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TRANSCRIPT
Assembling the Crystal Ball: Using Demand Signal Repository to
Forecast Demand
Authors: Ahmed Rashad & Santiago Spraggon
Advisor: Shardul Phadnis
Sponsor: Niagara Bottling LLC.
MIT SCM ResearchFest May 22-23, 2013
Agenda
• Overview
• Methodology
• Conclusion
May 22-23, 2013 MIT SCM ResearchFest 2
Agenda
• Overview
• Methodology
• Conclusion
May 22-23, 2013 MIT SCM ResearchFest 3
• Demand forecasting technique
• Using external Signals
• Aggregated in a single Repository
What is Demand Signal Repository (DSR)?
May 22-23, 2013 MIT SCM ResearchFest 4
External Signals
Repository (Database)
When to use DSR?
1. What are we forecasting?
2. What data is available?
3. What stage in the product lifecycle?
4. Is the investment worth it?
May 22-23, 2013 MIT SCM ResearchFest 5
When to use DSR?
May 22-23, 2013 MIT SCM ResearchFest 6
Trends and Patterns
Time Series
Special Events
Qualitative
Special Events + Trends and
Patterns
DSR
• Depends on what are we forecasting
Base Demand
Trend
Seasonality
Unexplained
Time
Dem
and
When to use DSR?
• Depends what data is available
May 22-23, 2013 MIT SCM ResearchFest 7
Sufficient History
Time Series
Little or No History
Qualitative
Sufficient History + External Data
DSR
• Depends on stage in the product lifecycle
When to use DSR?
May 22-23, 2013 MIT SCM ResearchFest 8
Time
Sale
s
Qualitative
Time-Series
Causal
Causal
Qualitative
Introduction Growth Maturity Decline
When to use DSR?
• Depends on the investment
May 22-23, 2013 MIT SCM ResearchFest 9
Forecast Accuracy
Co
sts
Total System Cost
Cost of Inaccuracy
Cost of Forecasting
Target Area
How can we develop a Demand Signal
Repository (DSR) to better predict demand?
May 22-23, 2013 MIT SCM ResearchFest 10
Agenda
• Overview
• Methodology
• Conclusion
May 22-23, 2013 MIT SCM ResearchFest 11
Method Used
May 22-23, 2013 MIT SCM ResearchFest 12
Initiation
• Planning
• Literature Review
• Interviews
• Requirements
Data Management
• Collection
• Validation
Modeling • Initial Models
• Analysis
• New Models
Modeling
May 22-23, 2013 MIT SCM ResearchFest 13
Product
All cases
All liters
Category
SKU
Customer
All Niagara
Top 12
Top 3
Geography
All Niagara
Region
State
City
3-Digit Zip code
Time
Annual
Quarterly
Monthly
Weekly
Daily
Growth
Seasonality
Wholesale Price
Merchandizing
Retail Price
Natural Disasters
Weekly Cycles
Buying Patterns
Temperature
Food Stamps
• 240+ Models • 60%+ Customer – State - Daily • 85%+ Customer – State - Weekly
Dependent Variables Independent Variables
Liters per Customer, in a State, per day or week
Agenda
• Overview
• Methodology
• Conclusion
May 22-23, 2013 MIT SCM ResearchFest 14
Key Findings
• Most Significant:
• Ordering patterns & POS quantity
• Seasonality
• POS revenue (proxy for price)
• Least Significant:
• Temperature
• POS quantity and revenue
May 22-23, 2013 MIT SCM ResearchFest 15
Challenges and Caveats
• Accuracy vs. Practicality
• Recording Data
• Retailer Policies
• How much Technology?
May 22-23, 2013 MIT SCM ResearchFest 16
Conclusion
• DSR could Significantly increase forecast accuracy (60%-85%)
• Accurate models are good, Simple models are better (>5 Factors)
• Perceptions can be misleading (Temperature)
May 22-23, 2013 MIT SCM ResearchFest 17
Assembling the Crystal Ball: Using Demand Signal Repository to
Forecast Demand
Authors: Ahmed Rashad & Santiago Spraggon
Advisor: Shardul Phadnis
Sponsor: Niagara Bottling LLC.
MIT SCM ResearchFest May 22-23, 2013