When and how much to invest? Investment and capacity choice under product life cycle uncertainty

Lukas, Elmar; Spengler, Thomas S.; Kupfer, Stefan; Kieckhäfer, Karsten

doi:10.1016/j.ejor.2017.01.027

Cited by 31 publications

(15 citation statements)

References 27 publications

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“…Based on these arguments, we propose the following hypothesis: H1: CAPEX from internally generated cash flows is positively correlated with long-term price trends Furthermore, investments and cash flows are highly interdependent. Investments are associated with projected future cash flow volumes, and anticipated sales positively impact investment volumes [15]. In addition, investments are sensitive to the availability of internal funding.…”

Section: A Price Trend and Demand Uncertaintymentioning

confidence: 99%

Manufacturing Management in Process Industries: The Impact of Market Conditions and Capital Expenditure on Firm Performance

Moser

Okwir

Seifert

2021

IEEE Trans. Eng. Manage.

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Process industries operate in commodity markets with low product variety, little product differentiation, and highly capital-intensive manufacturing. Because prices are volatile and demand is uncertain, manufacturing capabilities become the main strategic competitive lever. The purpose of this paper is to evaluate the interplay between market conditions, capital expenditure, manufacturing flexibility, and production capacity to effectively analyze the consequences of manufacturing investments in both the short and the long terms. In order to achieve this, we developed an econometric model linking market conditions and operational decisions. We tested the model on a sample of 480 firms from the mining sector and 1053 firms from the oil and gas sector. The results show that firms in process industries follow the market with their investments, which impacts their operations in the short term as well as their capacity growth in the long term. Additionally, from our findings, we propose a firm value driver model that managers could consider when deciding on capital expenditures. Finally, our results indicate that the managers of financially healthy companies should resist the stock market's short-term pressure to reduce fixed costs and instead play the long game.

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Section: A Price Trend and Demand Uncertaintymentioning

confidence: 99%

Manufacturing Management in Process Industries: The Impact of Market Conditions and Capital Expenditure on Firm Performance

Moser

Okwir

Seifert

2021

IEEE Trans. Eng. Manage.

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“…For instance, Nishihara and Ohyama (2008), based on Weeds (2002), investigated the choice between two alternative technologies, while Nishihara (2012) studied dynamic management of multiple investment projects with synergies. Several papers including Huisman and Kort (2015), Shibata and Nishihara (2015a), and Lukas, Spengler, Kupfer, and Kieckhafer (2017) examined both investment timing and sizing decisions.…”

Section: Model Setupmentioning

confidence: 99%

Valuation of an R&D project with three types of uncertainty

Nishihara

2018

EURO Journal on Decision Processes

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This paper develops an R&D decision-making model in the real options framework. The model is generic enough to capture three types of uncertainty in an R&D project, namely, uncertainty of research duration and costs, market value of technology, and a competitor's technology development. I derive analytical solutions, which help practitioners and researchers to evaluate various cases of R&D investment. Further, by analyzing the model with a wide range of parameter values, I reveal the following effects of the three types of uncertainty on R&D investment: Higher uncertainty of research duration and costs, unlike market value uncertainty, speeds up investment, especially combined with a higher risk of competition. The investment timing can be U-shaped in the strength of competition because of the trade-off between the preemptive investment effect and the decreased project value effect. These results can account for empirical findings about the uncertainty-investment relation in industries with high R&D intensity and severe competition.

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“…Doing so, benefits the investment decisions of companies regarding the introduction of new products. Our methodological framework may also be of use in optimal stock management [18].…”

Section: Discussionmentioning

confidence: 99%

Forecasting product sales with a stochastic Bass model

Grasman

Kornelis

2019

J.Math.Industry

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With the Bass model and data of previous sales a point estimate of future sales can be made for the purpose of stock management. In order to obtain information about the accuracy of that estimate a confidence interval can be of use. In this study such an interval is constructed from a Bass model extended with a noise term. The size of the noise is assumed to be proportional with the yearly sales. It is also assumed that the deviation from the deterministic solution is sufficiently small to make a small noise approximation. This perturbation takes the form of a time dependent Ornstein-Uhlenbeck process. For the variance of the perturbation an exact expression can be given which is needed in order to obtain confidence intervals.

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When and how much to invest? Investment and capacity choice under product life cycle uncertainty

Cited by 31 publications

References 27 publications

Manufacturing Management in Process Industries: The Impact of Market Conditions and Capital Expenditure on Firm Performance

Manufacturing Management in Process Industries: The Impact of Market Conditions and Capital Expenditure on Firm Performance

Valuation of an R&D project with three types of uncertainty

Forecasting product sales with a stochastic Bass model

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