Supply Chain Management

Transkript

1 Supply Chain Management Forecasting Methods Prof. Dr.-Ing. Burkhard Schmager Department of Industrial Engineering EAH Jena Sept 2016 SS 2016 Kapitel 2-1

2 Material Planning Approaches MRP - methods deterministical methods stochastical methods heuristical methods orders Consumption/period expectations II III III II III II I I I today time today time today time I,II,III - products SS 2016 Kapitel 2-2

3 Time Series Forecasting Use Case Data: Cups out of a Coffee Automat Period Demand ??? Maintenance: every 300 cups Question: What is the demand in period 13? SS 2016 Kapitel 2-3

4 Time Series Forecasting Use Case Data: Cups out of a Coffee Automat Week Cups ??? Maintenance: every 300 cups Question: How many cups in week 39? SS 2016 Kapitel 2-4

5 Prognosemethoden Die verbrauchsgesteuerte Bedarfsermittlung ermittelt auf der Basis der Verbrauchsmengen und Bestände in der Vergangenheit die zukünftigen Verbrauchsmengen Eine Bedarfsprognose erfolgt z.b. auf Basis der Mittelwertbildung x(i+1) = x(1)+ x(2)+x(3)+ x(4)+.+x(i) / i oder Exponentiellen Glättung x(i+1) = α*v(i) + (1-α) * x(i) x(i+1) = Prognosewert V = Verbrauchswert der letzten Periode α = Glättungsfaktor SS 2016 Kapitel 2-5

6 Forecasting methods Demand forecasting can be based on different forecasting methods (e.g. constant level forecasting methods): - Last value method - Simple averaging method - Moving average method - Weighted moving average method - Exponential smoothing method SS 2016 Kapitel 2-6

7 Forecasting methods But what about seasonality, trends and most important forecasting errors??? Can we use these methods anymore? - Last value method - Simple averaging method - Moving average method - Weighted moving average method - Exponential smoothing method SS 2016 Kapitel 2-7

8 Time Series Forecasting Use Case Data: Cups out of a Coffee Automat Week Week Demand Demand ??? Question: How many cups will be sold in week 52? SS 2016 Kapitel 2-8

9 Forecasting Error Forecast error can be defined by several measures: et = Ft Dt for t = 1,2,3,..,n - MAD (Mean abolute deviation) - MSE (Mean squared error) - STD (Standard deviation of forecast error) - Bias (Bias measures of over- or underestimating) - MAPE (Mean absolute percentage error) SS 2016 Kapitel 2-9

10 Forecasting Error et = Ft Dt for t = 1,2,3,..,n - MAD - MSE - STD SS 2016 Kapitel 2-10

11 Forecasting Error et = Ft Dt for t = 1,2,3,..,n - Bias - MAPE SS 2016 Kapitel 2-11

12 Seasonality in Forecasting Using the seasonality index for adjusting the forecast to repeating patterns (in 5 steps): Step 1: Compute the seasonality index for any period given Step 2: Adjust actual demand to get deseasonalized demand Step 3: Select an appropriate forecasting method Step 4: Apply the selected method on deseasonalized demand Step 5: Compute actual forecast SS 2016 Kapitel 2-12

13 Use Case Use Case Data: Cups out of a Coffee Automat Week Week Demand Demand ??? Question: How do we calculate SI for weeks 31 up to 52? SS 2016 Kapitel 2-13

14 Seasonality in Forecasting (Textbook example) SS 2016 Kapitel 2-14

15 Forecasting and Trend 1. ) Simple Linear Trend Model: Ft = E(Xt) = a + b t Estimate level (a) and trend (b) parameter using Least Square Regression Method and computing a and b by following equations: getting Fn+1 = a + b (n+1) for period t = n+1 SS 2016 Kapitel 2-15

16 Forecasting and Trend 2. ) Holt`s Method (Double Exponential Smoothing): Ft+1 = Lt+1 + Tt+1 Estimate level (L) and trend (T) parameter by using Fn+1 = E (Xn+1) for period n+1 SS 2016 Kapitel 2-16

17 Seasonality and Trend in Forecasting 1. Method using Static Seasonality Indices Look for the 5- Step method for seasonality and combine it with Holt s method! 2. Winter s method SS 2016 Kapitel 2-17

18 Seasonality and Trend in Forecasting (Example) Example using Holt s method SS 2016 Kapitel 2-18

19 Seasonality and Trend in Forecasting (Example) Winter s method SS 2016 Kapitel 2-19

20 Forecast Accuracy Monitoring Forecast Accuracy by Tracking signal TS SS 2016 Kapitel 2-20