Industry 4.0 adoption is a journey. At Facteon, we believe every manufacturer sits on the Industry 4.0 adoption spectrum. Whether you're an Industry 4.0 novice or expert, there are a range of projects you can undertake to better optimise your operations. Each of these projects is focused on maximising return and results, while minimising capital investment. 

Where do you sit?

Analogue

Businesses that have yet to bring digital technology very far into their operations. Data collection likely uses the machine’s standard sensors and there is no plan for what data needs to be collected and therefore, what sensors are needed. The data collected is only analysed on an ad hoc basis and this analysis is reactive, rather than preventative. For example, machine data is only accessed after a breakdown to find out what caused it. The manufacturer is aware that they should be collecting data, but they don’t know where to start. It’s likely that the business feels it lacks the time and money required to collect the right data, interpret it and make decisions. In the same vein, the business may perceive factory maintenance solutions as too costly and time-consuming to implement.

Key Attributes: 

• Production planning completed manually without the support of a central IT system
• Data is collected with sensors but it is not used for decision making
• IT systems are isolated and data entry is manual
• No Industry 4.0 goals have been defined and set
• Reactive approach to operational maintenance.

I'm at the Analogue level 

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Transitional

Businesses that are looking to adopt more efficient, technology-driven manufacturing processes. A business will have legacy installations that potentially have some operational sensors installed and is looking to improve efficiency or reduce downtime through programmes such as predictive maintenance through sensors. Data collection and analysis is probably fairly manual, however the machinery is not end-of-life and is modern enough to be tied into a more sophisticated data collection system through the addition of wireless sensors. The business will likely already have some clear goals about how it wants to cut waste and improve productivity.

Key Attributes: 

• MES and PLC systems in place
• A number of sensors (operational and informational) in use. The data collected is interpreted on occasion.
• Utilising advanced analytics for production analysis and operational efficiency. Often, this process occurs in silos with limited cross-facility collaboration.
• Homogeneous IT architecture in-house
• Industry 4.0 goals are defined but a project has not commenced.

I'm at the Transitional level 

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Digital

Your typical Industry 4.0 user at this level is likely using PLCs and MES, but in very task-oriented ways; they’re using the data for discrete, use-specific functions rather than feeding it into deep analytics. They may have already tied plant sensors into a site-wide network that enables central collection and analysis. As a result of an existing focus on improving efficiency, businesses at this level will likely already have a culture in place that is receptive to implementing more advanced projects. The business has many of the building blocks of more sophisticated Industry 4.0 operations in place and is now looking to find a project that leverages this for some big benefits.

Key Attributes:

• MES and PLC systems in place
• A number of sensors (operational and information) in use. The data collected is frequently interpreted, but in a task-specific manner.
• Homogeneous IT architecture in-house
• Operational and informational sensors installed
• Industry 4.0 goals are defined but a project has not commenced. 

I'm at the Digital level 

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Immersed

A business sitting at the immersed level has already run a project or two, analysed the pros and cons and deployed what they have learned. What sets a business at this level apart from other businesses is they are collecting data at multiple levels (either from a particular piece of equipment or interactions across a wider plant) and are now starting to track and look at trends over time and interrelationships. They might be using that data to automatically manage the supply chain and stock control. They could have done the analysis to work out the variation in individual machines’ performance on a production line and used that to tweak the line for optimal efficiency. Whatever projects businesses here have launched, the point is that they have embraced data not because of theoretical benefits, but because of proven ROI.

Key Attributes:

• MES, PLC and other systems in place
• Utilising near real-time analytic capabilities to service monitoring, controlling and optimising manufacturing and smart devices throughout the supply and value chains.
• Homogeneous IT architecture in-house
• Industry 4.0 goals are clearly defined and related processes are implemented.
• Broadly proactive approach to operations.

I'm at the Immersed level 

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Not sure where you sit? Download our Industry 4.0 Handbook here.