Digital transformation is the key to the further innovative development of my country's manufacturing industry, and determines the survival and development of enterprises in the future.
However, for enterprises, how to accelerate the pace of transformation and upgrading of the manufacturing industry in a low-energy, low-cost, and high-efficiency manner is still a problem that many manufacturing enterprises need to solve.
Go deep into the digital transformation scenarios of manufacturing enterprises, tap common pain points, form a data application system that can be implemented, promote the transformation of manufacturing industries in a more efficient way, and use data to grow businesses.
Problems Existing in Manufacturing Data Application
1. Rough management
Although many manufacturing companies already have a certain data foundation, they still use fixed reports to present data, without actually implementing analysis and application.
2. Data islands are serious
There is a lack of a unified platform between internal information systems to correlate and integrate data, resulting in the inability to work together in various links such as production, sales and storage, and it is difficult to fully release the true value of data.
3. Lack of data management mechanism and guarantee
Due to the lack of planning of the data management mechanism in the early stage, the data quality is uneven, the basic data is scattered, inconsistent, and inconsistent, and it is difficult to provide support for the upper-level data statistical analysis application.
4. Slow response to data analysis requirements
The analysis report is mainly based on tables, with a single dimension and a fixed form. The timeliness of responding to analysis requirements is poor, and it cannot meet the fast, flexible and changeable data analysis requirements.
5. High cost and high risk
Traditional BI platforms have high project costs, slow results, and high risks. After a long period of data warehouse construction and modeling, it is still impossible to see the value brought by the data.
Architectural Ideas for Manufacturing Digital Transformation
After several years of accumulation, most of the medium and large manufacturing enterprises in my country have established a relatively complete basic information system and accumulated a large amount of historical data. Therefore, the current key issue of digitalization in manufacturing enterprises is to connect data from various systems, establish a unified data analysis and processing and visualized BI platform, and realize data-based refined operations and predictive decision-making.
Based on this, combined with the construction and service experience of many manufacturing enterprise customers, a set of effective architectural ideas has been summed up to promote the real implementation of data applications.
1. Overall structure
The first is to build a unified data management model to integrate and process the respective data in a unified way, so that the original discrete data can be aggregated to release value.
Agile BI is based on a one-stop big data BI platform, integrates internal and external data of the enterprise, strengthens the management of enterprise data assets, and realizes data integration in various fields. Business personnel use the self-service analysis mode to mine data value, quickly build data applications, and achieve strategic goals in the fields of business decision-making, product development, store operations, finance, marketing, and production.
2. Technical Architecture
After having a clear overall structure, it is necessary to build a technical structure according to the actual scenarios and needs of the manufacturing industry. The entire platform technology architecture is divided into five levels:
Data source layer: connect data from different information systems and channels, and realize association and integration of heterogeneous data sources.
ETL layer: define unified data API standard interface, perform data cleaning, conversion, loading, etc.
Warehouse bazaar layer: Store the detailed data after ETL in the data warehouse in a star or snowflake model, and according to the needs of business analysis topics, after dividing the data model topics, import them into the data mart for calculation Speed up processing.
Application layer: For different levels of business personnel and analysis subject needs, establish rich data application scenarios such as ad hoc query, multi-dimensional analysis, data report, and in-depth analysis.
Presentation layer: Present the analysis results to the decision-making layer, management, IT personnel and business users with rich and beautiful chart display methods and flexible and changeable interactive methods. All users can access the system through browsers or mobile terminals.
3. Build a manufacturing enterprise data application ecosystem
At the data application level, the most important thing is to build application scenarios suitable for manufacturing enterprises, and build a data application ecosystem based on key business nodes.
Agile BI builds a system that covers full business process applications from supply chain, production, logistics, and sales to finance, human resources, and marketing. On the one hand, use data to improve production efficiency, control production quality, and promote product innovation; on the other hand, improve the company's internal data capabilities, transform data into business strategies, improve the company's business decision-making level, and reduce business risks. In this way, the value of business and management will be realized, a virtuous circle will be formed, and the huge value brought by data will be truly brought into play.
Manufacturing multi-scenario practice
Every industry has unique scenarios. Go deep into the whole process scenario of manufacturing enterprises, discover its characteristics and pain points, and propose targeted solutions:
1. Improve the production process of the manufacturing industry
The key to the production process of the manufacturing industry lies in refined management. Quantification and visualization through data can more accurately and timely discover problems in the process, so as to optimize the production process, improve process efficiency and quality assurance. Especially with the support of Agile BI, data feedback and time are greatly shortened. Real-time data, dynamic changes, and rapid feedback further enhance the effect of data on improving production processes.
Case: Analysis of production efficiency of a large manufacturing enterprise
Take material management as an example. In the past, whether the company’s material shortage was verified manually, although there is a material requirements planning system (MRP), the data volume is huge and the detailed indicators are complicated. When business personnel conduct specific data analysis, it often requires IT personnel to cooperate with modeling and calculation. Time-consuming and labor-intensive, and the lag is serious.
