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

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

  1. 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. 2. eSupply Chain Solutions to Reduce the Bullwhip Effect Anjali Sood Elham Mohammad Pour Irum Maqsood Pilar Mata Sergio Maldonado Shymaa Slangor
  3. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 18. eSupply Chain Solutions Forecasting Simple Moving Average (Sun,2005)
  19. 19. eSupply Chain Solutions Forecasting Weighted Moving Average(Sun,2005)
  20. 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. 21. eSupply Chain Solutions Forecasting Amazon Demand Forecasting Source: www.amazon.com/wishlist
  22. 22. eSupply Chain Solutions Just-In-Time Just-In-Time • Introduced by Toyota in 1950s • Inventory = Waste • From Push to Pull processes
  23. 23. eSupply Chain Solutions Just-In-Time Technologies that support JIT • Old days: Kanban Cards • Present time: Internet, RFID, Sensors
  24. 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. 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. 26. eSupply Chain Solutions Case Study Traditional model of how the retailer is doing business
  27. 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. 28. eSupply Chain Solutions Case Study Benefits Supply Chain Costs Before and After Information Visibility Source: (Boone and Ganeshan, 2008)
  29. 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. 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. 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

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