Four Ways Artificial Intelligence will Transform the Future of Project Management

Digital Transformation Opportunities to Deliver Augmented Project Management Intelligence.

Executive Summary

For Asset-intensive Industries, Capital Projects are a Billion-dollar engine for growth and change. These Projects have become an area of focus across major Energy and EPC Companies. In this whitepaper, we will examine future trends for Project Intelligence that are possible through the use of Artificial Intelligence (AI) and Machine Learning (ML).

Digital Transformation Opportunities for ERP-connected Project Management Intelligence

Artificial intelligence has already made some important inroads in ERP Cloud applications, with even greater potential to be realized. From project estimating and resourcing to project scheduling and managing project supplier deliverables. With ERP-connected project management systems, AI/ML opportunities can now span multiple ERP Modules.

AI/ML will not just take over repetitive Project tasks or provide routine enhancements. AI/ML will allow Project Management to leverage other ERP modules, like purchasing, to incorporate key Project Purchasing intelligence data points. Project scheduling can leverage visibility across project purchasing to incorporate supplier updates from PO promise dates to ASN’s and PO Receipts.

Project Intelligence AI/ML focus areas below are key opportunity areas for delivering transformational Projects AI/ML applications:

• Projects Machine Learning: Analyzing Project dependencies

• Align Project Schedules with Purchasing and Project Suppliers timelines

• Risk Management: Identify Project Exception Conditions Faster

• Predictive Analytics: Project Change Order Impact Analysis

Note: Other Projects AI/ML opportunity areas for future consideration include the use of Drones to Track/Analyze Project Progress.

Table of Contents

Executive Summary 2

Table of Contents 3

Four Ways Artificial Intelligence will Transform the Future of Project Management 4

I. Introduction 4

II. Using Machine Learning to Improve Project Planning Accuracy 5

III. Align Project Task Schedules with Purchasing & Project Suppliers Timelines 6

IV. Risk Management: Identify Project Exception Conditions Faster 7

V. Predictive Analytics: Analyze Project Change Orders and Related Project Risks 8

VI. Conclusion 9

I. Introduction

Capital Projects are described as a Billion-dollar engine for growth and change in the Asset-intensive Industries, where these Projects have become an area of focus across major Energy and EPC Companies. Projects are executed to build the vital assets for the Energy and EPC Value Chain, including the initial Asset Construction/Acquisition/Installation through the Asset handover to Operations & Maintenance

For Asset-intensive companies, Capital Planning and Capital Project execution is a high value area identified by industry analysts for process improvements in key areas to manage costs. These Asset-intensive Companies can improve their Capital Planning effectiveness with improved operational visibility to their Capital Project Portfolios, and Intelligent Project Management.

According to McKinsey’s 2017 AI Survey of early adopters, early evidence suggests that AI can deliver real value to serious adopters and can be a powerful force for disruption. In their survey, early AI adopters that combine strong digital capability with proactive strategies have higher profit margins and expect the performance gap with other firms to widen in the future. More recently, Cloud has set it sites on AI and ML technologies that can be applied across ERP Cloud Applications.

Digital Transformation via ERP-connected Project Intelligence -

The Project Intelligence AI/ML focus areas below are key opportunity areas for delivering transformational Projects AI/ML applications :

• Projects Machine Learning: Analyzing Projects with similar Tasks & Asset assignments

• Align Project Schedules with Purchasing and Project Suppliers timelines

• Risk Management: Project Exceptions Identification to Identify Project Task Exception Conditions Faster

• Predictive Analytics: Analyze Project Change Orders and Related Project Risks

Other Projects AI/ML opportunity areas for future consideration include the use of Drones to Analyze Project Progress.

In this Whitepaper, we will explore specific Artificial Intelligence and Machine Learning Opportunity areas to deliver Project Intelligence on an ERP-connected Projects Platform. Project Intelligence AI & ML offer beneficial capabilities to assist Project Managers in identifying potential exception conditions in their projects, and help project managers respond before projects reach a critical state.

ERP-Connected Projects significantly expand the real potential for AI and ML applications that can be implemented for Project Intelligence, by providing an ideal Project Intelligence AI/ML platform to leverage the interconnected nature of ERP Applications across Procurement, Financials, and Inventory/SCM areas.

