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Gül E. Okudan - One of the best experts on this subject based on the ideXlab platform.

  • ANALYSIS OF DYNAMIC PRICING SCENARIOS FOR Multiple-Generation PRODUCT LINES
    Journal of Systems Science and Systems Engineering, 2015
    Co-Authors: Nil Kilicay-ergin, Chun-yu Lin, Gül E. Okudan
    Abstract:

    In technology-intensive markets, it is a common strategy for companies to develop long-term Multiple Generation product lines instead of releasing consecutive single products. Even though this strategy is more profitable than sequentially introducing single product Generations, it can also result in inter-product line cannibalization. Cannibalization of Multiple-Generation product lines is a complex problem that needs to be taken into account at the early product line planning stage in order to sustain long-term profitability. In this paper, we propose an agent-based model that can simulate the potential cannibalization scenarios within a Multiple-Generation product line. We view a Multiple-Generation product line (MGPL) as complex adaptive system where each product Generation in the MGPL adjusts its sales price over time based on the shifts in the market demand. The proposed model provides insights into how various pricing strategies impact the overall lifecycle profitability of MGPL and can be used to assist companies in developing appropriate dynamic pricing strategies at the early product line planning stages.

  • Planning for Multiple-Generation product lines using dynamic variable state models with data input from similar products
    Expert Systems with Applications, 2013
    Co-Authors: Chun-yu Lin, Gül E. Okudan
    Abstract:

    Highlights? Proposed a model to forecast the sales behaviors and introduction timings of a new MGPL. ? The model incorporates a functional similarity analysis technique, DSVM and the use of Monte Carlo forward iteration. ? The model the advantages of low application difficulty and low computational complexity. ? The proposed model can effectively generate reasonable predictions. Multiple-Generation product lines require carefully planned strategies in order to reap the benefits of utilizing technology assets and resources efficiently over an elongated time span. In this paper, we build on our previous work in which we successfully implemented dynamic variable state models (DVSMs) to forecast the introduction timing of future Generations for an existing Multiple-Generation product line. Here we implement DVSMs for the design of a new Multiple-Generation product line. We investigate the potential for using historical sales data about similar products to generate a complete set of forecasts and relevant strategic moves using DVSMs. We present a case study implementing the proposed framework on Apple Inc.'s iPad product line. Results show that the forecast performance of the model matches the realized real data, and hence we deem the DSVM to be appropriate for modeling a new multi-Generation product line.

  • Application of Dynamic State Variable Models for Multiple-Generation Product Lines With Cannibalization Across Generations
    Volume 3: 38th Design Automation Conference Parts A and B, 2012
    Co-Authors: Chun-yu Lin, Gül E. Okudan
    Abstract:

    Multiple-Generation product strategy is favored in a variety of markets. Instead of introducing a single product to the market, companies incline to introduce a line of Multiple-Generation products to the market to better utilize technology assets and resources in an elongated time span. For such product development and launch scenarios, cannibalization can occur however. That is, when a new product Generation comes to the market, the current Generation is not withdrawn from the market but remains in the market to compete with the new one. In this research, we propose a new framework to predict the sales and introduction timing for every product Generation in a Multiple-Generation product line. Based on historical sales trend from a similar product of an existing and mature market, the proposed framework can effectively predict the performance of the entire product line over its lifecycle. In this study, we demonstrate a case study implementing the proposed framework on Apple Incorporation’s iPhone product line. The result shows that the forecast performance of the model is very close to real data.

  • Complex Adaptive Systems - Agent-based Modeling of Dynamic Pricing Scenarios to Optimize Multiple-Generation Product Lines with Cannibalization
    Procedia Computer Science, 2011
    Co-Authors: Chun-yu Lin, Nil Kilicay-ergin, Gül E. Okudan
    Abstract:

    Abstract In today's market, companies tend to develop long-term Multiple-Generation product strategies instead of releasing consecutive single products to maintain market competitiveness. Even though this strategy affords market vitality, it also can bring about inter-product-line cannibalization. Cannibalization within a Multiple-Generation product line is a complex problem, and it has seldom been explored. It indeed is a critical subject that companies need to take into account at the product line planning stage in order to ensure their long-term profitability. In this study, we view a Multiple-Generation product line as a complex adaptive system, and propose a new model that can simulate the potential cannibalization scenarios within a Multiple-Generation product line. The model concentrates on the price variations over time for every single Generation of product in a Multiple-Generation product line and is built upon an agent-based-methodology. Every product Generation in the product line is regarded as an independent agent, and is authorized to adjust its sales price according to the shifts in the market demand. The proposed model can assist companies in developing appropriate dynamic pricing strategies at the early product line planning stages.

Vivien M Cumming - One of the best experts on this subject based on the ideXlab platform.

