Introduction to Demand Planning & Forecasting

Transcription

1 CTL.SC1x -Supply Chai & Logistics Fudametals Itroductio to Demad Plaig & Forecastig MIT Ceter for Trasportatio & Logistics

2 Demad Process Three Key Questios What should we do to shape ad create demad for our product? Demad Plaig Product & Packagig Promotios Pricig Place What should we expect demad to be give the demad pla i place? Demad Forecastig Strategic, Tactical, Operatioal Cosiders iteral & exteral factors Baselie, ubiased, & ucostraied How do we prepare for ad act o demad whe it materializes? Demad Maagemet Balaces demad & supply Sales & Operatios Plaig (S&OP) Bridges both sides of a firm Material adapted from Lapide, L. (2006) Course Notes, ESD.260 Logistics Systems. 2

3 Forecastig Levels Level Horizo Purposes Strategic Tactical Operatioal Year/Years Quarterly Moths/Weeks Days/Hours Busiess Plaig Capacity Plaig Ivestmet Strategies Brad Plas Budgetig Sales Plaig Mapower Plaig Short-term Capacity Plaig Master Plaig Ivetory Plaig Trasportatio Plaig Productio Plaig Ivetory Deploymet Material adapted from Lapide, L. (2006) Course Notes, ESD.260 Logistics Systems. 3

4 Ageda Forecastig Truisms Subjective vs. Objective Approaches Forecast Quality Forecastig Metrics

5 Forecastig Truisms 1: Forecasts are always wrog 5

6 1. Forecasts are always wrog Why? Demad is essetially a cotiuous variable Every estimate has a error bad Forecasts are highly disaggregated w Typically SKU-Locatio-Time forecasts Thigs happe... OK, so what ca we do? Do t fixate o the poit value Use rage forecasts Capture error of forecasts Use buffer capacity or stock 6

7 Forecastig Truisms 2: Aggregated forecasts are more accurate 7

8 2. Aggregated forecasts are more accurate Aggregatio by SKU, Time, Locatio, etc. Coefficiet of Variatio (CV) Defiitio: Stadard Deviatio / Mea = σ/µ Provides a relative measure of volatility or ucertaity CV is o-egative ad higher CV idicates higher volatility 200 Red: µ=100, σ=45, CV=0.45 Blue: µ=100, σ=1, CV= Daily Demad /26/11 3/28/11 4/27/11 5/27/11 6/26/11 7/26/11 8/25/11 8

9 Aggregatig by SKU Coffee Cups ad the Sadwich Shop Large ~N(80, 30) CV = 0.38 Medium ~N(450, 210) CV = 0.47 Small ~N(250, 110) CV = Small Medium Large /14/13 9/13/13 10/13/13 11/12/13 12/12/13 1/11/14 2/10/14 3/12/14 4/11/14 5/11/14 6/10/14 9

10 Aggregatig by SKU What if I desig cups with a commo lid? Commo Lid ~N(780, 239) CV = 0.31 µ = ( ) = 780 uits/day σ = sqrt( ) = 239 uits/day 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 1,600 1,400 1,200 1, /14/13 9/13/13 10/13/13 11/12/13 12/12/13 1/11/14 2/10/14 3/12/14 4/11/14 5/11/14 6/10/14 Example of Modularity or Parts Commoality Reduces the relative variability Icreases forecastig accuracy Lowers safety stock requiremets 10

11 1,600 1,200 Aggregatig by Time Daily Demad for Lids ~N(780, 239) CV=0.31 Forecasts with loger time buckets have better forecast accuracy. The time bucket used should match the situatio /14/13 9/13/13 10/13/13 11/12/13 12/12/13 1/11/14 2/10/14 3/12/14 4/11/14 5/11/14 6/10/14 8,000 Weekly Demad for Lids ~N(5458, 632) CV=0.12 6,000 4,000 2, ,000 Mothly Demad for Lids ~N(21840, 1264) CV= ,000 20,000 15,000 10,000 5,

12 Aggregatig by Locatios Suppose we have three sadwich shops Weekly lid demad at each ~N(5458, 632) CV=0.12 CV reduces as we aggregate over SKUs, time, or locatios. ~N(5458, 632) ~N(5458, 632) ~N(5458, 632) ~N(16374, 1095) What if demad is pooled at a commo Distributio Ceter? Weekly lid demad at DC ~N(16374, 1095) CV=0.07 CV id = σ µ CV agg = σ µ = σ µ = CV id 12

13 Forecastig Truisms 3: Shorter horizo forecasts are more accurate 13

14 3. Shorter horizo forecasts are more accurate????

