Data Analytics – The Alchemy in Manufacturing Sector
Published on : Wednesday 19-10-2022
Data-driven manufacturing is no longer a choice but a strategic necessity, says Dr Rishi Mohan Bhatnagar, President at Aeris Communications.
Manufacturing remains an integral part of the world’s economic engine. On the other hand, technological advancements have bolstered production efficiency to a great extent. Over the last two decades, manufacturers have reduced waste and variability in their production processes and dramatically improved both—product quality and yield. Implementing programs such as lean and Six Sigma has helped achieve this.
However, this is not where it ends. In certain processing environments such as mining, pharmaceuticals and chemicals and Internet of Things (IoT), extreme swings in variability are a hard-hitting fact—despite application of programs like lean or Six Sigma. Given the sheer complexity and numbers of production activities that influence yields in these and other industries, manufacturers need a more granular approach to diagnose flaws. Big data or advanced data analytics provides just the same approach.
What is big data or data analytics?
Big data or data analytics refers to extremely huge and complex data sets that may be analysed using advanced statistical tools and methods that are designed to reveal trends, patterns and associations, especially related to processes. Advanced data analytics makes use of high-level methods and tools to focus on uncovering dependencies, identifying cause and effect and projecting future behaviours and trends.
One of these statistical methods is multivariate data analytics that helps analyse data with more than one variable at a time. This gives manufacturers the power to perform advanced statistical models such as “What If” calculations, pinpoint where processes deviate, and future-proof various aspects of their operations.
How manufacturers are using data analytics?
Data analytics or Big Data is being used to achieve productivity and efficiency gains and unearth new insights that will drive innovation. By using data analytics manufacturers can not only discover new information but also identify patterns that enable them to improve processes and increase supply chain efficiency, and identify variables that affect production quality, volume or consistency.
Data analytics assists manufacturers to unearth defects in processes, speed up product development, improve product quality and reduce variation in quality. These include some of these examples:
a. Predictive analytics tools are being used by process manufacturers to assess the makeup of chemicals, minerals and other raw materials to ensure they meet production requirements.
b. Manufacturers in the biopharmaceuticals sector are implementing advanced data analytics to significantly increase production for biologics such as vaccines without incurring extra capital expenditures.
c. Manufacturers in the chemical sector also implement advanced data analytics to compare and measure the effect of various production inputs such as coolant pressure, carbon dioxide or temperature flow on yield – often finding surprising and unexpected dependencies that are impacting out.
d. Companies dealing in precious metals and mines are able to gain insights from disjointed production data across multiple processes to find connections between specific variables.
e. Pharma manufacturers use data analytics to verify that processes—especially those created in batches; conforming to standards that will define the exact characteristics.
Data analytics is used by manufacturers to manage long-term operational health of production equipment. Predictive analytics is also being used not only to prevent breakdowns for apparatus using IoT but also to reduce unscheduled down-time.
Benefits of big data in manufacturing
Big data analytics is changing the face of manufacturing in a rapid way. Some of the biggest benefits big data or data analytics in manufacturing include the following:
i. Reduced machine downtime
Data analytics is used for performing predictive and preventive maintenance. A common issue in manufacturing happens to be hardware downtime that requires immediate troubleshooting. Now, thanks to data analytics, manufacturers can predict machine failures and take proactive measures to repair the equipment and ensure the production process continues.
ii. Growth of enterprise
Data analytics tools help to draw a comparison between the performances of different sites and point out the reasons for differences. Data analytics can help analyse production plants, develop what-if scenarios and apply predictive models.
iii. Better management of costs
Implementing predictive analytics can make budget planning much easier. It becomes possible to understand the costs required for problem-solving. Also, big data analytics helps track the root causes behind too much overhead costs. Manufacturing businesses cannot start reducing their indirect costs without knowing the average amount they spend on things each month. Data analytics assists in this area by providing baselines that intimate manufacturers of their most substantial indirect expenses. Then it becomes possible to begin figuring out where to improve upon.
iv. Enhanced customer experience and services
Needless to say, the success of any business, even manufacturing, depends upon the satisfaction of your customers. With big data one can analyse their experiences thereby making it better.
Manufacturing and Internet of Things (IoT)
In several ways, manufacturing has been an integral part of the Internet of Things (IoT) throughout its entire history. Sensor-based technology has been used by several companies for decades in their devices without fully realising their potential. Interestingly, it was the manufacturing sector that was one of the first to adapt robotics and automated processes. Many of these machines signalled distress with a notification-providing sensor thereby addressing any issue just right on time and reducing downtime.
All thanks to IoT that today, availability of analytical forecasting models and new data processing technology, the entire manufacturing sector has been transforming rapidly.
Final words
Data-driven manufacturing is no longer a choice but a strategic necessity that can help in unifying internal and external data. It can turn out to be a critical tool for realising improvements especially in terms of yields, particularly in any manufacturing environments in which processes are complex.
On a final note, it can be said that companies that successfully build their capabilities in conducting quantitative assessments that empower smart technology can set themselves apart from competitors.
Dr Rishi Mohan Bhatnagar is an established leader in starting strategic initiatives, executing and profitably scaling. Dr Bhatnagar has managed large business units with around USD 500 mill revenue in an MNC and also started a technology startup.
Experience of managing global customers, travelled across globe and handled multiple regions and market segments, telecom, BFSI, Automotive, Healthcare, Government and Education being key. He has managed large scale outsourcing projects in multi-vendor and multi-lingual environments.