Estimation of system assembly and test manufacturing yields through product complexity normalization

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Manufacturing Program at MIT, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 52-53).Cisco Systems, Inc. (Cisco) has recently adopted Six Sigma as the main platform to drive quality improvements in its manufacturing operations. A key component of the improvement strategy is the ability to define appropriate manufacturing yield goals. Cisco's manufacturing operations can be divided, at a very high level, in two major steps: Printed Circuit Board Assembly (PCBA) and System Assembly and Test. The company has already deployed a global yield goal definition methodology for the PCBA operation, but the creation of a similar methodology for the System Assembly and Test operation proved difficult: Cisco lacked a universal methodology to determine the expected variation on manufacturing performance resulting from differences on product design and manufacturing processes attributes. This thesis addresses this gap by demonstrating a methodology to relate relevant design and process attributes to the System Assembly and Test manufacturing yield performance of all products. The methodology uses statistical analysis, in particular Artificial Neural Networks, to generate a yield prediction model that achieves excellent prediction accuracy (4.8% RMS error). Although this study was performed using Cisco Systems' product and manufacturing data, the general process outlined in this exercise should be applicable to solve similar problems in other companies and industries. The core components of the methodology outlined can be easily reproduced: 1) identify the key complexity attributes, 2) design and execute a data collection plan and 3) generate statistical models to test the validity and impact of the selected factors.by Andres Olivella Sierra.M.B.A.S.M

Quality Measurement for Mobile M-ERP Applications

Mobile business boosting the popularization of the M-ERP(Mobile ERP) in the enterprise: The high penetration of mobile phones enables each employee to become an effective information carrier of the enterprise. The information application of the enterprise becomes more clear-cut. The mobile M-ERP pivots on the frequently changing key indexes of the enterprise operation, and takes the personalized and format-based data collection, analysis and processing platform as tool. It consolidates and unifies quickly diverse people, internal data, and external applications of the enterprise into an integrated whole, and provides real-time, key, and overall data reports to the management personnel. The recent wave of enterprise resource planning (M-ERP) systems adoption is a significant commitment of resource and may affect almost all business processes. M-ERP systems are integrated systems in that they promote cooperation among groups, teamwork, and process expertise and business knowledge. Firm that successfully implements an M-ERP system should raise revenues or decrease costs. The main purpose of this paper is to investigate the relationship among M-ERP system internal control, quality and performance in Korean firms. To investigate the relationship, the questionnaires were collected from 131 M-ERP – adopting Korean manufacturing companies. The following results were determined by verifying six hypotheses using LISREL. Internal control support perceived quality and quality support perceived performance. We expect that the results of the research can be used as the guidance of the implementation strategy of M-ERP systems. And these results provide important insights that complement extant research findings and also raise future research issues

The role of networking in the competitiveness profile of Spanish firms

Two main forces coincide nowadays in the characterisation of productive systems. On the one hand, the internationalisation of markets and economic activities has resulted in an increasing competition worldwide and a new and more global division of labour. On the other, the greater complexity of technology makes innovation a key factor in manufacturing firms’ competitiveness. Cooperative network relationships seem to be important in both processes. This paper aims to explore this aspect in the competitiveness behaviour of four Spanish manufacturing industries: food, chemicals, electronics and vehicles. Data has been obtained from a survey conducted specifically for this purpose at the firm level. Findings from the empirical analysis, based on the application of the Polytomus Logit Universal Model (PLUM), confirm the positive effects of the ability to network on company performance, particularly, intra-firm cooperation, cooperation between competitors and user-producer relationships.La internacionalización de mercados, de actividades económicas y la creciente competencia global son algunos de los factores que caracterizan los sistemas productivos actuales. A ello se suma una intensa complejidad tecnológica que afecta tanto a productos como a procesos productivos, concediéndole a la innovación un papel clave en la competitividad de las empresas manufactureras. En ambos procesos, las relaciones de cooperación empresarial se erigen como forma organizativa de creciente importancia. En este documento se explora la relación entre cooperación y comportamiento competitivo en cuatro industrias manufactureras: alimentación, química, electrónica y automóviles. La información estadística se ha obtenido a partir de una encuesta realizada a nivel microeconómico y diseñada específicamente con este fin en España. Los resultados que se derivan del análisis empírico, basados en la aplicación de un modelo Logit Universal (PLUM), confirman los efectos positivos de las relaciones cooperativas en los resultados empresariales, concretamente las relaciones intra-empresa, la cooperación entre competidores y las relaciones usuario-proveedor.Co-operation, Networking, Innovation, Competitiveness.

Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies

Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin

Human-automation collaboration in manufacturing: identifying key implementation factors

Human-automation collaboration refers to the concept of human operators and intelligent automation working together interactively within the same workspace without conventional physical separation. This concept has commanded significant attention in manufacturing because of the potential applications, such as the installation of large sub-assemblies. However, the key human factors relevant to human-automation collaboration have not yet been fully investigated. To maximise effective implementation and reduce development costs for future projects these factors need to be examined. In this paper, a collection of human factors likely to influence human-automation collaboration are identified from current literature. To test the validity of these and explore further factors associated with implementation success, different types of production processes in terms of stage of maturity are being explored via industrial case studies from the project’s stakeholders. Data was collected through a series of semi-structured interviews with shop floor operators, engineers, system designers and management personnel

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Security aspects in cloud based condition monitoring of machine tools

In the modern competitive environments companies must have rapid production systems that are able to deliver parts that satisfy highest quality standards. Companies have also an increased need for advanced machines equipped with the latest technologies in maintenance to avoid any reduction or interruption of production. Eminent therefore is the need to monitor the health status of the manufacturing equipment in real time and thus try to develop diagnostic technologies for machine tools. This paper lays the foundation for the creation of a safe remote monitoring system for machine tools using a Cloud environment for communication between the customer and the maintenance service company. Cloud technology provides a convenient means for accessing maintenance data anywhere in the world accessible through simple devices such as PC, tablets or smartphones. In this context the safety aspects of a Cloud system for remote monitoring of machine tools becomes crucial and is, thus the focus of this pape

Maintenance Strategies to Reduce Downtime Due to Machine Positional Errors

Manufacturing strives to reduce waste and increase Overall Equipment Effectiveness (OEE). When managing machine tool maintenance a manufacturer must apply an appropriate decision technique in order to reveal hidden costs associated with production losses, reduce equipment downtime competently and similarly identify the machines’ performance. Total productive maintenance (TPM) is a maintenance program that involves concepts for maintaining plant and equipment effectively. OEE is a powerful metric of manufacturing performance incorporating measures of the utilisation, yield and efficiency of a given process, machine or manufacturing line. It supports TPM initiatives by accurately tracking progress towards achieving “perfect production.” This paper presents a review of maintenance management methodologies and their application to positional error calibration decision-making. The purpose of this review is to evaluate the contribution of maintenance strategies, in particular TPM, towards improving manufacturing performance, and how they could be applied to reduce downtime due to inaccuracy of the machine. This is to find a balance between predictive calibration, on-machine checking and lost production due to inaccuracy. This work redefines the role of maintenance management techniques and develops a framework to support the process of implementing a predictive calibration program as a prime method to supporting the change of philosophy for machine tool calibration decision making. Keywords—maintenance strategies, down time, OEE, TPM, decision making, predictive calibration

Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment

Energy efficiency is an important factor in the marine industry to help reduce manufacturing and operational costs as well as the impact on the environment. In the face of global competition and cost-effectiveness, ship builders and operators today require a major overhaul in the entire ship design, manufacturing and operation process to achieve these goals. This paper highlights smart design, manufacturing and operation as the way forward in an industry 4.0 (i4) era from designing for better energy efficiency to more intelligent ships and smart operation through-life. The paper (i) draws parallels between ship design, manufacturing and operation processes, (ii) identifies key challenges facing such a temporal (lifecycle) as opposed to spatial (mass) products, (iii) proposes a closed-loop ship lifecycle framework and (iv) outlines potential future directions in smart design, manufacturing and operation of ships in an industry 4.0 value chain so as to achieve more energy-efficient vessels. Through computational intelligence and cyber-physical integration, we envision that industry 4.0 can revolutionise ship design, manufacturing and operations in a smart product through-life process in the near future