Cost Estimation Framework Research Articles

Fuzzy activity-based costing: An investment evaluation approach for management information system of a smart factory

The investment in a smart manufacturing factory with corresponding management information systems is strategic, complex, and costly. This paper aims to develop a cost estimation framework for a smart factory management information system (SFMIS). This paper proposed a fuzzy activity-based costing (FABC) model for the evaluation of the SFMIS under conditions of uncertainty. The model presented a multi-step procedure to do the identification, estimation, determination, calculation, and computation of the resource costs, activity costs, and systems as cost objects. The fuzzy Delphi method is adopted, and seventeen experts are surveyed in two rounds of a questionnaire assessing the importance of resources, activities, systems, and industry type and company size. The proposed FABC cost estimation model is illustrated by a case study. This model can identify and allocate resources and estimate SFMIS costs accurately and efficiently. By using this FABC cost evaluation framework, policymakers facilitate smart factory implementation for its management information system under uncertainty.

A data-driven framework for conceptual cost estimation of infrastructure projects using XGBoost and Bayesian optimization

ABSTRACT Cost estimation is a key component of project plans, yet it is challenging to provide reliable and efficient estimations using conventional methods in the conceptual phase of infrastructure projects. This study proposes a framework that integrates feature selection, extreme gradient boosting (XGBoost), Bayesian optimization (BO), and SHapley Additive exPlanations (SHAP) to provide conceptual cost estimations and explain the results for early decision-making. Correlation analysis and forward search are combined to select the key features. XGBoost is developed as the estimator and enhanced by BO in accuracy and efficiency. Model explanations were presented using SHAP. The framework is demonstrated through a case study of electric substations containing 605 samples. The results show that the proposed framework can provide satisfactory performance on conceptual cost estimations, where BO-XGBoost outperforms the benchmark models (with ~0.9567, adjusted ~0.9549, RMSE ~ 0.8690, and MAE ~ 0.4875). SHAP reveals how the features contribute to the cost based on both global and local explanations. The framework provides a guideline for more accurate, efficient, and explainable cost estimations in the conceptual phase of infrastructure projects. It can support the government and project planners in early decision-making, including reliable project budget and plan alternatives selection.

Cost-Effectiveness of Off-Road Equipment Electrification Incentives: Cost Estimation, Evaluation, and Usage Framework

Effective incentive strategies have played a major role in the electrification of on-road vehicles. Similar approaches to the off-road sector will be needed to continue decarbonizing the transportation sector. Estimating cost-effectiveness with given incentive funds is crucial to successfully develop and implement financial incentive strategies. Because of the lack of cost information for electric off-road equipment, forecasting cost-effectiveness of electrifying the off-road fleet is not feasible. We developed cost projection models for major components of off-road equipment and combined them into an equipment cost simulation tool. This simulation tool was then used to estimate NO x, particulate matter, and CO2 emission-based cost-effectiveness when diesel off-road equipment is replaced with equivalent electric equipment. Use of the developed cost-effectiveness framework in developing incentive strategies was also demonstrated. Finally, a sensitivity analysis was conducted to address potential variations of the assumptions made for projecting the cost of components and the resulting cost-effectiveness estimates. The framework demonstrated in this paper will inform governmental inventive funding programs to design the most cost-effective strategies, considering equipment vocation, model year, and usage.

Estimating coastal flood damage costs to transit infrastructure under future sea level rise

Future sea level rise (SLR) and associated increases in the frequency and intensity of coastal flooding poses significant threats to coastal communities and transit systems. Yet current literature and practice lack methods for estimating flood damage costs to transit systems. Here, we construct an event-specific flood damage cost estimation framework for transit systems, simulating separately flood ingress into underground spaces. We apply this framework to the MBTA rail transit system in Boston, estimating damages under several coastal flood events with SLR and project expected annualized losses (EAL) through 2100 with uncertain SLR. We estimate EALs to the MBTA system have doubled since 2008 to $24.4 M/year and are expected to reach $58 M/year by 2030 under all SLR scenarios. Our results suggest that absent adaptation schemes, particularly at tunnel ingress locations, coastal flood risk will continue to accelerate, potentially resulting in permanent inundation of underground and low-lying sections of the transit system.

