Most manufacturers aren’t facing cost and process inefficiencies because they don’t have enough data to streamline their workflows. The problem is how fast they can leverage the data they generate to support real-time process improvements.
As a manufacturer, your plant manager could be waiting days to receive a report that would otherwise take minutes with the right BI system.
Even better, advanced analytics can help you connect data across departments, providing context for the inefficiencies and recommending fixes before the issues affect your production lines or output quality.
This guide breaks down what advanced analytics in manufacturing means, including how to roll it out for your plant, and the most essential types of analytics to monitor.
What Is Advanced Analytics in Manufacturing?
Advanced analytics simply means combining data from various information systems to generate holistic predictions in real-time, as opposed to creating separate reports from each source.
A common inquiry from manufacturers is how advanced analytics differs from traditional reporting. The main difference is who the output is built for.
Traditional reporting required more human input, i.e., someone has to pull the data, format it, and hand it off to the production team.
In contrast, advanced analytics involves formatting the data into a version that the end user understands, so that it is consumed directly by the production team. Additionally, advanced analytics incorporates memories to ensure consistent answers.
Let’s outline other differences between traditional reporting and advanced analytics in the table below:
| Dimension | Traditional Reporting | Advanced Analytics |
| Data sources | Single system (ERP or MES) | Cross-system (ERP, MES, IoT, QC) |
| Output | Descriptive charts and summaries | Predictions, recommendations, anomaly alerts |
| Timing | Retrospective (daily or weekly) | Real-time or near-real-time |
| Primary user | Data analyst or IT | Operations leader, plant manager |
Advanced analytics highlight anomalies and recommend improvements, meaning the production teams proceed straight to interventions instead of spending time interpreting the charts as with traditional reporting.
5 Types of Analytics for Manufacturing
As a manufacturer, your information systems probably generate hundreds of gigabytes every day, which you can leverage to address process inefficiencies. However, the real advantage comes from knowing how to analyze that data to support your decisions.
Advanced analytics support different types of analyses for the manufacturing industry, with each layer supporting a different decision level.
Let’s discuss the types of analytics in detail below:
Descriptive Analytics
Descriptive analytics explain what happened, such as during a production shift or throughout the week. An example of descriptive analytics would be a production summary showing that Machine 2 Line 3 ran at 7% below its target output.
Advanced analytics provide reports on most production KPIs, including:
- Production output
- Equipment utilization
- Cycle times
- Scrap rates
- Downtime
- Order fulfillment
As a production manager, you can use these reports to establish plant performance and production trends quickly.
Diagnostic Analytics
Diagnostic analytics provide context to descriptive analytics by answering the question: Why did it happen? Using the same example of dipped output on Machine 2 Line 3, advanced analytics cross-references the descriptive data with other recorded metrics, such as machine sensor logs, operator shift changes, and raw material batches, to isolate the most likely cause.
For example, the diagnostic analytics report may show that a specific supplier’s raw materials had a higher moisture content than recommended or the machine had been on standby for an hour following a safety incident, hence the performance disparity.
Predictive Analytics
As the name suggests, predictive analytics forecast what is most likely to happen next, following what happened in the past (descriptive analytics). Predictive analytics combine historical and real-time manufacturing data to identify patterns in equipment performance, production processes, and demand trends to help you anticipate future events before they occur. For example, the reports may show that Machine 2 Line 3 may continue operating below optimal levels for at least the next 14 days until the current raw material volume is exhausted.
Additionally, it may indicate how this may affect your product inventory levels over time.
Prescriptive Analytics
Prescriptive analytics explain what you should do about the highlighted issues to subvert the anticipated outcomes.
It may provide a cost vs. benefit analysis for different interventions to help you quickly determine the best-fit solution.
In the case of Machine 2 Line 3, the recommended actions may include:
- Adjusting the machine setting
- Rerouting production to a different line
- Reordering from a backup supplier
Proactive Analytics
Proactive analytics focuses on preventing ‘what happened’ from occurring again. Instead of waiting for the plant manager to run descriptive analytics to know where the issue was, the system flags anomalies as soon as they happen to support fast interventions.
For example, advanced analytics may have flagged the defective supplier’s raw materials for the high moisture content, allowing the production team to act fast, thus reducing the impact on Machine 2 Line 3’s output.
