How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Computer Aided Civil and Infrastructure Engineering

Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning

https://pure.hw.ac.uk/ws/files/784859/AEI_2009.pdf

Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction

Three models of strategy that are implicit in the literature are described--linear, adaptive, and interpretive. Their similarity to Boulding's (1956) hierarchical levels of system complexity is not...

Three Models of Strategy.

Towards automated progress assessment of workpackage components in construction projects using computer vision

Automating progress measurement of construction projects

Artificial neural networks, which simulate neuronal systems of the brain, are useful methods that have attracted the attention of researchers in many disciplinary areas. They have many advantages over traditional methods in situations where the input-output relationship of the system under study is not explicitly known. This paper investigates the feasibility of using neural networks for predicting the cost flow of construction projects, explains the need for cost flow forecasting, and demonstrates the limitation of the existing models. It then introduces neural networks as an alternative approach to those mathematical and statistical methods. The method used in collecting data and modelling the cost flow is described. Results of the testing are presented and discussed.

A neural networks approach for cost flow forecasting

Purpose – The purpose of this paper is to identify and assess the extent of occurrence and impact of risk factors responsible for the variation between the forecast and actual construction cash flow.Design/methodology/approach – The study was conducted through a structured questionnaire administered to UK contracting organizations. Adopting a project‐by‐project approach, respondents were asked to provide opinions on the extent of occurrence of some identified risk factors and their impacts on cash flow forecast. Respondents were split into three groups of small, medium and large contracting firms based on their annual turnover so as to be able to investigate statistical differences of opinions between the groups. Statistical analyses were carried out using mean response analysis and univariate analysis of variance (ANOVA) in order to determine significant risk factors and also to investigate differences of opinions between respondents' groupings.Findings – The research identified 11 significant risk facto...

An evaluation of risk factors impacting construction cash flow forecast

Cash flow forecasting and control are essential to the survival of any contractor. The time available for a detailed pre-tender cash flow forecast is often limited. Therefore, contractors require simpler and quicker techniques which would enable them to forecast cash flow with reasonable accuracy. This paper identifies causes behind the inaccuracy of current standard value S-curves (which are often used as an alternative approach for cash flow forecasting) and proposes the use of standard cost commitment models. The process of developing and testing the cost commitment models involved first collecting actual data for 150 completed projects. Several criteria were identified to classify these projects. Tests were conducted to identify which of these criteria affected the shape of the cost commitment curves. Projects were then distributed into different groups and S-curves were fitted into each using the logit transformation technique. Errors incurred when fitting these curves were measured and compared with...

Ammar Peter Kaka

Papers

Towards automated progress assessment of workpackage components in construction projects using computer vision

Automating progress measurement of construction projects

A neural networks approach for cost flow forecasting

An evaluation of risk factors impacting construction cash flow forecast

Modelling standard cost commitment curves for contractors' cash flow forecasting