Big Data Age: using manufacturing intelligence to wake up sleeping data

Lead: In the big data age, manufacturing enterprises should take advantage of manufacturing intelligence (MI) technology to fully explore the huge business value behind the data. Enhance http://www.aliyun.com/zixun/aggregation/9139.html "> product quality, reduce quality cost, in order to stand out in the fierce market competition.

"In the big data age, manufacturers must re-examine their production quality data and, with the help of manufacturing Intelligence (MI) technology, tap into the huge business value behind the data." With the strength of data, improve product quality, reduce quality costs, so in the fierce market competition to stand out. "Ying Fei Unlimited (INFINITYQS) China technical director Shu Dechun, in the Hangzhou Machinery Engineering Association to host the" Quality, help enterprise transformation and upgrade "technology salon, said to reporters.

"Leaders in any industry have seen the unprecedented potential and significance of the big data age," according to a new survey by the McKinsey Global Research Institute (MGI) and McKinsey's Business technology office. "In 2009, businesses with more than 1000 employees in all sectors of the economy produced an average of at least 200 trillion bytes of data (twice times larger than Wal-Mart's 1999-year database)," he said. All walks of life have a large number of data available for analysis, and data analysis in the field of product manufacturing has been parallel to the labor force, capital status.

With the Internet, E-commerce, finance and other industries to the full data mining, in China's manufacturing enterprises, production information has become popular situation, but the further excavation of all kinds of data information is still in the initial stage-we have been concerned about the quality of data is also so.

In the manufacturing enterprise that the reporter visits, the enterprise records the data more in two kinds of forms: 1, the traditional paper pen record, 2, Excel spreadsheet record. These seemingly simple operation of data management, in the waste of human and material resources, but also for the production and quality control of enterprises buried a huge hidden dangers. And the real value behind the data mining, it is impossible to talk about.

Seemingly simple paper recording data must be placed in an independent archive. A slightly more advanced Excel table, while storing data as a file in a computer, will have to open dozens of or even hundreds of files if the engineers want to compare the existing data--of course, in a small amount of data.

For example: If the leader wants to know the operation of the production line A in the past 3 months, the production line will produce 200 products a day. To make a data recording calculation every day for each production line, to make a vertical comparison of the data over the past 2 weeks, an engineer would open at least 14 Excel files to fetch the data. Imagine how many files the engineer would have to open if compared with the data for the last 3 months, if it were more than the last 1 years? Of course, companies use Excel forms to record relevant data, which is fortunate enough for the engineer. What would be a disaster for engineers if quality data were all written on paper and 3 months of data analysis?

These cases are just one of the drawbacks of traditional data management. Luckily, leaders and customers don't have to look at reports every day. The data in the folders/archives, like lying on an isolated island, sleeps, only to meet the needs of engineers. Therefore, we see that enterprises in the face of transformation and upgrading, often try regret. Unfortunately, no one will think of the sleeping data and the huge amount of business information behind it.

"Reduce quality costs, improve product quality", for manufacturing enterprises, can not only be "empty cheque." Where is the solution? In the speech of Shu Dechun, technical director of the China region, we found the answer--The New Enterprise Quality Center, the unlimited proficient SPC software based on manufacturing Intelligence (MI) technology.

Manufacturing Intelligence (MI) is not a strange term for us, but it is the first time to apply manufacturing intelligence technology to the SPC (statistical process Control) of quality management. Manufacturing enterprises such as: All kinds of spot check table storage, difficult to find, electronic data dispersion, no analysis or very little analysis, the existing analytical tools can not guarantee good results, such as a series of production quality management problems, in the proficient SPC software system, solve.

Compared with the traditional data management model, the proficient SPC software, with the central SPC analysis engine as the core, through the "cloud" or local flexible deployment, in data acquisition and integration, real-time monitoring and analysis, workflow management, advanced reporting packages and SPC Quality Center, and many other functions under the joint Action, The active monitoring of the real-time production quality data of the enterprise helps the manufacturing enterprise to break the temporal and spatial limitation of the traditional tools such as pen and paper and Excel. Its independent database storage mode allows arbitrary data transfer to become a reality. With multistage Pareto lateral, multi-level box line diagram represented by the 300 of statistical analysis chart can help manufacturing enterprises all-round, multi-angle to the quality parameters of the arbitrary comparative analysis, to identify potential quality hazards, reduce the risk of stealth quality, in giving data two life, at the same time, fully explore the enormous value of the data.

At the end of the interview, Ms. Shu Dechun said to us: "In the big Data age, the Power Manufacturing Intelligence (MI) technology, through more transparent, more available data, enterprises can release more data contained in the value." Real-time, effective first-line quality data can better help enterprises improve product quality, reduce production costs. Enterprise leaders can also be based on real and reliable data to formulate the correct strategic management decisions, so that enterprises truly achieve a high level of ' manufacturing intelligence '.