How to Create a Manufacturing Dashboard in Looker with AI
Getting a clear, real-time picture of your factory floor can feel like trying to solve a complex puzzle in the dark. You know the data is there — in your MES, ERP, and scattered across spreadsheets — but turning it into actionable insights is slow and complicated. This article guides you on how to change that by building a manufacturing dashboard in Looker and shows you how new AI tools are making this process faster and more accessible than ever before.
Why a Manufacturing Dashboard is a Game-Changer
A manufacturing dashboard isn't just a collection of charts, it's a command center for your entire production line. It moves you from making decisions based on week-old reports and gut feelings to reacting instantly to what's happening on the floor. Think about it: instead of learning about a production bottleneck during a Tuesday morning meeting, you see it developing live on a screen.
With a centralized, real-time dashboard, you can:
Spot and Solve Problems Faster: A sudden spike in the defect rate or an unexpected machine slowdown becomes immediately visible, allowing you to react in minutes, not days.
Optimize Production Efficiency: By tracking metrics like cycle time and Overall Equipment Effectiveness (OEE), you can identify which machines or shifts are underperforming and find opportunities to improve output.
Reduce Operating Costs: Monitoring metrics like machine downtime and scrap rates helps you pinpoint sources of waste, enabling you to make targeted improvements that directly impact your bottom line.
Improve Quality Control: Live tracking of yields and defect rates lets you maintain higher quality standards and catch issues before they affect a large batch of products.
Ultimately, a dashboard transforms raw operational data into a powerful tool for making smarter, faster business decisions.
Key Metrics for Your Manufacturing Dashboard
A great dashboard only tells a clear story if it’s tracking the right things. While the specifics can vary based on your industry, most high-impact manufacturing dashboards keep a close eye on a core set of Key Performance Indicators (KPIs). Here are some of the most essential ones to include.
Overall Equipment Effectiveness (OEE)
OEE is the gold standard for measuring manufacturing productivity. It combines three critical factors into a single score:
Availability: The amount of time your equipment is actually running versus its planned production time.
Performance: How fast your machines are running compared to their designed speed.
Quality: The number of good parts produced versus the total parts started.
A perfect OEE score of 100% means you are manufacturing only good parts, as fast as possible, with no stop time. It’s an excellent high-level metric for understanding overall efficiency.
Cycle Time
This metric measures the average time it takes to produce one complete unit, from the start of the process to the end. Monitoring cycle time helps you understand your production capacity, identify process inefficiencies, and provide more accurate customer delivery estimates.
Production Volume
A simple but fundamental metric, production volume tracks the total number of units produced over a specific period (e.g., per hour, shift, or day). It gives you a clear and immediate picture of your output and helps you assess whether you're meeting production targets.
Defect Rate / First Pass Yield
First Pass Yield (FPY) measures the percentage of units that are manufactured correctly and meet quality standards the first time through the process, without any rework. The flip side, Defect Rate, tracks the percentage of parts that are scrapped or require rework. Tracking this KPI is vital for monitoring quality and identifying problems in the production process.
On-Time Delivery Rate
This metric measures the percentage of orders delivered to customers on or before the promised delivery date. It’s a crucial indicator of customer satisfaction and operational efficiency, reflecting the performance of your entire supply chain, from production scheduling to shipping.
Machine Downtime
Downtime tracks the amount of time that equipment is not operational. Your dashboard should let you categorize downtime by reason (e.g., unplanned maintenance, tooling changes, operator breaks) to help you find the root causes of lost productivity and work on preventative solutions.
Gathering Your Data: The Foundation of Your Looker Dashboard
Before you can build visualizations in Looker, you need to bring your data together. For manufacturers, this data is often locked away in a variety of systems:
Manufacturing Execution Systems (MES): The core system that tracks and manages the entire production process.
Enterprise Resource Planning (ERP): Systems like SAP or Oracle that handle inventory, orders, and supply chain logistics.
SCADA and IoT Systems: Sensors and controllers on the factory floor that generate streams of machine-level data.
Quality Management Systems (QMS): Databases that store information on inspections, compliance, and defects.
