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Understanding the Role of Big Data in Industry 4.0

The introduction of Industry 4.0 in 2011 could be seen as a necessary measure to wake up a declining industry. The decade before its introduction saw global manufacturing productivity drop to 1% and to spur growth a German study group considered business models that leveraged data sets produced within shop floors.

These data sets were expected to be analyzed to gain insight into the manufacturing process at a level never witnessed before, but first, a way to capture both structured and unstructured data had to be implemented.

This post will discuss:

  • The process of capturing big data within the industrial sector
  • The role of unified architecture and shop floor interconnectivity in Industry 4.0
  • The application of data analytical tech solutions to support Industry 4.0 business models

 - Success in Simulation and Scheduling

Defining Big Data in Manufacturing

Big data refers to the collection of large data sets and the process of analyzing the captured data to reveal patterns, trends, or to gain insight into a process. The average manufacturer produces large data sets across every stage of the production cycle. These data sets include data from customer demand, the supply chain, manufacturing equipment, and operators.

The data sets can be categorized as either structured or unstructured data. Capturing and analyzing structured data is generally a straightforward process because it is defined data produced from equipment. Unstructured data is generated from processes and may require extensive analytical technologies to analyze data.

Capturing data from the shop floor may also come with difficulties such as the challenges with collecting data from the legacy assets that still play important roles within the shop floor. To capture data from legacy assets, engineers spend approximately 70% of the time devising means to collect the data old equipment produce. Note that legacy equipment depends on analog communication systems and do not have digital I/O modules or Wi-Fi capability to ease the data collection and transfer process.

Technology advancements have created solutions to solve the data collection challenges within the manufacturing industry. Examples include the use of edge computing hardware such as sensors to capture data from processes and plugging legacy assets to human-machine interfaces (HMIs) or smart devices to collect data.

Industry 4.0 intends to bring automation to the shop floor and for this to occur assets within the shop floor must be capable of supporting machine-to-machine and machine-to-cloud data exchanges. Here, a special mention of the introduction of unified architecture by the OPC foundation must be made. OPC UA provides standards that enable manufacturers to unify both legacy and modern assets into a facility-wide network. The network supports data interexchange which powers industrial automation.

Use Cases for Big Data in Manufacturing

Big data support the application of diverse Industry 4.0 business models in the manufacturing industry. Historical data from previous customer demand cycles play an important role in demand forecasting and this goes for developing predictive maintenance strategies. Big data and its analytics also support machine vision when implementing the use of autonomous mobile robots within industrial facilities.

Other use cases of big data in Industry 4.0 include optimizing supply chain management by predicting delivery timelines and developing alternative plans to deal with a disrupted supply chain. Analyzing big data enables manufacturers to detect anomalies that could affect production cycles, as well as, optimize the life-span of tools used in the manufacturing process.

Utilizing Big Data Analysis to Enable Industry 4.0 Initiatives

Analyzing the big data sets from industrial processes to gain insight requires specific technology tools. These tools make it possible to connect the dots and see into the future of manufacturing facilities operations. These tools include:

  • Intelligent Risk-based Simulation and Scheduling Software – Simulation modeling and scheduling is a powerful digital transformation technology that can be used to evaluate diverse scenarios to gain insight. For example, a manufacturing outfit expecting increased demand can answer questions such as “how do we increase production capacity, decide the number of resources required, and how should these resources be allocated?”

Risk-based scheduling also enables manufacturers to anticipate risk and automate the creation of optimized schedules that consider these risk factors. The introduction of automation to scheduling means optimized schedules can be updated in real-time to ensure a facility meets its production requirements.

  • Digital Twin Software or Platform – The digital twin is a virtual representation of physical items or processes. The digital twin interacts with the physical entity through sensors and IoT devices that track the entities operations, in this case, a manufacturing facility’s operation. Thus, the digital twin relies on data sets from the shop floor to function.

With a digital twin, manufacturers can implement remote monitoring strategies and evaluate a manufacturing facility’s capabilities in real-time to solve complex operational problems.

  • Demand Forecasting Software – The ability to foresee future demand cycles is the foundation for accurate production planning. Demand forecasting software utilizes historical data and trend analysis to determine fluctuations in customer demand. To ensure the accuracy of forecast results, the historical data sets used for the analysis should also be accurate.
  • Manufacturing Enterprise Software or IIoT Platform – These tech solutions are the tools manufacturers utilize to support the interconnectivity of shop floor processes. IIoT platforms capture manufacturing data and support the building of applications to analyze the captured data. The applications and features of these enterprise platforms enable manufacturers to capture data from inventory management, work order generation, supply chain, and scheduling software to optimize every aspect of the production process.

Conclusion

The importance of big data to achieving Industry 4.0 cannot be questioned. The ability to capture accurate data sets from the shop floor ensures Industry 4.0 solutions have the fuel they require to function. Whether your Industry 4.0 strategy is focused on predictive maintenance or data-driven plant optimization, you must have a big data analytical tool in place to achieve the desired results.

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