Top 10 uses of Big Data in smart manufacturing

 

"Top 10 uses of Big Data in smart manufacturing"

 10 | Logistics

Delivery Tracking

Within logistics, many of the key players are harnessing Big Data infrastructures to track freights and analyse weather and road conditions in real-time. With this capability trucks can be diverted any time onto a fast or more cost effective route.

9 | Testing and simulation of new processes

Testing and simulation of new processes

With the use of digital twins and virtual reality environments, manufacturers can now test and simulate new processes and products without the need for risking a real life environment. 

As part of this digital transformation manufacturers are implementing data platforms to make decision making processes more strategic.

8 | Overhead tracking

Overhead tracking

In order to have true control and visibility for overhead costs, Big Data environments can be harnessed alongside connected data sources & advanced analytics capabilities

7 | Predictive maintenance

Predictive maintenance

Via sophisticated sensor technology, manufacturers can collect and analyse operational data in real-time for machinery and consumer products. 

By analysing this data organisations can identify upcoming failures and predict the need for maintenance well in advance, reducing downtimes and related maintenance costs, as well as prolong the lifespan of machines by preventing irreversible failures.

6 | Data-driven enterprise growth

Data-driven enterprise growth

With the adoption of big data, organisations have the ability to compare the performance of sites and identify the reasons for the differences. Not only can organisations analyse internal production and sales data, but they can also analyse the market and develop predictive models.

5 | Tracking

Tracking

To ensure manufacturing operations are optimised, there needs to be a daily flow of data from production lines to highlight any discrepancies and opportunities in real-time. 

By harnessing Big Data from multiple points within the production line, organisations can drive  continuous opportunities for optimisation, cost-savings and prevention with the right data analysis tools.

4 | After-sales

After Sales

By harnessing big data, manufacturers can predict and avoid warranty or recall issues, resulting in a reduction in costs.

These issues are commonly related to the manufacturing process by using smart analytics tools manufacturing processes and the quality of goods can be improved.

3 | Improving product quality

Improving Quality

By harnessing predictive analytics within the testing process, manufacturers could significantly improve quality levels and reduce quality related costs. 

By using pattern recognition and predictive analytics the number of tests could be reduced to performing the ones that are required rather than all tests.

Production line quality can also be improved with the use of Big Data analytics, detecting defects early to reduce downtime and costs.  

2 | Build to order 

Build to Order

With many sectors within the manufacturing industry seeing an increasing trend of ‘products to order’, manufacturers need to ensure they have a well defined data platform that can analyse customer behaviour and sales data in order to see real growth from a ‘build to order’ (BTO) production approach. 

1 | Risk management

Risk Management

By harnessing Big Data, suppliers have the ability to share their production data, creating transparency and higher levels of communication. 

As a result, manufacturers can see:

• Exactly where the supplier is with production and adjust the related processes accordingly to avoid potential waiting times
• Production and product-related quality metrics from their suppliers 
• Clear visibility on a supplier’s portfolio and have insightful data for supplier contract negotiations

Having this visibility can provide the data and insights needed for better risk management and fact based decision making for a more strategic management of operations.