The dynamics and performance of the building and construction product system

During 1999 the Department of Industry, Science and Resources (ISR) published 4 research reports it had commissioned from the Australian Expert Group in Industry Studies (AEGIS), a research centre of the University of Western Sydney, Macarthur. ISR will shortly publish the fifth and final report in this series. The five reports were commissioned by the Department, as part of the Building and Construction Action Agenda process, to investigate the dynamics and performance of the sector, particularly in relation its innovative capacity. Professor Jane Marceau, PVCR at the University of Western Sydney and Director of AEGIS, led the research team. Dr Karen Manley was the researcher and joint author on three of the five reports. This paper outlines the approach and key findings of each of the five reports. The reports examined 5 key elements of the ‘building and construction product system’. The term ‘product system’ reflects the very broad range of industries and players we consider to contribute to the performance of the building and construction industries. The term ‘product system’ also highlights our focus on the systemic qualities of the building and construction industries. We were most interested in the inter-relationships between key segments and players and how these impacted on the innovation potential of the product system. The ‘building and construction product system’ is hereafter referred to as ‘the industry’ for ease of presentation. All the reports are based, at least in part, on an interviewing or survey research phase which involved gathering data from public and private sector players nationally. The first report ‘maps’ the industry to identify and describe its key elements and the inter-relationships between them. The second report focuses specifically on the linkages between public-sector research organisations and firms in the industry. The third report examines the conditions surrounding the emergence of new businesses in the industry. The fourth report examines how manufacturing businesses are responding to customer demands for ‘total solutions’ to their building and construction needs, by providing various services to clients. The fifth report investigates the capacity of the industry to encourage and undertake energy efficient building design and construction.

A systematic approach to evaluate the process improvement in lean manufacturing organizations

Numerous tools and techniques have been developed to eliminate or reduce waste and carry out Lean concepts in the manufacturing environment. However, in practice, manufacturers encounter difficulties to clearly identify the weaknesses of the existing processes in order to address them by implementing Lean tools. Moreover, selection and implementation of appropriate Lean strategies to address the problems identified is a challenging task. According best of authors‟ knowledge, there is no method available to quantitatively evaluate the cost and benefits of implementing a Lean strategy to address the weaknesses in the manufacturing process. Therefore, benefits of Lean approaches cannot be clearly established. The authors developed a methodology to quantitatively measure the performances of a manufacturing system in detecting the causes of inefficiencies and to select appropriate Lean strategies to address the problems identified. The proposed methodology demonstrates that the Lean strategies should be implemented based on the contexts of the organization and identified problem in order to achieve maximum cost benefits. Finally, a case study has been presented to demonstrate how the procedure developed works in practical situation.

Manufacturing practices and performances: A Malaysian study

The purpose of this study is to identify the most common manufacturing practices adopted by the Malaysian manufacturers, company performance factors and relationship between practices and performances. To fulfil the study objectives, 400 manufacturers were surveyed by a standard 400 questionnaire. Three research methodologies such as descriptive analysis, ANOVA and regression analysis have been employed in this study. The analysis revealed that Malaysian manufacturers focus on optimizing three critical performance factors: product development, less customer return rate and on time delivery (OTD). The most important competitive factor was found to be company reputation and design and manufacturing capacity is the least important factor. The findings also proved that manufacturing practices significantly influence company performances

Manufacturing Yield Improvement by Clustering

Dealing with product yield and quality in manufacturing industries is getting more difficult due to the increasing volume and complexity of data and quicker time to market expectations. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large databases. Growing self-organizing map (GSOM) is established as an efficient unsupervised datamining algorithm. In this study some modifications to the original GSOM are proposed for manufacturing yield improvement by clustering. These modifications include introduction of a clustering quality measure to evaluate the performance of the programme in separating good and faulty products and a filtering index to reduce noise from the dataset. Results show that the proposed method is able to effectively differentiate good and faulty products. It will help engineers construct the knowledge base to predict product quality automatically from collected data and provide insights for yield improvement.

