Manufacturing is undoubtedly the backbone sector of industry, particularly in a market like Italy. Manufacturing companies are the ones that most use tools, equipment and machinery, often heavy equipment, to create products. To a superficial observer, it might seem that these companies are at the antipodes of innovation and digital transition, but this is not the case. Disciplines such as data science help companies make products more competitive, produce them in a more timely manner, and optimize them. Today, in fact, the simple production of objects, even having all the necessary know-how, is no longer sufficient to survive in the market.
Data science and manufacturing: a winning synergy
The various applications of Data Science are beginning to play a key role of the manufacturing industry, helping to increase productivity, efficiency and margins. This is due to the ever-increasing amount of data that a company can produce, which, when analyzed through the typical tools of Big Data, enables important strategic advantages. The combination is such that since 2020 there has been talk of artificial intelligence-based machinery capable of performing various tasks precisely on the basis of data, returning as output, in addition to the product, new data for analysis.
On the surface, these machines are capable of performing the same sets of operations as traditional versions, for example, creating a product, checking for defects, packaging it, and so on. The major difference lies in the quantity and quality of data collected, which experts, thanks to typical Data Science techniques such as visualization, can use to analyze flows. For example, if the increase in production is matched by a surge in the percentage of defective products, the trend can be corrected in real time, slowing down the rates until the equilibrium point is found.
In addition to real-time remediation and analysis, data science can also help with medium- and long-term optimization. To return to the previous example, if production information for the past six months shows an optimal production pace, it will be possible to make data-driven decisions about the strategy to be undertaken, for example, slowing down and optimizing raw material purchases or, conversely, acquiring new production lines. Through data science, these decisions are no longer made on the basis of subjective stakeholder experiences and sensitivities, but on the basis of real and incontrovertible data. For this to happen, however, a data mining process is also necessary, aimed at Isolate meaningful information From the set of those produced.
Data science also supports production processes
A fairly common mistake when thinking about the use of data is to think of it as something extremely distant from production lines. In reality, data science can be extremely useful at the plant level as well. There are some areas where the benefits are quite obvious, for example, optimizing consumption and containing costs, or being able to apply predictive maintenance practices. However, in this case, we want to delve even further into the aspects related to actual production.
Reduction of errors
Through analysis and prevention tools throughout the production chain, it is possible to considerably reduce The number of defective parts. For example, by varying the production speed, or other parameters such as processing temperature or pressure, depending on the quality of raw materials, which may be variable for different batches or batches.
Optimization of internal logistics
Through data analysis, it is possible, for example, to determine the optimal path for different stages of assembly or production of an object or product. By identifying bottlenecks even in the transition between different stages of processing or in the procurement of raw materials, a Data Scientist can bring significant improvements to productivity. Another theme that is gaining momentum is that of the production space optimization. Again, through Data Science it is possible, for example, to reorganize space to allow the annexation of a new production line.
Management of machinery and equipment
By taking advantage of an increasing number of sensors, it is now possible to identify potential problems before they occur. Certainly predictive maintenance is the ultimate expression of this potential. But even before we get to actual maintenance, through data analytics it is possible, for example, to manage production flows so as to avoid overheating or overstressing of machinery, increasing its life and reducing the production of defective products.
Equally, if the production line involves the use of mobile equipment, it is possible both to optimize its use so as to avoid bottlenecks and to allow their location within the plant.
Quality control
From managing raw materials, throughout the supply chain to managing customer satisfaction, Data Science techniques are a considerable support for production activities. The ability to manage and process data in real time makes it possible, for example, to identify any critical issues as soon as the first specimens of a product arrive on the market, with the possibility of intervening on the specimens still in production, in a much more timely manner than was once the case.
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