Predictive Analytics: Analyzing Project Change Orders and Associated Design Risks
Other project impacts include project change orders, which can change and expand project scope/revenue/cost. Project Change Requests/Orders can be a major factor in over-budget projects. Project change orders may include required project changes to project equipment/resources based on changes in project scope or process specification.
Project risk management is the primary responsibility of the project manager throughout any project, identifying and implementing risk mitigation strategies, particularly the risk associated with project changes.
For example, if a project change order is created for a project task that is linked to a released project order, the project manager must be notified that a revision of the order may be required to process the project change order
These project change orders can have cascading effects, which may include project orders for engineering equipment issued to project suppliers.These design change scenarios may require a revision of the purchase order to reflect material specification changes. Additional project change order use cases are listed below –
• Identify specific change orders that impact the project schedule and/or budget and all dependent activities.
• Project manager notification When a project change order is created for a project task that is linked to a released project order, the project manager must be notified that a revision of the order may be required.
• Inform the project manager if a project change order affects a specific project resource (large equipment), identify other activities affected/dependent on the same project resource.
• Notify the project manager when a project change order has been created for project tasks with orders already received.
Project Intelligence AI/ML applications deployed on cloud-based ERP platforms can create significant opportunities to improve project management in resource-intensive industries by giving project managers greater insight into variable projects in other ERP areas, that are integrated into the project, e.g. B. Procurement, SCM and Finance.
Project Management Use cases include machine learning projects to improve project planning accuracy, align project plans with procurement, and project planning functionality; Risk management to monitor project exception conditions and predictive analytics for project change orders. Cloud customers are beginning to see the benefits of ERP-driven AI. Machine learning covering ERP modules and using a unified ERP data model.