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

Agenda Introduction & Scope Key Question Methodology Results Implications

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

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

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

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?

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?

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

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

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

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

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

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

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

Results: Rate of Growth/Decline Dx 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%

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

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

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

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

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

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

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