Cost Uncertainty Research Articles

Operational cost uncertainty and financial performance of manufacturing firms in Kenya

The main objective of this study was to find out if uncertainty about the operational costs of manufacturing firms in Kenya affects financial performance. Kenyan manufacturing firms have not been performing as expected. They are meant to contribute to economic growth through GDP increments and market share, attract the largest strategic investments in the key processing industry, increase sales locally and internationally, and employ 20% of the Kenyan population. However, manufacturing firms have been facing various financial and non-financial challenges, including declining profit and sales, and some firms have moved out of the market. Many factors have been cited as contributing to declining financial performance. However, the influence of operational cost uncertainty on the financial performance of manufacturing firms in Kenya is not conclusive. Some studies found a negative relationship, while others found a positive relationship. Hence, the current study necessitates examining the influence of firm operational cost uncertainty on the financial performance of manufacturing firms in Kenya. The study anchored its variable on agency theory, which states that during financial uncertainty, operational costs are volatile. Indicators of operational cost uncertainty were the labour cost ratio and the research and development ratio, and proxies of performance were ROS and ROE. The study adopted positivism, philosophy, and an explanatory design. The target population was 856 manufacturing firms registered with the Kenya Association of Manufacturers. A sample of 90 firms was drawn from the population using the Nasuirma (2000) formula. The stratified random sampling technique was applied to 14 sectors, and each sample was picked by random sampling. The study covered 12 years, starting from 2009 to 2020. Panel data was collected from audited financial statements using a data collection instrument. Results showed that operational cost uncertainty had a positive and significant influence on the financial performance of manufacturing firms in Kenya. The labour cost ratio had a negative and significant relationship with the financial performance of manufacturing firms in Kenya. The research and development ratio had a positive and significant impact on the financial performance of manufacturing firms in Kenya. The study recommends manufacturing firms have enhanced technology in place to help reduce production costs. The study recommends having enhanced research and development in place that will take advantage of the market niche for products and technology for production.

Hybrid data-driven and physics-based simulation technique for seismic analysis of steel structural systems

This paper proposes 1) a new hybrid analysis technique by integrating a data-driven method with a physics-based technique to perform nonlinear analysis of steel structural systems under seismic loading, 2) two component-based data-driven models (PI-SINDy and DPI-SINDy) for predicting the nonlinear hysteretic response of steel seismic fuses with and without hysteretic degradation. The proposed hybrid data-driven and physics-based simulation (HyDPS) technique offers an efficient approach for seismic analysis of structures and is expected to address the challenges associated with computational cost and modeling uncertainties inherent in physics-based numerical simulations. In this technique, the well-understood components of the structure modeled numerically are combined with the critical components of the structure simulated using one of the data-driven models developed in this study. The proposed data-driven models were trained using experimental and numerical hysteresis data. The results show that these data-driven models can accurately and efficiently predict the nonlinear hysteretic response of steel structural components with and without degradation. Furthermore, the performance of the HyDPS technique powered by the PI-SINDy model is verified in the presence of noise using response history analyses performed on a steel buckling-restrained braced frame.

Whether and When to Invest in Transportation Projects: Combining Scenarios and Real Options to Manage the Uncertainty of Costs and Benefits

Transportation infrastructure projects are a cornerstone of economic growth. However, the issue of whether new transportation infrastructure projects deliver the expected benefits has come under considerable scrutiny. The growing economic uncertainty and the tightening of budget constraints have made the design, evaluation, and selection of such high-cost projects particularly critical. There are disagreements as to how project decision-makers can evaluate the long-term costs and benefits of infrastructure projects. The objective of this article is to address such disagreements. We develop and apply an innovative methodological approach that combines real options with scenarios to help policymakers assess the costs and benefits of transportation projects. While these techniques have been widely adopted in corporations, there is little empirical evidence regarding their combined use by project decision-makers dealing with complex infrastructure projects. In this article, we fill this gap in the planning and project studies literature. We show that scenarios and real options can be very helpful in developing a more comprehensive understanding of long-term impacts of major infrastructure projects and thus in selecting the most relevant projects. Overall, our article assists the debate on the management of the uncertainty of long-term costs and benefits of infrastructure projects and helps cope with such uncertainty.

