Chapman and Ward HMPOR case slide 1 © 2011 John Wiley & Sons Ltd Samdo case study discussion Chris Chapman and Stephen Ward

Chapman and Ward HMPOR case slide 1 © 2011 John Wiley & Sons Ltd

Samdo case study discussion

Chris Chapman and Stephen Ward


Discussion starting point

Start with a working assumption about the objective:

maximizing the expected value of M = R – C,

where M = margin (contribution to profit) per year

R = revenue

C = cost (direct and amortisation of capital)

Assume we want to understand the expected

values of M, R and C plus associated opportunity

and risk, and begin by considering R


Key components of R (revenue)

• base load power sales to Ontario Hydro

• waste heat (low pressure steam)

• back-up emergency power

We need to size these components,

then identify associated key sources of uncertainty,

and associated key responses (decisions)


Key components of C (cost)

• amortised capital cost

• fuel cost

• other costs

We need to size these components,

then identify associated key sources of uncertainty,

and associated key responses (decisions)


Creative thinking putting this together

• New untested design CCTG plant?

• Defer emergency power business?

• Back-to-back contract with Ontario Hydro?

• Back-to-back contract with gas supplier?

• Timing issues?


Some concluding comments

• The top-down process starting point is useful here• Many of the key generic process ideas can be

applied to all opportunity, risk and uncertainty management processes

• Designing processes for contexts is an overarching key idea

• Seeking simplicity systematically in these processes is another key idea, part of the overarching opportunity efficiency concept


Transcon 1 case study discussion

Chris Chapman and Stephen Ward

1 of 4 2011



Start with a working assumption about the objective:

maximizing the expected value of

M = B – C,

where M = margin (contribution to profit)

B = bid (price)

C = cost (direct),

and assume we want to understand the expected

values of M, P and C and associated risk.

Start with C for two components which are useful

examples.


Initial operations and training example

Direct cost (£m)

Astro Zoro

0.7 0.9 1.1 1.3

Cum

ula

tive

pro

babi

lity

0.2

0.4

0.6

0.8

1

01.5

indicates expected cost (also median)


Convert existing programmes example

0.5 1.0 1.5 2.0

Direct cost (£m)

Cum

ula

tive

pro

babi

lity

0.2

0.4

0.6

0.8

1

0

indicates expectedcost (also median)

Sysdoc 1 Sysdoc 2 Sysdoc 3 Datapol


Linking this to common practice

• The value of simple ‘other objective’ assessments

early on• The value of simple initial cost estimates• The value of the ‘risk efficiency’ concept and its

assessment via simple linear cumulative probability distributions



• The key estimating process ideas have been used very successfully by a limited number of organisations

• Many of the key ideas can be applied to all opportunity, risk and uncertainty management processes



Chris Chapman and Stephen Ward2 of 4 2011



Keep the working assumption that we are

maximizing the expected value of M = B – C,


B = bid (price)

C = cost (direct),

and we want to understand the expected value

of C, associated risk and decisions.

Now look at C when discrete events are explicitly involved.


Decision tree for the additional memory issue

0.8Key:

decision nodechoices available indicated above ‘choice branches’expected values indicated below ‘choice branches

chance nodealternative outcomes indicated above ‘chance branches’probabilities indicated below chance branches’

Cost to Astro

£0.6m

£1.0m

£0m

£0m

pre-install

expected cost = £0.6m (1.0 x £0.6m)

post-install if necessary

expected cost = £0.1m(0.2 x 0.5 x £1.0m)

extra memory needed

0.2

Astro have to pay

0.5 ?

Astro do not have to pay

0.5

no extra memory needed

probability = 1.0

Astro pay in advance


Cumulative probability distribution portrayal

indicatesexpectedcost

pre-installpost-installif necessary

0.5

0.2

0.4

0.6

0.8

1

0 1.0

Cum

ula

tive

pro

babi

lity

Direct cost (£m)


The ‘post-install if necessary’ option’s extra memory‘risk’ consistent with probability-impact grid portrayal

0.5

0.2

0.4

0.6

0.8

1

0 1.0

Pro

bab

ility

Direct cost (£m)


A revised decision tree to generalise

0.8Key:

decision nodechoices available indicated above ‘choice branches’expected values indicated below ‘choice branches

chance nodealternative outcomes indicated above ‘chance branches’probabilities indicated below chance branches’

Cost to Astro

£0.4–0.8m

£0.9-1.1m

£0m

£0m

pre-install

expected cost = £0.6m (1.0 x £0.6m)

post-install if necessary

expected cost = £0.1m(0.2 x 0.5 x £1.0m)

extra memory needed

0.1 – 0.3

Astro have to pay

0.3 – 0.7

Astro do not have to pay

0.7- 0.3.

