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

eSupply Chain Solutions to Reduce the Bullwhip Effect 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

Agenda

Bullwhip Effect

• Definition

• Causes

• Impacts

eSupply Chain Solutions

• Information Sharing and Partnerships

• Inventory Management

• Forecasting

• Just-In-Time

Case Study

Conclusions and Critiques

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

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

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

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’

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.

Agenda

Bullwhip Effect

• Definition

• Causes

• Impacts

eSupply Chain Solutions

• Information Sharing and Partnerships

• Inventory Management

• Forecasting

• Just-In-Time

Case Study

Conclusions and Critiques

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)

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)

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

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

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

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

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

eSupply Chain Solutions Forecasting

Simple Moving Average (Sun,2005)

eSupply Chain Solutions Forecasting

Weighted Moving Average(Sun,2005)

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))

eSupply Chain Solutions Forecasting

Amazon Demand Forecasting

Source: www.amazon.com/wishlist

eSupply Chain Solutions Just-In-Time

Just-In-Time

• Introduced by Toyota in 1950s

• Inventory = Waste

• From Push to Pull processes

eSupply Chain Solutions Just-In-Time

Technologies that support JIT

• Old days: Kanban Cards

• Present time: Internet, RFID, Sensors

Agenda

Bullwhip Effect

• Definition

• Causes

• Impacts

eSupply Chain Solutions

• Information Sharing and Partnerships

• Inventory Management

• Forecasting

• Just-In-Time

Case Study

Conclusions and Critiques

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

eSupply Chain Solutions Case Study

Traditional model of how the retailer is doing business

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

eSupply Chain Solutions Case Study

Benefits

Supply Chain Costs Before and After Information Visibility Source: (Boone and Ganeshan, 2008)

Agenda

Bullwhip Effect

• Definition

• Causes

• Impacts

eSupply Chain Solutions

• Information Sharing and Partnerships

• Inventory Management

• Forecasting

• Just-In-Time

Case Study

Conclusions and Critiques

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.

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