1. Systematic Asset Management Jan Holmström DIEM, SCI, Aalto University 2015-10-05

2. Overview Potential benefits of asset management Paradigmatic example: Arizona Pavement Management System Systematic asset management: combining asset information modeling and monitoring for continuous improvement Enablers for increasing the scope of asset management

3. 3 Three viewpoints on a system Integrated viewpoint * Production or value adding process User or asset owner Service provider Performance management Service delivery * Note: Performance interests also the society. E.g. energy consumption! Asset management

4. 4 Systematic Management perspective Integrated viewpoint Mechanisms (M): Production or value adding processes User or asset owner Service provider Evidence of outcome (O): Performance management Interventions (I): Service delivery Problem in contexts (C): The opportunities for service interventions

5. Different types of generative mechanisms for performance outcomes Provider interventionsGenerating mechanism Outcome for asset owner SpecializeReuse of skillsImproved availability of inputs Focus on core competencies StandardizeReuse of processes and designs Reduced cost and improved quality of inputs TrackExtend use of assets and resources Reduced value destruction Improved quality of lifecycle

6. 6 Performance improvement: specialize and standardize Value creation Time Specialize and standardize Productivity: Reduced cost and improved quality of delivery

7. 7 Performance improvement: informed mode Value creation Time Specialize and standardize Productivity Longer life cycle + asset accumulation Track and upgrade

9. Why look at pavement management system? GENERAL: SIMPLE “FRUIT-FLY” EXAMPLE It is a challenge to model wear and tear, as well as the impact of maintenance actions in preventive, predictive, and condition based maintenance. Pavement management system gives an idea of what is required for a solution. SPECIFIC: INFRA SUCCESS CASE Management wants close control over costs and maintenance actions. Pavement management example shows how this can be achieved for a highway network. 9

10. Problem situation 1970’s Shift from new construction to preserving existing roads Federal funds could only be spent on roads meeting specific guidelines Seven districts planned and performed maintenance almost autonomously => Corrective and wasteful maintenance actions Budget cuts. Important to be able to evaluate alternative preservation policies quickly and reliably Keeping track of the condition of the roads is difficult, let alone knowing the proper maintenance action to take Many factors must be considered in deciding how to maintain a particular mile of the road … altitude, average temperature, moisture conditions, structural properties, and traffic density … 10

11. Core process for predictive maintenance Core process is: model + plan + track + improve –Model Identify the asset “individuals” to be managed Condition indicators; Roughness and cracking Model effect of maintenance actions, Predict time to first cracking –Plan Aim for improved performance at fixed cost level Plan maintenance actions –Track Maintenance actions; Condition (Visual inspection, Mays meter) –Improve Missing information? Change model? Basis for effectiveness is not that the model is accurate, but more accurate than ad hoc allocation of work. Note benefit of long-term management of network, based on better “road individual” info. 11

12. Arizona PMS: Dividing the problem Roads divided into nine road categories Defined as combinations of average daily traffic and a regional environmental factor that depends on several climatic conditions. Elevation and rainfall were the primary variables used to define the regional factor on a scale of 0 to 5. Traffic density and the regional factor are independent of the preservation action, each pavement remains in one road category, regardless of what actions are taken. =>This way nine networks, each of which can be modeled using the conditions and actions 12

13. Asset management model 13 MANAGEMENT INPUTS ENGINEERING INPUTS Model Tracking & Planning Long-term performance standards Cost estimates, feasible actions, condition states, road categories Short-term performance standards, planning horizon, discount factor Transition probabilities Current road condition Long-term actions, average costs and road conditions Short-term actions, expected cost, predicted road conditions

14. How it all works Recommends preservation policies that achieve long- term and short-term standards for road conditions at lowest possible cost. When the optimal policy is followed a steady-state condition will be achieved. The proportion of roads in each condition and the expected budget requirements will remain constant To reach a steady-state uncertainties in the budget, and what the budget can buy needs to be reduced. Probabilistic nature of road deterioration may mean that at times a large proportion of roads may need immediate repair or some work needs to be postponed to future 14