  • re os geochronology and os isotope fingerprinting of petroleum sourced from a type i lacustrine kerogen insights from the natural green river petroleum system in the uinta basin and hydrous pyrolysis experiments
    Geochimica et Cosmochimica Acta, 2014
    Co-Authors: Vivien M Cumming, David Selby, Paul G Lillis, Michael D Lewan
    Abstract:

    Abstract Rhenium–osmium (Re–Os) geochronology of marine petroleum systems has allowed the determination of the depositional age of source rocks as well as the timing of petroleum Generation. In addition, Os isotopes have been applied as a fingerprinting tool to correlate oil to its source unit. To date, only classic marine petroleum systems have been studied. Here we present Re–Os geochronology and Os isotope fingerprinting of different petroleum phases (oils, tar sands and gilsonite) derived from the lacustrine Green River petroleum system in the Uinta Basin, USA. In addition we use an experimental approach, hydrous pyrolysis experiments, to compare to the Re–Os data of naturally generated petroleum in order to further understand the mechanisms of Re and Os transfer to petroleum. The Re–Os geochronology of petroleum from the lacustrine Green River petroleum system (19 ± 14 Ma – all petroleum phases) broadly agrees with previous petroleum Generation basin models (∼25 Ma) suggesting that Re–Os geochronology of variable petroleum phases derived from lacustrine Type I kerogen has similar systematics to Type II kerogen (e.g., Selby and Creaser, 2005a , Selby and Creaser, 2005b , Finlay et al., 2010 ). However, the large uncertainties (over 100% in some cases) produced for the petroleum Re–Os geochronology are a result of Multiple Generation events occurring through a ∼3000-m thick source unit that creates a mixture of initial Os isotope compositions in the produced petroleum phases. The 187Os/188Os values for the petroleum and source rocks at the time of oil Generation vary from 1.4 to 1.9, with the mode at ∼1.6. Oil-to-source correlation using Os isotopes is consistent with previous correlation studies in the Green River petroleum system, and illustrates the potential utility of Os isotopes to characterize the spatial variations within a petroleum system. Hydrous pyrolysis experiments on the Green River Formation source rocks show that Re and Os transfer are mimicking the natural system. This transfer from source to bitumen to oil does not affect source rock Re–Os systematics or Os isotopic compositions. This confirms that Os isotope compositions are transferred intact from source to petroleum during petroleum Generation and can be used as a powerful correlation tool. These experiments further confirm that Re–Os systematics in source rocks are not adversely affected by petroleum maturation. Overall this study illustrates that the Re–Os petroleum geochronometer and Os isotope fingerprinting tools can be used on a wide range of petroleum types sourced from variable kerogen types.

Antonio Hernando Escobar Zuluaga - One of the best experts on this subject based on the ideXlab platform.

  • Multiobjective Transmission Expansion Planning considering Multiple Generation Scenarios
    2014 IEEE PES Transmission & Distribution Conference and Exposition - Latin America (PES T&D-LA), 2014
    Co-Authors: Carlos Adrian Correa, Ricardo Andrés Bolaños, Antonio Hernando Escobar Zuluaga
    Abstract:

    This paper shows a methodology for solving the Transmission Expansion Planning (TEP) problem when Multiple Generation Scenarios (MGS) are considered. MGS are a result of the market based environment introduced by electricity deregulation. The solution to this problem is carried out by using multiobjective evolutionary strategies for the optimization process, implementing a new hybrid modified NSGA-II/Chu-Beasley algorithm. The proposed methodology is validated using the 6-bus Garver system and the IEEE-24 bus system. The TEP is based on the DC model of the network and non-linear interior point method is used to initialize the population. A set of Pareto optimal expansion plans with different levels of cost and load shedding is found for each system, showing the robustness of the proposed approach.

  • Multi-objective transmission expansion planning considering Multiple Generation scenarios
    International Journal of Electrical Power & Energy Systems, 2014
    Co-Authors: Carlos Adrián Correa Flórez, Ricardo Andrés Bolaños Ocampo, Antonio Hernando Escobar Zuluaga
    Abstract:

    Abstract This paper shows a methodology for solving the Transmission Expansion Planning (TEP) problem when Multiple Generation Scenarios (MGS) are considered. MGS are a result of the Multiple load flow patterns caused by realistic operation of the network, such as market rules, availability of generators, weather conditions or fuel prices. The solution to this problem is carried out by using multiobjective evolutionary strategies for the optimization process, implementing a new hybrid modified NSGA-II/Chu–Beasley algorithm and taking into account variable demand and Generation. The proposed methodology is validated using the 6-bus Garver system and the IEEE-24 bus system. The TEP is based on the DC model of the network and non-linear interior point method is used to initialize the population. A set of Pareto optimal expansion plans with different levels of cost and load shedding is found for each system, showing the robustness of the proposed approach.

