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Manufacturing Labor

Manufacturing Labor Tool

As part of the digital transformation initiative, this tool provides a user-centric solution enabling quick comparison between global standard metrics and local data, empowering users to make efficient, informed decisions.

Home

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Use Cases

/

Manufacturing Labor

Manufacturing Labor Tool

As part of the digital transformation initiative, this tool provides a user-centric solution enabling quick comparison between global standard metrics and local data, empowering users to make efficient, informed decisions.

Home

/

Use Cases

/

Manufacturing Labor

Manufacturing Labor Tool

As part of the digital transformation initiative, this tool provides a user-centric solution enabling quick comparison between global standard metrics and local data, empowering users to make efficient, informed decisions.

Objectives

The framework

Research

Define

Design

Conclusion

Objectives

Enhance factory labor decision-making by simplifying data collection across processing, packaging, maintenance, and quality control through data visualization tool.

Enhance factory labor decision-making by simplifying data collection across processing, packaging, maintenance, and quality control through data visualization tool.

My contribution

UI Design, technical research, user research, data visualization.

The team

Product owner(1), Power BI devs(3), Data analyst(2), Product designer

Tools

Miro, Figma, Power BI, Spreadsheet, ADO, Sharepoint, Power Apps

The framework

Envision the process as a continuous cycle of analysis, questioning, executing, and resolving user needs.

Envision the process as a continuous cycle of analysis, questioning, executing, and resolving user needs.

Research - Discover and observe

As part of the digital transformation process, we carry out the research and user experience process to implement solutions globally.

As part of the digital transformation process, we carry out the research and user experience process to implement solutions globally.

Purpose

Research

Constrains

Conclusion

From complexity towards resolution

Manufacturing Labor was developed to meet the needs for a tool that allows visualization and clear tracking in real time the labor allocation within production lines and the capability to compare those allocations with global standards.

Our research responded to accumulated data from the first users and product meetings.

The challenges

Currently using a variety of manual and ad hoc solutions to plan and schedule labor.

Faster analysis.

Comparison between local/site metrics and global standards to assess opportunity

Reduce labor costs.

Increase production efficiency. 

Optimize and automate labor management processes globally.

Labor allocation visibility to identify & close gaps between current state vs. global standards. 

More accurate labor planning.

Intelligent planning & forecasting.

Integrated manufacturing operations management for predictive planning & holistic optimization. 

Better planning decisions, optimized across all operations areas. 

Crew scheduling and management.

Enables dynamic labor planning to right size crew & reduce labor costs. 

Purpose

Research

Constrains

Conclusion

From complexity towards resolution

Manufacturing Labor was developed to meet the needs for a tool that allows visualization and clear tracking in real time the labor allocation within production lines and the capability to compare those allocations with global standards.

Our research responded to accumulated data from the first users and product meetings.

The challenges

Currently using a variety of manual and ad hoc solutions to plan and schedule labor.

Faster analysis.

Comparison between local/site metrics and global standards to assess opportunity

Reduce labor costs.

Increase production efficiency. 

Optimize and automate labor management processes globally.

Labor allocation visibility to identify & close gaps between current state vs. global standards. 

More accurate labor planning.

Intelligent planning & forecasting.

Integrated manufacturing operations management for predictive planning & holistic optimization. 

Better planning decisions, optimized across all operations areas. 

Crew scheduling and management.

Enables dynamic labor planning to right size crew & reduce labor costs. 

Purpose

Research

Constrains

Conclusion

From complexity towards resolution

Manufacturing Labor was developed to meet the needs for a tool that allows visualization and clear tracking in real time the labor allocation within production lines and the capability to compare those allocations with global standards.

Our research responded to accumulated data from the first users and product meetings.

The challenges

Currently using a variety of manual and ad hoc solutions to plan and schedule labor.

Faster analysis.

Comparison between local/site metrics and global standards to assess opportunity

Reduce labor costs.

Increase production efficiency. 

Optimize and automate labor management processes globally.

Labor allocation visibility to identify & close gaps between current state vs. global standards. 

More accurate labor planning.

Intelligent planning & forecasting.

Integrated manufacturing operations management for predictive planning & holistic optimization. 

Better planning decisions, optimized across all operations areas. 

Crew scheduling and management.

Enables dynamic labor planning to right size crew & reduce labor costs. 

Define

Synthesizing our knowledge to uncover hidden insights that illuminates the path forward. Adding clarity, reframing our point of view and define our thoughts about what's important.

Synthesizing our knowledge to uncover hidden insights that illuminates the path forward. Adding clarity, reframing our point of view and define our thoughts about what's important.

Problem

Data

Architecture

Conclusion

Problem definition

Key questions to identify key needs. As our understanding evolved, we inquired about the intent and motivations of the users to ensure that we were aligned with the real needs.

1 / 7

Problem

Data

Architecture

Conclusion

Problem definition

Key questions to identify key needs. As our understanding evolved, we inquired about the intent and motivations of the users to ensure that we were aligned with the real needs.

1 / 7

Problem

Data

Architecture

Conclusion

Problem definition

Key questions to identify key needs. As our understanding evolved, we inquired about the intent and motivations of the users to ensure that we were aligned with the real needs.

1 / 7

Design

After we've determined the audience and purpose of the application reports, we can identify the most valuable insights that we want to drive.

After we've determined the audience and purpose of the application reports, we can identify the most valuable insights that we want to drive.

Design scenarios by priority

Priority 1

  • MVP Implementation.

  • Visualization shifts by line/area and quick comparison and integration with global data.

  • Generate dashboards/visuals for each use case.

  • Integrate different teams into the tool's workflow within the tool.

Priority 2

  • Accelerate decision-making.

  • Integrate teh tool into the deep-dive by performing periodic user test sessions with end users.

  • Agile data loading performance.

Priority 3

  • Align global and local data structures.

  • Collaborate with all teams from prpoposal to execution.

Explore the Figma file

The password to unlock it is included in the CV I’ve shared with you. Make sure to have it handy to gain access!

Explore the Figma file

The password to unlock it is included in the CV I’ve shared with you. Make sure to have it handy to gain access!

Explore the Figma file

The password to unlock it is included in the CV I’ve shared with you. Make sure to have it handy to gain access!

Conclusion

"Ideally I need one digital intuitive tool that will automate ML process, enable collaboration between local site and global team and reduce the effort required to complete ML deep dive."

Predictions based on data analytics traditionally require skilled teams to manage, model and clean data. Generating narratives that make data understandable and interactive makes it a key planning tool. Getting the most out from data goes beyond representing it in a graph; reading data has a lot to do with how users interact, engage and act on the data to generate narratives that anyone can understand at a glance.

Understanding this approach was crucial in delivering user-centric solutions, providing the tools needed to unlock insights and drive decisions.

As a professional conclusion it was very interesting to participate and support user research initiatives in a shared environment with data scientists and data analysts. Complementing my work as a product designer with that of a data visualization designer, led us to rethink the figure within the team of a data visualization designer focused on generating data-driven narratives and being the connector between the data and design teams.

As a result, the tool is being implemented globally and user testing continues to be carried out for future deployments.

Thank you for making it here!

Made by a human · ©2024

Thank you for making it here!

Made by a human · ©2024

Thank you for making it here!

Made by a human · ©2024