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.

A Generic Cost Estimating Approach for a Composite Manufacturing Process Assessment

Cost estimation helps build confidence in the feasibility of the development of novel manufacturing processes. This paper focuses on the exploration of the cost estimation for novel manufacturing processes for decision support. One of the aspects of estimation is building credibility around the analysis, especially, in the early stages of planning. Cost estimating guidelines provide a good overview of the cost estimation steps but there is a requirement for guidelines for cost estimation model development. Through building on an understanding of the cost estimation principles, as well as cost estimation modelling, a high-level generic approach for process cost estimation is proposed. Further, a demonstration of a cost estimation modelling approach used for composites manufacturing in the automotive sector is provided, outlining the steps in cost estimation model development.

Predictive Statistical Cost Estimation Model for Existing Single Family Home Elevation Projects

One of the most preferred flood mitigation techniques for existing homes is raising the elevation of the lowest floor above the base flood elevation (BFE). Determination of project effectiveness through benefit-cost analysis (BCA) relies on the expected avoided flood loss and the project cost. Conventional construction cost estimates are highly detailed, considering specific details of the project; however, mitigation project decisions must often be made while considering only highly generalized building details. To provide a robust, generalized project cost estimation method, this paper implements data modeling and mining methods such as multiple regression, random forest, generalized additive model (GAM), and model evaluation and selection with cross-validation methods to hindcast elevation costs for existing single-family homes based on average floor area, increase in floor elevation, number of stories, and foundation type. Project cost data for homes elevated in Louisiana, United States, between 2005 and 2015 are used in cost prediction analysis. The statistical modeling results are compared with detailed estimations for several types of home foundations over a range of elevations. The results show substantial agreement between regression predictions and detailed estimates using RSMeans cost data.

Pipelined XPath Query Based on Cost Optimization

XPath query is the key part of XML data processing, and its performance is usually critical for XML applications. In the process of XPath query, there is inherent seriality between query steps, which makes it difficult to parallelize the query effectively as a whole. On the other hand, although XPath query has the characteristics of data stream processing and is suitable for pipeline processing, the data flow of each query step usually varies a lot, which results in limited performance under multithreading conditions. In this paper, we propose a pipelined XPath query method (PXQ) based on cost optimization. This method uses pipelined query primitives to process query steps based on relation index. During pipeline construction, a cost estimation model based on XML statistics is proposed to estimate the cost of the query primitive and provide guidance for the creation of a pipeline phase through the partition of query primitive sequence. The pipeline construction technique makes full use of available worker threads and optimizes the load balance between pipeline stages. The experimental results show that our method can adapt to the multithreaded environment and stream processing scenarios of XPath query, and its performance is better than the existing typical query methods based on data parallelism.