Chun-yu Lin - One of the best experts on this subject based on the ideXlab platform.

  • ANALYSIS OF DYNAMIC PRICING SCENARIOS FOR Multiple-Generation PRODUCT LINES
    Journal of Systems Science and Systems Engineering, 2015
    Co-Authors: Nil Kilicay-ergin, Chun-yu Lin, Gül E. Okudan
    Abstract:

    In technology-intensive markets, it is a common strategy for companies to develop long-term Multiple Generation product lines instead of releasing consecutive single products. Even though this strategy is more profitable than sequentially introducing single product Generations, it can also result in inter-product line cannibalization. Cannibalization of Multiple-Generation product lines is a complex problem that needs to be taken into account at the early product line planning stage in order to sustain long-term profitability. In this paper, we propose an agent-based model that can simulate the potential cannibalization scenarios within a Multiple-Generation product line. We view a Multiple-Generation product line (MGPL) as complex adaptive system where each product Generation in the MGPL adjusts its sales price over time based on the shifts in the market demand. The proposed model provides insights into how various pricing strategies impact the overall lifecycle profitability of MGPL and can be used to assist companies in developing appropriate dynamic pricing strategies at the early product line planning stages.

  • Strategic decision making for Multiple-Generation product lines using dynamic state variable models: The cannibalization case
    Computers in Industry, 2014
    Co-Authors: Chun-yu Lin, Gül E. Okudan Kremer
    Abstract:

    Multiple-Generation product lines require carefully planned strategies. Under a Multiple-Generation product development strategy, companies introduce a line of products to the market instead of introducing a single product to better utilize technology assets and resources in an elongated time span. For such product development and launch scenarios, cannibalization can occur, however. That is, Multiple product Generations compete in the same market and partition the company's market shares. In the paper, we propose a new framework to predict the sales and introduction timing for every product Generation in a Multiple-Generation product line while considering cannibalization. We demonstrate a case study implementing the proposed framework on Apple Inc.'s iPhone product line. The results show that the forecast performance of the model matches the realized data. Moreover, because the proposed framework is not computationally prohibitive, it can be used widely.

  • Planning for Multiple-Generation product lines using dynamic variable state models with data input from similar products
    Expert Systems with Applications, 2013
    Co-Authors: Chun-yu Lin, Gül E. Okudan
    Abstract:

    Highlights? Proposed a model to forecast the sales behaviors and introduction timings of a new MGPL. ? The model incorporates a functional similarity analysis technique, DSVM and the use of Monte Carlo forward iteration. ? The model the advantages of low application difficulty and low computational complexity. ? The proposed model can effectively generate reasonable predictions. Multiple-Generation product lines require carefully planned strategies in order to reap the benefits of utilizing technology assets and resources efficiently over an elongated time span. In this paper, we build on our previous work in which we successfully implemented dynamic variable state models (DVSMs) to forecast the introduction timing of future Generations for an existing Multiple-Generation product line. Here we implement DVSMs for the design of a new Multiple-Generation product line. We investigate the potential for using historical sales data about similar products to generate a complete set of forecasts and relevant strategic moves using DVSMs. We present a case study implementing the proposed framework on Apple Inc.'s iPad product line. Results show that the forecast performance of the model matches the realized real data, and hence we deem the DSVM to be appropriate for modeling a new multi-Generation product line.

  • Application of Dynamic State Variable Models for Multiple-Generation Product Lines With Cannibalization Across Generations
    Volume 3: 38th Design Automation Conference Parts A and B, 2012
    Co-Authors: Chun-yu Lin, Gül E. Okudan
    Abstract:

    Multiple-Generation product strategy is favored in a variety of markets. Instead of introducing a single product to the market, companies incline to introduce a line of Multiple-Generation products to the market to better utilize technology assets and resources in an elongated time span. For such product development and launch scenarios, cannibalization can occur however. That is, when a new product Generation comes to the market, the current Generation is not withdrawn from the market but remains in the market to compete with the new one. In this research, we propose a new framework to predict the sales and introduction timing for every product Generation in a Multiple-Generation product line. Based on historical sales trend from a similar product of an existing and mature market, the proposed framework can effectively predict the performance of the entire product line over its lifecycle. In this study, we demonstrate a case study implementing the proposed framework on Apple Incorporation’s iPhone product line. The result shows that the forecast performance of the model is very close to real data.