Looker doesn't store this data itself. Instead, it connects to a database or data warehouse where you've consolidated it. This typically means setting up a process (often called ETL — Extract, Transform, Load) to pull data from your sources and load it into a central repository like Google BigQuery, Snowflake, or Amazon Redshift. Having your data cleaned and centralized is the most critical first step.
Building Your Manufacturing Dashboard in Looker: A Step-by-Step Guide
Once your data is in a connected data warehouse, you can start building your dashboard inside Looker. Traditionally, this process involves a few distinct stages.
Step 1: Connecting to Your Data Warehouse
The first step is establishing a connection between Looker and your database. Looker has built-in connectors for dozens of popular databases, and the setup process is typically straightforward, involving you providing credentials and connection details to point Looker to your data.
Step 2: Defining Your KPIs in LookML
This is where Looker's real power — and complexity — lives. Looker uses a modeling language called LookML to define your business logic. Think of LookML as a dictionary that you create to tell Looker what your data actually means. You'll write LookML code to define dimensions (the attributes you group by, like ‘Product Line’ or ‘Shift’) and measures (the numbers you calculate, like ‘Average Cycle Time’ or ‘Total Units Produced’). This ensures everyone in the company is using the same definitions for key metrics, creating a single source of truth.
Step 3: Creating Individual Charts (Looks)
With your LookML model in place, business users can start exploring the data without writing SQL. In Looker's "Explore" interface, you can select dimensions and measures, add filters, and create a single visualization, known as a "Look." This could be a line chart showing production volume over time, a pie chart of downtime reasons, or a single number showing your current OEE.
Step 4: Assembling Your Dashboard
Finally, a dashboard is simply a collection of these Looks arranged on a single page. You can drag and drop your saved Looks onto a new dashboard canvas, resize them, arrange them logically, and add filters that allow users to drill down — for example, filtering the entire dashboard to view data for a specific production line or time frame.
The AI Advantage: Building Dashboards Faster
The traditional Looker workflow is incredibly powerful but comes with a steep learning curve. Writing LookML requires technical skills, which means non-technical team members often have to wait for a data analyst to model the data or build new reports. This is where AI changes the game.
From Code to Conversation
New AI-driven analytics tools remove the technical bottlenecks. Instead of writing code or navigating complex menus, you can simply ask for what you need in plain English. Imagine typing a prompt like:
“Create a bar chart showing the five products with the highest defect rates last month.”
The AI can interpret your request, identify the necessary data, and generate the chart for you instantly. This turns hours of technical work into a 30-second task, bypassing the need for a deep understanding of LookML or Looker's interface. It understands requests like "phone traffic" and knows you mean "mobile devices," making data interaction incredibly intuitive.
No More Waiting on a Data Analyst
AI democratizes data access. With a natural-language interface, your plant manager, shift supervisor, or quality control specialist can get the insights they need on their own. Instead of filing a ticket with the data team and waiting days for a new report, they can ask follow-up questions in real-time. A chart showing a spike in downtime can immediately be followed by the question, “What were the main reasons for downtime on production line B yesterday?” This rapid, interactive exploration leads to faster problem-solving and a more data-driven culture.
Deeper Insights, Instantly
One of the best side effects of this speed is the ability to follow your curiosity. A dashboard often raises more questions than it answers. When you spot an interesting trend, AI allows you to instantly drill down and explore it. This turns data analysis from a static reporting task into a dynamic conversation where each answer sparks a new question, helping you uncover underlying issues you might otherwise have missed.
Final Thoughts
Building a manufacturing dashboard in a tool like Looker provides unmatched visibility into your operations, trading guesswork for data-driven precision. These dashboards are your key to boosting efficiency, improving quality, and lowering costs, and new developments in AI are removing the traditional technical hurdles, making these insights more accessible than ever before.
We are focused on this shift from complexity to simplicity. At Graphed target="_blank" rel="noopener" we’ve built an experience that turns data analysis into a conversation. Instead of battling with complex setups or learning a new BI tool, you connect your data sources — even a simple Google Sheet fed by your MES — and just ask your questions in plain English. We instantly generate real-time dashboards and reports, so you can stop wrestling with data and get back to making decisions that grow your business.