Tips On How Manufacturers Can Take Advantage of IOT, Machine Learning and Big Data

Industrial ioT For Manufacturers | IIOT For Manufacturers

Internet of things (IoT) adoption from the manufacturing business has been happening at a measured pace, but awareness is becoming more powerful. In 2018, only 10 percent of companies deployed industrial IoT applications.

But 34% of industry players are deep into the consideration stage: tracking developments, but not being active with the projects just yet.

The subsequent five years, however, are poised to become crucial in terms of growth. The global IoT in the manufacturing sector is expected to witness a significant CAGR of 29.4% within the 2017-2024 period. Such exceptional growth can be attributed to several factors:

Increased digitization among enterprise manufacturers and a growing number of SMEs, building their operations employing advanced tech stack from day one.

A large percentage of modern ERP systems are already IoT-ready and include gateways to get information from sensors installed in gear, making IoT adoption the upcoming logical step towards developing a greater line of command.

Fast advances in machine learning, and profound learning, in particular, enable companies to strategically automate low-value procedures and streamline the manufacturing pipeline with the assistance of AI. New algorithms are capable of operationalizing new kinds of data including audio, video and image cues — the data that is off-limits into the majority of traditional analytics solutions.

IoT interoperability conundrum is being addressed by blockchain. Lack of unified protocols for securely storing and transmitting data has been slowing down IoT adoption across the B2C and B2B verticals.

Blockchain: an immutable digital ledger technology — has emerged as a new protocol for seamless and fast data exchanges inside large-scale networks of connected gadgets. Together, blockchain and IoT can revolutionize supply chain management; enhance track and traceability, warranty management and maintenance, repair and overhaul (MRO) processes.

So, what is the Web of Things role in the future of manufacturing?

Big Data and Machine Learning: The 2 Pillars of IoT-Based Solutions

The Internet of Things and the future of manufacturing are strongly connected to the businesses’ abilities to operationalize Big Data.

The majority of enterprises already has certain analytics capabilities to “crunch” proprietary data. Unfortunately, the problem, however, is that legacy solutions are no longer fast or effective enough per se.

For example, legacy monitoring systems can only locate failures in a few hours before they occur. They come with a limited capacity and visibility to the entire manufacturing process, and can simply identify failures at certain verticals. Creating custom engineering algorithms with predictive functionality for each procedure type is a lengthy and cost-prohibitive endeavor for many businesses.

Machine learning methods effectively address these flaws. When connected with the proprietary data warehouse, ML-algorithms can provide near-real-time insights from the data at hand. Specifically, the key benefits of machine learning approaches to manufacturing reside in the following areas:

  • Greater adaptability to different asset families;
  • Easy-to-scale across the whole plant;
  • Rapid model development (and self-optimization over time if profound learning is applied);
  • Flexible deployment approaches;
  • Proprietary historical and infrastructure data gets unclogged and used to maximize effectiveness.

IoT gadgets, powered by ML and connected to Big Data analytics programs, can instantly notify your teams about any abnormalities from the equipment performance such as changes in pressure, temperature, vibration and so forth. 

This provides enterprises an opportunity to make operational predictions around 20 days earlier and with greater accuracy when compared to traditional BI tools.

So, let us take a better look at the successful Internet of Thing manufacturing examples and use cases.

IoT has the potential to completely revamp the current assembly line arrangement, just like Ford’s new approach did back in the day. Fully digital factories controlled and managed remotely have already come into existence.

The Internet of Things in manufacturing can enable higher visibility to the production line – from packing to shipment of the final product. Sensors can deliver real-time data that may be further analyzed and translated to better process management and price optimization suggestions. Furthermore, IoT can provide stakeholders with information about lags in production; helping further reduce the waste and unnecessary inventory.

The benefits of IoT further expand towards supply chain management, allowing manufacturers to optimize the delivery process; gain additional visibility to the products are handled by the distributors and at what stages the bottlenecks happen.

All of that information can be “fed” to ERM, PLM and other proprietary systems for analysis. Capturing such data from providers can help you locate the key interdependencies, call when certain issues may arise and optimize manufacturing cycle times.

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