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Engineer-to-order (ETO) firms produce complex -one of a kind -products and desire shorter lead times as a key component to cost competitiveness. In ETO firms, the engineering process is the largest controllable consumer of lead time. Given that lead time is a function of completion rate and scheduling policy, one critical process is to accurately sequence jobs in front of the engineering function. However, unlike other manufacturing models, such as make-to-stock or make-to-order models, the design for an ETO product is not realized until after the engineering process has been completed. Hence, the only information available does not include data normally required by most sequencing algorithms. Therefore, the problem becomes the determination of an accurate schedule within a complex transactional process for jobs which have not even been designed yet. This paper investigates this topic in the context of the engineering process within the ETO model. Based on research conducted in conjunction with multiple firms, common factors are identified which drive complexity, and a new framework and algorithm are presented for using these factors to sequence jobs. Using discrete event simulation, the performance of this new algorithm is found to be a significant improvement over current industry and published methods.

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For the past 4 years, as part of the National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) grant award number UL1TR001436, the Clinical Translational Science Institute of Southeast Wisconsin (CTSI) has used process engineering approaches to identify and understand barriers that local researchers and other stakeholders face when engaging in clinical and translational science. We describe these approaches and present preliminary results. We identified barriers from published and unpublished work at other CTSA hubs, supplemented by surveys and semi-structured interviews of CTSI faculty. We then used a multifaceted approach to organize, visualize, and analyze the barriers. We have identified 27 barriers to date. We ranked their priority for CTSI to address based on the barrier’s impact, the feasibility of intervention, and whether addressing the barrier aligned with CTSI’s institutional role. This approach provides a systematic framework to scope and address the “barriers to research problem” at CTSI institutions.