Cost Uncertainty, Financial Aid, and the Enrollment Choices of Low-Income Students

Cost Uncertainty, Financial Aid, and the Enrollment Choices of Low-Income Students

Homo Heuristicus: From Risk Management to Managing Uncertainty in Large-Scale Infrastructure Projects

Large-scale infrastructure projects tend to experience variances between estimated and final costs. Governments, in response, have been using statistical methods (including probabilistic approaches) such as reference class forecasting to try and mitigate cost overruns. While helpful in accommodating risk, such statistical methods often fail to account for a project's cost uncertainty. To reduce cost variance under uncertainty, it is necessary to use heuristics when formulating an infrastructure project's cost contingency. This article argues a need to adopt a vision of human nature described as Homo heuristicus (heuristics-using-person). We provide awareness and rationale for developing an “adaptive toolbox” of heuristics for ecologically rational and contextually aligned cost contingency decision-making. However, the selection, recognition, and evaluation heuristics for determining cost uncertainty have yet to be examined. Thus, future research is required to cultivate the heuristics needed to address the uncertainty that surrounds the determination of a cost contingency.

Optimization of Neutrosophic Vendor-Buyer Economic Order Quantity Model Using Particle Swarm Optimization

This research introduces the Neutrosophic Vendor-Buyer Economic Order Quantity (EOQ) model, integrating Neutrosophic Set Theory and Particle Swarm Optimization (PSO) for advanced inventory management. Addressing uncertainties in demand and costs, Neutrosophic Sets quantify truth, indeterminacy, and falsity degrees for key parameters. The model, employing PSO inspired by collective behaviour in nature, aims to minimize the combined total cost (C) encompassing vendor and buyer expenses. A grocery store scenario illustrates the approach, demonstrating substantial total cost reduction through the optimization of decision variables. MATLAB R2015a visualizations include a mesh plot depicting cost changes across varying EOQ and demand variability values, emphasizing optimal solutions. A bar chart compares initial and optimized total costs, showcasing efficiency gains. Cost breakdowns and pie charts detail the impact on vendor and buyer expenses. Sensitivity analysis systematically explores variable influences, aiding decision-makers in understanding trade-offs and optimal ranges by using Python. This comprehensive framework contributes empirical insights for practical implementation, enabling businesses to make informed decisions and enhance adaptive inventory strategies efficiently.

Operational research application to minimise the uncertainty of time and cost in the main domains of industrial engineering by using quality engineering techniques

Operational research application to minimise the uncertainty of time and cost in the main domains of industrial engineering by using quality engineering techniques

Optimal Operation of Distribution Networks Considering Renewable Energy Sources Integration and Demand Side Response

This paper demonstrates the effectiveness of Demand Side Response (DSR) with renewable integration by solving the stochastic optimal operation problem (OOP) in the IEEE 118-bus distribution system over 24 h. An Improved Walrus Optimization Algorithm (I-WaOA) is proposed to minimize costs, reduce voltage deviations, and enhance stability under uncertain loads, generation, and pricing. The proposed I-WaOA utilizes three strategies: the fitness-distance balance method, quasi-opposite-based learning, and Cauchy mutation. The I-WaOA optimally locates and sizes photovoltaic (PV) ratings and wind turbine (WT) capacities and determines the optimal power factor of WT with DSR. Using Monte Carlo simulations (MCS) and probability density functions (PDF), the uncertainties in renewable energy generation, load demand, and energy costs are represented. The results show that the proposed I-WaOA approach can significantly reduce costs, improve voltage stability, and mitigate voltage deviations. The total annual costs are reduced by 91%, from 3.8377 × 107 USD to 3.4737 × 106 USD. Voltage deviations are decreased by 63%, from 98.6633 per unit (p.u.) to 36.0990 p.u., and the system stability index is increased by 11%, from 2.444 × 103 p.u. to 2.7245 × 103 p.u., when contrasted with traditional methods.

