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Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

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Page 1: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Value Analysis Studies

Five cases applying AHP to supply chain risk management

We demonstrate SMART on same data

Finland 2010

Page 2: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Blackhurst, Scheibe & JohnsonInternational Journal of Physical Distribution & Logistics Management 38:2 [2008]

• Risk by product and by supplier• Purpose to identify degree of risk for

alternative suppliers

Finland 2010

Page 3: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Blackhurst et al. – SMART WeightsRisk Rank Based on 1st Weight

Defects/million parts 1 100.0 0.18

Ease of problem resolution 2-3 83.3 0.15

Timeliness of corrective action 2-3 83.3 0.15

Fire 4 66.7 0.12

Product complexity 5 50.0 0.09

Labor availability 6-7 33.3 0.06

Supplier bankruptcy 6-7 33.3 0.06

Labor dispute 8-10 22.2 0.04

Political issues 8-10 22.2 0.04

War and terrorism 8-10 22.2 0.04

Value of product 11 16.7 0.03

Earthquake 12-13 11.1 0.02

Flood 12-13 11.1 0.02

Finland 2010

Page 4: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Blackhurst et al. – ScoresCriteria Weights Supplier1 Supplier2 Supplier3 Supplier4

Defects/million 0.18 0.700 0.267 0.850 0.900

Ease of resolution 0.15 0.800 0.214 0.900 0.850Product complexity 0.09 0.800 0.761 0.700 0.850

Timeliness to correct 0.15 0.800 0.169 0.850 0.850

Product value 0.03 0.700 0.686 0.650 0.750Earthquake 0.02 0.850 0.650 0.950 0.350

Fire 0.12 0.850 0.200 0.300 0.700

Flood 0.02 0.950 0.650 0.800 0.600

Labor availability 0.06 0.850 0.300 0.800 0.650

Labor dispute 0.04 0.800 0.150 0.650 0.750

Political issues 0.04 0.800 0.400 0.850 0.600

Supplier bankruptcy 0.06 0.950 0.900 0.650 0.650

War and terrorism 0.04 0.750 0.400 0.750 0.700

Finland 2010

Page 5: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Blackhurst et al. – Value Scores

Supplier Score RankSupplier 1 0.799 1Supplier 4 0.779 2Supplier 3 0.746 3Supplier 2 0.355 4

Finland 2010

Page 6: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Value Analysis• Focus on improvement of alternatives• Supplier 2 clearly inferior – discard• IF SCORES VERY CLOSE– Consider additional criteria– Discard criteria where remaining alternatives have

equal performance• EITHER WAY– Consider improving existing alternatives– Broaden search to find additional suppliers– Seek actions to improve existing supplier performance

where they are weak

Finland 2010

Page 7: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Implications

• Supplier1 weak on important criteria– Weakest rating – defects/million parts

• Supplier3 best on production-related criteria– Slight disadvantage in defects– Greater disadvantage on product complexity, product value– Low on exposure to fire– Slight disadvantage with respect to labor– IMPROVE product design, quality

• Supplier4 – weak on external risk– IMPROVE by relocation?

Finland 2010

Page 8: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Wu, Blackhurst & ChidambaramComputers in Industry 57 350-365 [2006]

• AHP model for inbound supply risk• Two suppliers • 18 risk factors– We selected top 10

Finland 2010

Page 9: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Wu et al. – SMART weightsRisk Rank Based on 1st Weight

Cost 1 100 0.251

Quality 2 94 0.236

On-time delivery 3 81 0.203

Continuity of supply 4 51 0.128

Engineering/Production 5 17 0.043

Second Tier supplier 6 13 0.033

Demand 7 12 0.030

Internal legal issues 8 11 0.028

Natural/man-made disasters 9 10 0.025

Politics/Economics 10 10 0.025

TOTAL 399 1.0

Finland 2010

Page 10: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Wu et al. – Scores & ValueCriteria Weight Supplier1 Supplier2

Cost 0.251 0.801 0.801Quality 0.236 0.903 0.701On-time delivery 0.203 0.804 0.602Continuity of supply 0.128 0.907 0.507Engineering/Production 0.043 0.623 0.522Second Tier supplier 0.033 0.731 0.631Demand 0.030 0.929 0.612Internal legal issues 0.028 0.923 0.923Natural/man-made disasters 0.025 0.627 0.710Politics/Economics 0.025 0.917 0.917TOTAL 0.84 0.68

Finland 2010

Page 11: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Wu et al. - Sensitivity

• Many of their original 18 criteria didn’t discriminate

• Of the 10 used here, Cost, Internal legal issues, Politics/economics the same for both

• Supplier1 clear choice– Quality, delivery, continuity, demand– Weak on location – might relocate

• Supplier2 needs to improve:– Product features

Finland 2010

Page 12: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Kull & TalluriIEEE Transactions on Engineering Management 55:3 [2008]

• Used AHP to evaluate supplier ability to respond to risks

• Fed into goal programming model• Three candidate suppliers• Five risk categories– 14 specific measures

