statistical forecasting for the semiconductor industry
TRANSCRIPT
ARA Consulting
Statistical Forecasting
August’15- © 2015 - 1
ARA Consulting
Semiconductor IndustryDemand Forecasting Using
Custom Models
Russ / Tony 5/28/2015
Russ EliasTony Alvarez
June 2015Russ EliasTony Alvarez
Statistical Forecasting
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If You Forecast Like Everyone Else
You’ll Get The Same Results ThatEveryone Else Gets
Statistical Forecasting
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Typical Demand Forecast Process
CustomerForecast
StatisticalForecast
DistributionSell-Through
DesignWins
“External”Variables
DemandCurrent/Historical
MarginOptimization
Strategy“Alignment”
DemandShaping/Promo
Demand TeamForecast
ConsensusDemand Forecast
SalesForecast
MarketingForecast
Typically a Three Stage ProcessWith Multiple Inputs
Statistical Forecasting
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ARA Consulting Forecasting Overview
Forecast = Trendt-1 + Seasonalityt-1 + Cyclicalt-1 + Irregularitiest-1 +Causal Factor(s) + Random (Unexplained) Variation
Trend Seasonality
Cyclical Irregular
Time
Time
Time
Causal
X1
Time
Statistical Forecasting
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A Challenge in Statistical Forecasting is Disaggregating
These Factors to Provide Sufficient Insight Into The Forecast
Forecasting Overview
Statistical Forecasting
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ARA Consulting Typical ProgressionNo Seasonality
orTrends?
TrendsBut
No Seasonality?
Trends&
Seasonality?
Trends, Seasonality&
Causal Factors?ARIMAX
Holts-Winters Smoothing(Multiplicative & Additive)
or ARIMA
Holt’s Linear Method(Double Exponential Smoothing)
Simple (Single) Exponential Smoothing(Filters Noise/Irregularities)
Statistical Forecasting
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Basic Capabilities Required Level 1: Limiting & Damping, Seasonal Smoothing, Demand
Filtering, Reasonability Tests
Level 2: Seasonal-with-Trend, Moving Average and Low-levelPattern Fitting
Level 3A: Trend Models For Products With Sporadic, Low-Volume Demand
Level 3B: Weighting of Historical Demand Seasonality; But“System Doesn’t Know It’s Christmas Until It Sees It Twice.”
Level 3C: Outlier Detection (Irregular Events); DeterminingWhich Elements Are Anomalous and Should Be Filtered
Statistical Forecasting
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“Boxed” Forecasting Software Typical Sequence
1)Product History Analyzed Using Variety (Dozens!) of Algorithms2)Automatically Selects Best Algorithm For Each Product3)Selection Based on How Well Algorithm Fits Historical Product Data4)Winning Algorithm Used to Project Future Sales
Forecasting Algorithm Will Always Produce Fcst; ButThat Fcst Won’t Always Be a Good One
“Over-Fitting” – Occurs When “Fit Noise in Data RatherThan Discovering Underlying Structure”
Pick Model That is Most Appropriate For Good Fcst;May Not Be Model That “Best” Fits Historical Data
Statistical Forecasting
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That’s What You Get From “Boxed”
Solutions in Typical Forecasting
Packages
What’s Missing?
Statistical Forecasting
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Forecast Out of the Box!