15 3. Shorter horizo forecasts are more accurate Postpoed fial customizatio to closer time of cosumptio Risk poolig of compoet (e.g., ham) icreases forecast accuracy.?

16 Forecastig Truisms Forecasts are always wrog è Use rages & track forecast error Aggregated forecasts are more accurate è Risk poolig reduces CV Shorter time horizo forecasts are more accurate è Postpoe customizatio util as late as possible 16

17 Subjective & Objective Approaches 17

18 Fudametal Forecastig Approaches Subjective Judgmetal Sales force surveys Jury of experts Delphi techiques Experimetal Customer surveys Focus group sessios Test marketig Causal / Relatioal Ecoometric Models Leadig Idicators Iput-Output Models Time Series Objective Black Box Approach Past predicts the future Idetify patters Ofte times, you will eed to use a combiatio of approaches

19 Forecastig Quality 19

20 Cost of Forecastig vs Iaccuracy ß Overly Naïve Models à ß Good Regio à ß Excessive Causal Models à Total Cost Cost Cost of Errors I Forecast Cost of Forecastig Forecast Accuracy

21 How do we determie if a forecast is good? What metrics should we use? Example - Which is a better forecast? Squares & triagles are differet forecasts Circles are actual values time

22 Accuracy versus Bias Accuracy - Closeess to actual observatios Bias - Persistet tedecy to over or uder predict Accurate Not Accurate Biased Not Biased

23 Forecastig Metrics 23

24 Forecastig Metrics e t = A t F t Mea Deviatio (MD) Mea Squared Error (MSE) MD MSE = = t= 1 t= 1 e t e 2 t Mea Absolute Deviatio (MAD) Root Mea Squared Error (RMSE) MAD = RMSE = t= 1 t= 1 e e t 2 t Mea Percet Error (MPE) Notatio: MPE = e t t=1 A t Mea Absolute Percet Error (MAPE) MAPE = A t = Actual value for obs. t e t = Error for observatio t F t = Forecasted value for obs. t = Number of observatios t=1 e t A t

25 Example: Forecastig Bagels For the bagel forecast ad actual values show below, fid the: Mea Absolute Deviatio (MAD) Root Mea Square of Error (RMSE) Mea Absolute Percet Error (MAPE) MAD = t= 1 e t Forecast Actual Moday Tuesday Wedesday RMSE = t= 1 e 2 t Thursday Friday MAPE = t=1 e t A t 25

26 Example: Forecastig Bagels Solutio: 1. Graph it. 2. Exted data table: Forecast Actual w Error: e t =A t -F t w Abs[error] = e t w Sqr[error] = e 2 w AbsPct[error] = e t /A t 3. Sum ad fid meas Daily Bagel Demad F t A t e t e t e 2 e t /A t Moday % 30 Moday Tuesday Wedesday Thursday Friday Tuesday % Wedesday % Thursday % Friday % Sum % MAD = 54/5 = 10.8 RMSE = sqrt(126.8) = 11.3 MAPE = 104%/5 = 21% Mea % 26

27 Key Poits from Lesso 27

28 Key Poits Forecastig is a meas ot a ed Forecastig Truisms Forecasts are always wrog Aggregated forecasts are more accurate Shorter horizo forecasts are more accurate Subjective & Objective Approaches Judgmetal & experimetal Causal & time series Forecastig metrics Capture both bias & accuracy MAD, RMSE, MAPE

29 CTL.SC1x -Supply Chai & Logistics Fudametals Questios, Commets, Suggestios? Use the Discussio! Jaie Photo courtesy Yakee Golde Retriever Rescue ( MIT Ceter for Trasportatio & Logistics