Industry

What are the challenges

Industrial companies have an unprecedented opportunity to create value with data science applications. The sector has many ‘asset heavy’ companies. In these companies, aspects such as logistics, supply chain, maintenance, system failure, planning etc. are important aspects of business operations. A lot of data can be generated during the operation, provided that the correct sources are located and sensors and other means for collecting data are used.

Thom van Egmond

CFO at Shimano Europe

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A lot of data can be generated during the operation, provided that the correct sources are unlocked and sensors and other means for collecting data are used. This can be used to improve solutions in the following fields.
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Planning of employees

Due to the high dependence on people in these sectors, correct planning is of great importance. A lot of value can be lost in this. Data science (machine learning) is increasingly being used to plan employees, certainly in environments with a high degree of complexity (many preconditions or constraints in the planning).

Predictive maintenance

Many companies still do maintenance based on the condition of parts or machines, or time based. Especially in equipment that is time-critical, or where expensive components are involved, predictive maintenance can lead to a more efficient and cheaper method of maintenance.

Reduction of stock

Companies can hold a lot of cash in surplus stocks. By using internal and external data for highly accurate demand forecasts, inventory levels throughout the entire supply chain can be reduced.

Quality improvement

Controlling the output of production processes, in which visual inspection takes place, are particularly expensive and labor intensive. Use of image recognition to automatically detect and locate defects can increase value. As well as the use of data to perform AI-enriched root cause analysis enhances value.

Other solutions we offer for value creation: