4 Limitations of Traditional SCADA Software:

What is SCADA Software?

Supervisory Control and Data Acquisition (SCADA) systems utilized in smart factories are critical for manufacturers. They enable production line performance monitoring, prompt response to issues, and efficient operation of production processes. These systems monitor real-time data gathered from sensors, controllers, Programmable Logic Controllers (PLCs), and other equipment. In case of a failure, they issue alerts to operators, providing the capability to monitor and control processes within the factory remotely.

Despite these advantages, traditional SCADA systems face several limitations due to innovation stagnation. Here are the key drawbacks:

1. Limited Scalability

SCADA software was initially designed to monitor and control automated machinery, specifically Programmable Logic Controllers (PLCs), as a replacement for physical control panels. The primary objective was to enable the centralized control of numerous machines on a site from a single location. Essential functions such as start/stop control, setting parameters, and data monitoring were implemented through simple graphics.

However, SCADA software development varies for each factory, production line, and company due to differences in equipment and purposes. In essence, SCADA software is tailored to a specific factory at a particular point in time. This characteristic poses challenges when attempting to update the software to incorporate the latest technologies and standards, limiting its scalability.

Compatibility and interoperability issues may arise during attempts to integrate SCADA software with new hardware or software components. As technologies like machine vision advance, the diversity of data types increases, posing difficulties in collecting and analyzing the latest data using traditional SCADA software.

Operationalizing the software requires a skilled developer or data analyst, introducing additional costs and time constraints. This dependency on expertise can be a disadvantage in terms of resource allocation.

2. Limited Data Analytics Capabilities

As manufacturers transition from smart factories to digital twins, the imperative is to collect comprehensive data generated by machines throughout the entire factory. This involves establishing a relational database by interconnecting the collected data and employing state-of-the-art industrial AI technology for real-time simulations, ultimately extracting valuable business insights.

However, achieving this goal proves challenging with traditional SCADA software. As mentioned earlier, SCADA software is predominantly tailored around equipment within individual sites, particularly focusing on PLC monitoring and control.

Expanding data collection to include additional machines and building a relational database becomes a formidable task. This is due to the fact that different machines utilize diverse communication protocols, necessitating the development of separate software for comprehensive data collection. Similarly, converting and standardizing disparate data formats from various machines, followed by relational linking, demands a dedicated module accommodating the diverse data formats from the production floor.

Consequently, once developed, integrating these advanced features into existing SCADA software can entail significant additional time and financial investments.

3. Difficult Installation Process

Traditional SCADA software is commonly developed as Windows-based software and typically distributed via DVD or downloaded for installation on a PC. The company responsible for developing the SCADA software may frequently dispatch personnel to your location to carry out the installation, a process that can span several hours to several days. Furthermore, if you intend to enable monitoring and control from multiple computers rather than a single one, the installation process must be repeated on each client computer. This duplication increases the time and cost of implementing the new software.

4. Limited Accessibility

Traditional SCADA software faces constraints in terms of accessibility. For instance, monitoring from an office away from the factory may prove challenging. The only feasible method for achieving this is by installing and utilizing a remote desktop, yet such a setup lacks the capability for concurrent monitoring or control by multiple users.

Most critically, remote desktops pose significant security risks. The inability to assign distinct monitoring or control permissions to different individuals increases the likelihood of inadvertent process interruptions. Additionally, tracking and attributing specific actions to particular users can be a cumbersome task.

5 Reasons to Adopt Data CAMP Today

What is Data CAMP?

Data CAMP is Aha Labs’ integrated solution for collecting, analyzing, monitoring, and predicting various data generated by various equipment and sensors on the manufacturing site.

1. Unlimited Data Collection and Preprocessing

Data CAMP supports most communication protocols used by industrial field equipment. This capability enables the simultaneous collection of various data types, including inspection images, results, CCTV footage, acoustic data, vibration data, PLC data, and more. The platform also includes a data preprocessing function that automatically converts diverse data formats into the desired formats before delivery.

In essence, Data CAMP operates as a data-centric solution, facilitating limitless data collection and connection as more equipment or processes are added.

2. Various Analysis Functions

Data CAMP employs ‘Statistical Process Control’ (SPC) analysis techniques to process collected data, effectively detecting product and facility abnormalities. It identifies trends within the data to pinpoint abnormalities, showcasing their location and trend of occurrence. Additionally, the platform allows for analyzing process efficiency and measuring the current equipment’s quality level.

As an example, you can utilize a machine learning artificial intelligence model trained on existing data for Image Quality Analysis, evaluating the quality of optical images currently captured. If issues such as incorrect angles or changes in focus and brightness due to aging lighting are detected, an alarm is triggered for field engineers to take necessary actions. The user-friendly structure of Data CAMP makes these analytics easily accessible.

When combined with LISA, AHHA Labs’ industrial AI solution, Data CAMP enables process analytics that traditional SCADA software cannot achieve. For instance, it facilitates the identification of processes prone to problems or the determination of specific inspection thresholds leading to rejects (error correlation). Conversely, it allows for process optimization by identifying areas of low correlation and reducing unnecessary inspections.

3. Easy No-Code Setup for Non-Experts

Perhaps the most appealing aspect is that you can configure all of this through the user interface alone, without the need for intricate coding or additional software development. From initial process settings to data conversion formats and anomaly detection conditions, everything can be effortlessly set up. The most significant advantage lies in its operation with low-code/no-code, making it accessible even for individuals well-versed in manufacturing processes but lacking development knowledge. Data CAMP allows you to explore data collection and analysis seamlessly, contributing to the swift digital transformation of your manufacturing floor.

• Note: “Low-code/no-code” is a platform that simplifies user software development. With a basic understanding of data management and the backend, users can create applications through ‘dragging and dropping’ features. No-code platforms provide templates for application development, eliminating the need for development knowledge.

4. Highly Accessible

Data CAMP leverages web-based technology, providing a comprehensive view of all production floor data without time and location constraints. Its web-based nature eliminates the need for repetitive installations on multiple computers, allowing data access anytime, anywhere by simply registering a client computer.

Scheduled for a software-as-a-service (SaaS) upgrade in 2024, Data CAMP aims to address the cautious stance of manufacturing companies toward external cloud services, ensuring data security. The upgraded version will enable selective data transfer to the cloud, allowing companies to experience cloud scalability while safeguarding their data sources.

For instance, the enhanced Data CAMP will support selective data transmission to the cloud. This means storing original data in internal storage and sending only shareable or reprocessed data externally. Previously, receiving specific alarms from a factory on another office computer or smartphone was challenging due to the lack of connectivity between factory information and the cloud. With Data CAMP, you can opt to transmit only abnormal alarm information detected after preprocessing data to the cloud, enabling push alarms.

The concept of a digital twin, a recent buzzword, aims to address real-world issues by rendering both the digital environment and physical site “visible.” To implement a digital twin on your manufacturing floor, the initial step involves creating an environment that collects and seamlessly connects all site data. Subsequently, industrial AI can be employed to analyze the data, facilitating advanced smart manufacturing endeavors such as predictive maintenance or process streamlining. Data CAMP simplifies the initiation of this transformative journey.