Dynamic clustering of inventory parts to enhance warehouse management

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Inventory management in today's complex manufacturing environments has become increasingly challenging. Ineffective management of inventory can lead to material shortages, excessive inventories, long lead times, waste of space, and poor customer service. Nowadays, various companies are using information systems to establish effective linkages to suppliers, customers, and other agents in the supply chain. These information systems include comprehensive data warehouses that integrate operational data within the supply chain including part usage, customer demand, defect rates, etc. The data can be used in analytics models to improve warehouse operations and inventory management. In this research, an approach is proposed for warehouse inventory management based on part clustering. The proposed approach categorises inventory parts based on their pick frequency, age, price, and sensitivity to transportation. Part grouping helps the decision makers to identify whether to keep the part in the warehouse, move it to an offsite inventory storage, or scrap it. The approach also determines when and how many parts should be moved from the offsite storage to the internal warehouse in order to balance the inventory and minimise the transportation costs. Dynamic reports are generated on a regular basis to effectively manage the inventory.

Original languageEnglish (US)
Pages (from-to)469-485
Number of pages17
JournalEuropean Journal of Industrial Engineering
Volume11
Issue number4
DOIs
StatePublished - Jan 1 2017

Fingerprint

Warehouses
Supply chains
Information systems
Data warehouses
Defects
Costs
Industry

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Cite this

@article{53cca425b073469cb30180938cfe99fa,
title = "Dynamic clustering of inventory parts to enhance warehouse management",
abstract = "Inventory management in today's complex manufacturing environments has become increasingly challenging. Ineffective management of inventory can lead to material shortages, excessive inventories, long lead times, waste of space, and poor customer service. Nowadays, various companies are using information systems to establish effective linkages to suppliers, customers, and other agents in the supply chain. These information systems include comprehensive data warehouses that integrate operational data within the supply chain including part usage, customer demand, defect rates, etc. The data can be used in analytics models to improve warehouse operations and inventory management. In this research, an approach is proposed for warehouse inventory management based on part clustering. The proposed approach categorises inventory parts based on their pick frequency, age, price, and sensitivity to transportation. Part grouping helps the decision makers to identify whether to keep the part in the warehouse, move it to an offsite inventory storage, or scrap it. The approach also determines when and how many parts should be moved from the offsite storage to the internal warehouse in order to balance the inventory and minimise the transportation costs. Dynamic reports are generated on a regular basis to effectively manage the inventory.",
author = "Faisal Aqlan",
year = "2017",
month = "1",
day = "1",
doi = "10.1504/EJIE.2017.086184",
language = "English (US)",
volume = "11",
pages = "469--485",
journal = "European Journal of Industrial Engineering",
issn = "1751-5254",
publisher = "Inderscience Enterprises Ltd",
number = "4",

}

Dynamic clustering of inventory parts to enhance warehouse management. / Aqlan, Faisal.

In: European Journal of Industrial Engineering, Vol. 11, No. 4, 01.01.2017, p. 469-485.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Dynamic clustering of inventory parts to enhance warehouse management

AU - Aqlan, Faisal

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Inventory management in today's complex manufacturing environments has become increasingly challenging. Ineffective management of inventory can lead to material shortages, excessive inventories, long lead times, waste of space, and poor customer service. Nowadays, various companies are using information systems to establish effective linkages to suppliers, customers, and other agents in the supply chain. These information systems include comprehensive data warehouses that integrate operational data within the supply chain including part usage, customer demand, defect rates, etc. The data can be used in analytics models to improve warehouse operations and inventory management. In this research, an approach is proposed for warehouse inventory management based on part clustering. The proposed approach categorises inventory parts based on their pick frequency, age, price, and sensitivity to transportation. Part grouping helps the decision makers to identify whether to keep the part in the warehouse, move it to an offsite inventory storage, or scrap it. The approach also determines when and how many parts should be moved from the offsite storage to the internal warehouse in order to balance the inventory and minimise the transportation costs. Dynamic reports are generated on a regular basis to effectively manage the inventory.

AB - Inventory management in today's complex manufacturing environments has become increasingly challenging. Ineffective management of inventory can lead to material shortages, excessive inventories, long lead times, waste of space, and poor customer service. Nowadays, various companies are using information systems to establish effective linkages to suppliers, customers, and other agents in the supply chain. These information systems include comprehensive data warehouses that integrate operational data within the supply chain including part usage, customer demand, defect rates, etc. The data can be used in analytics models to improve warehouse operations and inventory management. In this research, an approach is proposed for warehouse inventory management based on part clustering. The proposed approach categorises inventory parts based on their pick frequency, age, price, and sensitivity to transportation. Part grouping helps the decision makers to identify whether to keep the part in the warehouse, move it to an offsite inventory storage, or scrap it. The approach also determines when and how many parts should be moved from the offsite storage to the internal warehouse in order to balance the inventory and minimise the transportation costs. Dynamic reports are generated on a regular basis to effectively manage the inventory.

UR - http://www.scopus.com/inward/record.url?scp=85028744470&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85028744470&partnerID=8YFLogxK

U2 - 10.1504/EJIE.2017.086184

DO - 10.1504/EJIE.2017.086184

M3 - Article

AN - SCOPUS:85028744470

VL - 11

SP - 469

EP - 485

JO - European Journal of Industrial Engineering

JF - European Journal of Industrial Engineering

SN - 1751-5254

IS - 4

ER -