Finland 2010

Page 13: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Kull & Talluri – SMART WeightsRisk Category Rank Based on 1st Weigh

tQuality management Quality 1 100 0.342

Reliable material availability Delivery 2 72.8 0.249

Reliable cycle time Delivery 3 61.2 0.209

Protection against natural disaster Delivery 4 19.9 0.068

Excess capacity Delivery 5 11.6 0.040

Legal/Environmental control Quality 6 11.1 0.038

Power in the relationship Cost 7 5.2 0.018

Flexibility in processes Flexibility 8 3.4 0.012

Cost management capabilities Cost 9 2.6 0.009

Stable supply market Confidence 10 2.1 0.007

Information systems Confidence 11 0.7 0.002

Relations/Communications Confidence 12 0.7 0.002

Research capabilities Flexibility 13 0.7 0.002

Stable currency Cost 14 0.4 0.001Finland 2010

Page 14: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Kull & Talluri – Scores & ValueRisk Weight Supplier A Supplier B Supplier C

Excess capacity 0.040 1.0 0.50 0.25

Reliable material availability 0.249 0.33 1.0 0.33

Reliable cycle time 0.209 1.0 0.33 1.0

Protection against natural disaster 0.068 1.0 0.33 0.33

Cost management capabilities 0.009 0.33 0.33 1.0

Power in the relationship 0.018 1.0 0.33 1.0

Stable currency 0.001 1.0 1.0 1.0

Quality management 0.342 1.0 1.0 1.0

Legal/environmental control 0.038 1.0 0.33 1.0

Research capabilities 0.002 1.0 1.0 1.0

Flexibility in processes 0.012 1.0 0.175 0.413

Information systems 0.002 0.438 0.109 1.0

Stable supply market 0.007 1.0 1.0 0.33

Good relations/Communications 0.002 1.0 0.33 1.0

FINAL SCORE 0.825 0.737 0.746Finland 2010

Page 15: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Sensitivity

• Criteria that didn’t matter this choice– Stable currency, quality management, research– NOT A MATTER OF IMPORTANCE– A MATTER OF CONTEXT

• Supplier A won – weak on:– Material availability, cost management, IS

• Supplier C close – needs to improve on:– Protection against natural disaster, excess capacity,

process flexibility, supply market stability

• Supplier B weak on IS, process flexibilityFinland 2010

Page 16: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Schoenherr, Rao Tummala & HarrisonJournal of Purchasing & Supply Management 14 [2008]

• Considered five outsourcing options1. Sourcing finished goods from Mexico2. Sourcing finished goods from China3. Sourcing parts from China and assembling in the U.S.4. Sourcing parts from China, assembling in a Mexican

Maquiladora without investment5. Sourcing parts from China, assembling in a Mexican

Maquiladora with investment

• 17 Criteria

Finland 2010

Page 17: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Schoenherr et al. SMART WeightsRisk Factor Sub Obj Main Obj Rank Relative Weight

Product cost Cost Product 1 100 0.256

Product defect rate Quality Product 2 96.1 0.246

Order fulfillment risk Service Partner 3 25.4 0.065

Transportation risk Environment 4 24.6 0.063

ANSI compliance Quality Product 5 24.2 0.062

Competitor cost Cost Product 6 19.9 0.051

Supplier fulfillment risk Service Partner 7 19.9 0.051

On-time/budget delivery

Service Partner 8 19.9 0.051

Logistics risk Service Partner 9 16.8 0.043

Sovereign risk Environment 10 8.6 0.022

Wrong partner risk Mngt Capabilities Partner 11 8.2 0.021

Overseas risk Mngt Capabilities Partner 12 8.2 0.021

Supplier risk Mngt Capabilities Partner 13 7.8 0.020

Finland 2010

Page 18: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Schoenherr et al. Scores, ValueRisk factor weight FG-

MexFG-Chi

Parts Chi, US assy

Parts Chi, Maq no invest

Parts Chi, Maq w/invest

Product cost 0.256 0.25 1.0 0.22 0.94 0.44

Product defect rate 0.246 0.73 1.0 0.62 0.73 0.85

Order fulfillment risk 0.065 0.37 1.0 0.93 0.47 0.53

Transportation risk 0.063 1.0 0.59 0.38 0.33 0.31

ANSI compliance 0.062 1.0 1.0 1.0 1.0 1.0

Competitor cost 0.051 0.09 1.0 0.26 0.49 0.53

Supplier fulfillment risk 0.051 0.25 0.33 0.53 1.0 0.72

On-time/budget delivery 0.051 1.0 0.28 0.31 0.09 0.17

Logistics risk 0.043 1.0 0.49 0.22 0.16 0.16

Sovereign risk 0.022 1.0 0.54 0.54 0.20 0.20

Wrong partner risk 0.021 1.0 1.0 0.63 1.0 0.19

Overseas risk 0.021 0.46 0.125 1.0 0.125 0.23

VALUE SCORE 0.583 0.823 0.505 0.660 0.552

Finland 2010

Page 19: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Sensitivity

• China – big advantage in cost, quality– Weak – overseas risk, demand risk, natural

disaster, on-time/budget, supplier management– Could insure against overseas risk, natural disaster– Hedge against demand risk– Train or BPR for on-time, supplier management

Finland 2010

Page 20: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Second choice – make in China, Assemble in Maquiladora, without investment

• Relative advantages:– Reduce supplier fulfillment risk, wrong partner risk

• Disadvantages:– Transportation risk management– Order fulfillment risk– On-time deliver

Finland 2010

Page 21: Value Analysis Studies Five cases applying AHP to supply chain risk management We demonstrate SMART on same data Finland 2010

Third: Outsource to Mexico

• Outsource to Mexico– Weak: cost (most important)– Average: product quality (2nd most important)

• Build new facility in Mexico– Bad on cost

Finland 2010