Cost/risk evaluation and optimal timing of DCS replacements

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1 Cost/risk evaluation and optimal timing of DCS replacements John Woodhouse 1

2 Phases needing a Life Cycle view Business impact Acquisition CAPEX Asset health & utilisation benefits OPEX, Risks & Minor Changes CAPEX Degradation, technology overtake, obsolescence, demand changes Normal Operation & Maintenance 2

3 Asset health, performance, condition, value e-related damage mechanisms Asset age 3

4 BSI PAS 55:2008 requirements 4

5 Major investments/project decisions Short term success pressure & KPI s Lack of data Departmental conflicts/boundaries Quality, reliability, performance & cost mix? Uncertain equipment life Time value/cost of money - DCF 5

6 Normal LCC Limitations Weak data No quantification of risk/reliability Asset life or horizon predetermined Difficult to compare options with different lives Taxation/accounting issues Capital rationing/budgeting Complexity & selling the recommendation KPI s often reinforce short-termism, cheapest solutions 6

7 European MACRO project EU1488 Best practices in asset management decision-making November May 2000 Primary sponsors: The Woodhouse Partnership (Project Managers) Asset Performance Tools (Software Development) Anglesey Aluminium UK Government (DTI) Brown & Root Det Norske Veritas National Power Institute of Asset Mgmt National Grid Railtrack Shell Norway ICI Eutech PDVSA Yorkshire Electricity Decision types: Capital investments Equipment purchase (life cycle cost) Renewal timing Upgrades & modifications Opportunities & shutdown bundles O&M decisions Maintenance intervals Inspection/condition monitoring intervals Condition reaction points Functional testing strategies Purchasing & materials decisions Purchase quantities & thresholds etc Strategic spares 7

8 All the variables 8

9 What is the right objective? Business impact ( k/yr) TRUE OPTIMUM Balance point but not optimal TOTAL IMPACT Failure risks + O&M costs Planned Replacements Life Cycle (years) 9 9

10 Cost/risk/perf. calculation Sophisticated analysis now: G.I.G.O. Better Decisions How we interpret and use the data Iterative, what if? analysis: find which assumptions really matter & what data is therefore worth collecting Subjective judgement Bad Decisions 2 to 3 (-ish) Get better data first usually fails (what data, when, how good?) Quality of Data Guesstimates 10 Measurement

11 Using uncertain information Total costs & risks ( k/day) Inspection Interval (days) 11

12 Weak data the disciplines 1. Ask the RIGHT QUESTIONS 2. Of the RIGHT PEOPLE 3. In the RIGHT WAY 4. And SHOW THEM HOW THE DATA IS BEING USED 12

13 Normal LCC Limitations Weak data No quantification of risk/reliability Asset life or horizon predetermined Difficult to compare options with different lives Taxation/accounting issues Capital rationing/budgeting Complexity & selling the recommendation KPI s often reinforce short-termism, cheapest solutions 13

14 Different life determinations 0.12 Probability of Failure (per month) 0.1 Reactive opex-driven decisions Risk optimised strategy Useful Life rule-based 14 Age (months)

15 Quantifying the bath-tub 1. Range estimate the CUMULATIVE effects e.g. infant mortality = 1-5%, thereafter most (90+%) would reach 12 months but few (<20%) could reach 48 months without failure Survival % Time since new (months) 2. Use computer to fit curves & calculate probability implications Failure probability Failure distribution 15

16 The Maths! - T h(t)dt Fcost X 1 - e 0 T - h(x)dx e dt 0 h(t) = Hazard Rate at time t since last maintenance/renewal 16 16

17 Normal LCC Limitations Weak data No quantification of risk/reliability Asset life or horizon predetermined Difficult to compare options with different lives Taxation/accounting issues Capital rationing/budgeting Complexity & selling the recommendation KPI s often reinforce short-termism, cheapest solutions 17

18 Equivalent Annual Cost All immediate & future cashflows converted into an annualized, ongoing equivalent rate & discounted into today s equivalent value EAC = r A + M 1 + n i= 2 1 p p i 1 n M i p n S n A = Acquisition cost. M i = Operating & Maintenance cost in year of life i. S n = Resale value at age n r = Discount rate p = 1/(1+r) i.e. the discount factor 18

19 APT-LIFESPAN modelling Decision Support Tools Ltd

20 Describing the problem Guided capture of tacit knowledge (quantification of deterioration, performance, risk patterns etc) Uncertainty in costs & failure consequences Audit trail for assumptions Utilities to look up hard data, standard/ default rates, operational impact etc Decision Support Tools Ltd 2007 * see 20

21 Result: optimal replacement time Proposed new asset: optimal life cycle and total cost of ownership Optimal time to replace current asset 21

22 SABIC IP DCS renewal example 22

23 Sensitivity testing 23

24 Conclusions & implications Failure modes often interact so simplistic models (FMEA, Weibull etc, that treat each risk separately) are rarely valid Life Cycle modelling a) must include CAPEX, OPEX & Risks/Performance b) should not use NPV if different lives are being explored Structured use of tacit knowledge is vital (range-estimating is often more valuable that lots of confusing, mixed quality hard data) Data sensitivity is not what everyone thinks! Business impact of such optimisation (refining what is worth doing, and when) is c.10x more valuable than just improving efficiency (doing things faster or cheaper) 24

25 3-year R&D project: Best practice definition Process & methods guidance Decision-support tools Case studies & templates Started Sept 2009 Completion due Scope: identification of optimal strategies and decisions for managing asset aging & degradation, obsolescence, renewals, refurbishments and changing inspection & maintenance requirements.

26 SALVO Project: systematic version Asset population & system segmentation: e.g. Types, age, condition, performance, criticality Whole portfolio needs, costs & performance prediction e.g. Capital investment plans, resourcing needs, performance forecasts Hard data & tacit knowledge Problem characterisation & quantification e.g. Degradation, risk, performance, costs Asset health, performance, condition, value Uncertain mix of random/external and age- or use-related damage mechanisms Identification of suitable actions & options e.g. Inspection, maintenance, modification, renewal Asset age Asset health, performance, condition, value Uncertainty compounded by Variable deterioration rates Quality of measurement Variable functional demand & usage 1. Various options to repair, refurbish & extend life Asset age 2. Renewal options & timing optimisation 3. Upgrade, change or dispose? Asset whole life cycle integration & optimisation e.g. Design, operation, inspection, maintenance, modification, renewal, disposal Optimisation of individual interventions or actions on individual assets. 26