Many manufacturing systems are missing this layer of analytics, yet it is the difference between a production team that responds to operational breakdowns and one that prevents them before they occur.
How to Implement Advanced Analytics in Manufacturing
Rolling out advanced analytics in manufacturing should be in phases to reduce disruptions to operations.
Below is a step-by-step guide for integrating advanced analytics into a manufacturing system:
Phase 1: Build Your Data Foundation
The backbone of advanced analytics is creating a reliable database that ensures you only have a single source of truth. As such, consolidate your manufacturing data from the various systems you use, including ERP, MES, IoT, quality management systems, maintenance software, and inventory platforms, into a cloud-based warehouse.
Next, clean up the data to maintain accuracy and consistency by using standard data formats, eliminating duplicate records, and establishing governance policies.
Phase 2: Prioritize Use Cases by Impact
Identify high-value use cases that will deliver the most significant outcomes for your manufacturing process, as opposed to attempting to address every inefficiency simultaneously.
For example, you may prioritize operational challenges with the greatest financial impact, such as quality defects, production bottlenecks, or inaccurate demand forecasts.
Additionally, it is important to assess the suggested use cases for implementation complexity, available data, and expected return on investment, to ensure alignment with business goals.
Phase 3: Select the Right Platform
Research the available advanced analytics platforms and shortlist the most suitable options based on core features and client reviews.
Additionally, check that the platform is compatible with your data warehouse and needs very little modification, if any, as opposed to requiring you to overhaul your information system.
Phase 4: Run a Contained Pilot
Start by defining objectives for the pilot phase, such as reducing downtime, improving product quality, increasing throughput, or lowering maintenance costs, and establishing baseline performance metrics. You may then implement advanced analytics for the prioritized use cases on a single production line or shift and measure the results against your baseline data.
Additionally, collect feedback from operators, engineers, and maintenance teams, especially on whether the analytics are meeting their decision support needs.
Phase 5: Scale Across the Floor
Should the pilot phase produce consistent results, you can expand the advanced analytics to other lines and machines gradually until they cover your entire manufacturing line.
However, you should start by training your production team, including operators, supervisors, and engineers, to interpret the analytics outputs, as they apply to their departments. Additionally, you should standardize data collection, reporting, governance, and performance metrics to ensure consistency throughout the organization.
Plant managers and operators don’t need another dashboard; they need visibility into production lines and answers they can trust.
At Zenlytic, our platform provides a Clarity Engine that ensures your reports align with governed business logic and Artifacts that regenerate frequently to remain relevant to your live data.
Discover how Zenlytic helps manufacturing teams spend less time building reports and focus on acting on the customized insights to streamline production.
Advanced Analytics Examples Across Manufacturing
Advanced analytics transforms operational data into actionable insights that manufacturing teams can leverage to improve decision-making.
The following examples illustrate how manufacturing teams can benefit from applying advanced analytics in the real world:
Example 1: Predictive Maintenance
You can integrate your IoT sensors into your advanced analytics to monitor changes in vibration, temperature, pressure, and energy consumption on your CNC machines, conveyors, compressors, and industrial motors.
Advanced analytics combines real-time data from your sensors with historical data (maintenance records) to establish trends that signal potential equipment failure. As such, your maintenance team can replace parts before they break down completely, reducing disruptions on your production lines.
Example 2: Supply Chain and Inventory Optimization
You can use predictive analytics for inventory and demand forecasting to gain end-to-end visibility across your supply chain, including evaluating inventory levels, supplier performance, lead times, demand fluctuations, and logistics disruptions. This helps you detect anomalies that would be difficult to identify manually and intervene before they become bottlenecks.
For example, your procurement and supply chain teams could reduce disruptions by optimizing reorder points or diversifying supplier risks to reduce pressure on your production team.
Example 3: Real-Time Quality Optimization
You can analyze data from machine sensors, quality inspection systems, environmental sensors, and production equipment to detect process deviations before they affect product quality.
In response, the platform alerts operators and recommends adjustments such as modifying machine parameters or changing production speeds to maintain consistent output quality.
How to Choose the Right Advanced Analytics Platform
The right advanced analytics tool should help your team identify manufacturing anomalies in real time and provide a prescriptive analytics report to help the production team intervene before there’s a total operational breakdown.
