Controls engineers in scientific facilities manage numerous projects, support multiple Customer Units (CUs), and balance operational demands with new initiatives. This often leads to growing backlogs and staff stress. Meanwhile, managers struggle to allocate resources across CUs, frequently prioritizing short-term goals at the expense of long-term strategy. At ALBA, such pressures prompted a new organizational approach. Building on lessons from successful past transitions - including the 2013 shift to operations * and a major staff turnover ** - the Controls Section adopted the Kanban method to optimize the resource allocation and maximize the throughput. Tasks were categorized by size/complexity and Class of Service (CoS), and a unified board with multiple views was implemented to visualize Work In Progress (WIP) and support follow-up. All work begins in a visible backlog, jointly prioritized with CUs based on CoS. Dedicated engineering teams were formed to improve coordination and knowledge sharing. Policies and metrics are clearly defined and transparent. The implementation was done using Jira and Confluence (Atlassian ecosystem). First results of the new approach are included in the paper. This initiative aligns with broader organizational efforts such as the Activity Plan for ALBA (APA) and project tracking within the Computing division \***, laying the groundwork toward the ALBA II upgrade to a 4th generation synchrotron light source.

Controls engineers in scientific facilities manage numerous projects, support multiple Customer Units (CUs), and balance operational demands with new initiatives. This often leads to growing backlogs and staff stress. Meanwhile, managers struggle to allocate resources across CUs, frequently prioritizing short-term goals at the expense of long-term strategy. At ALBA, such pressures prompted a new organizational approach. Building on lessons from successful past transitions - including the 2013 shift to operations * and a major staff turnover ** - the Controls Section adopted the Kanban method to optimize the resource allocation and maximize the throughput. Tasks were categorized by size/complexity and Class of Service (CoS), and a unified board with multiple views was implemented to visualize Work In Progress (WIP) and support follow-up. All work begins in a visible backlog, jointly prioritized with CUs based on CoS. Dedicated engineering teams were formed to improve coordination and knowledge sharing. Policies and metrics are clearly defined and transparent. The implementation was done using Jira and Confluence (Atlassian ecosystem). First results of the new approach are included in the paper. This initiative aligns with broader organizational efforts such as the Activity Plan for ALBA (APA) and project tracking within the Computing division \***, laying the groundwork toward the ALBA II upgrade to a 4th generation synchrotron light source.

Sardana* and Taurus\*\* are community-driven, open-source SCADA solutions that have been used for over a decade in scientific facilities, including synchrotrons (ALBA, DESY, MAX IV, SOLARIS) and laser laboratories (MBI-Berlin). Taurus is a Python framework for building both graphical and command-line user interfaces that support multiple control systems or data sources. Sardana, is an experiment orchestration tool that provides a high-level hardware abstraction and a sequence engine. It follows a client-server architecture built on top of the TANGO control system\*\*\*. In the last two years, significant developments have been made in both projects. Sardana focused on enhancing continuous scans, introducing multiple synchronization descriptions to support passive elements (e.g. shutters) and detectors reporting at different rates. The configuration tool has also been extended, following the roadmap defined by the community\*\*\*\*. Taurus has seen substantial performance gains, particularly in GUI startup times, as part of an optimization effort that started nearly three years ago. Latest improvements take profit of new TANGO event subscription asynchronous modes\*\*\*\*\*. Continuous codebase modernization is underway, and support for Qt6 is planned for the July 2025 release. This presentation will overview these recent advancements in both Sardana and Taurus and outline their current development roadmap.

Sardana* and Taurus\*\* are community-driven, open-source SCADA solutions that have been used for over a decade in scientific facilities, including synchrotrons (ALBA, DESY, MAX IV, SOLARIS) and laser laboratories (MBI-Berlin). Taurus is a Python framework for building both graphical and command-line user interfaces that support multiple control systems or data sources. Sardana, is an experiment orchestration tool that provides a high-level hardware abstraction and a sequence engine. It follows a client-server architecture built on top of the TANGO control system\*\*\*. In the last two years, significant developments have been made in both projects. Sardana focused on enhancing continuous scans, introducing multiple synchronization descriptions to support passive elements (e.g. shutters) and detectors reporting at different rates. The configuration tool has also been extended, following the roadmap defined by the community\*\*\*\*. Taurus has seen substantial performance gains, particularly in GUI startup times, as part of an optimization effort that started nearly three years ago. Latest improvements take profit of new TANGO event subscription asynchronous modes\*\*\*\*\*. Continuous codebase modernization is underway, and support for Qt6 is planned for the July 2025 release. This presentation will overview these recent advancements in both Sardana and Taurus and outline their current development roadmap.

BL31-FaXToR is the only hard-X-ray micro-tomography and radiography beamline at the third-generation ALBA synchrotron * **. It enables 3D imaging with sub-second temporal resolution under either monochromatic or white-beam conditions. The beamline features a dual-detection system enabling high speed or high resolution acquisitions. For high speed data acquisitions of the detector utilizes multiple frame grabbers and an IBM Storage Scale clustered file system. The goal of the high speed detector is to provide live reconstructions during scans with minimal latency ***. The Detectors are managed through a REST client or LImA device server. The control system is based on Tango and Sardana, providing an efficient, distributed Python environment with full user access to hardware via both graphical and command-line interfaces. The synchronization elements also include a voice coil actuated fast shutter capable of 10 ms openings and a periodic chopper, which introduced new challenges for Sardana requiring the implementation of multiple synchronizations in time and position domain. The experimental GUI was developed using Taurus and LavuE. This paper outlines the BL31-FaXToR control system architecture, presents implementation examples, and discusses upcoming planned features.