Forecasting & Capacity Planning - IWSM Mensura · Single Exp Smoothing =PFW7D+DFW7PREDICTION...

Niteen Kumar | Demand Forecasting Techniques | IWSM 2019

Niteen KumarCapgemini

Demand Forecasting & Capacity Planning Using TimeSeries Data

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“ The goal of

forecasting is not to

predict the future but

to tell you what you

need to know to take

meaningful action in

the present ” - Paul Saffo

Forecast is the estimate of the future demand

WHAT IS TIME SERIES?


TIME ORDER

TREND

SEASIONALITY

CYCLICAL

RANDOMNESS

COMPONENT OF TIME SEREIS


WEEK DATA TYPE TECHNIQUE

W1 30 L

SIMPLE AVERAGE

MOVING AVERAGE

EXPONENTIAL SMOOTHING

W2 32 L

W3 31 L

W4 30 L t

d


W1 30 L+T

LINEAR REGRESSION

HOLT’S METHOD

W2 34 L+T

W3 36 L+T

W4 39 L+T t

d


W1 30 L+T+S

BASIC MODEL

WINTERS MODEL

W2 34 L+T+S

W3 38 L+T+S

W4 32 L+T+S

W1 32 L+T+S

W2 37 L+T+S

W3 40 L+T+S

W4 35....+N L+T+S

t

d


W1 30 L+T+S+C

SINUSOIDAL MODEL

W2 34 L+T+S+C

W3 38 L+T+S+C

W4 32 L+T+S+C

W1 32 L+T+S+C

W2 37 L+T+S+C

W3 40 L+T+S+C

W4 35...+N L+T+S+C

t

d

CAN YOU FORECAST?


WEEK DATA

W1 33

W2 34

W3 35

W4 37

W5 38

W6 34

W7 39

W8 44

+39+(39-34)

WEEK DATA

W1 33

W2 30

W3 33

W4 23

W5 30

W6 53

W7 47

W8 36

Simple Average

AVERAGE(W1:W7)

WEEK DATA FACTOR

W1 33

W2 30

W3 33 10%

W4 23 15%

W5 30 20%

W6 53 25%

W7 47 30%

W8 40

Weighted Moving Average

SUM(D*F) FOR ALL WEEKS

WEEK DATA PREDICTION

W1 33 -

W2 30 33

W3 33 31

W4 23 32

W5 30 27

W6 53 29

W7 47 43

W8 46

Single Exp Smoothing

=PF*W7D+DF*W7PREDICTION

Primary Factor (PF): 60%Damping Factor (DF): 40%


W1 33 26,5

W2 30 30,3

W3 33 34,1

W4 23 34,7

W5 30 36,5

W6 53 43,1

W7 47 47,2

W8 47,2

Double Exp Smoothing

Primary Factor: 0,20Secondary Factor: 0,03

CAN YOU FORECAST?


WEEK DATA

W1 33

W2 34

W3 35

W4 37

W5 38

W6 34

W7 39

W8 44

+39+(39-34)

WEEK DATA

W1 33

W2 30

W3 33

W4 23

W5 30

W6 53

W7 47

W8 36

Simple Average

AVERAGE(W1:W7)

WEEK DATA FACTOR

W1 33

W2 30

W3 33 10%

W4 23 15%

W5 30 20%

W6 53 25%

W7 47 30%

W8 40

Weighted Moving Average

SUM(D*F) FOR ALL WEEKS


W1 33 -

W2 30 33

W3 33 31

W4 23 32

W5 30 27

W6 53 29

W7 47 43

W8 46

Single Exp Smoothing

=PF*W7D+DF*W7PREDICTION

Primary Factor (PF): 60%Damping Factor (DF): 40%


W1 33 26,5

W2 30 30,3

W3 33 34,1

W4 23 34,7

W5 30 36,5

W6 53 43,1

W7 47 47,2

W8 47,2



MEASURING FORECAST ACCURACY



W1 33

W2 30 26,5

W3 33 30,3

W4 23 34,1

W5 30 34,7

W6 53 36,5

W7 47 43,1

W8 47,2



ERROR MAD MSD MAPE

3,54 3,54 12,50 0,12

2,69 2,69 7,22 0,08

-11,07 -11,07 122,61 0,48

- 4,75 - 4,75 22,55 0,16

16,45 16,45 270,71 0,31

3,92 3,92 15,38 0,08

7,07 75,16 0,21

BASIC STEPS IN FORECASTING


Problem definition

Gathering Data

Exploratory Analysis

Choosing & Fitting Model

Evaluating Forecast Model

PROBELM DEFINITION


Forecasting monthly service demand and required capacity to service those demand with higher accuracy