Below are three foundational questions to help you build your selection criteria:
- What is the Current State of Our Data Warehouse?: If your team only uses a CRM and ERP, you only need an advanced analytics platform with forecasting capabilities.
- Who Needs the Answers?: Some advanced analytics platforms are built strictly for plant managers and departmental heads, while others provide support at multiple decision layers, including machine operators.
- How Will We Know We Can Trust the Data?: The best way to guarantee you can trust the output is to limit your search to platforms with strong data governance and semantic modeling, ensuring metrics mean the same across departments.
Once you have the answers, use the following criteria to help you pick the best-fit advanced analytics tool for your plant:
- Has Strong Reporting Capabilities: Go for a platform that can provide customized reports that meet your team’s decision support needs. Additionally, the platform should support self-service analytics, including being usable by non-technical users, to reduce incidents of team members routing questions to a data analyst.
- Confirm Seamless Integration: The advanced analytics platform should connect directly with your databases, spreadsheets, CRMs, ERP systems, and cloud applications, without needing extensive configurations.
- Check for Scalability: Pick an advanced analytics platform with the capacity to meet your reporting needs even as your operations grow, such as introducing new production lines or adding a secondary plant.
- AI-Powered Capabilities: Go for a platform that integrates AI capabilities, such as natural language queries, to help team members verify reports and find citations, reducing the time they spend validating the analytics manually.
Advanced Analytics vs. Other Analytics Approaches
Understanding how advanced analytics compares to other analytics approaches helps manufacturers build a technology stack that delivers both immediate operational insights and long-term competitive advantages.
Here’s how the various types of analytics compare:
Advanced Analytics vs. Predictive Analytics
Predictive analytics focuses on forecasting future outcomes using historical and real-time manufacturing data, making it a subset of advanced analytics. It complements advanced analytics by providing context to ‘what happened’ (diagnostic analytics) and providing foundational data for answering the question: what should we do about it?
This means that advanced analytics provides a broader/bigger picture view of manufacturing inefficiencies compared to predictive analytics, which only identifies the root cause.
Advanced Analytics vs. Augmented Analytics
Augmented analytics relies on artificial intelligence, machine learning, and natural language processing to make data reports easy to consume for non-technical users. It explains manufacturing output trends using simple-to-understand language. As such, manufacturers can use augmented analytics to make advanced analytics outputs more user-friendly, as opposed to selecting one over the other.
Advanced Analytics vs. Business Intelligence
Legacy BI tools were used to visualize already known performance metrics into a customizable dashboard. Some manufacturers have opted to power up their BI tools by embedding augmented analytics to mimic advanced analytics. However, this has made the analytics systems complex for many manufacturing teams, as you can replace the systems with a single advanced analytics platform.
Frequently Asked Questions (FAQs)
This section answers common questions manufacturers ask about advanced analytics.
What Is the Difference – AI vs. Advanced Analytics?
The main difference between AI and advanced analytics comes down to their disciplines. AI refers to multiple technologies, such as machine learning models and natural language processing, which are what power advanced analytics. On the other hand, advanced analytics refers to the process of using manufacturing data to monitor, diagnose, predict, and prescribe outcomes to improve production efficiency.
What Data Sources Feed Advanced Analytics in Manufacturing?
The most common data sources for advanced analytics in manufacturing include:
- ERP systems
- IoT sensors
- Quality control systems
- Manufacturing execution systems (MES platforms)
How Long Does It Take to See Results From a Manufacturing Analytics Initiative?
There is no timeline for how soon to expect results after implementing advanced analytics in manufacturing, as it depends on the available historical data and the extent of integration. However, most advanced analytics platforms can generate measurable results with a few production cycles.
Conclusion
Applying advanced analytics in manufacturing helps bridge the gap between the problems highlighted on your reporting dashboard and how long it takes to implement the remedies to reduce disruptions in your operations. Unfortunately, most legacy BI platforms don’t provide comprehensive predictive analysis or require additional systems to make their reports usable for non-technical teams.
We built Zenlytic to help manufacturers replace legacy systems with an advanced analytics platform that delivers finished answers, including citing every answer. Additionally, production teams can ask questions in natural language (plain English) and get consistent, easy-to-understand reports from the warehouse.