Technology has integrated itself with manufacturing processes at a speed and scale never seen before. The modern manufacturing industry is investing heavily in technologies such as Big data analytics, artificial intelligence, cloud computing and IoT to augment visibility of information, address system complexities, improve performance whilst mitigating costs.
Welcome to the age of smart manufacturing factories that have digitized systems as one of the key features of their operations. These factories are a highly digitized bunch that relentlessly collect and share data through various devices and systems.
In this pursuit of boosting productivity, manufacturers are yet to optimize one very significant asset they have—data. Most smart manufacturing companies generate large volumes of data available but they fall short of making potent use of this stockpile of intelligence. Leveraging the insights available from data can help optimize production, manage and avoid downtime, upgrade the supply chain, reduce costs, and improve product quality! And that is just the beginning. Companies need to include data analytics in their primary business strategy and ensure that it is integrated into all touchpoints. This article covers the important types of big data and how they can be exploited to boost manufacturing performance.
Put the Data to Work
The digital industrial revolution has created an abundance of opportunities. Yes, even in a ‘physical’ set up like a factory, there is plenty of room for automation. Smart manufacturing businesses can analyze their data in three major ways—understand their performance, prevent potential issues, and forecast future trends.
Descriptive Data – to Answer ‘What’
Descriptive analytics is the most common type of data analysis and has a wide range of uses in smart manufacturing. It provides all the basic, real-time information about different functions within the company. Despite being simple, it is extremely beneficial in understanding the performance. The data is usually available in the form of tables, charts, graphs, dashboards, and reports.
The main aspect of descriptive data, as the name suggests, is to describe what has happened and what is happening. These insights can help determine patterns, changes, and more by using historical and real-time data. You can use it to see the outcomes of previous business decisions, the effects of certain operations, and more. The data you choose to analyze can be customized based on time period, customer demographics, business functions, or a combination of any of these.
This data can be effectively used to analyze previously conducted production cycles, machine performance, revenue details, and more. These reports provide great insights that can be used to improve productivity levels, monitor downtime, and provide benchmark data for different time periods and manufacturing operations. This allows companies to set up a foundation for the future by identifying best practices and removing bottlenecks.
Diagnostic Data – to Answer ‘Why’
Diagnostic analysis also works with historical and real-time data. However, the key difference between this and descriptive data is the question it answers. While descriptive data helps understand what was happening, diagnostic data delves deeper into the processes and is all about figuring out why certain things are happening. For example, descriptive data for a machine could identify the number of hours it has worked and its performance levels. On the other hand, diagnostic data will tell you that its performance levels have changed because of an update to the system.
Smart manufacturing companies need to ensure that they use diagnostic data to turn insights into actionable outcomes. This critical information can be leveraged to make more data-driven and informed business decisions across the organization. For example, if you want to investigate the root cause of say increase in customer churn, diagnostic analytics can take you there and enable you to take immediate remedial steps.
Diagnostic data reports can help collect machine performance and maintenance data, downtime data, and performance data. This data is then correlated and analyzed to assess the cause of success or failure. Companies can apply their learnings and inferences to ensure optimal performance in the future.
Predictive Data – to Answer ‘What Next’
In comes after diagnostic analysis is predictive analysis. It also uses historical data but to predict what lies ahead. Going by the above example, where diagnostic data analysis helps reveal the reason for an increase in customer churn, predictive data analysis sheds light on steps that might help reverse the outcome.
Data insights in predictive analytics are usually highly accurate and can be used for both short-term and long-term plans. This is done with not just data analytics but also with machine learning, AI, automation, and smart manufacturing technology.
The question ‘What next?’ can be answered with respect to the ordering of raw materials, customer demand, production schedules, and more. Forecasting can be done on the basis of raw materials and parts, customer expectations and demographics, as well as time periods, to name a few. Predictive analytics can help determine future schedules, especially when companies have to cater to increased demands. This will help avoid stock outs and overproduction. The analysis also helps determine downtime. As will most agree, ‘prevention’ of breakdowns is much better than ‘curing’ breakdowns. The consequence of all this is, of course, greater productivity levels. Planning based on data can lead to cost-effective production, improved employee experience, and better customer satisfaction. Efficient machinery combined with motivated employees is a recipe for success indeed.
Big Data – A Winner’s Solution
Data analytics in manufacturing is truly a game-changer. Apart from the endless list of benefits mentioned above, the use of data analytics can also help build new and robust revenue models. Data matters not only to optimize production levels but also to provide better quality and value. Customers expect fast, and high-quality products and smart manufacturing companies need to incorporate data-driven strategies to meet these expectations
At Quinnox, we believe that data is at the very core of our business strategy. Our data analytics services and solutions help premier organizations unlock highly useful insights by tapping into the right kind of data, thereby empowering them to solve complex business problems. Contact us to learn how you can accelerate your manufacturing performance.