Unit of Measurement Power App
Role
Lead Designer, End-to-end UX/UI and interaction designer
Team
Cross-functional collaboration with IT leadership, business analysis, and engineering.
Scope
Single enterprise application · Data normalization & governance · Conversion management
Timeline
Short- cycle delivery . 3 Weeks
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Impact
Pricing Accuracy
Improved pricing and budget accuracy by standardizing per-unit cost calculations across purchasing and forecasting workflows.
Data Governance
Enabled data stewards to govern conversion accuracy through a centralized workflow, improving long-term data integrity and trust.
Single Source of Truth
Established a single source of truth for unit-of-measure conversions, reducing ambiguity and inconsistency across vendor and manufacturer data.
Reduced Ordering Risk
Supported downstream purchasing, budgeting, and forecasting decisions with normalized data, improving cost control and reducing variance between estimated and actual spend.
More Accurate Material Planning
Gave procurement teams consistent quantities across 300+ vendors, improving material planning and reducing the risk of both excess inventory and supply shortages.
Project Overview
A centralized Power App designed to standardize units of measure across vendors and manufacturers, improving pricing accuracy and consistency throughout the organization.
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Standardize inconsistent units of measure into a common base unit.
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Normalize vendor and manufacturer packaging for accurate comparisons.
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Improve per-unit pricing accuracy across purchasing decisions.
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Give data stewards control over conversion rules and data quality.
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Support procurement, finance, and operations with consistent, reliable data.
The Challenge
Procurement and Data Steward teams faced two layers of inconsistency:
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manufacturer UOMs and vendor-specific purchasing units.
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Manufacturers defined materials by pack, box, pallet, spool, or other units, while vendors could repackage the same materials using their own units and quantities.
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This required procurement to manually convert quantities and calculate a comparable price per unit, such as converting cable to price per foot. The process was time-consuming and error-prone, increasing the risk of incorrect vendor comparisons, over-ordering and excess costs, or under-ordering and material shortages.
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Business Requirements
Standardizing unit-of-measure data to enable accurate pricing, fair vendor comparison, and governed procurement decisions at scale.
Requirement
Goal
Unit Normalization
Convert vendor-specific units into standardized base units.
Pricing Accuracy
Calculate reliable per-unit costs for fair comparisons.
Procurement Support
Help teams make purchasing decisions using consistent pricing data.
Data Governance
Manage conversion rules through a centralized, controlled process.
Operational Consistency
Ensure standardized unit data across procurement, finance, and operations.
Early Exploration: Separate Role-Based Views
At the initial stage of the project, the business requested separate views for data stewards and procurement. Each experience reflected their workflows, procurement focused on vendor pricing and comparison, while data stewards worked through validation, error handling, and conversion logic across multiple data sources.
Below are the initial rough drafts for the two separate views.
Why this made sense initially
This approach aligned with how teams were structured, allowing each role to focus on their specific responsibilities without unnecessary complexity.
What we learned from early exploration
As we got deeper into the workflows and talked through them with stakeholders, it became clear that splitting the experience was creating friction. Users needed visibility across both sides, and jumping between views made it harder to validate data, compare information, and stay in context.
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This decision reduced the need for context switching and allowed users to access all relevant information in one place. It also supported better collaboration between roles by increasing visibility and aligning workflows around shared data.
Design Shift: Unified Experience
To support both roles in a single experience, I broke the screen into clear sections, validation at the top, invoice context on the left, and UOM definitions below. Combined with filtering and flexible layouts, this lets users focus on their task while still having full context when they need it.
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This shift resulted in a more cohesive workflow, reducing friction and making it easier for teams to validate, compare, and act on data in one place.
Design Focus
Instead of adding more workflows, I focused on removing the confusion around pricing by standardizing how units are defined, converted, and shown. I kept the interface intentionally minimal, focusing on accuracy and consistency so teams can rely on the numbers without having to double-check or interpret them manually.
This screen was designed to support how power users work with large datasets.
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It gives them the ability to quickly filter and narrow results while maintaining clear visibility.
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The left panel can be collapsed to free up space for the table, especially when more fields are added, helping reduce the need for horizontal scrolling.
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Color is used to provide instant feedback on changes, making it easier for users to understand what’s been updated.
These decisions were intentional to make the experience more efficient and user-friendly.
I designed this search to use field-specific queries, so users can target a specific attribute; like invoice, UPC, or part number; instead of running a broad query across everything. Narrowing the query scope like this improves accuracy, reduces noise, and helps them get to the right results faster.
This is a predictive search that surfaces results as users type, handling both exact and close matches. Even partial input brings up relevant options, making it faster to find the right vendor.
What this Enabled
There wasn’t a centralized system in place before this, unit conversions were handled manually and inconsistently across teams. The introduction of a governed approach to unit-of-measure normalization, made pricing, budgeting, and procurement decisions much more reliable. It brought conversions into a clear system of record, reducing errors and giving data stewards more control. More importantly, it set a foundation for consistent, scalable analysis across vendors, projects, and forecasting, helping teams choose the right vendor, buy the correct quantities, and avoid overpaying or over-ordering.
Before
Solution
Impact
Unit conversions handled manually and inconsistently across projecs and teams.
A centralized approach that created consistency and control.
