| dc.description.abstract | Artificial intelligence have fascinated scientist with the ability of imitating human behaviour
and mindset through mathematical computing. It can also be argued that artificial intelligence
have the potential to surpass human intelligence. The opportunities within utilizing
the technology can be combined with endless combinations; however, attributes for an algorithm
comes at a cost that can affect an organization’s business strategy and technical
infrastructure. It is therefore important that compromises are taken in a well reflected
manner and appropriate conditions that suits the dynamic market that the organization lie
within.
This master’s thesis aims to illuminate opportunities and limitation associated with the implementation
of an artificial intelligence driven rolling forecasts in a Norwegian fiberglass
manufacturing plant using a qualitative research methodology that consists of interviews
with key personnel from the organization. Experimental models have been developed to
achieve an understanding of the implementation scope through a comparison of an autoregressive
integrated moving average and long short-term memory predictive models.
The study results have been compared to a theoretical framework revolving around the main
themes of the thesis: financial forecasting, management accounting & control and artificial
intelligence where the following outcomes have been identified: (1) implementation of the
technology require prerequisites in the form of integrated business processes, (3) forecasting
and budgeting must have distinguished roles, (2) the management have to increase technological
focus within business activities, (4) a systematic approach towards data collection
have to be established, (5) optimal balance between objective and subjective forecasting
is preferred and (6) prior manual activities that the predictive model is dependent on can
increase the risk of inaccurate forecasts. | |