In each 21st-century manufacturing plant, production is controlled to a more or lesser extent by software. Individual machines are controlled by software running on a PLC, a soft-PLC server or a dedicated controller. Production lines are controlled by production control software (PCS) and whole factories by manufacturing execution systems (MES). For planning and logistics, a dedicated or commercially available enterprise resource planning (ERP) system is added. At the same time, a lot of plants still use spreadsheets and written notes to analyze production performance, machine configuration or logistics planning.
Smart Industry, or at least part of it, aims at integrating these software systems and the data they use and generate into a cleverer solution. Combining all the data allows for more thorough and accurate analysis, and based on that, process improvements and cost/benefit optimizations. This can be done in the context of a single factory but also across factories, with or without including logistics.
With this basic concept in mind, and while working on a system for pet food manufacturing, I realized that, while a large part of our current industry still hasn’t heard of this 4th industrial revolution, the combination of data gathering and analysis, and machine learning would be a basis for improving the agility of production facilities. Production agility, the name I put on this, for the time being, isn’t new. In the works of Eliyahu Goldratt in the 80s, the ideas of reducing work in progress, eliminating bottlenecks and working with small batches were already used as a starting point for more cost-efficient production – followed by lean manufacturing in the 30 years after.
At the core lies the data that’s available in the factory, about all parts of production, and although in Goldratt’s initial works, the role of computers and software in analyzing this data plays a crucial role, there are still a lot of production facilities that fail to make optimal use of it. Often because the software only works with parts of the available data or because data analysis is reduced to human labour, performed by people using spreadsheets instead of dedicated, domain-specific and optimized analysis tools. This can be fixed, by introducing software solutions that combine flexible data gathering with proper data analysis tools and possibly also machine learning.
In essence, what I’m aiming at is using the data coming out of factories to analyze where bottlenecks or suboptimal processes can be found in the production and logistics chain – either in a single factory or across multiple factories. The results can then be fed to machine learning algorithms, which focus on optimizing what has been found. The output of that can be any combination of process changes, machine or production configurations or even changes to the logistics processes in and around the factories.
Within Smart Industry, a number of technologies or technology areas are identified that can help facilitate this. I want to introduce three that potentially play a major role in improving production agility.
First of all, the solution I have in mind will require the integration of factories and factory equipment with the industrial internet of things (IIoT). The IIoT is a network of industrial devices that are connected to the internet – either directly or through a so-called gateway. Having this connection in place allows data that’s available to or provided by these devices to be transferred over the internet so that it can be combined and fed to analysis applications.
These applications, connected to the internet, are a possible application of the second technology area, the cloud. Cloud applications may live on dedicated servers owned by an organization or be hosted on a public platform like Amazon Web Services (AWS), Microsoft Azure or Google Cloud. Running applications on these platforms can be done very cost-effectively, as it reduces or even eliminates the need for having to maintain own servers and data centres.
A third technology area, which can optionally also be hosted in the cloud, is machine learning. In this form of artificial intelligence, algorithms use data coming from machines to make the behaviour of these machines more efficient and effective. A machine-learning algorithm could, for example, be used to change the order of production steps or the parameters of a single production step to improve the results.
I plan to set up a company that helps manufacturers apply these technologies to reap the benefits of Smart Industry. I’ve seen a lot of situations where having the appropriate data and analysis tools available would lead to faster and better solutions. We have a lot of IT and software running our world, but in manufacturing, there’s a lot to gain.
I aim to make two potential customers into lead customers and build a company around the solution we can develop for them. The exact shape and technology choice of that solution isn’t entirely clear yet, but that will change rapidly over the coming months.
In doing this, I’m collaborating with partners in the Netherlands and Italy, also thanks to my connection to the Brainport High Tech Software Cluster and Intellimech. These two organizations both have Industry 4.0 as a key focus, and being a liaison between the two allows me to work with a lot of people that can contribute to my plans.