  • Complex Adaptive Systems - Agent-based Modeling of Dynamic Pricing Scenarios to Optimize Multiple-Generation Product Lines with Cannibalization
    Procedia Computer Science, 2011
    Co-Authors: Chun-yu Lin, Nil Kilicay-ergin, Gül E. Okudan
    Abstract:

    Abstract In today's market, companies tend to develop long-term Multiple-Generation product strategies instead of releasing consecutive single products to maintain market competitiveness. Even though this strategy affords market vitality, it also can bring about inter-product-line cannibalization. Cannibalization within a Multiple-Generation product line is a complex problem, and it has seldom been explored. It indeed is a critical subject that companies need to take into account at the product line planning stage in order to ensure their long-term profitability. In this study, we view a Multiple-Generation product line as a complex adaptive system, and propose a new model that can simulate the potential cannibalization scenarios within a Multiple-Generation product line. The model concentrates on the price variations over time for every single Generation of product in a Multiple-Generation product line and is built upon an agent-based-methodology. Every product Generation in the product line is regarded as an independent agent, and is authorized to adjust its sales price according to the shifts in the market demand. The proposed model can assist companies in developing appropriate dynamic pricing strategies at the early product line planning stages.

Gwo-hshiung Tzeng - One of the best experts on this subject based on the ideXlab platform.

  • Multiple Generation Product Life Cycle Based Marketing Promotion Mix Strategy Definitions by Hybrid MCDM Methods
    2010
    Co-Authors: Chi-yo Huang, Ya-lan Cheng, Gwo-hshiung Tzeng
    Abstract:

    Marketing promotion strategies, which are about the advertisement, public relationship development, sales promotion, etc., are one of the most important components of the high technology marketing mix programs. In an era of fast product and process innovations, the innovative product life cycles (PLC) are becoming daily shorter. Most dominant high technology electronic system products including mobile phones, personal computers, etc. have demonstrated Multiple Generation characteristics. Thus, appropriate marketing promotion strategies are especially important for such Multiple Generation PLC system products so that suitable actions can be taken aiming at promoting the fast cycling electronic products with comparatively shorter PLCs. Meanwhile, marketing communication costs can be reduced by defining strategies for Multiple Generation products instead of aiming at some specific Generation. However, most existing researches on marketing promotion strategies in general and high technology marketing promotion strategies in special usually neglected the Multiple Generation characteristics of products. Further, most existing researchers on Multiple Generations PLCs have been focused more on technological forecasting while ignored the impacts of marketing mix variables. Be aware of the situation, this paper intends to propose an analytic framework for selecting the optimum portfolio of marketing promotion strategies at different PLC stages of electronic system products. A Multiple criteria decision making (MCDM) framework consisting of the Decision Making Trial and Evaluation Laboratory (DEMATEL) as the tool for configuring the decision problem structure, the Analytical Network Process (ANP) as the tool for calculating weights of each criterion, and finally, the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) as the tool for ranking the alternatives have been proposed for defining the marketing promotion strategies of Multiple Generation PLC system products. An empirical study on the liquid crystal display (LCD) televisions (TVs), one of the most popular consumer electronic products which have emerged during the past decade and demonstrated clear multi-Generation characteristics, will be selected to demonstrate the feasibility of this proposed hybrid MCDM methods. Possible marketing promotion strategies were first derived by literature review. Then, experts being invited from Taiwanese LCD TV vendors and market research institutes were invited for providing opinions for the proposed marketing promotion strategies. The Empirical analysis result shows that the best marketing promotion strategies over each PLC stage include: (1)

  • Multiple Generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method
    Technological Forecasting and Social Change, 2008
    Co-Authors: Chi-yo Huang, Gwo-hshiung Tzeng
    Abstract:

    Abstract Product life cycle (PLC) prediction plays a crucial role in strategic planning and policy definition for high-technology products. Forecast methodologies which can predict PLCs accurately can help to achieve successful strategic decision-making, forecasting, and foresight activities in high-technology firms, research institutes, governments, and universities. Over the past few decades, even though analytic framework strategies have been proposed for production, marketing, R&D (research and development), and finance, aiming at each stage of PLCs, forecast methodologies with which to predict PLCs are few. The purpose of this research is to develop a novel forecast methodology to allow for predictions of product life time (PLT) and the annual shipment of products during the entire PLC of Multiple Generation products. A novel two-stage fuzzy piecewise regression analysis method is proposed in this paper. In the first stage, the product life-time of the specific Generation to be analyzed will be predicted by the fuzzy piecewise regression line that is derived based upon the product life-time of earlier Generations. In the second stage of the forecast methodology, the annual shipment of products of the specified Generation will be predicted by deriving annual fuzzy regression lines for each Generation, based upon the historical data on the earlier Generations' products. An empirical study predicting the life-time and the annual shipment of the 16 Mb (Mega bit) DRAM (Dynamic Random Access Memory) PLC is illustrated to validate the analytical process. The results demonstrate that two-stage fuzzy piecewise regression analysis can predict Multiple Generation PLT and PLC precisely, thereby serving as a foundation for future strategic planning, policy definitions and foresights.