scholarly journals Robust Design for Profit Maximization Under Uncertainty of Consumer Choice Model Parameters Using the Delta Method

2011 ◽  
Author(s):  
Camilo B. Resende ◽  
C. Grace Heckmann ◽  
Jeremy J. Michalek
Keyword(s):  
Optimal Design ◽  
Product Design ◽  
Choice Model ◽  
Profit Maximization ◽  
Downside Risk ◽  
Delta Method ◽  
Model Parameters ◽  
Design Decision ◽  
For Profit ◽  
Misspecification Error

In new product design, risk averse firms must consider downside risk in addition to expected profitability, since some designs are associated with greater market uncertainty than others. We propose an approach to robust optimal product design for profit maximization by introducing an α-profit metric to manage expected profitability vs. downside risk due to uncertainty in market share predictions. Our goal is to maximize profit at a firm-specified level of risk tolerance. Specifically, we find the design that maximizes the α-profit: the value that the firm has a (1−α) chance of exceeding, given the distribution of possible outcomes. The parameter α∈[0,1] is set by the firm to reflect sensitivity to downside risk (or upside gain), and parametric study of α reveals the sensitivity of optimal design choices to firm risk preference. We account here only for uncertainty of choice model parameter estimates due to finite data sampling when the choice model is assumed to be correctly specified (no misspecification error). We apply the delta method to estimate the mapping from uncertainty in discrete choice model parameters to uncertainty of profit outcomes and identify the estimated α-profit as a closed form function of design decision variables. This process is described for the multinomial logit model, and a case study demonstrates implementation of the method to find the optimal design characteristics of a midsize consumer automobile.

Robust Design for Profit Maximization With Aversion to Downside Risk From Parametric Uncertainty in Consumer Choice Models

2012 ◽  
Vol 134 (10) ◽  
Author(s):  
Camilo B. Resende ◽  
C. Grace Heckmann ◽  
Jeremy J. Michalek
Keyword(s):  
Optimal Design ◽  
Product Design ◽  
Choice Model ◽  
Profit Maximization ◽  
Downside Risk ◽  
Risk Tolerance ◽  
Parameter Estimates ◽  
Model Parameters ◽  
For Profit ◽  
Misspecification Error

In new product design, risk averse firms must consider downside risk in addition to expected profitability, since some designs are associated with greater market uncertainty than others. We propose an approach to robust optimal product design for profit maximization by introducing an α-profit metric to manage expected profitability vs. downside risk due to uncertainty in market share predictions. Our goal is to maximize profit at a firm-specified level of risk tolerance. Specifically, we find the design that maximizes the α-profit: the value that the firm has a (1 − α) chance of exceeding, given the distribution of possible outcomes. The parameter α ∈ (0,1) is set by the firm to reflect sensitivity to downside risk (or upside gain), and parametric study of α reveals the sensitivity of optimal design choices to firm risk preference. We account here only for uncertainty of choice model parameter estimates due to finite data sampling when the choice model is assumed to be correctly specified (no misspecification error). We apply the delta method to estimate the mapping from uncertainty in discrete choice model parameters to uncertainty of profit outcomes and identify the estimated α-profit as a closed-form function of decision variables for the multinomial logit model. An example demonstrates implementation of the method to find the optimal design characteristics of a dial-readout scale using conjoint data.

Measuring the effects of tourists’ relative willingness to spend and third-degree price discrimination on inbound tourism expenditure differentials

2021 ◽  
pp. 135481662110300
Author(s):  
Usamah F Alfarhan ◽  
Khaldoon Nusair ◽  
Hamed Al-Azri ◽  
Saeed Al-Muharrami ◽  
Nan Hua
Keyword(s):  
Price Discrimination ◽  
Profit Maximization ◽  
Tourism Industry ◽  
Theoretical Argument ◽  
Total Expenditure ◽  
Conditional Quantile ◽  
For Profit ◽  
Inbound Tourism ◽  
Tourism Expenditure ◽  
Quantile Estimates

Tourism expenditures are determined by a set of antecedents that reflect tourists’ willingness and ability to spend, and de facto incremental monetary outlays at which willingness and ability is transformed into total expenditures. Based on the neoclassical theoretical argument of utility-constrained expenditure minimization, we extend the current literature by applying a sustainability-based segmentation criterion, namely, the Legatum Prosperity IndexTM to the decomposition of a total expenditure differential into tourists’ relative willingness to spend and an upper bound of third-degree price discrimination, using mean-level and conditional quantile estimates. Our results indicate that understanding the price–quantity composition of international inbound tourism expenditure differentials assists agents in the tourism industry in their quest for profit maximization.

scholarly journals Impact of emission trading and renewable energy support scheme on the optimality of generator side bidding

2020 ◽  
Vol 167 ◽  
pp. 05008 ◽  
Author(s):  
A Arya ◽  
SPS Mathur ◽  
M Dubey
Keyword(s):  
Renewable Energy ◽  
Ghg Emissions ◽  
Profit Maximization ◽  
Emission Trading ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Developed Countries ◽  
Test System ◽  
For Profit ◽  
System Operator

