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.

PARAMETRIC COST MODELLING OF COMPONENTS FOR TURBOMACHINES: PRELIMINARY STUDY

The ever-increasing competitiveness, due to the market globalisation, has forced the industries to modify their design and production strategies. Hence, it is crucial to estimate and optimise costs as early as possible since any following changes will negatively impact the redesign effort and lead time.This paper aims to compare different parametric cost estimation methods that can be used for analysing mechanical components. The current work presents a cost estimation methodology which uses non-historical data for the database population. The database is settled using should cost data obtained from analytical cost models implemented in a cost estimation software. Then, the paper compares different parametric cost modelling techniques (artificial neural networks, deep learning, random forest and linear regression) to define the best one for industrial components.Such methods have been tested on 9 axial compressor discs, different in dimensions. Then, by considering other materials and batch sizes, it was possible to reach a training dataset of 90 records. From the analysis carried out in this work, it is possible to conclude that the machine learning techniques are a valid alternative to the traditional linear regression ones.

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

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

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.

Parametric Cost Estimation Model for Li-ion Battery Pack of E-motorcycle Conversion based on Activity Based Costing

Universitas Sebelas Maret (UNS) through SMART UNS Company has conducted research and development of e-motorcycle conversion using Li-ion battery pack as a substitute for ICE energy source from the conventional motorcycle. Currently, the battery-pack that used for e-motorcycle conversion is in the development phase towards commercialization. The challenge of estimating production costs is the complicated production process and storing hidden expenses that can be a problem. This hidden cost is often a missing or varied factor that costs less or more expensive. This study presents an integrated parametric cost estimation model with activity-based cost assignments to estimate production costs through cost calculations for each activity. Activity-based costs break the production process into a specific cost element for each step. Each activity's cost is put into a parametric cost estimation model to calculate the cost of each activity into the total cost of production. Cost estimation results will be analyzed using a regression method to determine which variables most affect the production cost of Li-ion battery packs for the conversion of e-motorcycles in the SMART UNS company.