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The current stage witnesses the widespread application of digital technologies such as artificial intelligence, blockchain, cloud computing, big data, edge computing, and the Internet of Things. Enterprises are facing significant opportunities and challenges in their digital transformation. As the manufacturing industry holds a pivotal position in the national economy, the digital transformation of this sector has become a focal point in the economic transition and development of various countries worldwide. Many nations have consequently released national strategies, competing to seize the initiative in the digital transformation of the manufacturing industry.
For instance, Germany first proposed “Industry 4.0” in 2013, followed by the United States introducing the concept of the “Industrial Internet.” In 2015, China put forward “Made in China 2025.” Digital transformation demands both the achievement of management automation and production flexibility and the intelligentization of decision-making processes, whhich implies substantial changes in enterprise business processes and internal production relationships. Simultaneously, as digital transformation employs a new way of linking and utilizing digital technologies, it blurs the traditional boundaries of businesses.
For instance, Germany first proposed “Industry 4.0” in 2013, followed by the United States introducing the concept of the “Industrial Internet.” In 2015, China put forward “Made in China 2025.” Digital transformation demands both the achievement of management automation and production flexibility and the intelligentization of decision-making processes, whhich implies substantial changes in enterprise business processes and internal production relationships. Simultaneously, as digital transformation employs a new way of linking and utilizing digital technologies, it blurs the traditional boundaries of businesses.
1. Data Collection and Processing
In the apparel manufacturing industry, data collection is the initial step, and it is crucial to gathering relevant data accurately and comprehensively. During the manufacturing process, various data such as temperature, pressure, speed, humidity, and other indicators can be collected in real-time using sensors, monitoring devices, and similar means. Simultaneously, a significant amount of data relating to equipment operating status and employee performance can be gathered during the process. Through data collection, manufacturing companies can acquire a wealth of valuable information to provide ample support for subsequent data analysis.
Building a data warehouse is an important part of the process,and it involves several steps. Initially, constructing the data warehouse is the starting point. Based on predefined analytical subject matters and the data warehouse modeling method, a clothing work hours data warehouse is designed. Subsequently, using the established dimension and fact tables of the data warehouse, data is extracted, transformed, and cleansed from existing data sources before being loaded into the data warehouse.
After the data warehouse is built, online analytical processing is conducted to provide support and a basis for managerial decision-making.
Finally, constructing mining models for data mining results in obtaining specific functionalities. For instance, by employing association mining models for analysis, factors influencing work hours, such as garment styles categorized into tops and bottoms, combined with factors like work experience and materials, allow an evaluation of existing standard work hour parameters based on an actual work time study. This evaluation then assists in making decisions to modify and define reasonable standard parameters.
Regarding the collected data, Apparel manufacturing need to undergo reasonable processing and analysis. The data processing procedure can utilize existing data analysis tools and artificial intelligence algorithms to screen and organize data, eliminating abnormal and irrelevant information to obtain a clear and accurate dataset. In the process of data analysis, various statistical methods, machine learning algorithms, and others can be employed to thoroughly explore the data in order to uncover patterns and value hidden within the data.
2. Process Optimization
Process optimization in the apparel manufacturing industry is a significant application of data analysis. By analyzing various data during the production process, manufacturing companies can identify bottlenecks, understand the efficiency of different stages, and pinpoint causes. In-depth data analysis can reveal potential opportunities to optimize the production process. For example, analyzing equipment operation data can determine whether each device is in its optimal state, allowing timely maintenance and adjustments to ensure equipment efficiency. Moreover, process optimization, through data analysis, can improve production scheduling, reduce production costs, and enhance capacity utilization.
3. Quality Control
Quality control is an indispensable aspect in the apparel manufacturing industry. Through data analysis, manufacturing companies can monitor the quality of products during the production process. By collecting and monitoring various key data during the product manufacturing process, such as temperature, humidity, pressure, and other indicators, it is possible to track the quality of products in real-time and promptly identify any anomalies.
Through data analysis, quality control models can be established to predict and optimize product quality. For instance, by analyzing data related to quality, it’s possible to identify the primary factors affecting product quality and improve product quality by adjusting process parameters, enhancing production processes, and employing other means.
4. Supply Chain Management
Data analysis plays a crucial role in supply chain management. Apparel manufacturing can employ data analysis to monitor and manage supply and demand information in real-time. This allows for more accurate prediction of market demands and facilitates rational production planning.
Simultaneously, by analyzing various segments within the supply chain, it is possible to identify bottlenecks and weak points, enabling targeted optimization. Through data analysis, clothing manufacturing enterprises can access information from other businesses within the supply chain and conduct effective collaborative management to enhance the overall efficiency of the supply chain.
Data analysis has become an important means to enhance production efficiency in the manufacturing industry. Through data collection, process optimization, quality control, and supply chain management, Apparel manufacturing can optimize production and distribution, minimizing waste to the maximum extent. However, data analysis is not a one-time task, manufacturing enterprises need to continuously carry out data collection and analysis. They must adjust according to actual circumstances and continuously enhance production efficiency driven by data to achieve sustainable development.