Triangular Linguistic Fuzzy based performance measurement model for supply chain to assess leanness attributes

Increasing global competitiveness worldwide has forced manufacturing organizations to produce high-quality products more quickly and at a competitive cost. In order to reach these goals, they need good quality components from suppliers at optimum price and lead time. This actually forced all the companies to adapt different improvement practices such as lean manufacturing, Just in Time (JIT) and effective supply chain management. Applying new improvement techniques and tools cause higher establishment costs and more Information Delay (ID). On the contrary, these new techniques may reduce the risk of stock outs and affect supply chain flexibility to give a better overall performance. But industry people are unable to measure the overall affects of those improvement techniques with a standard evaluation model .So an effective overall supply chain performance evaluation model is essential for suppliers as well as manufacturers to assess their companies under different supply chain strategies. However, literature on lean supply chain performance evaluation is comparatively limited. Moreover, most of the models assumed random values for performance variables. The purpose of this paper is to propose an effective supply chain performance evaluation model using triangular linguistic fuzzy numbers and to recommend optimum ranges for performance variables for lean implementation. The model initially considers all the supply chain performance criteria (input, output and flexibility), converts the values to triangular linguistic fuzzy numbers and evaluates overall supply chain performance under different situations. Results show that with the proposed performance measurement model, improvement area for each variable can be accurately identified.

Digital human modelling for vehicle design and manufacturing

Digital human modelling (DHM) has today matured from research into industrial application. In the automotive domain, DHM has become a commonly used tool in virtual prototyping and human-centred product design. While this generation of DHM supports the ergonomic evaluation of new vehicle design during early design stages of the product, by modelling anthropometry, posture, motion or predicting discomfort, the future of DHM will be dominated by CAE methods, realistic 3D design, and musculoskeletal and soft tissue modelling down to the micro-scale of molecular activity within single muscle fibres. As a driving force for DHM development, the automotive industry has traditionally used human models in the manufacturing sector (production ergonomics, e.g. assembly) and the engineering sector (product ergonomics, e.g. safety, packaging). In product ergonomics applications, DHM share many common characteristics, creating a unique subset of DHM. These models are optimised for a seated posture, interface to a vehicle seat through standardised methods and provide linkages to vehicle controls. As a tool, they need to interface with other analytic instruments and integrate into complex CAD/CAE environments. Important aspects of current DHM research are functional analysis, model integration and task simulation. Digital (virtual, analytic) prototypes or digital mock-ups (DMU) provide expanded support for testing and verification and consider task-dependent performance and motion. Beyond rigid body mechanics, soft tissue modelling is evolving to become standard in future DHM. When addressing advanced issues beyond the physical domain, for example anthropometry and biomechanics, modelling of human behaviours and skills is also integrated into DHM. Latest developments include a more comprehensive approach through implementing perceptual, cognitive and performance models, representing human behaviour on a non-physiologic level. Through integration of algorithms from the artificial intelligence domain, a vision of the virtual human is emerging.

Lean supply chain performance evaluation method

Increasing global competitiveness worldwide has forced manufacturing organizations to produce high-quality products more quickly and at a competitive cost which demand of continuous improvements techniques. In this paper, we propose a fuzzy based performance evaluation method for lean supply chain. To understand the overall performance of cost competitive supply chain, we investigate the alignment of market strategy and position of the supply chain. Competitive strategies can be achieved by using a different weight calculation for different supply chain situations. By identifying optimal performance metrics and applying performance evaluation methods, managers can predict the overall supply chain performance under lean strategy.

