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Rajiv D. Banker , Dmitri Byzalov , Shunlan Fang , Yi Liang; Cost Management Research. Journal of Management Accounting Research 1 December 2018; 30 (3): 187–209. https://doi.org/10.2308/jmar-51965

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The traditional view of cost behavior assumes a simple mechanistic relation between cost drivers and costs. In contrast, contemporary cost management research recognizes that costs are caused by managers' operating decisions subject to various constraints, incentives, and psychological biases. This conceptual innovation opens up the “black box” of cost behavior and gives researchers a powerful new way to use observed cost behavior as a lens to study the determinants and the consequences of managers' operating decisions. Banker and Byzalov (2014) presented an overview of the economic theory of cost behavior and major estimation issues. The research literature on cost management has grown rapidly in the past few years and has enhanced the understanding of how managerial decisions influence observed costs. In this study, we provide a comprehensive review of recent findings and insights, with a particular emphasis on the implications of cost management for understanding issues in cost, managerial, and financial accounting, and challenges and opportunities for future research.

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Cost estimation and prediction in construction projects: a systematic review on machine learning techniques

• Review Paper
• Published: 15 September 2020
• volume  2 , Article number:  1703 ( 2020 )
• Sanaz Tayefeh Hashemi 1 ,
• Omid Mahdi Ebadati   ORCID: orcid.org/0000-0002-2688-9595 2 &
• Harleen Kaur 3  

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Construction cost predictions to reduce time risk assessment are indispensable steps for process of decision-making of managers. Machine learning techniques need adequate dataset size to model and forecast the cost of projects. Therefore, this paper presents analysis and studied manuscripts that proposed for cost estimation with machine learning techniques for the last 30 years. The impact of this manuscript is deep studied of machine learning techniques and applied an analysis methodology in cost estimation based on direct cost and indirect cost of construction projects, which consists of two parts. In the first part, for study the proposals, we focus on collecting related studied from Google Scholar and Science Direct journals. The interested application areas for project cost estimation are building, highway, public, roadway, water-related constructions, road tunnel, railway, hydropower, power plant and power projects. The second part is regarded to the analysis of the proposals. For cost analysis, there are possibilities to consider two approaches as qualitative and quantitative. However, reflect to the machine learning techniques the quantitative approach is studied. In quantitative approach, we categorized the models in three parts, as statistical, analogues and analytical model and analyse them based on their features. Correspondingly, papers have been thoroughly investigated based on the application area, method applied, techniques implemented, journals, which have been published in, and the year of publication. The most important outcome of this study is to find out the different analytics methods and machine learning algorithms to predict the cost estimation of construction and related projects and aid to find out the suitable applied methods.

Avoid common mistakes on your manuscript.

1 Introduction

Cost prediction is a vital process for every business in that it is a predecessor for budget prices and resource allocation in a project life cycle. Actually, it is hard to obtain input data for cost estimation process, while the scope of work is barely known in that it might lead to poor and rough estimates. The more, the project scope is known there are more chances to generate estimates that are more accurate in that more specifications of the project are defined. However, it should be taken into account that, on the other hand, by the progressive elaboration, the process of cost control becomes more difficult if the project is based on inaccurate cost estimates. Furthermore, construction industry due to its characteristics and large amounts of capital needed to initiate and continue the project, are the project types which need more attention because they are high-risk [ 1 ]. Either overestimating or underestimating the cost of these projects will lead to future deviations in budget vs. realized cost. Hence, the methods used in this realm, their respective accuracy, and even their gaps have shown growing interest. Methods with more consistent results can facilitate and smooth the path for cost estimators provided that their related gaps can be investigated and overcome in order to acquire better results. In conventional methods, by knowing work packages and their prices and how they are distributed along the project lifetime; the total project cost can be estimated. Which this will be an input for project resource allocation and further budget calculations. The conventional methods have shown that they are not merely enough. Thereby the lack of a systematic approach in order to reduce the error of the estimation process has entailed in studies that most of all have tried to take advantage of mathematical models, machine learning techniques, and so on to overcome inaccurate or may even erroneous predictions. What is estimated as project construction cost is different from tender price in that the tender price contains other amounts, including company profit and contingency reserve. Contingency reserve is the amount allocated to known risks during the project execution, which is an estimated amount of reserve. The components of project cost are depicted in Fig.  1 due to the contractor’s viewpoint [ 2 ].

Bid structure and analysis in projects

As shown in Fig.  1 [ 3 ], the project cost includes the project indirect cost and direct cost. The project direct cost itself is composed of costs directly spent in the project and the indirect part, which is mainly the overhead of the project, incurred either in the project itself or on the staff side.

This classification is described as follows:

Direct costs Direct costs can be defined as costs that are directly spent in the project and its production activities, which can be well estimated, while adequate information is available about site condition, construction method used, and the resources utilized. In fact, direct costs are composed of several items such as cost of the labor assigned to the project, equipment used, materials and crews and the subcontractors, which the work packages are assigned to, on behalf of the general contractor.

Indirect costs Indirect costs are classified into the following categories:

Project overheard These costs are mainly the costs, which are indirectly incurred in the project and are in charge of the project work packages, but cannot be directly assigned to them such as utilities, supervisory, etc.

General overheard These costs, in contrary to project overhead, cannot be attributed to each project individually and are mainly the staff side costs, such as an amount of money spent in the head office, personnel cost, and so on, which can be attributed to projects proportionate to their costs toward the total costs of the contractor’s organization.

Markup The company bid price is the summation of project’s cost, and an amount regarded as markup which itself is comprised of the following amounts of money:

Profit The amount of money attributed to company’s profit, which depends on the business objectives, the industry competition level, and also how much the contractor wills to win the project over its rivals.

Risk contingency Usually known as identified risks or known unknown, which is also considered in markup and is the amount of money, set aside for uncertain situations, which can affect the project performance, including unexpected events, labor issues, etc. [ 3 ].

Aims and objectives

The objectives of this systematic review include:

Investigating the criteria for construction projects cost estimation.

Determine the criteria of construction projects based on application area, method applied, techniques implemented, journals, and the year of publication.

Reviewing the existing models of machine learning techniques in cost estimation of construction projects.

Assessing the methods, techniques and criteria for construction project cost estimation.

The rest of the paper is structured as follows; Sect.  2 , explores the research methodology, the way to retrieve data, cost estimation techniques and analytics models. Section  3 , concisely deliberates about the results and related discussion and distribution methods, and the paper is concluded in Sect.  4 , and brief about the final results and methods, limitations and future work.

2 Research methodology

2.1 types of studies.

This research investigates the available models and criteria in the field of the smart-grid project for cost estimation from the past 30 years. This is to emphasize that the present review paper does not include all the articles done in this scope and just the ones with the defined keywords and in the domain of construction projects. This study will impose no restriction on the type of proposal work conducted on the subject and no limitations on the date of publication of the documents as well.

2.2 Information sources and search strategy

Databases such as Google Scholar and Science Direct will be searched to access the relevant documents. These two main sources of academic database are totally included more than 400 million documents. Database will be searched using following keywords to obtain relevant papers: “Construction”, “Cost estimation”, “Cost Prediction”, “Regression Analysis”, “Case Based Reasoning”, “Analogy”, “Artificial Intelligence Techniques”. The used keywords in this study are the most important guidelines in this area, which can help to reach to relevant papers. For such, no limitations impose on the publication status of the extracted studies.

Google Scholars

All fields from 1985–2020 ((((Cost Estimation AND Construction) OR (Cost Prediction AND Construction) OR (Cost Estimation AND Regression Analysis) OR (Construction AND Regression Analysis) OR (Case Based Reasoning) OR (Analogy) OR (Construction AND Artificial Intelligence Techniques) OR (Cost Prediction AND Artificial Intelligence Techniques) OR (Cost Prediction AND Analogy) OR (Regression Analysis)))) AND ((Machine Learning Techniques OR forecasting)).

Science Direct

All Article Types in journals or books, years 1985–2020 ((((Cost Estimation AND Construction) OR (Cost Prediction AND Construction) OR (Cost Estimation AND Regression Analysis) OR (Construction AND Regression Analysis) OR (Case Based Reasoning) OR (Analogy) OR (Construction AND Artificial Intelligence Techniques) OR (Cost Prediction AND Artificial Intelligence Techniques) OR (Cost Prediction AND Analogy) OR (Regression Analysis)))) AND ((Machine Learning Techniques OR forecasting)).

2.3 Selection process

We briefly investigate the papers to identify relevant manuscripts based on the title and abstract. The results entered into the EndNote and remove the duplicates. Following determining the relevant headlines used to consider eligibility of the manuscript criteria to study the full-texts of any potentially related discussions identified so far. To find other theoretically relevant articles, the references of the extracted papers also are examined. Using the comments made by specialists of this field, key journals of the field identified and the relevant articles have been reviewed in details in terms of the following sections, which are embedded in a form used to retrieve data from paper (Table 1 ).

