Investment In Capacity Expansion Research Articles

Sustainable and Resilient Capacity Expansion Planning for Chemical Processing Industry: A Bi-Objective Approach

According to United Nations reports, the worldwide population is expected to reach around 9.6 billion by 2050. This forecast emphasizes the critical role of energy and raw materials that are needed to meet the tremendous demand for goods. Consequently, firms feel pressured to establish sustainable and resilient strategic plans to acquire new process technologies and expand their capacity on time. These decisions are made at the highest management level, supported by a set of capacity expansion portfolios, to improve their competitiveness, especially in capital-intensive industries such as chemical processing. This paper investigates the sustainable and resilient capacity expansion problem for such industries within a long-term horizon. The main objective is to develop a holistic capacity expansion planning framework that fits the chemical processing industries and can be used to generate resilient scenarios while enhancing their sustainability measures. To this end, a bi-objective mixed-integer programming model was developed to solve the sustainability–resilience–profitability dilemma. The results showed a controversial relationship between the profitability of capacity expansion investments and a company’s commitment to sustainability and resilience. Furthermore, capacity expansion decisions were shown to primarily depend on the importance assigned to maximize profit as a managerial choice. However, there is no clear trend in sustainability preferences based on the sustainability weighting choice.

ICU capacity expansion under uncertainty in the early stages of a pandemic

We propose a general modular approach to support decision‐makers' response in the early stages of a pandemic with resource expansion, motivated by the shortage of Covid‐19‐related intensive care units (ICU) capacity in 2020 in Italy. Our approach uses (1) a stochastic extension of an epidemic model for scenarios of projected infections, (2) a capacity load model to translate infections into scenarios of demand for the resources of interest, and (3) an optimization model to allocate this demand to the projected levels of resources based on different values of investment. We demonstrate this approach with the onset of the first and second Covid‐19 waves in three Italian regions, using the data available at that time. For epidemic modeling, we used a parsimonious stochastic susceptible‐infected‐removed model with a robust estimation procedure based on bootstrap resampling, suitable for a noisy and data‐limited environment. For capacity loading, we used a Cox queuing model to translate the projected infections into demand for ICU, using stochastic intensity to capture the variability of the patient arrival process. Finally, we used stochastic dynamic optimization to select the best policy (when and how much to expand) to minimize the expected number of patients denied ICU for any level of investment in capacity expansion and obtain an efficient frontier. The frontier allows a trade‐off between investment in additional resources and the number of patients denied intensive care. Moreover, in the panic‐driven early days of a pandemic, decision‐makers can also obtain the time until which they can postpone action, potentially reducing investment costs without increasing the expected number of denied patients.

Dynamic expansion of flexible capacity with and without pricing coordination

This research presents an optimal multi-period dynamic capacity expansion framework for a shared flexible capacity producing two products. The framework allows modeling coordinated and non-coordinated capacity and marketing decisions under demand uncertainties. In our two-level decision process, the upper level considers the strategic capacity expansion and formulates multi-period uncertainty as a stochastic dynamic programming (DP) problem, and the lower level considers each period’s pricing and capacity allocation decisions in response to available capacity and market demand. We compare capacity expansion decisions under centralized pricing schemes that coordinate the pricing decisions of different products based on the overall interest of the firm and the decentralized pricing schemes based on the interests of individual product managers. Numerical results show that adopting a decision coordination strategy may help firms to maintain flexibility and reduce capacity expansion investment while enhancing overall profitability.

