Douglas Grabenstetter, Ph.D.

Certified Six Sigma Master Black Belt

BMG University, Denver, CO

Ph.D.

Industrial and Systems Engineering

Mississippi State University

2012

M.S.

Industrial Engineering

Northern Illinois University

2001

Top Plus Divisional Bronze Medal Award

2002
Siemens Energy and Automation

Second Place, Distinguished Master’s Thesis Competition

2002
Northern Illinois University, DeKalb, IL

Keynote Speaker

PEAK Training Session  2016

Scheduling Heuristic

Siemens Energy and Automation Corp

2004

Sequencing jobs in an engineer-to-order engineering environment

Grabenstetter, D.H., Usher, J.M.

2015

Engineer–to-order (ETO) firms produce complex – one of a kind – products and desire shorter lead time 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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Developing due dates in an engineer-to-order engineering environment

Grabenstetter, D.H., Usher, J.M.

2014

Engineer-to-order (ETO) firms comprise approximately one-fourth of all North American manufacturing, and the number is growing. These firms produce complex one-of-a-kind products and, like most firms, 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 using one-half of the total. Hence, one critical process is to accurately determine the engineering due date. However, unlike other manufacturing models such as Make to Stock or Make to Order, the design for an ETO product is not realised until after the engineering process has been completed; therefore, the only information available does not include data normally required by most due date-setting algorithms. The question then becomes how does one accurately determine the engineering due date in a complex transactional process when the job has not even been designed yet? This paper investigates this issue in the context of the engineering process within the ETO model. Analytical research is conducted in conjunction with multiple ETO firms. Several common factors are identified which drive complexity in the ETO engineering environment. A new framework and algorithm are then presented for using these factors to predict ETO engineering flow times in the absence of normally assumed information. Comparison of the performance of this new algorithm with that reported in the literature shows it to be a statistically significant improvement.

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Determining job complexity in an engineer to order environment for due date estimation using a proposed framework

Grabenstetter, D.H., Usher, J.M.

2013

The engineer to order (ETO) environment is a common operating strategy found in industry today. ETO is a growing strategy as customers are increasingly demanding personalized solutions. In ETO, the engineering process is the largest controllable consumer of lead-time consuming one half of the total. A critical process is to determine engineering complexity for purposes of flow time prediction. One distinguishing factor of ETO is that each product is the culmination of a unique design prepared for a particular customer order. The only information available is limited to that which has been gathered during the quoting stage. Hence, the question becomes how does one determine the job difficulty in a complex transactional process when the job has not even been designed yet? This paper presents the results of a study that was conducted in conjunction with multiple ETO firms to identify factors which drive complexity in the engineering environment. One important application of these complexity factors is as a potential input to the accurate prediction of flow times. This paper presents a framework for using these complexity factors to predict ETO engineering flow times.

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