As a major Green House Gases (GHG) producer, CO2 in particular, the electricity industry’s emissions have turned in to a matter of immense concern in many countries, especially in India. India’s economy and fast economic development has attracts the attention of the world. Emission trading schemes (ETS) and renewable energy support schemes (RESS) are implemented by the various developed countries to alleviate the affect of GHG emissions. In this paper, an optimization based market simulation approach is proposed with the consideration of emission trading schemes and renewable support schemes. To simulate the bidding strategy and for profit maximization, a particle swarm optimization (PSO) algorithm is used. As above problem is a multi-objective optimization problem, Where, in the first level each Genco submit the bid to the independent system operator and in the next level a optimization method is used for the determination of optimal bidding with the implementation of emission trading schemes and renewable support schemes. It is assumed that each generator should submit bid as a price taker’s in sealed auction based on pay-as-bid market clearing price mechanism. The practicability of proposed optimization method is checked by an IEEE-30 bus test system consists of six suppliers.

scholarly journals Cyber-Empathic Design: A Data Driven Framework for Product Design

2016 ◽  
Author(s):  
Dipanjan D. Ghosh ◽  
Junghan Kim ◽  
Andrew Olewnik ◽  
Arun Lakshmanan ◽  
Kemper E. Lewis
Keyword(s):  
Product Design ◽  
Structural Equation ◽  
Design Space ◽  
Equation Modeling ◽  
Design Decision ◽  
Interaction Data ◽  
Model Framework ◽  
Empathic Design ◽  
Product Interaction

One of the critical tasks in product design is to map information from the consumer space to the design space. Currently, this process is largely dependent on the designer to identify and map how psychological and consumer level factors relate to engineered product attributes. In this way current methodologies lack provision to test a designer’s cognitive reasoning and could therefore introduce bias while mapping from consumer to design space. Also, current dominant frameworks do not include user-product interaction data in design decision making and neither do they assist designers in understanding why a consumer has a particular perception about a product. This paper proposes a new framework — Cyber-Empathic Design — where user-product interaction data is acquired via embedded sensors in the products. To understand the motivations behind consumer perceptions, a network of latent constructs is used which forms a causal model framework. Structural Equation Modeling is used as the parameter estimation and hypothesis testing technique making the framework falsifiable in nature. To demonstrate the framework and demonstrate its effectiveness a case study of sensor integrated shoes is presented in this work, where two models are compared — one survey based and using the Cyber-Empathic framework model. It is shown that the Cyber-Empathic framework results in improved fit. The case study also demonstrates the technique to test a designers’ cognitive hypothesis.

Ride service outsourcing for profit maximization

2009 ◽  
Vol 45 (1) ◽  
pp. 138-148 ◽  
Author(s):  
Yihua Li ◽  
Xiubin Wang ◽  
Teresa M. Adams
Keyword(s):  
Profit Maximization ◽  
Service Outsourcing ◽  
For Profit

scholarly journals Overcoming the Problem With Solving Business Problems: Using Theory Differently to Rejuvenate the Case Method for Turbulent Times

2020 ◽  
Author(s):  
Todd Bridgman ◽  
C McLaughlin ◽  
Stephen Cummings
Keyword(s):  
Management Education ◽  
Profit Maximization ◽  
Case Method ◽  
Core Element ◽  
Capitalist System ◽  
For Profit ◽  
Not For Profit ◽  
Turbulent Times ◽  
Business As Usual ◽  
Critical Questioning

© 2018, The Author(s) 2018. A questioning of the neoliberal consensus in the global economic order is creating turbulence in Western democracies. Long regarded as the only viable capitalist model, neoliberalism is now subjected to increasing scrutiny. Management education that has been aligned to a neoliberal worldview must now respond to this shifting landscape in order to retain its legitimacy. One core element of management education undergoing revision as a result is the case method of teaching. The case method’s traditionally narrow focus on training students to solve business problems is increasingly problematic in an environment where the structure of the capitalist system in which firms operate is now a topic of debate. To address this, we argue for a reconceptualization of the case method’s relationship with theory. This has conventionally taken two forms: a hostility to any inclusion of theory in the analytical process and an approach that uses theory as an instrument for profit maximization. We propose an alternative third approach that encourages students to engage in a critical questioning of business-as-usual capitalism from the perspective of multiple stakeholders, including managers, employees, unions, not-for-profit organizations, government, and the natural environment.

scholarly journals BAYESIAN APPROACH TO THE D-OPTIMAL FOR MIXTURE EXPERIMENTAL DESIGN

2019 ◽  
Vol 3 (2) ◽  
pp. 109
Author(s):  
Uqwatul Alma Wizsa
Keyword(s):  
Optimal Design ◽  
Quadratic Model ◽  
Model Parameters ◽  
Potential Term ◽  
Lattice Design ◽  
Simplex Lattice Design ◽  
Simplex Lattice ◽  
Mixture Experimental Design ◽  
Special Case ◽  
Experimental Region

A mixture experiment is a special case of response surface methodology in which the value of the components are proportions. In case there are constraints on the proportions, the experimental region can be not a simplex. The classical designs such as a simplex-lattice design or a simplex-centroid design, in some cases, cannot fit to the problem. In this case, optimal design come up as a solution. A D-optimal design is seeking a design in which minimizing the covariance of the model parameter.  Some model parameters are important and some of them are less important. As the priority of the parameters, the prior information of parameters is needed in advance. This brings to a Bayesian D-optimal design. This research was focus on a baking experiment in which consisted of three ingredients with lower bounds on the proportion of the ingredients. The assumption model was a quadratic model. Due to the priority of the model parameters, the Bayesian D-optimal design was used to solve the problem. A point-exchange algorithm was developed to find the optimal design. Nineteen candidates is used to choose twelve design points. It found that the potential term is feasible to the actual model and design points represent overall points in the design area.

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