It is important to identify your goals and priorities to optimize the manufacturing process during the Smart Factory journey. This could be more specific such as increasing production capacity without new hiring or producing a new product for your manufacturing line. These are some of the opportunities for your company to implement new technologies which will aid with smart factory development. On the other side, you might have more general goals and priorities on current manufacturing lines where the objective is to increase productivity, efficiency, reliability, and better decision-making. This could be optimizing Overall Equipment Effectiveness to boost performance and profitability, enhancing automation levels to relieve the recruitment process or pressure on team retention.

At the primary stage, it is essential to audit your existing Smart Factory status which includes all the aspects of your operation process such as existing manufacturing lines equipment, system integration, data usage, IT infrastructure, systems (cloud and on-site),technology, and connectivity. It will not only provide you with an overview of the existing status but also assist you in understanding what can be accomplished by utilizing new Industry processes and technologies. Then this info can be utilized to recognize the opportunities that best suit with company’s goals and priorities, and this will also assist to provide the best payback.

The main goal of equipment systems integration is to link all possible sources of data to a common system which includes all the manufacturing lines, specific machines, and different types of equipment under it. Additionally, it also includes data that are within and outside the company and are not directly linked to product manufacturing. Some of the examples of data within your company include data from other departments such as HR, Sales, Marketing, and others. This is called vertical integration where the aim is to integrate Information and Operational technology which allows the company to streamline its process and better performance.

Once the above process for equipment systems integration is completed company will be able to collect data from different sources. The next step is to pull the valuable data and derive value to improve the business. Data visualization is the key and implementing it will make data simpler for the human brain to understand and aid to get insights by identifying trends, patterns, and visuals. It is key for decision-makers to know and act accordingly. This is beneficial to everyone in the company including engineers, supervisors, managers, and the CEO.

Automated decision-making includes the use of data, algorithms, and machines to take decisions in a range of situations. The system takes decisions itself with specific rules and scenarios. The best example of this is automated decision-making with a preventive maintenance schedule for any equipment. Instead of the maintenance manager or supervisor scheduling maintenance based on the timeline provided by the equipment manufacturer, automated sensors will collect data from different sources and will decide the best time for preventive equipment maintenance. In this way, decisions will be based on actual run time for specific equipment rather than generalized and preventive maintenance will be carried out before a failure.

The next step is to allow your Smart Factory to understand the decisions it makes and the data it collects. So as in the above equipment preventive maintenance case, the system will understand as the machine runs in the manufacturing environment and makes the preventive maintenance schedule decisions accordingly. Statistical modeling and digital twins are other key technologies under Smart Factory. With the help of this process can be improved by running data-driven simulations and planning for any changes with the existing manufacturing line including producing new products. Some of the other major benefits are accelerated risk assessment, better decision-making, effective research, predictive maintenance, product design, and many more. Machine Learning opportunities are with data from all different sources of the business.