Cost Estimation: How to Estimate the Cost of Your Project

Abstract 

Cost estimation plays a critical role in the allocation of resources. All projects or activities depend on budgets, which are based on estimates. Budgeting is the initial activity and relies on estimates to determine the allocation of resources. If cost estimation is done correctly, it enhances the allocation of resources as well as the ultimate financial performance of a company. In construction companies, success and profitability rely on the ability to estimate the of the project correctly. The same case applies to capital-intensive industries such as oil and gas. All other companies have to calculate the cost of developing a new product, service, marketing, enhancing quality, among other activities, or face failure along the way as resources dry up. This literature review explores the link between cost estimation and resources allocation. 

Introduction 

Any activity related to the production of goods or services consumes resources. Cost estimation refers to the process of estimating the resources required to produce a product, service or deliver a project. Cost drivers are the specific elements that consume resources. As a standard part of planning, cost estimation is critical to guide the organization in the allocation of resources. If the estimation is weak or off the mark, the result is cost overruns, where some activities fail to get resources to produce deliverables. This literature review seeks to assess why cost estimation is vital in business and how companies can ensure effective use of cost estimation. 

History 

The emergence of estimation 

The methods for estimation are not new. The invention of mathematics necessarily led to the rise of estimation, where people use the known to make predictions. The human brain operates similarly, trying to make predictions given the uncertainty in the world. In the majority of cases, all the information required to make decisions is often unavailable, and that demands to work with estimates based on the past. With the rise of the industrial revolution and the modern world, cost estimation is critical when building products or making decisions. Therefore, over the years, scientists, mathematicians, and engineers have developed sophisticated modeling techniques to ease cost estimation. 

Estimation and decision-making 

Cost estimation is essential for decision-making. According to Swan & Ugursal (2009), policymakers have to rely on estimations to arrive at greenhouse emissions as well as make predictions. Such estimations are, and they have to include elements such as macro-economic variables, unemployment, inflation, estimates of energy use per person, among other considerations. Policy makers use information from such estimates to evaluate the impact of their policy choices and to make a business case for specific intervention. Similarly, businesses also need to make cost estimates to justify the need for additional resources. 

In more complex systems, companies use dynamic structuring tools as estimation tools. According to Klerk, Rixen & Voormeeren (2008), some of these tools include the random walk, linear regression, least squares, and the simplex method, among other modeling tools. However, cost estimation using traditional tools often produce inaccuracies due to their manual nature and experience needed to use them correctly. Therefore, in recent years, companies have been moving towards intelligent tools such as machine learning, which use artificial intelligence to process a massive amount of data. 

Current situation 

Cost Estimation in modern companies 

Manufacturing companies 

According to Datta & Roy (2010), manufacturing companies no longer exclusively sells products along but services as well. If a company is selling sophisticated equipment, the terms of the contract might oblige the company to offer service for decades until product amortization. Traditionally, most manufacturing companies did not factor in the cost of the offering services. Consequently, most incurred considerable costs in offering the services because they neglected to conduct a proper cost estimation of offering those services. Without a doubt, offering services without cost estimation to assess the cost of offering the services lead to losses. Every activity in the cost, schedule consumes costs or resources, and the companies must factor in those costs in their operations (Erkoyuncu, Rajkumar & Essam, 2009). The modern corporation does not only provide products or services for the immediate consumption by the consumer, but also offer services in the long term. Proper cost estimation is critical in developing realistic cost estimates to avoid losses. Moreover, with the right cost estimate, the profits generated from those activities make the operations of the company sustainable in the long term. 

Projects 

Newnes et al. (2008) argue that estimating the cost of products is critical in today's environment. Most products often overrun the budget, and that has implication for the project completion. If the cost overrun is too high, the project might stall at some time, as the owners have to look for additional funds, a process that takes time. However, despite the critical need for cost estimation, design changes often complicate matters, making costs increase beyond initial estimates. In some sectors such as defense, governments are slashing budgets, but the cost of items continues to increase. With those complexities, there is a growing realization that whole-life costing can improve the cost-estimating process. For existing products, cost estimation is easy as engineers can use similar existing products to develop a realistic or very accurate estimate. However, for a new product, cost estimation is not straightforward hence complex. 

