful manner. The report includes the calculation and presentation of a wide variety of product-level, machine-level, unit-level, and factory-level KPI reporting metrics that were previously unavailable at production plants worldwide.

[0056] A schematic diagram of a preferred embodiment of the MPI system 1300 is shown in relation to Figure 13. In this embodiment, the MPI computing system 1320 incorporates several feature units, including a raw factory data database 1326, a display database 1328, an optional browse / cache database 1302, and an MPI backend application server 1332. In the preferred embodiment, the MPI backend application server 1332 provides and updates data in the system for all frontend applications, provided as a .NET 7 framework Single Application Programming Interface (API) and hosted within Microsoft Azure App Service or built on other equivalent technologies.

[0057] Access to the MPI computing system 1320 is preferably controlled by an authentication system. In an exemplary embodiment, an authentication module 1342, such as Microsoft Identity Platform, is used to provide authentication via OAuth / Open ID Connect for convenient integration with the human resources systems of participating factories. In a preferred embodiment, authenticated access to the MPI computing system 1320 is enabled via one or more web servers 1330 that provide modules such as an MPI display module 1360, an MPI online retrieval mask module 1346, and an MPI management module 1304. In a preferred embodiment, these modules are implemented as a Preact single-page application.

[0058] In exemplary embodiments, users are assigned permission levels that may include or prevent access to various MPI modules available to them, such as an analyst user role 1306, a reporter user role 1308, or a management / administrative user role 1310. Generally, an analyst user 1306 is permitted to view and filter aggregated data, a reporter user 1308 is permitted to report or modify raw plant data, and an administrative user 1310 is permitted to manage other users and plant-level account information (e.g., enrollment data and initial configuration data).

[0059] In a preferred embodiment, the MPI system 1300 also includes a communications service module 1312. The communications service module is preferably configured to facilitate communication between the MPI computing system 1320 (particularly the MPI backend 1332) and users such as, but not limited to, an analyst 1306, a reporter 1308, and an administrator 1310. Exemplary communications may take the form of, for example, email, push notifications, or text messages. These can be used, for example, to prompt users for periodic input of outstanding data, to notify users of changes in rankings, or to report the publication of new KPI data.

[0060] While preferred embodiments of the present invention have been shown and described, those skilled in the art will understand that many modifications and alterations can be made that affect the described invention and remain within the scope of the claimed invention. Furthermore, many of the above elements may be altered or replaced by different elements that provide the same results and remain within the spirit of the claimed invention. Therefore, it is intended that the present invention be limited only as indicated by the claims.

[0061] All documents and similar materials cited in this application, including but not limited to patents, patent applications, articles, books, professional texts, and web pages, are expressly incorporated by reference in their entirety, regardless of the form of such documents and similar materials. If one or more of the incorporated references and similar materials differ from or contradict this application, including but not limited to defined terms, use of terms, or described techniques, this application shall have control over such differences.

[0062] As stated above, and as used herein, the singular forms “a,” “an,” and “the” refer to both singular and plural unless the context clearly indicates otherwise. The term “equipped with” as used herein is synonymous with “contains,” “contains,” or “characterized by,” and is comprehensive or open-ended and does not exclude additional unlisted elements or method steps. Many methods and materials similar or equivalent to those described herein can be used, but certain suitable methods and materials are described herein. Unless otherwise indicated in the context, an enumeration of numerical ranges by endpoints includes all numbers contained within that range. Furthermore, a reference to “one embodiment” is not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the listed features. Furthermore, unless expressly stated otherwise, embodiments that “contain” or “have” an element or more elements having a particular characteristic may include additional elements, whether or not they possess that characteristic.

[0063] The terms “substantially” and “about” describe and take into account small variations resulting from variations in processing or operating range that are apparent to those skilled in the art from this disclosure, for example. For example, these terms may mean ±5% or less, e.g., ±2% or less, e.g., ±1% or less, e.g., ±0.5% or less, e.g., ±0.2% or less, e.g., ±0.1% or less, e.g., ±0.05% or less, or 0%.