2.4 Cost estimation techniques

The total number of 92 papers have been studied thoroughly, in terms of application area, applied methods, techniques implemented, journal published in, and the year of publication.

In cost estimation scope, many methods and techniques are used, out of which Artificial Neural Networks (ANNs), hybrid models of ANN with secondary artificial intelligence or meta-heuristic methods, Radial Basis Function Neural Network (RBFNN); Case-Based Reasoning (CBR), Regression Analysis (RA), Particle Swarm Optimization (PSO), Decision Tree (DT), and Expert Systems are investigated here.

Artificial neural networks are one of the many algorithms, which are modelling biological learning processes by computers. They are classified under a major classification named machine learning. In fact, machine learning is the process of programming the computers to optimize a performance based on a past available data or experience [ 4 ].

The first mathematical model of an artificial neural network model was formulated by McCulloch and Pitts in 1943 [ 5 ]. Artificial neural networks known as neural networks are analogy-based, non-parametric information-processing systems that have inspired their functionality and structure from the brain’s biological neural networks [ 6 ]. The most challenging problems, which neural networks are used for, are pattern recognition, clustering/categorization, and prediction/forecasting [ 7 ]. In forecasting problems, neural networks are trained based upon past data and depending upon their generalization ability; they can provide forecasting for novel cases.

Neural networks have several advantages, including their capability to perform predictions with less required developed statistical trainings, ability to detect intricate nonlinear relationships among variables, ability to discover all possible interrelations between variables, and the capacity to be developed through the use of numerous training algorithms. However, like any other subject, there remain some disadvantages, including their “black box” mechanism leading to discouragement in finding the origin of the results, their difficult applicability to some problems, their need for high computational resources, and their vulnerability in overfitting and experimental construction, which are highly in need of resolving several matters such as their topology and other methodological matters [ 8 ].

On the other hand, ANN is extremely data driven and will show low prediction performance, while being fed with a small number of data, leading to over specification, which means that they can perform well with the available data, but are incapable of predicting novel cases [ 9 ]. In their point of view, the application of heuristic rules such as preventing the model from being further trained, while there seems to be no more improvement in the network MSE and also using fewer numbers of nodes in hidden layers can mitigate this possibility.

Despite the black box mechanism of neural networks, they have been widely used in prediction problems demonstrating reasonable results as scrutinized in the literature. Developing hybrid model of back propagation neural networks and genetic algorithm will lead to more accurate predictions and prevent the model from presenting erroneous performance hence can overcome the encapsulated shortcomings [ 10 ]. The use of hybrid models of ANN with secondary artificial intelligence or meta-heuristic methods such as genetic algorithms, bee colony algorithm, and artificial immune systems have been proposed in numerous articles in order to cover the drawbacks of ANNs and thus enable them to be applied in diverse problems [ 11 ]. Genetic Algorithm (GA), one of these meta-heuristic methods and a family of evolutionary computation models, was first invented by John Holland in 1960s [ 12 ]. As the optimization problems are occurring in dynamic settings, they require a kind of feedback from the environment, which the problem is taking place regarding the success or even failure of the current applied strategy, that will exploit the earned knowledge in order to evolve the applied strategies and recombine the best pieces of competing strategies to reproduce much more fitting individuals [ 13 ].

Furthermore, CBR is a data mining technique, which remembers the information and also uses the solutions implemented for the similar past cases in solving new problems [ 2 ]. The main source of this information and knowledge is the case, which is reused though through matching by some kind of tolerance [ 14 ].

On the other hand, RA can be classified as a data oriented technique that deals with just the data in hand and not the characteristics behind them and is divided to two linear and nonlinear models [ 15 ]. In addition, decision trees are widely used for solving classification problems. Decision tree is constructed continuously based on the feature that best satisfies the branching rule. This process is then performed iteratively for each branch [ 16 ]. Classification and regression decision trees deal with predicting a dependent variable based upon a predictor variable. The response variable in the former includes a finite set of values, while in the latter contains continuous or discrete set of variables [ 17 ]. Regression trees are good substitution for basic regression methods. Decision tree is mainly constructed based on those attributes in the dataset that are pertinent to the classification case, thus it can be mostly regarded as a feature selection problem [ 18 ].

Besides expert systems are well known by their application of knowledge, facts and methods elicited from human experts that have been affirmed to be effective in solving the cases of the similar domain [ 19 ].

Furthermore, the papers are categorized by the year of publication and the journals within, which papers have been published. In addition, the papers are studied in terms of the area within, which the cost estimation method has been applied. The current fields are as followings: building projects, highway projects, public projects, road way projects water-related construction projects, road tunnel projects, railway projects, hydropower projects, and power plant projects. Besides, the cost estimation methods in these papers are investigated from the applied technique's viewpoint.

Cost estimating methods are classified into two main categories: qualitative and quantitative methods, which will be described in detail later. The total view of cost estimation modelling techniques is depicted in Fig.  2 (Modification of [ 20 ]).

(Modification of [ 20 ])

Cost estimation modelling techniques.

2.4.1 Qualitative approaches

Qualitative approaches are based on estimator’s knowledge of the project, the scope of work, and influencing factors and are divided into two classes: expert judgment and heuristic rules. Expert judgment depends on the good or bad results of the past estimations based on judgment. According to [ 21 ], expert judgment technique is mainly taking advice from the more experienced experts and peers to check the validity of the estimating results. This technique is, in fact, intuition-based and mainly relies on unspoken yet not well documented extrapolation techniques, which are the power in hands of experienced experts that can professionally assure the reliability of estimations [ 21 ].

On the other hand, the heuristic rules in cost estimating are due to intuitive judgments and are done as a rule of thumb to ease the process of estimating and are extracted from relative similar projects.

2.4.2 Quantitative approaches

Quantitative approaches can be defined as methods relying on the process of collecting and analysing historical data and applying quantitative models, techniques, and tools to estimate the project’s cost. Quantitative cost estimating approaches are classified into three main categories: statistical, analogous, analytical ones.

2.4.3 Statistical methods in cost estimation

Statistical methods, on the other hand, are based on formulas or other alternative approaches to establish a causal correlation between final costs and its corresponding characteristics [ 20 ].

Parametric cost estimating methods evaluate the cost through regarding characterizing parameters like mass, volume, and cost without considering little details [ 22 ]. In fact, in this way, the project cost is estimated based on defining its causal link with these parameters, and the result will be a mathematical function of the corresponding variables. This approach is efficient at early stage of a project, where there is little information available about the project [ 23 ], however, it suffers from the minimal necessary result justification [ 22 ]. There are three types of parametric cost estimation methods as follows [ 24 ]:

The method of scales This method is applicable in prevailing technologies, which simple products of different sizes are produced. Evaluating the most influencing technical parameters is the prerequisite of this method. Thereafter, this evaluation is compared with those of finished projects, which makes this method a combination of analogous and parametric approaches. The main disadvantage of this method is assuming that the cost and considered parameters are interrelated through a linear function [ 24 ].

Statistical models In this method, the activities are divided into major different scopes through, which the final mathematical formulae is constructed. This model is composed of three main data types [ 24 ]:

Technical specifications

Relationships between the data and final variables

Cost estimation formulae (CEF) CEF is a mathematical relationship between the final cost and a limited set of technical parameters. The major parameter categories are as follows [ 24 ]:

Physical values According to functional description

Dimensioning values According to solution description

The most probably prevailing parametric methods are regression analysis and optimization techniques [ 20 ].

Parametric cost estimation methods are faced with different drawbacks, which some of them are described as follows; through application of these methods, different results are the sole issue without giving a vision about the origin of them. On the other hand, lack of necessary parameters during early stages will result in uncertainty of the results. In addition, the designer should be aware of the influence of each parameter on the final cost. CEFs in particular, are incapable of solving specific cases. Sometimes also, there is a need to obtain results of regression analysis in four or five similar cases to reach to the most reliable cost. Despite all these disadvantages, they are considered as useful cost estimation methods due to their rapidity of execution [ 24 ].

Analogous models

Analogous models are based on similar past cases, which are reused and adjusted in different cases [ 25 ]. This similarity is due to functional or geometrical homogeny between cost structures, which are alike [ 20 ].

According to [ 21 ], analogous methods are known to be the simplest method of estimating through. The cost of projects is estimated in compared to their similar completed projects that are available as a historical database. Thus, project managers have to consider the most available parameters to include in their process of estimating to reach better results; however, this method is a kind of rough estimate, which is easy to use, but with lower levels of complexity and accuracy as well [ 21 ].