Application of machine learning techniques for cost estimation of engineer to order products

Cost engineering capabilities are becoming increasingly important for the competitiveness of industrial firms, especially for engineer to order products (ETOPs). Despite this relevance, the literature on the use of advanced data-driven methodologies, such as machine learning (ML), for early cost estimation (CE) of ETOPs is quite sparse. Furthermore, ML has still seen little use in real industrial applications due to several challenges. Accordingly, the objective of this paper is threefold: (a) to develop a solid early CE approach for ETOPs, including feature selection; (b) to investigate the benefits of adopting ML for ETOPs’ CE; (c) to identify how ML can be introduced into real industrial context with little knowledge on ML. Long action research has been carried out with a large industrial company that produces Oil & Gas ETOPs. We observed how ML facilitates the exploration of the relationships between the choices of early design stages and the CE. ML algorithms also allowed to both capture the high variability of the data and test different combinations of cost drivers in very effective ways. The project resulted in an accurate CE framework with an iterative feature selection process and an approach for introducing ML into a real industrial context.

A framework for cost estimation in product-service systems: A systems thinking approach

Original Equipment Manufacturers (OEMs) are increasingly undergoing a transition into the offering of Product-Service System (PSS) solutions. This transition has proven to be a difficult challenge characterised by inadequate contract decisions due to cost under-estimations and performance overpromises. Despite the relevance of cost estimation for the OEM’s transition into the PSS scheme, little research attention has been received until recently for the development of cost estimation frameworks appropriate for the PSS context. To fill this gap, this paper proposes a framework for cost estimation in the PSS context based on the “systems-thinking” approach, composed of a set of iterative modules and methodological foundations that bring into discussion several properties relevant to the PSS context. A numerical control multi-axis machine centre PSS is illustrated to evidence the capability of the framework to analyse the cost impacts on both the customer and on the OEM side and to support the design and improvement of PSS configurations and associated contract mechanisms.

CONCEPTUAL COST ESTIMATION FRAMEWORK FOR MODULAR PROJECTS: A CASE STUDY ON PETROCHEMICAL PLANT CONSTRUCTION

Modularization, which allows for pre-assembly away from a construction site, has been known to be more cost-effective than stick-built; however, contractors have difficulty ascertaining the benefits and adopting it. Calculating the benefits and costs of adopting modularization precedes decision making. However, modular cost estimation is challenging since relevant information in the early stages of a project and historical data about industrial modularization both have limited availability. To solve this problem, this study developed a conceptual cost estimation framework for industrial modular projects by converting stick-built project information. The framework is composed of eight steps based on two approaches. This study conducted a case study to demonstrate the applicability of the framework, which compared the project cost of modularization scenarios 1 and 2 with that of the stick-built version of the ongoing project. In addition, the estimated modular cost was compared with the engineers’ estimation to verify the accuracy of the framework. The contributions of this study are in identifying and quantifying the factors influencing the differences in cost between the modularization and stick-built versions, and developing the conceptual cost estimation framework for an industrial modular project. This framework is expected to support deciding on adopting modularization, budgeting, and project viability.

A multi-perspective approach to support collaborative cost management in supplier-buyer dyads

Joint cost management is a decisive factor for sustainable collaboration in supplier-buyer dyads. For both parties the establishment of an accurate cost estimation (CE) framework supports managing suppliers' costs as well as manufacturer's quotation costing. Grounded on resource dependence theory and following a design science research approach, this study introduces a multi-perspective CE system inspired by statistical learning, deep learning, decision making, and multi-agent theory. We evaluate our system by a single case and computer simulation study, using empirical data coming from observations and archives at a large Bavarian original equipment manufacturer (OEM). The results indicate that our CE approach allows to select the most significant cost-drivers and predict total costs of parts and assemblies with high accuracy. This supports the supplier in efficiently managing its costs. In making the CE blackbox model transparent using a combination of model agnostic post-hoc explainable artificial intelligence approaches we foster user acceptance for both suppliers and OEMs. All CE artifacts are ensembled in a multi-agent system to automatically manage costs with suppliers and furthermore, as a model extension, can lead to a collaborative price agreement. Our system supports supply chain managers on both sides in entering into a sustainable long partnership.

