DEA-Based PROMETHEE II Distribution-Center Productivity Model: Evaluation and Location Strategies Formulation
Abstract
:1. Introduction
2. Relevant Literature
3. Methods
3.1. PROMETHEE II
3.2. DEA
4. Application
4.1. Background
4.2. DEA-Based PROMETHEE II Model Development
- Vehicle Off Road (VOR)
- Number of Employees (NE)
- Stock Efficiency (SE)
- Stock Month (SM)
- Immediate Supply Job Card Fill Rate (JCFR) Task Achievement
- Service Rate (SR)
- Guest Delight Index (GDI)
5. Results and Discussion
6. Implications of DCs Locations
- Strategy 1
- Strategy 2
- Strategy 3
- Strategy 4
- Strategy 5
- Strategy 6
7. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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DC | NE | SE | GDI | SR | SM | JCFR | VOR |
---|---|---|---|---|---|---|---|
LJC | 7 | 51.7 | 98.1 | 75.17 | 1.1 | 91.9 | 0.03731 |
MKK | 9 | 48.4 | 98 | 99.10 | 1.1 | 89.5 | 0.02173 |
PSC | 6 | 62.2 | 94.5 | 92.40 | 1.8 | 79.3 | 0.03021 |
MKO | 6 | 40.8 | 94.1 | 92.50 | 2.7 | 80.1 | 0.01655 |
MDR | 11 | 66.9 | 93.6 | 93.30 | 1.5 | 95.2 | 0.01485 |
RRC | 8 | 59.1 | 93.5 | 96.50 | 1.1 | 91.7 | 0.01934 |
RBC | 2 | 9.6 | 90.6 | 91.30 | 2.4 | 77 | 0.01165 |
TFC | 3 | 38.1 | 89.9 | 94.30 | 1.5 | 73 | 0.01552 |
MKR | 13 | 21.9 | 89 | 96.30 | 1.1 | 87.2 | 0.01508 |
DC | Rank | |||
---|---|---|---|---|
LJC | 0.1867 | 0.3023 | −0.1156 | 8 |
MKK | 0.2192 | 0.1339 | 0.0853 | 3 |
PSC | 0.1765 | 0.1722 | 0.0043 | 5 |
MKO | 0.2370 | 0.1173 | 0.1197 | 1 |
MDR | 0.2333 | 0.1175 | 0.1159 | 2 |
RRC | 0.1846 | 0.1207 | 0.0639 | 4 |
RBC | 0.2350 | 0.2362 | −0.0012 | 6 |
TFC | 0.1501 | 0.2219 | −0.0718 | 7 |
MKR | 0.0995 | 0.3000 | −0.2005 | 9 |
Potential Improvements for Output and Input Measures | |||||||||
---|---|---|---|---|---|---|---|---|---|
DC | Efficiency Score | Rank | SE | GDI | SR | SM | JCFR | NE | VOR |
LJC | 0.582 | 9 | +71% | +113% | +192% | +218% | +85% | … | −2% |
MKK | 0.727 | 8 | +37% | +39% | +40% | +124% | +37% | … | … |
PSC | 0.832 | 7 | +20% | +85% | +98% | +62% | +79% | … | … |
MKO | 1 | 1 | … | … | … | … | … | … | … |
MDR | 1 | 1 | … | … | … | … | … | … | … |
RRC | 0.932 | 5 | +7% | +24% | +23% | +73% | +13% | … | … |
RBC | 1 | 1 | … | … | … | … | … | … | … |
TFC | 1 | 1 | … | … | … | … | … | … | … |
MKR | 0.881 | 6 | +13% | +26% | +17% | +150% | +13% | −65% | … |
DC | DEA Efficiency Score | DEA-Based PROMETHEE II Score | ||||
---|---|---|---|---|---|---|
MKO | 0.1197 | 0.119747 | 1.0000 | 1 | 1.0000 | |
MDR | 0.1159 | … | 0.9879 | 1 | 0.9879 | |
MKK | 0.0853 | 0.8924 | 0.727 | 0.6488 | ||
RRC | 0.0639 | 0.8255 | 0.932 | 0.7693 | ||
PSC | 0.0043 | 0.6395 | 0.832 | 0.5321 | ||
RBC | −0.0012 | 0.6223 | 1 | 0.6223 | ||
TFC | −0.0718 | 0.4020 | 1 | 0.4020 | ||
LJC | −0.1156 | 0.2652 | 0.582 | 0.1543 | ||
MKR | −0.2005 | −0.20051 | 0.0000 | 0.881 | 0.0000 |
Title of Previous Studies | Publication Date | MCDM Theoretical Base & Assumptions (Context, Criteria, Alternatives, Decision Making Units (DMU), … etc.) | Methodology (MCDM Tools and Techniques) | Dissimilarities in the Contribution Compared to the Current Study (Including Any Potential Improvements to Be Handled in Further/Future Studies, Gaps/Aspects not Covered, or Motives for Further Investigations) |
---|---|---|---|---|
An integrated benchmarking approach to distribution center performance using DEA modelling [29]. | 2002 | Context: Distribution Center Performance; Criteria: 3 inputs (fleet size, experience, and the mean order throughput time in days (MOT)); and 4 outputs (sales volume of 4 different products); Alternatives: Distribution Centers. | DEA | Use of DEA alone (i.e., efficiency focused model) All outputs focused on sales volumes |
Freight village design using the multicriteria method PROMETHEE [30]. | 2007 | Context: Freight village design; Criteria: freight village layout, the cross-docking options of the modules and direct railway access, and the circulation conditions; Alternatives: three alternative designs of the freight village layout are compared by means of multicriteria analysis | PROMETHEE | The context of the “freight village” is more generic and comprehensive compared to the context of the “distribution centers” Based on a single technique (PROMETHEE) |
Evaluating Efficiency and Effectiveness of Logistics Infrastructure Based on PCA-DEA Approach in China [31]. | 2009 | Context: Logistics Infrastructure; Criteria: 6 inputs (number of staff and employed workers in transport, possession of civil motor vehicles, possession of watercraft, railway density, waterway density, highway density) and 2 outputs (freight traffic, and turnover volume of freight traffic); Alternatives: logistics infrastructure for 31 major regions (23 provinces, 4 municipalities, and 4 autonomous regions) in China | Principal Component Analysis (PCA) and DEA | Logistics infrastructure and location oriented, not performance oriented. Not clarified—how the effectiveness has been measured (as DEA is commonly, and scientifically, known to be employed to measure the efficiency (i.e., not effectiveness) |
