AI + PLM - Insight to Execution : Change Management Implementation

In my previous edition, I tried to explain how AI integrated PLM solution can bring massive value to Industrial Workflows - from Design Optimisation to Field Operations. From my experience with different industries and readings, one process stands out a mission critical and always complex to manage within any industry : Change Management. 

According to me, AI integrated PLM solution will definitely raise the bar and make this process more proactive than reactive. Also it will be insight driven engine of innovation across organisation to manage changes effectively and more efficiently. 

To understand How exactly can it be implemented ? let’s break down the process right from Requirement Collection >> Request for Change >> Change Implementation and Execution. Let’s explore how AI tools can help PLM system to make this process much leaner and effective.   

Change Cycle

Requirement Collection 

Normally in any industry, any change starts with particular requirement then it may come from customer complaint, environmental compliances, legal constraints or any internal product enhancement program. Traditionally, we collect this data through reports, emails, and some documents. it means these sources are error prone and fragmented. 

AI Advantage :

• NLP (Natural Language Processing) can be used on particular set of data from emails, reports and Documents to extract relevant data for requirements. 

• Semantic Analysis will help to identify the context and categorise those requirements according to our logic. 

PLM Advantage :

• All historical requirements, specifications and engineering documents should be archived and stored properly which can be accessed by some interface. 

• Well structured metadata should be defined for requirement objects to train AI Models. 

Requirement Analysis & Approval 

Once requirement is captured in particular from all sources, it should be properly validated, check feasibility and align it with business priorities. This is very important step where all cross functional team has to work together and approve these requirements.

AI Advantage :

• AI Model can analyse all past requirements and suggest which requirement specification will get impacted. 

• Also, it can suggest impacted areas and identify duplicate or conflicting requirements which will help for easy decision making. 

PLM Advantage :

• Role based access to requirements for approval. 

• Decision rationales are captured for all accepted and rejected requirements.

• maintaining digital thread between requirement objects and other PLM Objects (CAD data, Documents, Specification, Parameters etc)

Request for Change

All approved requirements are formalised into Change Requests which orchestrate a controlled change process. 

AI Advantage :

• Based on requirement context, AI can identify the type of change request to carry change process. 

• AI can auto populate information needed for Change Request creation from requirements by identifying affected objects, linked documents. 

• Using organisation structure information, AI can help to identify actors for Change Process execution. 

PLM Advantage :

• Change Request configuration with correct set of metadata and workflows. 

• Proper People and Organisation structure for correct roles and access to relevant data. 

• Defined product structure, linked documents, and historical changes in a very structured manner. 

Impact Analysis

Most important step in Change Execution which defines how Change will be executed across the value stream. It identifies and analyse which objects (CAD Object, MFG Objects, Routes, Documents), configurations, processes, and suppliers will be affected by this change. 

AI Advantage :

• Use linked Object data to provide graphical analysis of impacted objects. 

• Predicts the ripple effect across CAD Files, BOMs, Configurations, Processes, MFG Objects and Suppliers. 

• Compare historical data to provide dependencies and risk areas to implement. 

PLM Advantage : 

• Structured and Linked object model (CAD Files, Documents, MFG Objects, Routes, Configurations, Suppliers, BOMs)

• All change logs and dependency data. 

• APIs to expose PLM data for AI Model. 

Change Implementation

After proper Impact Analysis, Change Request gets approved and users come to know the quantum of work to be done for this Change Implementation. As per identification of impacted objects, change moves in to execution such as CAD File changes, BOM revisions, Document Updates, Production updates, and Suppliers notes.

AI Advantage :

• AI Models will help to assign Change actions/tasks to relevant users for change execution. 

• AI Model can also track completion and delays under each change request orchestration to update models. 

PLM Advantage :

• Integration with all downstream applications (ERP, CRM etc) to share Changes. 

• Closed loop feedback mechanism using Change Request and Change Actions workflows.

• Capturing history for Change which helps AI to learn more about which works and which didn’t work. 

From Data to Decisions: Engineering AI-Ready PLM Systems

To implement this vision, we need following things to be implemented in PLM - 

• Structured and linked data in PLM 

• Change History logs 

• Configured Change Management Objects and its workflows 

• API Integration with AI Platforms 

• Feedback loop for AI Model improvements

Finally, AI is not here to replace our processes and structured workflows but to make it more smarter, faster and proactive. From my experience, I can assure you that how connected data can enable smarter decision making. Once you combine this with AI, then your PLM engine will become true engine of innovation and resilience.