Analytical models

Analytical models instead, are the process of estimating costs by accurately defining the cost corresponding to each processing phase attribute in details, and afterwards using a bottom-up approach for aggregating the project total cost, thus this approach is leading to a more accurate result [ 25 ].

3 Results and discussion

This section discusses the findings of this study. Initially, an overview of the data analysis describes. Then, it presents the report and discussion of the study findings according to the research methodology in the separate subsections. Furthermore, illustrate the result of comparison of different models within the context.

3.1 Data analysis

The present study explores the existing methods and techniques for the cost estimation of projects and extracts approaches components. A classify analysis is conducted using the existing methods and tools and comparison made for different models. The components extracted from all the studied papers classified in terms of application area, methods, techniques, journals and year of publications. The results are discussed in the following sub-sections.

The total studied proposal papers are 92, which based on the considered features, they categorized for different approaches. The sum of 69 of articles are directly reviewed in the field of cost estimation in construction projects and 48 of them have focused on machine learning techniques. Elfaki et al. [ 26 ] have also reviewed the application of intelligent techniques in the construction cost estimation field. All these results have been summarized in “Appendix 1 ”. This Appendix shows; the total view of the present reviewed papers, in terms of the reference, year of publication, first author, area within which the method(s) has/have been applied and the method(s) in order of superiority of performance.

3.2 Application area

Table 2 shows an overall view of the reviewed papers applied in different areas. As it is shown in this table, most of the articles have studied building projects in general and less than half have scrutinized specific construction projects.

Cost estimation in building projects has been studied in a wide range of studies. In fact, building projects in this paper is meant the projects related to constructing buildings and such cases. The aforementioned projects' distributions are shown in the time horizon in Fig.  3 . As it is shown in this figure, the most studies are done in the year 2011 and 2017 with building project standing on the top; on the other hand, hydropower projects, own the least number of studies in this spectrum.

Distributions of different projects studies in years

3.3 Methods

Machine learning techniques have been defined as a system that can learn from data. In general, the main strong point of machine learning techniques can be identified as: the ability of handle uncertainty in methods, the ability to manage and perform with incomplete data, and the ability to decide and conclude the new cases based on experiences from analogous cases.

Khalaf et al. [ 27 ] have applied PSO in estimating cost and duration of 60 construction projects at the early stage. What has been inferred from this study is that PSO has been well performed with high accurate results, while it is encountering parameters with a wide range of variability. The other strength of this model is that it is based on existing projects and is more reliable than the projects based on judgement and experimental cases. However, this paper tries to examine the model with a wider range of parameters and also apply it to green buildings. On the other hand, [ 28 ] have studied the application of ANN in cost estimation of building projects, and it compared the results with RBFNN paper methods, and showed the ANN outperforms. Then, the study followed by optimizing the model accuracy, and applying it to other types of projects, and using other methods for cost factors' screening. In addition, [ 1 ] have proposed a cost model, which is a quantity based one, through which the results will be finally multiplied by the desired prices. Although the recommended model outperforms the CBR method is compared to it, there is a need to conduct more researches to compare the results with further parametric methods to validate the reliability of the current model. This study also, takes advantage of a parameter making process, which its role is to summarize many effective cost factors into a package of influential parameters. On the other hand [ 29 ] have investigated the capability of multilayer feed forward neural network model with a backpropagation learning algorithm for estimating the cost of 78 building projects in India, along with testing the effectiveness of either the early stopping or Bayesian regularization approach on the generalization competency of the network and on the overfitting error as well; where the later approach surpasses. Furthermore, [ 30 ] have implemented fuzzy logic to predict the cost of building projects. As their model is not dynamic in response to market prices, the need for more agile model is felt. Furthermore, [ 31 ] have used an integration of BP neural network and genetic algorithm to estimate the cost of residential buildings. The role of GA is to improve the ANN performance by preventing it from falling into local maximum point and increasing the convergence speed. Besides, [ 32 ] it takes the advantage of multiple regression analysis to estimate the cost of residential buildings. In the research point of view, 92% of the cost of residential building is affected by the land area and building area, and the remaining 8% is stemmed from other factors.

Cost estimation of residential buildings with the use of multifactor linear regression has been considered in [ 33 ], which has reached an accuracy around 92% in the end. The research has recommended to compare the results with those researches that implemented neural network technique to see the differences. Actually, the study is highly advocated the use of cost estimation models in construction projects instead of conventional methods. In [ 34 ], application of Back-Propagation Artificial Neural Network (BPANN) in order to predict the cost of building projects in Nigeria can be seen, however, the model can only be implemented in institutional type of buildings and no other types of buildings or any other projects cannot be estimated by this method. Also, the criterion for the model performance is the prediction errors and other means of evaluations have not been taken into account. Furthermore, [ 35 ] have conducted a survey to investigate the most influencing factors on the cost estimating process, then developed the ANN model, and eventually conducted a sensitivity analysis. They have reached remarkable results with MLP neural network, while applying it at the very early stage of the project. Furthermore, [ 36 ] have implemented ANN for cost prediction of building projects in Philippines. They have concluded that ANN oftentimes can show an acceptable performance despite the incomplete available datasets; however, the enriched datasets is highly recommended. Besides, [ 37 ] have implemented a hybrid model of ANN and GA in order to overcome some drawbacks of ANN, including the slow convergence and being trapped in local minimums. Also, [ 38 ] have applied a hybrid method of CBR and GA in early stages of high-rise building projects to estimate the cost, in a less erroneous way. The application of GA has successfully improved the process of the estimation model by defining the weights of cost factors, though, they recommend to include other cost categories for these types of buildings such as engineering fees and contingencies, considering indexes for different locations, applying other algorithms, instead of GA in order to improve the weights, implementing the model with larger projects dataset, and determining other different cost factors that are effective on the cost estimation process.

On [ 39 ], has comprehensively studied different possible ANN architectures with different learning rates and eventually has compared them, and it is concluded that the best one is an MLP neural network with two hidden layers. It has reached to key findings in the research such that, the number of neurons in the hidden layer, and the learning parameters have more effects on the network generalization ability rather than on its accuracy ability. In addition, the number of hidden neurons is more effective than the learning parameters. On the other hand, the network is highly sensitive to the number of inputs, so that the more inputs; the more the possibility of overlearning in the network. Finally, the study suggests to implement the model in other types of buildings and to compare the current results with other cost estimation methods such as multiple linear regression. Moreover, the development of ANN and Support Vector Machine (SVM) for predicting the cost of building projects and schedule is presented in [ 40 ], out of which, SVM has shown superior performance; therefore, ANN is more applicable in nonlinear sample data. The paper also recommends using an ensemble of ANN and SVM, while it should be taken into account that early planning is considered a key factor in project success. In addition, [ 41 ] have conducted a survey and implemented data analysis in order to extract the main influencing input parameters of their fuzzy model. They have mentioned that the use of two-sided membership function has shown better results than other studied models. They also suggest that comparing the result with other single or combined methods can also be useful. Besides, [ 42 ] have taken advantage of Multiple Regression Analysis (MRA) capabilities to revise CBR in order to enhance the prediction accuracy. They suggest considering also nominal variables and investigating the origins of the increase in the error rate. Son et al. [ 43 ] have also applied a hybrid model of principal component analysis and Support Vector Regression (SVR) and compared them with SVR, ANN, Decision Tree, and Multiple Linear Regression (MLR) out of which eventually, they presented that SVR algorithm is outperformed.

In another research, [ 44 ], the authors have successfully applied case adaptation in order to enhance CBR performance. They suggest that implementing this model in other types of projects as a future research. Also, they proposed the uses of qualitative factors are effective on the model and highly recommend considering the bias resulted from data originated from different regions. In addition, [ 45 ] in their study, have studied BPANN model and compared it with regression in cost estimation of building projects. The best architecture of the neural network is chosen after a process of trial and error out of which eventually, the neural network showed a better performance in compared to regression analysis. In this research, it has been recommended that larger dataset with more accurate information can be used in the future researches. Application of a hybrid model (Modified PSO and fuzzy neural network) in cost estimation of construction projects has also been scrutinized in [ 46 ] as a novel approach within, which the model is capable of being applied to other new cases. Further, [ 47 ] have investigated a BP-ANN model to predict cost of building projects. The best promising architecture is generated after several trials. They also called for larger dataset as an input for the network, in order to improve its performance. Cheng et al. [ 48 ] have integrated neural network with fuzzy logic in order to handle uncertainties as a novel approach. They claim that the hybrid neural network is more effective than the mere neural network in predicting cost of construction projects at very early stage of the project. In addition, there is a concrete evidence that the hybrid neural network is able to address both linear and nonlinear connections in the hidden layer. Cheng et al. [ 49 ] have also taken simultaneous advantage of GA, fuzzy logic, and ANN for global optimization, approximate reasoning, and input–output mapping, respectively. Their cost estimation method is applicable to early stages of the project for project manager’s decision-making process.

