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Incorporating end-user models and associated uncertainties to investigate multiple stakeholder preferences in system design


The essence of systems engineering lies in enabling rational decision-making that is consistent with the preferences of the system’s stakeholders. Modern approaches, such as value-driven design, attempt to convey the true preferences of the stakeholder using mathematical formulations like value models. A critical step to the formation of value models is the identification of the stakeholders. A primary stakeholder must be identified and then it must be determined how the other stakeholders’ preferences impact the preference of the primary, if they do at all. This paper looks at three stakeholders of an electric vehicle system, all of which could be considered the primary stakeholder dependent on the situation. Novel customer, commercial, and government-oriented value models are created. To understand the impact of customers on the primary stakeholder’s designs, an end-user value-based demand model is developed and a method for integrating end-user preferences into the manufacturer’s value model is demonstrated. Uncertainties associated with the end-users, including those associated with the economy, are quantified and incorporated into a value-based design framework through Monte Carlo simulations. Possible stakeholder risk attitudes are discussed and a rational decision-making strategy to maximize stakeholder’s system value under uncertainty is presented. The resulting designs and the influences of the multiple stakeholders are discussed, showing that the identification and incorporation of the important stakeholders are critical to the systems engineering process and value-based design in particular.

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\({\text{Vt}}\) :

Top-level customer value model

\({\text{Ct}}\) :

Top-level representation of costs occurring to the customer

\({\text{Bt}}\) :

Top-level representation of customer benefits

\({\text{Sp}}\) :

Purchase cost or selling price of the vehicle

\({\text{Gt}}\) :

Taxes associated to the vehicle

\({\text{Gp}}_{\text{j}}\) :

Government penalty/incentive on vehicle

\({\text{CPM}}\) :

Cost to operate vehicle per mile

\({\text{MC}}_{\text{j}}\) :

Annual maintenance cost of the vehicle

\({\text{CI}}_{\text{j}}\) :

Annual insurance cost of the vehicle

\({\text{SV}}_{\text{j}}\) :

Salvage value of the vehicle

\(r_{\text{p}}\) :

Customer’s discount rate

\(l\) :

Number of years the customer owns the vehicle

\({\text{Br}}\) :

Average annual cost of a rental vehicle

\({\text{Bn}}\) :

Benefit of passenger capacity

\({\text{Bp}}\) :

Benefit of performance

\({\text{Hp}}\) :

Horsepower of the vehicle

\({\text{Pd}}\) :

Downtime penalty function due to time lost at recharging

\({\text{Rev}}\) :

Range of the vehicle

\({\text{Rave}}\) :

Average range of a gas-powered vehicle

\(t_{\text{charge}}\) :

Charging time of the vehicle

\(\pi\) :

Profit of the commercial manufacturer

\({\text{IC}}_{\text{total}}\) :

Costs occurring to the commercial manufacturer

\(Q\) :

Quantity of vehicles the commercial manufacturer sold

\(Q_{\text{comp}}\) :

Quantity of vehicles the competitor sold

\({\text{CI}}_{\text{k}}\) :

Startup investment cost occurring to the government

\({\text{Cv}}\) :

Investment/infrastructure cost of the manufacturer

\({\text{Cd}}\) :

Total design cost of the vehicle

\({\text{Cm}}\) :

Manufacturing cost of the vehicle

\({\text{Ctr}}\) :

Transportation cost of the vehicle (plant to store)

\(C_{\text{pv}}\) :

Cost to manufacture a single vehicle

\(r_{\text{c}}\) :

Discount rate of the manufacturer

\(m\) :

Project duration in years

\({\text{PM}}\) :

Profit margin of the manufacturer

\(V_{\text{comp}}\) :

Perceived customer value of the competitor

\(V_{\text{new}}\) :

Perceived customer value of the new vehicle

\({\text{ElPop}}_{\text{comp}}\) :

Eligible population of the competitor

\({\text{ElPop}}_{\text{new}}\) :

Eligible population of the new vehicle


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Topcu, T.G., Mesmer, B.L. Incorporating end-user models and associated uncertainties to investigate multiple stakeholder preferences in system design. Res Eng Design 29, 411–431 (2018). https://doi.org/10.1007/s00163-017-0276-1

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  • Value-based design
  • Systems engineering
  • Design under uncertainty
  • Complex engineered systems
  • End-user modeling