2016
DOI: 10.1007/s12159-016-0140-0
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Quantifying the effects of additive manufacturing on supply networks by means of a facility location-allocation model

Abstract: Additive manufacturing (AM), or popular scientific 3D printing, disseminates in more and more production processes. This changes not only production processes themselves, e.g. by replacing subtractive production technologies, but AM will in all likelihood also impact the configuration of supply networks. Due to a more efficient use of raw materials, transportation relations may change and production sites may be relocated. How this change will look like is part of an ongoing discussion in industry and academia… Show more

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Cited by 25 publications
(22 citation statements)
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“…Here, we review the quantitative results. Barz et al (2016) study the impact of a more efficient raw material utilization of AM technology on the supply chain layout using mixed-integer programming to analyze a twostage supply network. In the first stage, raw materials are delivered to production sites.…”
Section: Quantitative Models Am Literaturementioning
confidence: 99%
“…Here, we review the quantitative results. Barz et al (2016) study the impact of a more efficient raw material utilization of AM technology on the supply chain layout using mixed-integer programming to analyze a twostage supply network. In the first stage, raw materials are delivered to production sites.…”
Section: Quantitative Models Am Literaturementioning
confidence: 99%
“…The 'buy-to-fly ratio' is the weight of raw material bought, versus the material weight actually used in the part [73,83]. It is not a new concept, but AM enhances its potential and creates spin-offs.…”
Section: Buy-to-fly Spin-offs Mechanismmentioning
confidence: 99%
“…Procedure 1 shows the decoding algorithm for the priority-based encoding and its trace table, with the priority-based encoding being random. The CDCLAP is solved in two stages [14]. In the first stage, the CDC location is chosen and the transportation between the CDCs and retailers calculated.…”
Section: Encoding and Decoding Algorithmmentioning
confidence: 99%
“…Kaya and Urek [12] presented a facility locationinventory-pricing model without uncertainty to determine optimal facilities locations. Barz [14] proposed an optimization model for a two-stage capacitated facility location and allocation problem with additive manufacturing, in which all variables were certain. Jindal and Sangwan [15] developed a multiobjective model for a CLSC network design problem with the economic and environmental factors being fuzzy uncertain variable and the DC and CC were separate.…”
Section: Introductionmentioning
confidence: 99%