Achieving quality improvement in the mask manufacturing industry by using Six Sigma technique

The Six Sigma technique is one of the quality management strategies and is utilised for improving the quality and productivity in the manufacturing process. It is inspired by the two major project methodologies of Deming’s "Plan – Do – Check – Act (PDCA)" Cycle which consists of DMAIC and DMADV. Those two methodologies are comprised of five phases. The DMAIC project methodology will be comprehensively used in this research. In brief, DMAIC is utilised for improving the existing manufacturing process and it involves the phases Define, Measure, Analyse, Improve, and Control. Mask industry has become a significant industry in today’s society since the outbreak of some serious diseases such as the Severe Acute Respiratory Syndrome (SARS), bird flu, influenza, swine flu and hay fever. Protecting the respiratory system, then, has become the fundamental requirement for preventing respiratory deceases. Mask is the most appropriate and protective product inasmuch as it is effective in protecting the respiratory tract and resisting the virus infection through air. In order to satisfy various customers’ requirements, thousands of mask products are designed in the market. Moreover, masks are also widely used in industries including medical industries, semi-conductor industries, food industries, traditional manufacturing, and metal industries. Notwithstanding the quality of masks have become the prioritisations since they are used to prevent dangerous diseases and safeguard people, the quality improvement technique are of very high significance in mask industry. The purpose of this research project is firstly to investigate the current quality control practices in a mask industry, then, to explore the feasibility of using Six Sigma technique in that industry, and finally, to implement the Six Sigma technique in the case company to develop and evaluate the product quality process. This research mainly investigates the quality problems of musk industry and effectiveness of six sigma technique in musk industry with the United Excel Enterprise Corporation (UEE) Company as a case company. The DMAIC project methodology in the Six Sigma technique is adopted and developed in this research. This research makes significant contribution to knowledge. The main results contribute to the discovering the root causes of quality problems in a mask industry. Secondly, the company was able to increase not only acceptance rate but quality level by utilising the Six Sigma technique. Hence, utilising the Six Sigma technique could increase the production capacity of the company. Third, the Six Sigma technique is necessary to be extensively modified to improve the quality control in the mask industry. The impact of the Six Sigma technique on the overall performance in the business organisation should be further explored in future research.

Additive manufacturing of tissues and organs

Additive manufacturing techniques offer the potential to fabricate organized tissue constructs to repair or replace damaged or diseased human tissues and organs. Using these techniques, spatial variations of cells along multiple axes with high geometric complexity in combination with different biomaterials can be generated. The level of control offered by these computer-controlled technologies to design and fabricate tissues will accelerate our understanding of the governing factors of tissue formation and function. Moreover, it will provide a valuable tool to study the effect of anatomy on graft performance. In this review, we discuss the rationale for engineering tissues and organs by combining computer-aided design with additive manufacturing technologies that encompass the simultaneous deposition of cells and materials. Current strategies are presented, particularly with respect to limitations due to the lack of suitable polymers, and requirements to move the current concepts to practical application.

Predicting the effects of bagasse depithing operations on boiler combustion performance

The sugar industry is pursuing diversification options using bagasse as a feedstock. Depithing, the removal of the smaller bagasse particles, is an integral part of the manufacturing processes for bagasse by-products such as pulp and paper. There are possible environmental and economic benefits associated with incorporating depithing operations into a sugar factory. However there have only been limited investigations into the effects of depithing operations on a sugar factory boiler station. This paper describes a modelling investigation, using the lumped parameter boiler design tool BOILER and the CFD code FURNACE, to predict the effects of pith, depithed bagasse and mixed bagasse/pith firing on the efficiency, fuel consumption and combustion performance of a typical sugar factory boiler.

Knowledge sharing leveraging new product development activities to derive enhanced business performance : mixed method study

Knowledge has been widely recognised as a determinant of business performance. Business capabilities require an effective share of resource and knowledge. Specifically, knowledge sharing (KS) between different companies and departments can improve manufacturing processes since intangible knowledge plays an enssential role in achieving competitive advantage. This paper presents a mixed method research study into the impact of KS on the effectiveness of new product development (NPD) in achieving desired business performance (BP). Firstly, an empirical study utilising moderated regression analysis was conducted to test whether and to what extent KS has leveraging power on the relationship between NPD and BP constructs and variables. Secondly, this empirically verified hypothesis was validated through explanatory case studies involving two Taiwanese manufacturing companies using a qualitative interaction term pattern matching technique. The study provides evidence that knowledge sharing and management activities are essential for deriving competitive advantage in the manufacturing industry.

