B a c kg r o u n d
Industrial automation
From manufacturing data to continuous process improvement As a software professional and a member of the Brainport High Tech Software Cluster, Angelo Hulshout has been looking into the possibilities of Smart Industry for some years now. This spring, he took up the challenge to bring the benefits of production agility, as he calls it for now, to the market and set up a new business around that. Currently, he’s working out the plan and making the first realization steps – with first potential customers in the Netherlands and Italy. Angelo Hulshout
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n 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.
Production agility
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 combi42
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nation 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 labor, 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.
