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

Grabenstetter, Douglas H.; Usher, John M.

doi:10.1080/00207543.2013.787169

Cited by 18 publications

(27 citation statements)

References 16 publications

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“…The complexity drivers at the NSC level identified in the first step should be evaluated in terms of their appropriateness for inclusion in the complexity index. Thus, different considerations should be taken into account: (1) data availability in the early quotation phase (Grabenstetter and Usher 2013), (2) the correlation between drivers (Bearden 2003;Budde, Nagler, and Friedli 2015) and 3 to assign a value. However, to determine the parameter in the early quotation phase, a discrete parameter could be more appropriate (for example, size represented by a part, a unit or a module).…”

Section: Step 2: Define a Complexity Index For Nscmentioning

confidence: 99%

Understanding the impact of non-standard customisations in an engineer-to-order context: A case study

Johnsen

Hvam

2018

International Journal of Production Research

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Companies operating with an engineer-to-order (ETO) manufacturing strategy produce customised solutions for their customers. While they may be able to build on a base of existing sub-solutions, e.g. standard product structures, modules or parts when engineering a customer-specific solution, they often have to create something completely new to satisfy customers' requirements. However, it is not always clear to ETO companies what the costs associated with making customer specific solutions are, or which product or project characteristics drive costs and in what business processes. Therefore, it is not clear to companies if it is actually profitable for them to fulfil all of their customers' requirements. Hence, making it relevant to understand how creating non-standard customisations impact project profitability. This paper presents a framework for how ETO companies can quantify the impact of the complexity associated with non-standard customisations when cost data is only available at the project level. The framework is theoretically founded; it is based on statistical regression and a definition of a complexity index for non-standard customisations. The framework is validated in the context of an ETO case company and empirical data is presented.

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Section: Step 2: Define a Complexity Index For Nscmentioning

confidence: 99%

Understanding the impact of non-standard customisations in an engineer-to-order context: A case study

Johnsen

Hvam

2018

International Journal of Production Research

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“…Engineering-to-order (ETO) has become increasingly important in production systems, particularly for delivering customized products (Gosling and Naim, 2009;Radke and Tseng, 2012;Grabenstetter and Usher, 2013). However, highly customized ETO environments have received much less attention from researchers than high volume, standardized, make-to-stock (MTS) environments (Gosling and Naim, 2009;Yang, 2013).…”

Section: Introductionmentioning

confidence: 98%

“…In this context, many authors recognize that a capacity planning system that quickly analyses the impact of potential orders on capacity plans is of major importance during the order acceptance phase to determine reliable due dates and price quotations (Giebels, 2000;Gademann and Schutten, 2005;Hans et al, 2007;Sawik, 2009;Montreuil et al, 2013;Nobibon et al, 2015). These decisions are crucial in the ETO context and are related to setting important milestones for each project and for bid preparation (Bertrand and Muntslag, 1993;Gademann and Schutten, 2005;Hans et al, 2007;Zorzini et al, 2008;Grabenstetter and Usher, 2013;Montreuil et al, 2013).…”

Section: Introductionmentioning

confidence: 98%

Tactical capacity planning in a real-world ETO industry case: An action research

Carvalho

Oliveira

Scavarda

2015

International Journal of Production Economics

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“…Therefore, it was decided to use a simple materialbased approach similar to what is currently utilized in industry. As such, jobs are grouped into three classes based upon perceived difficulty as predicted by the job material content (Grabenstetter & Usher, 2013). To accomplish this task, the 1276 jobs in the data-set were grouped by the three classes and an average process time by class determined.…”

Section: Methodsmentioning

confidence: 99%

“…However, given the nature of the ETO engineering environment, nothing is known ahead of time in regards to job processing time. To overcome this problem, Grabenstetter and Usher (2013) identified seven engineering factors that can be used in this context to predict job processing times using a regression-based approach. These factors are:…”

Section: Regression-driven Complexity-based Flow Time Predictionmentioning

confidence: 99%

Sequencing jobs in an engineer-to-order engineering environment

Grabenstetter

Usher

2015

Production & Manufacturing Research

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

Cited by 18 publications

References 16 publications

Understanding the impact of non-standard customisations in an engineer-to-order context: A case study

Understanding the impact of non-standard customisations in an engineer-to-order context: A case study

Tactical capacity planning in a real-world ETO industry case: An action research

Sequencing jobs in an engineer-to-order engineering environment

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