[0064] Underlined or italicized headings and subheadings are used for convenience only and do not limit the disclosed subject matter, nor are they referenced in connection with the interpretation of the description of the disclosed subject matter. All structural and functional equivalents to elements of the various embodiments described through this disclosure, which are known to those skilled in the art, or which will hereafter become known to those skilled in the art, are expressly incorporated by reference herein and are intended to be included in the disclosed subject matter. Furthermore, nothing disclosed herein is intended to be made available to the public, whether such disclosure is expressly stated in the above description or not.

[0065] Many alternative methods may exist for carrying out the disclosed technology. Various functions and elements described herein may be divided in a manner different from those shown without departing from the scope of the disclosed technology. General principles defined herein may be applied to other embodiments. Different numbers of given modules or units may be used, different types of given modules or units may be used, given modules or units may be added, or given modules or units may be omitted.

[0066] In this disclosure, the term “multiple” refers to two or more. Unless otherwise specifically defined, orientations or positional relationships indicated by terms such as “top” and “bottom” are based solely on the orientations or positional relationships shown in the figures, for the purpose of facilitating and simplifying the description of the disclosed technology, and do not indicate or imply that the referenced device or element is in a particular orientation or must be constructed or operated in a particular orientation; therefore, they should not be interpreted as limiting the disclosed technology. Terms such as “connected,” “attached,” and “fixed” should be understood in a broad sense. For example, “connected” may be a fixed connection, a removable connection, or an integral connection, a direct connection, or an indirect connection via an intermediate medium. To a person skilled in the art, the specific meanings of the above terms in the disclosed technology may be understood according to the particular circumstances.

[0067] It should be understood that all combinations of the aforementioned and additional concepts described in more detail herein (unless such concepts are mutually inconsistent) are considered to be part of the disclosed art. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are considered to be part of the art disclosed herein. The disclosed art is illustrated by the description of exemplary embodiments, which are described in particular details, but there is no intention to limit or in any way restrict the scope of the appended claims to such details. Further advantages and modifications will be readily apparent to those skilled in the art. Thus, the disclosed art, in its broader form, is not limited to any of the particular details, representative devices and methods, and / or exemplary examples shown and described. Accordingly, deviations from such details may be made without departing from the spirit or scope of the general inventive concept.

[0068] Specific details are provided in the above description to provide a complete understanding of the disclosed technology. However, it is understood that the disclosed embodiments and models can be implemented without these specific details. For example, circuits can be shown in block diagrams to avoid unnecessarily obscuring the disclosed embodiments. In other examples, well-known circuits, processes, algorithms, structures, and techniques can be shown without unnecessary detail to avoid obscuring the disclosed embodiments.

[0069] Embodiments of the techniques, blocks, steps, and means described above can be achieved in a variety of ways. For example, these techniques, blocks, steps, and means can be implemented in hardware, software, or a combination thereof. In the case of hardware embodiments, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to perform the functions described above, or a combination thereof.

[0070] The disclosed technology can be described as a process shown as a flowchart, flow diagram, data flow diagram, structure diagram, or block diagram. While a flowchart can describe an operation as a sequential process, many operations can be performed in parallel or simultaneously. Furthermore, the order of operations can be rearranged. A process terminates when its operation is complete, but it may have additional steps not shown in the diagram. A process can correspond to a method, function, procedure, subroutine, subprogram, etc. If a process corresponds to a function, its termination corresponds to the function's return to the calling function or main function.

[0071] Furthermore, the disclosed technologies can be implemented in hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware, scripting languages, or microcode, the program code or code segments for performing the required tasks can be stored in a machine-readable medium such as a storage medium. Code segments or machine-executable instructions can represent procedures, functions, subprograms, programs, routines, subroutines, modules, software packages, scripts, classes, or any combination of instructions, data structures, or program statements. Code segments can be coupled to other code segments or hardware circuits by sending or receiving information, data, arguments, parameters, and / or memory contents. Information, arguments, parameters, data, etc., can be passed, transferred, or transmitted via any suitable means, including memory sharing, message passing, ticket passing, network transmission, etc.

[0072] In the case of firmware or software embodiments, the disclosed methodology can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described herein. When implementing the methodology described herein, any machine-readable medium that materializes the instructions can be used. For example, software code can be stored in memory. Memory can be implemented within or outside the processor. As used herein, the term “memory” means any type of long-term, short-term, volatile, non-volatile, or other storage medium, and is not limited to any particular type of memory or number of memories, or the type of medium in which the memory is stored.

