Examples of Supply Chain Decisions Trading Off Criteria
We encountered five recent cases in supply chain risk management that applied analytic hierarchy process (AHP) models. This approach can yield equivalent results from simple multiattribute utility models, a linear form of multiattribute utility theory. In
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Examples of Supply Chain Decisions Trading Off Criteria
We encountered five recent cases in supply chain risk management that applied analytic hierarchy process (AHP) models. This approach can yield equivalent results from simple multiattribute utility models, a linear form of multiattribute utility theory. In this chapter we review these four cases, taking the original AHP input data and constructing a SMART model, an implementation of value analysis. These cases were usually applied to evaluate alternative suppliers, either relatively as sources of various types of risk, or in a selection decision. One case, by Gaudenzi and Borghesi, applied AHP in a different mode, to assess a risk scorecard for organizational departments.
Case 1: Blackhurst et al. (2008) This study focused on a method to identify specific areas of risk by product and by supplier.1 They used multicriteria analysis, but not with the intent of selecting suppliers, but rather identifying degree of risk, using heat graphs.2 However, they provided data that would be applicable to supplier selection considering risks. First, they gave a list of criteria. Based on the data provided in their paper, we can infer the following risks, ordered by importance: Risk
Rank
Based on 1st
Weight
Defects/million parts Ease of problem resolution Timeliness of corrective action Fire Product complexity Labor availability Supplier bankruptcy Labor dispute Political issues War and terrorism Value of product
1 2–3 2–3 4 5 6–7 6–7 8–10 8–10 8–10 11
100.0 83.3 83.3 66.7 50.0 33.3 33.3 22.2 22.2 22.2 16.7
0.18 0.15 0.15 0.12 0.09 0.06 0.06 0.04 0.04 0.04 0.03
D.L. Olson, D. Wu, Enterprise Risk Management Models, C Springer-Verlag Berlin Heidelberg 2010 DOI 10.1007/978-3-642-11474-8_8,
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8 Examples of Supply Chain Decisions Trading Off Criteria
Risk
Rank
Based on 1st
Weight
Earthquake Flood Total
12–13 12–13
11.1 11.1 555.4
0.02 0.02 1.00
Scores for each supplier on each criterion are given below, along with resultant valuescores. Criteria
Weights
Supplier1
Supplier2
Supplier3
Supplier4
Defects/million Ease of resolution Product complexity Timeliness to correct Product value Earthquake Fire Flood Labor availability Labor dispute Political issues Supplier bankruptcy War and terrorism
0.18 0.15 0.09 0.15 0.03 0.02 0.12 0.02 0.06 0.04 0.04 0.06 0.04
0.700 0.800 0.800 0.800 0.700 0.850 0.850 0.950 0.850 0.800 0.800 0.950 0.750
0.267 0.214 0.761 0.169 0.686 0.650 0.200 0.650 0.300 0.150 0.400 0.900 0.400
0.850 0.900 0.700 0.850 0.650 0.950 0.300 0.800 0.800 0.650 0.850 0.650 0.750
0.900 0.850 0.850 0.850 0.750 0.350 0.700 0.600 0.650 0.750 0.600 0.650 0.700
Note that we are using the data for a different purpose than Blackhurst et al. We are demonstrating how the SMART multiattribute system could be applied. Supplier1 is best on a number of disaster factors, to include fire, flood, labor, supplier bankruptcy, and war and terror. Supplier4 is best on four of the quality factors, while Supplier3 is best on three. In this data, Supplier1 dom
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