Facility Location Selection using PROMETHEE II Method [32]. | 2010 | Context: Facility Location Selection Problem; Criteria: (closeness of market, closeness to raw material, land transportation, air transportation, cost of labor, availability of labor, community education, and business climate); Alternatives: 3 locations | PROMETHEE | The criteria are suitable for a selection model, not for performance evaluation. Based on a single technique (PROMETHEE) Efficiency not considered |
Fuzzy AHP-PROMETHEE methodology to select bus garage location: a case study for a firm in the urban passenger transport sector in Istanbul [33]. | 2011 | Context: Garage Location Selection Problem (busses); 6 main Criteria: (cost, infrastructure, accessibility, social and economic structure, macro factors, and environmental factors); Alternatives: 3 garage locations | Fuzzy AHP and PROMETHEE | Irrelevant context Selection focused (i.e., not evaluation) Efficiency not considered |
Use of Promethee method to determine the best alternative for warehouse storage location assignment [34]. | 2014 | Context: warehouse storage location assignment; Criteria: space, picking (the total distance travelled when the pick is issued from a single command), total cost of picking a single command, time to products (the round trip), the average time it takes to serve a client, and the average time it takes to serve a group; Alternatives: Warehouses | PROMETHEE | Warehouse functions focus on storing, which is limited compared to the distribution centers functions Location oriented, not performance oriented Use of PROMETHEE alone |
A framework for measuring transport efficiency in distribution centers [35]. | 2016 | Context: transport efficiency in distribution centers; Criteria: 3 inputs (number of vehicles, fuel costs and total vehicle time in operation) and 3 outputs (total distance driven, tons shipped, and vehicle utilization); Alternatives: Distribution Centers | DEA | Concentration on transportation (i.e., other productivity aspects were not considered due to the scope of the study) Use of DEA alone (i.e., efficiency focused model) |
Visual management of performance with PROMETHEE productivity analysis [36]. | 2018 | Context: Facility Productivity of British Universities; Criteria: 2 inputs (Staff and Facilities spent) and 7 outputs (student satisfaction, research quality, admissions service, graduate prospects, graduates’ achievement, completion rate, and the total number of students); Alternatives: The British Universities | PROMETHEE Productivity Analysis (PPA) | Irrelevant application to the “distribution centers”. Although a graphical representation was provided in order to “distinguish between efficient, effective, frugal and ineffective actions” in the proposed approach [36], it focused mainly on the efficiency (outputs/inputs) rather than the effectiveness (i.e., not well linked with the simple definition of productivity that considers both efficiency and effectiveness). |
Measuring performance of government-supported drug warehouses using DEA to improve quality of drug distribution [37]. | 2020 | Context: Drug warehouses; Criteria: 4 inputs (warehouse storage capacity, temperature-controlled storage capacity, number of skilled employees and operational cost) and 6 outputs (fill rate, number of generic drugs, volume of drugs, consumption points, inventory turns ratio and time efficiency); Alternatives: Warehouses. | DEA | Warehousing orientation (for drugs), which is relatively different from the distributing orientation (automotive spare parts industry) Focusing on efficiency only via the utilization of DEA alone |
A novel hybrid fuzzy PROMETHEE-IDEA approach to efficiency evaluation [38]. | 2021 | Context: Facility EU national steel sectors; Criteria: 3 inputs (number of employees, cost of labor, electricity consumption) and 3 outputs (production value, cost of emissions trading, and net export of the final products); Alternatives: 6 selected sectors (6 EU Countries) | DEA and PROMETHEE | The application was built based on an example that considers 6 different EU steel sectors, which is relatively irrelevant application. Although both DEA & PROMETHEE were employed, the focus was on efficiency (the consideration of outputs/inputs), not on the effectiveness (the achievement with respect to each criterion) |
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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
Alidrisi H. DEA-Based PROMETHEE II Distribution-Center Productivity Model: Evaluation and Location Strategies Formulation. Applied Sciences. 2021; 11(20):9567. https://doi.org/10.3390/app11209567
Chicago/Turabian StyleAlidrisi, Hisham. 2021. "DEA-Based PROMETHEE II Distribution-Center Productivity Model: Evaluation and Location Strategies Formulation" Applied Sciences 11, no. 20: 9567. https://doi.org/10.3390/app11209567