Article
DEA-Based PROMETHEE II Distribution-Center Productivity
Model: Evaluation and Location Strategies Formulation
Hisham Alidrisi
Citation: Alidrisi, H. DEA-Based
PROMETHEE II Distribution-Center
Productivity Model: Evaluation and
Location Strategies Formulation.
Appl. Sci. 2021, 11, 9567. https://
doi.org/10.3390/app11209567
Academic Editor: José Machado
Received: 10 September 2021
Accepted: 12 October 2021
Published: 14 October 2021
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Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University,
Jeddah 21589, Saudi Arabia; hmalidrisi@kau.edu.sa
Abstract:
The current era of industrial economics necessitates warehouse and logistic distribution
centers (DCs) to contribute productively toward an organization’s success. Playing such a critical pro-
ductive role implies that logistics activities must be practiced effectively and efficiently. However, the
indistinguishability between effectiveness and efficiency leads to a somewhat shallow interpretation,
and consequently, a diluted evaluation. Hence, this paper aims to develop a productivity evaluation
model for nine DCs belonging to an international automotive vehicles and spare parts company.
The developed model was set up based on two multi-criteria decision making (MCDM) approaches:
the Preference Ranking Organization Method for Enrichment of Evaluations II (PROMETHEE II)
and data envelopment analysis (DEA). PROMETHEE II was employed to evaluate the effectiveness,
while the DEA was utilized in order to measure the efficiency of the investigated DCs. The resulting
hybrid model collectively creates what can conceptually and practically be considered a productivity
evaluation model. The results also provide six different strategies through which distribution center
locations can be evaluated in order to implement potential future initiatives.
Keywords:
DEA; PROMETHEE II; distribution centers; logistic centers; warehouses; inventory;
evaluation; productivity; location selection; strategies
1. Introduction
In the current industry 4.0 era, almost all fields of industry are characterized by sophis-
ticated manufacturing approaches associated with various services that are fully dependent
on rapid technological changes. Such a situation necessitates that warehouse and logistic
distribution centers (DCs) accomplish their critical roles for the productive success of any
organization. Such a productive role implies the use of well-established measurement ap-
proaches for effectiveness and efficiency while considering the differentiation among these
approaches both conceptually and practically [
1
]. DCs are expected to perform effectively,
thus enabling future evaluation of the extent to which they attain the strategic and/or
operational objectives of the firm. DCs should also be operated efficiently, allowing firms
to monitor to what extent DCs are capable of converting the different kinds of available
resources into tangible, measurable forms of outputs. However, within the context of the
relevant MCDM-based research applications [
2
–
10
], neither the original version of DCs, i.e.,
warehouses [
11
], nor the extended version of DCs (or what is currently known as logistic
centers) [
12
] have provided robust evidence for handling the issue of the indistinguisha-
bility between effectiveness and efficiency (i.e., the two wings of productivity). In other
words, none of the previous MCDM applications have attempted to research interweaving
MCDM approaches in order to evaluate and/or select DCs, whether as warehouses [
2
,
4
,
13
]
or as logistic centers [
3
,
5
–
10
,
14
–
21
], with regard to effectiveness and efficiency. Hence,
this paper aims to develop a productivity evaluation model for a set of DCs belonging
to an international automotive vehicles and spare parts firm located in the Middle East,
North Africa, and Turkey (MENAT). The developed model was established based on the
employment of two MCDM techniques: the Preference Ranking Organization Method for
Appl. Sci. 2021, 11, 9567. https://doi.org/10.3390/app11209567 https://www.mdpi.com/journal/applsci
