{"id":7966,"date":"2025-01-21T08:31:22","date_gmt":"2025-01-21T07:31:22","guid":{"rendered":"https:\/\/thinkupc.com\/activa-industria-4-0\/"},"modified":"2025-03-26T12:29:38","modified_gmt":"2025-03-26T11:29:38","slug":"activa-industria-4-0","status":"publish","type":"page","link":"https:\/\/www.thinkupc.com\/en\/activa-industria-4-0\/","title":{"rendered":"ACTIVA Industria 4.0"},"content":{"rendered":"\t\t
\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

We offer personalized consulting, funded by the Ministry of Industry, Trade and Tourism, to facilitate the digital transformation of industrial SMEs.<\/h2>

\"ACTIVA<\/p>

ThinkUPC is one of the consulting firms selected by the ACTIVA Industria 4.0<\/strong><\/a> program to provide specialized and individualized consulting<\/strong> to Spanish industrial SMEs.<\/span><\/p>

This program enables companies to obtain a diagnosis of their current situation<\/strong> along with a transformation plan<\/strong> that identifies the necessary actions in their digital transformation process and establishes a roadmap for implementation.<\/strong><\/p>

Our knowledge of the industrial sector and our experience in implementing Industry 4.0 projects make us the ideal partner<\/strong> to carry out this specialized consulting in digital transformation.<\/p>

Below, we share some of our representative projects developed in the industrial sector, ranging from defining a roadmap for Industry 4.0 adoption within the company to practical examples of digital transformation initiatives that emerged from our consulting engagements.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Catalogue of Digitalization Solutions\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\n\t\t\t\t\t
Industry 4.0 Consulting (ACCI\u00d3 vouchers for Industry 4.0) <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/path><\/svg><\/span>\n\t\t\t<\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
    \n
  1. Digitalization and automation of processes through AI.<\/strong>\n

    This initiative includes two parts: updating the technological tool with the latest trends in web application development and enhanced web application security, and incorporating AI (artificial intelligence) to extract information from unstructured data and improve process efficiency. AI technologies are applied in image recognition, natural language processing, and supervised learning models to simplify processes. <\/p>\n<\/li>\n

  2. Digitalization of customs processes.<\/strong>\n

    Consultancy to improve efficiency through the use of technology for process automation and data transmission between the client and customs, thereby reducing the efforts involved in processing customs operations.<\/p>\n

    Creation of the integrated dashboard in Power BI for managing the operations described above.<\/p>\n<\/li>\n

  3. Predictive analytics to detect pests.<\/strong>\n

    Implementation of AI algorithms within a corporate solution for pest management that predicts potential infestations based on historical data extracted from control points.<\/p>\n<\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

    \n\t\t\t\t\n\t\t\t\t\t
    Digitalization of production plants or industrial machinery <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/path><\/svg><\/span>\n\t\t\t<\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
    \n\t\t\t\t
    \n\t\t\t\t\t\t\t\t\t
      \n
    1. Deployment of an IoT MIIMETIQ platform to monitor the production process in the plant.<\/strong>\n

      Deployment of an IIoT data acquisition and processing platform that enables the application of AI models to support decision-making in production processes.<\/p>\n

      The platform allows the acquisition of all kinds of data\u2014both sensor-generated signals from the plant and any other data from production management systems. This data is stored in a processed database, ensuring complete traceability of the entire manufacturing process. This traceability is essential for subsequently applying any kind of data analytics, whether it’s descriptive analysis or AI models designed to help prevent failures or support decision-making in general. <\/p>\n<\/li>\n

    2. Heijunka board digitization infrastructure on Kanbanize.<\/strong>\n

      Development of a communication platform for the various stakeholders in the production process. This solution integrates with SAP for acquiring manufacturing orders, with Kanbanize to visualize the production process, and includes the development of an assistant to convert manufacturing orders into tasks on Kanban boards. <\/p>\n<\/li>\n<\/ol>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