Improving supply system reliability against random disruptions: Strategic protection investment

Supply chains, as vital systems to the well-being of countries and economies, require systematic approaches to reduce their vulnerability. In this paper, we propose a non linear optimisation model to determine an effective distribution of protective resources among facilities in service and supply systems so as to reduce the probability of failure to which facilities are exposed in case of external disruptions. The failure probability of protected assets depends on the level of protection investments and the ultimate goal is to minimise the expected facility-customer transport or travel costs to provide goods and services. A linear version of the model is obtained by exploiting a specialised network flow structure. Furthermore, an efficient GRASP solution algorithm is developed to benchmark the linearised model and resolve numerical difficulties. The applicability of the proposed model is demonstrated using the Toronto hospital network. Protection measures within this context correspond to capacity expansion investments and reduce the likelihood that hospitals are unable to satisfy patient demand during periods of high hospitalisation (e.g. during a pandemic). Managerial insights on the protection resource distribution are discussed and a comparison between probabilistic and worst-case disruptions is provided.

Time to build: A real options analysis of port capacity expansion investments under uncertainty

Port capacity investments are large, expensive and uncertain. They also involve long construction lead times. Therefore, choosing the timing and size of port investments appropriately to serve trade growth is important. This paper presents a real options model to value the option of single-phase (service) port expansions with flexible investment timing and size under uncertainty. The model moreover takes the construction lead time into account. This model allows comparing investment in the expansion of a port already operating and generating revenues with an investment in a greenfield project. An existing port has incentives to protect users from congestion resulting in delays and to protect their port revenues. Therefore, the higher the port users’ aversion to waiting, the earlier the port is expanded and the smaller the expansion. Oppositely, in a greenfield project, increased waiting-time aversion leads to projects being undertaken later and being larger. The impact of increased time to build on both the investment timing and size is ambiguous, since this construction lead time increases uncertainty, but at the same time also reduces the present value of cash flows. Increased public ownership leads to earlier investment, but not necessarily more capacity.

Applying Innovative Performance Metrics for Corridor Evaluation

Widespread application of traditional performance metrics such as vehicle level of service has resulted in transportation networks that are often suited for only one mode of transportation to the detriment of others. Moreover, the continued investment in capacity expansion intended to meet level of service expectations increases financial burdens on transportation agencies for the capital, operating, and maintenance costs of these facilities, without regard to whether those resources are being used efficiently. Shifting focus to alternative and more comprehensive performance metrics can result in a more economical approach to transportation infrastructure, allowing communities to understand what investment combinations provide the best bang for the buck. Innovative performance metrics also allow communities to consider priorities beyond level of service, and design infrastructure accordingly. The Wasatch Front Central Corridor Study, undertaken in the Salt Lake City metropolitan area, is one example of a regional application of efficiency-focused performance metrics such as person throughput, seat utilization, benefit–cost ratio, access to employment, and economic impacts. Further research opportunities could include development of best practices to standardize benefit–cost analysis for transportation projects, other methods of evaluating overall system efficiency, and the role of parking policy and land use change in maximizing use of transportation resources.

Capacity expansion investment of supplier under make-to-order and make-to-stock supply chains

Capacity, and production mechanisms significantly affect the decisions and performance of supply chain with a supplier and a buyer. This study examines the influence of capacity expansion investment and supplier's make-to-order supply chain (MTO mechanism) or make-to-stock supply chain (MTS mechanism) on the equilibrium decisions and performance of supply chains. For each supplier's production mechanism, we derive the corresponding equilibrium wholesale price, order quantity, and capacities. We show that, when a supplier has sufficient existing capacity or the cost of capacity investment is small, the optimal capacity investment and total profit of a supply chain under MTS mechanism are always higher than that under MTO mechanism. By contrast, a MTS mechanism may induce low capacity and gain total profits lower than that of MTO mechanism. Our computational experiment shows that MTO mechanism outperforms MTS in scenarios with low uncertainty of demands; otherwise, MTS mechanism outperforms MTO mechanism. And if the cost of capacity is relatively small, the supply chain efficiency under MTS mechanism is always higher than that under MTO mechanism.

