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research article

Utilizing additive manufacturing and mass customization under capacity constraints

Lacroix, Rachel  
•
Timonina-Farkas, Anna  
•
Seifert, Ralf W.  
January 1, 2023
Journal Of Intelligent Manufacturing

Additive manufacturing (AM), originally used for prototyping, is increasingly adopted for custom final part production across different industries. However, printing speed and production volume are two barriers for the adoption of AM for product customization at large scale. Nevertheless, manufacturers could aim to combine the benefits of AM for product customization with traditional mass customization (MC) technologies over the product life cycle (PLC). This approach is showcased in our paper as a manufacturing opportunity and is addressed via a non convex-concave optimization model that considers a monopolist manufacturer producing horizontally differentiated products at scale. To satisfy individual customer preferences under capacity considerations, the firm jointly decides on the inventory, production quantity, product variety, optimal technology-switching times (between AM and MC) and pricing strategy. Our approach can be implemented by decision-makers to leverage customer-centricity and benefit from this novel hybrid manufacturing practice. By deriving a closed-form solution for the production quantity based on an adaptive inventory policy, the resulting optimization problem is solved using the Sample Average Approximation framework grounded by analytical results. Our results demonstrate that the new usage of AM with MC can benefit a manufacturer for customer-centric driven strategies. Significant profit improvements can be achieved with an AM-MC-AM technology-switching scenario under certain capacity conditions and with an increasing-decreasing pricing strategy. Our results also indicate that the benefits of pricing flexibility are highest when capacity is unlimited or when the firm does not hold inventory. Under capacity constraints, a simple decreasing pricing policy combined with inventory performs very well.

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Type
research article
DOI
10.1007/s10845-022-02007-x
Web of Science ID

WOS:000860430200001

Author(s)
Lacroix, Rachel  
Timonina-Farkas, Anna  
Seifert, Ralf W.  
Date Issued

2023-01-01

Published in
Journal Of Intelligent Manufacturing
Subjects

Computer Science, Artificial Intelligence

•

Engineering, Manufacturing

•

Computer Science

•

Engineering

•

manufacturing

•

mass customization

•

inventory policy

•

pricing

•

product life cycle

•

product diffusion

•

inventory

•

variety

•

demand

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
TOM  
TOM  
Available on Infoscience
October 24, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/191534
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