Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics
Introduction: Data Envelopment Analysis (DEA) is used to measure the relative performance of a series of distribution centers (DCs), using key indicators based on reverse logistics for a company that produces electric and electronic supplies in Colombia. Objective: The aim is to measure the relat...
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| Format: | Article |
| Language: | English |
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Universidad de la Costa
2018-12-01
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| Series: | Inge-Cuc |
| Subjects: | |
| Online Access: | https://revistascientificas.cuc.edu.co/ingecuc/article/view/1783 |
| _version_ | 1818543710117298176 |
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| author | César David Ardila Gamboa Frank Alexander Ballesteros Riveros |
| author_facet | César David Ardila Gamboa Frank Alexander Ballesteros Riveros |
| author_sort | César David Ardila Gamboa |
| collection | DOAJ |
| description | Introduction: Data Envelopment Analysis (DEA) is used to measure the relative performance of a series of distribution centers (DCs), using key indicators based on reverse logistics for a company that produces electric and electronic supplies in Colombia.
Objective: The aim is to measure the relative performance of distribution centers based on Key Performance Indicators (KPI) from a supply network with reverse logistics.
Methodology: A DEA model is applied through 5 steps: KPIs selection; Data collection for all 18 DCs in the network; Build and run the DEA model; Identify the DCs that will be the focus of improvement; Analyze the DCs that restrict or diminish the total performance of the system. Results− KPIs are defined, data is collected and KPI’s for each DCs are presented. The DEA model is run and the relative efficiencies for each DCs are determined. A frontier analysis is made and DCs that limit or reduce the performance of the system were analyzed to find options for improving the system.
Conclusions: Reverse logistics, brings numerous advantages for companies. The analysis of the indicators allows logistics managers involved to make relevant decisions for higher performance. The DEA model identifies which DCs have a relative superior and inferior performance, making it easier to make informed decisions to change, increase or decrease resources, and activities or apply best practices that optimize the performance of the network. |
| first_indexed | 2024-12-11T22:38:56Z |
| format | Article |
| id | doaj.art-b1e7aaa0820e43299edd2f2b555bc8fb |
| institution | Directory Open Access Journal |
| issn | 0122-6517 2382-4700 |
| language | English |
| last_indexed | 2024-12-11T22:38:56Z |
| publishDate | 2018-12-01 |
| publisher | Universidad de la Costa |
| record_format | Article |
| series | Inge-Cuc |
| spelling | doaj.art-b1e7aaa0820e43299edd2f2b555bc8fb2022-12-22T00:47:52ZengUniversidad de la CostaInge-Cuc0122-65172382-47002018-12-0114213714610.17981/ingecuc.14.2.2018.131783Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse LogisticsCésar David Ardila Gamboa0Frank Alexander Ballesteros Riveros1Universidad Militar Nueva Granada, Bogotá (Colombia)Universidad Militar Nueva Granada, Bogotá (Colombia)Introduction: Data Envelopment Analysis (DEA) is used to measure the relative performance of a series of distribution centers (DCs), using key indicators based on reverse logistics for a company that produces electric and electronic supplies in Colombia. Objective: The aim is to measure the relative performance of distribution centers based on Key Performance Indicators (KPI) from a supply network with reverse logistics. Methodology: A DEA model is applied through 5 steps: KPIs selection; Data collection for all 18 DCs in the network; Build and run the DEA model; Identify the DCs that will be the focus of improvement; Analyze the DCs that restrict or diminish the total performance of the system. Results− KPIs are defined, data is collected and KPI’s for each DCs are presented. The DEA model is run and the relative efficiencies for each DCs are determined. A frontier analysis is made and DCs that limit or reduce the performance of the system were analyzed to find options for improving the system. Conclusions: Reverse logistics, brings numerous advantages for companies. The analysis of the indicators allows logistics managers involved to make relevant decisions for higher performance. The DEA model identifies which DCs have a relative superior and inferior performance, making it easier to make informed decisions to change, increase or decrease resources, and activities or apply best practices that optimize the performance of the network.https://revistascientificas.cuc.edu.co/ingecuc/article/view/1783Data envelopment analysisRelative performanceReverse LogisticsReturnable packagesWarehousing |
| spellingShingle | César David Ardila Gamboa Frank Alexander Ballesteros Riveros Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics Inge-Cuc Data envelopment analysis Relative performance Reverse Logistics Returnable packages Warehousing |
| title | Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics |
| title_full | Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics |
| title_fullStr | Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics |
| title_full_unstemmed | Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics |
| title_short | Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with Reverse Logistics |
| title_sort | data envelopment analysis to measure relative performance based on key indicators from a supply network with reverse logistics |
| topic | Data envelopment analysis Relative performance Reverse Logistics Returnable packages Warehousing |
| url | https://revistascientificas.cuc.edu.co/ingecuc/article/view/1783 |
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