eSupply Chain Solutions to Reduce the Bullwhip Effect
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eSupply Chain Solutions to Reduce the Bullwhip Effect - professor Mohamed Baymout, EBC6230, Winter 2014, Telfer School of Management ...

eSupply Chain Solutions to Reduce the Bullwhip Effect - professor Mohamed Baymout, EBC6230, Winter 2014, Telfer School of Management

This presentation provides an overview of ways ESupply Chains can be used to mitigate the Bullwhip Effect.

Submitted to:
Dr. Mohamed Baymout

Prepared by:
Anjali Sood Elham Mohammad Pour Irum Maqsood Pilar Mata
Sergio Maldonado Shymaa Slangor

Agenda:
Bullwhip Effect
Definition
Causes
Impacts eSupply Chain Solutions
Information Sharing and Partnerships
Inventory Management
Forecasting
Just-In-Time
Case Study Conclusions and Critiques


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eSupply Chain Solutions to Reduce the Bullwhip Effect Presentation Transcript

  • 1. E-Supply Chain Technologies & Applications EBC 6230 – Winter Session 2014 Title :eSupply Chain Solutions to Reduce the Bullwhip Effect Submitted to: Dr. Mohamed Baymout Prepared by: Anjali Sood Elham Mohammad Pour Irum Maqsood Pilar Mata Sergio Maldonado Shymaa Slangor
  • 2. eSupply Chain Solutions to Reduce the Bullwhip Effect Anjali Sood Elham Mohammad Pour Irum Maqsood Pilar Mata Sergio Maldonado Shymaa Slangor
  • 3. Agenda Bullwhip Effect • Definition • Causes • Impacts eSupply Chain Solutions • • • • Information Sharing and Partnerships Inventory Management Forecasting Just-In-Time Case Study Conclusions and Critiques
  • 4. Agenda Bullwhip Effect • Definition • Causes • Impacts eSupply Chain Solutions • • • • Information Sharing and Partnerships Inventory Management Forecasting Just-In-Time Case Study Conclusions and Critiques
  • 5. Definition Bullwhip Effect (Boute, Disney , Lambrecht,& Houdt, 2008) • Jay Forester (1961): the tendency of replenishment orders to increase in variability as it moves up the Supply Chain. • Procter and Gamble: “Bullwhip Effect”. • Most famous game describing the Bullwhip effect: “the Beer Distribution Game”. Source: stevekeifer.wordpress.com
  • 6. Bullwhip Effect Causes (Lee, Padmanabhan, & Whang, 1997) (Joseph & Wilck , 2006): 1. Demand Forecast Updating: Upstream suppliers Downstream operation Demand forecast readjustment Order placement Upstream manager Additional factors: distorted demand concepts, multiple forecasts, long lead times.
  • 7. Bullwhip Effect Causes (Lee, Padmanabhan, & Whang, 1997) (Joseph & Wilck, 2006): 2. Order batching: • Types: periodic ordering, push ordering. • The Bullwhip Effect depends on the type. • Additional factors: high fixed order costs, random ordering, and correlated ordering. 3. Price fluctuation: • The effect of “promotions”. • Customers buy in bulks. Customer buying pattern Mistranslated consumption pattern Bullwhip Effect
  • 8. Bullwhip Effect Causes (Lee, Padmanabhan, & Whang, 1997) (Joseph & Wilck, 2006): 4. Rationing and shortage gaming: • “Gaming” is placing numerous orders for one product by one customer with the intention of receiving the fastest order fulfilment. • Causes a false spike in the demands. • “Rationing” is done by manufacturers whenever the product demand exceeds the available supply. • The manufacturer allocates the amount in proportion to the amount ordered. • Only 50% of orders of the real demand will be fulfilled. • Reason: customers exaggerate their real needs. • ‘Free Returns Policy’
  • 9. Bullwhip Effect Impacts (Boute,Disney , Lambrecht, & Houdt,2008) Many inefficiencies result from the Bullwhip Effect, such as: • Excessive inventory investment. • Poor customer service. • Lost revenues. • Wrong capacity plans. • Ineffective transportation. • Missed production schedules.
  • 10. Agenda Bullwhip Effect • Definition • Causes • Impacts eSupply Chain Solutions • • • • Information Sharing and Partnerships Inventory Management Forecasting Just-In-Time Case Study Conclusions and Critiques
  • 11. eSupply Chain Solutions Information Sharing and Partnerships Uncertainty caused by lack of perfect information between members of the supply chain have been identified as a major cause of order amplification. Information Sharing as a solution… (Yu Zhenxin 2001)
