Co-evolution of demand and supply under competition

In this paper, we derive strategies to enforce dominance in a business-to-consumer market with heterogeneous, competing products, while the market segmentation evolves through interaction of demand and supply. By using evolutionary economic notions, we extend operations management studies on manufacturing facing demand diffusion. We arrive at a synthesis of a Forrester delay manufacturing model and a technology substitution-diffusion model and show that the actual operationalization of product attractiveness, reflecting what consumers deem important, as well as the responsiveness of production capacity scaling greatly determine the market dynamics and asymptotic outcome. We obtain analytic results on absolute dominance in case of the constant inherent attractiveness of products, say technical performance, and numerical results on instability and quasi-stability in case of more encompassing definitions of attractiveness involving price and service level. We conclude, in general, that in establishing market dominance, firms should focus on timely entry to capture first-buyers, high responsiveness and predatory pricing. Scale advantages and resilience through responsiveness are essential in obtaining and subsequently retaining the market share when other firms already provide or are about to enter with technically superior products. We also hint on how to extend our model to study several other issues on industry dynamics

Optimal lot-sizing, pricing, and product intergenerational lifestyle decisions for the case of disruptive innovations in fashion

The objective of this dissertation is to determine production schedules, production quantities, selling prices, and new product introduction timing to fulfill deterministic price-dependent demand for a series of products in such a way as to maximize profit per period. In order to accomplish the above task, some main assumptions are made. First, it is assumed that the series of products being considered are associated with sequential non-disruptive innovations in technology as well as disruptive innovations in fashion. That is to say, the products represent subsequent generations in the same family of products in an industry that experiences repeated minor technological innovations and in which product success is due in part to fashionability (Fisher, 1997). Second, it is assumed that the planning horizon is sufficiently long and product lifecycles are sufficiently short that several generations of the product family are planned. Third, it is assumed that the producer is following a solo-product roll strategy (Billington, Lee, & Tang, 1998). This means that the inventory of one product iteration is exhausted at the same time that the next product iteration is introduced and ready for sale. Fourth, it is assumed that demand for each product iteration is governed by a modified version of the Bass (1969) diffusion model that incorporates price. Fifth, it is assumed that the various demand and cost characteristics being considered do not change from one product iteration to the next. Sixth, it is assumed that no backlog of demand is maintained and that any unmet demand is lost. Seventh, it is assumed that the manufacturer is a monopolist or at least the dominant member of a market that is made up of it and smaller competitors that are not large enough to affect the market in a meaningful way. The formulated profit maximization problem uses the Thomas (1970) model which in turn depends in its solution on theorems first presented by Wagner and Whitin (1958a). An extensive numerical study that aims at examining the sensitivity of the planned product lifecycle length and profit per period to changes in model parameters is performed using software developed especially for that purpose. The results of the analysis reveal that the above two measures are more sensitive to changes in market-oriented parameters than to changes in operations-oriented parameters. Managerial implications of the research findings are discussed

A System Dynamics Innovation Diffusion Model Applied to Carbon Nanotube Manufacturing

The transition of advanced technologies from their nascent state to viable commercial entities is a critical step in assuring the United States\u27 national technological superiority and support is often required to incubate such technological developments. We propose a spiral development to investigate the possible scenarios, underlying economics, and risk associated with scaling up carbon nanotube (CNT) production to a commercially viable scale. As the first stage of this proposed effort, this study characterizes the potential scenarios by which a CNT manufacturing company can generate positive annual net revenue and potentially be considered a competitive manufacturer of CNT products on an industrial scale. Subsequent stages of this effort will determine the potential risks associated with investing in the current CNT research and production efforts from a macro perspective. This study investigates the necessary adoption fractions, contact rates, production capacities, production costs, product prices, monetary support, and time necessary for the company of interest to be considered a commercially viable producer of CNT products. The subsidization required to generate varied profit margins is also explored. The application of sys- tem dynamics models determined to approximately represent the real diffusion and production of both CNT sheet and CNT yarn products generates insight regarding policy improvement for the company to achieve competitive commercial CNT production and provide an assessment of when CNT production may be profitable. This study should not be used as the basis for decision making due to the fact that the analysis is based on notional data and scenarios