A combination of the AHP-based and simulation-based cost model can be seen in [ 50 ] for a single project. They have reached to reasonable results, and they suggest that a wider range of data be fed to their model will be better results. In addition, they recommend a cash allocation system for multiple projects can be developed with a user interface to work around. Their model can be applied to other construction projects as well, and they provided that a modification in weights and evaluation criteria are considered. On the other hand, a combination of rough set (RS) theory and artificial neural network (ANN) is implemented in [ 51 ]. In fact, rough set theory is used to filter the main effective factors in a cost estimation process. They recommend that this hybrid network be implemented in construction projects in that it surpasses the mere ANN results. In their point of view, the less input data can cause, less overfitted network. They recommended combining their model with cost control methods, dealing with data and project cost index in a more scientific way as their future work. An et al. [ 52 ] have used CBR to estimate construction cost of residential buildings. What makes their method worth of use is the application of AHP method in order to interfere with expert’s knowledge in the estimation process. In addition, [ 53 ] have compared three models of NN to predict project’s cost, including BPANN, BPANN adjusted with GA, and NN modified with GA, where the second one outperforms the others. The future of this research is needed to more adjustment of the GA parameters rather than determining them manually.

In [ 54 ], the researchers have used regression analysis to estimate cost of building projects in Singapore and have selected principal components, while being encountered with a large amount of independent variables towards dependent variables. Thus, this will render a regression model with few uncorrelated principals that will eventually produce a better performance. Li et al. [ 55 ] also have investigated the application of regression analysis to estimate the cost of building projects, while incorporating a step-wise variable selection in order to scrutinize the relationship between the available independent variables and the cost of a project as a dependent one. This wise is noted by the authors that the accuracy of the model has improved towards the classic model. Comparison between MRA, ANN and CBR is delicately performed in [ 56 ] out of which, ANN outperforms in terms of accuracy, while CBR outperforms in terms of time spent for cost estimation process. In fact, in this study, three approaches for cost estimation consisting of multiple regression analysis, CBR and ANN have been compared, which finally CBR and ANN outperform MRA, and error associated with ANN is smaller. Also, CBR is the most appropriate model, due to fewer time-consuming features. The use of a BP ANN can be seen in [ 57 ], which is delicately applied to estimate the cost of structural systems of buildings and eventually the results have been compared with regression-based estimations, where the BPANN outweighs the other. Kim et al. [ 58 ] have also implemented BP-ANN, which has been improved through the application of GA algorithm. They have also compared the results of applying GA in order to omit the trial-and-error process of selecting the best ANN architecture with those of the model in the absence of GA, out of which, GA has shown an effective role in improving the model results.

Sonmez [ 59 ] have implemented RA and ANN in cost estimation of building care retirement community projects. They believe that there is not a distinct line between these two methods, and none of them can be called superior to the other; however, they have investigated the for and against of both methods in their case study. Again, Multivariate regression analysis has been implemented in [ 60 ], while accompanied with factor analysis in order to select the best promising factors in the cost estimation process. They believe that the factors effective on cost model accuracy should be more explored. Besides, additional analysis is needed for circumstances, where new projects with new specifications and technologies are added to the project portfolio. In their point of view, different project factors can be taken into consideration, such as regional factors, project categorization, and so on to improve the model performance. Future research shall be conducted to study the cost factor’s behaviour throughout the project lifetime. Emsley et al. [ 61 ] have also implemented ANN in addition to MRA and again factor analysis is implemented to help the process of retaining the best influencing factors in predicting construction cost. Setyawati et al. [ 62 ] have fully compared different situations under which, an ANN may perform better by including different inputs, different structures, data transformation, data preparation, size of dataset. Eventually, ANOVA Footnote 1 test has been implemented to investigate significant difference among four different input sets.

Besides, [ 63 ] have implemented BPANN for predicting the construction cost of school buildings by considering two proposed architectures, where the one, with more inputs outperforms the other. They claim that results that are more accurate stem from more data fed to the network in that neural networks are highly data driven. Boussabaine et al. [ 64 ] have presented an ANN approach developing 6 networks for different n(1 to 6) intervals of the project cash flow as completed intervals throughout the project and m(2 to 7 up to the end of the project) outputs as the remaining m intervals of the project, up to the completion of the project. Khosrowshahi et al. [ 65 ], have also implemented pure MRA to predict cost and time of housing projects in U.K. In this regard, they hope to generate a model, which is more general and can be applied to more diverse cases in terms of type, location, and so forth.

Cost estimation in highway projects has also been the main concern of some studies, which are scrutinized as follows. Mahalakshmi et al. [ 66 ] have estimated the cost of highway projects with the application of a multi perceptron neural network with the back-propagation learning algorithm. The model is composed of significant common cost factors such as topological index and project duration. In [ 67 ], a hybrid model of CBR and AHP is investigated in order to enhance the capabilities of CBR in many aspects, such as improving the accuracy of the results, saving the time, and improving the performance of the model. They claim that, the use of more comprehensive dataset will lead to higher accuracy in results. In addition, the application of indexes related to geographical locations and cost factors should be taken into consideration. On the other hand, [ 68 ] have considered an expert system based upon a regression model in order to facilitate the process of transportation cost estimation. The novel approach in this study is the process of separating quantity from price for removing the need for considering regional factors. Thereafter, when the quantity is estimated, it’ll be applied to unit price retrieved from an up-to-date database. Furthermore, [ 69 ] have implemented ANN in order to improve the estimation accuracy over conventional methods such as EVM. Footnote 2 They have considered several factors out of which, traffic volume, topography, weather conditions, evaluating date, contract duration, construction budget, percent of planned completion, and percent of actual completion, are assumed as the most effective parameters in the project cost. Wilmot and Mei [ 70 ] have also implemented and compared two models, including ANN model and Regression based model for forecasting highway construction cost and the associated escalation in a future, which finally shows the out-performance of the ANN model. In their point of view, factors such as facility (i.e. labour and price of equipment and material), the contract’s characteristics (i.e. terms of payment, duration, geographical location), and overall contract terms (i.e. changes in specifications, amendments and so forth) are the most influencing factors on a cost estimation process.

Sodikov [ 71 ] have successfully implemented ANN in forecasting cost of highway construction and strongly advocate the ANN capabilities in being applied in uncertain circumstances and is used in early stages in projects. Further research is also needed to apply a hybrid of ANN with fuzzy logic, case-based reasoning, and so forth. Hegazy and Ayed [ 23 ] have developed an ANN model in this scope and optimized the corresponding weight through three different methods, including back-propagation training, simplex optimization, and applying genetic algorithm, out of which, simplex optimization surpasses the others. Their model is adaptive to new cases and can be compatible based on new circumstances. Adeli and Wu [ 72 ] have taken into consideration a regularization neural network, while a cost function composed of a standard error is applied and regularization error in order to simultaneously improve the network performance and prevent the network from being overfitted.

They defined public projects for their model as, whatever projects that are related to public sector, such as, schools, warehouses, hospitals, highways, bridges, water-related projects, and so on. In fact, the projects with such cases have been considered in this category.

Alshamrani [ 73 ] have considered cost estimation in building projects by taking advantage of regression analysis. Hyari et al. [ 74 ] in their work, they have developed an ANN model for cost estimation of engineering services through which, the influencing factors on a cost estimation process are selected via interview and literature review and further the best architecture of network is chosen after a process of trial and error. They desired to expand their model by feeding it with diverse datasets from different places worldwide; and also applying it to specific projects like bridges and schools that may increase its accuracy by confining the inherent variance in the input variables. Besides, [ 75 ] present a new method called Principal Item Ratios Estimating Method (PIREM), including parametric estimating, ratio’s estimating, and cost significant model, which is capable of estimating costs under high fluctuations in prices, and it even can predict with least data available equal to only 20% of all cost factors.

Skitmore and Ng [ 76 ] have used a forward cross validation regression analysis to estimate time and cost in construction projects. They claim that, when the cost estimation model needs data such as the total amount of a contract, the accuracy of the cost estimation stems is derived from the accuracy of the total contract. Despite these limitations, the model can surpass the current risks and provide a practical tool in this scope. Bowen and Edwards [ 77 ] can also be regarded as a move from black-box mechanisms toward more logical and understandable methods like expert systems in the late twentieth century. They remind that the importance of historical data and expert’s knowledge in cost estimation scope should not be disregarded. In addition, they desired to integrate a resource allocation system with the current cost model in the future.

