Autonomous Monitoring in Manufacturing Industry. All you need to know about it

Exercising ways to reduce operating and labor costs has been a practice that industries have been following for a long time. However, the approach has redefined itself largely in the last few years. Today, advanced technologies like IoT, advanced automation, smart sensors, and Artificial Intelligence are creating new benchmarks in the segment by introducing automation like never before.

The Fourth Industrial Revolution (Industry 4.0) has blurred the lines between the physical, biological, and digital worlds. Industry 4.0 calls for autonomous and cognitive solutions that can manage the entire production process with its unbiased influence. Research by Mckinsey predicts that Industry 4.0 technologies are expected to create a market of $3.7 trillion by 2025. Out of which, AI alone is expected to generate upto $2 trillion in revenue.

Another report reveals that with the help of predictive analytics, industries can improve forecasting by 10-20%. This can eventually help decrease inventory cost by 5% and increase revenue by 2-3%.

Predictive analysis is just a form in which manufacturing industries can use AI. Autonomous monitoring does not just allow manufacturers to monitor their facility and productivity in realtime; it also collects a large number of operational data, including:

  • Tracking core KPIs like production rate, Overall Equipment Effectiveness (OEE), and production or scrap rate.
  • Avoid missing deadlines with precise forecasting.
  • Predict possible disruptions in the supply chain.
  • Resolving barriers in the production cycle
  • Identify and resolve equipment inefficiencies as and when needed.
 

Artificial Intelligence’s impact on the manufacturing industry has been game-changing. Numerous industrial units globally have adopted and noticed a wide range of benefits of autonomous monitoring.

Some Use cases of AI in manufacturing industries

1- Danone Group: The France-based food manufacturer uses AI for demand forecasting. The brand showed an exceptional fall in lost sales, forecasting errors, and demand planners’ workload.

2- Fanuc: It’s a Japanese automation company. The firm uses robotic workers to work at its factories to produce essential components for CNCs and Motors.

3- BMW Group: The automotive manufacturing company uses automated image recognition for inspections, quality checks, and eliminating any deviations from a target without any actual faults.

4- Porsche: Another automotive manufacturing company that uses AGVs (Automated Guided Vehicles) to automate crucial parts of their manufacturing process.

Benefits of autonomous monitoring in the manufacturing industry

Demand planning and forecasting

Machine learning algorithms can automate the detection and analysis of data faster and precisely compared to any human. For AI, any data is beyond just a cluster of keywords. It utilizes the same data to plan and forecast possible fluctuations in demand. This analysis helps you seamlessly reduce errors in the supply chain.

According to a McKinsey report, AI-powered forecasting can help reduce supply chain network errors by upto 50%. This will eventually result in a loss in sales due to out-of-stock inventory by upto 65% and decrease warehouse costs by 10-40%.

Acknowledging these figures, it’s undeniable that AI plays a vital role in transforming the traditional manufacturing modus operandi. By using the technology, companies can leverage faster and more precise forecasting. This is just the tip of the iceberg; multiple benefits follow data-backed forecastings like low buffer stocks, reduced working capital, lesser space consumption in warehouses, improved transport planning, optimized labor schedules, etc.

AI doesn’t just play a pivotal role in the way industries manufacture; it also helps evaluate what to manufacture. The smartly crafted algorithms can help identify changing consumer tastes, an inevitable part of demand forecasting. One example here can be packaged-food companies. They can reach and switch to certain ingredients to resonate with changing demands and have them as limited-specials.

Development and maintenance

Manually managing and maintaining your machinery can be a tedious task in the context of time and money. Besides, it also involves a major risk of equipment malfunction resulting in heavy loss of productivity and production schedule. Perhaps this is one reason humans predicting maintenance will fail compared to AI-based predictions.

Sensors collect data from machinery based on heat, movement, and vibrations. Simultaneously PLC (Programmable Logic Controller) tracks data related to machine inputs and outputs, and computer vision data is tracked using cameras installed across the factory. While the time-series data evaluate the machine’s condition by determining its history, external data sources like knock-on effects from related equipment or weather conditions are also considered. The collected data is then utilized for training models, optimizing production lines, and product development.

AI allows manufacturers to take required steps on time to upgrade equipment. Machine Learning algorithms like supervised and unsupervised learning can use data on a realtime basis. Before the blink of an eye, the system can identify previous products, processes, and workflow by using the collected data.

Communication strategies

AI doesn’t just help streamline operations; it can also assist in establishing communication channels by detecting patterns from different sources. This includes image, audio, and video sources to enhance communication between employees and customers. AI chatbots can help businesses save a lot of time.

By implementing chatbots, companies can alleviate repetitive efforts by call center teams or sales professionals while resolving/answering sales queries and boosting customer acquisition. Using chatbots and other tools, you can get acquainted with maintenance cycles and identify potential issues and upgrades required by your machinery.

You can also notify and serve your customers with solutions before they even contact you by automating the communication process. This will help you deliver exceptional customer service resulting in better brand goodwill and customer loyalty. With advancements in technology, companies can establish better connectivity among devices.

The same can be utilized through data analysis and automation. Businesses can use cloud applications to pool data in one accessible space. The information can be collected from material outsourcing data, fulfillment and return records, or FAQs by customers. This will help track customer concerns with a product and timely resolve them by managing the production and maintenance.

Concluding Note

Digitization has taken a peak during and after the Covid times. According to a report, around 43% of manufacturers in the United Kingdom believe that relying on traditional manufacturing technologies increases risks. The same report reveals that out of 95% of manufacturers that got negatively impacted due to the pandemic, 82% are now prepared to deal with such an event in the future.

The confidence is undoubtedly due to the vast adoption of technology and its unbiased results.

Not just huge manufacturing units like Siemens and Canon, even startups adopt AI to streamline their production cycle and enhance productivity.

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