Implications of uncertainty in technology cost projections for least-cost decarbonized electricity systems

Plans for decarbonized electricity systems rely on projections of highly uncertain future technology costs. We use a stylized model to investigate the influence of future cost uncertainty, as represented by different projections in the National Renewable Energy Laboratory 2021 Annual Technology Baseline dataset, on technology mixes comprising least-cost decarbonized electricity systems. Our analysis shows that given the level of future cost uncertainty as represented by these projections, it is not possible to predict with confidence which technologies will play a dominant role in future least-cost carbon emission-free energy systems. Successful efforts to reduce costs of individual technologies may or may not lead to system cost reductions and widespread deployments, depending on the success of cost-reduction efforts for competing and complementary technologies. These results suggest a portfolio approach to reducing technology costs. Reliance on uncertain cost breakthroughs risks costly outcomes. Iterative decision-making with learning can help mitigate these risks.

Stochastic Economic Ranking Addresses Risk and Uncertainty for Decision-Makers

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 213385, “Stochastic Economic Ranking: A Prudent Way To Address Risk and Uncertainty for Decision-Makers,” by Mahesh Narayanan, SPE, Mehaboob Abdulazeez, SPE, and Khadija Bukhamsin, SPE, Saudi Aramco, et al. The paper has not been peer reviewed. _ Project screening and ranking is inevitable even when an organization faces no resource bottlenecks. In project economic predictions, the stochastic approach is prevalent and proves to be the most natural approach to address technical and commercial uncertainties. In contrast with a deterministic approach, a stochastic Monte Carlo simulation helps in understanding the range of possible outcomes to enable effective decision-making. The objective of the complete paper is to present an integrated stochastic work flow that addresses the subsurface, surface, and cost uncertainties to screen, rank, and grade opportunities early in the derisking process. Introduction The proposed work flow simultaneously can address all technical and commercial uncertainties with a Monte Carlo model and predict a range of possible economic outcomes with customized economic analysis tools. In other words, it can achieve complete unbiased sample randomness in expected project outcomes. The work flow also creates a stage-gate decision-tree approach and reviews economics as a series of staged investments. This helps to expose incremental capital in the life cycle of a project responsibly and help with course correction where necessary. Value Addition: Stochastic Approach A common practice in the oil and gas industry is to simply examine low, middle, and high cases. This is a limited view of possible scenarios and provides a false sense of precision regarding expected project outcomes. The term “stochastic” refers to the property of being well-described by a random probability distribution. A stochastic approach features the following advantages: - A stochastic model addresses uncertainties in inputs with complete unbiased randomness in the outcomes. - By running thousands of calculations and using many different estimates of future economic conditions, stochastic models predict a range of possible future investment results, showing the potential upside and downside of each. - Once the work flow is followed and the model is set up, multiple realizations can be created with a single click. Proposed Integrated Stochastic Methodology In the world of project economics, classical stochastic economics is a tool to address uncertainty and risk. In simple terms, a mean or deterministic view of a production profile, along with one view of a schedule, is taken and blended with probabilistic distribution of cost inputs. This data is then taken through a Monte Carlo model to create multiple scenarios to predict a range of possible economic outcomes. This narrow range of economic outcomes only covers the uncertainty in cost but does not cover variability in well performance and time.

Whole Life Cycle Cost Analysis of Transmission Lines Using the Economic Life Interval Method

With the large-scale construction and commissioning of transmission lines over the past two decades, the grid is facing a large-scale centralized decommissioning of transmission lines. The transmission line’s economic life is crucial to rationalizing its construction and reducing the grid’s development costs. Based on the minimum economic life calculation principle, the static and dynamic transmission line economic life calculation model is established, considering the whole life cycle for transmission line cost. The improved gray GM (1,1) model is applied to forecast cost data during the economic life assessment of transmission lines with fewer samples. Considering the cost uncertainty in life-cycle costing, the interval cost model based on the coefficient of variation wave amplitude is proposed to determine the economic life intervals under different guarantees by using the normal distribution probability density function, which reduces the influence of cost fluctuations on the economic life calculation error. The economic life analysis of a 500 kV transmission line is used as a case study to verify the model’s accuracy and effectiveness. The method shows the economic life intervals under different guarantee degrees based on the most probable economic life determination, which provides theoretical support for calculating the economic life elasticity of transmission lines.

