Agile manufacturing: Enablers and an implementation framework

Tougher competitive situations have led to increasing attention being paid to customer satisfaction, of which timely and customized services are the key concepts. As the product life cycle becomes shortened, high product quality becomes necessary for survival. Markets become highly diversified and global, and continuous and unexpected change become the key factors for success. The need for a method of rapidly and cost-effectively developing products, production facilities and supporting software, including design, process planning and shop floor control system has led to the concept of agile manufacturing. Agile manufacturing can be defined as the capability to survive and prosper in a competitive environment of continuous and unpredictable change by reacting quickly and effectively to changing markets, driven by customer-designed products and services. This article details the key concepts and enablers of agile manufacturing. The key enablers of agile manufacturing include: (i) virtual enterprise formation tools/metrics; (ii) physically distributed manufacturing architecture and teams; (iii) rapid partnership formation tools/metrics; (iv) concurrent engineering; (v) integrated product/production/business information system; (vi) rapid prototyping tools; and (vii) electronic commerce. A conceptual framework for the development of an agile manufacturing system and future research directions are presented in this paper. This framework takes into account the customization and system integration with the help of business process redesign, legal issues, concurrent engineering, computer-integrated manufacturing, cost management, total quality management and information technology.     

Exploring soft TQM dimensions and their impact on firm performance: some exploratory empirical results

The aim of the paper is to study soft total quality management practices and their impacts on firm performance. In the research, the systematic literature review was used. After identifying a research gap, a theoretical framework was developed and research hypotheses were formulated. We have further checked the validity and reliability of measurement items using confirmatory factor analysis. The research framework was tested using multiple linear regressions under the effect of controllable variables (i.e. size of employees, supply uncertainty and demand uncertainty) of firm performance. The research hypotheses are well supported, which is consistent with previous extant literature.     

Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications

Performance measures and metrics are essential for effectively managing logistics operations, particularly in a competitive global economy. The global economy is featured with global operations, outsourcing and supply chain and e-commerce. The real challenge for managers of this new enterprise environment is to develop suitable performance measures and metrics to make right decisions that would contribute to an improved organizational competitiveness. Now the question is whether traditional performance measures can be used and out of them which ones should be given priority for measuring the performance in a new enterprise environment. Some of the traditional measures and metrics may not be suitable for the new environment wherein many activities are not easily identifiable. Measuring intangibles and nonfinancial performance measures pose the greater challenge in the so-called knowledge economy. Nevertheless, measuring them is so critical for the successful operations of companies in this environment. Considering the importance of nonfinancial measures and intangibles, an attempt has been made in this paper to determine the key performance measures and metrics in supply chain and logistics operations. This is based on a literature survey and some of the reported case experiences. Suggestions for future research directions are also indicated.     

OXYGEN LIMITATION IN MICROFLUIDIC BIOFUEL CELLS

Biofuel cells are devices that use biocatalysts (enzymes or microbes) to convert biochemical energy directly into electrical energy. Microfluidic biofuel cells exploit the lack of active mixing at microscale dimensions to eliminate the use of proton exchange membranes that separate anolyte and catholyte streams. Simulation of this system, by solving the governing 3-D conservation equations (flow, species transport), reveals that oxygen availability limits the performance of the cathode. An exponential decay in the availability of oxygen at the cathode is observed along the length of the microchannel, indicating that increasing the number of electrode pairs reduces the overall current density. This conclusion is consistent with experimental observations. Increasing electrolyte flow rates can reduce the mass transport limitations by decreasing the diffusion boundary-layer thickness, but disparity between the flow rates of the anolyte and catholyte can induce wastage of dissolved oxygen.     

Indium tin oxide-coated glass modified with reduced graphene oxide sheets and gold nanoparticles as disposable working electrodes for dopamine sensing in meat samples

Sensitive, rapid, and accurate detection of dopamine (DA) at low cost is needed for clinical diagnostic and therapeutic purposes as well as to prevent illegal use of DA in animal feed. We employed a simple approach to synthesize reduced graphene oxide sheets (rGOS) and gold nanoparticles (AuNPs) at room temperature on indium tin oxide-coated glass (ITO) slides as disposable working electrodes for sensing DA. Graphene oxide (GO) was directly reduced on ITO to remove oxygenated species via a rapid and green process without using chemical reducing reagents. AuNPs were electrochemically deposited in situ on rGOS-ITO with fairly uniform density and size. The sensitivity of the AuNPs-rGOS-ITO sensor for DA detection is 62.7 μA mM(-1) cm(-2) with good selectivity against common electrochemically interfering species such as ascorbic acid (AA) and uric acid (UA), and the detection limit measured by differential pulse voltammetry (DPV), at a signal-noise ratio of 3, was 6.0 × 10(-8) M. The electrochemical catalysis of DA was proven to be a surface process with an electron transfer coefficient (α) of 0.478 and a rate constant (k(s)) of 1.456 s(-1). It correlates well with the conventional UV-vis spectrophotometric approach (R = 0.9973) but with more than thrice the dynamic range (up to 4.5 mM). The sensor also exhibited good stability and capability to detect DA in beef samples, and thus is a promising candidate for simple and inexpensive sub-nanomolar detection of DA, especially in the presence of UV-absorbing compounds.     

