Digital transformation is no longer an option for Italian SMEs: it is a necessity. In particular, for SMEs active in manufacturing, logistics and production, the digitization of processes is the first, fundamental step to increase efficiency, quality and competitiveness. But where to start?

The information systems of a manufacturing enterprise are traditionally divided into three hierarchical levels, each with distinct purposes, technologies and users:

A. Upper Level – Managerial and Management Systems

At this level we find strategic information systems used by senior management, executives and staff functions. It includes applications such as:

• ERP (Enterprise Resource Planning), for integrated enterprise resource planning;
• CRM (Customer Relationship Management), for customer relationship management;
• BI (Business Intelligence) and management control, for data analysis and decision support.

These tools provide a comprehensive and integrated view of the enterprise, support medium- to long-term planning, and facilitate monitoring of business KPIs.

B. Intermediate Level – Operating and Functional Systems.

This is the level of area operational applications, used by middle staff and those involved in production, logistics and administrative functions. Examples:

• WMS (Warehouse Management System) for advanced warehouse management (integration with barcode, RFID, handheld terminals, storage maps);
• MES (Manufacturing Execution System) for real-time production management and monitoring;
• Systems for managing facility maintenance management (CMMS), quality, operations personnel or safety.

These systems interface both upward (with ERP and management) and downward (with automation), providing traceability, data consistency and operational control.

C. Lower Level – Automation and Process Control.

It is the level closest to the field, where the embedded hardware and software that govern machines, equipment, plants and production lines are located. It includes:

• PLC (Programmable Logic Controllers) and SCADA (Supervisory Control and Data Acquisition);
• HMI (Human-Machine Interface) for interaction with operators;
• Sensors, actuators, robots and industrial IoT devices for real-time monitoring and control.

This level is responsible for physically executing operations and collecting data from the field, often in real time, to send to higher levels.

Vertical integration between these three layers is critical to ensure information continuity, operational efficiency and strategic responsiveness. The adoption of modern architectures (such as those based on Industrial IoT, Edge Computing, hybrid cloud, and API-first architectures) today enables enterprises to overcome information silos and achieve a truly integrated and flexible model consistent with Industry 4.0 paradigms.

Widecons suggests a practical and modular approach to digitization, which starts at the automation and process control level and is based on three strategic steps: survey and collect data from the field that already exists, generate data where it is not yet available, and finally structure it in a centralized system (Database, Data Warehouse or Data Lake), and then analyze it through intelligent dashboards, apply predictive analytics, etc.

In this first part of the article we will present the three strategic phases, and in the second and final part we will present the applications (dashboards, predictive analytics, etc.).

The starting point: survey and collect data from the existing field

Many small and medium-sized enterprises are in a situation of partial digitization: the production process is up and running, but it is not tracked in an integrated and centralized manner. Production data, when available, are often scattered and disorganized, stored in unconnected Excel files, paper documents, or displayed locally on machine, plant, or test equipment displays.

This fragmentation is a significant limitation: without an overview of processes, the company is unable to identify where productivity losses or operational inefficiencies are generated.

The first step to overcome this situation is to survey all existing information sources and initiate structured and centralized data collection. Where intelligent devices or PLCs are already in place ( Programmable Logic Controller) capable of generating data, it is critical to connect them to a broader information system that can process, analyze and enhance this information within a truly effective digital transformation strategy.

Generate data where it is not available

When machines are not equipped with digital components, Widecons offers customized solutions designed with an Industrial Internet of Things (IIoT) perspective. Basically, sensors and hardware devices are installed near the machines to monitor their operational performance in real time.

The process has four stages:

• Analysis of the type of machine and its operational characteristics;
• Identification of the available communication protocol, such as Modbus, OPC-UA or other industry standards;
• Installation of dedicated firmware for collecting, controlling and sending data;
• System configuration To enable remote and continuous acquisition of information.

Although this phase may appear complex, it represents a strategic and high-return investment for the enterprise. The data collected becomes a valuable tool for:

• Compare performance between machines, batches or production runs;
• Detect abnormal variations, early signs of deterioration or inefficiencies;
• Preventing failures, activating predictive maintenance interventions.

Through this digital integration, even traditional plants become part of a smart, interconnected production system.

Data storage: DB, DW, Data Lake

Once the data has been collected, it is essential to organize it properly so that it can be used to make data-driven decisions. The main architectures available are:

• Database (DB): optimal for managing data from the field in real time. Ideal for daily monitoring of production, logistics or quality.
• Data Warehouse (DW): A structured system for historical analysis and integration of data from heterogeneous sources. It is the reference tool for the development of KPIs, performance analysis and predictive models.
• Data Lake: a flexible platform designed to handle large volumes of unstructured data, such as machine logs, audio/video files, technical documentation and other heterogeneous sources.

Widecons designs and implements tailored scalable solutions, designed for the specific needs of SMEs. The architectures developed are general and easily adaptable, ensuring rapid implementation time and optimized costs.