Wealth of InformationBeyond Historical Product Data
Statistical Forecasting
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Application of Custom Models ForSemiconductor Forecasting
Data Typically Available:Historical Product Demand & Delivery Data
Product Inventory Levels
Product Delinquency
Specific End-Market Forecasts
General Macro-Economic Trends
Customer Product Backlog
Statistical Forecasting
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ARA Consulting Application of DSF ForSemiconductor Forecasting
Demand Signal Forecasting (DSF)• Forward-Looking Approach to Custom Models• Utilizes Customer Backlog (VOC) as a Leading Indictor to
Augment Historical Data• But, Customer Backlog Lead Time is Typically Less That What is
Required to Initiate Product Builds – Need ‘Gap Fill’ Forecast
Demand Signal Forecasting (DSF) + Indicator Variables• Can Further Customize Model By Incorporating Indicator Variables• Example: Inventory Levels, Delinquency, End-Market Forecasts,
and Macro-Economic Trends to Further Refine & Customize Model
Statistical Forecasting
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DSF Application Example Background
• Customer’s Backlog is Often a Weak Forward Looking Signal Beyond OneMonth; i.e. 30 Day Backlog Usually Reliable, 60 and 90 Day BacklogUsually Subject to Significant Changes
• Manufacturing Cycle Times Range From ~90 Days (Si Start to Ship) to 15– 30 Days (Wafer Bank to Ship)
• Mis-Match Between Backlog Signal Timing & Mfg Cycle Time Can BeManaged With Inventory Staging, But at a Cost
• But, Tradeoff Inventory Risk vs. Customer Delinquency/Satisfaction
Demand Signal Forecasting Generations• Gen 1: Backlog as Leading Indicator Variable (Elias 2000 Thesis)• Gen 2: Mid-Range Backlog Imputation in a Transfer Function Based
Custom Model to Create a Better Leading Indicator (Elias and Alvarez,2014 Unpublished Work)
DSF Gen 2 Addresses Mis-Match Between Customer BacklogTiming as a Useful Leading Indicator & Mfg Cycle Time
Statistical Forecasting
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D = DemandC = Custom DSF2E = EWMA
DSF2 Applied to High Volume Consumer Product• One Product• One Customer• One Market Segment
Statistical Forecasting
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ARA Consulting DSF2 Model Results
D = DemandC = Custom DSF2e = Error
Statistical Forecasting
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ARA Consulting DSF Gen 2 – First Step
00.10.20.30.4
1 2 3 4 5 6 7
Weighting by AgeDSF2 Looks Backward and DevelopsOptimal ARIMA Model Based on PastDemand and Past Forecast Errors
Statistical Forecasting
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DSF Gen 2 – Second Step
DSF2 Then Looks Forward and Uses aTransfer Function to Blend in Backlog
0
0.2
0.4
1 2 3 4 5 6 7
Weighting by Age
Backlog
0
0.2
0.4
1 2 3 4 5 6 7
Weighting by Age
forecasts:Yt+1, Yt+2, Yt+3
(Idealized)
Statistical Forecasting
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ARA Consulting Forecast Benchmarks DSF2 Compared to Four Alternate Forecasting Methods:
• Exponentially Weighted Moving Averages (EWMA), with AutomatedSmoothing Coefficient Optimization
• Holt-Winters Seasonal Decomposition• Auto-Regressive Integrated Moving Average (ARIMA), with Monthly
MAPE-Optimal Model Parameterization• Sales and Operational Planning (S&OP) Consensus Forecasting
Forecast Methods Comparison Metrics:• Bias % (Cum Actual – Cum Forecast)*100/Cum Actual• Mean Absolute Percent Error (MAPE)• Normalized Inventory Dollar Maximum Delinquency and Final Period
Delinquency or Inventory Level• Forecast Performance Graphs Showing Head-to-Head Results
Statistical Forecasting
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Forecast Method Comparisons:Statistical Metrics
Rank Method Bias MAPE5 S&OP 58% 63%4 EWMA -1.3% 55%3 Holt-Winters -9.6% 42%2 ARIMA -7.9% 34%1 DSF Gen 2 4.3% 27%
The Custom DSF2 Model Outperformed Both the S&OPConsensus Forecast and the Conventional Statistical Models
Available in Demand Planning Software Packages
Interesting, But Where’s The Money?
Statistical Forecasting
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Forecast Method Comparisons:Financial Metrics
Rank Method MaxDelinquency
Period End Inventoryor (Delinquency)
Period EndMonths Inventory
5 S&OP $17M ($14M) n/a4 EWMA $9.5M $12M 6 Months3 Holt-Winters $3M $14M 7 Months2 ARIMA $2M $11M 5.5 Months1 DSF Gen 2 $3M $3M 1.5 Months
Bottom Line High ROI:Less Delinquency/Missed Sales & Higher Customer Satisfaction
Less Inventory Build/Working Capital, Reduced Inventory ExposureIn Reality Implications More Severe as Assumed that 100% of Delinquency
