Introduction to Demand Planning & Forecasting - edXMITx+CTL.SC1x_1+2T2015+type@ass… ·...
Transcript of Introduction to Demand Planning & Forecasting - edXMITx+CTL.SC1x_1+2T2015+type@ass… ·...
MIT Center for Transportation & Logistics
CTL.SC1x -Supply Chain & Logistics Fundamentals
Introduction to Demand Planning
& Forecasting
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Demand Process – Three Key Questions Demand Planning
n Product & Packaging n Promotions n Pricing n Place
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What should we do to shape and create demand for our product?
What should we expect demand to be given the demand plan in place?
How do we prepare for and act on demand when it materializes?
Demand Forecasting n Strategic, Tactical, Operational n Considers internal & external factors n Baseline, unbiased, & unconstrained
Demand Management n Balances demand & supply n Sales & Operations Planning (S&OP) n Bridges both sides of a firm
Material adapted from Lapide, L. (2006) Course Notes, ESD.260 Logistics Systems.
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Forecasting Levels
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Level Horizon Purposes
Quarterly • Brand Plans • Budgeting • Sales Planning • Manpower Planning
Strategic Year/Years • Business Planning • Capacity Planning • Investment Strategies
Tactical
Months/Weeks • Short-term Capacity Planning • Master Planning • Inventory Planning
Operational Days/Hours • Transportation Planning • Production Planning • Inventory Deployment
Material adapted from Lapide, L. (2006) Course Notes, ESD.260 Logistics Systems.
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Agenda
• Forecasting Truisms • Subjective vs. Objective Approaches • Forecast Quality • Forecasting Metrics
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Forecasting Truisms 1: Forecasts are always wrong
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CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
1. Forecasts are always wrong Why?
n Demand is essentially a continuous variable n Every estimate has an “error band” n Forecasts are highly disaggregated
w Typically SKU-Location-Time forecasts
n Things happen . . .
OK, so what can we do? n Don’t fixate on the point value n Use range forecasts n Capture error of forecasts n Use buffer capacity or stock
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CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Forecasting Truisms 2: Aggregated forecasts are more accurate
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CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
2. Aggregated forecasts are more accurate
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• Aggregation by SKU, Time, Location, etc. • Coefficient of Variation (CV)
n Definition: Standard Deviation / Mean = σ/µ n Provides a relative measure of volatility or uncertainty n CV is non-negative and higher CV indicates higher volatility
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Red: µ=100, σ=45, CV=0.45 Blue: µ=100, σ=1, CV=0.01
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Aggregating by SKU
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• Coffee Cups and Lids @ the Sandwich Shop n Large ~N(80, 30) CV = 0.38 n Medium ~N(450, 210) CV = 0.47 n Small ~N(250, 110) CV = 0.44
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CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics 10
• What if I design cups with a common lid? • Common Lid ~N(780, 239) CV = 0.31
n µ = (80 + 450 + 250) = 780 units/day n σ = sqrt(302 + 2102 + 1102) = 239 units/day
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Aggregating by SKU
Example of Modularity or Parts Commonality • Reduces the relative variability • Increases forecasting accuracy • Lowers safety stock requirements
Large ~N(80, 30) CV=0.38 Med. ~N(450, 210) CV=0.47 Small ~N(250, 110) CV=0.44
Lids ~N(780, 239) CV=0.31
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics 11
Aggregating by Time
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Daily Demand for Lids ~N(780, 239) CV=0.31
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Weekly Demand for Lids ~N(5458, 632) CV=0.12
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Monthly Demand for Lids ~N(21840, 1264) CV=0.06
• Forecasts with longer time buckets have better forecast accuracy.
• The time bucket used should match the situation.
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Aggregating by Locations • Suppose we have three sandwich shops
n Weekly lid demand at each ~N(5458, 632) CV=0.12
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• What if demand is pooled at a common Distribution Center? n Weekly lid demand at DC ~N(16374, 1095) CV=0.07
~N(5458, 632) ~N(5458, 632) ~N(5458, 632)
CV reduces as we aggregate over SKUs,
time, or locations.