Roadway projects are related to projects in the scope of paving roads, asphalt, and road-related works such as constructing bridges over roads, and mainly earth works. Few studies are done in this realm, which are as follows. In [ 78 ] the comparison of applying three different ANNs, including Multi-Layer Perceptron (MLP), General Regression Neural Network (GRNN), and RBFNN, has shown that GRNN is capable of estimating the cost of roadway projects with higher accuracy towards the two others. This type of neural network has shown outstanding performance, while encountering with incomplete datasets. They believe that a homogeny in data set will also lead to better results in future researches in which, they have considered roadway projects with diverse specifications.

Swei et al. [ 79 ] have applied an integration of a Maximum Likelihood (ML) and Least Angle Regression (LAR) to estimate the cost of road pavement. They suggest that more cost inputs can be taken into consideration, in the model for the future. They also recommend that their model can be implemented by using actual cost rather than a bid price for further studies. Besides, the use of other methods such as regression analysis is also proposed.

Peško et al. [ 80 ] have considered comparing ANN and SVM capabilities in cost estimation for construction of urban roads out of which, SVM has shown superior result compared to ANN. They claim for more expanded database in the future researches. Also, they raise the need for a cost model that is capable of estimating at very early stage of the project for management purposes.[ 81 ], for instance, have taken advantage of simulation in management with the use of stochastic models and Monte Carlo simulation. On the other hand, [ 82 ], have applied CBR and GA for cost estimation of bridge construction projects. On the other hand, [ 83 ] have probed the application of ANN in cost estimation of bridge repair and maintenance projects compared to work package methods, which finally the results of ANN are more outstanding. A conclusion is drawn that the model performs well at the early stages of the project, and a hybrid of the current method with up-to-date techniques in general, and fuzzy logic in particular are recommended.

Water-related projects, here are referred to whatever project beyond the scope of water, sewer installation services, and so on. Cost estimation in this type of projects is less investigated, which are studied as follows. Marzouk and Elkadi [ 84 ] have determined variables effective on cost estimation process and conducted a survey to implement a factor variable reduction through Exploratory Factor Analysis (EFA). Eventually, the best ANN is selected from different architectures with an error almost equal to 22%. ANN has also been the main concern for cost estimation in [ 85 ], since it is capable of tackling non-linearity in early stages of projects. Furthermore, [ 86 ] have used ANN model to predict water and sewer services construction cost and selected the best network architecture based on trial and error through, which have reached an accuracy of 80%. They claim that one of the drawbacks of their model is the lack of regional factors, which can be effective in improving the performance and accuracy of the current model.

In roads, tunnels project area, two types of neural networks have been implemented in [ 87 ], and the results have been compared with those of multiple regression analysis, out of which neural networks show better performance. Their model can be implemented in other types of buildings as well. However, as they claim, the model needs to be updated to be compatible to newly complete and added projects to their database. Petroutsatou and Lambropoulos [ 88 ], on the other hand, have approached the construction cost estimation via application of a Structural Equation Model (SEM) and compared the results with ANN and RA models, which SEM performs better. They try to follow their research in future to be able to predict the project profit and schedule programming as well as project cost.

On the other hand, in railway project’s scope, comparison between MRA and ANN in estimating the cost of light rail transit and metro track works can be seen in [ 89 ] out of which, the MRA result is superior to the other due to the small number of available instances. This shows that the higher the number of the cases the higher will be the ANN results' accuracy. Besides, the work of [ 90 ] has depicted the effect of GA on optimizing CBR attributes weights for estimating the cost of railway bridge projects.

Recently, [ 15 ] have investigated forecasting hydroelectric power plant project’s cost via ANN through which, three different architectures have been generated and examined, while seeking the best performance. The results have been compared with those of RA and concluded that the ANN shows better promising results. They set forth that, the model can be used for different parts of hydroelectric power plant projects as well. Singal et al. [ 91 ] probe RA in their study and compared the results with actual cases to validate their model.

Hashemi et al. [ 92 ] have thoroughly investigated effective parameters in cost estimation of power plant projects, while simultaneously considering risk in these projects by embedding PERT technique. The sensitivity analysis conducted in this research shows that the type of power plant is the most influencing factor in the model inputs. This data is finally fed into the hybrid of ANN and GA, to estimate the cost of these types of projects with an accuracy equal to 94.71%.

Hence, the determinative role of ANNs is highlighted again in Fig.  4 . Afterwards, as it is mentioned before, RA is the most powerful method applied in cost estimation studies. As it can be seen, SVM, PSO, RBFNN, and Fuzzy ANN have been used only in building projects.

Application area versus method applied

Figure  5 shows the distribution of these methods and as it is depicted so, ANNs have the first ranking among all methods. This strongly shows the power of Neural Networks as the artificial intelligence tool to deal with estimating problems. Further, Regression Analysis stands on the second step as an outstanding tool in the field of parametric methods.

Applications of different methods in construction cost estimation studies

3.4 Distributions and techniques

Studies on the distribution of the cost estimation techniques suggest the need for categorization. These techniques are based on the studied papers considered as an analogous, analytical, parametric and intuitive approach. To continue analysing the reviewed papers, based on these criteria, the results have been represented in “Appendix 2 ”. The summary of “Appendix 2 ” is illustrated in Fig.  6 . As the result shown in Fig.  6 , most of the adopted techniques belong to the analogous category, and the least one is the analytical one, which is the decision tree method adopted in [ 43 ].

Distributions of articles by approach types

In the construction cost estimation, the qualitative model confides in the specialist judgment or heuristic and mathematical rules. The qualitative models can classify into statistical, intuitive, and analytical models. On the other hand, quantitative models can categorize into three main techniques of analogous, parametric, and analogy-based models. Among all the methods applied to the proposal techniques, only 2% of them are qualitative, which belong to intuitive methods such as AHP. Therefore, based on this result, the rests of the studies are done based on quantitative approaches (Fig.  7 ).

Approach type distribution

One step further, the sub domains of each approach type are shown in Fig.  8 i.e. intuitive, analytical, analogous, and parametric. As shown in this figure, analytical methods such as decision trees have the least proportion of all methods applied. These categories are also shown in each application area and presented in Fig.  8 .

Approach types applied in different areas

Moreover, the distribution of these approaches in the time spectrum is shown in Fig.  9 , As it is presented analogous approaches have the most portion of studies conducted. Total categories of cost estimation methods applied in cost estimation of construction projects can also be seen in “Appendix 3 ”.

Approaches implemented in time horizon

3.5 Journals

Table 3 also summarizes the papers reviewed by their journals, and the journal’s portion of total.

As shown in the above table, the Journal of Construction Engineering and Management, Building and environment, Construction Management and Economics, Expert System with Applications are the top journals with the most published papers in construction cost estimation scope.

3.6 Year of publication

Figure  10 has also depicted the distribution of cost estimation studies in years. As shown in this figure, a smooth growth has been occurred in years 2009 until 2011, and 2017 until 2019, after a decline in years 2006, and 2007. However, again a diminution has been observed afterwards until 2016. Furthermore, as it presents, the most studies have been done via ANN as a powerful machine learning technique.

Distributions of applied cost estimation methods in years

As it has been contemplated more in diverse applied methods, the ANNs’ contribution to cost estimation problems observed in Fig.  11 . Cost models, expert systems, AHP, Footnote 3 CBR, Monte Carlo, fuzzy logic, and decision tree methods are all summarized as other methods in this diagram.

Proportions of each method studied in time horizon

4 Conclusion

Cost estimation in construction projects has been reviewed in articles published within years from 1985 to 2020. A conclusion is drawn that in almost all the cases, estimating at the very early stage of the project is of a great concern. Most of the proposed estimation techniques tried to meet the expectation by generating models to be applied at even tendering level to help process of decision-making of managers. Fundamentally, effective cost factors shall be explored and scrutinized exactly. Not only, the effective cost factors should be studied, but also the factors affecting the cost model accuracy must be reviewed in deep. One of the cost factors that have been noted repeatedly is the regional factor, which shows the importance of differentiating between projects with diverse geographical origin. Additionally, the ability of the model to expand generally and the applicability to novel cases has the high degree of importance.

