Volume flexibility and capacity investment under demand uncertainty

Hagspiel, Verena; Huisman, Kuno; Kort, Peter M.

doi:10.1016/j.ijpe.2016.05.007

Cited by 76 publications

(57 citation statements)

References 17 publications

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“…Thus, the utilization rate decreases with uncertainty when the firm produces below capacity right after the investment. The utilization rate being decreasing with uncertainty for an intermediate α, α = 0.02 for example, is consistent with the findings in Hagspiel et al (2012). If α is large, i.e.…”

Section: Market Uncertaintysupporting

confidence: 87%

“…The utilization rate is equal to the ratio q * /K * with q * = q(θ * , K * ). It has been shown that for the unbounded demand function p(t) = θ(t) − γq(t), at given level of the market trend, the utilization rate decreases significantly with market uncertainty (see Hagspiel et al (2012)). However, this section shows that for our model, if the market trend is large enough, the utilization rate increases with market uncertainty.…”

Section: Numerical Analysismentioning

confidence: 99%

“…This leads to the at first sight counterintuitive result that the utilization rate increases with uncertainty. However, an intermediate market trend still results in an utilization rate being decreasing with uncertainty as in Hagspiel et al (2012), and a moderately large market trend yields a non-monotonic utilization rate.…”

Section: Introductionmentioning

confidence: 99%

“…When entering a market, it is not only the timing (investment trigger or threshold) that is important, but also the size of the production capacity market demand is so high that the firm produces up to capacity right after the investment, while Hagspiel et al (2012) do consider such a possibility and derive that the utilization rate decreases when the market uncertainty increases. Compared to Hagspiel et al (2012), we adopt a slightly different demand function, which, however, leads to different results.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to Hagspiel et al (2012), we adopt a slightly different demand function, which, however, leads to different results. The difference in demand function is that the market size is unbounded in the work of Hagspiel et al (2012), while we have a bounded market size. The latter can be motivated by the presence of market maturity.…”

Section: Introductionmentioning

confidence: 99%

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Volume flexibility and capacity investment: a real options approach

Wen

Kort

Talman

2017

Journal of the Operational Research Society

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This paper considers the investment decision of a firm where it has to decide about the timing and capacity. We obtain that in a fast growing market, right after investment the firm produces below capacity, where the utilization rate (the proportion of capacity that is used for production right after the investment) increases with market uncertainty for a very big market trend, and shows no monotonicity for a moderately large market trend. On the other hand we get that, for a slowly growing or shrinking market, the firm produces up to capacity right after investment. In the intermediate case, the firm produces up to capacity right after investment when uncertainty is low and below capacity when uncertainty is high, whereas the utilization rate decreases with the market uncertainty.

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Section: Market Uncertaintysupporting

confidence: 87%

Section: Numerical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Volume flexibility and capacity investment: a real options approach

Wen

Kort

Talman

2017

Journal of the Operational Research Society

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Modularity measures: Concepts, computation, and applications to manufacturing systems

Shao

Zavala

2020

AIChE Journal

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We propose a measure to quantify the modularity of industrial production (manufacturing) systems and optimization formulations to compute it. From a manufacturing perspective, we argue that a system is deemed modular if: (a) the equipment units that comprise it form clusters (modules) of dense connectivity (i.e., difficult module assembly tasks are performed off-site), (b) connectivity between modules is sparse (i.e., easy assembly tasks are performed on-site), (c) the number of modules is small, and (d) the module dimensions facilitate transportation. We show that the measure proposed satisfies these requirements and that it can be computed by solving a convex mixed-integer quadratic program. We provide a discussion on advantages and disadvantages of alternative modularity measures used in different scientific and engineering communities. Our results seek to highlight conceptual and computational challenges that arise from the need to define and quantify modularity in a manufacturing context.

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When to invest in electric vehicles under dual credit policy: A real options approach

et al. 2023

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This research aims to investigate traditional vehicle manufacturers' green technology investment theory under dual credit policy from the perspective of real options, overcoming earlier investigations of this issue that considered it only from a stability or single uncertainty perspective. An analytical real options model was first provided for traditional automaker investment. Then we solved the analytical solution for the electric vehicle investment threshold based on the uncertainty of credit price and fuel vehicle market scenarios. The optimal electric vehicle investment timing is demonstrated using numerical simulation. Results show that (1) when the fuel vehicle market demand falls to a certain level, automakers will choose to make electric vehicle investments regardless of how the credit price changes in the market; (2) the effect of volatility on the investment threshold depends on the covariance or correlation coefficient; (3) the numerical simulation results revealed that the credit price drift rate, risk‐free rate, correlation parameters, and electric vehicle production cost all have a positive impact on the electric vehicle investment region, whereas the drift rate of fuel vehicle and electric vehicle production cost have a negative impact. These results can be used to make theoretical conclusions about electric vehicle investments.

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Volume flexibility and capacity investment under demand uncertainty

Cited by 76 publications

References 17 publications

Volume flexibility and capacity investment: a real options approach

Volume flexibility and capacity investment: a real options approach

Modularity measures: Concepts, computation, and applications to manufacturing systems

When to invest in electric vehicles under dual credit policy: A real options approach

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