68,289 research outputs found
Beyond enterprise resource planning projects: innovative strategies for competitive advantage
ABSTRACT A rapidly changing business environment and legacy IT problems has resulted in many organisations implementing standard package solutions. This 'common systems' approach establishes a common IT and business process infrastructure within organisations and its increasing dominance raises several important strategic issues. These are to what extent do common systems impose common business processes and management systems on competing firms, and what is the source of competitive advantage if the majority of firms employ almost identical information systems and business processes? A theoretical framework based on research into legacy systems and earlier IT strategy literature is used to analyse three case studies in the manufacturing, chemical and IT industries. It is shown that the organisations are treating common systems as the core of their organisations' abilities to manage business transactions. To achieve competitive advantage they are clothing these common systems with information systems designed to capture information about competitors, customers and suppliers, and to provide a basis for sharing knowledge within the organisation and ultimately with economic partners. The importance of these approaches to other organisations and industries is analysed and an attempt is made at outlining the strategic options open to firms beyond the implementation of common business systems
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
Retrosynthetic reaction prediction using neural sequence-to-sequence models
We describe a fully data driven model that learns to perform a retrosynthetic
reaction prediction task, which is treated as a sequence-to-sequence mapping
problem. The end-to-end trained model has an encoder-decoder architecture that
consists of two recurrent neural networks, which has previously shown great
success in solving other sequence-to-sequence prediction tasks such as machine
translation. The model is trained on 50,000 experimental reaction examples from
the United States patent literature, which span 10 broad reaction types that
are commonly used by medicinal chemists. We find that our model performs
comparably with a rule-based expert system baseline model, and also overcomes
certain limitations associated with rule-based expert systems and with any
machine learning approach that contains a rule-based expert system component.
Our model provides an important first step towards solving the challenging
problem of computational retrosynthetic analysis
Nonterrestrial utilization of materials: Automated space manufacturing facility
Four areas related to the nonterrestrial use of materials are included: (1) material resources needed for feedstock in an orbital manufacturing facility, (2) required initial components of a nonterrestrial manufacturing facility, (3) growth and productive capability of such a facility, and (4) automation and robotics requirements of the facility
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019
A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands
of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector
that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the
potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent
modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the
main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the
time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing.
Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy
prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify
system and market effects effectively
On systematic approaches for interpreted information transfer of inspection data from bridge models to structural analysis
In conjunction with the improved methods of monitoring damage and degradation processes, the interest in reliability assessment of reinforced concrete bridges is increasing in recent years. Automated imagebased inspections of the structural surface provide valuable data to extract quantitative information about deteriorations, such as crack patterns. However, the knowledge gain results from processing this information in a structural context, i.e. relating the damage artifacts to building components. This way, transformation to structural analysis is enabled. This approach sets two further requirements: availability of structural bridge information and a standardized storage for interoperability with subsequent analysis tools. Since the involved large datasets are only efficiently processed in an automated manner, the implementation of the complete workflow from damage and building data to structural analysis is targeted in this work. First, domain concepts are derived from the back-end tasks: structural analysis, damage modeling, and life-cycle assessment. The common interoperability format, the Industry Foundation Class (IFC), and processes in these domains are further assessed. The need for usercontrolled interpretation steps is identified and the developed prototype thus allows interaction at subsequent model stages. The latter has the advantage that interpretation steps can be individually separated into either a structural analysis or a damage information model or a combination of both. This approach to damage information processing from the perspective of structural analysis is then validated in different case studies
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