Supply chain risks by Levi 2015
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Supply Chain Risk Mitigation by Prof David S-Levi, 2015 Oct 21
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Supply chain risks by Levi 2015
- 1. A New Approach to Manage Supply Chain Risk Featuring MIT professor David Simchi-Levi, a thought leader on supply chain management and business analytics OCTOBER 21, 2015 Delivered by
- 2. Questions? Type your question in the chat box in the lower left corner of your screen and click “send”
- 3. Download Webinar Materials Click on the resources icon to download webinar materials
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- 5. Follow the Conversation on Twitter Use #HBRwebinar @HBRExchange OCTOBER 21, 2015
- 6. Today’s Speaker MIT professor David Simchi-Levi, a thought leader on supply chain management and business analytics A New Approach to Manage Supply Chain Risk OCTOBER 21, 2015 @HBRExchange | #HBRwebinar
- 7. Identifying Risks and Mitigating Disruptions in the Supply Chain David Simchi-Levi Professor, MIT Chairman, OPS Rules
- 8. © David Simchi-Levi 2015
- 9. • Significant increase in supply chain risk Outsourcing and offshoring Supply chain is geographically more diverse Lean manufacturing Just‐in‐time JIT manufacturing and low inventory levels Intel Sales are down Giant blames Thai flood for $1B drop in sales goals. Toyota, Honda, Goodyear, Canon, Nikon, Sony… have cut production and lowered financial forecasts because of the flooding in Thailand. The Wall Street Journal, 2011 General Motors truck plant was shutting down General Motors truck plant in Louisiana announced that it was shutting down temporarily for lack of Japanese-made parts because of the earthquake and tsunami had struck Japan. New York Times, 2011 © David Simchi-Levi 2015
- 10. • Significant increase in supply chain risk Outsourcing and offshoring Supply chain is geographically more diverse Lean manufacturing Just‐in‐time JIT manufacturing and low inventory levels 0 50 100 150 200 250 Quake/Tsunami Floods Tornadoes Floods Japan Thailand USA Australia Natural Disasters 2011 Cost ($B) © David Simchi-Levi 2015
- 12. Supply Chain Disruption and Stock Performance • Mattel, the world’s largest toy maker; • Recalled 18 million toys made in China on August 2007; • The reason: hazards such as lead paint $‐ $0.50 $1.00 $1.50 $2.00 $2.50 2003Q3 2003Q4 2004Q1 2004Q2 2004Q3 2004Q4 2005Q1 2005Q2 2005Q3 2005Q4 2006Q1 2006Q2 2006Q3 2006Q4 2007Q1 2007Q2 2007Q3 2007Q4 2008Q1 2008Q2 2008Q3 2008Q4 Hasbro Mattel Stock Performance ($1 invested in 2003) Product Recall 12
- 13. • Natural disasters • Geopolitical risks • Epidemics • Terrorist attacks • Environmental risks • Volatile fuel prices • Rising Labor costs • Currency fluctuations • Counterfeit parts and products • Port delays • Market changes • Suppliers’ performance • Forecasting accuracy • Execution problems Unknown-Unknown Known-Unknown Uncontrollable Controllable © David Simchi-Levi 2015
- 14. • Natural disasters • Geopolitical risks • Epidemics • Terrorist attacks • Environmental risks • Volatile fuel prices • Rising Labor costs • Currency fluctuations • Counterfeit parts and products • Port delays • Market changes • Suppliers’ performance • Forecasting accuracy • Execution problems Unknown-Unknown Known-Unknown Uncontrollable Controllable © David Simchi-Levi 2015
- 15. • Natural disasters • Geopolitical risks • Epidemics • Terrorist attacks • Environmental risks • Volatile fuel prices • Rising Labor costs • Currency fluctuations • Counterfeit parts and products • Port delays • Market changes • Suppliers’ performance • Forecasting accuracy • Execution problems Unknown-Unknown Known-Unknown Uncontrollable Controllable © David Simchi-Levi 2015
- 16. Epidemics Fuel Prices Geopolitical Problems Currency Fluctuations Commodity Prices Port Delays Product Design Problems Forecast Accuracy Suppliers‘ Performance The Risk Framework Expected Impact Ability to ControlLOW Unknown-Unknown HIGH HIGH Known-Unknown LOW Counterfeits Government Regulations Natural Disasters Environmental Risks 16
- 17. • • • • Mostly ad‐hoc, intuition, gut feeling Exposure to risk may reside in unlikely places May lead to the wrong actions and wasted resources No ability to prioritize mitigation investment © David Simchi-Levi 2015
