6 Ways Advanced Analytics Optimize Manufacturing Processes

Updated Date: 18 October 2024

Do You Want to Accelerate Your Manufacturing Operations?

Do you want to accurately predict customer demand? The key to getting real-time insights and visibility lies in integrating advanced analytics into your existing workflow. These analytics tools utilize the latest technology like AI and machine learning to analyze historical data and optimize business operations. Manufacturers, especially, can develop a strategy that can be more effective, economical, and optimized with advanced analytics.

According to McKinsey’s research, manufacturing companies that use advanced analytics have improved their operational productivity by at least 50%.

Optimizing manufacturing processes can help with revenue growth and meeting customer demands. So, in this blog post, we will talk about how advanced analytics is improving manufacturing processes, along with how it can help reduce waste and boost efficiency.

Traditional Manufacturing Process is Taking a Backseat: Here’s Why

Manufacturing processes have relied on traditional methods like manual assembly and paper-based record-keeping for a long time. But, as technology advances, these old ways are not enough to keep up with the demands of customers today. This is mostly because traditional methods offer inconsistent quality and lack real-time data, leading to slower production. Let's explore the challenges of traditional methods before talking about how advanced analytics could enhance industrial operations.

• Operational Inefficiencies: Traditional processes often waste time and resources due to manual tasks and outdated machinery.
• High Costs: Traditional operation costs can be quite high, which also includes labor, energy, and material wastage.
• Lack of Real-Time Data: Real-time data can help combat issues quickly, which are often lacking in traditional methods that heavily depend on manual processes.
• Reactive Maintenance: Traditionally, equipment is only fixed after it breaks down, which can lead to unexpected downtime and lost productivity.
• Data Silos: In the traditional format, crucial data is often scattered across various departments or systems. This makes it difficult to get a complete picture of manufacturing operations.

How Advanced Analytics Helps Manufacturers?

Advanced analytics is a combination of techniques that provide real-time visibility into every aspect of the manufacturing processes, including equipment and strategies. With advanced analytics, manufacturing companies can:

Improve Their Business Strategies by:

• Improving customer experience
• Driving innovation
• Monitoring market trends
• Predicting future risks and customer demands
• Assessing supply chain inefficiencies
• Future-proofing manufacturing operations

Reduce Costs by:

• Tracking raw materials
• Identifying and mitigating bottlenecks
• Reducing energy and waste
• Detecting early anomaly signs before they turn into costly failures
• Reducing downtime

Achieve Overall Equipment Effectiveness (OEE) by:

• Reducing the time taken for root cause analysis
• Improving the lifecycle of the equipment
• Enhancing maintenance procedures
• Increasing the overall product quality

Advanced Analytics Techniques that Help Manufacturing Processes

From AI to data mining, advanced analytics consists of a wide range of the latest techniques that can help businesses in multiple ways. The key techniques that can optimize various manufacturing processes for better efficiency and productivity are:

1. Artificial Intelligence (AI): AI can streamline operations in assembly lines, automate repetitive tasks in production, and enhance quality control by detecting issues in real-time.
2. Machine Learning (ML): ML can optimize production processes, improve production planning by predicting customer demands, and forecast equipment failures with the help of predictive maintenance.
3. Natural Language Processing (NLP): NLP can analyze machine logs, customer feedback, and maintenance reports to help manufacturers detect potential issues and create appropriate maintenance strategies.
4. Data Mining: Data mining can detect inefficiencies in production, predict customer demands, and enhance the quality of operational data by identifying future trends.
5. Predictive Analytics: Predictive analytics can forecast future demand changes or potential equipment failures, allowing manufacturers to adjust production schedules and prevent costly downtime.
6. Prescriptive Analytics: Prescriptive analytics helps managers choose the best course of action to reduce costs and increase productivity based on predictions.