Many of these AI/ML capabilities simply would not be possible using standalone Project Management tools. ERP-connected Project execution significantly expands the potential for Projects AI/ML applications to deliver more intelligent Project execution.

II. Using Machine Learning to Improve Project Planning Accuracy

Machine Learning can guide project managers to create more accurate project schedules and estimates by providing suggestions using ML from completed projects, based on project types with similar Tasks and Project Assets. Machine Learning provides a new capability for Project Intelligence to analyze project history and improve project planning and resourcing accuracy.

Over time, Project ML can build a significant Project knowledge base to help Project Managers improve their project scheduling and budgeting accuracy, reducing costly project budget overruns. Other ML benefits include using the identified Tasks which are running late to identify any associated Assets (from like Asset assignments, etc.) to identify the specific Project Assets which are impacted by these late tasks.

For Capital Projects, Projects can have multiple CIP Assets assigned at the Task-level to capture project-related Asset costs. These Task-level Asset assignments can be used to identify the impacted Asset(s) for a Project. When PM’s know which Project Assets are impacted by a Late Tasks or Project Change Orders is going to be important information for Project Managers and Stakeholders to prepare remediation plans.

Project ML can also help identify other tasks which reference these same Project assets to identify other impacts (in addition to the task predecessor-successor relationships). Note: each Project Asset will have estimated In-Service dates, as well.

For example, in the case of a Project Task that is running late, Machine Learning capabilities can be used to analyze other Projects with similar tasks (and assigned Project Assets for common asset types) to compare Task effort and resources assigned to see if the late task was estimated / resourced correctly. Projects ML can also alert a Project Manager if they missed a Project Task dependency, such as a facility permitting step or a construction funding approval gate.

ML may be able to determine if a predecessor relationship for a prerequisite task was not identified. Other factors can include long lead project equipment delivery schedules for engineered/major equipment that requires fabrication. Other Projects ML opportunity areas include -

• Provide Task estimating and Resourcing suggestions

• Provide Supplier Lead times for Major/Engineered Equipment (long lead equipment) based on prior projects (with similar Assets)

• Provide shortest and longest project durations for similar Projects based on prior project history.

• For Tasks running late, use ML to identify any associated Assets (Asset assignments) to identify the specific Project Assets which are impacted by these late tasks.

III. Align Project Task Schedules with Purchasing & Project Suppliers Timelines

Project Managers can realize significant benefits from Project Intelligence AI/ML applications that leverage these existing ERP Cloud integrations to Projects. For example, Intelligent Project Alerts can notify Project Managers when Supplier Promise dates for Project Purchase Orders exceed Project schedule dates, or the engineered equipment needed for a Project will require a 10 week lead time for fabrication and delivery.

ERP Cloud Purchasing capabilities includes support for various complex project-based purchasing requirements, for both goods and services. For Project-related Purchase Orders, this capability would allow Project Managers to ensure that Project Tasks are aligned with the Purchase Order Promise Dates related to those Tasks (when PO Promise dates are later than the estimated Task completion date).

This capability would leverage the Projects and Purchasing integrations to drive additional AI benefits to help project managers track PO delivery dates from Project suppliers, and alert PM’s when product / equipment is received, or delivery dates are not met by the Supplier.

• Alert PM’s when PO Promise dates are later than the estimated Task completion date.

• Alert PM’s when product/item is received, or delivery dates are not met by the Supplier.

• Alert PM’s if product/item received quantity does not match PO quantity, or product is placed in quarantine for quality inspection issue (and provide the PO receiving warehouse location).

• Alert PM’s in the event of a product substitution by the Supplier.

The Purchase Order entry process includes Line level detail associated with a specific Project & Task combination which captures the requested date from the requestor, and promised delivery dates from the Supplier. This information may not always be readily available to the Project Manager, especially if the supplier provides a promise date that is different from the requested date – which should be a noted condition.

IV. Risk Management: Identify Project Exception Conditions Faster

Managing Project Risk is a critical responsibility of the Project Manager during each project to help quantify the impact of the risk on project schedule and costs, as well as respond with Project Risk Mitigation Plans. Project Risk Identification and Control is also an opportunity area for implementing Artificial Intelligence (AI) and Machine Learning (ML) to analyze common factors that cause projects to run late and over budget, and alert the Project Manager.

