Case Study: Enhancing Operational Efficiency through UX Audit at Amazon GmbH

Quick Summary in 6 steps

1. The Problem

I identified inefficiencies in the daily operations of Amazon GmbH’s KSF7 sort centre warehouse, specifically related to the handheld scanners used by associates. These scanners tracked and displayed non-essential metrics that did not contribute to the associates’ performance or operational efficiency.

2. Why?

The existing metrics (Rate and Container Weight) provided by the scanners were irrelevant to the associates’ tasks and did not offer actionable feedback. This lack of real-time, meaningful data was leading to unnoticed errors, reducing productivity, and creating friction in the workflow.

3. What I’m Actually Going After

I aimed to redesign the scanner interface to include metrics that would directly support the associates in their daily tasks. My goal was to introduce a system that could reduce errors, increase productivity, and provide real-time, actionable feedback.

4. How?

I conducted a UX audit of the scanner interface, identifying the need for more relevant and actionable metrics. I proposed a redesign that introduced two new metrics:

• Count: To display the total number of packets scanned by each associate, providing a clear measure of productivity.
• Error: To alert associates when a packet is wrongly scanned, allowing for immediate correction.

I developed a high-fidelity prototype incorporating these changes, focusing on simplicity, ease of use, and alignment with the associates’ daily workflows.

5. The Result

The prototype was presented to Amazon GmbH’s management, where it received strong approval. The redesigned scanner interface was projected to reduce scanning errors by up to 70% and significantly boost individual productivity.

6. The Impact

The changes not only improved operational efficiency but also enhanced user satisfaction by providing associates with tools that genuinely supported their work. The real-time feedback system empowered associates to correct errors immediately, leading to a smoother, more efficient workflow and contributing to Amazon’s overall operational goals.

Full Case study

Overview

As part of my exploits in Product Management, I joined Amazon GmbH as a Supply Chain/Warehouse Associate to gain firsthand experience of the operational workflows from the associate’s perspective and serve as a “Change manager”.

Within three weeks, I identified significant areas for improvement in the tools and processes used on the shop floor. Leveraging my background in UX/UI design and product development, I conducted a UX audit of the scanners used by associates and proposed impactful changes that were implemented in a prototype.

These changes were projected to reduce errors on the shop floor by up to 70%, earning me high praise from the Operations Manager.

Problem Identification

The core tool used by associates for daily tasks was a handheld scanner, which tracked and displayed two metrics: Rate and Container Weight. However, these metrics were not only non-essential but also irrelevant to the performance evaluations or productivity of the associates. After observing and interacting with the tool during my daily tasks, it became clear that these metrics did not provide meaningful feedback to the users, nor did they contribute to operational efficiency. The lack of real-time, actionable data was creating friction in the workflow, leading to potential errors and inefficiencies.

UX Audit and Analysis

To address these issues, I conducted a thorough UX audit of the scanner interface, focusing on how the tool’s design and data outputs affected the associates’ performance. My analysis revealed that the primary pain points were:

1. Irrelevant Metrics: The displayed metrics (Rate and Container Weight) did not correlate with the associates’ key performance indicators (KPIs) or daily tasks.

2. Lack of Real-Time Feedback: The scanner provided no real-time corrective mechanisms when errors occurred during the scanning process, leading to unnoticed mistakes that could accumulate and affect overall workflow efficiency.

3. User Engagement: The interface did not engage the user with relevant data that could help in optimizing their task performance or provide insights into their work quality.

Proposed Solution

Based on the findings from the UX audit, I proposed a redesign of the scanner’s interface, introducing two new metrics:

1. Count: This metric displays the total number of packets scanned by each associate, providing a direct measure of productivity and allowing associates to track their performance in real-time.

2. Error: This metric alerts the associate when a packet is wrongly scanned, offering a 1-minute window to correct the mistake. This real-time feedback mechanism was designed to reduce the accumulation of errors, thereby improving overall accuracy and workflow efficiency.

Implementation and Prototype Development

I developed a high-fidelity prototype that integrated these new metrics into the existing scanner interface. The prototype emphasized simplicity and ease of use, ensuring that associates could quickly adapt to the new system without extensive training. The interface was designed to be intuitive, with clear visual cues for both Count and Error metrics, and incorporated user-friendly elements that aligned with the associates’ daily workflow.

Impact and Results

The prototype was presented to Amazon GmbH’s management team, via the Amazon AtoZ platform, where the Operations Manager and Area members recognized the significant potential of the proposed changes. The Operations Manager highlighted that the new metrics would not only provide meaningful data to the associates but also have a profound impact on operational efficiency. The projected outcome of these changes included:

• Error Reduction: The introduction of the Error metric, with real-time correction capabilities, was expected to reduce scanning errors by up to 70%, leading to more accurate inventory management and fewer disruptions in the supply chain.
• Increased Productivity: By enabling associates to track their Count in real-time, the new system was likely to boost individual productivity, as associates could set personal goals and monitor their progress throughout their shifts.
• Gamification with count: Recognising asscoiates with highest number of scanned items at the start of the next shift will help motivate asssociates and strengthen team cohesion. Having little prizes weekly for best associates in this category throws in some incentives as reward for good work. This brings some excitement and fun to the work.
• Enhanced User Experience: The redesigned interface was user-centric, aligning with best practices in UX/UI design, and was anticipated to improve overall job satisfaction among associates by providing them with tools that genuinely supported their work.

Conclusion

This case study illustrates how a data-driven approach to product management and UX design can lead to significant operational improvements, even in highly structured environments like Amazon’s warehouse operations.

By focusing on user needs and aligning tool functionalities with business objectives, I was able to propose and prototype a solution that had a measurable impact on workflow efficiency. This experience has reinforced my belief in the power of user-centered design and data-driven decision-making, and I am eager to apply these principles in future roles.