A Multi-Perspective Approach to Support Collaborative Cost Management in Supplier-Buyer Dyads

Joint cost management is a decisive factor for sustainable collaboration in supplier-buyer dyads. For both parties the establishment of an accurate cost estimation (CE) framework supports managing suppliers’ costs as well as manufacturer’s quotation costing. Grounded on resource dependence theory and following a design science research approach, this study aims to develop a multi-perspective CE system inspired by statistical learning , deep learning- decision-making and multi-agent theory. We evaluate our system in a single case and computer simulation study using empirical data coming from observations and archives at a large Bavarian original equipment manufacturer (OEM). The results indicate that our CE approach can select the most significant cost-drivers and predict total costs of parts and assemblies with high accuracy to support the supplier in efficiently managing its costs. In making the CE blackbox model transparent using a combination of model agnostic post-hoc explainable artificial intelligence approaches we create user acceptance for both suppliers and OEMs. All CE-artifacts are ensembled in a multi-agent system being able to automatically manage costs with suppliers and furthermore, as a model extension, can lead to a collaborative price agreement. Our system supports supply chain managers on both sides in entering into a sustainable long partnership.

A Review of Building Construction Cost Research: Current Status, Gaps and Green Buildings

An overall scoping review was conducted to examine research on building construction costs in the past decades. The aim is to provide a better understanding of conventional building construction cost estimation methods, gaps and potential improvement strategies that may mitigate the high risk of the cost overrun in conventional and sustainable building. This study first examined the components included in a building's construction cost and the commonly used calculation methods for cost estimation. Then, additional components included for sustainable buildings were identified and explained. The causal factors for construction cost overruns in sustainable building werediscussed as well. The findings concluded the following: (a) there is no consistent cost definition used in the industry; (b) a variety of cost estimation methods create ambiguity and confusion; and (c) newer cost estimating methods and tools, such as parametric cost estimation, which integrate risk and uncertainties have not been broadly adopted by the building industry. The current practice used to determine a sustainable building's cost estimation is the same traditional method that has been used over several decades, which is based on the material and labor costs. Such a conventional approach does not consider other factors, including the complexity of the sustainable building system, an organization's environment, and the capability of teams, among others. To respond to such a knowledge gap, a comprehensive and consistent cost estimation framework was proposed to integrate risk and uncertainty consideration, which is particularly prevalent in sustainable building.

Revolutionising cost structure for integrated project delivery: a BIM-based solution

PurposeThe amalgamation of integrated project delivery (IPD) and building information modelling (BIM) is highly recommended for successful project delivery. However, IPD lacks an accurate cost estimation methodology at the “front-end” of projects, when little project information is available. This study aims to tackle this issue, through presenting analytical aspects, theoretical grounds and practical steps/procedures for integrating target value design (TVD), activity-based costing (ABC) and Monte Carlo simulation into the IPD cost structure, within a BIM-enabled platform.Design/methodology/approachA critical review was conducted to study the status of cost estimation within IPD, as well as exploring methods and tools that can enhance the cost estimation process for IPD. Thereafter, a framework is developed to present the proposed methodology of cost estimation for IPD throughout its entire stages. A case project is used to validate the practicality of the developed solution through comparing the profit-at-risk percentage for each party, using both traditional cost estimation and the proposed solution.FindingsAfter applying the proposed IPD's cost estimation framework, on a real-life case project, the findings demonstrated significant deviations in the profit-at-risk value for various work packages of the project (approximately 100% of the finishing package and 22% of openings package). By providing a precise allocation of overhead costs, the solution can be used in real-life projects to change the entire IPD cost structure and ensure a fair sharing of risk–rewards among the involved parties in IPD projects.Practical implicationsUsing the proposed methodology of cost estimation for IPD can enhance the relationship among IPD's core team members; all revealed financial deficiencies will be considered (i.e. compensation structure, profit pooling), hence enhancing the IPD performance.Originality/valueThis paper presents a comprehensive solution for integrating BIM and IPD in terms of cost estimation, offering three main contributions: (1) an innovate approach to utilise five-dimensional (5D) BIM capabilities with Monte Carlo simulation, hence providing reliable cost estimating during the conceptual TVD stage; (2) mathematical models that are developed through integrating ABC into the detailed 5D BIM to determine the three IPD's cost structure limbs; and (3) a novel mechanism of managing cost saving (rewards) through distinguishing between saved resources from organisation level, to daily task level, to increase trust among parties.