Optimization of weld characteristics of friction welded AA 6061-AA 6351 joints using grey-principal component analysis (G-PCA)

Friction welding is a solid state joining process in which the quality of welded joint is influenced by the input parameter setting. The objective of the present study is to conduct experimental investigation of the bond strength and hardness of the friction welded joints involving AA 6061 and AA 6351 alloys by conducting experiments designed by Taguchi’s L⁹ orthogonal matrix array. A systematic approach becomes essential to find the optimal setting of friction welding parameters. Hence a new approach named grey-principal component analysis (G-PCA) is presented in which the principal component analysis (PCA) is used to generate weights for the grey relational coefficients obtained in the grey relational analysis (GRA). The results of the confirmation experiment conducted with the optimal setting predicted by the G-PCA have shown improvements in the performance characteristics. Hence G-PCA can be used for experimental welding optimization.     

Multifunctional carbon-nanotube cellular endoscopes

Glass micropipettes, atomic force microscope tips and nanoneedles can be used to interrogate cells, but these devices either have conical geometries that can damage cells during penetration or are incapable of continuous fluid handling. Here, we report a carbon-nanotube-based endoscope for interrogating cells, transporting fluids and performing optical and electrochemical diagnostics at the single organelle level. The endoscope, which is made by placing a multiwalled carbon nanotube (length, 50-60?μm) at the tip of a glass pipette, can probe the intracellular environment with a spatial resolution of ～100?nm and can also access organelles without disrupting the cell. When the nanotube is filled with magnetic nanoparticles, the endoscope can be remotely manoeuvered to transport nanoparticles and attolitre volumes of fluids to and from precise locations. Because they are mounted on conventional glass micropipettes, the endoscopes readily fit standard instruments, creating a broad range of opportunities for minimally invasive intracellular probing, drug delivery and single-cell surgery.     

Using Planar Embedded DRAM in Memory Intensive Signal Processing Circuits: Case Studies on LDPC Decoding and Motion Estimation

This paper studies the feasibility and potential of using planar embedded DRAM (eDRAM), which is completely compatible with CMOS logic process, to improve circuit implementation efficiency of memory-hungry signal processing algorithms. In spite of its apparent cell area efficiency advantage over SRAM, planar eDRAM is not being widely used in practice, mainly due to its very short retention time (e.g., few $\upmu $ s and even a few hundreds ns). In this work, we contend that short retention time may not necessarily be a fundamental issue for implementing signal processing algorithms because they typically handle streaming data, which exhibits regular and predictable data access patterns, and has a large algorithm/architecture design space. This study elaborates on the rationale and application of using a planar eDRAM in memory-hungry signal processing circuit implementations, and discusses the possible algorithm and architecture design strategies to better embrace the use of planar eDRAM. For the purpose of demonstration, we use low-density parity-check (LDPC) code decoding and motion estimation in video encoding as test vehicles. Beyond a straightforward SRAM replacement, we propose an interleaved read/write page-mode DRAM operation to reduce planar eDRAM energy consumption by leveraging LDPC code decoding data access pattern, and we investigate the potential of using planar eDRAM to enable a higher degree of image data reuse in motion estimation by proposing a folded scan structure to further improve its effectiveness. We carried out detailed planar eDRAM SPICE simulations at 45 nm node to obtain its characteristics, based on which we quantitatively evaluate the effectiveness of using planar eDRAM in these two case studies.     

Mathematical modeling and verification of pulse electrochemical micromachining of microtools

Pulse electrochemical micromachining (PECMM) is an unconventional manufacturing method suitable for the production of micro-sized components on a wide range of electrically conductive materials. PECMM in this study has been used to manufacture microtools. The non-contact nature of PECMM has necessitated the modeling of the process to estimate the anodic profile (microtool profile). This paper presents a mathematical model for predicting the diameter of the microtools fabricated by PECMM process. Tungsten microtools of diameters less than 100 μm were fabricated using an in-house built microelectrochemical machining system. Experimental results confirm the theoretical prediction of reduction in tool diameter with respect to increasing machining time. Further, from the experimental verification, it was found that the deviations in the tool diameters were within 9 % of the theoretical predictions.     

A Study on Process Parameters of Ultrasonic Assisted Micro EDM Based on Taguchi Method

Experimental investigation of ultrasonic assisted micro electro discharge machining was performed by introducing ultrasonic vibration to workpiece. The Taguchi experimental design has been applied to investigate the optimal combinations of process parameters to maximize the material removal rate and minimize the tool wear. Analysis of variance (ANOVA) was performed and signal-to-noise (S/N) ratio was determined to know the level of importance of the machining parameters. Based on ANOVA, ultrasonic vibration at 60% of the peak power with capacitance of 3300 PF was found to be significant for best MRR. The machining time plays a significant role in the tool wear. The results were confirmed experimentally at 95% confidence interval.