Competitor Collaboration Before a Crisis: What the AI Industry Can Learn

Overview: For artificial intelligence (AI) technology to impact society positively, the major AI companies must coordinate their efforts and agree on safe practices. The social legitimacy of AI development depends on building a consensus among AI companies to prevent its potentially damaging downsides. Consortia like the Partnership on AI (PAI) aim to have AI competitors collaborate to flag risks in AI development and create solutions to manage those risks. PAI can apply valuable lessons learned from other industries about how to facilitate collective action but do so proactively rather than after the fact. The Dynamic Capabilities Framework of “sensing, seizing, and transforming” provides a process map for the AI industry to create processes to reduce the risk of a major disaster or crisis

Costa Rica’s Development Strategy based on Human Capital and Technology: how it got there, the impact of Intel, and lessons for other countries

human development, technology

Management: A bibliography for NASA managers

This bibliography lists 630 reports, articles and other documents introduced into the NASA Scientific and Technical Information System in 1991. Items are selected and grouped according to their usefulness to the manager as manager. Citations are grouped into ten subject categories: human factors and personnel issues; management theory and techniques; industrial management and manufacturing; robotics and expert systems; computers and information management; research and development; economics, costs and markets; logistics and operations management; reliability and quality control; and legality, legislation, and policy

Towards an integrated perspective on fleet asset management: engineering and governance considerations

The traditional engineering perspective on asset management concentrates on the operational performance the assets. This perspective aims at managing assets through their life-cycle, from technical specification, to acquisition, operation including maintenance, and disposal. However, the engineering perspective often takes for granted organizational-level factors. For example, a focus on performance at the asset level may lead to ignore performance measures at the business unit level. The governance perspective on asset management usually concentrates on organizational factors, and measures performance in financial terms. In doing so, the governance perspective tends to ignore the engineering considerations required for optimal asset performance. These two perspectives often take each other for granted. However experience demonstrates that an exclusive focus on one or the other may lead to sub-optimal performance. For example, the two perspectives have different time frames: engineering considers the long term asset life-cycle whereas the organizational time frame is based on a yearly financial calendar. Asset fleets provide a relevant and important context to investigate the interaction between engineering and governance views on asset management as fleets have distributed system characteristics. In this project we investigate how engineering and governance perspectives can be reconciled and integrated to enable optimal asset and organizational performance in the context of asset fleets

Quality Improvement of Foundry Operation in Nigeria Using Six Sigma Technique

In this paper Six Sigma DMAIC analysis was applied in an aluminium mill in order to identify sources and causes of waste with the intention of providing veritable solutions. The foundry section was the segment under scrutiny. Re-work or defects in this firm was found to be on the average of about 37.05% of total production for the twenty-three months under study (January 2009- December 2010). Defect reduction was therefore chosen as the Critical-to-Quality (CTQ) factor. The sigma level of 1.87 in the firm indicated the existence of opportunities for improvement. Analysis was carried out using SPSS, SPC for Excel to perform regression analysis, process capability analysis, generate descriptive statistics, histograms and run charts. The results of these analyses identified three major defects and some of their behaviours. Based on the analysis, solutions were proffered in the Improve and Control phases of this project. Implementation of the proffered solutions resulted in noticeable improvement and led to the firm operating with near- perfect processes thus proving the applicability of Six Sigma

Technology transfer and sustainable industrial development for developing countries

This paper reports on a part of work for the UNIDO initiative on technology transfer for sustainable industrial development. The proposed technology transfer framework, adapted from the East Asian late industrialisers model, identifies two categories of countries requiring support for enhancing their technological capabilities: (a) very late industrialisers (“low income” developing countries), and (b) slow industrialisers (countries with sizeable manufacturing sectors but limited success in gaining international competitiveness) and three technology transfer routes: (a) through trade and aid to strengthen indigenous production for domestic markets (Route 1); (b) through FDI and contracting to develop export oriented firms (Route 2), and (c) through the supply chain of capital equipment and materials to develop local subcontracting capacity (Route 3). Very late industrialisers need support to start with Route 1 in selected sectors and upgrade through imported mature technologies. Appropriate product innovations are also possible. The slow industrialisers have more scope for increased technology transfer through Routes 2 and 3

The effect of Total Productive Management practices on manufacturing performance through SECS/GEM Standard for electronic contract manufacturing companies (TOC, Abstract, chapter 1 and Reference only)

In an environment of intense global competition, it pays to consider both creative and proven systems that can be used to bring about effective and efficient manufacturing operation. Many electronic contract manufacturing companies have put forth huge amounts of effort and resources to achieve precise and reliable measurement of equipment performance. The objective of a concise measurement is to optimise this piece of asset for every dollar invested. However, it has failed on numerous attempts to achieve the desirable result due to hardware limitations, low degrees of data accuracy and the need for manual intervention. Integrating Total Productive Maintenance (TPM) methodology with SEMI Equipment Communication Standard (SECS) with Generic Equipment Model (GEM) enables data acquisition in a concise manner and keeps track of all real-time transactions that have taken place between the operator and the equipment. To achieve this integration process, a fast-track TPM implementation approach is required by re-engineering the TPM implementation process. The Re-Engineered TPM approach comprises of three TPM pillars (Asset Productivity (AP), Autonomous Maintenance (AM) and Planned Maintenance (PM)) instead of the original eight pillars. Apart from three TPM pillars, also included are SECS/GEM standard, direct and indirect labour utilisation hours, material and overhead cost. The main objective of this study is to determine whether the re-engineering effort, based on these three TPM pillars, SECS/GEM standard together with labour and cost, are able to minimise losses in production process and have positive impact on Output (Manufacturing Performance). The study also aims at evaluating whether the SECS/GEM standard integration with Autonomous Maintenance has the capability of real-time monitoring equipment performance on the production floor. Furthermore, the study aims to assess the impact on productivity, namely, the Output (Manufacturing Performance). The three years, monthly data for the study was collected from ten Electronic Contract Manufacturing (ECM) companies in Johor, Malaysia. The data was analysed through descriptive statistics, regression analysis and panel data analysis. Based on the panel data analysis, the Hausman Test revealed that the Fixed Effects model was found to be the optimal model for this study. The result shows that six independent variables were significant, while one independent variable was not. The insignificant independent variable was SECS/GEM standard integration with Autonomous Maintenance. Further analysis was conducted through a qualitative study. The additional analysis shows that ECM companies do not fully understand the possible application of the SECS/GEM standard integration with Autonomous Maintenance in their manufacturing environment. Therefore, minimum effort was deployed by ECM companies in incorporating this standard into their equipment maintenance platform. However, these days many ECM companies have started to purchase equipment with SECS/GEM standard in order to facilitate smoother future integration with Autonomous Maintenance or with other TPM pillars. This total integration of TPM (three pillars), SECS/GEM standard, labour and cost provides an avenue to monitor and address the operational losses in the production equipment in a timely manner. This system paves the way to improving Output (Manufacturing Performance)