Investment Plan and Evaluation of the Transition of a Farm to a Wellness Tourist Farm

This study aimed to develop a commercial concept for transforming farms into wellness tourist destinations. The proposed scenario involved the renovation of a hayrack into a highend tourist accommodation, comprising relaxation rooms with panoramic glass walls and the renovation of the barns, as well as an outdoor swimming pond. The research methods includeddescription, compilation, and synthesis to explore the legislation in Slovenia related to investment performance and trends in wellness tourism to support the realisation of the proposed investment. Furthermore, the study used the method of financial estimation of investment using cost-benefit analysis to facilitate the transition. Four datasets were used for the estimation: investment income, investment costs, end value of the investment, and annual interest rate. The estimated investment cost was €530,000 and the total estimated revenue was €192,720, with total costs amounting to 50% of the total income, as well as an annual cash flow of €96,360, which was used in the assessment of the investment return period. According to the findings, the investment return period is 15 years with the lowest annual cash flow and interest rate of 3.5%. However, caution is advised due to uncertainties in the long-term costs of raw materials and energy.

Quantifying uncertainties in greenhouse gas savings and abatement costs with cellulosic biofuels

Abstract Cellulosic biofuels from non-food feedstocks, while appealing, continue to encounter uncertainty about their induced land use change (ILUC) effects, net greenhouse gas (GHG) saving potential and their economic costs. We analyse the implications of multiple uncertainties along the biofuel supply chain from feedstock yields, land availability for production to conversion to fuel in the refinery on these outcomes. We find that compared to corn ethanol, cellulosic biofuels have a substantially smaller and less uncertain ILUC-related GHG intensity and lead to larger GHG savings at lower welfare costs of abatement, indicating the potential to make robust and substantial contributions to cost-effective climate change mitigation.

Influence of Human Resources Management Practices on Organisational Performance of Construction Firms in Lagos State –Nigeria

The success of any organisation depends on the effective and efficient utilisation of its human resources. In every project-based industry, such as the construction industry, project performance is tied to the effectiveness of its human resources. Labour turnover in the Nigerian construction industry and the poor performance of the Nigerian construction industry in terms of project delay, cost uncertainty, and quality deficiency is caused by inadequate utilisation of human resources management practices. The study aimed to assess the influence of human resources management practices on the organisational performance of construction firms in Lagos State in order to improve their competitive edge. A quantitative research method was adopted involving administration of 140 well-structured questionnaires to registered construction firms. The results revealed that human resources management practices (selection, training, development and reward) have a positive and statistically significant influence on organisational performance, with a p-value of <.001, <.003, <.003 and <.002 respectively. While the other practices (recruitment and compensation) did not significantly influence organisational performance. This indicates that human resources management practices are important to the success of organisations. Therefore, in order to overcome the challenges of labour dissatisfaction at work resulting from organisational instability, poor working conditions, lack of training, and reducing the low performance of the employees in the construction industry, appropriate and adequate human resources management strategies, policies, and action plan provisions should be put in place in order to improve organisational performance and to maintain competitive advantages.

Modeling the robust facility layout problem for unequal space considering health and environmental safety criteria under uncertain parameters

This study examines the robust facility layout problem (RFLP) while taking into account unpredictable health and environmental safety standards. This problem's major goal is to arrange the departments in various departments of a hall, allot each department the appropriate amount of space, and identify the kind of amenities and equipment needed for each chosen sector. To accomplish the aforementioned objective, five criteria were taken into account: the total cost of department transfer and selection; access to more facilities and equipment; access to firefighting equipment; access to favorable climatic conditions; and the separation of noisy departments from one another. The fuzzy programming approach is utilized in this research to regulate the uncertainty parameters due to the uncertainty of the transfer cost and transfer time parameters. Additionally, by supplying an appropriate chromosome, the precise Epsilon constraint approach, NSGA II, and MOPSO have been employed to tackle the issue. The computational sizes of larger-sized sample problems solved demonstrate the strong performance of the NSGA II in quickly finding effective solutions.

A real option budget framework for asset management funding models in higher education institutions

This study applies a novel funding model framework in contemporary decentralized responsibility center management (RCM) budget model for asset management. The novel funding model is used in real option theory which reflectes potential needs for risk management and estimate future budget requirements in an objective and quantitative manner. The framework consists of three phases: (1) cost estimation, (2) option model, and (3) funding benefit. The proposed framework is expected to enhance the capabilities for securing sufficient deferred maintenance (DeM) funding by hedging against the risks associated with the uncertainty in DeM costs in a bilateral setting. Furthermore, the funding model framework could further contribute to the methodological development of contemporary RCM models to account for their behavioral aspects under budgeting uncertainty.