‘Catch-up’ Builds are Sold; Typically Some Portion Gets Cancelled Resulting inLost Sales and Therefore Even More Excess Inventory
Note: Semiconductor Industry ASP ~$1.25, For Illustrative Purposed Normalized Unit ASP Set to $1 and Normalized Units to 1M/Month
Statistical Forecasting
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D = DemandC = Custom DSF2E = EWMA
DSF2 vs. EWMA Forecast
Statistical Forecasting
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D = DemandC = Custom DSF2H = Holt-Winters
DSF2 vs. Holt-Winters Forecast
Statistical Forecasting
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D = DemandC = Custom DSF2A = ARIMA
DSF2 vs. ARIMA Forecast
Statistical Forecasting
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D = DemandC = Custom DSF2S = S&OP Fcst
DSF2 vs. S&OP Lead 3 Forecast
Statistical Forecasting
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ARA Consulting Inventory Observations
S&OP: Keeps Predicting Product’s Demise – Never Caught Up With DemandEWMA: Slow to Respond - Initially Delinquent Then Over-Builds
Holt-Winters: Moderate Bullwhip Effect Evident – Builds-Delinquent-BuildsARIMA: Starts Off Reasonably, Doesn’t Respond to Final Rapid Drop
DSF2: Starts Off Reasonably, By Utilizing Customer Backlog Keeps From Overbuilding
Delinquent SupplyExcess Inventory
Statistical Forecasting
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ARA Consulting ConclusionForecasting System Designed to Quickly Track Changes in Behavior
Can Create “Noisy” Forecasts During Periods of Relative Stability
Forecasting System Designed to Give Smooth Forecasts WillTypically Lag True Changes
If Only Looking Back, There is No Reliable Way to Forecast What WillHappen When Established Patterns or Relationships Change
It Follows That Without Forward Looking Data/Information,Quantitative Methods & Corresponding Predictions are Only as
Reliable as The Stability of Patterns Modeled in Their Past History
This is Where Demand-Signal ForecastingComes In
Statistical Forecasting
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ARA Consulting CaveatsKeep Modeling Approach “As Simple as Possible, But Not Simpler”
Custom Models Take Work, The DSF2 Model in This Example Took~40 Hrs Hours to Develop & 1 Hr/Mth Maintenance
While Modest Within Overall Costs Associated With an S&OP Effortand Very High ROI, the Optimal Method Depends on The Situation
DSF2 Was The Optimal Approach in This Example (And Others), ButThat Will Not Be True in All Situations
In Course of This Work, Multiple Custom Models Were Utilized ForDifferent Products; In All But One Case The Statistical Models Out-
Performed the S&OP Consensus Forecast
DSF2 Works Best When Customer Order Patterns are Subject toRapid Changes and Historical Data is Insufficient to Provide a Good
Predictor of the Future
Therefore, ….
Statistical Forecasting
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One Size Does NotFit All
Statistical Forecasting
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Optimal Forecasting Method Depends onMany Variables
Segmentation Often Used to MatchForecasting Method to Product Category
Best to Use Simplest/Lowest Cost“Acceptable” Method (Forecast Value Add)
Statistical Forecasting
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Conventional Segmentation
Forecasting Technique vs. Product Category(After Demand Driven Forecasting)
Statistical Forecasting
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Recommended Segmentation Variables:Product Lifecycle PositionVolume (Pareto Principle)Degree of Intermittency
Margin
Semiconductor Segmentation
Fast Ramp
Slow Ramp
Delayed Ramp
New Product Mid-Life Product
Top 20 Low VolumeSteady State
Fast Ramp
Slow Ramp
Controlled EOL
EOL Product
Low VolumeIntermittent
Build toOrder
(Lead Times13 – 16Weeks)
Low Margin
BTO
High Margin
Bank to Fcst& FTO
Bank to Fcst
Finish toOrder (FTO)
(Lead Times4– 6 Weeks)
Statistical Forecasting
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RecommendedConsiderNot Required or Inapplicable
When to Use Custom Model
Fast Ramp
Slow Ramp
Delayed Ramp
New Product Mid-Life Product
Top 20 Low VolumeSteady State
Fast Ramp
Slow Ramp
Controlled EOL
EOL Product
Low VolumeIntermittent
Build toOrder
(Lead Times13 – 16Weeks)
Low Margin
BTO
High Margin
Bank to Fcst& FTO
Bank to Fcst
Finish toOrder (FTO)
(Lead Times4– 6 Weeks)
Statistical Forecasting
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Top “20”Typically 50 – 80% of a Company’s or Business Unit’s Revenue
Bank to Fcst: Custom Models Worth The Effort; Heuristics Need to Be UnderstoodFinish to Order (FTO): Theoretically No Fcst Required; Lead 1 Backlog Could be Used to
