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Spending on Data Tools Expected to Jumpstart the Manufacturing Industry Post-Pandemic

Getting the manufacturing industry to its pre-pandemic levels is one challenge majority of CIOs have to deal with as economies open up. On top of the challenges Covid-19 brings, stakeholders in the manufacturing industry must grapple with supply chain disruptions, the political fallout from tariffs and a likely trade war on the horizon. The listed challenges lead to the question, how do manufacturers deal with both the old and new problems the industry currently faces?

Jo De Vliegher, CIO of Norsk Hydro, states that before the pandemic the Aluminum giant had constantly discussed the possibility of harnessing its data and using it across its facilities. But with the pandemic, the need to work remotely has led to a heightened focus on data tools and their ability to unlock hidden value.

The ongoing disruptions in the manufacturing industry mean a higher level of insight is needed by decision-makers to make drastic changes to existing systems which no longer serve their intended purposes. Data tools can be categorized under the jurisdiction of digital transformation where digital technology is used to extract data from every process within a facility, production line or cycle.

IoT hardware and edge computing currently lead the way in enabling the extraction of data from the production line. In modern or Greenfield factories, shop floor scanners, temperature sensors, and other hardware now work behind the scenes to capture data from diverse sections of the manufacturing cycle. The deployed hardware can track the movement of supplies, throughput, machine utilization etc.

IoT hardware and other hands-on data extraction tools do the admirable task of assigning figures to manufacturing processes but to receive insight from these figures a different set of data tools are required.

Michael Larner, a principal analyst at ABI, explains the need for analytical tools that provide insight by highlighting the fact that captured data reports what’s happening but proactively analyzing what might happen is required to take specific actions.

The type of data tools manufacturers must rely on to move to the analytical and decision-making phase, are forecasting tools such as simulation modeling and real-time analytical tools such as the digital twin.

Simulation is the Name, Efficiency is its Result

Simulation modeling takes the data sets captured by deployed hardware and historical data to recreate models of the operational conditions within manufacturing facilities. The virtual environment then provides the foundation for analyzing production line operations against diverse manufacturing variables.

Take, for example, the automotive industry which is beset by specific challenges reminiscent of Nokia’s economic fall such as the need to reduce production cost while increasing revenues, and the need to optimize production in an unstable environment with regards to the metal supply chain. Navigating through these challenges require extensive forecasting and testing to improve the automobile industry’s product development cycle.

Simulation modeling provides the environment for extensive manufacturing scenario tests and cycle evaluations. For example, Daimler, the automotive giant, employed the use of simulation to evaluate the effects of changes within its facilities to the production cycle. The simulation model assisted Daimler with evaluating how a reduction or increase in workstations, size of the provision area, and available personnel will affect plant performance. Insight into the impact of plant aesthetics and operations helped Daimler sped up its go-to-market timelines, as well as, the dynamics at play when opening a new factory.

Applying Daimler’s example to today’s challenges within the manufacturing industry, manufacturers can evaluate the effect of alternative supply chains and the use of reduced workstations due to the pandemic on production cycles. Simulation also provides answers to what-if questions which mean social distancing on the shop floor must not translate into productivity loss.

With simulation modeling, manufacturers can answer questions such as what distances should operators maintain while working and the throughput to expect with distance or limited workstations as a constraint. The answers to this provide insight into the expansion and optimization strategies required to ensure specific production targets are met.

The Digital Twin is the Name, Real-time Strategizing is the Game

The digital twin provides an avenue for harnessing the real-time capabilities of the data captured by hardware data tools on the factory floor and across other manufacturing processes. A digital twin is a virtual representation of physical events where data is shared between the cyber-physical environments it enables.

With enough data coming from data tools deployed on the factory floor, the performance of every element within the production line can be studied in real-time. Thus, managers can see potential bottlenecks a mile away and develop strategies to avoid them.

The digital twin provides multiple use cases for remotely monitoring manufacturing operations and workflows thus reducing the number of operators on the shop floor. It also provides the data-driven insight needed to optimize shop floor operations and evaluate the impact of external changes to existing systems.

By increasing spending on data capturing tools and data analytical tools, manufacturers are confident of understanding the challenges ahead and developing the strategies needed to navigate the changing production landscape and to surpass today’s limited manufacturing capacities.

Simio software is a best-in-class data analytics tool which provides the flexibility and features needed to analyze the complex data coming from your shop floor. You can learn more about Simio software and its simulation and digital twin capabilities by contacting us today.

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