As shown by results, among the various methods (ANN, Fuzzy NN, SVM, PSO, RBF, RA, CBR, PSO, Decision Tree, AHP, Monte Carlo, fuzzy logic) used by researchers, the most popular machine learning techniques that used in the reviewed papers are ANN and RA respectively. In contrast to other methods, the ANN and RA are the most popular and successful methods implemented in these studies respectively. However, the hybrid models of ANN with fuzzy logic, CBR, GA and so forth have surpassed the mere ANN applied. The point that shall be considered in ANN application is its sensitivity to input data. Since this machine learning technique is data driven, it will perform more accurately, if a large amount of data and homogenous dataset exists to extract relations between available data. On the other hand, the number of input neurons (known as cost factors), has a direct effect on system malfunction. Accordingly, when the number of input cost factors increases, the complexity of the system will increase and in case of construction cost estimation, it showed the accuracy of the estimation will decrease. This study finds out in the hidden layer, the number of neurons and the corresponding weights have a direct effect on the generalization ability of the model. Indeed, the number of factors is important rather than learning parameters, and it directly affects the estimation model accuracy. In addition, ANN is known as a powerful model in tackling with nonlinear problems. Tuning the ANN parameters, such as the number of hidden factors and weights have also been the concern of many studies, which have been overcome by combining it with GA algorithm. Nevertheless, the expert knowledge to select cost factors in the estimation models has a valuable influence. Furthermore, the building and highway projects assign the most attention of the researchers to themselves in cost estimation studies. Among these studies, the methods have been categorized based on their approach, including intuitive, parametric, analogous, and analytical, which the most studies belong to the analogous group.

This study provides several guidelines for applying machine learning models in construction projects as follows: (1) understand the fundamental and validation of machine learning models and cooperate with existing applications and models; (2) select the best models, which ability is well matched with the research impacts and goals; (3) construct the dataset priority for proposal machine learning models and check the sufficiency and efficiency of the dataset; (4) parallel use of machine learning models with current or ordinary models at the early stage of a project; and (5) find the project priority of factors and required datasets in the research association.

The limitations of this research paper can be summarised as: (a) data is collected from Google Scholars and Science Direct scientific database, therefore, the articles did not cite in these two databases did not consider in the study as well; (b) the study had the limitation of exploring the English language papers in the cost estimation for construction projects domain only and not considered the other languages.

Based on this study, deep-learning techniques did not get attention of researchers in the field of cost estimation for construction projects; therefore, this systematic review suggests these techniques and models for future propose work and study.

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Sanaz Tayefeh Hashemi

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Omid Mahdi Ebadati

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Appendix 1: Reviewed articles by methods used

Appendix 2: reviewed articles by approach type implemented, appendix 3: categorization of cost estimation methods applied in construction projects.

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Tayefeh Hashemi, S., Ebadati, O.M. & Kaur, H. Cost estimation and prediction in construction projects: a systematic review on machine learning techniques. SN Appl. Sci. 2 , 1703 (2020). https://doi.org/10.1007/s42452-020-03497-1

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Implementation of strategic cost management in manufacturing companies: overcoming costs stickiness and increasing corporate sustainability

• Mohammad Mahdi Rounaghi   ORCID: orcid.org/0000-0002-9640-678X 1 ,
• Hajer Jarrar 2 &
• Leo-Paul Dana 3  

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In today's competitive world, three factors: price, quality and time have critical roles in the success of the companies to achieve success in the competition. For this purpose, the companies have to also adapt themselves to changes in technology and environment. Strategic cost management is the best way to improve the sustainable management models in the manufacturing companies. Strategic cost management has solved many of the problems and shortcomings of traditional accounting system and by accurate determination of costs, their proper allocation to products and elimination of waste, tries to create value for shareholders by using continuous improvement. The objective of this paper was to develop a management model called strategic cost management that reduced costs stickiness and increased corporate sustainability. Using strategic cost management approach can create competitive advantage for the companies, because it provides accurate cost price information so that the users can easily understand the information. The aim of the paper by introducing strategic cost management was to contribute toward accurate pricing, which could result in the increased profitability and competitiveness of the manufacturing companies in a highly competitive global market and at a market‐based price. Also, due to the growing competition among companies in providing high quality products with reasonable prices, a precise system of measurement of the cost of the product is necessary.

Introduction

In recent years, economic analysis in the planning process and in the monitoring process of the production process shows that three factors: price, quality and time have critical roles in the success of the companies to achieve success in the competition. The world faces the problem of integration between sustained business functions. The sustainability data are not sufficiently integrated. To solve this problem, organizations need information systems to facilitate their sustainability initiatives [ 1 , 2 ]. Also, businesses and academics worldwide agree regarding the benefits of sustainable development (SD). Improving reputation and branding and increasing revenues by reducing costs are the primary strategic objectives of any entity [ 3 , 4 ]. In this paper, we introduce the strategic cost management approach that helps manufacturing companies for overcoming the costs stickiness and monitoring the life cycle of products and it introduces integrated sustainable development system for manufacturing companies.

Strategic cost management is a process connecting financial management, cost management and strategic management. It involves cost optimization and financial resources preparation which are needed to achieve desired strategic market position in cost effective manner. The importance of managing costs and aligning them with the business strategy of an entity is critical especially in the midst of challenging economic times faced by businesses today. Traditionally companies have been under pressure to cut cost in the short-term without really thinking about sustainable change, impact on the people and integration with the overall business strategy. In the current business environment of increased global competition, new markets, increasing regulation and changing demographics, successful companies are changing their approach to cost structuring and control.

Over the last decade, research in management accounting has challenged the fundamental assumption that cost behavior is symmetric for activity increases and decreases. Cost behavior is an important issue in cost accounting and management accounting, as it widely affects decision-making processes. Moreover, several techniques generally used by managerial accountants and financial analysts depend mainly on cost behavior, such as conventional ABC, cost estimation and cost-volume-profit analysis. Quality management (QM) has been widely viewed as a management paradigm that enables firms to gain a competitive. Therefore, overcoming on cost stickiness is a critical issue for mangers of manufacturing companies. Also, understanding cost behavior is an essential element of cost and management accounting [ 5 – 8 ].

Cost stickiness, also referred to as asymmetric cost behavior, is a well-documented result of managerial discretion underlying the development of corporate cost compared to changes in firm activity. Managers’ decisions to maintain the resource allocations due to product market competition can be costly, especially during periods of sales decreases. Under the traditional model of cost behavior, costs are assumed to be either fixed or move proportionately and symmetrically with sales changes. The traditional model of cost behavior distinguishes between fixed and variable costs and posits a proportional relation between variable costs and underlying activity levels. Understanding sticky cost behavior is important and has direct benefits for the economy as it provides useful information to managers making decisions on cost control and to external stakeholders (e.g., financial analysts) assessing firm performance. As the global economy integrates and competes, strengthening cost management and operational efficiency becomes increasingly important to firms’ survival and development [ 9 – 14 ].

Cost management is an important part of business management in the manufacturing industry. The degree of cost management implementation is a comprehensive index to measure the level of enterprise management. In particular, firms with limited access to capital have higher costs of securing external financing during the capacity expansion periods, which increases the upward adjustment costs. When activity decreases, firms with limited access to capital may suffer more decrease in the present value of revenue generated by a marginal capacity, as these firms have higher opportunity cost of capital and thus higher discount rates compared to firms with better access to capital. Therefore, we hypothesize that limited access to capital not only reduces contemporary capacity expansions associated with sales increases, but also weakens the degree of cost stickiness when sales decrease [ 15 , 16 ].

On the other hand, cost management is an important part of business management in the manufacturing industry. The degree of cost management implementation is a comprehensive index to measure the level of enterprise management. From investors’ perspective, investors depend on the published financial statements prepared by the management that are based on available information regarding the determinants of cost behavior. From financial analysts’ perspective, predicting cost behavior is an essential part of earnings prediction [ 16 – 18 ].

In many production firms, it is common practice to financially reward managers for firm performance improvement. For decades, firms have devoted to improving the speed and efficiency of material and information flows in the supply chain, acknowledging the importance of time-based competitive advantage in the dynamic business environment. As one of the key factors in decision-making process, the evolution of product price passes critical information. Managing costs by utilizing resources effectively is regarded as fundamental to success in today's competitive environment. Cost behavior as “sticky” if costs increase more for activity increases than they decrease for an equivalent activity decrease. Sticky behavior is the result of decisions made by managers when activity decreases. When activity drops, the manager must decide whether to (a) maintain committed resources and bear the cost of unutilized capacity at least in the short-term or (b) immediately reduce committed resources and incur potentially large retrenching costs in the current period and, if activity increases in the future, incur further costs to replace resources. Traditional accounting cost models assume that fixed costs are independent of the level of activity and variable costs change proportionately with changes in the level of activity. In the common traditional model of the behavior of costs, which is generally accepted in accounting literature, costs are usually divided into two categories of fixed and variable ones in terms of changes in activity level: fixed occupants are variable. Most management accounting texts assume that unit variable costs are linear and proportional to changes in activity and that fixed costs are fixed. The proportionality and symmetry between costs and activity implies that a 1% increase in activity results in a 1% increase in costs, and a 1% decrease in activity results in a 1% decrease in costs. Stickiness might also be conditioned by existing capacity [ 5 , 19 – 26 ].

