How Big Data Can Boost Manufacturing Efficiency

big data

 

Over the last decade, manufacturers have been able to reduce waste in and enhance product yield from their manufacturing processes by implementing Six Sigma and Lean techniques. However, the volatile nature of today's manufacturing sector, particularly in chemicals, pharmaceuticals, and mining, requires a more granular approach to discovering and fixing process problems. Nowadays, more and more manufacturers are turning to using Big Data to help tackle this problem. Market research has shown that the global Big Data in manufacturing industry  is projected to reach a market value of $9.11 billion by 2026. In this blog, we’re going to take a look at how the use of Big Data can help boost manufacturing efficiency.

 

What Is Big Data?  

Big data involves exceptionally big or complicated data sets that can be studied using advanced statistical methods and tools to identify patterns, trends, and relationships. In particular, those relating to processes, behaviour, and interactions. Big data analytics employs sophisticated methodologies and tools to reveal relationships, determine cause and effect, and forecast trends, events, and behaviour.  

How Does Big Data Improve Manufacturing Efficiency? 


Big data and advanced analytics influence several critical decisions in manufacturing as they can be used to uncover insights to improve efficiency and quality. The us e of big data can also accelerate innovation, which has become increasingly vital among competing businesses. The crucial segments in the manufacturing sector where big data is making an impact are: 

 

  • Yield Improvement & Waste Reduction 

Using big data technology to analyse large historical data sets can allow manufacturers to find hidden patterns in their manufacturing process. Using big data provides manufacturers with a more granular approach to diagnosing and correcting process flaws which ultimately leads to yield improvements and reduction of waste. 


  • Supply Chain Management

Big data analytics is used by manufacturers to improve supply chain management. It can be used to address various issues at the strategic, operational, and tactical level. Implementing supply chain analytics may ensure data-driven decisions to decrease costs and improve service. 


  • Real-time Process Insights 

One of the most challenging aspects for manufacturers is manufacturing line delay. Production process delays can occur because of factors such as maintenance downtime, defects or lack of expertise. The use of smart sensors that can detect where errors exist can provide real-time monitoring in this case. These sophisticated sensors detect the source of the delay and when it began, and how long it lasted. Analysis of real-time monitoring metrics enables manufacturers to spot problems, allowing them to intervene and improve quality. 


  • Market Research 

In conjunction with IoT technological advancement and other sophisticated analytics, Big Data analytics aid in analysing the market by incorporating feedback, conducting surveys, and collecting data to modify products and discover a niche in the market to maximise profits. In production, this aids in pre-planning the business strategy and keeping track of the requirements so that no resource is lost in relation to the industry. 


How Can Manufacturers Benefit from Big Data Analytics? 


The most significant improvement in the manufacturing industry in recent years has been the incorporation of big data analytics into the manufacturing process, which has provided manufacturers with better production capacities. Let's look at the advantages of data analytics in manufacturing and how it's helping to improve product quality. 

 

  • Data Analytics Enables Better Product Design 

Traditionally, delivering a new product design to the market requires a significant amount of trial and error. Often, the earliest product design iterations receive lukewarm reception from consumers because of poor ergonomics and design. Machine learning and artificial intelligence combined with modern data analytics technologies help build computerised product designs and assist manufacturers in putting them through their paces to increase acceptability. 

 

  • Analytics Algorithms Fuel Manufacturing Automation 

Manufacturers can reduce unexpected downtime with automated manufacturing systems by optimising analytics algorithms to detect potential anomalies in the production process or equipment and execute proactive maintenance. 

 

  • Effective Product Management 

Data modelling and predictive analytics employ historical demand data to mimic future market conditions to generate accurate demand forecasts, allowing firms to fulfil market demand without wasting resources. 

 

  • Advantages Well Worth The Effort 

With a robust big data and analysis foundation, you are almost ready to launch your big data solution to users. However, considerable training is necessary because the big data environment may differ significantly from typical database and data warehouse technologies. Nonetheless, big data's commercial advantages and benefits are well worth the effort. Big data is an integral part of modern business and one of your most valuable resources for generating smart, sustainable change in an organisation and gaining a competitive advantage over rivals. 


How To Utilise Big Data To Boost Manufacturing Efficiency 


There are endless ways in which manufacturers can utilise big data to improve their manufacturing processes. However the key to doing so is having the right tools and platforms that teams can use to harness the power of big data and gain actionable insights from. It is not always easy to find a solution that can process big data in an efficient and user-friendly way. This is what the SmartX Advanced Process Automation solution provides. It is a cutting-edge data platform that acts as a process automation solution for full-bed operations, increasing efficiency, productivity, and process robustness. SmartX uses Power BI to directly access process data making it easy for the user to quickly and easily build batch visualisations. 


If you’re looking for ways to improve manufacturing through using big data, our experts can help you incorporate this into your manufacturing business in the most efficient way possible. Download the SmartX brochure here or get in touch with our expert team today to find out how SmartX can enhance your manufacturing efficiency. 

 

Claude Lacey

Claude Lacey leads the Innopharma Technology software development team and has worked in software development for over 20 years.  Prior experience includes software development management in the medical device industry where he led the development of digital pathology software applications.  His areas of expertise include cloud architecture, imaging and data driven systems, UX design, agile and lean thinking.  He has a special interest in IoT, machine learning and computer graphics technologies. Claude holds a BSc in Computer Science and PgDip in Pharmaceutical Business and Technology.

Girish Mallya

Girish Mallya is a Senior Software Developer working at Innopharma Technology with 12 years of professional experience developing image analysis solutions and is responsible for the technical aspects of software R&D projects that involve image analysis/computer vision. Apart from image processing and computer vision, he has experience in C++, Python, MATLAB, Machine learning for image analysis.

Volodymyr Rudiakov

Volodymyr Rudiakov is a Senior Software Developer at Innopharma Technology with 8 years of experience of using C++, Python, JavaScript, NodeJS, Java and different technologies for data exchange, storage and processing, IoT devices and systems integration, and process automation. Overall, more than 20 years of networks specialist and systems integrator experience.

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