How Advanced Analytics Optimizes Manufacturing

With advanced analytics, manufacturing companies can significantly improve their efficiency and productivity. From predicting equipment failures to forecasting customer demands, advanced analytics helps manufacturers make informed decisions. With AI and ML, they can ensure resources are used more effectively and reduce overproduction or underproduction. Here’s a detailed overview:

1. Improve Manufacturing Processes

Advanced analytics can spot inefficiencies and provide insights for potential improvement opportunities. It can recommend the best settings to evaluate the use of raw materials, machine performance, and production time.

Example: A factory that makes auto components can use machine learning (ML) to examine machine data and determine the best operating parameters for pressure, temperature, and speed. Manufacturers can decrease energy usage, guarantee consistent quality, and increase production speed.

2. Optimize Supply Chain

Advanced analytics helps manufacturers predict and optimizing supply chain problems to ensure that raw materials are available when needed. Suppliers can prevent understocking or overstocking of raw materials.

Example: An electronics manufacturer can use data mining techniques to determine a rise in demand for a particular component. This ensures they have enough inventory to meet production demands.

3. Real-Time Visibility

With advanced analytics, companies can get real-time insights into every step of the manufacturing process, from sourcing raw materials to product creation. This helps make smarter decisions, such as finding backup suppliers or modifying production schedules to avoid delays.

Example: A smartphone manufacturer can use data analytics to follow every step of production and identify possible reasons for delays.

4. Predictive Maintenance

Predictive analytics can forecast when a machine or any of its parts is likely to fail, allowing the manufacturer to repair the machine before a failure happens.

Example: A manufacturer can use AI to track temperature, vibration, and other sensor data to predict when a component will wear out and schedule maintenance to prevent unscheduled halts.

5. Energy Efficiency

Advanced analytics can help manufacturing companies reduce energy usage by identifying inefficient processes and suggesting ways to optimize energy consumption.

Example: In a steel manufacturing plant, advanced analytics can analyze data from sensors on furnaces to identify periods of high energy consumption and suggest adjustments to minimize energy wastage.

6. Demand Forecasting and Production Planning

Advanced analytics allows manufacturers to accurately forecast future demands using historical data, market trends, and other factors. This helps avoid overproduction or underproduction and ensures customer demands are met.

Example: A beverage manufacturer can use predictive analytics to predict demands for certain drinks during a particular season and adjust production accordingly.

Benefits of Advanced Analytics in Manufacturing

• Increase Efficiency: Advanced analytics helps produce more in less time with fewer resources by optimizing production processes, reducing downtime, and improving machine performance.
• Improve Product Quality: By monitoring the production process and identifying potential problems early, advanced analytics can help ensure consistent product quality.
• Reduce Cost: It helps lower operation costs with predictive maintenance, better energy management, and reduced waste.
• Make Informed and Faster Decisions: With real-time insights and automated tasks, manufacturers can improve response times and decision-making.
• Stay Ahead of Competitors: Advanced analytics can help manufacturers stay ahead of competitors by increasing productivity, reducing operational costs, and delivering high-quality products.

Don’t Lag Behind: Integrate Advanced Analytics in Your Manufacturing Process Now!

Many manufacturing companies are already using advanced analytics to increase efficiency and production. By utilizing techniques like AI, ML, and predictive analytics, manufacturers can gain deeper and more accurate insights into their operations to make smarter decisions.

The role of advanced analytics in manufacturing processes is growing, and with our expertise, you can integrate these technologies into your processes too.Cozentus’ custom solutions use advanced analytics to streamline production, optimize inventory, and enhance supply chain management. Here’s how:

1. We can create a custom model that can:
   • Predict when tools of a machine will break down by using CNC (Computer Numerical Control) machine data.
   • Test a product’s quality using image recognition and computer vision. If any defect is found, we can trace the root cause using production data.
   • Allocate required workforce to optimize production using historical production data.
2. For a manufacturing company of crucial products like semiconductors, we can analyze fluctuations in semiconductor wafer production and correlate process parameters with final yield to suggest modifications that maximize the yield.