Notions of cost behavior are a key element in management accounting [ 27 ]. There are two main views about the existence of expense stickiness: rational decision-making and motivational. The rational decision-making view treats expense stickiness as a consequence of management rationally choosing between alternatives after comprehensively weighting costs and benefits. The second view is motivation-based and relates expense stickiness to managerial incentives, suggesting that managers are not expected to behave as if they were in an ideal world. Among their dysfunctional behavior, perks and earnings management reflecting different contracting stimulations are often observed [ 28 ].

Planning and control are of the important tasks of management. Cost related information that managers need them to perform these tasks may be received from classified information reflected in the financial statements. The required information in this regard cannot be easily extracted from the financial statements [ 29 ]. A business entity expenses can show different behaviors suitable to the level of activity. In traditional cost model it is often assumed that administration, general and selling costs varies according to activity level. However, recent experimental studies have revealed evidence that shows that administration, general and selling costs behave asymmetrically [ 30 ]. An asymmetric behavior is a behavior in which cost increase more rapidly. In other words, the reduction in costs at the time of declining sales is lower than when the cost increases at the time of the same level of sales. This cost behavior is called cost stickiness. Expanding researches show that economic factors such as increase in assets and uncertainty about the future can have an impact on the asymmetric behavior of cost.

Costs stickiness

Cost behavior is defined as cost reaction in response to changes in activity level. Managers who understand how costs behave, have better circumstances for predicting spending trends in various operational positions. This position allows them to plan their activities and thus plan their operating revenues better. The traditional view related to costs indicates that changes in costs have a proper relationship with increased and decreased activity level. However, recent researches about costs behaviors indicate costs stickiness. Thus the degree of increase in costs level as a result of increase in activity level is higher than the degree of reduction in costs level as a result of decrease in activity level.

According to the idea of Anderson et al. [ 31 ], there are many reasons for costs stickiness. Some of these reasons include natural reluctance to lay off employees when downsizing, firm costs and the need for time to approve a reduction in the volume of activity and management decisions for maintaining used resources which could be the result of individual consideration and leads to imposing cost to the firm. By determining the stickiness of cost, the company owners can analyze whether managers incur costs to the firm or not [ 32 ].

Managers of manufacturing companies must consider the relationship of costs with income and the effect of income changes on the costs rate when planning and budgeting the company activities for predicting the future costs and thus offer a more comprehensive budget [ 33 ]. The ultimate goal of any business unit is maximizing profits and consequently, an increase in equity. Management of each profit-oriented enterprise tries to gain maximum benefit and efficiency from using the fewest resources and one of the simplest ways to reduce consumption of resources is cost control. But this requires complete knowledge of how costs behave and the factors influencing the behavior of the cost. One of the items that should be considered in the analysis of cost behavior is the phenomenon of cost stickiness. The public and dominant view is that with declining sales, costs should also be changed accordingly. But in fact, it does not happen [ 34 ].

Today, increasing competition in domestic and international markets has forced managers to better understand their cost structure and become aware of cost orientations means how the costs change. The meaning of cost orientation is a model according which costs react to changes in activity level [ 35 ]. Therefore, it is suggested that managers calculate their costs stickiness and consider all aspects of this important issue in their decisions. Orientation or the concept of cost stickiness gives a great help to investors and shareholders. Because in companies with strong stickiness, by reduced selling, costs will change more than the time when selling increases and this will be considered as a weakness of management by the investors and shareholders; while one of the main reasons of cost stickiness is bearing the current costs to avoid more losses in the future and or more profit in the future and it depends on management decisions [ 36 ].

Review of literature

Sustainable development refers to an economic, environmental and social development that meets the needs of the present and does not prevent future generations from fulfilling their needs. In manufacturing companies, collaboration between supply chain members is important for the sustainability and competitive advantage of a supply chain. The collaborative activities in a supply chain include various joint activities for cost reduction, research and development (R&D), product development, manufacturing, marketing, distribution, and service. The commitment of companies to corporate sustainability has been frequently discussed in theory and practice. Such a commitment to corporate sustainability demands a strategic approach to ensure that corporate sustainability is an integrated part of the business strategy and processes. Also, the effective adoption of continuously developing new technologies is a critical determinant of organizational competitiveness [ 37 – 41 ].

For the first time [ 5 ] tested the hypothesis that costs are sticky and approved the presence of stickiness in the costs behavior. They established a model with administration, general and sales costs as a function of sales, and found that costs increase by an average of 55% in response to a 1% increase in net income, but decrease only by 35% against 1% reduced income. In other words, a 1% increase in net sales, costs increase by 55% but by 1% decrease in net sales, costs decrease only by 35%. Due to the lack of public information about costs related drivers, they used data of administration, general and sales costs and net income of sales for the analysis of cost stickiness, and stated that they can analyze the behavior of administration, general and sales costs based on sales net income because sales volume stimulates many parts of this cost. Subramaniam and Weidenmier Watson [ 25 ] tested the presence of behavior of stickiness in the cost price of goods sold, and the results showed a positive relationship. They also tested the effect of different economic conditions, such as rates of GDP and the different characteristics of companies, such as total assets and number of employees of companies on costs stickiness. Their results showed that in periods of economic growth, the severity of stickiness is more and in the periods that income decrease happened in its previous periods, severity of stickiness decreases. Also, by increasing the ratio of total assets to sales and an increase in the number of personnel of companies, severity of cost stickiness increases. Stickiness of sales and distribution and general and administration costs has been studied in another study by Anderson et al. [ 31 ]. The main hypothesis of this study is public sale and administration costs. After collecting data related to cost of general sales and administration and sales revenue costs of 7629 American companies in a 20-year period (1979–1998), the relationship between costs and sales was examined by multi-varibale regression relationship. The results of this study did not confirm the main hypothesis of the research and announce the general sale and administration costs of companies in the statistical population of the research, sticky.

The results obtained by Weiss [ 18 ] from a sample of 2520 out of 44,931 industrial companies from 1986 to 2005 show the issue that the sticky behavior of costs increased the accuracy of analysts in predicting revenue in total, considering the fact that prediction horizon and especial effects of industry have put this analysis under control. With regard to the classification of costs into sticky and non-sticky costs, the results of Weiss's research [ 18 ] show that the accuracy of analysts in forecasting revenues for firms with sticky cost behavior is on average 25 percent less than that of people who analyze for companies with non-sticky cost behavior. Obviously, the behavior of cost has a considerable influence on the accuracy of analysts' prediction.

In Kordestani and Mortazavi, research [ 30 ], the power of profit prediction was compared with other models by the model based on variability and stickiness of cost. The study showed that the accuracy of prediction of the model based on the variability of costs and stickiness of cost is significantly higher than the other models. In several domestic researches, stickiness of various costs has been studied. According to the results of Ghaemi and Nematollahi's research, the cost price of the sold goods and selling and distribution and general and administration costs are sticky. Another study from the same researcher showed that overhead costs are sticky, but the costs of raw materials, direct wages and financial costs are not sticky.

In other study, Khani and Shafiei [ 42 ] examined cost stickiness and its relationship with sales and the results of their research indicate an undeniable relationship between the amount of sales and company earnings with the level of company's costs. Although their findings indicate that costs do not increase in proportion to profit increase, but there is a significant relationship between them.

In other study, Banker et al. [ 43 ] examined the relationship between uncertainty and sticky behavior of cost. By examining administration, general and sales costs, number of employees and their working hours, they evaluated cost stickiness. The results indicate the presence of cost stickiness in the sample under investigation. Sepasi et al. [ 44 ] examined the characteristics of management behavior toward costs stickiness. Their studied a sample consisting 14,568 year-company and examined administration, general and sales costs for the years 1992–2011. The results showed behavioral changes in managers about cost stickiness so that the occurrence of cost stickiness phenomenon increases the confidence of managers.

Management of strategy and strategic cost management

Effective strategic management, plays an important role in the success of the company or organization. Increase in competition in the international arena, new technologies and changes in business processes, caused management to become more dynamic and important than before. Managers should always have a competitive attitude and for this purpose the company's competitive strategy is essential. Strategic attitude leads the manager to anticipate changes and products and their production process will be designed based on anticipated changes in demand and customer's needs. In this situation, flexibility is important.

In developed countries, most organizations use data of cost management. But the extent of their reliance on this information depends on the nature of the competitive strategy of the company. Many companies compete on the basis of the provision of goods and services at the lowest cost price. Some companies compete on the basis of being a leader in production and offering superior and differentiated products. The role of cost management is supporting corporate strategy by providing the information through which one can be successful in products development and their marketing. For achieving corporate sustainability, we suggest to use the instruments of strategic cost management in manufacturing companies . Today, managers use strategic cost management tools to accomplish strategies and achieve main success producer factors.

