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From Ambulances to Ward Boundaries Daniel Haight U of A Centre for Excellence in Operations

Darkhorse Analytics


Analytics


The Goal


Analytics

<Combining math, data, and computers to improve insight and efficiency>


Finance

Computer Science

Math

Data

Computers

Accounting IT/MIS


Calgary EMS: Q: Whatâ&#x20AC;&#x2122;s going on?


Response time % Response < 8min

92% 90%

91% 89%

89%

88% 86%

86% 84% 83%

82% 80% 2000

Data from 2000-2004 â&#x20AC;&#x201C; priority 1 calls.

2001

2002

2003

2004


Response time % Response < 8min

100%

89%

91%

90%

89%

86%

80%

83%

70% 60% 50% 40% 30% 20%

10% 0%

2000

Data from 2000-2004 â&#x20AC;&#x201C; priority 1 calls.

2001

2002

2003

2004


Priority 1 calls 12:00am


Priority 1 calls 1:00am


Priority 1 calls 2:00am


Priority 1 calls 3:00am


Priority 1 calls 4:00am


Priority 1 calls 5:00am


Priority 1 calls 6:00am


Priority 1 calls 7:00am


Priority 1 calls 8:00am


Priority 1 calls 9:00am


Priority 1 calls 10:00am


Priority 1 calls 11:00am


Priority 1 calls 12:00pm


Priority 1 calls 1:00pm


Priority 1 calls 2:00pm


Priority 1 calls 3:00pm


Priority 1 calls 4:00pm


Priority 1 calls 5:00pm


Priority 1 calls 6:00pm


Priority 1 calls 7:00pm


Priority 1 calls 8:00pm


Priority 1 calls 9:00pm


Priority 1 calls 10:00pm


Priority 1 calls 11:00pm


Ward Criteria • Geographical – Contiguity – Compactness – Natural boundaries

• Socio-political – Population equality (± 10%) – Electoral equality (± 25%) – Groups of interest (community leagues, socio-demographics) – Similarity to existing solution


64 Units

1

2

3

4

5

6

5

4

2

3

4

5

6

5

4

3

3

4

5

6

7

7

5

3

4

5

5

6

8

7

6

5

5

5

6

7

9

7

6

6

5

6

7

9

12 11

9

8

6

5

4

9

10

9

7

5

2

3

5

7

8

9

3

2

4 Districts

360 Population

1

2

3

4

5

6

5

4

1

2

3

4

5

6

5

4

1

2

3

4

5

6

5

4

2

3

4

5

6

5

4

3

2

3

4

5

6

5

4

3

2

3

4

5

6

5

4

3

3

4

5

6

7

7

5

3

3

4

5

6

7

7

5

3

3

4

5

6

7

7

5

3

4

5

5

6

8

7

6

5

4

5

5

6

8

7

6

5

4

5

5

6

8

7

6

5

5

5

6

7

9

7

6

6

5

5

6

7

9

7

6

6

5

5

6

7

9

7

6

6

5

6

7

9

12 11

9

8

5

6

7

9

12 11

9

8

5

6

7

9

12 11

9

8

6

5

4

9

10

9

7

5

6

5

4

9

10

9

7

5

6

5

4

9

10

9

7

5

2

3

5

7

8

9

3

2

2

3

5

7

8

9

3

2

2

3

5

7

8

9

3

2

90 each

90 each

88, 88, 91, 93


Edmonton Journal â&#x20AC;&#x201C; Page A1 April 10, 2009


4

3 2 1

7 6

10

5 9

8 11

12


“Many months of our election planners’ time were saved due to the computer-based approach without sacrificing any of the criteria relevant to the council”

“I would like to emphasize how an OR implementation such as this has had a profound effect on how we carry out one of our critical tasks at the City of Edmonton”


The Supernet


The Problem Use as few of the blue lines as possible to connect all the red dotsâ&#x20AC;Ś


Why use few?


How do you solve it?


8,426,642m


8,248,888m


Original Solution

Our Solution

High River

High River

Vulcan

Vulcan

Fort Macleod

Fort Macleod Lethbridge

Difference in solutions: 14 km

Lethbridge


Original Solution

Total kms:

8,426,642m

Our Solution

8,248,888m

Potential savings: 178 km or 2.1% (Note: Cost is ~ $12/m)


Alberta Education: Q: How many teachers should we hire?