Nevertheless, despite the complexity, customers are demanding "good quality estimates of acquisition, maintenance, upgrading, refurbishment, and disposal through the product's lifecycle (p. 100).” Under those conditions, accurate cost estimation is not an option but an imperative. Newnes et al. (2008) note that 85% of costs are inbuilt at the concept stage, which makes life-cycle costing critical to ensure that costs are optimized throughout the product development, from concept stage to manufacturing. 

Cost estimation and sustainability 

Cost estimation is not useful for production costs, but also guide the sustainability of a business. Before marketers and engineers create a product, they have to consider many things. One of the considerations is the cost as it has a bearing on the final price of the product. If the price is too high, the product might fail if the majority of targeted customers cannot afford it. Also, if the cost of raw materials is too high, the business underlying the product is not sustainable. 

Moreover, with lower projects and climate change concerns, sustainability is a critical consideration when undertaking any project. According to Presley & Meade (2010), firms in the construction industry should include sustainability measures in their benchmarking efforts. Benchmarking is a necessary process where companies, compare the various elements of their projects such as time, cost, quality, etc. against some extreme measure. In the benchmark, Presley & Meade (2010) propose the use of strategic and activity based criteria to catch the cost used in construction. 

Modern cost estimation techniques 

ElSawy, Hosny & Razek (2011) agrees that estimating the cost of building construction project is a vital role of management because the quality depends on the accurate cost estimation. The management has to undertake project cost estimation within the limits of the scope of the budget prepared by the project owner. If a construction firm costs a project within the right range, then it is likely to win a project. Also, after completing the project, the value of the project will be less than the project price, creating a profit margin. The survival of any firm depends on its ability to earn profits from its operations. However, despite the importance of the cost estimation, the firm’s failure to achieve that goal due to the complexity of the process and information overload. Cost departments have to consider massive information and contingencies, which increases the chances of not considering some crucial variables. In the end, simple failures leading to weak estimation resulting in cost overruns. ElSawy, Hosny & Razek (2011) suggests that costing departments can use Artificial Neural Network to estimate costs. The network can evaluate a considerable amount of data and present usable results. With the cost estimation process becoming more complicated due to the massive amount of data available, the use of Artificial Neural Network can make sense out of a vast amount of data which is not humanly possible to analyze using traditional techniques. 

Keung, Kitchenham & Jeffery (2008) notes that cost estimation is complicated. Currently, most companies use data-intensive methods for estimation that include linear regression. The main drawback with the method is the inability to assess whether a dataset is appropriate. In those circumstances, heuristic models such as the one suggested by Hosny & Razek (2011) that uses the neural network is critical. As Hosny & Razek (2011) demonstrates, heuristic algorithms can identify unusual project cases. Keung, Kitchenham & Jeffery (2008) makes a strong case of the use of the Mantel correlation randomization test, which uses “distance matrix of project features and the distance matrix of known effort values of the dataset (p. 471).” With the model, companies can estimate costs in projects or products with a vast amount of data or variables to consider. The method makes sense, considering that a typical product uses hundreds of components sometimes provided by different suppliers. Cost estimation determines the financial performance of a product; hence, the better the estimation, the better the outcome. 

As noted earlier, the use of advanced algorithms that include artificial intelligence is an excellent solution to the complex problem of cost estimation. With big data, cloud computing, and the internet of things, there is enough data around to help companies conduct cost estimation using artificial intelligence (Banker, Mashruwala & Tripathy, 2014). Without tools such as machine learning, manufacturing companies rely on manufacturing process data associated with ERP systems. Keung, Kitchenham & Jeffery (2008) notes that companies use linear regression widely in cost estimation, but not suitable in a world of a massive amount of data. Sajadfar & Ma (2015) highlight the use of data mining. With data mining, companies can use existing manufacturing process data associated with ERP systems together with the linear regression and data mining techniques. The two methods have their unique strengths and using them both allows companies to discover cost features; hence, make accurate cost estimations. 

