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
Discussion starting point 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 1 0.8 0.6 0.4 0.2 0 indicates expected cost (also median) Cumulative probability Astro Zoro 0.7 0.9 1.1 1.3 1.5 Directcost (£m)
Convert existing programmes example 1 0.8 0.6 0.4 0.2 0 Datapol Sysdoc 2 Sysdoc 1 Sysdoc 3 Cumulative probability indicates expected cost (also median) 0.5 1.0 1.5 2.0 Direct cost (£m)
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
Some concluding comments 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
Transcon 2 case study discussion Chris Chapman and Stephen Ward 2 of 4 2011
Discussion starting point Keep the working assumption that we are maximizing the expected value of M = B – C, where M = margin (contribution to profit) 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 Cost to Astro pre-install Astro pay in advance £0.6m probability = 1.0 expected cost = £0.6m (1.0 x £0.6m) post-install if necessary extra memory needed Astro have to pay £1.0m 0.2 0.5 ? expected cost = £0.1m (0.2 x 0.5 x £1.0m) Astro do not have to pay £0m 0.5 no extra memory needed £0m Key: 0.8 decision node choices available indicated above ‘choice branches’ expected values indicated below ‘choice branches chance node alternative outcomes indicated above ‘chance branches’ probabilities indicated below chance branches’
Cumulative probability distribution portrayal 1 0.8 0.6 0.4 0.2 0 post-install if necessary pre-install indicates expected cost Cumulative probability 0.5 1.0 Direct cost (£m)
The ‘post-install if necessary’ option’s extra memory‘risk’ consistent with probability-impact grid portrayal 1 0.8 0.6 0.4 0.2 0 Probability 0.5 1.0 Direct cost (£m)
A revised decision tree to generalise Cost to Astro Astro pay in advance pre-install £0.4–0.8m expected cost = £0.6m (1.0 x £0.6m) probability = 1.0 post-install if necessary extra memory needed Astro have to pay £0.9-1.1m expected cost = £0.1m (0.2 x 0.5 x £1.0m) 0.3 – 0.7 0.1 – 0.3 Astro do not have to pay £0m 0.7- 0.3. no extra memory needed £0m Key: 0.8 decision node choices available indicated above ‘choice branches’ expected values indicated below ‘choice branches chance node alternative outcomes indicated above ‘chance branches’ probabilities indicated below chance branches’
Revised cumulative probability distributionwith 0.7- 0.9 probability post-install needed 1 0.8 0.6 0.4 0.2 0 post-install if necessary Cumulative probability indicates expected cost pre-install 0.5 1.0 Direct cost (£m)
Some concluding comments 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
Transcon 3 case study discussion Chris Chapman and Stephen Ward 3 of 4 2011
Discussion starting point 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 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 1.0 even if precise non-linear curves are used, this portrayal suggests limited cost risk Cumulative probability 0.5 1 2 3 … 5 0 Cost (£)
Linking this to common practice • 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
Some concluding comments • Many of the key 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, introducing complexity where it pays being a crucial part of this
Transcon 4 case study discussion Chris Chapman and Stephen Ward 4 of 4 2011
Discussion starting point 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 1 0.8 0.6 0.4 0.2 0 Key: c1 example discrete values c2 discrete values of particular interest c3 b preliminary probability of winning curve assumed underlying curve P(B), probability of winning possible extrapolations a 10 15 20 25 B, Bid (£m)
Bid decision summary sheet example Assuming expected direct cost estimate C = £13 million B P(B) conditional M unconditional M notes • 0.8 15 -13 = 22 x 0.8 = 1.6 buy work? • 0.66 3 2.0 • 0.52 4 2.1 optimum for M • 0.38 5 1.9 • 0.24 6 1.4 nominal price • 0.1 7 0.7 overstretched? The key risk is loosing business you want?
Linking this to common practice • 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?
1 0.8 0.6 0.4 0.2 0 Probability of winning curves for composite competitor k and component competitor i, where k = i + j the space between these lines indicates the impact of competitor j composite competitor k competitor i Probability of winning 10 15 20 Adjusted bid (£m)
Some concluding comments • 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 • Designing processes for contexts is an overarching key idea • 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
