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Case: Sandvik Mining & Construction Oy

The manufacturing industry is going through enormous change in the dynamic global business environment where new business models, updated procedures, intelligent data, global equipment base, and more extensive supplier network are among the major forces driving companies to adapt their businesses to operate in more efficient and customer oriented way.


By utilizing data mining, the process of analyzing data from multiple different perspectives and summarizing it into useful information for the benefit of business operations, companies can create cost savings and provide competitive advantage. A pilot project concerning data mining was implemented at Tampere site of Sandvik Mining & Construction, a Swedish stock listed company providing solutions for various heavy industries. Project was aimed at figuring out how well data acquired from the company`s fleet operations could be turned into relevant, business supporting information with the help of analytics software, automatically.

- The key is to move beyond data reporting and basic analytics to the predictive analytics – to make the data as a real parameter in decision making.

 

Apply data as a parameter in decision making

Sandvik Mining Construction already had huge amounts of data at their disposal. They for example collected various types of data from their drilling operations and machinery used. This data is stored in cloud. From there, a customer could easily get access to the machine maintenance history data as well as, for example, the machinery location information.

In a recent co- work with Quva Oy, the company explored the potential of modern machine learning to refine data and support decision making.

- We are driven by the opportunities to improve our customers’ productivity and processes. Internet of Things and analytics have potential to bring significant added value from the data that resides in different systems and machines, summarizes the Research Manager of Sandvik, Tuomo Pirinen.

The ultimate aim for this pilot project was to figure out, how we could turn Quva’s customers` existing data into meaningful, business supporting information as automatically as possible, describes the CEO of Quva Oy, Emil Ackerman.

According to Tuomo Pirinen, one the biggest advantages from the pilot project was definitely the growing understanding of different analytics tools and related know-how. “We would have not been able to come up with all of those important findings without automatically analyzed data, and because of that, we now have been able to prevent incorrect conclusions already at an early stage.”

 

Data analytics complements domain knowledge

One should remember, that automated analytics and related tools can bring value for a company, but analytics itself is just a means to an end. At its best, the use of analytics can bring readily cultivated information to be utilized for decision making by the professionals with deep domain knowledge. However, without the relevant domain expertise, analytics may be in vain.

- Analytics can help and guide to right directions, though the final decisions are still made by a human being. Quva Oy`s CEO, Emil Ackerman, encourages companies to carry out similar projects as they do not require heavy investments and can in many ways help companies to understand and improve their operations.

- Complex operational environment with multiple, simultaneously occurring activities is simply too much for a human being to understand and furthermore, to coordinate accordingly. This is where analytics` significance comes to play: With utilizing data mining and analytics, a company can automatically interpret complex, multilayered correlations and gain meaningful information to support decision making.

Information gained from machines and processes will be utilized in a much greater scale in the near future. While the operational environment gets even more and more complex, making relevant, strategic business decisions can actually get easier with the help of data mining. Data analytics gives companies a true competitive advantage, reminds Ackerman.




We got very good results by combining our domain knowledge and Quva’s tools and expertise on advanced analytics. This allowed a large number of different hypotheses to be tested over an extensive dataset quickly.

Tuomo Pirinen, Research Manager, Sandvik Mining & Construction Oy


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Quva Oy

Business ID: 2348506-3

Address: Sumeliuksenkatu 18 B
33100 Tampere, Finland

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Emil Ackerman
+358 45 2086 816
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Juho Liljeroos
+358 40 7418 498
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