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Data-driven manufacturing is here

data driven manufacturing

What is data-driven manufacturing?

Important decisions that impact the manufacturing process should always be based on facts, not guesses, wishes, theories or opinions. Today’s emerging technology helps by enabling both people and the equipment to collect and process the facts they need to achieve better results. 

The accelerated deployment of low-cost sensors and their connection to the internet has created a lot of hype about the future of manufacturing. The Internet of Things (IoT) and its application of big data and analytics has led to the creation of the next generation of manufacturing. This involves using data to reduce costs through new age sales and operations planning, dramatically enhanced productivity, supply chain and distribution optimization, and new types of after-sales services. 

Data-driven manufacturing is clearly the next wave of manufacturing operations to drive efficient and responsive production systems. Manufacturers are finally in a better position to incorporate data into their daily decision-making activities in a meaningful and productive way. 

Challenges in data-driven manufacturing

  1. The integration with legacy systems- Though industrial automation is an evolutionary process and introducing advanced technologies is exciting, it is also crucial to find a way to make them work alongside well-established and proven legacy systems. A modern factory has multiple system levels.  Iit can become a challenge when the original developer of the homegrown legacy systems cannot completely interface with the new-age systems with scarce documentation. It is important to understand that it is not about starting from a clean sheet of paper but integrating efficiently within the existing design and manufacturing environment.
  2. System security challenges: Distributed control systems connected through the internet can expose existing systems to unauthorized access by attackers. As more IoT devices are being increasingly connected via gateways, this also opens up avenues to enable control and access from anywhere. Most of the traditional manufacturing system gateways would need a great amount of hardening against new-age security challenges that IT services have. This implies adding sufficient computing power to handle networking and security tasks.
  3. Beyond just data exchange toward data sharing- Creating a unified data model and integrating together all the independent systems in the manufacturing process can be a challenge. This data needs to be seamlessly mapped in and shared to every business unit to minimize the wasted resources and materials.  Using IoT driven sensors which can detect potential failures across equipment can be one way of minimizing data exchange failures. 
  4. Inaccurate or incomplete data- When existing manufacturing data itself is incomplete or inaccurate, this can impact the decision making, especially for critical projects where data is the backbone for success. This also means a lot of time, effort and resources are used to completing the daa records or making sure that it is factual and authentic.

How can data-driven manufacturing help?

According to Forrester, data-driven organizations report a 30% annual growth in addition to being profitable and acquiring and retaining new customers. 

  1. Deriving unexpected insights for decision-making : Developing unexpected data-driven insights by using advanced analytics can reveal further opportunities to make quick and accurate decisions. The right data allows manufacturers to focus on the most important problems and opportunities. A clear understanding of whether manufacturers are measuring the right things by establishing KPIs for problems can help easily solve them 
  1. Deep insights into manufacturing processes: Advanced analytics can help manufacturers unearth unseen opportunities to increase production yields. Many times they may assume that all possible process improvements have been implemented, using data they can further dig into deeper prospects for improvement. With these data-driven insights, solutions to problems that have been lingering for a while can also be found, further enhancing the scope of operations using existing resources. 
  1. Cost savings: A manufacturing company that uses real-time, shop floor data as well as sophisticated statistical assessments can easily take what were once isolated data sets, aggregate the data, and then analyze it to reveal critical insights.This can go a long way in  reducing operating costs while accelerating the speed of the results. 
  1. Predict market trends: The data-driven manufacturer can leverage analytics platforms for enhanced prediction of customization demands. This happens by identifying fluctuating patterns and trends in customer behaviour.  Data analytics  allows a granular view of manufacturing processes that enable smarter and more accurate production decisions guided by predictive analysis. 

Artificial Intelligence for data-driven manufacturing

As most modern manufacturing processes require high high levels of accuracy, non-stop enhancements in the production quality, and the highest quality of maintenance processes, Artificial intelligence (AI) finds its way in easily delivering these results for this industry. 

Using AI, manufacturing becomes more data oriented, giving manufacturers the opportunity to increase productivity as well as profits. It also helps them lead the way to continue to grow with its many AI-driven analytical applications including smart maintenance, quality 4.0, predictive intelligence, human-robot collaboration etc. 

data driven manufacturing ELI screen

Towards data-driven bottleneck elimination technology

ThroughPut’s ELI is an AI-Powered Bottleneck Elimination Engine that analyzes your existing industrial data in real-time. ELI continuously detetects, identifies, prescribes and prevents your shifting operational bottlenecks to save millions in delays, inefficiencies & lost revenue. YOu finally can breakthrough bottlenecks that clog productivity, growth and profitability using data-driven decisions. 

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