Evaluating Resiliency of Supply Chain Network: A Data Envelopment Analysis Approach
Abstract
:1. Introduction
2. Literature Review
2.1. Conceptual Foundation
2.2. Measuring Resiliency of Supply Chain
3. Methodology
3.1. DEA Model
- (1)
- There is no measurement error in the data.
- (2)
- If DMUs (network configurations) are observed to operate with certain input and output sets, the combination of the input and outputs sets are also operable (convexity).
- (3)
- If a specific network configuration can operate with a certain input and output set, it can also operate at a point that uses larger input and yields less output (strong disposability).
3.2. Selected Variables
3.2.1. Positive Factor
Available Capacity
Node Degree and Clustering Coefficient
Number of Supply Nodes
3.2.2. Negative Factor
3.2.3. External Factor
4. Application of the Model to a Case Study in Korea
4.1. Case Study: LPG Supply Chain in Korea
4.2. Data Collection
5. Results and Discussion
6. Conclusions
Appendix A. Hub Location Allocation Model
Acknowledgements
Author Contributions
Conflicts of Interest
References
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Resiliency Measure | Reference |
---|---|
Simulation (Performance Measure) | |
customer service level, production change over time, average inventory at each distribution center, total average network inventory | [16] |
the integral of time multiplied by the absolute error, customer service level | [32] |
order fill rate | [17] |
downstream market share | [33] |
order fulfillment rate | [37] |
order fulfillment rate, the cost of backorders | [38] |
lead time ratio | [39] |
shipment delay time | [40,41,42] |
Complex Network Theory (Topological Measure) | |
the degree of distribution, average path length, clustering coefficient | [21] |
the size of the largest functional network, clustering coefficient and characteristics path length | [20] |
connectivity, characteristic path length, clustering coefficient | [19] |
effective demand node ratio, supply mileage | [34] |
node degree | [15] |
flows, costs, capacities, and other topological measures | [35] |
the degree of distribution, average path length, clustering coefficient | [21] |
Conceptual (Survey Scale) | |
flexibility, redundancy, disruption occurrences | [18] |
robustness, ability | [36] |
capability (ability to bounce back, market position, financial strength, recovery), vulnerability (turbulence, deliberate threats, external pressures) | [14] |
commitment management, reporting culture, learning, awareness, preparedness and flexibility, self-organization, teamwork, redundancy, and fault-tolerance | [22,23] |
SCN Config. | Ave. Node Degree | Clustering Coefficient (105) | No. of Supply Nodes | Available Capacity (Ton) | Total Distance (Km) | Population Density * |