Leveraging power of learning capability upon manufacturing operations

Learning capability (LC) is a special dynamic capability that a firm purposefully builds to develop a cognitive focus, so as to enable the configuration and improvement of other capabilities (both dynamic and operational) to create and respond to market changes. Empirical evidence regarding the essential role of LC in leveraging operational manufacturing capabilities is, however, limited in the literature. This study takes a routine-based approach to understand capability, and focuses on demonstrating leveraging power of LC upon two essential operational capabilities within the manufacturing context, i.e., operational new product development capability (ONPDC), and operational supplier integration capability (OSIC). A mixed-methods research framework was used, which combines sources of evidence derived from a survey study and a multiple case study. This study identified high-level routines of LC that can be designed and controlled by managers and practitioners, to reconfigure underlying routines of ONPDC and OSIC to achieve superior performance in a turbulent environment. Hence, the study advances the notion of knowledge-based dynamic capabilities, such as LC, as routine bundles. It also provides an impetus for managing manufacturing operations from a capability-based perspective in the fast changing knowledge era.

A fuzzy TOPSIS based multi criteria performance measurement model for lean supply chain

Increasing global competition, rapid technological changes, advances in manufacturing and information technology and discerning customers are forcing supply chains to adopt improvement practices that enable them to deliver high quality products at a lower cost and in a shorter period of time. A lean initiative is one of the most effective approaches toward achieving this goal. In the lean improvement process, it is critical to measure current and desired performance level in order to clearly evaluate the lean implementation efforts. Many attempts have tried to measure supply chain performance incorporating both quantitative and qualitative measures but failed to provide an effective method of measuring improvements in performances for dynamic lean supply chain situations. Therefore, the necessity of appropriate measurement of lean supply chain performance has become imperative. There are many lean tools available for supply chains; however, effectiveness of a lean tool depends on the type of the product and supply chain. One tool may be highly effective for a supply chain involved in high volume products but may not be effective for low volume products. There is currently no systematic methodology available for selecting appropriate lean strategies based on the type of supply chain and market strategy This thesis develops an effective method to measure the performance of supply chain consisting of both quantitative and qualitative metrics and investigates the effects of product types and lean tool selection on the supply chain performance Supply chain performance matrices and the effects of various lean tools over performance metrics mentioned in the SCOR framework have been investigated. A lean supply chain model based on the SCOR metric framework is then developed where non- lean and lean as well as quantitative and qualitative metrics are incorporated in appropriate metrics. The values of appropriate metrics are converted into triangular fuzzy numbers using similarity rules and heuristic methods. Data have been collected from an apparel manufacturing company for multiple supply chain products and then a fuzzy based method is applied to measure the performance improvements in supply chains. Using the fuzzy TOPSIS method, which chooses an optimum alternative to maximise similarities with positive ideal solutions and to minimise similarities with negative ideal solutions, the performances of lean and non- lean supply chain situations for three different apparel products have been evaluated. To address the research questions related to effective performance evaluation method and the effects of lean tools over different types of supply chains; a conceptual framework and two hypotheses are investigated. Empirical results show that implementation of lean tools have significant effects over performance improvements in terms of time, quality and flexibility. Fuzzy TOPSIS based method developed is able to integrate multiple supply chain matrices onto a single performance measure while lean supply chain model incorporates qualitative and quantitative metrics. It can therefore effectively measure the improvements for supply chain after implementing lean tools. It is demonstrated that product types involved in the supply chain and ability to select right lean tools have significant effect on lean supply chain performance. Future study can conduct multiple case studies in different contexts.