Improving competitiveness through the application of cost estimation models

Optimal costing decisions are required in order to ensure that organisations are globally competitive. The case study company is a global affiliate based in Durban, KwaZulu-Natal, South Africa and is involved in the manufacture and assembly of automobiles and automotive components. It was noticed that cost estimation models were only introduced at an advanced stage in the project life cycle. The concept of cost estimation and its application to improve various factors of a business has been investigated previously and existing evidence could be utilised to support further study in the field. Therefore, the aim of the study was to improve the quality of sourcing decisions by means of the introduction of a parametric cost estimation model and business process re-engineering. A case study approach was adopted. The first objective was to develop an overview of the current sourcing processes and understand the factors which influenced sourcing decisions. The methods used included the generation of a Standard Operating Procedure (SOP) for the current sourcing process, an online survey and interviews. It was concluded that there was a need to develop a detailed SOP which identified and included all impacted departments. The second objective was to redesign the sourcing process. It was concluded that the current sourcing processes did not take cost estimates into account at the early stages of the project life cycle and the inability to accurately predict costs consequently negatively impacted the cost competitiveness of the organisation. The third objective was to develop and implement a parametric cost estimation model. The model was created using Microsoft Excel. The results revealed that the Parametric Cost Estimation Model (PCEM) needed to focus on small injection moulded components as they were the highest contributor to the high Cost Index Manufacturing (CIM), which made the organisation globally uncompetitive. The results revealed that with the introduction of the PCEM and the revised sourcing process, the selected component was competitively priced.Recommendations were made for continuous process improvement and a roadmap for the further introduction of cost estimation models. Further research could also be conducted to develop an optimal cost estimation model based on analogous costing techniques or to develop a comprehensive database for other complex commodities.

Machine Learning Modeling of Forest Road Construction Costs

The economics of the forestry enterprise are largely measured by their performance in road construction and management. The construction of forest roads requires tremendous capital outlays and usually constitutes a major component of the construction industry. The availability of cost estimation models assisting in the early stages of a project would therefore be of great help for timely costing of alternatives and more economical solutions. This study describes the development and application of such cost estimation models. First, the main cost elements and variables affecting total construction costs were determined for which the real-world data were derived from the project bids and an analysis of 300 segments of a three kilometer road constructed in the Hyrcanian Forests of Iran. Then, five state-of-the-art machine learning methods, i.e., linear regression (LR), K-Star, multilayer perceptron neural network (MLP), support vector machine (SVM), and Instance-based learning (IBL) were applied to develop models that would estimate construction costs from the real-world data. The performance of the models was measured using the correlation coefficient (R), root mean square error (RMSE), and percent of relative error index (PREI). The results showed that the IBL model had the highest training performance (R = 0.998, RMSE = 1.4%), whereas the SVM model had the highest estimation capability (R = 0.993, RMSE = 2.44%). PREI indicated that all models but IBL (mean PREI = 0.0021%) slightly underestimated the construction costs. Despite these few differences, the results demonstrated that the cost estimations developed here were consistent with the project bids, and our models thus can serve as a guideline for better allocating financial resources in the early stages of the bidding process.

An Enterprise Management Framework for Costs Engineering Estimation Models Based on Scopus Scientometrics. An Aerospace Engineering Case Study

The cutting-edge industrial product creation faces battling goals. There is an additional expenditure need to enhance new product reliability, and nevertheless, an enterprise should reduce product costs to receive the long-term development funds. This conflict resolution depends on a design engineer, who should take into account the future product costs as early, as possible. So, the purpose of this article is to elicit the most widespread cost estimation models at all design stages. I purposely investigate the models' limits to propose their common frame. The research methodology is Scopus scientometrics. First, I elicit the most authoritative reviews on the design cost estimation topic. Then I made the reviews content analysis and summarize the models’ limitations. Findings show that the design cost estimation models significantly vary. They do not eliminate or substitute for each other. Each model is suitable in an appropriate designing process place. Scientometric analysis points that advanced cost estimation models are poorly evolved for enterprise efficiency prediction including the aerospace industry. To overcome these obstacles, I propose an enterprise goal model. This study’s novelty is that a fitting combination of cost estimation models ensures the whole enterprise's economical effectiveness.

Understanding the costs of urban sanitation: towards a standard costing model

There is a dearth of reliable cost data for urban sanitation. In the absence of high-quality global data, the full cost of sustainable implementation of urban sanitation remains uncertain. This paper proposes an approach for developing bespoke parametric cost estimation models for easy and reliable estimation of the costs of alternative sanitation technologies in a range of geographical contexts. A key requirement for the development of these models is the establishment of a large database of empirical information on the current costs of sanitation systems. Such a database does not currently exist. Two foundational tools are proposed. Firstly, a standard metric for reporting the costs of urban sanitation systems, total annualised cost per household. Secondly, a standardised approach to the collection of empirical cost data, the Novel Ball-Park Reporting Approach (NBPRA). Data from the NBPRA are presented for 87 individual sanitation components from 25 cities in 10 countries. Broad cost ranges for different archetypal systems have been estimated; these currently have high levels of uncertainty. Further work is proposed to collect additional data, build up the global database, and develop parametric cost estimation models with higher reliability.

AN ANALYTICAL COST MODEL FOR INVESTMENT CASTING

Analytical cost estimation of investment casted products during design phase is a complex task since the quantity of parameters to be evaluated. So far, there is a short literature on such cost estimation models. This paper attempts to improve the cost model presented by Boothroyd and Dewhurst. Improvements (mainly focused on cluster assembly and investment, sintering and melting phases) were defined and verified in cooperation with two foundries. Tested on eight components, deviation between estimated and actual costs is around 14% for manual production lines and 6% for automatic ones.