The Essential Steps in Your Smart Factory Evolution
Moving your business closer to becoming a Smart Factory is widely regarded as being essential to remain competitive and profitable. Driving efficiency savings, making productivity gains, dealing with skills shortages – these are just some of the challenges that manufacturers face which can be considerably mitigated with Smart Factory solutions.
How do you get to the Smart Factory, though? What should you do next, how far should you go now, and what are the priorities?
The answers to these and similar questions are not easy as there is no one-size-fits-all solution. Instead, the many different variables that exist mean the solution is different for every production environment.
Those variables include production inefficiencies that currently exist, productivity issues, and the product/s being manufactured as well as the wider business processes for procurement, distribution into the supply chain, sales, and more.
Steps Towards the Smart Factory
The steps typically involved to move your facility from where it is now to a Smart Factory include:
- Enabling communication and the collection of data – this is known as Equipment Systems Integration
- Data visualisation – to make it possible for you to access and process the data collected
- Automated decision-making – where many manual and time-consuming decisions are automated
- Machine learning – where the system in your facility automatically improves by learning from the real-time data it collects and the decisions it takes
Step 1 – Equipment Systems Integration
The ultimate objective of equipment systems integration projects is to connect all potential sources of data to a common system. This includes the production line as a whole as well as individual machines and equipment.
It can also include data not directly connected to manufacturing the product, i.e. data from other units and departments in the business.
Once systems are integrated and connected to a common system, you can start making changes that will deliver real and tangible benefits for the company. This includes automating additional processes where it wasn’t possible to do so before because the equipment and platforms couldn’t connect or communicate with each other.
Your system will also be able to collect and store data, setting you up to move to the next step.
Step 2 – Data Visualisation
Once you have gone through an equipment systems integration process, your production facility will have the capability to collect data from a vast range of sources.
This is only one step in the journey, however. The real work begins when you start putting the data you collect to good use. In other words, the data your system collects must have value.
After all, data in itself won’t improve your business. You’ll derive value from how you use that data.
Implementing data visualisation solutions is a key part of this process.
Data visualisation makes it possible for decision-makers to make sense of the data collected. Those decision-makers can then act on what the data tells them. This applies to everyone from operatives to engineers to engineering managers to CEOs.
Again, however, this is only a step on the Smart Factory journey as the next point, automated decision-making, will improve productivity even further.
Step 3 – Automated Decision-Making Based on Data
So, instead of the system presenting data to a person, the system makes the decision itself.
Automating decision-making offers a range of benefits:
- Decisions can be taken immediately any time of the day or night as there is no need to wait for a decision-maker to be available
- Improved productivity and enhanced OEE as a result of eliminating decision-making delays
- Eliminating the risk of human error in decision-making
One of the most straightforward examples of automated decision-making is equipment maintenance schedules.
Instead of a manager scheduling maintenance according to a timetable created by the manufacturer of the equipment, sensors collect data which is then used to determine the best time to schedule maintenance. This decision can be based on specific business objectives like conducting the maintenance before a failure occurs and completing the maintenance when it will have the least impact on output.
Step 4 – Machine Learning
Following on from the above, the next step is to enable your Smart Factory to learn from the decisions it takes and the information it receives.
So, in the equipment maintenance example above, the system will learn as the machine operates in the live production environment, tailoring the maintenance scheduling decisions it takes accordingly.
Another technology that becomes important in this part of the Smart Factory journey is statistical modelling and digital twins where you can run data-driven simulations to improve processes, plan for production line changes such as new product introductions, and more.
The above is a general guide to the steps typically required on a Smart Factory development journey. What should you do now, however?
At SL Controls, we can help you answer this question through services like digital maturity assessments, Smart Factory road-mapping, automation strategy development, and creating a business case. Find out more today.