[0073] Furthermore, as disclosed herein, the term “storage medium” can mean one or more memories for storing data, including read-only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage medium, optical storage medium, flash memory device, or other machine-readable medium for storing information. The term “machine-readable medium” includes, but is not limited to, portable or fixed storage devices, optical storage devices, wireless channels, or various other storage media that can store or carry instructions or data.

[0074] While the principles of this disclosure are described above in relation to specific apparatuses and methods, it should be clearly understood that this description is provided for illustrative purposes only and not as a limitation on the scope of this disclosure.

Claims

1. For each piece of equipment within multiple facilities, The administrator provides initial configuration data such that at least one manufacturing unit and at least one manufactured product are associated with each piece of equipment in multiple facilities, From the reporter user, a set of operational data from each piece of equipment within multiple facilities, To receive this, the steps include: acquiring manufacturing data using the equipment performance data acquisition module, The steps include storing the manufacturing data acquired via the equipment performance data acquisition module in an unprocessed database, A step of calculating at least one aggregated manufacturing KPI using a KPI analysis module; a step of authenticating a user using a remote computing device via an authentication module; and a step of receiving a request from the user via the remote computing device to display a manufacturing KPI report, wherein the manufacturing KPI report includes the at least one aggregated manufacturing KPI. A method comprising the step of displaying the manufacturing KPI report on the remote computing device.

2. The method according to claim 1, wherein the set of operation data from each of the multiple pieces of equipment is received via an operation data filter mask.

3. The method according to claim 2, wherein the operation data filter mask includes a digital file containing ordered raw equipment data corresponding to a defined operation period.

4. The method according to claim 2, wherein the operation data filter mask includes an automatic data acquisition function that receives unprocessed equipment data from a reporting system.

5. The set of operation data from each of the multiple pieces of equipment is, via the operation data filter mask option presented to the reporter user, A digital file containing ordered, unprocessed equipment data corresponding to a defined operating period, and The method according to claim 1, which is received as an automatic data acquisition function that receives unprocessed equipment data from a reporting system.

6. The method according to claim 1, wherein the user is an analyst user authorized to view and filter aggregated data.

7. The method according to claim 1, wherein the remote computing device is a mobile device.

8. The method according to claim 1, wherein the at least one aggregated manufacturing KPI includes one or more selected from the group consisting of labor productivity, machine productivity, efficiency management, energy management, and raw material yield.

9. The method according to claim 1, wherein manufacturing data is acquired via a remote computing device comprising a desktop computer system.

10. The method according to claim 1, wherein the manufacturing KPI report is displayed on the remote computing device as a visual display including tachometer features.

11. The method according to claim 1, wherein the manufacturing KPI report is displayed as an absolute ranking on the remote computing device.

12. The method according to claim 1, wherein the manufacturing KPI report is displayed in percentile ranking information format.

13. A factory performance indicator system, One or more processors, The system includes a memory for storing computer executable instructions, and when the computer executable instructions are executed by one or more processors, the factory performance indicator system is configured to: For each piece of equipment within multiple facilities, The administrator provides initial configuration data such that at least one manufacturing unit and at least one manufactured product are associated with each piece of equipment in multiple facilities, From the reporter user, a set of operational data from each piece of equipment within multiple facilities, To receive this, the steps include: acquiring manufacturing data using the equipment performance data acquisition module, The steps include storing the manufacturing data acquired via the equipment performance data acquisition module in an unprocessed database, A step of calculating at least one aggregated manufacturing KPI using a KPI analysis module; a step of authenticating a user using a remote computing device via an authentication module; and a step of receiving a request from the user via the remote computing device to display a manufacturing KPI report, wherein the manufacturing KPI report includes the at least one aggregated manufacturing KPI. A factory performance indicator system that causes the system to perform an operation including the step of displaying the manufacturing KPI report on the remote computing device.

14. A computer program comprising instructions operable to perform the method described in any one of claims 1 to 12 when executed on a suitable device.

15. A computer storage medium containing the computer program described in claim 14.

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