Developing prototype cost model for embedded motherboards assembly- A case study
Organizations today need cost estimation in product early conceptual stage to compete in the market. This study aims to examine the impact of types of components, number of components, memory type, memory size, IPC (Institute for Printed Circuits) class of Printed Circuit Board, types of test/accept...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | eng eng |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://etd.uum.edu.my/10856/1/s900128_01.pdf https://etd.uum.edu.my/10856/2/s900128_02.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Organizations today need cost estimation in product early conceptual stage to compete in the market. This study aims to examine the impact of types of components, number of components, memory type, memory size, IPC (Institute for Printed Circuits) class of Printed Circuit Board, types of test/acceptance criteria, number of batch lot sizes, and form factor on the estimated cost of Embedded Motherboard (EM) PCBA (Printed Circuit Board Assembly) at the Prototype Build stage. Learning Curve theory is used as underpinning theory. 77 sample size of suppliers’ quotation of different models of EM in the prototype build stage were collected with case study sampling technique used. Multiple regression was performed for data analysis. The results showed that types of components, IPC Class, types of test, number of batch lot sizes, and form factor significantly impacted the total predicted cost. However, number of components, memory type and memory size have insignificant impact on the total predicted cost. The findings furnish significant input to NPD team members to predict prototype PCBA cost despite minimum information at the early design stage. Theoretical implication includes new cost estimation model to improve cost engineering knowledge while practical implication includes cost savings to company in terms of wastage reduction. Practical implication include cost savings to company in terms of wastage reduction. Future research is suggested to embark on an automated parametric cost estimation model to capture, incorporate and store each estimation into a database that can be kept and retrieved for future cost estimation. |
|---|