The impact of judgment on statistical forecasts

The thesis will aim to empirically assess how judgment impacts a statistical forecast in a spare parts supply chain.The thesis will investigate what impact judgment has on forecast accuracy that is, does it improve the statistical forecast. If so, is there specific types of demand series, spare parts types or expertise which can affect the accuracy improvement. The results will be used to provide a matrix showing where judgment should or should not be applied to a statistical forecast with regards to accuracy improvement.The size and direction of the judgmental adjustment will be scrutinised to explore where any correlation can be found to accuracy improvement. The experiment will be for a 12 months longitudinal period using forecast experts who are working in a company and are forecasting the same spare parts on a day to day basis. The statistical forecast used will be the method that the company uses on a day to day basis. In order to benchmark the performance of the experts a senior academic will also be forecasting the spare parts involved over the same period in order to show another comparison but with a more considered, complex statistical forecast rather than the relatively simple average based statistical forecast the company used.Insights into further research, limitations of the experiment and a conclusion stating the impact to academic knowledge and possible practitioner usage will be discussed.

Level dc:type.qualificationlevel

Doctoral (Level 8)

Year dc:date.issued

2020

Author dc:creator

• Keyes, MA

Language dc:language

en

Identifier

oai:salford-repository.worktribe.com:1358487

OAI identifier oai:identifier

oai:salford-repository.worktribe.com:1358487