“Today, the real output of automation is not how many sensors we read, but how accurately and how quickly we make decisions.”
Automation in industry is no longer merely a technological investment aimed at speeding up machines. Instead, it has become a comprehensive transformation area centered on how data is collected, protected, and converted into decisions. The success of software projects is often determined not by the code itself but by the robustness of the infrastructure and the continuity of systems. vMind CEO Volkan Duman evaluates software-driven automation through four main pillars: infrastructure, decision-making over the cloud, the continuity of digital twins, and a locally developed end-to-end IT architecture. According to Duman, the real output of automation lies not in the number of sensors but in making the right decisions at the right time.
1. Why are IT and cloud infrastructure critical in software-driven automation projects?
“We do not see automation merely as a software project; for us, automation is a living system that defines how an organization operates. What makes this system valuable is the flow of data before the code, the reliability of integrations, the auditability of security, and the uninterrupted continuity of operations. In the field, the most critical threshold is the correct integration of OT and IT. If we cannot bring together the data from the production line with MES, ERP, and the analytics world, automation cannot scale, and the gains remain limited to a single production line.
Cloud and modern infrastructure provide scalability, centralized management, rapid deployment, and standardization. We do not leave automation at the level of ‘making the application run’; instead, we establish the platform, design the security, standardize observability and manageability, and ensure operational continuity.”
2. How does placing production data in the cloud change decision-making processes?
“Reading the impact of the cloud on manufacturing merely as data storage would be incomplete. The real difference lies in standardizing production data across facilities, making KPIs comparable, and accelerating decision-making mechanisms. When indicators such as downtime, quality deviations, energy consumption, or maintenance requirements can be monitored in real time, organizations no longer wait for reports; they manage the field through instant signals.
Once the data flow is established, visible improvements begin in predictive maintenance, quality optimization, inventory, and supply chain decisions. We first design the security of information, access management, and auditability. Then we transform data into an architecture ready for analytics and artificial intelligence. Today, the real output of automation is not how many sensors we read, but how accurately and how quickly we can make decisions.”
3. What infrastructure conditions are required for a digital twin to function effectively?
“A digital twin is more than a flashy dashboard; it is a living model connected to the operational decision-making mechanism. For it to function properly, three conditions are critical: low latency and reliable connectivity, a properly designed edge-cloud balance, and a performance computing infrastructure. If GPU or AI-ready resources are not provided according to need, the digital twin becomes merely a monitoring tool.
More importantly, continuity is essential. When a digital twin is disrupted, not only the application but also production optimization, maintenance planning, and quality forecasting are affected. Therefore, business continuity, disaster recovery, 24/7 monitoring, and a culture of regular testing and drills are indispensable. Technology only moves beyond being a project when supported by operational maturity.”
4. Why is a local and end-to-end IT infrastructure a strategic issue for industrial companies?
“In industry, infrastructure selection is no longer simply an IT procurement decision; it is directly a matter of risk management and competitive strength. Production data and process knowledge represent the organization’s most confidential intellectual assets. Where this data is stored, how it is protected, and how systems recover during crises are of strategic importance.
A local and end-to-end approach ensures data sovereignty, auditability, regulatory compliance, and operational sustainability. We address this integrity through cloud, security, managed services, and integration layers. It is also becoming clear that automation is not limited to the production floor; there is significant efficiency potential in office processes as well. For this reason, with our upcoming RPA-focused initiatives, we aim to extend automation from the factory floor to the office, creating an end-to-end enterprise automation layer.”
