background
With the development of big data technology and the popularization of the concept of digital transformation, traditional enterprises, especially the manufacturing industry, have also begun to invest manpower and capital to establish their own data analysis teams, hoping to empower the development/transformation of enterprises through data analysis. Although domestic leading manufacturing companies such as Huawei, Midea, Haier, Sany, and Xugong have shared some cases of data technology empowering enterprise development/transformation, small and medium-sized manufacturing companies want to drive enterprise development/transformation through data analysis technology. It is still a complex, long-term, exploratory matter. In the data dilemma of the manufacturing industry, the author recorded his feelings of working in the electronics manufacturing industry for two years. I've been thinking about it lately:
- How should the manufacturing industry build a framework for enterprise-level data analysis?
- How to establish a data management organization? What kind of data management organization needs to be established?
- How to do data governance?
- How to realize data-driven business value?
Enterprise-level data analysis framework
The group has established a relatively complete digital supply chain data analysis index system, excellent smart factory data analysis index system, and digital marketing data analysis index system. The data analysis index system in R&D, manufacturing, finance, after-sales and other business fields is also being gradually established. A partial screenshot of the supply chain data analysis indicator system is as follows:
The founder of the digital supply chain data analysis index system, based on Deloitte's EVM shareholder value map analysis framework and the supply chain SCOR framework, builds and promotes the digital supply chain data analysis index system from 0 to 1 to implement in the group.
For specific design ideas, interested friends can refer to the article: https://blog.csdn.net/weixin_43727334/article/details/123772717
Thinking about enterprise-level data analysis
When applying the data analysis system built by my data analysis colleagues, the author has been thinking about:
- The data analysis framework in the supply chain field can be based on EVM and SOCR model models;
- The data analysis framework in the manufacturing field can be based on Six Sigma and lean production theory.
So, how to further simplify the framework of data analysis?
The author concludes:
- According to the operating logic of the three levels of corporate strategy, corporate management, and corporate operations, top-down dismantling, bottom-up summary, continuous iteration, and repeated analysis.
- Corporate strategy is to ensure that the company is profitable and profitable. Build sustainable and long-term profitable corporate genes through vision, values, and corporate culture; realize corporate profitability through products and services as means.
- Business management, undertake business strategy. Establish a reasonable and efficient process to manage a series of activities required to produce products and services and the organizations involved in these activities. Through informatization means and technology, enterprise processes and management experience are precipitated into a series of informatization systems.
- Enterprise management, in-depth analysis and mining of data generated by information systems, to achieve data-driven enterprise management:
- Increase revenue and reduce cost
- Efficiency improvement and quality assurance
- prevent risk
The framework of enterprise data analysis is:
- Use data to continuously improve and iterate the enterprise information system;
- Use data to mine process value and drive process reengineering;
- Use data to optimize enterprise activities, promote organizational restructuring and business transformation
- Use data to drive product innovation and realize value-added services
- Finally, realize the increase of enterprise profits through data