  • 12. eSupply Chain Solutions Information Sharing and Partnerships Benefits of Information Sharing: • Reduced costs • Reduced Inventories • Mitigate uncertainty that leads to order amplification • Products are manufactured at the right time, right quantity and distributed to the right location Standards and Technologies that support information sharing: • Electronic Data Interchange (EDI): Transmission of POS data in real-time to all players of the supply chain • Point of Sale (POS) • Vendor Managed Inventories (VMI)
  • 13. eSupply Chain Solutions Information Sharing and Partnerships Causes of Uncertainty (Mason-Jones R. et al. 1998): • Manufacturing process • Supply Side Lean Thinking Partnership Source Programme • Demand Side • Planning and control systems Information Sharing
  • 14. eSupply Chain Solutions Inventory Management Vendor Managed Inventories (VMI) Image Source: http://www.supplychain247.com/article/retailers_are_driving_rfid_adoption_and_propagating_the_benefits/omni_id/D2
  • 15. eSupply Chain Solutions Inventory Management Share POS Data • POS data provides “Actual Demand” figures • Sharing POS data enables businesses to compare Shipment data with Actual Demand Data and therefore allows for better shipment scheduling Source: http://www.opsrules.com/supply-chain-optimizationblog/bid/313709/How-to-Use-POS-Data-to-Improve-Supply-ChainPerformance
  • 16. eSupply Chain Solutions Inventory Management RFID – Radio Frequency Identification Image Source: http://www.supplychain247.com/article/retailers_are_driving_rfid_adoption_and_propagating_the_benefits/omni_id
  • 17. eSupply Chain Solutions Forecasting Forecasting techniques in e-supply chain to reduce the bullwhip effects are as bellow: • Simple Moving Average • Weighted Moving Average • Exponential Smoothing method
  • 18. eSupply Chain Solutions Forecasting Simple Moving Average (Sun,2005)
  • 19. eSupply Chain Solutions Forecasting Weighted Moving Average(Sun,2005)
  • 20. eSupply Chain Solutions Forecasting Exponential Smoothing Method (Sun, 2005) (Chen et al, 1999): Forecast = (Actual Demand Previous Period x ά) + (Previous Demand x (1-ά))
  • 21. eSupply Chain Solutions Forecasting Amazon Demand Forecasting Source: www.amazon.com/wishlist
  • 22. eSupply Chain Solutions Just-In-Time Just-In-Time • Introduced by Toyota in 1950s • Inventory = Waste • From Push to Pull processes
  • 23. eSupply Chain Solutions Just-In-Time Technologies that support JIT • Old days: Kanban Cards • Present time: Internet, RFID, Sensors
  • 24. Agenda Bullwhip Effect • Definition • Causes • Impacts eSupply Chain Solutions • • • • Information Sharing and Partnerships Inventory Management Forecasting Just-In-Time Case Study Conclusions and Critiques
  • 25. eSupply Chain Solutions Case Study Reducing Bullwhip effect by Centralizing Internal Information (Boone and Ganeshan, 2008) Background • Midsize retailer with annual sales of $1 billion operating in more than 20 locations • Each location could have more than one department store, convenience store etc. • Corporate Headquarters are responsible for : • Setting the overall financial goals • Merchandising policies • Coordinating resources across retail locations • Maintaining responsibility for financial reporting
  • 26. eSupply Chain Solutions Case Study Traditional model of how the retailer is doing business
  • 27. eSupply Chain Solutions Case Study Implemented New System • Installed 128-bit scanners that captured the product bar codes. • Information captured was stored in a centralized database • Corporate Headquarter can now look at this centralized system which will help them make better decisions
  • 28. eSupply Chain Solutions Case Study Benefits Supply Chain Costs Before and After Information Visibility Source: (Boone and Ganeshan, 2008)
  • 29. Agenda Bullwhip Effect • Definition • Causes • Impacts eSupply Chain Solutions • • • • Information Sharing and Partnerships Inventory Management Forecasting Just-In-Time Case Study Conclusions and Critiques
  • 30. Conclusions and Critiques • Information sharing is considered one of the important strategies for reducing or mitigating the bullwhip effect. • Information sharing through e-supply chain systems not only facilitates effective sharing of information, it also allows fast dissemination of important data. • It is essential for organizations to adopt measures to capture and store data that can then be used for effective communication, inventory management, forecasting and reporting. • There are increasing number of third party vendors that provide out of the box, cloud, and open source solutions that can be adopted by organizations of various sizes. • e-Supply chains are playing an important role in mitigating the bullwhip effect and the scope to leverage them is only limited by the cost and technology used by the organizations.
