Regression analysis for supply chain logged data: A simulated case study on shelf life prediction

Xuan Tien Doan; P. T. Kidd; R. Goodacre; B. D. Grieve

doi:10.1109/ICOSP.2008.4697709

Regression analysis for supply chain logged data: A simulated case study on shelf life prediction

Xuan Tien Doan, P. T. Kidd, R. Goodacre, B. D. Grieve

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The paper illustrates that valuable information can be mined from temperature data collected along the perishable food produce supply chain. Three regression techniques: Ordinary Least Square (OLS), Principal Component Regression (PCR) and Latent Root Regression (LRR) have been used to predict remaining shelf life of tropical seafood products. The results show that LRR is the best of the three regression techniques and works well in predicting remaining shelf life for tropical seafood. The results demonstrate the potential usefulness of utilizing automated temperature data collection (e.g. using RFID sensors) to help achieve a challenging business objective-remote real-time prediction of remaining shelf life of chilled foods. © 2008 IEEE.

Original language	English
Title of host publication	International Conference on Signal Processing Proceedings, ICSP\|Int Conf Signal Process Proc
Publisher	IEEE
Pages	2717-2720
Number of pages	3
ISBN (Print)	9781424421794
DOIs	https://doi.org/10.1109/ICOSP.2008.4697709
Publication status	Published - 2008
Event	2008 9th International Conference on Signal Processing, ICSP 2008 - Beijing Duration: 1 Jul 2008 → …

Conference

Conference	2008 9th International Conference on Signal Processing, ICSP 2008
City	Beijing
Period	1/07/08 → …

Keywords

Business objectives, Chilled foods, Latent root regression, Ordinary least squares, Principal component regression, Real-time prediction, Regression techniques, RFID sensors, Seafood products, Shelf life, Shelf-life prediction, Temperature data; Curve fitting, Signal processing, Supply chain management, Supply chains; Regression analysis

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10.1109/ICOSP.2008.4697709

Cite this

@inproceedings{5d3e47dd7a8a46f3828313b31705669e,

title = "Regression analysis for supply chain logged data: A simulated case study on shelf life prediction",

abstract = "The paper illustrates that valuable information can be mined from temperature data collected along the perishable food produce supply chain. Three regression techniques: Ordinary Least Square (OLS), Principal Component Regression (PCR) and Latent Root Regression (LRR) have been used to predict remaining shelf life of tropical seafood products. The results show that LRR is the best of the three regression techniques and works well in predicting remaining shelf life for tropical seafood. The results demonstrate the potential usefulness of utilizing automated temperature data collection (e.g. using RFID sensors) to help achieve a challenging business objective-remote real-time prediction of remaining shelf life of chilled foods. {\textcopyright} 2008 IEEE.",

keywords = "Business objectives, Chilled foods, Latent root regression, Ordinary least squares, Principal component regression, Real-time prediction, Regression techniques, RFID sensors, Seafood products, Shelf life, Shelf-life prediction, Temperature data; Curve fitting, Signal processing, Supply chain management, Supply chains; Regression analysis",

author = "Doan, {Xuan Tien} and Kidd, {P. T.} and R. Goodacre and Grieve, {B. D.}",

year = "2008",

doi = "10.1109/ICOSP.2008.4697709",

language = "English",

isbn = "9781424421794",

pages = "2717--2720",

booktitle = "International Conference on Signal Processing Proceedings, ICSP|Int Conf Signal Process Proc",

publisher = "IEEE",

address = "United States",

note = "2008 9th International Conference on Signal Processing, ICSP 2008 ; Conference date: 01-07-2008",

}

Doan, XT, Kidd, PT, Goodacre, R & Grieve, BD 2008, Regression analysis for supply chain logged data: A simulated case study on shelf life prediction. in International Conference on Signal Processing Proceedings, ICSP|Int Conf Signal Process Proc. IEEE, pp. 2717-2720, 2008 9th International Conference on Signal Processing, ICSP 2008, Beijing, 1/07/08. https://doi.org/10.1109/ICOSP.2008.4697709

Regression analysis for supply chain logged data: A simulated case study on shelf life prediction. / Doan, Xuan Tien; Kidd, P. T.; Goodacre, R. et al.
International Conference on Signal Processing Proceedings, ICSP|Int Conf Signal Process Proc. IEEE, 2008. p. 2717-2720.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Regression analysis for supply chain logged data: A simulated case study on shelf life prediction

AU - Doan, Xuan Tien

AU - Kidd, P. T.

AU - Goodacre, R.

AU - Grieve, B. D.

PY - 2008

Y1 - 2008

N2 - The paper illustrates that valuable information can be mined from temperature data collected along the perishable food produce supply chain. Three regression techniques: Ordinary Least Square (OLS), Principal Component Regression (PCR) and Latent Root Regression (LRR) have been used to predict remaining shelf life of tropical seafood products. The results show that LRR is the best of the three regression techniques and works well in predicting remaining shelf life for tropical seafood. The results demonstrate the potential usefulness of utilizing automated temperature data collection (e.g. using RFID sensors) to help achieve a challenging business objective-remote real-time prediction of remaining shelf life of chilled foods. © 2008 IEEE.

AB - The paper illustrates that valuable information can be mined from temperature data collected along the perishable food produce supply chain. Three regression techniques: Ordinary Least Square (OLS), Principal Component Regression (PCR) and Latent Root Regression (LRR) have been used to predict remaining shelf life of tropical seafood products. The results show that LRR is the best of the three regression techniques and works well in predicting remaining shelf life for tropical seafood. The results demonstrate the potential usefulness of utilizing automated temperature data collection (e.g. using RFID sensors) to help achieve a challenging business objective-remote real-time prediction of remaining shelf life of chilled foods. © 2008 IEEE.

KW - Business objectives, Chilled foods, Latent root regression, Ordinary least squares, Principal component regression, Real-time prediction, Regression techniques, RFID sensors, Seafood products, Shelf life, Shelf-life prediction, Temperature data; Curve fit

U2 - 10.1109/ICOSP.2008.4697709

DO - 10.1109/ICOSP.2008.4697709

M3 - Conference contribution

SN - 9781424421794

SP - 2717

EP - 2720

BT - International Conference on Signal Processing Proceedings, ICSP|Int Conf Signal Process Proc

PB - IEEE

T2 - 2008 9th International Conference on Signal Processing, ICSP 2008

Y2 - 1 July 2008

ER -

Regression analysis for supply chain logged data: A simulated case study on shelf life prediction

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