      \n\t\t\t\t\n\t\t\t\t\t
      Smart manufacturing solutions and digital replication <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/path><\/svg><\/span>\n\t\t\t<\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
      \n\t\t\t\t
      \n\t\t\t\t\t\t\t\t\t
        \n
      1. Reduction of electricity consumption in EAF furnaces by applying AI.<\/strong>\n

        Steel melting furnaces have high energy consumption that can reach up to 1,740 MWh per day. This makes energy one of the main costs in the industry. This consumption varies depending on multiple parameters and input conditions that are often difficult for operators to control due to the process’s complexity. <\/p>\n

        Using data fusion techniques that combine over 400 variables, and data-driven AI models, it has been possible to determine the causes of excessive energy consumption and develop an application that generates recommendations for process operators and engineers. This solution helps reduce the energy consumption of these furnaces.<\/p>\n<\/li>\n

      2. Detection and diagnosis of anomalies in hot rolling mills applying AI.<\/strong>\n

        This rolling process consists of four interrelated subprocesses (furnace, rolling mill train, cooling plate, and edger) such that a malfunction or misalignment in one, or the combination of minor deviations in more than one of these subprocesses, can result in shape defects in the final product, potentially leading to its rejection by the end customer.<\/p>\n

        Thanks to AI-based modeling of the rolling process and the development of a real-time monitoring application, it is possible to detect and identify anomalies in the process operation. This enables early detection and reduction of defects in the final product. <\/p>\n<\/li>\n

      3. AI-based digital replication to improve the performance of the wastewater treatment process.<\/strong>\n

        In water treatment processes, it is crucial to ensure specific levels of certain parameters, such as Total Organic Carbon (TOC), nitrogen (NT), and turbidity (NTU). If these parameters are not controlled, the treated water may fail to meet the required specifications set by regulations. <\/p>\n

        Thanks to AI-based modeling of the wastewater treatment process, it has been possible to monitor in real time the stability and behavior of all the basins in the treatment plant.<\/p>\n<\/li>\n

      4. Study of the impact of the rolling process operation on the occurrence of surface defects using AI.<\/strong>\n

        During the rolling process, the tubes go through several subprocesses: heating, pressing, perforation, and rolling. A deviation in the operation of any of these subprocesses\u2014or certain combinations of operations across them\u2014can cause surface defects in the tubes. In the best-case scenario, surface treatments will be necessary to mitigate these defects. <\/p>\n

        Thanks to process modeling using AI techniques and anomaly detection algorithms, it is possible to identify process anomalies that have a high probability of triggering a specific surface defect in the tube being manufactured.<\/p>\n<\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

        \n\t\t\t\t\n\t\t\t\t\t
        Operational Excellence Solutions <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/path><\/svg><\/span>\n\t\t\t<\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
        \n\t\t\t\t\t
        \n\t\t\t\t
        \n\t\t\t\t\t\t\t\t\t
          \n
        1. Digitization of the LEAN planning Heijunka board using KANBAN cards.<\/strong>\n

          Consultancy for the digitalization of a physical Heijunka board into a fully digitalized board for a plant producing plant protection products. This board plans the execution of production orders and supports the management of delays. At the same time, it eliminates indirect tasks, accelerates operational decision-making, and automates tasks such as raw material supply management, warehouse management, etc. <\/p>\n<\/li>\n

        2. Planning and sequencing the production of fresh and frozen products to obtain the greatest possible profit.<\/strong>\n

          Generation of a Mixed Integer Linear Programming (MILP) model that calculates the optimal production quantity for each of the various products to maximize profits. This model takes into account the estimated demand forecast, existing inventories, expiration dates, costs of unmet demand, and manufacturing constraints across production lines and products, among others. The output of this model is then used to feed a heuristic model, which proposes the optimal weekly production sequence to closely align with the quantities determined by the MILP model. <\/p>\n<\/li>\n<\/ol>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

          \n\t\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t\t\t

          Characteristics that differentiate us<\/h2>