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29 Sep 2022
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How to Build Flexible Ecosystems with Smart Manufacturing to Deliver Scalable Innovation?

 

Smart manufacturing is a technology-led approach that connects the Internet of Things by using systems to monitor and manage the production process. It uses data analytics to analyze production runs and machine performance to optimize the efficiency of automotive manufacturing solutions and thereby open up many new opportunities.

Advantages of a flexible ecosystem based on smart manufacturing

Smart manufacturing based on automotive IoT can help manufacturers tackle all kinds of problems and reduce costs within their organizations.

The positive effects of smart manufacturing include:

  • Greater capacity and flexibility.
  • Enhanced competitiveness.
  • Higher productivity levels due to being able to respond better to disruptions.

With an ecosystem that can communicate between devices and businesses, manufacturers can grow the business by delivering more value through more resources to more satisfied end customers.

Challenges Facing Smart Manufacturing Ecosystems

Technical standards for sensors are yet to be widely adopted, with communication currently stifled by a lack of standard ways in which different machines can share data and interact with each other. Other challenges include the vast cost of implementing sensors on a large scale and the complexity of developing predictive models.

Many industry experts have argued that it isn’t small chunks that result in the big returns in smart manufacturing but pieces of a bigger puzzle. Suppose a company views the digital transformation initiative as an outward-looking innovation for the entire value chain instead of a strictly inward-looking improvement for individual factories. In that case, they are likely to see better results now and in the future.

How to build your flexible smart manufacturing ecosystem

Smart manufacturing deployments involve sensors embedded in industrial and automotive solutions and equipment to collect data on operational status and performance. By analyzing the data streaming off these devices, manufacturing engineers and data analysts can look for signs that particular parts may fail, enabling preventive maintenance to avoid unplanned downtime on vehicles and other large machines.

Manufacturers can also analyze trends in their data to spot where their process is slowing down or inefficient in its use of materials. Additionally, data scientists and other analysts can use the same data to run simulations of alternative processes, which could help identify the most time-efficient ways of producing various products.

For building a flexible ecosystem approach, manufacturers can follow the below-mentioned steps –

First, define the scope and refrain from building capabilities you don’t need. The scope of the solution becomes easier once you understand the nature of the business issue. Then, it is important to act with speed and scale fast. A flexible ecosystem has got scalable capabilities ready to respond anytime. Finally, it is important to systemize the process by creating repeatable steps for activating new business use cases. This makes it easier to tap into the ecosystem readily.

The ecosystem shaped by technologies like digital twins, artificial intelligence, edge computing, and cloud is set to shape the future of automotive manufacturing solutions in the decades to come.

Conclusion

The future of manufacturing lies in the ability to build flexible ecosystems that can deliver scalable innovation. This will require shifting from the traditional linear manufacturing model to a more dynamic and agile approach that can accommodate change and uncertainty.

Smart manufacturing technologies will enable this transition as they can collect and analyze data across the entire value chain, identify inefficiencies and optimize resources in real-time. This will allow manufacturers to respond quickly to changes in customer demand and market conditions while reducing waste and maximizing efficiency.

To build these flexible ecosystems, manufacturers will need to partner with various stakeholders, including suppliers, distributors, and customers. This collaborative approach will allow for the sharing of data and insights and the development of new business models that can support the delivery of scalable innovation.

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