Guide for Customer-Driven Benchmarking of Maintenance Activities (2004) / Chapter Skim
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Appendix D - Assessing Value Added to Customers
Appendix D - Assessing Value Added to Customers
Pages 214-230
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From page 214... ...
AVOIDED USER COSTS There is a well-established convention among transport economists that road user costs should be calculated by the following formula: User Costs = Travel Time Costs + Vehicle Operating Costs + Accident Costs. In customer-driven benchmarking, it may be desirable to employ avoided user costs or some component of user costs as a customer-driven outcome measure.
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From page 215... ...
Indeed, many management and decision-support systems include estimation of road user costs, and the algorithms in those systems potentially can be used to develop performance measures for benchmarking. The Pontis Bridge Management System calculates travel time costs, vehicle operating costs, and accident costs as a function of deficiencies in clear deck width, vertical clearance, and load capacity of bridges.
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From page 216... ...
AVOIDED EXTERNAL COSTS A good example of external costs that can be avoided by road maintenance is the infestation by noxious weeds of farmland adjacent to a highway. Certain types of noxious weeds are destructive to crop yields and can significantly reduce the income of farmers.
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From page 217... ...
DISCOUNTING Three different types of economic costs have just been discussed: avoidable user costs, avoidable life-cycle costs, and avoidable external costs. Estimated avoidable costs do not all occur at the same time, but rather at different times in the future.
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From page 218... ...
The example reveals that a stream of avoidable future costs of $1,000 per year totaling $10,000 over 10 years has a present worth or discounted present value of $7,023.58 225 WILLINGNESS TO PAY Customers of road transport and, in turn, of road maintenance are willing to pay various amounts for different types of road maintenance. Road users and others do indeed pay gas taxes, property taxes, and other fees in order to support road maintenance costs.
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From page 219... ...
In a large number of the stated preference surveys conducted in the transportation field, the experiments are designed so that each factor influencing a choice is independent, thus allowing an independent estimate of the strength of each factor that influences a choice. Consultants assisting MnDOT developed stated preference survey instruments to assess willingness to pay for litter removal and various types of vegetation control.
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From page 220... ...
The results obtained from administering the stated preference survey were eventually incorporated into MnDOT's prototype decision-support system for benchmarking. 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 2 4 6 8 10 Litter Indicator Value W ill in gn es s to P ay (m in)
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From page 221... ...
Survey Instrument for Litter Control: Impact of Litter on Work Trips This survey was administered to focus groups of rural and urban residents. Each scenario was illustrated using digital photos of Roads A and B that were systematically altered in accordance with the experimental design to show focus group participants each level of each factor that affects their choice of taking Road A or Road B
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From page 222... ...
229 Figure D-4. Visual Graphics for Litter Control Surveys Road B (hardly any litter)
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From page 223... ...
An accounting framework that exhausts all benefits including avoidable agency costs and changes in consumer surplus would consist of the following: • Avoidable user costs, • Avoidable life-cycle costs, Appendix D: Assessing Value Added to Customers 230
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From page 224... ...
Any deviation from these actions, assuming the selection of actions is optimal, increases road user and life-cycle costs. These are the avoidable costs of optimal maintenance actions, ignoring, of course, externalities and changes in consumer surplus.
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From page 225... ...
Rather than be content with benchmarking output and outcome measures, MnDOT held firm to its conviction that above all, the value to the customer of road maintenance is the fundamental issue and should be the focus of any benchmarking effort. Accordingly, MnDOT contracted with a private firm for the development of a benchmarking process and prototype software to explore the relationships among inputs, outputs, outcomes, and the value added of maintenance products and services in a manner that adjusts for uncontrollable environmental factors such as weather, terrain, and road type.
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From page 226... ...
Production functions for certain outcomes and outputs for selected activities in MnDOT's Clear Roads and Attractive Roadsides products and service areas were successfully estimated based upon the fact that their coefficients were found to be statistically significant. Value Added The benchmarking framework developed was carefully structured to permit an assessment of the additional economic value a customer receives because of some incremental change in resources or uncontrollable factors.
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From page 227... ...
associated with estimated production based on average or other prescribed levels of resources with or without adjusting for uncontrollable factors. Figure D-5 shows an example output screen from the MnDOT decision-support software that illustrates a comparison among the output, outcome, and economic value of an activity instance involving plowing and sanding compared with estimated production using the same level of resources.
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From page 228... ...
Value: 43.94 $2,585.99 $68.49 36.72 10.46 $4,951.71 $131.15 7.22 $29.28 -5.00 $2,365.72 $62.66 $75,000.00 Output Elasticity Activity Instance Activity Estimate Activity Actual Net Value $148.98 5.46 1.00 $8.00Value of Time ($/hr) Cost/ Accident PRODUCTION FUNCTION
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From page 229... ...
The lower left quadrant of the screen shows the coefficients for two Cobb–Douglas production functions: one for the output production function and the other for the outcome production function. A Cobb–Douglas function has the property that the coefficients are equal to their elasticities: Output or Outcome Y = a0 X1a1 X2a2 X3a3, where X1 is a factor input and a1 is a coefficient.
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From page 230... ...
Both MnDOT staff and the consultant team recognized that the quality and completeness of the data would need to be improved over time and that the production functions would need to be re-estimated using less restrictive functional forms than the Cobb–Douglas production function. However, the feasibility of estimating production functions and making comparisons based on outcomes, outputs, and value added after adjusting for uncontrollable variables such as weather and terrain was established.
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