Browsing by Author "Demirci, Ece Zeliha"

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Open Access
Designing intervention strategy for public-interest goods
(2016-09) Demirci, Ece Zeliha
Public-interest goods, which are also referred as goods with positive externalities, create benefits to individual consumers as well as non-paying third parties. Some significant examples include health related products such as vaccines and products with less carbon emissions. When positive externalities exist, the good may be under-produced or under-supplied due to incorrect pricing policies or failing to value external benefits and that is why a need for intervention arises. A central authority such as government or social planner intervenes into the system of these goods so that their adoption levels are increased towards socially desirable levels. The central authority seeks to design and finance an intervention strategy that will impact the decisions of the channel in line with the good of the society, specified as social welfare. A key issue in designing an intervention mechanism is choosing the intervention tools to incorporate. The intervention tools can target the supply or demand of the good. One option for the intervention tool is investment in demand-increasing strategies, which affects the level of stochastic demand in the market. Second option is investment in strategies that will improve supply of the good. Alternatives for this option include registering rebates or subsidies and investment in yield-improving strategies when production process faces imperfect yield. As several real life cases indicate, central authority operates under a limited budget in this environment. Thus, we introduce and analyze social welfare maximization models with the emphasis on optimal budget allocation. We model the lower level problem, which represents the channel as a newsvendor problem. We then utilize bilevel programming for modeling the environment incorporating the role of central authority. After obtaining single level equivalent formulations of the problems, we analyze and solve them as non-linear programs. Our first problem is to analyze an intervention strategy, which uses only subsidy issued per unit order quantity. We explore the subsidy design problem for single retailer and n retailers cases. We show that all of the budget will be used under mild conditions and present structural results. Also, we analyze subsidy design problem for two echelon setting, where the central authority gives subsidy both to retailer and manufacturer. We consider centralized and manufacturer-driven problems and present numerical results. In the remaining part of the thesis, we focus on joint intervention mechanisms in which two intervention tools are applied simultaneously. First, we study a joint mechanism composed of demand-increasing strategy and rebate. We present two models and associated structural properties. First model aims to find optimal budget and allocation of it among intervention tools. We deduce that rebate amount may be independent of investment made in demand-increasing strategies and improvement pattern of demand. Second model decides on the optimal allocation of a given budget between intervention tools. We show that central authority will allocate all budget under mild conditions. Furthermore, we use real-life data and information of California electric vehicle market in order to verify the proposed models and show benefits of taking such an approach. We also explore the application of the joint mechanism under a given budget for exponentially distributed demand family and fully characterize the optimal solution. The analysis of the solution reveals that designing an intervention scheme without considering an explicit budget constraint will result in budget deficit and excess money transfers to the retailer. As the second modeling environment we consider a joint mechanism consisting of demand-increasing strategy and yield-improving strategy in a setting where yield uncertainty exists. We introduce lognormal demand and yield models that take into account the investments made for improving them. We test the suggested model with a case study relying on the available estimates of US influenza market. The results indicate that addressing both demand and yield issues by the proposed mechanism will increase vaccination percentages remarkably.
Embargo
Integrating efforts for product development and market penetration
(Elsevier BV, 2023-07-16) Demirci, Ece Zeliha; Erkip, Nesim
In this paper, we build a decision model to explore an innovative firm’s budget allocation problem, which needs to be solved for each successive generation of a product. The firm introduces the product to the market through a distributor while aiming to maximize the market potential. This goal can be achieved by investing in R&D and increasing availability using subsidies registered to the distributor. We analyze the problem using a game theoretical model and provide a guideline for the funding strategy. We show that the optimal budget allocation decision is characterized by two budget thresholds and a threshold on the cost efficiency of R&D. We identify and analyze the effects of two significant parameters, total available budget and efficiency level of R&D, on the optimal solution. In addition, we assess the model’s applicability by examining the expected excess budget requirement and the distributor’s expected profit. We provide valuable managerial insights on when and how to prioritize the two components of the budget.
Open Access
Order timing for seasonal products with demand learning and capacity constraints
(2009) Demirci, Ece Zeliha
Order time and order quantity of seasonal products significantly affect profits gained at the end of the period due to high demand uncertainty. Delaying order time enables a company to gain more information on demand, while decreasing the possibility of realizing the best order quantity due to capacity constraints. This thesis analyzes the problem of determining the best order time for a seasonal product manufacturer in an environment, where there exists a single opportunity for ordering and capacity is a decreasing function of the order time. Main feature of the study is utilizing demand information collected until the order time for resolving some portion of the demand uncertainty. A Bayesian update procedure is utilized to capture the essence of the gathered demand information. Three models are proposed for determining the order time, each having a different level of flexibility with respect to possible order times considered. Analytical results for structural properties, as well as extensive numerical results are obtained. A computational study is carried out in order to compare the performance of the models under different settings and to identify the conditions under which the demand learning is most beneficial.