no extra memory needed

probability = 1.0

Astro pay in advance


Revised cumulative probability distributionwith 0.7- 0.9 probability post-install needed

indicatesexpectedcostpre-install

post-installif necessary

0.5

0.2

0.4

0.6

0.8

1

01.0

Cum

ula

tive

pro

babi

lity

Direct cost (£m)



• The value of simple ‘other objective’ assessments early on

• The value of simple initial cost estimates• The value of decision trees that do not need exact

probabilities or consequences• The value of the generality of a minimalist view of

uncertainty as part of a clarity efficient perspective• Many of the key ideas can be applied to all risk

management processes






Still use the working assumption that the

objective is maximizing the expected value of

M = B – C,


B = bid (price)

C = cost (direct),

but assume we want to understand the

expected value of C in total, and associated risk.


Cost estimate summary sheet example

comp item/option base min max exp choices/assumptions 1 mainframe etc 3.6 3.6 3.6 3.6 no choice 2 Astro 0.3 0.3 0.3 0.3 no choice Zenith 1.0 1.1 1.3 1.2 total 1.5 3 Zoro 1.0 1.2 1.4 1.3 if no hostile takeover Astro 0.8 0.7 1.1 0.9 preferred option 4 … omitted 5 to avoid making this slide too complex___________________________________________________ total direct cost 10.9 14.2 12.5 (£ million) Could we interpret this as 12.5 +/- 2 £ million?


Layered curves can show contributions, including simple linear curves if discrete outcomes are not portrayed, as shown here

0.5 1 2 3 … 5

0

1.0

Cost (£)

Cum

ula

tive

pr

obab

ility

even if precisenon-linear curvesare used, this portrayal suggestslimited cost risk



• The value of simple estimating processes

• The value of more complex estimating processes in

their own right and as the basis of simple estimates

• The key estimating process ideas have been used very successfully by a limited number of organisations



• Many of the key process ideas can be applied to all opportunity, risk and uncertainty management processes


• Seeking simplicity systematically in these processes is another key idea, introducing complexity where it pays being a crucial part of this






Still use the working assumption that the objective is maximizing the expected value of M = B – C, where M = margin (contribution to profit) B = bid (price) C = cost (direct), but assume now that we want to use an expected value for C from part 3, assume values for B, and understand the implications for M.


Cost estimate summary sheet example

comp item/option base min max exp choices/assumptions

1 mainframe etc 3.6 3.6 3.6 3.6 no choice

2 Astro 0.3 0.3 0.3 0.3 no choice

Zenith 1.0 1.1 1.3 1.2

total 1.5

3 Zoro 1.0 1.2 1.4 1.3 if no hostile takeover

Astro 0.8 0.7 1.1 0.9 preferred option

4

5

___________________________________________________

total direct cost 10.9 14.2 12.5 (£ million)

Say we round £12.5 million to £13 million


Probability of winning curve examples

Key:

example discrete values

discrete values of particular interest

preliminary probabilityof winning curve

assumed underlying curve

possible extrapolations

10 15 20 25B, Bid (£m)

P(B

),

prob

abili

ty o

f w

inn

ing

0.2

0.4

0.6

0.8

1

0

c1

c2

c3 b

a


Bid decision summary sheet example

Assuming expected direct cost estimate C = £13 million

B P(B) conditional M unconditional M notes

15 0.8 15 -13 = 2 2 x 0.8 = 1.6 buy work?

16 0.66 3 2.0

17 0.52 4 2.1 optimum for M

18 0.38 5 1.9

19 0.24 6 1.4 nominal price

20 0.1 7 0.7 overstretched?

The key risk is loosing business you want?



• The value of simple ‘other objective’ assessments early on

• The value of simpler and more complex cost estimates• The value of simpler and more complex bid curve

(probability of winning) estimates

What do more complex bid curve

estimates involve?


Probability of winning curves for composite competitor k and component competitor i, where k = i + j

competitor icompositecompetitor k

the space betweenthese lines indicates theimpact of competitor j

10 15 20

Adjusted bid (£m)

Pro

bab

ility

of

win

ning

0.2

0.4

0.6

0.8

1

0



• The key bidding process ideas have been used very successfully by a limited number of organisations

• Many of the key process ideas can be applied to all opportunity, risk and uncertainty management processes


• Seeking simplicity systematically in these processes is another key idea, introducing complexity where it pays being a crucial part of this, part of the overarching opportunity efficiency concept

Documents

Chapman and Ward HMPOR case slide 1 © 2011 John Wiley & Sons Ltd Samdo case study discussion Chris Chapman and Stephen Ward