5. What is the cause for the bullwhip effect?
• Trade Promotion
• Information/Communication lags
• Delivery lags
• No forecast-related information
sharing
Create demand variability
amplification
6.
7. What can one do to alleviate the bullwhip effect?
1. Operational improvements
• Lead time reduction from manufacturing
• Efficient order transmission
• Smooth delivery
8. What can one do to alleviate the bullwhip effect?
2. Information sharing
• Point-of-sales scanner data
• Logic for orders each period
• Revision of upcoming forecast
9. What can one do to alleviate the bullwhip effect?
3. Channel alignment
• Manufacturing Location
• Distribution
10. Improving Promotion Forecast Accuracy and Collaborative
Planning
• Promotions and the Consumer
• Low price promotion, high consumer response
• High-low pricing strategy for premium brands
11. Improving Promotion Forecast Accuracy and Collaborative
Planning
• Efficient Promotion Forecasts
i) information sharing between
manufacturer and retailer
12.
13. Improving Promotion Forecast Accuracy and Collaborative
Planning
• Efficient Promotion Forecasts
ii) variety effects,
• effects of offering multiple package sizes
• small package high price/piece
• large package low price/piece
23. The S.M.A.R.T.S. Model
• Package Size Switching and Stockpiling
Behaviour
• These idiosyncracies lead us to believe that
one needs to incorporate a consumer choice
model where sales are affected by the price
differences between package sizes each
period.
24. The S.M.A.R.T.S. Model
• Package Size Switching and Stockpiling
Behaviour
• we aggregated demand of all packages and
determined the average price paid as the
demand-weighted price across the various
package sizes
25. The forecasting model using two customer segments
• First, we have to solicit consumer reactions to price promotions, e.g., determine
their switching behavior across package sizes.
• Second, the aggregate forecast has to be broken down into forecasts at the
individual stock-keeping-unit (SKU) level.
27. An Out line for careful consideration of consumer response to
promotions
• a. Aggregate the sales data to see unexplainable issues at the
SKU level
• b. Model the customer reaction to promotions, i.e., switching
across package sizes and stockpiling
• c. Synchronize shipments with demand pull information to
make promotions work for both the manufacturer and the
retailer
28. Promotion Forecasts and CPFR
• Retailer Perspective on CPFR
• Market research data, POS data and information obtained through loyalty programs
• Manufacturer Perspective on CPFR
• Product attributes, trade promotions and overall logistics capability
• Supply Chain Collaboration
• The joint retailer-manufacturer considerations represent the most accurate picture of SKU-level demand
in the supply chain. Clearly this suggests the value of collaboration between the manufacturer and the
retailer to better understand the consumer response.
