Data Envelopment Analysis to measure relative performance based on key indicators from a supply network with reverse logistics
Ardila Gamboa, César David
Ballesteros Riveros, Frank Alexander
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AbstractIntroduction− 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 perfor-mance 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 net-work; 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 are analyzed to find options for improving the system.Conclusions−Reverse logistics, brings numerous ad-vantages for companies. The analysis of the indicators allows logistics managers involved to make relevant deci-sions for higher performance. The DEA model identifies which DCs have a relative superior and inferior perfor-mance, 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.
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