{"id":130433,"date":"2023-07-27T12:42:55","date_gmt":"2023-07-27T11:42:55","guid":{"rendered":"https:\/\/www.pbctoday.co.uk\/news\/?p=130433"},"modified":"2023-07-27T12:42:55","modified_gmt":"2023-07-27T11:42:55","slug":"bam-looks-nplan-portfolio-improve-risk-cost-management","status":"publish","type":"post","link":"https:\/\/www.pbctoday.co.uk\/news\/digital-construction-news\/construction-software-news\/bam-looks-nplan-portfolio-improve-risk-cost-management\/130433\/","title":{"rendered":"BAM looks to nPlan Portfolio to improve risk and cost management"},"content":{"rendered":"

BAM has partnered with nPlan Portfolio and will use their AI and big data to assess and manage schedule risk across their projects<\/h2>\n

According to nPlan Portfolio, their product allows contractors to oversee 50 projects and plan future development. Contractors, such as BAM, can analyse the entire portfolio to identify potential schedule and cost risks.<\/p>\n

nPlan Portfolio uses machine learning that enables contractors to focus on specific key areas and uncover ways to make their portfolio schedule more efficient.<\/p>\n

Using machine learning to analyse construction schedules<\/h3>\n

nPlan’s approach to forecasting and reducing risks in construction projects involves analysing extensive past project data\u2014a task best suited for machine learning.<\/p>\n

nPlan collected the world’s largest dataset of past project schedules over 740,000 programmes) and used it to train an AI model that outperforms human operators in predicting project performance.<\/p>\n

When a customer uploads a project schedule to nPlan Insights, the AI generates a likely forecast for each activity in the schedule. The software then combines this data to provide an overall project forecast and identifies the activities that are most likely to cause delays.<\/p>\n

nPlan\u2019s approach to portfolio management approach has gained recognition from project teams at respected organisations such as Network Rail<\/a>, Shell, HS2, Transpennine Route Upgrade<\/a>, Kier, SCS, and more.<\/p>\n

Measuring delay risk for project portfolios and gaining insight is a challenging task. Human biases can affect project schedules and impact portfolio analysis. By using the same AI and big data-powered forecasting<\/a> engine as nPlan Insights these discrepancies can be avoided.<\/p>\n

The key features of nPlan Portfolio<\/h3>\n

Users can assess the workload on a monthly timeline, enabling them to spot potential bottlenecks in both planned and forecasted work.<\/p>\n

The platform offers customisation options, such as tags, grouping, and filtering, making it user-friendly for personalised organisations. Metrics are available for the created groups, aiding in data analysis.<\/p>\n

nPlan Portfolio highlights groups that contribute to delays, allowing professionals to address systemic issues and drive improvements. Each project has its own dedicated page, featuring health trends, workload forecasts, and key risk areas needing attention.<\/p>\n

\u201cAs our Clients are increasingly moving towards outcome-focused capital programmes we are seeing a shift in our leadership focus to the risks to delivery of those outcomes rather than just the risks to project delivery,\u201d said David Bunn, portfolio director at BAM.<\/p>\n

“Applying nPlan\u2019s AI capability at a portfolio oversight level allows us to quickly target our leadership interventions to the right areas across a large portfolio of projects, to mitigate risks to outcomes being delivered when they are needed,\u201d he added.<\/p>\n

\n<\/div>","protected":false},"excerpt":{"rendered":"

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