5 TECHNOLOGY TRENDS THAT WILL FORTIFY DIGITALIZATION IN MANUFACTURING INDUSTRY
In the previous blog post, we have talked about “Digital Transformation: Manufacturing Industry Reacts During The Pandemic.” In this one, we would like to tackle the five technological trends that will reinforce digitalization in the same sector.
Many businesses have been affected significantly by the pandemic. So what technological advances can boost the recovery phase in this industry. When manufacturers are forced to limit their operations due to the restrictions, it is essential to improve the remaining processes and procedures’ efficiency and productivity.
Automation in manufacturing
McKinsey Global Institute recently conducted a study of manufacturing work in 46 countries which are, automation possibilities in this sector.
The surveyed area is covering about 80 percent of the global workforce.
The study shows that despite manufacturing being one of the most highly automated sectors globally, there is still remarkable automation potential within the sites themselves, like supply chain and procurement. Manufacturing is the second industry with the top potential for automation. The noteworthy domain is data collection and processing, predictive physical potential sectors.
The final goal for manufacturing companies and sites is to capture more long-term value at each stage of automation maturity. And how to achieve this goal depends on the maturity levels in the automation spectrum.
The spectrum includes four stages of automation maturity levels.
- Low maturity: This stage has limited infrastructure. It may lack robotics and data collection systems.
- Mig maturity: More systems are installed during this stage, but the manufacturers use only a fraction of the potential.
- High maturity is manufacturing companies who are already proficient in the traditional automation infrastructure but lack automation in some less critical areas like support-function.
- Best-in-class maturity is manufacturing companies that use both traditional and advanced technological solutions for automation.
Manufacturers also need to deploy and install different solutions depending on different maturity levels.
For example, from the low to mid maturity level, it is the installation of necessary sensing infrastructure and simple task execution automation; it creates basic processes to identify, evaluate, and implement automation; the program automates the simplest tasks.
In the mid-to-high maturity level, manufacturers need to utilize traditional automation to create highly automated processes; install any advanced sensing equipment like vision systems, in-line sampling analysis; create automation centers of excellence with a fixed number of SMEs support, and take advantage of optimization routine via advanced programming (machine learning)
The high to best-in-class maturity level is where companies employ cutting-edge automation using advanced robotics (collaborative robotics, automated guided vehicles); latest optimization automation programming (neural networks, artificial intelligence, etc.)
IoT and Robotics in manufacturing
Digital transformation in the manufacturing sector requires constant innovation and the merge of cutting-edge technology for automation—IoT and robotics in manufacturing are one of the trends allowing companies to speed up their processes.
The International Federation of Robotics (IFR) anticipated that the number of robots shipped internationally would increase by 12 percent every year, starting from 2020 to 2022.
The IoT in manufacturing market size is expected to be valued at $994B in the next three years.
The combination of IoT and robotics solutions enables manufacturing sites to automate pair production lines, simplify repetitive processes, and monitor complicated procedures, giving employees and critical decision-makers the ability to analyze the business performance and understand the ground for significant improvements.
By deploy robotics in manufacturing, businesses will gain multiple benefits:
- It improves quality because robotics allows companies to provide better product quality, like industrial IoT or 3D printing.
- Reducing cycle times and introduce real-time monitoring.
- Increasing productivity
- Reducing any risks of injury under hostile conditions for workers
- Accessing real-time information since the use of sensors allows manufacturers to receive information about any processes and state of machinery.
Manufacturers can reduce costs and optimize their business processes by investing in predictive maintenance because of the growing number of IoT devices and their information about hardware, robots, and additional machinery.
Predictive maintenance allows the manufacturing industries to reduce the maintenance cost by 25 to 30 percent. In contrast, reactive maintenance usually costs a fortune, not to mention the extended downtime and disruption in the maintenance process.
The predictive maintenance flow contains seven stages: establishing conditional baseline - installing condition monitoring sensors - collecting conditional data - identifying baseline breaches - creating work order - performing maintenance - repeating predictive maintenance flow.
After the last stage, the flow will continue from the fourth stag (identifying baseline breaches)
Predictive maintenance’s obvious advantage is the control of their cost and schedules, reduced downtime, and the opportunity to eliminate causes of failure.
Data is the vital backbone behind any successful businesses; it is not exceptional in the manufacturing industry. Loads of manufacturing companies, especially in their later phases in the maturity spectrum, collect a massive amount of data from various sources. These data, if appropriately treated, will be the game-changing factor for manufacturers.
Digitalization in the manufacturing industry is a combination of Big Data, Machine Learning algorithms, and cloud computing software solutions. Digitalization plays a pivotal role in interpreting and predicting consumer behavior and demand, employee productivity, and safety issues, as well as gives manufacturers the ability to determine areas of improvement.
Some areas of predictive analytics in manufacturing organizations are costs (waste rates, inventory turns, value-added analysis, cost efficiency, overhead efficiency), production quality (vendor quality, production quality, data accuracy, cost of accuracy), lead times (cycle times, setup times, material availability, machine uptime, customer services time), delivery reliability (vendor delivery performance, schedule adherence, order, and schedule changes, lost sales)
If predictive analytics is used appropriately, it is beneficial to manufacturing. Predictive analytics allows users to access data-driven analytics, insights, and make decisive decisions, to improve forecasts and maximizes revenue and inventory management, to enhance operational efficiency in real-time.
The last digitalization trend in this dominant industry is LEAN manufacturing. LEAN is all about smart use of resources to solve some typical problems such as transportation, inventory, motion, non-utilized talent, overproduction, and so on.
This trend relies heavily on the previous trend (predictive analytics) in making smart decisions and automation in manufacturing to better use resources.
Some short and long-term benefits of LEAN manufacturing are also improved quality, efficiency, safer work environment, easier to manage complicated production sites; on top of that is the more significant potential for automation of manufacturing processes.
Side-note about TP&P Technology:
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