A framework for analytical cost estimation of mechanical components based on manufacturing knowledge representation

This paper presents a novel framework for manufacturing and cost-related knowledge formalization. This artefact allows industries to capitalize the knowledge of experienced practitioners in the field of manufacturing and assembly, so that it can be used by designers for quickly and analytically estimating the production costs of components during product development. The framework consists of the following: (i) a cost breakdown structure used for splitting out the manufacturing cost, (ii) a data model (cost routing) to collect the knowledge required to define a manufacturing process, (iii) a data model (cost model) for collecting the knowledge required to compute the manufacturing cost of each operation within a manufacturing process, and (iv) a workflow to define the manufacturing process. The proposed framework provides several advantages: (i) knowledge formalization of product manufacturing cost, (ii) knowledge sharing among design/engineering departments, and (iii) knowledge capitalization for decision-making process. The proposed framework is used to formalize the knowledge required for analytically estimating the manufacturing cost of open-die forged components. Results highlight that the framework addresses the most important requirements for a knowledge-based cost estimation system.

An end-to-end learning-based cost estimator

Cost and cardinality estimation is vital to query optimizer, which can guide the query plan selection. However traditional empirical cost and cardinality estimation techniques cannot provide high-quality estimation, because they may not effectively capture the correlation between multiple tables. Recently the database community shows that the learning-based cardinality estimation is better than the empirical methods. However, existing learning-based methods have several limitations. Firstly, they focus on estimating the cardinality, but cannot estimate the cost. Secondly, they are either too heavy or hard to represent complicated structures, e.g., complex predicates. To address these challenges, we propose an effective end-to-end learning-based cost estimation framework based on a tree-structured model, which can estimate both cost and cardinality simultaneously. We propose effective feature extraction and encoding techniques, which consider both queries and physical operations in feature extraction. We embed these features into our tree-structured model. We propose an effective method to encode string values, which can improve the generalization ability for predicate matching. As it is prohibitively expensive to enumerate all string values, we design a patten-based method, which selects patterns to cover string values and utilizes the patterns to embed string values. We conducted experiments on real-world datasets and experimental results showed that our method outperformed baselines.

Developing a population wide cost estimating framework and methods for technological intervention enabling ageing in place: An Australian case.

PurposeAgeing in place is one of the greatest desires of elderly people. Assistive digital technologies could potentially delay the institutionalization of the elderly people and allow them ageing in place. This study develops a population-wide cost estimating framework for adopting digital technologies that can improve the quality of life of elderly people through examining an Australian region.MethodsWe developed a five-stage cost estimation framework, which involved progressive forecasting of elderly population and direct cost estimation methods. The forecasting and cost estimation models have been set for a 10-year period because the prediction accuracy from cross-sectional data is better in the short to medium term compared to the long-term. For cost estimation, we categorised the ageing population on the basis of the number of chronic diseases that they have contracted. Costs of assistive technologies were collected from open sources. The model has been tested in the Fitzroy and Central West, a regional area of Queensland in Australia. A stakeholder panel discussion in a workshop format was used to validate the appropriateness of the proposed framework and the study findings.ResultsThis study identified eight common chronic diseases with different comorbidity patterns in Australia. We also identified the required assistive technologies to assist patients with chronic diseases. This study estimated that annual per capita cost for technological intervention could range from AUD 4,169 to AUD 7,551 on the basis of different price margins of the technologies.ConclusionThe approach of categorising the aged cohorts on the basis of the number of chronic diseases helps estimate population-wide costs compared to using single technology intervention costs for a particular chronic disease cohort. The cost estimation framework and the method developed in this study can assist the government to estimate costs for ageing-in-place programs.

An evidence-based model to consolidate medication adherence cost estimation: the medication adherence cost estimation framework.

Aim: To develop a standardized framework determining the economic impact of medication nonadherence. Materials& methods: Secondary analysis of existing literature reported cost data, aggregating cost outcome indicators. Weighted-average cost analysis performed, determining the proportional contribution to total cost. Results: Direct costs were reported in 92% of studies and indirect costs in 4% of studies. Three most utilized cost categories were hospital (68%), primary care (18%) and pharmacy costs (72%). Average unadjusted direct costs ranged from $625 to $154,203 contributing to 88% of the total cost; adjusted medical costs ranged from $565 to $56,313 representing 96% of the total cost. Conclusion: The medication adherence cost estimation framework enables the comparison of costing studies, facilitating informed health policy decision-making based on consistent evidence and terminology.

