For most manufacturers, the path to establishing a Smart Factory is still troublesome because of data overload. To overcome this hurdle, firms ought to view the transformation as a journey with four stages that reap ongoing advantages to their operations.

FREMONT, CA: The smart factory development is about building upon the progressions of the third industrial revolution by mechanizing the collection of data from applications and machines and changing that data into instantaneous insights. New technologies turn the tedious but decisive process of extracting insights from information into one that is instant, streamlined, and achievable for each manufacturer.      

Level One: Available Data

At this stage, the data is accessible but challenging to use in making decisions or implementing expansions. The information is in siloed systems needing manual work to incorporate and translate into valuable data. Additionally, problem-solving at this stage is possible, but exceedingly time-consuming. When a machinery or product quality issue arises, operators and engineers ought to scramble to manually gather information from a range of systems before they can discover the problem and ways to fix it. This manual method is not only frustrating but expensive; it drains resources, time, and money from the factory. So, manufacturers at level one must move to level two soon or risk wasting millions of dollars in lost production output from unplanned downtime each day.

Level Two: Accessible Data

A level two system combines all the disparate data sources into one single source of truth and incessantly gathers and tracks production information. Besides, a level two system also allows engineers to focus their attention on addressing high-value issues like changing materials, improving the product, or adopting a mass customization strategy. On the contrary, at level two, proactive analysis that enables factories to make developments before issues occur, still requires effort, time, and engagement from engineers. Furthermore, to move from level one to level two, firms need to implement a new data architecture that takes only a matter of months.

Level Three: Active Data

A level three system alters the manufacturing method from reactive problem solving into proactive analysis and enhancements. The system facilitates engineers and operators to be truly dynamic and preventative in solving problems, which would not be achievable in level two. To move from level two to three, one must build on the introductory level’s data architecture by toting up new system capabilities like Artificial Intelligence (AI) And Machine Learning (ML).

Level Four: Action-oriented Data

At level four, the information system essentially deploys the recommendations found from analyzing manufacturing data. For instance, an ML model will recognize the optimization, then generate and send the suggested settings to the machine, where it is mechanically executed.

Accomplishing level four requires datasets, which are large enough and have adequate validated cases to offer the data needed for the system to know the effects of a production change. The time required to move from level three to four varies depending on the amount of time it takes to collect the essential datasets.

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