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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