How Asset Irreversibility Influences the Investment-Uncertainty Relationship

This paper examines how industry-specific uncertainty affects firms’ investments for varying degrees of asset irreversibility (i.e. the wedge between purchase price and liquidation value of an asset). To identify more or less irreversible capital goods, we exploit unique survey data on German manufacturing firms over the sample period 2004 to 2012 in which managers provide information on investments’ purpose (capacity expansion, replacement, restructuring, rationalization, and other). We find that uncertainty only depresses capacity expansion investments, while non-capacity expansion investments such as replacement, restructuring and rationalization investments are not affected. Our results indicate that only investments into the most irreversible capital goods will decrease if uncertainty raises. We provide several tests confirming the robust impact of asset irreversibility on the investment-uncertainty relationship as we analyze several competing channels explaining the negative investment-uncertainty relation, such as the financial friction or the market power channel.

A stockyard planning problem

With over 100 millions tons of coal exported annually, the coal terminals at the Port of Newcastle are among the busiest in the world. To accommodate the anticipated rise in the export of coal in the next decade, effective stockyard management at the coal terminals will be essential. We have developed stockyard planning technology that can achieve substantially higher throughput levels. The technology intelligently combines greedy construction, enumeration, and integer programming. We demonstrate the efficacy of the technology on a variety of instances derived from real-life data and also show how the technology can be used to investigate the benefits of capacity expansion investments on throughput levels.

Determinants of Efficiency at Major Brazilian Port Terminals

This paper reports on the use of different approaches for measuring the efficiency of major Brazilian port terminals. Two of the most important approaches, DEA (Data Envelopment Analysis) and SFA (Stochastic Frontier Analysis), were performed on data collected from 25 terminals. The findings indicate that the majority of Brazilian terminals are running short on capacity due to the export boom that has occurred over the past few years and due to the lack of investment in capacity expansion. Furthermore, efficiency measurements derived from a reduced model with only one input and one output were explained based on variables such as type of cargo handled (container or bulk), connectivity of the terminal to railroads, and control (state or private) of the terminal. Implications and directions for future research are explored.

A multiobjective metaheuristic for a mean-risk multistage capacity investment problem with process flexibility

In this paper, we propose a multiobjective local search metaheuristic for a mean-risk multistage capacity investment problem with process flexibility, irreversibility, lumpiness and economies of scale in capacity costs. In each period, discrete decisions concerning the investment in capacity expansion, and continuous decisions concerning the utilization of the available capacity to satisfy demand are considered. We solve the capacity utilization problems with linear programming, in order to find the minimum capacity for each resource with the other resources remaining unchanged, this way providing information on the feasibility of the discrete investment decisions. Conditional value-at-risk is considered as a risk measure. Results of a computational study are presented, that show the approach is capable of obtaining high-quality approximations to the efficient sets, with a modest computational effort.

Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle

Semiconductor industry is capital intensive in which capacity utilization significantly affect the capital effectiveness and profitability of semiconductor manufacturing companies. Thus, demand forecasting provides critical input to support the decisions of capacity planning and the associated capital investments for capacity expansion that require long lead-time. However, the involved uncertainty in demand and the fluctuation of semiconductor supply chains make the present problem increasingly difficult due to diversifying product lines and shortening product life cycle in the consumer electronics era. Semiconductor companies must forecast future demand to provide the basis for supply chain strategic decisions including new fab construction, technology migration, capacity transformation and expansion, tool procurement, and outsourcing. Focused on realistic needs for manufacturing intelligence, this study aims to construct a multi-generation diffusion model for semiconductor product demand forecast, namely the SMPRT model, incorporating seasonal factor (S), market growth rate (M), price (P), repeat purchases (R), technology substitution (T), in which the nonlinear least square method is employed for parameter estimation. An empirical study was conducted in a leading semiconductor foundry in Hsinchu Science Park and the results validated the practical viability of the proposed model. This study concludes with discussions of the empirical findings and future research directions.

Cost of capital for incentives on capacity expansion investments

In this paper, the expression for the cost of capital is derived when capacity expansion investments and replacement investments exhibit differences in their effective prices. It is shown that the cost of capital derived by perturbing the optimal stock path should be constructed under an opportunity cost criterion.