  • 31. References • • • • • • • • • • • • • • • • • • • • • • • • • • • Al-Zubi , H. (2010). Applying Electronic Supply Chain Management Using Multi-Agent System: A Managerial Perspective . (pp. 106-113). International Arab Journal of e-Technology. Anatan, Lina. “INFORMATION SHARING AMONGST SUPPLY CHAIN PARTNERS:THE WAY TO SOLVE “BULLWHIP EFFECT”IN SUPPLY CHAIN MANAGEMENT”, Fakultas Ekonomi Universitas Kristen Maranatha Bandung Aprille, D., & Garavelli, A. C. (2007). BULLWHIP EFFECT REDUCTION: THE IMPACT OF SUPPLY CHAIN FLEXIBILITY. 19th International Conference on Production Research(ICPR-19). Chile. B.S. Sahay, Jayanthi Ranjan, (2008) "Real time business intelligence in supply chain analytics", Information Management & Computer Security, Vol. 16 Iss: 1, pp.28 – 48 Bottani, E., Montanari, R., & Volpi, A. (2010). The impact of RFID and EPC network on the bullwhip effect in the Italian FMCG Supply Chain. Int.J.ProductionEconomics, 426-432. Boute, R. N., Disney , S. M., Lambrecht, M. R., & Houdt, B. V. (2008). A win-win solution for the bullwhip problem. Disney, S. M., & Towill, D. R. (2003). The effect of vendor managed inventory (VMI) dynamics on the Bullwhip Effect in supply chains. Int. J. Production Economics, 199–215. Frank Chen,1 Jennifer K. Ryan,2 David Simchi-Levi3. 1999. The Impact of Exponential Smoothing Forecasts on the Bullwhip Effect HX Sun, YT Ren. 2005. The Impact of Forecasting Methods on Bullwhip Effect in Supply Chain Management Johansson H J, McHugh P., Pendlebury AJ. And Wheeler III WA. (1993). Business Process Re-engineering” (Willey). Joseph , H., & Wilck , I. (2006). Managing the Bullwhip Effect . Keifer, S. (2009). Why amazon.com has the best demand forecasting data. Retrieved January 2014, from gxsblogs: http://www.gxsblogs.com/keifers/2009/12/why-amazon-com-has-the-bestdemand forecasting-data.html Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The Bullwhip effect in Supply Chains . MIT Sloan Management Review , pp. 93-102. Mason-Jones R and Towill, D R (1998). “Shrinking the Supply Chain Uncertainty Circle”. Control Vol. 24,No. 7,pp 17-23. Mason-Jones Rachel and Towill Denis R., 2000, “Coping with Uncertainty:Reducing ”Bullwhip”. Behaviour in GlobalSupply Chains” , Supply Chain Forum, An International Journal. Napolitano, M. (2013). Retailers are Driving RFID Adoption and Propagating the Benefits Throughout their Supply Chains. Retrieved from SupplyChain24/7: http://www.supplychain247.com/article/retailers_are_driving_rfid_adoption_and_propagating_the_benefits/omni_id/D2 Sari, K. (2010). Exploring the impacts of radio frequency identification (RFID) technology on Supply Chain Performance. European Journal of Operational Research, 174-183. Schonberger, R. J. (2006). Japanese production management: An evolution—With mixed success. Bellevue, WA, United States: Journal of Operations Management. Srinivasan K, Kekre S., and Mukhopadhyay, T. (1994). “Impact of Electronic Data Interchange technology on JIT shipments”. Management Science, Vol. 40, pp1291-304. Sugimori, Y., Kusunoki, K., Cho, F., & Uchikawa, S. (1977). Toyota production system and kanban system: materialization of just-intime and respect-for-human system. International Journal of Production Research. Tonya Boone and Ram Ganeshan (2008). Forecast Process Improvement: The Value of Information Sharing in the Retail Supply Chain - Two Case Studies Traub, T. (2012, July). Wal-Mart Used Technology to Become Supply Chain Leader. Retrieved from Arkansas Business: http://www.arkansasbusiness.com/article/85508/wal-mart-used-technologyto-become-supply-chain-leader?page=all Wahl, M. (2013). HOW TO USE POS DATA TO IMPROVE SUPPLY CHAIN PERFORMANCE. Retrieved from OPS Rules Blog: Insights into Supply Chain and Operations Strategy: http://www.opsrules.com/supply-chain-optimization-blog/bid/313709/How-to-Use-POS-Data-to-Improve-Supply-Chain-Performance Wang, H., & He, B. (2011). Research on the Reducing Measures of Bullwhip Effect. 2011 International Conference on Software and Computer Applications (pp. 202-206). Singapore: IACSIT Press. Wilck, J. H. (n.d.). Managing the Bullwhip Effect. Yasushiro, M. (2012). Toyota Production System: An Integrated Approach to Just-In-Time, Fourth Edition. Auerbach Publications. Yu Zhenxin, Yan Hong and Cheng Edwing T.C. (2001). “Benefits of Information Sharing with Supply Chain Partnerships”. Industrial Management & Data Systems. 101/3. Pp114-119 Thank you – Q/A