Because the iterative algorithm for the simultaneous estimation of the background and model parameters involves heavy calculations, off-line optimization and on-line forecasting implementation are used to alleviate the computational costs.
This paper ends with a forecast for continuative implementation work and open research questions.
The group was facilitated through an action research approach incorporating three phases: (1) awareness; (2) forecasting; and (3) implementation.
This paper presents an analytic hierarchy prediction model based on the consistent fuzzy preference relations to help the organizations become aware of the essential factors affecting the success of Knowledge Management (KM) implementation, forecasting the possibility of successful KM project, as well as identifying the actions necessary before initiating KM.
As the program increased supervision, surveillance and program data quality improved, facilitating more effective logistical and technical forecasting, planning, and implementation.
The paper outlines both the methodological and algorithmic aspects of the modelling, forecasting and signal extraction procedures, including the design and implementation of forecasting support software with a specially designed Graphical User Interface within the Matlab® computing environment.
The mean values of the forecast range without program implementation (left) are compared to the lower forecast variants (right) with the program deployment.
Illustration of the "off-line optimization and online forecasting" scheme for the implementation of the ETAS model in CSEP.
As a result of the aforementioned works, the Health and Social Care Modelling Group (HSCMG) in collaboration with the Care Services Efficiency Delivery (CSED, Department of Health) developed a software implementation of the forecasting framework [ 10], known as FLoSC (Forecasting Length-of-Stay and Cost) (cf. http://www.healthcareinformatics.org.uk/flosc/).uk/flosc/
Various challenges like employees resistance, cross-functional conflict, poor facility planning, poor sales forecast, etc. erupt during implementation of JIT production.