A review of multi-factor capacity expansion models for manufacturing plants: Searching for a holistic decision aid

Investment in capacity expansion remains one of the most critical decisions for a manufacturing organisation with global production facilities. Multiple factors need to be considered making the decision process very complex. The purpose of this paper is to establish the state-of-the-art in multi-factor models for capacity expansion of manufacturing plants within a corporation. The research programme consisting of an extensive literature review and a structured assessment of the strengths and weaknesses of the current research is presented. The study found that there is a wealth of mathematical multi-factor models for evaluating capacity expansion decisions however no single contribution captures all the different facets of the problem.

Price, delivery time guarantees and capacity selection

This paper studies the impact of using delivery time guarantees as a competitive strategy in service industries where demands are sensitive to both price and delivery time. We assume that delivery reliability is crucial, and investment in capacity expansion is plausible in order to maintain a high probability of delivering the time guarantee. A mathematical framework is proposed to understand the interrelations among pricing, delivery time guarantee and capacity expansion decisions. Specifically, an optimization model is developed to determine the joint optimal selection of these three important decision variables, with an objective of maximizing the average net profit. We characterize the optimal decisions and study their qualitative behaviors as various parameters change. We further present a numerical example to illustrate how the results of our model can be used to provide useful managerial insights for selecting the best competing strategies for firms with different operating characteristics. Our model and results are also applicable to a make-to-order manufacturing environment.

Competition without privatisation – Norway's reforms in the power sector

Major reforms to Norway's electricity industry started in 1991, with the introduction of commercialisation and market-based pricing. Elements of reform include unbundling into separate entities for generation, transmission and distribution, the creation of a central regulatory body, the introduction of “point tariffs”, where the producers pay a tariff for deliveries into the grid and the consumers pay a tariff for taking power out of the grid, and the promotion of trading among around 300 entitites in spot markets, through bilateral contracts and futures markets. The main positive outcomes of the reforms are that real tariffs have dropped and investments for capacity expansion have reduced, reflecting greater efficiency in the sector.

An econometric analysis of the determinants of capacity expansion investment in the west european chemical industry

An econometric analysis of the determinants of capacity expansion investment in the west european chemical industry

The Leontief-Duchin-Szyld dynamic input-output model with reduction of idle capacity and modified decision function

The paper discusses modifications of the dynamic input-output model as formulated by Leontief, Duchin and Szyld. On the basis of theoretical arguments and empirical considerations a modification with respect to two properties of the model is proposed. Firstly, the reformulated model allows for the explicit retirement of idle capacity, a mechanism which is not included in the original model. Secondly, the decision function ruling the process of the implementation of capacity expansion investment is modified using the concept of capacity reserve. In ex post simulations for Germany it is demonstrated that the modified model is better in tracing the actual development of gross output and investment variables on the aggregate as well as on the sectoral level. It should be noted that the improved goodness of fit is at the expense of a complication of the model and a need for a kind of calibration process, if the model is implemented for different countries or different periods of time.

Discounted Cash-Flow Analysis of Capacity Expansion Investments by 0-1 Programming

Abstract The net present value criterion of discounted cash-flow analysis is the appropriate method for ranking the options in many capacity expansion decisions. This paper presents and discusses a 0-1 programming model that facilitates this type of analysis. Two actual implementations for Owens-Corning Fiberglas are discussed.

Investment Cost Minimization for Communications Satellite Capacity: Refinement and Application of the Chenery-Manne-Srinivasan Model

Chenery, Manne, and Srinivasan contributed to a model of investment in capacity expansion assuming a demand growth rate, a discount rate, and economies of scale. In their model, minimizing the sum of discounted future capacity investment costs always requires capacity replenishments to be equally spaced in time if summation is over an infinite time horizon. But the model assumes a static technology, no depreciation, and no effect of price on demand.