Aid Decision Making When Order “Smoothing” Required for Production Purposes
Semiconductor Segmentation
Fast Ramp
Slow Ramp
Delayed Ramp
New Product Mid-Life Product
Top 20 Low VolumeSteady State
Fast Ramp
Slow Ramp
Controlled EOL
EOL Product
Low VolumeIntermittent
Build toOrder
(Lead Times13 – 16Weeks)
Low Margin
BTO
High Margin
Bank to Fcst& FTO
Bank to Fcst
Finish toOrder (FTO)
(Lead Times4– 6 Weeks)
Statistical Forecasting
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Low Volume Steady StateHigh Margin: Consider Custom Models as Necessary; Could Be Very High ROI
“Boxed” Statistical Models May Be Acceptable (Low Effort) for Bank to Fcst
Low Margin: Build to Order (BTO)
Semiconductor Segmentation
Fast Ramp
Slow Ramp
Delayed Ramp
New Product Mid-Life Product
Top 20 Low VolumeSteady State
Fast Ramp
Slow Ramp
Controlled EOL
EOL Product
Low VolumeIntermittent
Build toOrder
(Lead Times13 – 16Weeks)
Low Margin
BTO
High Margin
Bank to Fcst& FTO
Bank to Fcst
Finish toOrder (FTO)
(Lead Times4– 6 Weeks)
Statistical Forecasting
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Low Volume IntermittentStatistical Models Probably Won’t Work Well or Worth the Effort
Best to Keep as BTO
Semiconductor Segmentation
Fast Ramp
Slow Ramp
Delayed Ramp
New Product Mid-Life Product
Top 20 Low VolumeSteady State
Fast Ramp
Slow Ramp
Controlled EOL
EOL Product
Low VolumeIntermittent
Build toOrder
(Lead Times13 – 16Weeks)
Low Margin
FTO
High Margin
Bank to FcstFTO
Bank to Fcst
Finish toOrder (FTO)
(Lead Times4– 6 Weeks)
Statistical Forecasting
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Treat Statistical Forecasting as a “Black Box” atYour Peril
Understanding The Story Behind The Data is aRequirement For Effective Forecasting
You Do Need to Understand the Heuristics
You Don’t Need to Understand the ComputationalDetails
Customized Demand Signal ForecastingModel is Demonstrated to Provide
Significant Financial Benefit
Statistical Forecasting
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PDCA: Demand ForecastingPlan
(Methods & Data)
Do(Forecast Compilation)
Check(Team Review)
Act(Adjust & Learn)
Statistical Forecasting
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Appendix
Statistical Forecasting
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Approaches to Forecasting
Three Categories of Forecasting Models(Logility – Seven Methods That Improve Forecast Accuracy)
Statistical Forecasting
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ARIMA = Auto Regressive Integrated Moving Average
ARIMAX
ARIMA + eXogenous variables
Advanced Statistical Algorithm That Produces ForecastsBased Upon Weighted Nonlinear Combinations of PastRealizations, Past Errors, and Future Leading Indicators
Let’s Look at ARIMA a more in Detail ...
Custom Modeling Background
Statistical Forecasting
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ARIMA Looks at These Two Series:1. The past demand values (D)2. The past forecast error values (e)
Future Forecasts Are Weighted Combinations ofPast Values of These Two Series ... How It WeightsThese Values is The Trick
D = Demande = Error
norm
aliz
ed u
nits
/mon
th
Statistical Forecasting
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Rolling Average Weighting ARIMA Makes Future Predictions Based Upon Weighted
Combinations of Past Values Let’s Explore Weighting Options... A Rolling Average Weights Past Predictions Based Upon Equal
Weights of Past Observations:
0
0.2
0.4
1 2 3 4 5 6 7
Weighting by Ageage weight
1 0.252 0.253 0.254 0.255 06 07 0
Statistical Forecasting
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EWMA Weighting An Exponentially Weighted Moving Average (EWMA) Weights Past
Predictions Based Upon Weights That Follow an Exponentially DecayingValue
Weights Can Be Tuned By Selection of Decay Factor, But They MustAlways Be Monotonically Decreasing With Age Of Observation
0
0.05
0.1
0.15
0.2
1 2 3 4 5 6 7
Weighting by Ageage weight1 0.162 0.1283 0.10244 0.081925 0.0655366 0.0524297 0.041943
Statistical Forecasting
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ARIMA Weighting An Autoregressive Integrated Moving Average (ARIMA) Weights Past
Observations and Past Forecast Errors Based Upon Weights That AreCalculated From Maximum Likelihood Estimation (MLE) Criteria
This Permits Weights to Assume Any Values Required; Constrained Only toSum to Unity
ARIMA’s Use of MLE For Parameter Estimation Gives it TheoreticalStatistical Optimality Qualities That EWMA and Holt-Winters Do Not Have
0
0.1
0.2
0.3
0.4
1 2 3 4 5 6 7
Weighting by Ageage weight1 0.252 0.143 0.384 0.075 0.046 0.097 0.03