Developing due dates in an engineer-to-order engineering environment

Grabenstetter, Douglas H.; Usher, John M.

doi:10.1080/00207543.2014.940072

Cited by 33 publications

(33 citation statements)

References 34 publications

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“…In order to provide a 95% percent confidence interval with a three-day half-length using the standard deviation of lateness (SDL) value based on an initial run of 30 replications, it was determined that 53 replications of the experiment would be required. Due dates for input into the model were calculated using the approach of Grabenstetter and Usher (2014). The process steps in the model follow those shown in Figure 2 and the processing times for those steps utilize industry values.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%

Sequencing jobs in an engineer-to-order engineering environment

Grabenstetter

Usher

2015

Production & Manufacturing Research

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“…In this context, customers change their requirements over the time of product fabrication and, thus, the ability to respond to these modifications is a prerequisite of success (Little et al, 2000;Cameron & Braiden, 2004;Zorzini et al, 2008;Montreuil et al, 2013) and the main order winning characteristic in this context is fitness for purpose (Little et al, 2000). Hicks & Braiden (2000), Cameron & Braiden (2004), Grabenstetter & Usher (2014) and Willner et al (2014) also highlighted that price, reduced lead times and delivery performance are important aspects of customer service as most contracts include financial penalties for late delivery.…”

Section: An Overview In Etomentioning

confidence: 99%

“…ETO products have deep and complex structures, with many assembly levels, needing coordination with component supply (Hicks & Braiden, 2000;Cameron & Braiden, 2004;Alfieri et al, 2011;Gosling & Naim, 2009; Grabenstetter & Usher, 2014). These products are super-value goods that are highly customised, produced in low volumes (often one-of-a-kind) and have a long engineering trajectory, with many disruptions and adaptations due to specification changes demanded by the customer (Hans et al, 2007;Pandit & Zhu, 2007;Alfieri et al, 2011;Powell et al, 2014).…”

Section: An Overview In Etomentioning

confidence: 99%

“…Management must rely on a rough estimation of the impact of an incoming order on resource utilisation and eventually must adjust capacity, as micro process planning (i.e., detailed technological planning of production activities that result in manufacturing instructions - Giebels, 2000) has not been performed yet (Zijm, 2000;Grabenstetter & Usher, 2014). Additionally, as the pattern of demand (i.e., in terms of the level and mix of work) fluctuates significantly over time, ETO companies cannot accurately forecast demand, neither order materials nor produce in advance .…”

Section: An Overview In Etomentioning

confidence: 99%

“…In a recent literature review, comprising 250 optimisation models for tactical production planning, Díaz-Madroñero et al (2014) outlined that, although some proposals have been validated in real environments, very few are reported to be implemented and incorporated into the planning systems of the companies considered. This issue is particularly concerning in engineering-to-order (ETO) contexts where customers require reliable due dates as part of the service mix offered (Hans et al, 2007;Grabenstetter & Usher, 2014;Mourtzis et al, 2014). To contribute to the fulfilment of this research-practice gap, Carvalho et al (2015) presented the findings of a tactical capacity planning problem solution applied in a real-world ETO production setting using an optimisation model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An optimisation approach for capacity planning: modelling insights and empirical findings from a tactical perspective

Carvalho

Scavarda

Oliveira³

2017

Prod.

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The academic literature presents a research-practice gap on the application of decision support tools to address tactical planning problems in real-world organisations. This paper addresses this gap and extends a previous action research relative to an optimisation model applied for tactical capacity planning in an engineer-to-order industrial setting. The issues discussed herein raise new insights to better understand the practical results that can be achieved through the proposed model. The topics presented include the modelling of objectives, the representation of the production process and the costing approach, as well as findings regarding managerial decisions and the scope of action considered. These insights may inspire ideas to academics and practitioners when developing tools for capacity planning problems in similar contexts.

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APS Feasibility in an Engineer to Order Environment

Alfnes

Hvolby

2019

IFIP Advances in Information and Communication Technology

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This paper discus the feasibility of using advanced planning and scheduling (APS) in an engineer to order manufacturing company. The starting point is a company producing customised variants of standard products involving engineering, machining, sub-assembly, and final assembly operations. There is limited degree of parts commonality as the product variety is high with respect to material and size. APS is normally used in repetitive manufacturing companies but we argue that some ETO companies with a somewhat fixed planning setup could benefit of implementing an APS engine on top of their ERP system. One of the critical elements is, however, whether the company is able to formalise their tacit knowledge in the complicated planning processes to rules and options in an APS environment.

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Developing due dates in an engineer-to-order engineering environment

Cited by 33 publications

References 34 publications

Sequencing jobs in an engineer-to-order engineering environment

Sequencing jobs in an engineer-to-order engineering environment

An optimisation approach for capacity planning: modelling insights and empirical findings from a tactical perspective

APS Feasibility in an Engineer to Order Environment

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