DEV Community: not me

📊 Analyzing Manufacturing Downtime: Insights From My Dashboard

not me — Mon, 29 Sep 2025 03:07:58 +0000

I have created a dashboard to monitor and analyze manufacturing line productivity and downtime factors. The goal of this project was to identify inefficiencies, understand operator performance, and estimate potential improvements if operators shared their best practices.

This post summarizes some of my key findings.

🔹 Product Efficiency

Among all products in the dataset, OR-600 stands out with the highest line efficiency. This makes it a benchmark product to compare against others in terms of process stability and operator handling.

🔹 Operator Performance

When comparing operators, Mac appears to be underperforming compared to his peers, despite the fact that Charlie actually logs more total downtime.

This is really strange, so I check for non-operator downtime.

It confirms that he's underperforming compared to his peers.

But there's another thing that needs attention, type of downtime:

Batch Change:

Mac struggles significantly here, while the other operators show little difficulty. This impacts his overall performance.

Machine Adjustment:

Interestingly, this is where the story flips. Machine adjustment is the single leading cause of downtime across all operators, but Mac actually performs better than everyone else in handling it.

This reveals that each operator has different strengths and weaknesses, and no one operator is simply “bad” or “good.”

🔹 Leading Downtime Factor

From the dashboard, machine adjustment emerges as the most significant contributor to downtime.

Most operators struggle with it.
Mac, however, manages it well, giving him an edge in this particular area.

Understanding this factor is crucial, because any improvement in machine adjustment processes will directly translate into large efficiency gains for the entire line.

🔹 What If Operators Shared Knowledge?

Instead of working in silos, what if operators could share their expertise?

By simulating this “knowledge sharing” scenario, I estimate a potential efficiency gain of around 16%.

Here’s the math:

Current average cycle time per batch = 101 minutes
With knowledge sharing = 85 minutes per batch

That’s a reduction of 16 minutes per batch, which compounds into significant time savings at scale.

🔹 Takeaways

This analysis highlights a few key points:

OR-600 is the most efficient product line and can serve as a benchmark.
Operator performance is nuanced—Mac work more slowly and underperform in batch changes but excel in machine adjustments.
Machine adjustment is the top downtime factor, making it the biggest opportunity for improvement.
Knowledge sharing could reduce average cycle time per batch by 16%, a major gain for overall productivity.

📊 Analyzing Cafe Rewards Offers with Looker Studio

not me — Thu, 17 Jul 2025 04:09:30 +0000

I created a dashboard using Looker Studio to explore a dataset on coffee rewards offers. I'd like to share my approach and findings, and would love to hear your feedback!

The dataset, Cafe Rewards Offers, was provided by Maven Analytics. It contains information about customer interactions with different promotional offers, including demographics, transaction history, and offer completions.

🟢 Question 1: How many reward offers were completed, and which offers had the highest completion rate?

To answer this, I used a table that displays the characteristics of each offer alongside the number of times it was completed. Since the offers don’t have unique names, I included attributes like offer type, difficulty, duration, and channels to help distinguish them.

This required joining two tables:

Events table – contains records of completed offers
Offers table – provides characteristics of each offer

I filtered the events table to include only offer completed events and joined it with the offers table. I then used a pie chart to visualize the proportion of completions per offer type.

🧠 Insight:
Offers with the highest completion rates were discount offers that had:

High difficulty
Longer duration
Were delivered via social channels

🟢 Question 2: How many informational offers were followed by transactions?

For this, I created a blended data source by joining two versions of the events table:

One filtered for transactions
One filtered for informational offers

I then displayed the result in a table, following a similar structure to the first question.

🧠 Insight:
Informational offers that led to more transactions tended to:

Have shorter durations
Be delivered through social channels rather than web

🟢 Question 3: How is customer demographics distributed?

I used three pie charts to show the distribution of:

Age groups
Gender
Income levels

These charts use the customers table to display a demographic breakdown.

🧠 Insight:
The largest customer group consisted of:

Male users
Undefined age
Earning $50k–$80k annually

🟢 Question 4: Are there demographic patterns in offer completions?

To explore this, I blended the events table and offers table, filtering for only completed offers. Then, I used pie charts (similar to the previous question) to visualize the demographics of users who completed offers.

🧠 Insight:
Once again, the users with the highest offer completions were:

Male
Undefined age
Earning $50k–$80k annually

🔗 Explore the Dashboard Yourself

You can view the full dashboard here:
👉 Looker Studio Dashboard

Let me know what you think! I'm open to any suggestions on improving both the analysis and the visual design of the dashboard.

Analyzing U.S. Hospital Service Quality: Trends, Gaps, and Opportunities for Improvement

not me — Sun, 06 Jul 2025 07:08:37 +0000

Improving patient experience is a national priority for hospitals in the United States. From how clearly nurses communicate to how well discharge instructions are explained, every interaction matters — and it's often measured through standardized surveys.

In this post, I analyzed trends in hospital service ratings across the U.S., identifying which areas have stagnated and where opportunities for improvement still exist. The goal is to explore how hospitals can better prioritize the services that most influence patient satisfaction.

🧪 Dataset: The dataset used in this project is from Maven Analytics Website and is entirely synthetic

Through visualizations and correlation analysis, I’ll show where the system is stuck — and where there’s room to lead.

