Bridging the Computational-Experimental Gap: A Supply Chain Management Framework for Just-in-Time Synthesis in Advanced Materials Discovery

Abstract

The fast pace of development of high-performance computing, density functional theory, and machine learning has reduced the process of screening new materials through theory from years to days. But the same pace has not been achieved in the physical supply chain of laboratory materials, thus causing a major hindrance in the pipeline of discovery of new materials. This paper focuses on the supply chain problems in advanced materials science and engineering laboratories and suggests a model for eliminating this hindrance.

The three major gaps that have been identified from a structured review of literature involving 50 papers and thematic analysis of eight stakeholders including Principal Investigators, Post-Doctoral Researchers, Lab Managers, Computational Scientists, Procurement Officers, Facility Engineers, Industry R&D Managers, and Sustainability Officers are: (1) disconnect between computational screening and procurement process, (2) lack of predictive lead-time models for laboratory chemicals, and (3) lack of closed-loop approach for R&D at circular economy. The quantitative results show that the average score for predictability of procurement lead-time is 2.0/5, whereas the average score for severity of delays is 4.4/5. The average recovery of precursors' mass is 26%. There are four main qualitative themes: "Wait-and-see Trap", "Customs and Bureaucracy Black Box", "Cost vs. Purity Dilemma", and "Desperately Predictive Need".

SynchroLabs' SCM Framework consists of four components: Predictive Procurement Engine, Lead-Time Intelligence Dashboard, Closed-Loop Recovery Engine, and Integrated Smart Inventory. Readiness for adoption was reported to be 4.4/5 on average, demonstrating high potential for implementation. Contribution to the theory of supply chain is that it applies the theory of closed-loop to laboratories and offers recommendations for laboratory managers, institution administrators, and developers of the technologies of synthesis and computational discovery.