1. New Product Forecasting in Volatile Markets
Alex Baldwin and Jason Shin
Adviser: Dr. Shardul Phadnis
MIT SCM Research Fest
May 22, 2014

2. Agenda Introduction & Scope Key Question Methodology Results
Implications

3. The Product Life Cycle Demand Time
Intro
Growth
Maturity
Decline
Time
Vernon (1966): Products have a finite life cycle, with distinct phases

4. Limited Life Cycles Involve a finite forecasting horizon
Order-of-magnitude shifts in demand are always near
Innovation influences competitiveness and thus volatility

5. Volatility in Market Shares
Stable Market
Volatile Market a a b b c c
Rate of Change in Features
Gradual
Rapid
Consumer Awareness
High
Limited
Rate of Entry and Exit
Low
High

6. Forecasting Techniques
Method
Issues
Quantitative Forecasting
Uses prior sales data to forecast (econometrics)
New products don’t look like old products
Judgment Forecasting
Uses judgment, i.e. expert opinions
Bias, dissonance between experts
Conjoint Analysis
Applies weight to multiple sources
How much weight to apply?

7. Key Questions
Is there an alternative to pure judgment forecasting for limited- life, volatile-market products?
Do volatile-market products behave differently than stable- market products?

8. Methodology
Do volatile-market products behave differently than stable-market products? What to measure? Who is impacted?

9. Who and what? Capacity Planning
Makes the right amount of production and distribution capacity available via capital and human resources
Valve Dx Dy
Key Measures: Rate of Change & Modality How much output flexibility is required in the supply chain, given a short time horizon?
Steep
Not As Steep
Rate
Modes

10. Who and what? Sales Forecasting
Sets and measures enterprise- wide targets for financial sales performance. Primarily interested in long-term demand trend (i.e. quarterly results)
Starpulse.com
Key Measure: Skew Do peak sales occur sooner or later in the product life cycle? How to monitor sales expectations accordingly?
Positive Skew
Negative Skew

11. Who and what? Inventory Management
Manages stocks of inbound materials and components, maintains production buffers, manages stock in outbound channels to ensure availability to customers
Key Measure: Variance Variance determines the appropriate level of safety stocks and buffers throughout the supply chain
Highly Variable
Not as Variable
Variance

12. Methodology – Summary Exogenous Influences Independent Variable
Measures
Length of Product Life Cycle
H1 Skew of Shipments - +
Rate Of Feature Change
H2 Modality of Shipments
Trend Of Product Demand Throughout Lifecycle
Volatility of Market Shares
Consumer Understanding +
H3 Variance of Shipments
Rate of Entry/ Exit From Market + +
H4 Rate of Growth/Decline +
Expected Risk/Return

13. Case Study Durable goods Life cycle <5 years 8 “Stable” Products
4 “Volatile” Products
Complete life-cycle sales data by month
Measure
Expected Volatile Behavior
Empirical Result
Statistical Significance
Skew
Greater Right Skew
Confirmed
Medium
Rate of Growth/ Decline
Greater Rate
Low
Variance
Greater Variance
Modality
Multi-Modal
???
None

14. Results: Skew
Hypothesis: Volatile products are more positively skewed due to early sales enthusiasm
Measure: Non-parametric Skew Statistic
Result: Confirmed
Positive Skew
Negative Skew
Volatile: NP .35
Stable: NP .09

15. Results: Rate of Growth/DeclineDx Dy
Steep
Not As Steep
Hypothesis: Volatile products grow/decline faster due to share changes from entry/exit
Measure: Dx/Dy (%)
Result: Confirmed
Volatile: 117% | 23% | -6% | -12%
Stable: 131% | 33% | -11% | -9%

16. Results: Variance
Hypothesis: Volatile products are more variable due to changing shares in market
Measure: CV (De-trended)
Result: Confirmed
Highly Variable
Not as Variable
Variance
Volatile: CV .86
Stable: CV .70

17. Results: Modality
Hypothesis: Volatile products have less clearly- defined modes (peaks)
Measure: Hartigan’s Test
Result: Inconclusive
Uni-Modal
Multi-Modal
Volatile
P: >0.5
Stable

18. Implications: Skew Volatile products are more positively skewed.
Sales Forecasting
Sales occur sooner, if at all. Sales can be predicted by expected life of product and skew (conjoint method)
Positive Skew
Capacity Planning
Must support an early peak, along with a long right tail

19. Implications: Rate of Growth/Decline
Volatile products grow and decline faster.
Capacity Planning
Faster production ramps call for increased flexibility and investment
Rate Dx Dy
Steep
Sales Forecasting
Revenue streams are concentrated into fewer accounting periods - accuracy

20. Implications: Variance
Volatile products are more variable.
Inventory Management
Increased levels of stocks and buffers are needed throughout the supply chain
Highly Variable
Not as Variable
Sales Forecasting
Financial trade-off between carrying inventory and meeting service levels
Variance

21. Implications: Modality
Volatile products have more modes (inconclusive).
Capacity Planning
Production flexibility is key: how to handle dips with reduced capacity?
Multi-Modal
Inventory Management
Stocks and buffers must be continuously altered rather than maintained at a constant level

22. Key Insights
Forecasting for volatile markets is not impossible, it’s just different!
Stability in market share quantifiably influences product demand for limited-life durable products
Insight about demand trends can improve conjoint forecasting – the combination of quantitative and qualitative forecasting techniques