Overall, hospitals across the country have shown minimal improvement in service quality. While a few states lacking key resources made some progress, the majority of hospitals have either plateaued or declined in performance. In fact, many service ratings have worsened in 2023.

This trend suggests that hospitals have not made substantial progress in any single service area. If improvements had occurred, we would expect to see at least a modest increase in the national ratings. The following chart shows the top-performing states and national rating for each hospital service over time. While minor fluctuations exist, most services remain stagnant or have declined slightly in 2023, indicating a lack of consistent improvement.

Interestingly, states with smaller rural populations—such as New York (NY), New Jersey (NJ), and the District of Columbia (DC)—tend to have lower hospital performance scores. In contrast, states with more rural areas—such as Nebraska (NE), South Dakota (SD), Kansas (KS), and Iowa (IA)—are consistently among the top performers.

The next chart highlights how these top-performing states have maintained relatively higher ratings over time compared to the low-performing states.

This finding challenges common assumptions and suggests that rural-dominant states may be better at delivering certain aspects of hospital care. The comparison in the following chart reveals that states with higher rural population percentages tend to have better average hospital service ratings. On the other hand, more urbanized states like New York and New Jersey show lower performance, suggesting rural hospitals may offer more consistent patient experiences in certain areas.

Although hospital ratings are generally stagnant and showed a noticeable decline in 2023, this trend also reveals a significant opportunity. The consistently low scores across multiple service areas indicate room for systemic improvement.

The most impactful way to improve overall hospital ratings is to focus on each hospital's weaknesses and services that show the highest correlation with overall satisfaction. These include care transition, cleanliness of the hospital environment, communication with nurses, and responsiveness of hospital staff. The following heatmap illustrates the strength of correlation between each patient experience measure and the overall hospital rating, highlighting which areas hospitals should prioritize to drive improvements in patient satisfaction.

H _CLEAN_HSP - Communication with Nurses
H_COMP_1 - Communication with Doctors
H_COMP_2 - Responsiveness of Hospital Staff
H_COMP_3 - Communication about Medicines
H_COMP_5 - Discharge Information
H_COMP_6 - Care Transition
H_COMP_7 - Cleanliness of Hospital Environment
H_QUIET_HSP - Quietness of Hospital Environment

The data reveals a clear stagnation in patient experience across U.S. hospitals, but also uncovers key opportunities for improvement. By focusing on high-impact areas like care transition, cleanliness, communication with nurses, and staff responsiveness, hospitals can make meaningful progress. These insights—though drawn from a synthetic dataset—mirror real-world challenges and offer a roadmap for improving patient outcomes nationwide.

What Drives Success in Pixar Films? A Data-Driven Analysis of Box Office, Awards, and Trends

not me — Tue, 20 May 2025 10:14:52 +0000

Have you ever wondered...

What kind of Pixar films earn the most worldwide?
Do the highest-rated films also win the most awards?
Are sequels more profitable than their originals?
Has the type of stories Pixar tells changed over time?
And what about film ratings—have they evolved?

I explored these questions using a dataset of Pixar films, looking into box office earnings, ratings, awards, genres, and release trends. Here's what I found 👇

🎬 Which films have performed best at the box office? Did they have the highest budgets?

Among the top five highest-grossing Pixar films, all had a production budget of $200 million.

But that doesn’t mean high budgets always lead to huge box office returns. In several cases, lower-budget films outperformed more expensive ones, reminding us that storytelling, timing, and audience connection often play a bigger role than budget alone.

🏆 Which films received the most awards? Are they also the best rated?

The five most awarded Pixar films — Coco, Soul, The Incredibles, Toy Story 3, and Up — are all among the highest-rated, each with an average score above 80 across several review platforms.

This shows a strong alignment between critical acclaim and industry recognition.

🔁 How do sequels compare to their originals?

Out of 9 sequels that Pixar has produced, 7 of them earned significantly more at the box office than their original films. This indicates that while sequels often bring in bigger earnings, it's not guaranteed for every franchise.

⏳ Have genres and ratings evolved over time?

Over the last 20 years, Pixar's genre themes have remained consistent, continuing to explore family, adventure, and emotional storytelling.

However, there's been a shift in film ratings. In the most recent decade (2015–2024), there’s been a noticeable rise in PG-rated films, while G-rated films have declined sharply.

This trend is essentially a reversal of the pattern observed between 2005–2014, suggesting changing audience expectations and industry standards.

❓ Questions This Analysis Sparked

Analyzing this data left me wondering:

Why do some high-budget films underperform at the box office?
Can a low-budget Pixar film match the success of top earners like Toy Story 3?
What makes a sequel more successful than its original?
What’s driving the decline in G-rated content?

📌 Final Thoughts

Pixar films continue to set benchmarks for storytelling, visual innovation, and emotional impact. While budgets and sequels play a role, data shows that success isn't one-dimensional. By looking at earnings, ratings, awards, and trends over time, we can start to understand what makes a Pixar film memorable — or wildly successful.

Tools used: Microsoft Excel

Data source: Maven Analytics

Thanks for reading! Feel free to share your thoughts or favorite Pixar movie in the comments 👇