Using lean manufacturing and machine learning for improving medicines procurement and dispatching in a hospital

Abstract

Industry 4.0 concepts are defined around the use of new technologies for improving industrial companies according to scientific, technological and organizational aspects. Enterprise complex problems could be solved in this frame. Many methods and concepts presented in the literature are focused on these aspects. A new method is being developed in Icam Paris-Sénart around the idea that sustainability must be the kernel of “industry of the future”. Indeed, the framework proposed is a combination of the previous aspects with environmental, social and societal aspects. The actual situation of the planet is an encouragement to make all future transformations with sustainability as kernel. These concepts could be imported in healthcare logistics and transport area for solving specific problems of this domain. Icam (French Engineer school) and FEI University are collaborating for proposing to healthcare hospitals a new framework (healthcare logistics 4.0) for solving complex problems of this domain.

This paper is focusing on how to improve medicines procurement and dispatching in a hospital. Indeed, the use of Artificial Intelligence in healthcare sector is growing up. Data treated and analyzed could give important insights for solving problems and improving the existing organization. As machine learning is already exploited in many production management problems (forecasting, storage, production, etc.), the idea is to use it for developing an aided tool in addition to a lean manufacturing classical approach. After presenting the real problem of medicines procurement and dispatching detected in healthcare hospitals, concepts of lean manufacturing and machine learning will be exposed. Then, the methodology proposed for solving the problem will be shown. An illustration will be given for validating concepts presented. Finally, the link with the global “Healthcare logistics 4.0” framework will be described.