Artificial Intelligence (AI) improves Project Intelligence capabilities to monitor and assess Project exception conditions and alert Project Managers when intervention may be required. Project Intelligence AI can monitor projects performance on a continuing basis to detect and report task-level issues before the Project reaches a critical threshold, which can lead to a schedule or budget overrun.

Intelligent Project Alerts can notify Project Managers when Exception conditions occurring outside of the core project schedule, in areas like Purchasing and Assets. For example, when project materials are received where quantities don’t match ordered quantities, or project materials are rejected during receiving. While Machine Learning will guide project managers to create more accurate project schedules and estimates faster and easier by providing suggestions using ML from completed projects.

Identification of Project Exceptions conditions to monitor Project Tasks that are running late, and generate Project alerts for exceptions which may have overall project schedule impacts. Also, Project Exceptions Dashboards can provide an ideal reporting platform for AI/ML generated Task Alerts and Exceptions to Analyze impacts on the Project schedule, and respond accordingly to mitigate project risks.

This capability can be used to identify which Project tasks will need attention, and build on Machine Learning capabilities for projects to recognize Project conditions where tasks run late, and may be under-estimated, missing the required materials/resources or other predecessor task dependencies.

• Recognize Project conditions where tasks run late, and may be under-resourced, missing the required materials/resources, or missing predecessor Task dependencies.

• Recognize Project conditions where Supplier-related events are delayed, including delays to PO Approval, PO Releases, and Supplier Delivery delays.

• During PO Receiving, a Risk or Exception can be noted if the product/equipment received is moved to quarantine for inspection-related issues, or the quantity received doesn’t match PO.

• IOT Cloud Services can be used on projects for equipment geolocation, as well as monitoring major/engineered equipment with sensitive electronics/components that may be affected by severe vibrations/shocks (or humidity/moisture) as it is transported to Project sites.

V. Predictive Analytics: Analyze Project Change Orders and Related Project Risks

Other factors impacting project execution include Project Change Orders which can alter and expand the project scope/deliverables/costs. Project Change Requests/Orders can be a primary causative factor for projects exceeding their Budget. Project Change Orders can include design changes required for Project Equipment/Assets based on project scope or process specification changes.

Managing Project Risk is a critical responsibility of the Project Manager during each project to identify and implement Risk Mitigation strategies, especially the risks that are associated with project change.

For example, when a Project Change order is created against a Project Task associated with a released Project Purchase Order, the Project Manager should be alerted that a Purchase Order revision may be needed to support the Project Change Order

These Project Change orders can have cascading effects that can include Project Purchase Orders for engineered equipment that have been released to Project suppliers. These Project Change Order scenarios may call for a Purchase order revision to reflect any equipment specification changes. Other Project Change Order use cases are included below -

• Identify specific Change Orders that impact Project Schedule and/or Budget and any dependent successor tasks.

• Alert PM when a Project Change order is created against a Project Task associated with a released Project Purchase Order, the Project Manager should be alerted that a Purchase Order revision may be needed.

• Alert PM when Project Change Order impacts a specific assigned Project Asset (Major Equipment), identify other Tasks that are assigned/dependent on that same Project Asset.

• Alert PM when Project Change Order created against Project Tasks with PO(s) that have already been received.

VI. Conclusion

Project Intelligence AI/ML applications delivered on ERP Cloud Platforms can create significant opportunities for Project Management improvements across Asset-intensive industries, equipping project managers with greater insights into Project variables in other Project-integrated ERP areas like Purchasing, SCM and Finance.

Project Management Use cases include Projects Machine Learning to improve Project Planning accuracy, align project schedules with project purchasing functions and timelines; Risk Management for monitoring Project Exception conditions, as well as Predictive Analytics for Project Change Orders. Cloud Customers are starting to identify the benefits of ERP-based AI & ML that spans across ERP modules, which leverages a unified ERP data model.

As ERP Projects integration capabilities expand (i.e., across Financials & EPM, Procurement, SCM, Asset Management, etc.), additional AI/ML opportunities will be available to improve Project Manager visibility and project financial insights.

Cloud-based AI/ML Apps can enable faster time-to-business insights, acceleration of key processes, decreased time to market, and improved customer experiences—all while reducing costs and improving productivity—without requiring data scientists.