Talk, The Conference for Users of R (useR!) 2018, Brisbane, Australia
In any customer-facing business, accurately predicting demand ahead of time is of paramount importance*. Workforce capacity can be flexibly scheduled at local area accordingly. In this way, we can ensure having sufficient workforce to meet volatile demand.In this case study, we focus on the gas boiler repairing field operation in the UK. We have developed a prototype capacity forecasting procedure which uses a mixture of machine learning techniques to achieve its goal. Firstly, it uses Generalised Additive Model approach to estimate the number of incoming work requests. It takes into account the non-linear effects of multiple predictor variables. The next stage uses a large random forest to estimate the expected number of appointments for each work request by feeding in various ordinal and categorical inputs. At this stage, the size of the training set is considerable large and does not fully-fit in memory. In light of this, the random forest model was trained in chunks / parallel to enhance computational performance. Once all previous steps have been completed, probabilistic input such as the ECMWF Ensemble weather forecast to give a view of all predicted scenarios.