Elfaki Alatawi & Abushandi (2014) argues that during the preliminary process of any project, most of the research efforts go towards cost estimation as part of construction management. In the construction industry, cost estimation is the process of predicting the cost of the performing work given the scope, project requirement, and time is given. Successful completion of a project depends on the accurate estimation, but the task itself is knowledge intensive. The final estimate depends on the skills of the person doing the estimation. With a skilled person, a company can do an excellent job of coming up with a cost estimate, but that is not the case with an inexperienced person. Typically, it takes engineers several years to master the process of cost estimation. Another problem with estimation is that the work is not documented, which makes the process prone to the subjectivity of the cost estimation engineer. Elfaki Alatawi & Abushandi (2014) notes that the process of cost estimation is a delicate process with numerous parameter must the estimator must address to improve accuracy. If the estimation is done poorly, the likely outcomes include construction delay or even business bankruptcy. Therefore, in agreement with Keung, Kitchenham & Jeffery (2008) and Keung, Kitchenham & Jeffery (2008), Elfaki Alatawi & Abushandi (2014) suggest the companies should use intelligent solutions to assist in cost estimation to avoid the pitfall of manual estimation that relies on the skill of the engineer. Some of these intelligent systems include machine learning, rule-based systems, evolutionary systems, agent-based systems, and hybrid systems. 

Other than construction, another industry that heavily relies on cost estimation is the oil industry. According to the Kaiser (2009), during the process of drilling oil wells, the objective for most companies is to make home quickly given the constraints of technology, operations, quality, and safety. However, the process is steeped in deep uncertainty coming from the market. For instance, changing oil prices influence the decision to dig a well or not, with high prices encouraging digging wells in expensive locations such as offshore. According to the complexities are easier to handle with the right approach to time and cost estimation. To achieve that objective Kaiser (2009) identifies statistical analysis and empirical modeling as critical. The models make it easy to use the existing variables and develop realistic cost estimates, even under challenging conditions or uncertainty. 

Cost estimation plays a critical role in allocating financial resources well. When a construction company estimates the costs of a project, all the relevant activities get the resources needed to produce deliverables. The same case applies to an oil company drilling an oil well in an offshore location. The management has to estimate the cost of drilling machines, steel pipes, offshore platform, labor, and other elements that goes into such a project. Once the project manager makes the estimate, he or she then distributes resources accordingly. However, cost estimates are not only about the ingredients that go towards building a product or a project. Another important consideration is quality because a product of inadequate quality is unlikely to succeed in the market. A construction project or an oil well has to pass quality tests; therefore, the management has to factor in the cost of quality. According to Lari & Asllani (2013), companies that seek to improve the performance of their operations have to consider the cost of quality. Lari & Asllani (2013) argues that there is a link between quality costs and operational processes, and the better the processes, the better the cost outcome of efforts to boost performance. Therefore, the management must estimate the cost of quality from the perspective of the existing processes. If the processes are weak, then investment must first go towards improving them. 

Poor cost estimation and cost overruns 

ElSawy, Hosny & Razek (2011) found that improper cost estimation often leads to costs overruns. Doloi (2011) also arrives at the same conclusion. Cost overruns happen when the cost of the project is more than the estimates. Consequences, the allocation of financial resources does not satisfy all the project activities. According to Doloi (2011), the main reason that leads to overrun is that inadequate cost estimation that fails to factor in direct and indirect factors associated with the project environment. He identifies political and legislative factors, which determine the business case of the project, and the legal and environmental issues related to the project. If the law is restrictive, compliance costs with environmental standards impose a massive cost to the project. Secondly, political and legislative factors demand the contractor to speed the project; the extra speed imposes an additional cost of the overall project, making it move beyond initial estimates (Cantarelli, Flybjerg & Bert van Wee, 2010). Moreover, public projects are sometimes subject to changing whims of the stakeholders such as politicians, and that means a modification of the initial project plan, scope, etc., leading to cost escalation. 

Methods and/or Standards 

In selecting the sources, the most important criteria were relevant. A source is relevant if it deals with the topic, which is “Cost Estimation." In searching for the relevant information in online databases, Google Scholar, and libraries, I used search terms that included different variations of the search term “Cost Estimation.” Another critical consideration was the reputation of the article with peer-reviewed one being the most reputable and reliable sources. The sources used in this review were from peer-reviewed journal articles. Time is also important. The resources reviewed were published in the last ten years. 

Questions for Further Research 

This study has raised an important question about which are the best tools for cost estimation. It turns out that while cost estimation is necessary, the process itself is more laborious and prone to the errors and subjectivity. With big data and growing complexity, traditional manual methods of data estimation are insufficient. Thus, cost estimation has to consider the tools for estimation and the environment itself. In public projects, laws and environmental regulations, as well as politics, are essential. In companies, the element of politics, the needs of stakeholders also influence costs. After finding that cost estimation is relevant from the literature, the research will further explore why cost estimation is vital in business and how companies can ensure effective use of cost estimation in resource allocation. Those questions are, considering that decision-making is a function of proper data and cost estimation is about using past data and some forecasting to make useful predictions. 