Through the system data collected by MES and MPR, it is connected to Agile BI for real-time multi-dimensional analysis. For example, the work of checking the complete set of materials used to require point-to-point screening of relevant personnel, but now the results of the inspection are displayed in real time on the analysis platform. The index system can be flexibly adjusted according to the situation, and the work efficiency has increased by more than 30%.
2. Better Quality Assurance
With the accumulation of data volume, the quality of data will also be improved under the scientific analysis system. Therefore, further use of data can enable manufacturing companies to improve the visibility of quality and the accuracy of forecasting supply performance. The use of data and in-depth analysis can allow manufacturing companies to see product quality and transportation accuracy in real time, and make the best decision when accepting product orders with high time requirements. In this way, the production situation can be comprehensively controlled and the production quality can be improved.
Case: Production line quality monitoring of a large manufacturing enterprise
The company’s on-site production process and quality management are all manually imported into the system data, and then simple analysis is performed using Excel’s built-in charts. The data lacks immediacy and accuracy, and consumes a lot of manpower and time. Management is out of touch with practice. The data did not maximize the support of production work.
With the help of Agile BI, IT personnel began to combine more business analysis dimensions to conduct exploratory analysis and analysis and forecasting. With the help of the big data analysis platform, the company's overall production and operation status was displayed at multiple levels from the production line, team and branch factory. On the one hand, it improves the breadth and depth of data analysis, on the other hand, it also reduces the work of manual data processing, improving work efficiency and data immediacy. In this way, it helped the company improve its core competitiveness in the production process, monitor materials and production processes in an all-round way, and improve production quality.
3. Reduce inventory cost
Inventory is one of the main cost components of a manufacturing enterprise. Reducing inventory and increasing turnover are important means to reduce costs and increase efficiency and ensure profitability. Through data analysis, an appropriate inventory model can be established to determine the optimal order time and order quantity, so that the inventory cost can be minimized under the constraint conditions.
Case: Scientific inventory optimization of a large manufacturing company
Although the business used ERP and WMS, the system's ability to provide basic inventory replenishment calculations and recommendations was not satisfactory.
Inventory optimization needs to calculate the optimal inventory level and mode based on constraints such as service level, order lead time, economic batch size, and transportation cost, and dynamically calculate inventory control levels, replenishment quantities, and replenishment time points based on demand forecasts.
While establishing visual inventory management, Agile BI uses a multi-level inventory optimization calculation model and incorporates the most cutting-edge simulated stochastic optimization algorithm to quickly calculate the best multi-level inventory model for large-scale supply chain networks under complex business rules. Enterprises reduce inventory costs by 5%-15%.
4. Understand user needs
For manufacturing companies integrating R&D, production and sales, it is often necessary to conduct a large amount of market research, including dynamic changes in the market, user demand analysis, and overall market forecasts and insights. Through the collection and precipitation of internal and external data, combined with scientific analysis technology, and visual dynamic and real-time display, enterprises can find the most reasonable optimization path among these intricate relationships, and can grasp user needs in the first place , to help improve existing product offerings.
Case: Product improvement of a large furniture manufacturing company
One of the company's products was underperforming in the market, while a competing product sold well. Therefore, through market research and data analysis, the company found the problem: because the product size is different from the mainstream market, users choose products with common sizes in the market due to their habitual preferences when shopping.
In addition, product features are very important decision-making factors for users when choosing products. The company uses data to analyze functions and competitors against benchmarks, and the product department can clarify user needs, develop and manufacture products with market feasibility.
In the process, Agile BI has solved the agility problem of the enterprise's big data application well. The report construction steps are simplified, the development and implementation cycle is short, the efficiency is high, and the report is flexible, which can meet the complex big data application. Especially in the context of accelerated market changes, Agile BI quickly responds to needs, presents and feeds back dynamic changes in a timely manner, and promotes rapid product improvement.
5. Market competition analysis
The application of data analysis can help manufacturing companies accurately understand market conditions and competition patterns. Through benchmarking analysis with competing products, they can better design products, distribute sales channels, understand market price feedback, and formulate new marketing strategies. In this way, we can promote continuous innovation, maintain agility, and improve profitability in the long run.
Case: Analysis of the retail market of a large home appliance manufacturer
During the product design stage, the company conducted a comprehensive analysis of the product market competition with the help of Agile BI. Through in-depth analysis of market share, increase and decrease, and major competitors, it positioned itself more accurately and closely monitored market changes. , Keep abreast of the market dynamics of competitors, and then take the lead in the market.
The project established multi-dimensional data analysis and display. For example, in terms of overall analysis, dimensions such as market share, industry competition situation, market movement, and year-on-year ratio have been established; in terms of product analysis, dimensions such as best-selling brands, product structure comparison, and brand distribution at all levels of the market have been established; Auxiliary thematic analysis has been established, including price trend analysis, marketing channel analysis, brand market analysis, etc., so as to truly achieve comprehensive and multi-angle big data analysis of the market and competitors.