Instruments of strategic cost management are as below:

The most common system that used in many companies is activity-based costing system. Activity-based costing system which is specifies the resources consumed by each activity during the relevant period; and thus the cost of each activity is precisely calculated. Then the aggregated costs of any activity are assigned to the considered product or customer, depending on the product consumption or the customer use of that activity [ 45 ]. The other instrument is bench-marking. Bench-marking is a process that the companies try to choose the best practice as of the right activity in comparison with the leading companies, then given the success-builder factors, the company processes are improved to the level of performance of its competitors or even reach to a better level. For identification of internal and external failure factors in the companies, we suggest to use total quality management technique. Total quality management a new concept that emphasizes on precise measurement of the costs and identification of internal and external failure factors, through which a way to lower production (lean production) by continuous improvement in company processes is created [ 46 ].

For finding the precise systems of measurement of the cost, in-time production system and kaizen costing are useful tools for manufacturing companies. In-time production system is a system based on the volume of demand. In this system, a piece of product will be purchased or produced only when a sign of its consumer is received. This prevents the accumulation of inventory in workstations. Among the main objectives of this system we can mention improvement of quality and increase in productivity with an emphasis on the kaizen concept. Kaizen costing is a managerial technique through which managers and employees of the company become committed to perform continuous improvement program in the quality and other key factors of success. In the path of continuous improvement, the processes are re-engineered and non-value activities in the manufacturing process are removed or left behind [ 47 ].

The other instruments are target costing and value engineering. In target costing method, the costs are determined according to the product price. It means that first the companies determine the product selling prices, by analyzes of the market and then according to their expected profit, determine the cost price of the product. In other words, goal-oriented costing system is profit planning and cost management system that in that base it was the price, and the essential emphasis is on customers. Goal-oriented costing system focuses on the design stage and requires the participation of all specialized units [ 48 ]. Value engineering is suggested with the aim of examination of all activities of a project, from the formation of the first thought to the design and implementation and then setting up and utilization, is known as one of the most efficient and the most important economic methods in the field of engineering activities [ 49 ]. The purpose of value engineering is eliminating or modifying any factor that leads to the imposition of unnecessary costs, without hurting the core and essential functions of the system. Value engineering is the continuous improvement of design and implementation and it is not merely a program to reduce costs, but is a way to maximize the value of designs [ 50 ].

Implementation stages of strategic cost management

Implementation stages of strategic cost management include value chain analysis, strategic situation analysis and analysis of structural and administrative costs drivers.

Analysis of the value chain

Value chain analysis is an instrument for strategic analysis that helps companies to better understand the competitive advantage. Value chain analysis focuses on the whole value chain of the product from design to production and after-sales service. The basic concept of analysis is that by a thorough examination of each of the activities in the value chain, one can reveal the activities that the companies have the highest or lowest success in them from competition perspective, and plan accordingly.

Analysis of strategic situation

At this stage, the company determines its potential and current competitive advantage by examining valued activities and cost drivers which have been specified in the previous stage. Companies which have competitive strategy of cost leadership are strongly trying to reduce their costs to the level of cost of cost leadership. Cost leadership focuses on cost reduction only as far as it makes sure that it is the leader in price and the holder of the lowest cost in the market. Reduction of costs is usually done by increasing productivity in the production process, distribution or general and administrative expenses. In this management strategy, maintaining stability is a priority and the company is not looking for innovation and risk-taking, but is looking for offering products and services at competitive prices. In contrast, competitive strategy of differentiation, allows the companies to raise the price of products higher than that of their competitors and without significant reduction in costs, have high profitability. These companies, by creating differentiation between the products and creating new features, make customers willing to pay a reasonable price as a result of this differentiation. Using the product differentiation strategy, one can reduce the intensity of competition and no threat of product substitution happens for the manufacturer, because all customers become loyal to the brand of the product [ 51 , 52 , 53 ].

Analysis of drivers of structural and executory cost

Strategic Analysis of cost drivers helps companies in improvement of their competitive situation. Drivers of structural and executory cost are used to facilitate operational and strategic decision-making.

Driver of structural cost, has strategic nature because it includes programs and decisions which have long-term effects. In this regard, the following items are necessary to be noted:

Scale: For example, a retail company shall determine the number of new stores it opens during the year in order to achieve the strategic goals and competitive success.

Technology: New technologies can significantly reduce the company costs. For example, some manufacturing companies in developed countries use computer technology to show number of products that their customers use (especially large retailers), so that whenever the customers run out of the inventory in the warehouse, they send for them quickly.

Complexity of products: companies that produce a high variety of products, have high cost of planning and management of production and also high distribution costs and after-sales service. Such companies usually use activity-based costing to determine the degree of profitability of their products.

Administrative cost drivers, are the factors that companies can manage them in the short term through operational decisions to reduce costs. These factors include:

Work commitment: work commitment causes reduction in costs. The companies in which there is a strong correlation between the employees, can significantly reduce their operating costs.

Design of Production process: the sequence arrangement of equipment and the frequency of processes lead to accelerating the production process in the company. Production technology innovations can significantly reduce costs.

Relationships with suppliers of raw materials of the company: the companies can reduce their costs significantly through agreements with suppliers of raw materials on quality, delivery time and other characteristics of their required raw materials.

Conclusions

Today, sustainability emphasizes various aspects of the organization in economic, social and environmental terms, so the importance of this issue is very important for current and future generations. Most companies have come to the conclusion that in order to improve the efficiency and effectiveness of production sustainability, they need to monitor, measure and control the characteristics of sustainable production. Therefore, measuring the sustainability of production has become an important issue in production and operations.

The purpose of this paper is to design a model for achieving a sustainable development index in order to integrate the economic, social and environmental performance data of manufacturing industries. By understanding the limitations and shortages of resources, the approach of the manufacturing companies includes the acquisition of new production mechanisms and technologies. To achieve newer and more innovative technologies tailored to their production processes in order to reduce production costs and increase their market share, these companies have conducted costly research. One way to deal with a shortage of resource for companies is reduce their costs. Companies regardless of sizes and operational scales must take economic opportunities into account in the long run, limiting opportunities, and incorporating innovative solutions, sustainable development, and positive social and environmental impact into their business activities.

Small-business owners face an ongoing challenge in trying to balance the need to serve customers and meet long-term business objectives while at the same time controlling the cost of doing business. A strategic cost management strategy in which cost decisions are made according to the value they add to both the business and the customer is often the most effective strategy a small business can adopt. Good financial decisions come from an effective cost management strategy designed to maximize value and minimize both initial and ongoing costs. Although a great many of a business’s cost-based decisions involve purchasing, pricing and inventory management, it’s also important for every small-business owner to consider costs involved inside the business.

In a competitive world, paying attention to cost management to reduce costs and increase customer satisfaction are priorities. Today, noting the proper role of the choosing quality and quantity of production factors, choosing between user processes or capital in the production process and selection of appropriate technology, in determining the cost price and producing products that meet the price reasonable in accordance with the customer' purchasing power appear more than before.

Providing the required information of cost management is possible only by establishing a modern system of management accounting including the design and use of various management accounting tools within the organization. Among these tools, there are activity-based costing, target costing, Kaizen costing, product life cycle costing. Strategic cost management is effective by accurate evaluation and identification of costs in the creation of income, profitability and value creation for companies.

By a correct understanding of their competitive situation and by using instruments of cost management, companies can reduce unnecessary costs. Also strategic cost management, by providing more accurate data for the managers, helps them in the short and long-term decision-making to achieve their strategic goals.

Given the importance of understanding the costs for those inside and outside the organization, such as managers, capital market analysts, investors and auditors recommendations for future research are presented as follows:

Examination of the effect of the changes in sales on costs stickiness.

Study of the relationship between management optimism with cost stickiness in various industries.

Examination of the relationship between the cost structure with behavior of each expense.

Availability of data and materials

This paper has no associated data.

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Independent Researcher, Mashhad, Iran

Mohammad Mahdi Rounaghi

USEK Business School, Holy Spirit University of Kaslik, PO Box 446, Jounieh, Lebanon

Hajer Jarrar

Rowe School of Business, Dalhousie University, Halifax, Canada

Leo-Paul Dana

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MMR carried out this research as a first author, Dr. HJ and Professor LPD supported the research as a supervisor. All authors have read and approved the manuscript.

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Correspondence to Mohammad Mahdi Rounaghi .

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Rounaghi, M.M., Jarrar, H. & Dana, LP. Implementation of strategic cost management in manufacturing companies: overcoming costs stickiness and increasing corporate sustainability. Futur Bus J 7 , 31 (2021). https://doi.org/10.1186/s43093-021-00079-4

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Received : 09 April 2021

Accepted : 11 June 2021

Published : 16 September 2021

DOI : https://doi.org/10.1186/s43093-021-00079-4

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• Strategic cost management
• Manufacturing companies
• Cost stickiness
• Corporate sustainability
• Continuous improvement