Conclusion 

From the review of literature, it is evident that cost estimation is useful in decision making regarding the allocation of resources. In the project, poor cost estimation leads to cost overruns, project delays, and might even cause bankruptcy. For companies that seek to improve performance, they must consider the cost of quality. The main idea is that if the cost is known, then the company can decide whether the cost is justifiable, considering that high cost might lead to high prices and subsequent poor sales. In construction, a reasonable cost estimate increases the chances of winning the project. When cost estimation is done well, it increases the chances of survival of the firm. If a company knows the cost of producing something of executing a particular task, it will distribute or allocate resources well. In other words, cost estimation is critical in proper planning, costing, and allocation of resources, funding requisitions, and making a business case for any project. However, cost estimation is complicated and requires the use of manual processes. In recent years, companies have started to move towards intelligent estimation tools to make the process objective and accurate. 

References  

Banker, R. D., Mashruwala, R., & Tripathy, A. (2014). Does a differentiation strategy lead to more sustainable financial performance than a cost leadership strategy? Management Decision, 52 (5), 872-896. doi:10.1108/md-05-2013-0282 

Cantarelli, C., Flybjerg, B., & Bert van Wee, M. (2010). Cost overruns in Large-Scale Transportation Infrastructure Projects: Explanations and Their Theoretical Embeddedness. European Journal of Transport and Infrastructure Research, 10 (1), 5-8. 

Datta, P. P., & Roy, R. (2010). Cost modelling techniques for availability type service support contracts: A literature review and empirical study. CIRP Journal of Manufacturing Science and Technology, 3 (2), 142-157. doi:10.1016/j.cirpj.2010.07.003 

Doloi, H. K. (2011). Understanding stakeholders perspective of cost estimation in project management. International Journal of Project Management, 29 (5), 622-636. doi:10.1016/j.ijproman.2010.06.001 

Elfaki, A. O., Alatawi, S., & Abushandi, E. (2014). Using Intelligent Techniques in Construction Project Cost Estimation: 10-Year Survey. Advances in Civil Engineering, 2014 , 1-11. doi:10.1155/2014/107926 

ElSawy, I., Hosny, H., & Razek, M. (2011). A Neural Network Model for Construction Projects Site Overhead Cost Estimating in Egypt. IJCSI International Journal of Computer Science Issue s, Vol. 8, Issue 3, No. 1, May 2011 

Erkoyuncu, J., Rajkumar, R., & Essam, S. (2009). Uncertainty challenges in service cost estimation for product- service systems in the aerospace and defence industries. 

Kaiser, M. J. (2009). Modeling the time and cost to drill an offshore well. Energy, 34 (9), 1097-1112. doi:10.1016/j.energy.2009.02.017 

Keung, J., Kitchenham, B., & Jeffery, D. (2008). Analogy-X: Providing Statistical Inference to Analogy-Based Software Cost Estimation. IEEE Transactions on Software Engineering, 34 (4), 471-484. doi:10.1109/tse.2008.34 

Klerk, D. D., Rixen, D. J., & Voormeeren, S. N. (2008). General Framework for Dynamic Substructuring: History, Review and Classification of Techniques. AIAA Journal, 46 (5), 1169-1181. doi:10.2514/1.33274 

Lari, A., & Asllani, A. (2013). Quality cost management support system: An effective tool for organisational performance improvement. Total Quality Management & Business Excellence, 24 (3-4), 432-451. doi:10.1080/14783363.2012.733258 

Newnes, L., Mileham, A., Cheung, W., Marsh, R., Lanham, J., Saravi, M., & Bradbery, R. (2008). Predicting the whole-life cost of a product at the conceptual design stage. Journal of Engineering Design, 19 (2), 99-112. doi:10.1080/09544820701803061 

Presley, A., & Meade, L. (2010). Benchmarking for sustainability: An application to the sustainable construction industry. Benchmarking: An International Journal, 17 (3), 435-451. doi:10.1108/14635771011049380 

Sajadfar, N., & Ma, Y. (2015). A hybrid cost estimation framework based on feature-oriented data mining approach. Advanced Engineering Informatics, 29 (3), 633-647. doi:10.1016/j.aei.2015.06.001 

Swan, L. G., & Ugursal, V. I. (2009). Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and Sustainable Energy Reviews, 13 (8), 1819-1835. doi:10.1016/j.rser.2008.09.033