YEAR MONTH QUARTER (X) Q-INCIDENTS ACTUALS REQUIRED FTE

2017 11 3002017 2

2017 32017 4

2 5402017 52017 62017 7

3 8852017 82017 92017 10

4 5802017 11

2017 12

2018 1

5 ?? ??2018 2

2018 3

DEMANDFORECAST

CAPACITYFORECAST

What are those magic numbers?

GATHERING DATA


0

200

400

600

800

1000

1200

1-j

un

6-j

un

8-j

un

13

-ju

n

15

-ju

n

20

-ju

n

22

-ju

n

27

-ju

n

29

-ju

n

4-j

ul

6-j

ul

11

-ju

l

13

-ju

l

18

-ju

l

20

-ju

l

25

-ju

l

27

-ju

l

1-a

ug

3-a

ug

8-a

ug

10

-au

g

15

-au

g

17

-au

g

22

-au

g

24

-au

g

29

-au

g

31

-au

g

5-s

ep

7-s

ep

12

-se

p

14

-se

p

19

-se

p

21

-se

p

26

-se

p

28

-se

p

3-o

kt

5-o

kt

10

-okt

12

-okt

17

-okt

19

-okt

24

-okt

26

-okt

31

-okt

2-n

ov

7-n

ov

9-n

ov

14

-no

v

16

-no

v

21

-no

v

23

-no

v

28

-no

v

30

-no

v

5-d

ec

7-d

ec

12

-de

c

14

-de

c

19

-de

c

21

-de

c

Incident Count By Date

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

Total

juni

juli

augustus

september

oktober

november

december

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

23

25

27

29

31

33

35

37

39

41

43

45

47

49

51

53

0

2000

4000

6000

8000

10000

12000

14000

jun

i

juli

augu

stu

s

sep

tem

ber

okt

ob

er

no

vem

ber

de

cem

ber

jan

uar

i

feb

ruar

i

maa

rt

apri

l

mei

jun

i

juli

augu

stu

s

sep

tem

ber

2016 2017

Critical

High

Low

Medium

EXPLORATORY ANALYSIS


REAL TIME DATA IS NOT SO CLEAR AND VISUAL INSPECTION IS NOT SO HELPFUL

0

200

400

600

800

1000

1200

1-j

un

6-j

un

8-j

un

13

-ju

n

15

-ju

n

20

-ju

n

22

-ju

n

27

-ju

n

29

-ju

n

4-j

ul

6-j

ul

11

-ju

l

13

-ju

l

18

-ju

l

20

-ju

l

25

-ju

l

27

-ju

l

1-a

ug

3-a

ug

8-a

ug

10

-au

g

15

-au

g

17

-au

g

22

-au

g

24

-au

g

29

-au

g

31

-au

g

5-s

ep

7-s

ep

12

-se

p

14

-se

p

19

-se

p

21

-se

p

26

-se

p

28

-se

p

3-o

kt

5-o

kt

10

-okt

12

-okt

17

-okt

19

-okt

24

-okt

26

-okt

31

-okt

2-n

ov

7-n

ov

9-n

ov

14

-no

v

16

-no

v

21

-no

v

23

-no

v

28

-no

v

30

-no

v

5-d

ec

7-d

ec

12

-de

c

14

-de

c

19

-de

c

21

-de

c

Incident Count By Date

CHOOSING A FITTING MODEL


YEAR MONTH QUARTER (X) Q-ACTUALS INCIDENTS

2017 1

1 3002017 2

2017 3

2017 4

2 5402017 5

2017 6

2017 7

3 8852017 8

2017 9

2017 10

4 5802017 11

2017 12

2018 1

5 4162018 2

2018 3

2018 4

6 7602018 5

2018 6

2018 7

7 11912018 8

2018 9

2018 10

8 7602018 11

2018 12

Does the data follow a normal distribution?


