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Dry goods - steel enterprise decision-making intelligent system case sharing

The steel industry is one of the most important raw material industries in China, and there is an urgent need to realize the upgrade from "big" to "strong". In the previous article, we shared the main difficulties and pain points of digital transformation of traditional enterprises from the perspective of Olin Technology's delivery team, and in this issue, we share the case of digital transformation in the steel industry.

A large steel group is a production capacity of more than ten million, more than ten billion tax large-scale iron and steel joint venture, as a domestic steel leader, the follow-up is ready to further implement the requirements of the old and new energy transformation, plans to reduce and replace, to create advanced steel production base. After years of information technology construction, the enterprise has vertically established the L1 ~ L4 layer of automation and information technology system, and horizontally realized the procurement, inventory, production, sales, logistics, finance and other aspects of end-to-end information technology full coverage.

But the iron and steel group information system is also facing new challenges:

1. low rate of data self-acquisition. In terms of information technology, there are manual input and adjustment of more links, multi-party input caused by data inconsistency, information in a timely manner to collaborate on the existence of misplaced phenomena and other issues;

2.

Data analysis has the core data management can not be independently upgraded and transformed, the performance of data access risk, the use of data in a single way, the closed loop of information processing in the system can not be fully formed, the data analysis ability is relatively weak and so on;

3.excellent experience knowledge has not been solidified.

A lot of business data analysis relies on manual completion and personal experience judgment, can not do real-time analysis and feedback and business synchronization, the use of historical data assets is relatively small, historical data has not yet constituted the intelligent analysis of business operations support;

4. traditional architecture there are risks.

The system extends the more traditional IOE type of information technology means, there is a technical support risk.

All of these issues have hindered the transformation of the steel company's digital intelligence.

Online Technology supports the two platforms of Omnicom Platform and EventNet, and opens up the whole process data of enterprise operation of procurement, production, inventory, sales, order and marketing, and conducts data analysis from the overall situation of the enterprise operation, and through the quantitative decision-making system, it helps customers to realize the optimization of the balance of the cost of the ore, the optimization of the quantitative decision-making of the enterprise, and the intelligent analysis of the enterprise operation, so as to build a complete set of decision-making intelligent auxiliary analysis system. The company's business model is based on the concept of "the best of the best", and is based on the concept of the best of the best.

The internal data and external supply chain data, industrial cycle data, macroeconomic data, competitive environment data, industrial big data, etc. to build a full range, based on the time dimension, the formation of three-dimensional multi-dimensional data model, according to the data model to give a quantitative analysis and insight based on big data, to events and risks pushed to the PC end and cell phone, for the leadership decision-making to provide direct advice. Provide direct recommendations for leadership decision-making.

The operational experience and key business nodes of each department are built into a digital model through artificial intelligence technology, and at the same time, different models are linked into the overall multi-dimensional business model of the enterprise through knowledge mapping, so that the data of each functional department can play a role in the global perspective of the enterprise and form global optimization; through continuous iterative model training, quantitative analysis and optimal solutions are provided to assist decision-making, forming a Lean management.

1. Business Management

We mapped and sorted out the steel group's data, processes, information technology systems, and business activities, and based on the results of the research and the range of information technology data available to the steel group, we carried out the implementation.

2. Master data management and establishment of a unified master data asset management platform

The master data asset management platform consists of a complete set of specifications and technologies for generating and maintaining master data. The complete platform includes metadata management, information system integration, data governance, data analysis, data exchange and other functions.

The implementation plan includes:

Combing the steel group master data system feasibility implementation plan (including data collection, data quality analysis, data source analysis, data resource census, management granularity, etc.).

Master data management system implementation (infrastructure deployment, prototype iteration and preview, master data aggregation, data cleansing, conversion, data mapping, master data quality management implementation, system performance tuning, etc.).

3. Data lake, the establishment of a unified data integration platform

The implementation of the program includes:

Combing the steel group information technology system data lake construction feasibility of the implementation of the program (including the data infrastructure, data access range, model and data integration standards, reasonable planning of the granularity of the data storage, the construction of the dimensions) Hierarchical structure to form a unified data center. Through the multi-level ETL extraction, transformation, cleaning, loading functions, to achieve the organic combination of various types of data sources, to ensure the quality of data sources, to ensure the integrity and consistency of information).

Data lake management system implementation (through the configuration and management of the front data extraction function of each information system, timely access to and integration of the management data of each professional system).

4. Comprehensive operational decision-making and establishment of a unified display platform

Through the intuitive display of key indicators, operators can access the company's operational information in a complete, timely, global and efficient manner, and achieve the purpose of business information penetration and transparency.

The implementation plan includes:

Combing the steel group's integrated operational decision-making and business experience curing related content (including the indicator system, existing business processes, ERP system docking, CRM system docking, cost analysis and costing, etc., and reasonably defining the division of functions between the business system and the business analysis system).

Comprehensive operational decision-making system implementation (theme data summarization and processing, algorithm model design and development, historical data deep learning and model optimization, management console customization, information technology system interactive docking customization, etc.).

5. Purchasing inventory optimization, establish purchasing inventory optimization auxiliary decision-making services and applications

Considering that purchasing and raw fuel and auxiliary material inventory optimization of the iron and steel group is an important and urgently needed to improve the part, the corresponding auxiliary module will be planned and implemented separately.

Through comprehensive modeling, analysis, and deep learning optimization of the steel group's pre-iron production data, pre-iron equipment maintenance data, procurement data, inventory data, iron and steel raw materials and auxiliary materials procurement price index, logistics data, ore allocation, pre-iron quality data, costing models, production planning data, production data, finished goods, and other data, the group will form a dynamic and intelligent recommendation of raw materials and auxiliary materials. The program can be used to optimize the procurement of raw materials and auxiliary materials, the inventory dynamic optimal program, and the optimization of the accounts payable structure.

Through the construction of the above five dimensions, we have successfully improved the steel group's operational decision-making ability: managers can grasp the company's operation situation at any time, and provide the basis for the daily data analysis and decision-making of the steel group's senior leaders and analysts of the business departments. At the same time, it can reduce the difficulty of user operation, reduce user training costs, and provide the company management with fast and rich statistical analysis data and decision-making support for human, financial, and material resources, so that they can focus more on business optimization and management to further enhance the steel group's operational efficiency and decision-making ability.

In addition to improving the steel group's operational decision-making capabilities, the following value benefits were realized:

? Transparency of the company's operations and management and global analysis of business and financial integration. Multi-dimensional comparisons of annual targets and historical data for sustained, robust growth.

The most important thing you can do for your business is to make sure that you have the right tools for the right job.

Predicting, simulating, and optimizing benefits, and analyzing the impact of market fluctuations.

? Contract Order Lifecycle Collaboration.

The contract order lifecycle is the main line of communication between production, supply, and sales, as well as the identification of problems.

? Decision support for production, supply, and marketing.

Supporting the smooth supply and production under the condition of reasonable capital utilization, and the dynamic balance of production and sales under the market changes.

As a large and complex process industry, steel companies have difficulty in obtaining internal production data for all processes, and most of them are opaque "black boxes".


Data Ready
Data Ready
Data Ready is a video ready tool that allows users to easily and intuitively make key adjustments to the data stored in their PCs, such as when they want to change their PC's settings, or when they want to change their PC's settings for the first time. By providing the steel group with digital decision-making and operation support capabilities, we have effectively enhanced the soft power of the enterprise and the competitiveness of the whole industry, and created a benchmark for digital transformation in the steel industry.

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