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Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly…
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 (AI) will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of AI techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different AI techniques to be considered and then shows how these AI techniques are used for the components of IMS.
This paper analyses the role of bed managers and the processes involved in admission, stay, transfer and discharge of patients in the hospital setting. The paper seeks to begin a discussion of the difficulties entailed in the allocation of beds within the context of confined resources. This is achieved by: a review of the somewhat sparse literature on bed management and associated issues; the development of frameworks of analysis with regard to what bed managers do and the information used to support the bed management function; and an explication of results from fieldwork. This is followed by a discussion of the scope of responsibility and career role of the bed manager as well as the potential and problems of bed data. Contacts with others investigating this field and other trusts indicate that the situation in Greater Manchester may be typical of most areas.
– The purpose of this paper is to provide a method for analysing and improving the operational performance of business processes (BPs).
The purpose of this paper is to provide a method for analysing and improving the operational performance of business processes (BPs).
The method employs two standards, Business Process Modelling Notation (BPMN 2.0) and Business Processes Simulation (BPSim 1.0), to measure key performance indicators (KPIs) of BPs and test for potential improvements. The BP is first modelled in BPMN 2.0. Operational performance can then be measured using BPSim 1.0. The process simulation also enables execution of reliable “what-if” analysis, allowing improvements of the actual processes under study. To confirm the validity of the method the authors provide an application to the healthcare domain, in which the authors conduct several simulation experiments. The case study examines a standardised patient arrival and treatment process in an orthopaedic-emergency room of a public hospital.
The method permits detection of process criticalities, as well as identifying the best corrective actions by means of the “what-if” analysis. The paper discusses both management and research implications of the method.
The study responds to current calls for holistic and sustainable approaches to business process management (BPM). It provides step-by-step process modelling and simulation that serve as a “virtual laboratory” to test potential improvements and verify their impact on operational performance, without the risk of error that would be involved in ex-novo simulation programming.
The purpose of this paper is to develop and empirically validate an instrument containing operational measures of lean service. The instrument is intended for use by both researchers and practitioners.
The instrument was developed and validated in an iterative process between theoretical and empirical insights. Drawing on a wide selection of frequently cited papers on lean service, a preliminary list of items was generated. These items were then vetted through four steps in order to achieve high validity. Empirical refinement and validation included workshops and semi‐structured interviews with expert practitioners, as well as testing the instrument's ability to discriminate between high and low adoption of lean and portray changes during lean service adoption.
The instrument contains 34 items that assess enablers of lean adoption, lean practices, and operational performance. Empirical validation suggested the instrument was able to discriminate between high and low adoption of lean service, as well as portray changes over time during lean adoption.
Practicing managers will be able to use the instrument in order to track progress during lean service adoption, thereby identifying and acting upon deviations from planned progress.
The paper represents the first comprehensive attempt to develop an instrument for assessing lean service adoption. Through this instrument, operational definitions of lean service will allow researchers to measure the level of lean service adoption, and using this information, to develop knowledge of for instance the contingencies to lean service adoption, the problems and pitfalls in lean service adoption and the feasibility of transferring practices to various service settings.