~N(16374, 1095)
CVind =σµ
CVagg =σ nµn
=σ
µ n=CVindn
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Forecasting Truisms 3: Shorter horizon forecasts are more accurate
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CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics 14
3. Shorter horizon forecasts are more accurate
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CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics 15
3. Shorter horizon forecasts are more accurate
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• Postponed final customization to closer time of consumption
• Risk pooling of component (e.g., ham) increases forecast accuracy.
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Forecasting Truisms
• Forecasts are always wrong è Use ranges & track forecast error
• Aggregated forecasts are more accurate è Risk pooling reduces CV
• Shorter time horizon forecasts are more accurate
è Postpone customization until as late as possible
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CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Subjective & Objective Approaches
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CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Fundamental Forecasting Approaches
Judgmental n Sales force surveys n Jury of experts n Delphi techniques
Experimental n Customer surveys n Focus group sessions n Test marketing
Causal / Relational n Econometric Models n Leading Indicators n Input-Output Models
Time Series n “Black Box” Approach n Past predicts the future n Identify patterns
Often times, you will need to use a combination of approaches
Subjective Objective
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Forecasting Quality
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CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Cost of Forecasting vs Inaccuracy
Cost of Forecasting
Forecast Accuracy
Cost
Cost of Errors In Forecast
Total Cost ß Overly Naïve Models à ß Excessive Causal Models à ß Good Region à
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
How do we determine if a forecast is good? • What metrics should we use? • Example - Which is a better forecast?
n Squares & triangles are different forecasts n Circles are actual values
time
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CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Accuracy versus Bias
Accurate
Not Accurate
Biased Not Biased
n Accuracy - Closeness to actual observations n Bias - Persistent tendency to over or under predict
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Forecasting Metrics
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CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Forecasting Metrics
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n
tte
MADn
==∑
1
n
tte
MDn==∑
MPE =
etAtt=1
n
∑n
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n
tte
MSEn
==∑
MAPE =
etAtt=1
n
∑n
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1
n
tte
RMSEn
==∑
Notation: At = Actual value for obs. t et = Error for observation t Ft = Forecasted value for obs. t n = Number of observations
et = At – Ft Mean Deviation (MD)
Mean Absolute Deviation (MAD)
Mean Squared Error (MSE)
Root Mean Squared Error (RMSE)
Mean Percent Error (MPE)
Mean Absolute Percent Error (MAPE)
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Example: Forecasting Bagels • For the bagel forecast and actual values
shown below, find the: n Mean Absolute Deviation (MAD) n Root Mean Square of Error (RMSE) n Mean Absolute Percent Error (MAPE)
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Forecast Actual Monday 50 43 Tuesday 50 42 Wednesday 50 66 Thursday 50 38 Friday 75 86
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n
tte
MADn
==∑
MAPE =
etAtt=1
n
∑n
2
1
n
tte
RMSEn
==∑
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Example: Forecasting Bagels • Solution:
1. Graph it. 2. Extend data table:
w Error: et=At-Ft w Abs[error] = |et| w Sqr[error] = e2 w AbsPct[error] = |et/At|
3. Sum and find means
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Ft At et |et| e2 |et/At| Monday 50 43 -7 7 49 16.3% Tuesday 50 42 -8 8 64 19.0% Wednesday 50 66 16 16 256 24.2% Thursday 50 38 -12 12 144 31.6% Friday 75 86 11 11 121 12.8%
Sum 0 54 634 104% Mean 0 10.8 126.8 21%
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Forecast Actual
MAD = 54/5 = 10.8 RMSE = sqrt(126.8) = 11.3 MAPE = 104%/5 = 21%
CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Key Points from Lesson
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CTL.SC1x - Supply Chain and Logistics Fundamentals Lesson: Demand Forecasting Basics
Key Points
• Forecasting is a means not an end • Forecasting Truisms
n Forecasts are always wrong n Aggregated forecasts are more accurate n Shorter horizon forecasts are more accurate
• Subjective & Objective Approaches n Judgmental & experimental n Causal & time series
• Forecasting metrics n Capture both bias & accuracy n MAD, RMSE, MAPE
MIT Center for Transportation & Logistics
CTL.SC1x -Supply Chain & Logistics Fundamentals
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