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5 Digitization Technologies for Implementing Industry 4.0

At the height of the pandemic, access to protective equipment such as masks and gloves, and the availability of test kits to improve tracing was at an all-time low. In a bid to secure citizens, countries turned to their manufacturing industries to repurpose production lines to aid the response efforts…and the global manufacturing industry’s response was one for the ages that will be comprehensively documented and studied for decades to come.

To aid the governmental response to a healthcare crisis, the manufacturing industry repurposed production lines to churn out millions of PPEs, test kits, ventilators and other crucial resources. Within a couple of weeks, the increased production capacity put certain countries in the position to aid struggling communities across the globe. According to the World Economic Forum, a major factor behind this rapid response was the industry’s reliance on digital transformation technologies to improve production planning.

The digital transformation of the manufacturing industry is the major driving factor behind building the smart factory and agile processes defined by Industry 4.0. Developing a proactive factory with the capacity to react to disruption also requires the integration of digitalization technology. This post will discuss the 5 crucial digital technologies for implementing Industry 4.0 business models.

IoT and Edge Computing

 - Success in Simulation and SchedulingThe smart factory of the future is powered by data collection, the interexchange of data and data analytics. These three elements alongside machine vision are the keys to implementing Industry 4.0 business models within traditional facilities. To manipulate data it must first be captured and collecting data from the legacy equipment that still make up a large portion of the assets on the shop floor provides its own challenges.

First, legacy equipment are not equipped with the technology to capture, transmit or be included in communication networks. Second, capturing data from the deepest parts of the shop floor such as data from material handling systems and complex heavy equipment also provides its challenges. IoT and edge devices serve as a means for collecting data on the shop floor and handling decentralized analysis.

The data collected by these edge devices provide the fodder for analytical tools used to gain insight. Utilizing data capturing devices, manufacturers can implement Industry 4.0 business models such as:

  • Condition monitoring – Continuously collecting data from shop floor equipment enables manufacturers to detect anomalies to develop proactive measures that reduce breakdown.
  • Predictive maintenance – The use of sensors to capture vibration data or other functional information is the basis for developing predictive maintenance strategies.

Simulation Modeling and Scheduling Software

Simulation modeling has been widely used across the manufacturing industry to answer ‘what-if’ questions pertaining to capacity planning and resource allocation. Although it is a digital transformation tool, it is certainly not new. What’s new is the convergence of simulation modeling with the accurate data collection solutions of today such as IoT and other smart devices.

Leveraging accurate data from the shop floor, simulation models now bring a level of accuracy that was unattainable in earlier decades. Manufacturers can accurately predict the effects of increased demand to production cycles and the resources required to meet demand.

Risk-based scheduling is another relatively new digital transformation solution with the capability to reduce the effect of downtime on manufacturing operations. Using intelligent object-based simulation modeling and risk-based scheduling software, manufacturers can determine the effects of risk factors in real-time. For example, a machine unexpectedly stops working during a tight shift and a new optimized schedule is required to ensure deadlines are met. Risk-based scheduling treats the defective machine as a constraint and reproduces an optimized schedule in real-time that optimizes productivity. Thus, simulation modeling and scheduling software can be used to implement the following Industry 4.0 concepts:

  • Data-driven plant productivity optimization – Simulation models rely on shop floor data to gain insight into complex scenarios. Applying the insight leads to increased productivity.
  • Reduce the effects of downtime – Risk-based scheduling integrates constraints to develop optimized schedules that mitigate risks.

Digital Twins

The digital twin is a digital replica of physical entities with the capacity to interact with the entity in real-time. Here again, data capturing technologies such as IoT and edge devices have roles to play. These data collection tools provide the information for recreating the digital twin and support the real-time transfer of information from the manufacturing floor to the digital twin and vice versa.

The fourth industrial revolution is expected to thrive on interconnectivity and real-time analytics and digital twins guarantee both use cases. For interconnectivity, the digital twin brings all assets and processes within a facility into a single virtual environment. The virtual environment enables manufacturers to gain insight into the different subsystems within the facility, how they interact, and work together to improve productivity. Secondly, digital twin platforms enable real-time data analytics as it collects real-time data and supports the application of analytics to solve problems.

The digital twin empowers manufacturers to implement Industry 4.0 business models such as:

  • Remote monitoring – Having access to the manufacturing floor and production processes from any location is an important aspect of Industry 4.0. The digital twin provides that access through the use of visualization dashboards and mobile applications.
  • Improving operational efficiency – The digital twin provides manufacturers with the tools to measure every interrelated process on the shop floor to improve productivity with data.

Cloud Computing

The connected shop floor Industry 4.0 proselytizes is expected to increase the data generated from industrial processes. Thus, both centralized and decentralized data storage and analytical technologies are required to effectively gain insight from data. Edge devices are a means to decentralize data analysis within the shop floor while the cloud provides the scalable infrastructure to collect as much data as the largest manufacturing facility can produce.

The scalability of cloud computing supports the use of analytical tools such as the digital twin, simulation modeling and scheduling software in diverse ways. Examples include the provision of enough storage space to store analytical results and a platform to enable stakeholders to easily access data. Cloud computing assist manufacturers with implementing Industry 4.0 business models such as:

  • Transferring and storing data – The industrial cloud provides unlimited storage space for storing manufacturing data and supporting the use of edge devices on the shop floor.
  • Platform as a Service – Developing industry-specific applications requires an industrial platform with the tools to support developers. The industrial cloud supports the development of PaaS platforms for the manufacturing industry.

Additive Manufacturing

The reputation of 3D printing as a supportive rapid prototyping and production tool was cemented during the pandemic. An example of its use includes the rapid production of test swabs by manufacturing outfits and healthcare centers to speed up testing. Advancements in 3D printing technology such as the Direct Metal Laser Sintering technique enable manufacturers to 3D print end-use products for public use.

Compared to other manufacturing processes such as CNC machining which takes 3 to 4 weeks to complete projects, 3D printing of complex parts can be done within 12 to 36 hours. Manufacturers can integrate 3D printing to implement industry 4.0 business models such as:

  • Lean Manufacturing – Utilizing a 3D printer to develop products or prototypes is considerably less expensive when compared to other manufacturing processes. The materials used are also reusable which reduces waste and the total cost of manufacturing an item.
  • Iterative Prototyping – The affordability and speed of the 3D printing process make it the perfect manufacturing technique for iterative prototyping. Manufacturers can utilize 3D printing to improve product design to develop a high-performing final product.

Conclusion

Industry 4.0 is the future of manufacturing and to achieve the smart interconnected system it promises, manufacturers must decide when and how to leverage the technologies listed here to improve productivity and decision making.

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