Industry 4.0

Smart Factory Solution

Your first step to Industry 4.0

The next manufacturing revolution: Powering lean production with the vision of a smart factory

Excelling in today's competitive world calls for a new age of manufacturing - one that combines lean thinking with cutting-edge solutions that transform typical shop floors into SMART, highly predictive factories.

What makes a factory "SMART"?

A smart factory has the ability to connect Machinery and Equipment to People and Services - generating new systems of data analytics and engagement.

Every day more and more industries are joining the industrial revolution, also known as Industry 4.0. Digital transformation has allowed physical elements to merge with digital ones, improving productivity. This results in the Smart Factory concept.

With IoT, we connect equipment and connect information, developing the ability to analyze a new free flow of data - expanding on previous limitations that hindered the line's growth and efficiency. This collaboration connects data by syncing not only the equipment to the production information - but also to human intelligence.

This new network of inter-connected data can be expanded to integrate not only into our own factories but also to suppliers and customers - providing new business value across multiple spectrums of the manufacturing network.

Steps to creating a "SMART" factory:

A smart factory uses various Industry 4.0 technologies to make manufacturing practices fully comprehensive. The focus is to deploy a series of technologies that will connect your entire manufacturing process and upload data into one safe and secure location. These technologies include:

Cloud Computing 

The cloud gives companies a safe and secure location to store all the data gathered by the connected machines and devices used in their manufacturing. Cloud computing has greater flexibility and is more cost-effective than traditional on-premises alternatives. Quickly uploading large amounts of data helps management make decisions near real-time, identifying issues and correcting inefficiencies as fast as possible. 

Big Data Analytics 

Big data analytics uses artificial intelligence (AI) to spot error patterns and run predictive quality assurance with high accuracy. With the sheer amount of data uploaded to the cloud, it would be impossible for a human to conduct fast, accurate analysis. Big data analytics is used for predictive maintenance. AI uses machine learning and past data to spot error patterns that lead to downtime, then uses these patterns to predict and schedule maintenance before faults occur. This reduces downtime, which increases productivity and saves money. Delivering the right information at the right time allows shop floors to improve optimally and at speed. 


Sensors are attached to devices and machines to collect data at different stages of the manufacturing process. This information is uploaded to the cloud and analyzed using machine learning to provide instant visibility for management teams over the various layers of the shop floor. Temperature sensors are used in cleanrooms to track and detect climate in the lab, this data is shared through an Internet of Things (IoT) gateway and AI is used to self-correct or alert relevant teams of any changes.

Industrial Internet of Things

Industrial Internet of Things (IIoT) refers to all the machines and devices used in manufacturing that are linked by data communication systems on the internet. Using IIoT devices allows for the exchange and use of data between people and machines. These machines use sensors to collect data at different layers of the shop floor and the data is used to improve a company’s manufacturing processes. IIoT devices enable operational efficiency, control, and visibility over different stages of manufacturing.

Digitalising an industry brings with it numerous benefits that affect the planning, quality and development of products and logistics in the supply chain. Here are the most important competitive advantages:

  • Speed and flexibility in the face of the unforeseen
  • Real-time digital and physical connection through sensors and IoT devices
  • Resource optimisation
  • Reliability of stored data
  • Two-way data flow between elements

In the long run we have other advantages, such as the benefits that Machine Learning provides. We require time to collect and store enough data to plot a forecast in demand, perform preventive and predictive maintenance or generate digital twins, these are only some of the actions that Machine Learning can do for us.

OS TRIO leverages multi-domain knowledge, technological expertise and innovative thinking to reinvent the manufacturing process. Our focus is helping our clients build “Smart factories of the future”.