Demand Modeling And Capacity Planning For Innovative Short Life-Cycle Products

This dissertation focuses on demand modeling and capacity planning for innovative short life-cycle products. We first developed a new model in the class of stochastic Bass formulations that addresses the shortcomings of models from the extant literature. The proposed model considers the common fact that the market potential of a product is not fixed and might change during a life-cycle due to exogenous (e.g., economic- or competitors-related) or endogenous (e.g., quality-related) factors. Allowing this parameter (market potential in the Bass model) to follow a geometric random walk, we have showed that the future demand of a product in each period follows a lognormal distribution with specific mean and variance. We also developed a novel stochastic capacity expansion model that can be used by a make-to-order manufacturer, who faces stochastic stationary/non-stationary demand, in order to optimally determine policies for specifying the sizes of capacity procurement. In addition to the cost of expansion decisions, the proposed risk-neutral expansion model considers procurement lead-times, irreversibility of investments, and the costs associated with lost sales and unutilized capacity. We provide necessary and sufficient conditions for the derived optimal policy. We then present an exact solution method, which is more efficient than classical recursive methods. Additionally, three extensions of the proposed expansion model that can address more complicated settings are presented. The first extension increases the capability of the model in order to tackle capacity planning for a multi-sourcing scenario. Multi-sourcing is a case in which the manufacturer can procure capacity from two supply modes whose marginal expansion costs and lead-times are complementary. The second extension addresses a scenario in which an installed capacity can be used for producing future generations of a product. The last extension accounts for salvage value of the installed capacity in the model and provides the necessary and sufficient conditions for the optimal policy. Finally, using the proposed stochastic Bass model, we present the results and managerial insights gathered from numerical experiments that have been conducted for the stochastic capacity expansion models

The Optimal Climate Policy Portfolio when Knowledge Spills across Sectors

This paper studies the implications for climate policy of the interactions between environmental and knowledge externalities. Using a numerical analysis performed with the hybrid integrated assessment model WITCH, extended to include mutual spillovers between the energy and the non-energy sector, we show that the combination between environmental and knowledge externalities provides a strong rationale for implementing a portfolio of policies for both emissions reduction and the internalisation of knowledge externalities. Moreover, we show that implementing technology policy as a substitute for stabilisation policy is likely to increase global emissions.technical change, climate change, development, innovation, spillovers

REGIONAL ECONOMIC DIVIDE AND THE ROLE OF TECHNOLOGICAL SPILLOVERS IN ITALY. EVIDENCE FROM MICRODATA

This paper assesses the impact of R&D efforts on production in the North and Centre-South of Italy by using a panel of 1203 manufacturing firms over the period 1998-2003. The estimations are based on a nonlinear translog production function augmented by a measure of R&D spillovers. This measure combines the geographical distance between firms, the technological similarity within each pair of firms and the technical efficiency of each firm. The estimation method takes into account the endogeneity of regressors and the potential sample selection issue regarding firms’ decision to invest in R&D. Results show that the external stock of technology exerts a higher impact in the Centre-South of Italy. Finally, it emerges that R&D capital and R&D spillovers are substitutes for Northern firms and complements for Centre-Southern firms.R&D spillovers, Italian economic divide, translog production function, technical efficiency

Economic Development and Technology Diffusion

I first present a New Economic Geography model and analyze the impact of R&D on economic development of integrating countries. I find that technology diffusion and skilled labor migration stimulates economic development through fix cost reduction on a firm level. As the inclusion of foreign technology matters for structurally backward countries, I second use time series data for Greece, Portugal, Spain and Ireland representing European integration during the 1980s and 1990s. In considering three different technology diffusion channels, estimates, however, reduces to Portugal as test procedures confirm nonstationarity and cointegration only for this country. I find empirical evidence for bilateral trade as a diffusion channel but not for FDI or foreign patents.Economic Geography, Agglomeration, Technology Diffusion, Nonstationary Time Series