- 19. Complex bill of materials and supply chain structure Over 50 manufacturing plants 10 tiers of suppliers 1400 tier 1 supplier companies with 4,400 manufacturing sites in over 60 countries 55,000 different parts 6 million vehicles produced annually © David Simchi-Levi 2015
- 21. Engine Plants Contract Manufacturers Assembly Suppliers Steel Bar Suppliers Raw Chemical Suppliers Sheet Steel Suppliers • Time‐To‐Recover TTR : The time it takes to recover to full functionality after a disruption Assembly Plants Stamping Plants © David Simchi-Levi 2015
- 22. Engine Plants Contract Manufacturers Assembly Suppliers Steel Bar Suppliers Raw Chemical Suppliers Sheet Steel Suppliers • Time‐To‐Recover TTR : The time it takes to recover to full functionality after a disruption Assembly Plants Stamping Plants TTR =2 Weeks © David Simchi-Levi 2015
- 23. Engine Plants Contract Manufacturers Assembly Suppliers Steel Bar Suppliers Raw Chemical Suppliers Sheet Steel Suppliers • Time‐To‐Recover TTR : The time it takes to recover to full functionality after a disruption Assembly Plants Stamping Plants 2 Weeks 1 Week 2 Weeks 2 Weeks 2 Weeks TTR =2 Weeks 2 Weeks © David Simchi-Levi 2015
- 24. • Time‐To‐Recover TTR : The time it takes to recover to full functionality after a disruption • Performance Impact PI : Impact of a disruption for the duration of TTR on a given performance measure Engine Plants Contract Manufacturers Assembly Suppliers Steel Bar Suppliers Raw Chemical Suppliers Sheet Steel Suppliers Assembly Plants Stamping Plants 2 Weeks 1 Week 2 Weeks 2 Weeks 2 Weeks TTR =2 Weeks 2 Weeks © David Simchi-Levi 2015
- 25. 2 Weeks $1.5B 1 Week $100M • Time‐To‐Recover TTR : The time it takes to recover to full functionality after a disruption • Performance Impact PI : Impact of a disruption for the duration of TTR on a given performance measure Engine Plants Contract Manufacturers Assembly Suppliers Steel Bar Suppliers Raw Chemical Suppliers Sheet Steel Suppliers Assembly Plants Stamping Plants 2 Weeks 2 Weeks 2 Weeks TTR =2 Weeks 2 Weeks 2 Weeks $400M 2 Weeks $100M 2 Weeks $2.5B TTR =2 Weeks PI = $400M 2 Weeks $300M © David Simchi-Levi 2015
- 26. 2 Weeks $1.5B • Time‐To‐Recover TTR : The time it takes to recover to full functionality after a disruption • Performance Impact PI : Impact of a disruption for the duration of TTR on a given performance measure • Risk Exposure Index REI : Normalizes the PI by the maximum PI over all disruption scenarios Engine Plants Contract Manufacturers Assembly Suppliers Steel Bar Suppliers Raw Chemical Suppliers Sheet Steel Suppliers Assembly Plants Stamping Plants 2 Weeks 1 Week 2 Weeks 2 Weeks TTR =2 Weeks 2 Weeks 2 Weeks $400M 1 Week $100M 2 Weeks $100M 2 Weeks $2.5B TTR =2 Weeks PI = $400M 2 Weeks $300M © David Simchi-Levi 2015
- 27. 2 Weeks 0.6 • Time‐To‐Recover TTR : The time it takes to recover to full functionality after a disruption • Performance Impact PI : Impact of a disruption for the duration of TTR on a given performance measure • Risk Exposure Index REI : Normalizes the PI by the maximum PI over all disruption scenarios Engine Plants Contract Manufacturers Assembly Suppliers Steel Bar Suppliers Raw Chemical Suppliers Sheet Steel Suppliers Assembly Plants Stamping Plants 2 Weeks 1 Week 2 Weeks 2 Weeks TTR =2 Weeks 2 Weeks 2 Weeks 0.16 1 Week 0.04 2 Weeks 0.04 2 Weeks 1.0 TTR =2 Weeks REI = 0.16 2 Weeks 0.12 © David Simchi-Levi 2015
- 28. • Ford and its supplier production portfolio and volume of production by site • Bill of materials for each vehicle and its corresponding parts • Volumes and profit margins of different vehicle lines • Pipeline inventories • Time duration of a disruption • Firm’s response after a disruption The response is simulated via optimization © David Simchi-Levi 2015
- 30. Number of Sites Performance Impact Another 2773 sites with No Impact 2773 805 142 252 154 408 1 201 401 601 801 1001 1201 1401 1601 1801 No Impact Very Low Low Medium High Very High © David Simchi-Levi 2015
- 31. © David Simchi-Levi 2015
- 32. • Long Term Contracts • Track Inventory • Partnership • Risk Sharing Contracts • Track Performance • Require Multiple Sites • Inventory • Dual Sourcing • New Product Design
