Streamline Your Windchill PLM Solution

Everything you need to streamline your Windchill PLM solution

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Overview: Streamline Your Windchill PLM Solution

Develop Options for Future State

Execute the design you created previously. Confirm out-of-the-box (OOTB) functionality meets your business needs, change your system, and manage your data.

01. Streamline customizations

At this point, it’s time to streamline the customizations you have identified. Standardize the customizations you plan to keep by making sure it follows PTC’s customization best practice, or optimize your Windchill PLM solution by removing the customizations you no longer need and leveraging OOTB functionality.

To build your solution, refer to the requirements and plans you established in the previous steps. This step includes:

  • Modifying existing customization to follow PTC best practices
  • Implementing configuration changes
  • Carrying out your plan agreed upon about handling historical data
  • Ensuring that the OOTB functionality works in a way that satisfies your business needs
  • Making approved updates
  • Documenting every execution step

02. Confirm OOTB functionality

Ensure that the OOTB functionality can satisfy your business needs in creating and managing the data moving forward. The development team and the governance team will make changes to the data model and business processes. They will also ensure application programming interfaces or loaders are available to support the new OOTB functionality.

03. Update your system to meet requirements

You will now implement the supported configuration based on the previously created plan. This could involve turning on and off features and updating settings to accommodate users.

Some examples of configurations you will need to create or change include:

  • Object types
  • Attributes
  • Lifecycle
  • Object initialization rules
  • Access control
  • Workflows
  • Preferences
  • Properties

Ensure that your configurations are done in a way that provides flexibility in case you need to make changes to your system later. Again, optimizing system usage and enhancing the performance of your system is the goal.

04. Manage historical data

In the process of standardizing customizations from Windchill PLM, you may also need to remove custom data attributes. However, regardless of what custom attributes you remove, you still need to manage your organization’s data in the system before removing customizations.

If you remove custom attributes, you should ask:

  • Does the data still have all the information the users need?
  • Does the process to manage that data still meet user needs after removing customizations?
  • Do the right individuals have the right access to view, create, or modify that data?

The data transformation and maintenance decisions you made earlier in the project will determine what management tactics are applied here. This task will require complex work and is a major activity. Data is extracted from Windchill PLM solution and converted before it is reloaded back into your Windchill PLM solution. This time, the data may appear as a different object type with different attributes. Data transformation is the collective steps taken to convert and extract data from one format to another to send to a new destination.

The data transformation process includes:

  • Data discovery: Identifying and understanding the data in its original format
  • Data mapping: Planning the data transformation
  • Code generation: Creating code to run the transformation
  • Code execution: Converting data to the desired format
  • Data review: Checking data to ensure everything is formatted correctly
  • Data cleaning: Correcting or removing data that is incorrect, mislabeled, duplicated, incorrectly formatted, or incorrect

Tools and technologies used for data transformation will vary based on the complexity of the changes your project requires. Depending on your project, organization, or regulatory requirements, not all data may need to be transformed.

05. Clean the data

Data cleaning techniques will not always be the same every time you perform them. It’s still helpful to create a template to outline your data cleaning process to ensure you are completing the same steps each time. Depending on your situation, some additional steps could include:

  • Removing duplicate data
  • Correcting structural errors
  • Combining data from multiple sources
  • Figuring out where to put the attributes removed during the transformation in the new data model
  • Handle missing data

In the end, you will need to validate the data.

In this step, your execution team will proceed with development using their chosen software development methodology.

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