Uncertainty propagation and sensitivity analysis in composite manufacturing cost estimation: ALPHA-framework and cost tool development

The presented ALPHA cost tool is a novel highly flexible bottom-up parametric hybrid cost estimation framework. It combines the benefits of both methods with the aim of providing cost information during all product development phases. The software offers full transparency to the user and advanced two-level uncertainty management to not only understand any project’s cost structure but also aid to identify its cost driving parameters. The implementation of sensitivity analysis makes the intrinsic uncertainty inevitable embedded in cost estimation become graspable. Gaussian error propagation offers direct feedback without extra calculation time while classic Monte Carlo Simulation gives detailed insight through post estimation analysis. From the vast number of commercially available or self-developed cost tools many probably already incorporate uncertainty measures similar to those proposed here. But this article shows both the potential of the additionally obtainable information from uncertainty propagation and demonstrates a way of integrating these risk considerations into a self-developed cost tool.

An efficient effort and cost estimation framework for Scrum Based Projects

Software Process Models from its inception instill standardization and creates a generic culture of developing software for various IT industries. A great paradigm shift has been observed in terms of embracing Agile Development methodology as a viable development methodology in cross key business units. There is a buffet of agile methodologies comes under the umbrella of ASD, out of which Scrum got the highest popularity and acceptability index. Agile based software development is the need of immediate environment. There is an increasing demand for significant changes to software systems to meet ever-changing user requirements and specifications. As Agile is volatile, so effort estimation is challenging and still striving for perfection to decide size, effort, cost, duration and schedule of projects with minimum error. This cause sensitizes potential researchers all across the globe to start working on addressing the issue of inaccurate predication of efforts. The gap between estimated and actual effort is because of limited or no inclusion of various estimation factors like people and project related factors, inappropriate use of size metric and cost drivers, ignorance of testing effort, team member’s inability to understand user story size and complexity etc. This paper attempts to bridge the gap of estimated and actual effort by the use of soft computing techniques thus taking the research to advance frontier area in terms of estimation.

Development of a Cost Estimation Framework for Potential Transload Facilities

As the cost of transportation continues to rise and there is a growing push for a more environmentally friendly transportation network, optimizing mode distributions becomes an attractive solution. One way to optimize mode distributions is through strategically located freight transfer facilities, such as transload facilities. While there are many benefits to this type of facility, such as the emissions savings or pavement damage reductions garnered by shifting commodity tonnage onto alternative modes of transport, it is also essential that transportation planners understand the costs associated with building transload facilities. Unfortunately, literature does not provide an adequately disaggregated and scalable cost estimation approach that could be applied to various configurations and types of transload facilities. In this research, a cost estimation framework was created to determine the basic cost of transload facilities by type using unit costs from a construction cost database, equipment costs from local dealers, the projected commodity tonnage, design recommendations from literature, and survey responses from local facilities. A case study based upon proposed facilities in Arkansas was completed to illustrate the effectiveness of this methodology. While there is currently no construction design for these facilities, this framework yielded costs consistent with those expected. A key finding was that storage costs could account for up to 81% of a transload facility’s costs. Overall, this cost framework is believed to balance general scalability with accuracy well to provide reasonable cost estimations for constructing new or expanded facilities.

Cost Estimation Framework for Optimal Retrofit Planning to Mitigate Residential Building Vulnerability to Wildfires

AbstractCommunities near forests are vulnerable to wildfires. Retrofit measures to reduce the vulnerability of residential buildings to wildfire include improvements that hinder, mitigate, or preve...

Data-driven cost estimation for additive manufacturing in cybermanufacturing

Cybermanufacturing is a new paradigm that both manufacturing software and hardware tools are seamlessly integrated by enabling information infrastructure and are accessed as services in cyberspace. This paradigm encourages tool sharing and reuse thus can reduce cost and time in product realization. In this research, a new cost estimation framework is developed based on big data analytics tools so that the manufacturing cost associated with a new job can be estimated based on the similar ones in the past. Manufacturers can use this cost analytics service in their job bidding process, which is currently ad hoc and subjective in industry practice. The new framework is implemented and demonstrated for additive manufacturing, where the similarities of 3D geometry of parts and printing processes are established by identifying relevant features. Machine learning algorithms for dynamic clustering, LASSO and elastic net regressions are applied to feature vectors to predict the cost based on historical data.