---|---|---|---|---|---|---|
1 | 1.995 | 0.000 | 2 | 53,002.5 | 162,700 | 4452 |
2 | 2.000 | 0.015 | 3 | 77,996 | 160,200 | 4652 |
3 | 2.000 | 0.008 | 3 | 56,401.172 | 131,010 | 9308 |
4 | 2.011 | 0.104 | 4 | 80,070.852 | 127,770 | 4940 |
5 | 2.000 | 0.008 | 3 | 57,487.392 | 131,010 | 9308 |
6 | 2.011 | 0.104 | 4 | 80,781.569 | 127,770 | 4940 |
7 | 2.011 | 0.133 | 4 | 57,815.846 | 106,600 | 6411 |
8 | 2.022 | 1.600 | 5 | 85,005.144 | 106,870 | 13,895 |
9 | 2.011 | 0.133 | 4 | 59,357.692 | 106,600 | 6411 |
10 | 2.022 | 1.600 | 5 | 87,422.65 | 106,870 | 13,895 |
11 | 2.028 | 0.628 | 5 | 59,890.152 | 101,270 | 13,554 |
12 | 2.049 | 4.130 | 6 | 85,179.567 | 96,459 | 13,650 |
13 | 2.028 | 0.628 | 5 | 62,120.714 | 101,270 | 13,554 |
14 | 2.049 | 4.130 | 6 | 87,644.823 | 96,459 | 13,650 |
15 | 2.049 | 1.360 | 6 | 60,072.998 | 89,489 | 14,191 |
16 | 2.077 | 9.640 | 7 | 85,752.84 | 74,066 | 9084 |
17 | 2.049 | 1.360 | 6 | 62,357.684 | 89,489 | 14,191 |
18 | 2.077 | 9.640 | 7 | 88,436.857 | 74,066 | 9084 |
19 | 2.077 | 3.830 | 7 | 61,021.554 | 67,730 | 17,880 |
20 | 2.109 | 28.200 | 8 | 86,936.811 | 72,123 | 13,373 |
21 | 2.077 | 1.360 | 7 | 63,626.922 | 67,730 | 17,880 |
22 | 2.109 | 9.640 | 8 | 90,025.922 | 72,123 | 13,373 |
Variable | Min | Med. | Max. | Std. |
---|---|---|---|---|
Positive Factor | ||||
Available capacity (Tons) | 53,002.5 | 70,811.5 | 90,025.9 | 13,239.2 |
Ave. node degree | 2.0 | 2.0 | 2.1 | 0.0 |
Clustering coefficient (105) | 0.0 | 1.4 | 28.2 | 6.2 |
Number of supply nodes | 2.0 | 5.0 | 8.0 | 1.7 |
Negative Factor | ||||
Total distance (Km) | 67,730.0 | 101,270.0 | 162,700.0 | 27,147.0 |
External Factor | ||||
Population density | 4452.0 | 13,373.0 | 17,880.0 | 4166.7 |
SCN Config. | Resiliency Score | Ranking | Resiliency Score Considering Population Density | Ranking Considering Population Density |
---|---|---|---|---|
1 | 0.000 | 22 | 1.000 | 1 |
2 | 0.001 | 19 | 1.000 | 2 |
3 | 0.001 | 20 | 0.002 | 21 |
4 | 0.008 | 17 | 0.998 | 9 |
5 | 0.001 | 21 | 0.002 | 22 |
6 | 0.008 | 18 | 1.000 | 3 |
7 | 0.012 | 15 | 0.128 | 17 |
8 | 0.127 | 11 | 0.154 | 13 |
9 | 0.012 | 16 | 0.128 | 18 |
10 | 0.127 | 12 | 0.154 | 14 |
11 | 0.058 | 13 | 0.069 | 19 |
12 | 0.293 | 7 | 0.338 | 11 |
13 | 0.058 | 14 | 0.069 | 20 |
14 | 0.260 | 8 | 0.300 | 12 |
15 | 0.131 | 10 | 0.142 | 16 |
16 | 0.639 | 6 | 0.992 | 10 |
17 | 0.132 | 9 | 0.143 | 15 |
18 | 0.642 | 5 | 1.000 | 4 |
19 | 1.000 | 1 | 1.000 | 5 |
20 | 1.000 | 2 | 1.000 | 6 |
21 | 1.000 | 3 | 1.000 | 7 |
22 | 1.000 | 4 | 1.000 | 8 |
SCN Config. | Available Capacity (Ton) | Average Node Degree | Clustering Coefficient | Number of Supply Nodes | Total Distance (Km) |
---|---|---|---|---|---|
1 | 143,115.5 | 3 | 63.6 | 16 | 0 |
2 | 115,108.5 | 3 | 62.6 | 15 | 0 |
3 | 101,517.8 | 2 | 51.2 | 12 | 0 |
