The Engineer to Order (ETO) model is used by a significant number of manufacturers across multiple sectors. Indeed, ETO firms comprise approximately one fourth of all North American manufacturing and are growing at a rate of twenty percent (Cutler [10]). In the ETO environment, the engineering process is the largest controllable consumer of lead-time in ETO firms. Since one half of the total lead-time is typically consumed by the engineering process, it is a critical task to accurately set the due date and later sequence the jobs in queue. However, unlike other manufacturing models such as Make to Stock or Make to Order, the product for each order is unique. Hence the resulting design is not realized until after the engineering process has been completed an the only information available is limited to information which has been gathered during the quoting stage of the order fulfillment process. These facts drive uncertainty into the front-end process. Therefore, the question becomes how does one predict the job difficulty let alone the due date in a complex transactional process when the job has not even been designed yet? In regard to the state of the art for the topic of design complexity, due date setting and sequencing, there is an abundance of research. Unfortunately little of it is aimed at the ETO environment. Additionally, there is not an agreed upon way in the literature to define complexity nor is there one overarching methodology for assessing complexity. Therefore, this research investigates the topics of job complexity, due date setting and job sequencing in the context of the Engineer to Order model. Analytical research is conducted with in conjunction with multiple ETO firms and several common factors are identified which drive complexity in the ETO engineering environment. These complexity factors are can be used is as an input to the accurate prediction of flow times for the ETO engineering process as well as sequencing. The research results in new innovative approaches for complexity assessment, due date setting and sequencing which outperform existing approaches.

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