YEAR MONTH QUARTER (X) Q-INCIDENTS ACTUALS

2017 1

1 3002017 2

2017 3

2017 4

2 5402017 5

2017 6

2017 7

3 8852017 8

2017 9

2017 10

4 5802017 11

2017 12

2018 1

5 4162018 2

2018 3

2018 4

6 7602018 5

2018 6

2018 7

7 11912018 8

2018 9

2018 10

8 7602018 11

2018 12




YEAR MONTH QUARTER (X) Q-ACTUALS INCIDENTS2017 1

1 3002017 2

2017 3

2017 4

2 5402017 5

2017 6

2017 7

3 8852017 8

2017 9

2017 10

4 5802017 11

2017 12

2018 1

5 4162018 2

2018 3

2018 4

6 7602018 5

2018 6

2018 7

7 11912018 8

2018 9

2018 10

8 7602018 11

2018 12

Is there a seasonality in the data?


QUARTER (X)Q-ACTUALS INCIDENTS

PERIOD AVERAGE

SEASONAL FACTOR

1 300 358 0,53

2 540 650 0,96

3 885 1038 1,53

4 580 670 0,99

5 416 0,53

6 760 0,96

7 1191 1,53

8 760 0,99

Can we deseasonalize the data?



QUARTER (X)

Q-ACTUALS INCIDENTS

PERIOD AVERAGE

SEASONAL FACTOR

DESESAONLIZEDINCIDENTS (Y)

1 300 358 0,53 569

2 540 650 0,96 564

3 885 1038 1,53 579

4 580 670 0,99 588

5 416 0,53 789

6 760 0,96 794

7 1191 1,53 779

8 760 0,99 770

Is there a trend in the data?


DATA UNDER ANALYSIS


QUARTER (X)

Q-ACTUALS INCIDENTS

PERIOD AVERAGE

SEASONAL FACTOR

DESESAONLIZEDINCIDENTS (Y)

1 300 358 0,53 569

2 540 650 0,96 564

3 885 1038 1,53 579

4 580 670 0,99 588

5 416 0,53 789

6 760 0,96 794

7 1191 1,53 779

8 760 0,99 770

Is there a trend in the data?

Model Summary

S R-sq R-sq(adj) R-sq(pred)

59,5622 75,59% 71,53% 60,93%

Regression EquationDE-SEASONLAIZED INCIDENTS COUNT = 500,7 + 39,62 QUARTER (X)


Can we forecastusing trend

Model Summary


59,5622 75,59% 71,53% 60,93%

Regression Equation

DE-SEASONLAIZED INCIDENTS COUNT = 500,7 + 39,62 QUARTER (X)

QUARTER (X)TREND FORECAST

500,7 + 39,62 QUARTER (X)FINAL FORECAST

9 857 452

10 897 859

11 937 1432

12 976 963

Can we also inclue the seasonality factor?



Can we forecastusing trend

Model Summary


59,5622 75,59% 71,53% 60,93%

Regression Equation

DE-SEASONLAIZED INCIDENTS COUNT = 500,7 + 39,62 QUARTER (X)

QUARTER (X)TREND FORECAST

500,7 + 39,62 QUARTER (X)

FINAL FORECAST(TREND + SEASON)

9 857 452

10 897 859

11 937 1432

12 976 963

Can we also include the seasonality factor?



HmmmmThe forecast is better compared to moving avg

EVALUATING THE FORECASTING MODEL

QUARTER (X)

Q-ACTUALS INCIDENTS

TREND FORECAST

FINAL FORECAST

FORECASTING ERROR

9 514 857 452 12%

10 907 897 859 5%

11 1376 937 1432 -4%

12 976 963


CAPACITY PLANNING

QUARTER (X)Q-ACTUALS INCIDENTS

TREND FORECAST

FINAL FORECAST

FORECASTING ERROR

PRODUCTIVITYPLANNED

FTEACTUAL REQUIRED

FTE

9 514 857 452 12% 6 5 6

10 907 897 859 5% 6 10 11

11 1376 937 1432 -4% 6 17 16

CAPACITY UTILIZATION IS BETTER W.R.T TO PLAN AVAILABITY


Will they have questions at the end?

Date post:	06-Oct-2020
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Forecasting & Capacity Planning - IWSM Mensura · Single Exp Smoothing =PF*W7D+DF*W7PREDICTION...

Documents

Forecasting & Capacity Planning - IWSM Mensura · Single Exp Smoothing =PFW7D+DFW7PREDICTION...