Upgrade project for the low-capacity and very slow railway in the north of the UK between York and Manchester (TPU) is using nPlan’s forecasting and risk management software to reduce optimism and salience biases in its project schedule forecasts. It is reported by the Railway Supply magazine with reference to the Railway Gazette.

TPU
Picture: Peter Devlin

Using a deep learning method, neural networks build a model by analyzing past project schedules and capturing how activities are performed under different conditions. Once the model has been trained, it can be fed schedules of upcoming projects, allowing it to provide detailed information about the risk profile of each activity, as well as generate a forecast of how the project will be implemented.

CFR repaired one of the railway lines to Ukraine

This allows you to focus on the most risky elements of each project and test the impact of various mitigation scenarios.

“Modernization of the Transpennine Route is an ambitious and challenging multi-year program,” explained Richard Palczynski, Head of Strategic Program Control. “With nPlan, we can take a new approach to analyzing our schedules, eliminating the human factor and increasing the efficiency of the process.”

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