A framework for generating product production information for mass customization

Dean, Paul; Tu, Yiliu; Xue, Deyi

doi:10.1007/s00170-007-1171-0

Cited by 29 publications

(16 citation statements)

References 30 publications

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“…For initial job selection scheme, five 1 Â S vectors are introduced as follows: Take an n-job 4-stage production problem as an example. Vector_1 should be [0,0,0,0], if Vector_3¼ [4,6,8,10], and Vector_5¼ [10,12,16,20], then Vector_2¼ [2,3,4,5] and Vector_5¼ [7,9,12,15].…”

Section: Initial Job Selectionmentioning

confidence: 99%

“…Moreover, unexpected disturbances frequently and randomly happen to affect the daily production on OKP shop floors, such as job insertion/cancellation, machine breakdown/ operator absence, and variations in processing times. Thus, OKP companies use mixed-product production on a flow line to improve production efficiency [3,4], and they have to adaptively schedule and control production online.…”

Section: Introductionmentioning

confidence: 99%

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An efficient heuristic for adaptive production scheduling and control in one-of-a-kind production

Nault

Xue

et al. 2011

Computers & Operations Research

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Section: Initial Job Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An efficient heuristic for adaptive production scheduling and control in one-of-a-kind production

Nault

Xue

et al. 2011

Computers & Operations Research

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“…According to [31], the mass customization paradigm involves a design-sell-make-assemble cycle. Dean et al and Daaboul et al [78][79][80] put this cycle into six fundamental processes, while Latorre-Biel et al [14] establishes a five step sequence for the execution of the mass customization approach, within an Industry 4.0 environment:…”

Section: The Mass Customization Production Systemmentioning

confidence: 99%

An Entropy-Based Formulation for Assessing the Complexity Level of a Mass Customization Industry 4.0 Environment

Martínez-Olvera

2020

Mathematical Problems in Engineering

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It has been stated that Industry 4.0’s goal is, among others, the sustainable success in a market characterized by exigent and informed consumers demanding personalized products and services, where the level of manufacturing complexity increases with level of product customization. Even though different manufacturing complexity measures have been developed, there seems to be a lack of a comprehensive metric that address both the mass customization variety-induced complexity, and the complexity derived from the adoption of the Industry 4.0 paradigm. The main original contribution of this paper is the development of an entropy-based (entropic) formulation to address this last issue. Its validity and usefulness is put to the test via a discrete-event simulation study of a mass customization production system operating within an Industry 4.0 context. Our findings show that the entropic formulation acts as a fairly good trend indicator of the system’s performance parameter increase/decrease, but not as an estimator of the final values. A discussion of the managerial implications of the obtained results is offered at the end of the paper.

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“…Numerous systems and tools are used within this process stage: web analytics and survey tools for collecting customer requirements, CASE tools for developing the website and configurator, CAD systems for developing the product, a computer-aided manufacturing system (CAM) for planning subsequent production, and product data management for collecting and managing data about the product [19,46]. The result of the sub-process "product definition" is a product specification including general modules and components as well as linking rules and valid standard configurations [9]. In addition to the product specification, a supply chain plan is prepared, spanning a supplier network in which suppliers are identified and contacted [39].…”

Section: Population Of the Wss Templatesmentioning

confidence: 99%

Analytics Use Cases for Mass Customization – A Process-based Approach for Systematic Discovery

Wache¹,

Dinter²,

Kollwitz³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Nowadays, mass customization (MC) is shaped by the application of digital technologies like computeraided design, computer aided manufacturing, and distribution planning. Within a MC process, various data is created, which can be used to gain knowledge about past and future business activities by means of modern data analytics methods. The paper at hand applies design science research and presents a process-based approach for identifying potential analytics use cases for MC. For this purpose, a generic MC process is derived from previous literature and a systematic analysis is carried out using the work systems method. The resulting artifact offers a differentiated view on customers, products, activities, participants, technologies, and information as well as on the information flows within the MC process. It enables manufacturers to identify valuable opportunities for analytics and to optimize current MC processes. Furthermore, it can be used to develop a systematic process for the discovery and evaluation of analytics use cases and novel business models in the future.

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A framework for generating product production information for mass customization

Cited by 29 publications

References 30 publications

An efficient heuristic for adaptive production scheduling and control in one-of-a-kind production

An efficient heuristic for adaptive production scheduling and control in one-of-a-kind production

An Entropy-Based Formulation for Assessing the Complexity Level of a Mass Customization Industry 4.0 Environment

Analytics Use Cases for Mass Customization – A Process-based Approach for Systematic Discovery

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