- 33. Time‐to‐Recover TTR : The time for a node in the supply chain to return to full functionality after a disruption. Time‐to‐Survive TTS : The maximum duration that the supply chain can match supply with demand after a node disruption © David Simchi-Levi 2015
- 34. 0 50 100 150 200 250 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 20 40 >50 NumberofSuppliers TTS (weeks) © David Simchi-Levi 2015
- 35. • Development of a Decision Support System for Risk Management Risk Analysis‐‐Strategic Identify Exposure to Risk associated with parts and suppliers Prioritize and allocate resources effectively Segment suppliers and develop mitigation strategies Identify opportunities to reduce risk mitigation cost Track changes in Risk Exposure‐‐Tactical Alert procurement executives to changes in their risk position Respond to a Disruption‐‐Operational Identify an effective way to allocate resources after a disruption
- 38. Plant Parent Child Plant‐X Part A Part B Plant‐X Part B Part C Plant Part From Plant‐X Part B Plant‐Y Plant‐X Part C Plant‐Z Part Feature Plant Part A CDHABC AP02A Part B CDHXYZ AP02A Plant A Plant B DCZSA > RF3S7R ‐ 7144 ‐ CA > 0132A > RF3S7R ‐ 7144 ‐ CA > AG9R ‐ 7144 ‐ EB0001 > AG9R ‐ 7144 ‐ EB0002 > AG9R ‐ 7144 ‐ EB0003 > AG9R ‐ 7144 ‐ EB0004 > AG9R ‐ 7144 ‐ EB0005 > AG9R ‐ 7144 ‐ EB0006 > AG9R ‐ 7144 ‐ EB > PCV6R ‐ 7015 ‐ GCA > CV6R ‐ 7015 ‐ GCA > CV6R ‐ 7002 ‐ GCC > AP02A >> CDHABCTD >> CDH T1 Sites Final Assembly Part Structure Part Supply Final Assembly Supply Lineage
- 40. Supplier Vehicle Impacted Total Part Cost Financial Impact Volume Impact Supplier Part Names x11 cc1 $$$ $$$ vvv x11 y11 x12 cc2 $$$ $$$ vvv x11 y12 x13 cc3 $$$ $$$ vvv x11 y13 x14 cc4 $$$ $$$ vvv x12 y21 x15 cc5 $$$ $$$ vvv x12 y22 x16 cc6 $$$ $$$ vvv x13 y31 x17 cc7 $$$ $$$ vvv x13 y32 x18 cc8 $$$ $$$ vvv x13 y33 x19 cc9 $$$ $$$ vvv x13 y34 x20 cc10 $$$ $$$ vvv x14 y41
- 41. Supplier Vehicle Impacted Total Part Cost Financial Impact Volume Impact Supplier Part Names x11 cc1 $$$ $$$ vvv x11 y11 x20 cc10 $$$ $$$ vvv x11 y12 x21 cc11 $$$ $$$ vvv x11 y13 x20 y21 x20 y22 x20 y31 x20 y32 x20 y33 x20 y34 x20 y35
- 43. • Provided an internal proactive tool for risk management • Generated critical supplier list / part list Previously, Ford monitored 1500 supplier sites The model identified 2600 suppliers’ sites, up to $2.5 billion risks on revenue Among the 2600 sites, 1100 sites were monitored by Ford Identified 1500 new sites that are not currently monitored About 400 sites have been assessed as low risks • Examples of the model in practice Risk model identified a sensor that has high vehicle exposure and is being supplied by two sites globally. The commodity team acknowledged the sourcing concentration and has investigated alternatives For the fastener commodity, the model enabled Ford to prioritize parts based on exposure level and triggered further investigation. Our investigation segment industry standard parts with short TTR into low‐risk while special or unique fasteners into potential high‐risk category Ford Supply Risk Specialists use the model routinely to prioritize commodities and supplier sites that represent the highest level of exposure during potential disruption events i.e. natural disasters, labor strikes, political unrest, etc. , enabling efficient use of resources © David Simchi-Levi 2015
- 44. Award Winning Technology Winner of the 2014 Informs Wagner Award ‐ Ford Motor Company: Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain HBR article : From Superstorms to Factory Fires describes a method developed by David Simchi‐ Levi to manage unpredictable supply chain disruptions. Received the Ford 2015 Engineering Excellence Award
- 45. • Risk Exposure method implemented in industries such as Telecommunication, High‐Tech, Pharmaceutical, Aerospace and Automotive • The UN Office of Disaster Risk Reduction applied the Risk Exposure method in developing countries Haraguchi, M. and U. Lall, “Flood Risks and Impacts”
- 46. • HBR: Find the Weak Link in Your Supply Chain • Software that implements the concepts in this talk http://www.opalytics.com/network‐risk/ © David Simchi-Levi 2015
- 47. Questions? Type your question in the chat box in the lower left corner of your screen and click “send”
- 48. Thank you for joining us! This webinar was made possible by the support of UPS. Delivered by OCTOBER 21, 2015