15. … and in detail: Using the asset model for optimization Transition probabilities link current road conditions and maintenance actions to future road conditions A transition probability specifies the likelihood that one mile of the pavement will change its condition from one condition to another if a preservation action is applied. Possible to calculate the proportion of the statewide network expected to be in any given condition for a given maintenance policy. The performance of the network is measured in terms of these proportions. Least-cost actions sought that maintain that at least a certain proportion of pavements are in, or above the desirable condition levels. 15

16. Tracking road condition Model and track individual road segments Pavement roughness and the amount of cracking are used to describe road condition –present roughness (3 levels) measured by a driving on the road with a "Mays Meter” –present amount of cracking (3 levels) is the highway engineers' rating using pictures showing different percentages of cracking –a specific state could be defined as roughness = 50 cm/km and cracking =5% For prediction an index to the first crack (5 levels) is used. –based on what and when was the last non-routine action (Note: only used for roads not cracked!) 16

17. Maintenance actions 17 alternate maintenance actions –routine maintenance –substantial corrective measures –action example is: resurface with three inches of asphalt. For every feasible action a pavement in a given condition state can only go to three or four states. For feasible actions, only 3 percent of the transition probability matrix is nonzero. 17

18. Setting performance goals 18 Average Daily Traffic Minimum Proportion of Roads with Acceptable Roughness Maximum Proportion of Roads with Un- acceptable Roughness Minimum Proportion of Roads with acceptable Cracking Maximum Proportion of Roads with Un- acceptable Cracking 0-2.0000,500,250,600,25 2.001-10.0000,600,150,700,20 > 10.0000,800,050,800,10

19. Outcome Savings of 30% realized in its first year (1980-81) from $46m to $32m Forecast savings were 101 million dollars over the next five years. Management decision process changed from a subjective non-quantitative method Actions from mostly corrective to preventive maintenance Similar systems were then immediately planned for other states, and eventually it became federally mandated that pavement management systems had to be implemented. 19

20. Learning from APMS: Direct learning points 1.What are the types of things you need to include in an asset model if you want to have predictive maintenance? 2.How can a predictive model and reality be kept synchronized? 3.What are the most important tasks left for management after an effective asset model has been introduced? 20

22. Increasing relevance beacuse of smart assets August 2007, Missisippi River Bridge near Minneapolis fails and results in 13 deaths ”If a car can be smart enough to spot when the oil is low or a brake has failed, why not do the same for bridgers, tunnels and buildings?” Technological enablers –Wireless sensor network to monitor temerature, vibration and strain –Examples: Humber Bridge UK; Golden Gate US; Jino Bridge South Korea 22

23. 23 Example: Performance improvement business Energy Service Companies (ESCo) are firms that help businesses and families to trim their energy bills –From $3.6 to $5.1 billion business to in the US from 2006 to 2011; yearly growth about 10 % Business model: –ESCos design a scheme to reduce a building's energy bill, borrow money for equipment, installs and maintains it over a fixed period. Clients pay ESCo's for the savings –ESCos unburden their balance sheets and lower their borrowing costs by securitizing revenues Challenge is to widen market to smaller customers where the transaction costs tend to outweigh the savings –One solution is for municipalities to aggregate many similar properties and “mediate” contracts between small customers and equipment manufacturers, ESCos and banks

24. Closing the gaps for performance improvement in built environment Performance modelling Solution design Use and operations User requirements Technical specification Budget User experience Technical performance Actual costs Construction As-built Identify improvement opportunities Evidence of outcome Performance monitoring As- designed

25. Closing the gaps: Possible energy futures? Guarantee performance of solutions (design and construction companies) Allocate investment where best pay back (property portfolio owners) Mobilize consumers and intelligent devices to deliver demand flexibility (utility companies) Alignment of business logic in multi-firm networks (utility, contractors, designers, service providers) Do you have more ideas?