=

/

=

+


Initial Teachers

Age Staff

Calculate Staff Attrition

Compare Staff and Students

Initial Population

Age Population

Calculate Population Migration & Births

Hire New Staff

Calculate Student Participation


Initial Population 50 45 40 35

Age

30 25 20 15 10 5 0 -30000

-20000

-10000

0

Population

10000

20000

30000


Age Population 50 45 40 35

Age

30 25 20 15 10 5 0 -30000

-20000

-10000

0

Population

10000

20000

30000


Migration 50 45

100 90

40

80

35

70 60

40

Age

30

50

25

30

20

20 10

15

0

50

45

40

35

30

25

20

15

10

5

0

10

Age

5 0 -30000

-20000

-10000

0

Population

10000

20000

30000


Migration 50 45

100 90

40

80

35

70 60

40

Age

30

50

25

30

20

20 10

15

0

50

45

40

35

30

25

20

15

10

5

0

10

Age

5 0 -30000

-20000

-10000

0

Population

10000

20000

30000


Migration 50 45

100 90

40

80

35

70 60

40

Age

30

50

25

30

20

20 10

15

0

50

45

40

35

30

25

20

15

10

5

0

10

Age

5 0 -30000

-20000

-10000

0

Population

10000

20000

30000


Migration 50 45 40 35

Age

30 25 20 15 10 5 0 -30000

-20000

-10000

0

Population

10000

20000

30000


Migration 50 45 40 35

Age

30 25 20 15 10 5 0 -30000

-20000

-10000

0

Population

10000

20000

30000


Migration 50 45 40 35

Age

30 25 20 15 10 5 0 -30000

-20000

-10000

0

Population

10000

20000

30000


New 0 yr-olds 50 45 14%

40

12%

35

X

8%

30

Age

10%

25

6%

20

4%

15

2%

10

0% 50

45

40

35

30

25

20

15

10

5

0

5 0 -30000

-20000

-10000

0

Population

10000

20000

30000


New 0 yr-olds 50

50

45

45

40

40

35

35

0%

2%

4%

6%

8%

10%

12%

14%

25

X

30

Age

30

25

20

20

15

15

10

10

5

5

0

0 -30000

-20000

-10000

0

Population

10000

20000

30000


School Aged 50 45 40 35

Age

30 25 20

20 18 16

15

14

School Aged

12

10

10 8 6

5

4 2

0

0

-30000

-20000

-10000

0

10000

20000

30000

-30000

-20000

-10000

0

10000

20000

30000

Population


Estimate Participation 20 18 16

100%

14

80% 60%

Age

12 10

40%

8

20%

6 4

0%

20

18

16

14

12

10

8

6

4

2

0

2

Age

0 30,000

20,000

10,000

0

Population

10,000

20,000

30,000


Estimate Participation 20 18 16

100%

14

80% 60%

Age

12 10

40%

8

20%

6 4

0%

20

18

16

14

12

10

8

6

4

2

0

2

Age

0 30,000

20,000

10,000

0

Population

10,000

20,000

30,000


Apply Participation 20 18 16

100%

14

X

60%

12

Age

80%

10

40%

8

20%

6 4

0%

20

18

16

14

12

10

8

6

4

2

0

2 0 30,000

20,000

10,000

0

Population

10,000

20,000

30,000


Apply Participation 20

20

18

18

16

16

14

14

0%

20%

40%

60%

80%

100%

10

X

12

Age

12

10

8

8

6

6

4

4

2

2

0

0 30,000

20,000

10,000

0

Population

10,000

20,000

30,000


Apply Participation 20 18 16 14

Age

12 10 8 6 4 2 0 30,000

20,000

10,000

0

Population

10,000

20,000

30,000


Apply Participation 20 18 16 14

Student Count

Age

12 10 8 6 4 2 0 30,000

20,000

10,000

0

Students

10,000

20,000

30,000


Initial Teachers

Age Staff

Calculate Staff Attrition

Compare Staff and Students

Initial Population

Age Population

Calculate Population Migration & Births

Hire New Staff

Calculate Student Participation


Teacher Workforce 71 66 61 56

Age

51 46 41 36 31 26 21 1,000

500

0 Teachers

500

1,000


Age Workforce 71 66 61 56

Age

51 46 41 36 31 26 21 1,000

500

0 Teachers

500

1,000


Apply Attrition Based on Age Specific Probabilities 71 66 61 56 50%

51 40%

Age

Probability of Attrition

60%

46

30%

41

20%

36

10%

31 26

0% 21

26

31

36

41

Age

46

51

56

61

66

21 1,000

500

0 Teachers

500

1,000


Apply Attrition Based on Age Specific Probabilities 71 66 61 56 50%

51 40%

Age

Probability of Attrition

60%

46

30%

41

20%

36

10%

31 26

0% 21

26

31

36

41

Age

46

51

56

61

66

21 1,000

500

0

500

1,000

Remaining Staff


Calculate Hires

30,000

20,000

10,000

20

71

18

66

16

61

14

56

12

51

10

46

8

41

6

36

4

31

2

26

0

21 0

10,000

= Required Staff

20,000

30,000

1,000

500

Age

Age

Students / Student to Staff Ratio

0

500

1,000

- Remaining Staff = Required Hires


Apply Hires 71

X

66

Hire Age/Gender Probability

61

5.0%

56

4.5%

51

Age

Probability

4.0% 3.5%

46

3.0% 2.5%

41

2.0% 1.5%

36

1.0%

31

0.5%

26

0.0% 21

26

31

36

41

46

Age

51

56

61

66

71

21 1,000

500

0

500

Required Hires

1,000


Apply Hires Required Hires 71

X

66

Hire Age/Gender Probability

61

5.0%

56

4.5%

51

Age

Probability

4.0% 3.5%

46

3.0% 2.5%

41

2.0% 1.5%

36

1.0%

31

0.5%

26

0.0% 21

26

31

36

41

46

Age

51

56

61

66

71

21 1,000

500

0

500

1,000


Apply Hires Required Hires 71

X

66

Hire Age/Gender Probability

61

5.0%

56

4.5%

Probability

4.0%

51

3.5%

46

3.0% 2.5%

41

2.0% 1.5%

36

1.0%

31

0.5%

26

0.0% 21

26

31

36

41

46

Age

51

56

61

66

71

21 1,000

500

0

500

1,000


Repeat


Lessons Learned • Process integration is key • It replaces supports decisionmaking • Interactivity fosters buy-in • Analytics is hard (IT, Stats, Communication) • Talent is rare


Starting Salaries 59000 57000 55000 53000 51000

49000 47000 45000

Accounting Finance Marketing HRM OM BusEcLaw Female Male

from Ambulance to Ward Boundaries Analytics  

by Dan Haight

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