SMART – Smart Predictive Maintenance for the Pharmaceutical Industry

Objective
The main objective of this project is to improve the productivity of two packaging lines within SweOps Steriles function, as measured by Overall Equipment Effectiveness (OEE) and competence in line staffing. We aim to achieve the objective by enabling next-best-action decision support for front-line operators.

Background
Modern pharmaceutical packaging lines are complex systems with multiple intricate physical and digital components. Operators gain domain expertise through extended exposure and interaction with the systems. They see, touch, and listen to the operating parts of the system. With time, they develop deep procedural knowledge and reach the level where they can predict when the physical systems need maintenance. How do they do this? This is the basic question of the project SMART: Smart Predictive Maintenance for the Pharmaceutical Industry, a collaboration between AstraZeneca and KTH.

Our approach deploys three pillars: 1) sensor networks in manufacturing, 2) machine learning predictive models, and 3) interactive immersive and contextual visualizations. We will observe and interview expert operators to acquire their procedural knowledge and focus on the sensing and machine learning tools to produce a rich sensor-based predictive model that we visualize peripherally in the plant and immersively to the operators. Some goals of the project are to enhance the operators’ abilities to perform predictive maintenance and expedite the transfer of these skills to novice operators via the use of novel digital tools.

About the Digital Futures Industrial Postdocs
Recruitment ongoing

Main supervisor
Lihui Wang, Professor and Chair of Sustainable Manufacturing at KTH

Co-supervisors
Jan Kronqvist, Assistant Professor at KTH
Ming Xiao, Associate Professor, Division of ISE at KTH EECS
Mario Romero, Associate professor at KTH EECS School, Division of Computational Science and Technology