Digital transformation is the way how to increase productivity
For a long-time open-source software was a black horse and only when businesses began to realise the importance of flexibility instead of pre-programmed, out of the box solutions with “best practices” proposed by giants like SAP, Oracle, and others.
Nowadays...
Nowadays...
Companies are keen to build their solutions using the newest approaches and combining modern technologies to support a variety of crucial processes.
Data commonality for many business units becomes a necessity for data-driven decision making and the ability to get a benefit of big data, data science and advantages of neural networks.
A sequence of steps of business digitalisation in fin-tech, government services, the travel sector and retail — more and more operations require no people’s actions. Different economy sectors are transforming with different speed and efficiency, but the baseline is definite. Hardware manufacturers getting into digitalisation slower, but software dedicated businesses are up to speed much faster.
If we look into users interaction with taxi or travel services, we see that we communicate with driver only when the ride begins and during the trip, but our interaction with the service begins much earlier and end only when we have been charged and get the driver rated. I trust that the same thing is coming into more complex, production industries, where digitalisation brings real value in terms of cost savings and increasing the overall quality of the products among with simplification, wow effect of enhanced knowledge of how the product is built.
A few decades ago, CAD systems turned the engineering process upside down, simplified every aspect of parts design. Simulations and FEA software changing the world of verifying the designs and getting physical testing to the end stages of the latest development stages as a final proving ground for the parts designs and simulations.
The next step would be an automated cost analysis for many different parts like casting, CNC machining, electronics development, plastic injection molding, and others. I would say that one of the largest impacts lays in cost optimisation by using automated analysis of shapes for metals and plastic parts as well as autonomous analysis of electronic circuits and electronic components alternatives in electronics design.
Another serious change would be to get a product lifecycle under full control and not only in terms of “a digital twin” of the product being manufactured product, but including product DNA into every part produced, collect details on raw materials used in the product and machines status during its assembly. If a product is a part of a bigger assembly we would want to keep full control of its usage as well as service operations and end of life, collecting as much data as possible for further analysis. A good example would be components in a car.
When we instantly can have access and control of any device in the car, we can manage everything there without even having physical access to it and configure it remotely.
Data commonality for many business units becomes a necessity for data-driven decision making and the ability to get a benefit of big data, data science and advantages of neural networks.
A sequence of steps of business digitalisation in fin-tech, government services, the travel sector and retail — more and more operations require no people’s actions. Different economy sectors are transforming with different speed and efficiency, but the baseline is definite. Hardware manufacturers getting into digitalisation slower, but software dedicated businesses are up to speed much faster.
If we look into users interaction with taxi or travel services, we see that we communicate with driver only when the ride begins and during the trip, but our interaction with the service begins much earlier and end only when we have been charged and get the driver rated. I trust that the same thing is coming into more complex, production industries, where digitalisation brings real value in terms of cost savings and increasing the overall quality of the products among with simplification, wow effect of enhanced knowledge of how the product is built.
A few decades ago, CAD systems turned the engineering process upside down, simplified every aspect of parts design. Simulations and FEA software changing the world of verifying the designs and getting physical testing to the end stages of the latest development stages as a final proving ground for the parts designs and simulations.
The next step would be an automated cost analysis for many different parts like casting, CNC machining, electronics development, plastic injection molding, and others. I would say that one of the largest impacts lays in cost optimisation by using automated analysis of shapes for metals and plastic parts as well as autonomous analysis of electronic circuits and electronic components alternatives in electronics design.
Another serious change would be to get a product lifecycle under full control and not only in terms of “a digital twin” of the product being manufactured product, but including product DNA into every part produced, collect details on raw materials used in the product and machines status during its assembly. If a product is a part of a bigger assembly we would want to keep full control of its usage as well as service operations and end of life, collecting as much data as possible for further analysis. A good example would be components in a car.
When we instantly can have access and control of any device in the car, we can manage everything there without even having physical access to it and configure it remotely.