A PLM-based data analytics approach for improving product development lead time in an engineer-to-order manufacturing firm

A PLM-based data analytics approach for improving product development lead time in an engineer-to-order manufacturing firm

Kai SunYunpeng Li Utpal Roy 

Department of Aerospace and Mechanical Engineering, Syracuse University, Syracuse, 13244, US

Corresponding Author Email: 
kasun@syr.edu
Page: 
69-74
|
DOI: 
https://doi.org/10.18280/mmep.040201
Received: 
|
Accepted: 
|
Published: 
30 June 2017
| Citation

OPEN ACCESS

Abstract: 

A critical challenge to Engineer-To-Order (ETO) manufacturing firms is the long lead time in product development, due to the nature of highly customized products produced in low volume. The emergent Data-Driven Design (D3) approach is a possible solution to alleviate this tension by utilizing rich product-related data throughout the product’s lifecycle and building reusable data analytics models, in order for more accurate lead time estimate in a continuously updated way. In this paper, we propose a product lifecycle management (PLM) based framework to enable Prescriptive Analytics capability in ETO companies to improve their lead time estimate. The PLM has been extended to support data analytics model lifecycle management by incorporating a formal data analytics process model, CRISP-DM. It also implements a model-based approach for optimization model development, management, and execution. The proposed PLM-based Predictive Analytics framework has been applied in a local ETO manufacturing company for testing and validation.

Keywords: 

CRISP-DM, Engineer-to-order (ETO), Model-based Optimization, Prescriptive Analytics, Product Lifecycle Management (PLM).

1. Introduction
2. Challenges
3. PLM-Based Prescriptive Analytics
4. Case Study
5. Conclusion
Acknowledgement