eSupply Chain Solutions to Reduce the Bullwhip Effect
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Transcript of eSupply Chain Solutions to Reduce the Bullwhip Effect
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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
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eSupply Chain Solutions to Reduce the Bullwhip Effect Anjali Sood
Elham Mohammad Pour
Irum Maqsood
Pilar Mata
Sergio Maldonado
Shymaa Slangor
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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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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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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
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Bullwhip Effect Causes (Lee, Padmanabhan, & Whang, 1997) (Joseph & Wilck , 2006):
1. Demand Forecast Updating:
Additional factors: distorted demand concepts, multiple forecasts, long lead times.
Downstream operation
Order placement
Upstream manager
Demand forecast re-adjustment
Upstream suppliers
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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
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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’
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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.
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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 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)
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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)
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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
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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
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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-optimization-blog/bid/313709/How-to-Use-POS-Data-to-Improve-Supply-Chain-
Performance
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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
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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
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eSupply Chain Solutions Forecasting
Simple Moving Average (Sun,2005)
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eSupply Chain Solutions Forecasting
Weighted Moving Average(Sun,2005)
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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))
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eSupply Chain Solutions Forecasting
Amazon Demand Forecasting
Source: www.amazon.com/wishlist
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eSupply Chain Solutions Just-In-Time
Just-In-Time
• Introduced by Toyota in 1950s
• Inventory = Waste
• From Push to Pull processes
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eSupply Chain Solutions Just-In-Time
Technologies that support JIT
• Old days: Kanban Cards
• Present time: Internet, RFID, Sensors
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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 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
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eSupply Chain Solutions Case Study
Traditional model of how the retailer is doing business
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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
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eSupply Chain Solutions Case Study
Benefits
Supply Chain Costs Before and After Information Visibility Source: (Boone and Ganeshan, 2008)
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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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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.
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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-best-
demand 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-technology-
to-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