4 | 73,942.7 | 2 | 49.9 | 10 | 0 |
5 | 100,431.6 | 2 | 51.2 | 12 | 0 |
6 | 73,231.9 | 2 | 49.9 | 10 | 0 |
7 | 70,679.4 | 1 | 41.5 | 8 | 0 |
8 | 43,815.6 | 1 | 40.2 | 7 | 0 |
9 | 69,137.6 | 1 | 41.5 | 8 | 0 |
10 | 41,398.1 | 1 | 40.2 | 7 | 0 |
11 | 62,180.4 | 1 | 39.0 | 6 | 0 |
12 | 31,091.8 | 1 | 33.6 | 5 | 0 |
13 | 59,949.8 | 1 | 39.0 | 6 | 0 |
14 | 28,626.5 | 1 | 33.6 | 8 | 0 |
15 | 47,796.7 | 1 | 33.6 | 4 | 0 |
16 | 3526.1 | 0 | 19.3 | 1 | 0 |
17 | 45,512.0 | 1 | 33.6 | 4 | 0 |
18 | 842.0 | 0 | 19.3 | 1 | 0 |
19 | 0.0 | 0 | 0.0 | 0 | 0 |
20 | 0.0 | 0 | 0.0 | 0 | 0 |
21 | 0.0 | 0 | 0.0 | 0 | 0 |
22 | 0.0 | 0 | 0.0 | 0 | 0 |
SCN Config. | Available Capacity (Ton) | Average Node Degree | Clustering Coefficient | Number of Supply Nodes | Total Distance (Km) | Population Density |
---|---|---|---|---|---|---|
1 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 |
2 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 |
3 | 25,128.8 | 0 | 15.2 | 3 | 37,133.0 | 0 |
4 | 710.7 | 0 | 0.0 | 0 | 0.0 | 0 |
5 | 24,042.6 | 0 | 15.2 | 3 | 37,133.0 | 0 |
6 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 |
7 | 24,783.5 | 0 | 3.5 | 1 | 0.0 | 0 |
8 | 5325.1 | 0 | 27.7 | 3 | 31,931.8 | 0 |
9 | 23,241.6 | 0 | 3.5 | 1 | 0.0 | 0 |
10 | 2907.6 | 0 | 27.7 | 3 | 31,931.8 | 0 |
11 | 28,223.3 | 0 | 28.0 | 3 | 28,170.8 | 0 |
12 | 3558.0 | 0 | 24.7 | 2 | 22,842.1 | 0 |
13 | 25,992.8 | 0 | 28.0 | 3 | 28,170.8 | 0 |
14 | 1092.7 | 0 | 24.7 | 5 | 22,842.1 | 0 |
15 | 32,181.6 | 0 | 28.6 | 2 | 12,954.4 | 0 |
16 | 2684.0 | 0 | 0.0 | 0 | 0.0 | 0 |
17 | 29,896.9 | 0 | 28.6 | 2 | 12,954.4 | 0 |
18 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 |
19 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 |
20 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 |
21 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 |
22 | 0.0 | 0 | 0.0 | 0 | 0.0 | 0 |
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Pourhejazy, P.; Kwon, O.K.; Chang, Y.-T.; Park, H. Evaluating Resiliency of Supply Chain Network: A Data Envelopment Analysis Approach. Sustainability 2017, 9, 255. https://doi.org/10.3390/su9020255
Pourhejazy P, Kwon OK, Chang Y-T, Park H. Evaluating Resiliency of Supply Chain Network: A Data Envelopment Analysis Approach. Sustainability. 2017; 9(2):255. https://doi.org/10.3390/su9020255
Chicago/Turabian StylePourhejazy, Pourya, Oh Kyoung Kwon, Young-Tae Chang, and Hyosoo (Kevin) Park. 2017. "Evaluating Resiliency of Supply Chain Network: A Data Envelopment Analysis Approach" Sustainability 9, no. 2: 255. https://doi.org/10.3390/su9020255
APA StylePourhejazy, P., Kwon, O. K., Chang, Y.-T., & Park, H. (2017). Evaluating Resiliency of Supply Chain Network: A Data Envelopment Analysis Approach. Sustainability, 9(2), 255. https://doi.org/10.3390/su9020255