On‐Chip Near‐Infrared Spectral Sensing with Minimal Plasmon‐Modulated Channels

AbstractSpectroscopy is widely used in biomedicine, agriculture, remote sensing, industrial process monitoring, etc. The emerging on‐chip integration techniques further extend these applications to on‐site and portable operating modes. However, realizing high‐applicability and low‐cost spectroscopy chips is still a major challenge particularly in infrared due to the requirements on a large number of spectral channels. Here, a compact and low‐cost near‐infrared spectral sensing platform is demonstrated, releasing the burden of scaling up the spectral channel and simultaneously keeping a high accuracy in chemical classification. The monolithically integrated spectral sensor consists of a small amount of plasmon‐modulated narrowband photodetection units and operates with on‐chip photoelectric responses via a statistical machine learning method. Both plasmonic resonances with low spectral correlation and advanced regression algorithms contribute to high sensing accuracy in the case of limited hardware resources. Chemical concentration quantification, plastic sorting, and scanning spectral imaging are realized using such a sensor with up to twelve spectral channels. This concept avoids the uncertainty of various spectral reconstruction algorithms, high cost, and processing complexity and therefore provides promising strategies for miniaturized spectral sensing platforms.

Investment strategies of information‐provision technology in the platform‐based supply chain

AbstractOn retailing platforms, several information‐provision technologies are adopted to gain profit, such as production video ads service, live streaming service, and virtual reality/augmented reality tech. In this article, we focus on the investment strategies of information‐provision tech and its impact on the platform‐based supply chain. To this end, we develop a general model under which the platform invests in information‐provision tech for homogenous sellers and consumers search for products on the platform. Our results show that the platform should adopt a higher investment level in information‐provision tech for the products with the unit search cost or products' information uncertainty degree being medium. Also, a more competitive environment can lead to a lower platform's investment level in information‐provision tech when the number of browsing products is sufficiently large. Interestingly, we find that for a large unit search cost or small uncertainty degree of products' information, investing in information‐provision tech can benefit the platform's and sellers' profit. In addition, if the number of browsing products is large, investing in information‐provision tech can increase the consumer surplus and social welfare. Lastly, our results hold for a broad class of distributions of products' information uncertainty value and other practical cases. Our studies can help the platform to understand the roles of information‐provision tech and provide some practical management insights.

Evaluation of Probabilistic Project Cost Estimates

Evaluation of cost estimates should be fair and give incentives for accuracy. These goals, we argue, are challenged by a lack of precision in what is meant by a cost estimate and the use of evaluation measures that do not reward the most accurate cost estimates. To improve the situation, we suggest the use of probabilistic cost estimates and propose guidelines on how to evaluate such estimates. The guidelines emphasize the importance of a match between the type of cost estimate provided by the estimators and the chosen cost evaluation measure, and the need for an evaluation of both the calibration and the informativeness of the estimates. The feasibility of the guidelines is exemplified in an analysis of a set of 69 large Norwegian governmental projects. The evaluation indicated that the projects had quite accurate and unbiased P50 estimates and that the prediction intervals were reasonably well-calibrated. It also showed that the cost prediction intervals were noninformative with respect to differences in cost uncertainty and, consequently, not useful to identify projects with higher cost uncertainty. The results demonstrate the usefulness of applying the proposed cost estimation evaluation guidelines.

Machine Learning for Daily Forecasts of Arctic Sea Ice Motion: An Attribution Assessment of Model Predictive Skill

Abstract Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR. Significance Statement We build and evaluate different machine learning (ML) models that make 1-day predictions of Arctic sea ice velocity using present-day wind velocity and previous-day ice concentration and ice velocity. We find that models that incorporate nonlinear relationships between inputs (a neural network) capture important information (i.e., have a higher correlation between observations and predictions than do linear and persistence models). This performance enhancement occurs primarily in deeper regions of the central Arctic where wind speed is the dominant predictor of ice motion. Understanding where these models benefit from increased complexity is important because future work will use ML to elucidate physically meaningful relationships within the data, looking at how the relationship between wind and ice velocity is changing as the ice melts.