When Data Meets People – How to Build a Scalable Future Factory
AI is here to stay. The discussion around it fluctuates between excitement and concern, but the fact is that those who engage with it early are in a better position, especially as its role in our work continues to grow.
For Darekon, integrating AI into our operations represents a key way in ensuring that available tools are used to make our employees’ daily work easier. We do not want to merely ride the wave of AI but be an active participant in the transformation, continuously learning and creating new technology and data on how to use it.
Scalability is about managing the whole entity
Our goal is not to replace people with AI but to harness it to support our employees’ daily routines, to reduce stress, and to ensure that the changes in scalability, quality control, and regulation can be integrated seamlessly into their work. In a proof of concept (POC) study done in collaboration with the University of Turku, we focused specifically on how production can be scaled in an environment where customer requirements, quality assurance demands, and regulations might change constantly.
The scalability of our production is especially important to us, because we work for several vastly different industries and have to be able to react quickly to decrease or increase production volume as well as react to changes in products and processes. This is all dependent on the know-how of our expert personnel and their readiness to integrate new technologies as part of their everyday routines. With the data from the study, we can help shift the focus of daily work from individual tasks to managing the system and its interfaces as a whole while the changes cutting across the entire production process accelerate and become more interconnected in the background.
Everything starts with data
The scalability of production can easily be seen only as an adjustment of volume. As the POC study progressed, we soon found out that it is a much broader field.
The study was structured around four areas that support one another. These were the scalability of production control, scalability of quality, people-centered change research, and the structuring of key performance indicators (KPI) and scalability factors. These areas were supported by change research done on the implementation of technology and people’s point of view.
Quality assurance utilized, for example, machine vision and SPI (solder paste inspection) that help us ensure the producibility of new product versions and estimate the quality of the end product. However, the true value of technology is in how the data it produces can help direct decision-making and processes.
Making data work for people
New systems, data solutions, and AI applications change the way of working, and they must be harnessed to support people’s everyday work. If employees have to depend on separate systems, disjointed information, and unclear interfaces, the stress increases and change slows down.
The goal is not to move the responsibility from people to machines but to build an operating model where systems produce true, real-time and coherent information to be used by the whole organization. When the information is not dependent on individuals, the employees can move from reactive execution to managing the system as a whole. This fundamentally changes the nature of the work: instead of guessing, the employees will have an exact understanding of where the change is happening and why.
Towards the operating model of a future factory
The POC was supported by two studies done with the University of Turku, focusing on personnel readiness for digital innovation and the integration of complex systems. The first one examined the readiness of the personnel to implement digital innovations in their work, the second (currently underway) will focus on the integration of complex systems and end-to-end management in a production environment. In addition, a master’s thesis on the scalability of the production operating model in the context of business renewal and growth brings together and synthesizes the overall findings of the POC.
Development areas in data integration were identified particularly between strategy, regulation, planning, and KPIs. The goal is to build a data system where KPIs go through different systems from the strategy and regulation of the data system to design, production, and quality assurance and so support the end-to-end management of the production. The POC study strongly highlighted the importance of KPIs and data integration.
Without a system like this, people might be forced to react to individual signals instead of the big picture. When data is coherent and transparent, it supports more manageable decision-making and can reduce the stress of the workload, since in a constantly changing environment uncertainty is a major source of stress.
The final goal is clear: to build a future factory operating model where the employees can control the continuous change rather than having to adapt to it.
More information:
Meeri Virkkala
ESGQ Manager, Darekon
meeri.virkkala(a)darekon.fi
+358 40 718 1720
DAREKON
Darekon is an international electronic product contract manufacturer. Founded in 1985, it serves the MedTech, CleanTech, Aerospace & Defence, Advanced Industrials, and HighTech segments. The company operates production facilities in Finland (Haapavesi, Klaukkala, and Ylivieska), as well as in Gdańsk, Poland, and Stockholm, Sweden. Darekon is known for its customer-centric approach, flexibility, and high-level certified quality systems.
