Data Analysis of Retailer Orders to Improve Order Distribution

Our paper attempts to improve the order distribution for a logistics service provider who accepts order from retailers for fast moving consumer goods. Due to the fluctuations in orders on a day to day basis, the logistics provider will need the maximum number of trucks to cater for the maximum order...

Full description

Saved in:
Bibliographic Details
Main Authors: CHEONG, Michelle Lee Fong, CHOY, Junyu, MA, Nang Laik
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2013
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/1830
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-2829
record_format dspace
spelling sg-smu-ink.sis_research-28292013-08-12T09:24:11Z Data Analysis of Retailer Orders to Improve Order Distribution CHEONG, Michelle Lee Fong CHOY, Junyu MA, Nang Laik Our paper attempts to improve the order distribution for a logistics service provider who accepts order from retailers for fast moving consumer goods. Due to the fluctuations in orders on a day to day basis, the logistics provider will need the maximum number of trucks to cater for the maximum order day, resulting in idle trucks on other days. By performing data analysis of the orders from the retailers, the inventory ordering policy of these retailers can be inferred and new order intervals proposed to smooth out the number of orders, so as to reduce the total number of trucks needed. An average of 20% reduction of the total number of trips made can be achieved. Complementing the proposed order intervals, the corresponding new proposed order size is computed using moving average from historical order sizes, and shown to satisfy the retailers’ capacity constraints within reasonable limits. We have successfully demonstrated how insights can be obtained and new solutions can be proposed by integrating data analytics with decision analytics, to reduce distribution cost for a logistics company. 2013-07-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/1830 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Data Analytics inventory policy order distribution Artificial Intelligence and Robotics Business Operations Research, Systems Engineering and Industrial Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Data Analytics
inventory policy
order distribution
Artificial Intelligence and Robotics
Business
Operations Research, Systems Engineering and Industrial Engineering
spellingShingle Data Analytics
inventory policy
order distribution
Artificial Intelligence and Robotics
Business
Operations Research, Systems Engineering and Industrial Engineering
CHEONG, Michelle Lee Fong
CHOY, Junyu
MA, Nang Laik
Data Analysis of Retailer Orders to Improve Order Distribution
description Our paper attempts to improve the order distribution for a logistics service provider who accepts order from retailers for fast moving consumer goods. Due to the fluctuations in orders on a day to day basis, the logistics provider will need the maximum number of trucks to cater for the maximum order day, resulting in idle trucks on other days. By performing data analysis of the orders from the retailers, the inventory ordering policy of these retailers can be inferred and new order intervals proposed to smooth out the number of orders, so as to reduce the total number of trucks needed. An average of 20% reduction of the total number of trips made can be achieved. Complementing the proposed order intervals, the corresponding new proposed order size is computed using moving average from historical order sizes, and shown to satisfy the retailers’ capacity constraints within reasonable limits. We have successfully demonstrated how insights can be obtained and new solutions can be proposed by integrating data analytics with decision analytics, to reduce distribution cost for a logistics company.
format text
author CHEONG, Michelle Lee Fong
CHOY, Junyu
MA, Nang Laik
author_facet CHEONG, Michelle Lee Fong
CHOY, Junyu
MA, Nang Laik
author_sort CHEONG, Michelle Lee Fong
title Data Analysis of Retailer Orders to Improve Order Distribution
title_short Data Analysis of Retailer Orders to Improve Order Distribution
title_full Data Analysis of Retailer Orders to Improve Order Distribution
title_fullStr Data Analysis of Retailer Orders to Improve Order Distribution
title_full_unstemmed Data Analysis of Retailer Orders to Improve Order Distribution
title_sort data analysis of retailer orders to improve order distribution
publisher Institutional Knowledge at Singapore Management University
publishDate 2013
url https://ink.library.smu.edu.sg/sis_research/1830
_version_ 1770571599674605568