IIoT and ML: The Secret to Reducing Waste and Maximizing ROI in Manufacturing

IIoT and ML: The Secret to Reducing Waste and Maximizing ROI in Manufacturing | SPONSORED

For modern manufacturers to stay relevant in an ever-expanding global market, they have to overcome a plethora of challenges that demand innovative solutions. Rising costs of raw materials and energy have placed considerable strain on profit margins, causing manufacturers to adopt more cost-efficient strategies. Concurrently, there is an increasing emphasis on sustainable practices that minimize environmental impact while maintaining production efficiency, requiring businesses to adopt practices that reduce waste and utilize resources more efficiently. To address these challenges, manufacturers must adopt advanced technologies, such as IIoT and machine learning (ML), to enhance efficiency, maintain a competitive edge, and positively impact their bottom line.

The industrial Internet of Things (IIoT) in manufacturing is a network of interconnected physical devices (or objects). These connected devices have sensors, software, and connectivity that allow them to monitor and control the production process by collecting, sharing, and exchanging data in real-time, laying the groundwork for the integration of machine learning.

Machine learning (ML), a subset of artificial intelligence (AI), functions as the cognitive engine that powers smart systems within IIoT. ML algorithms complement IIoT by learning from the large amounts of data generated by these interconnected devices, making them a good fit for dynamic industrial environments. ML applied in IIoT is found in predictive maintenance, anomaly detection, and real-time decision support.

According to McKinsey, the fourth industrial revolution (industry 4.0) is projected to generate $3.7 trillion in value in 2025. This proposition is based on the premise that AI/ML is continuously being integrated into manufacturing operations via IIoT. By integrating IIoT and ML, manufacturers can improve the productivity and quality of industrial processes, reduce costs associated with waste and inefficiencies, and maximize return on investment (ROI).

Understanding the problem: waste and inefficiency in manufacturing

Material Waste

Manufacturers deal with different types of waste, including:

• Overproduction: Producing more products than needed or producing them too early, results in increased storage and inventory costs. An example of overproduction is producing 100 units of a product when only 80 are needed, resulting in 20 units stored, thereby tying up valuable resources.
• Excess Inventory: This is the lost revenue created by unprocessed or unsold inventory. Waste from excess inventory can also be storage waste, waste of capital held in unprocessed inventory, and any other wasted resources associated with the entire production process and storage of goods.
• Scrap: The quality team must rework or scrap defective products that fail quality standards. For example, manufacturers must fix or discard products that don’t meet specifications.

Common causes of material waste include human error, equipment malfunction, and poor demand forecasting.

Operational Inefficiencies

Manufacturing operations lose money through operational inefficiencies, similar to how they lose it through waste.

• Inefficient machine scheduling and downtime: When a piece of equipment is not in operation, the manufacturer’s ability to meet production targets or deadlines can negatively affect its bottom line.
• Poor resource allocation and energy consumption: This includes inefficient use of labor, materials, equipment, and energy management.

Manufacturing operations lose money through operational inefficiencies, similar to how they lose it through waste.

Role of IIoT in waste reduction and process optimization

Real-Time Data Collection and Monitoring

As the backbone of IIoT systems, IIoT sensors collect data from the physical environment and send it to the cloud or edge device for analysis and action. Unlike traditional methods that rely on periodic checks by maintenance technicians, industrial IIoT sensors offer round-the-clock monitoring of critical assets. This ensures that equipment stays operational and minor issues are caught before they escalate into major breakdowns.

Advanced Connectivity and Communication

IIoT devices and systems communicate and exchange data continuously due to their interconnected nature in industrial environments.

For example, manufacturers use IIoT systems to track assets with RFID tags that serve as asset identifiers. The cloud stores data linked to these identifiers, including serial numbers, models, costs, and areas of use. Advanced algorithms encrypt all connections between end devices and the IIoT remote gateway or the central IIoT gateway—securing and streamlining the system.

Predictive Maintenance

As one of the most significant trends for business processes in industrial environments, manufacturers can utilize IIoT technology to monitor the health of equipment through IIoT devices and sensors, which detect early signs of wear or failure before they occur. In contrast to the traditional reactive or preventive approach, this proactive strategy saves time and reduces maintenance costs. Furthermore, it minimizes unplanned downtime, enhances equipment uptime, and ultimately extends the life cycle of aging machinery.

Leveraging machine learning for smarter decisions

Anomaly Detection and Quality Control

Machine learning models can efficiently analyze real-time production data to detect anomalies in network traffic and deviations from quality standards. By identifying defects early, manufacturers can reduce waste and continuously improve product quality.

Demand Forecasting and Inventory Optimization

Machine learning models analyze historical and real-time data to predict customer preferences and forecast demand. Manufacturers use predictive analytics to focus on product ideas that align with consumer interests, streamlining the design process, reducing development time, and optimizing inventory levels.

Process Automation and Adaptive Control

The growing adoption of smart technologies in manufacturing has reduced dependence on manually performed tasks. Manufacturers use machine learning to automate industrial processes, streamline workflows, and adjust production conditions in real-time to ensure quality and efficiency.

How InfluxDB 3 Powers IIoT and ML in Manufacturing

Time Series Data: The Foundation of IIoT and ML Solutions

In manufacturing, time series data captures metrics like temperature and pressure over time, making it an essential part of modern manufacturing. InfluxDB 3 supports the full range of time series data. InfluxDB Cloud stores all your time series data, including metrics, events, and traces. Consequently, it efficiently manages the large volumes of sensor data that IIoT and ML systems generate in industrial settings. As a result, manufacturers gain real-time visibility into their operations, enabling them to quickly monitor and respond to changes and enhance their operational efficiency.

In manufacturing, time series data captures metrics like temperature and pressure over time, making it an essential part of modern manufacturing.

Scalability and High Performance

InfluxDB 3 makes it easier to manage time series workloads at any scale. InfluxDB Clustered lets you scale ingestors for heavy ingestion, queriers for heavy querying, or both. Its suitability for IIoT lies in efficiently handling large time series data for predictive analytics and maintenance.

Ease of Integration with Machine Learning Models

InfluxDB 3 ingests and processes data in real-time. This capability bridges the gap between real-time operations and analytical tools in industrial settings. Teams can use InfluxDB for time series operational workloads. They can also leverage data access virtualization to train AI/ML models and run analytics in existing data lakehouses. This integration with machine learning models will improve decision-making and enhance operational efficiency across production processes.

Final thoughts

IIoT and ML are changing the manufacturing industry, addressing the challenges of waste and inefficiency. Using real-time data, predictive maintenance, automation, and advanced connectivity, manufacturers can reduce costs, boost productivity, and improve profits.

As a time series database, InfluxDB 3 is a strong proponent of smart manufacturing. Through features like real-time ingestion, scalability, and precision, it offers efficient tools that capture, store, and analyze real-time data. This makes it easier for manufacturers to run more efficient and profitable operations.

Manufacturers that need to optimize manufacturing, minimize waste, and maximize profits should check out the InfluxDB 3 product suite (InfluxDB Cloud Serverless, InfluxDB Cloud Dedicated, and InfluxDB Clustered) or contact the sales team to get started.

Sponsored by InfluxData