Data Analytics and Its Importance in The Manufacturing Industry Today

Data analytics is the practice of investigating vast quantities of data in order to make assumptions about the information they hold, progressively due to the help of specific software and methods. Data analytics practices and systems are broadly used in commercial industries to facilitate organizations to make more-enlightened business decisions and used by engineers and researchers to verify or disprove theories, models and hypotheses. Data analytics can help businesses increase revenues, improve operating efficiency, develop marketing campaigns and consumer service struggles, react faster to evolving market tendencies and gain an edge over industry competition, with the eventual goal of increasing business performance. Depending on the specific application, the data that's analysed can comprise of either past statistics or new data from the practice that has been gathered for real-time analytics use. Increased uncertainty and low growth have caused manufacturers to squeeze every asset for maximum value.

The next target is their own data. Process manufacturers have been receiving pressure from all areas in recent times as raw materials have become more expensive or challenging to source and growth has slowed. The majority of manufacturers have already made the most obvious changes to streamline their operations, using traditional approaches to get as much productivity out of their supply chains and operations as possible. To do more with less in a slow-growth and unstable environment, however, companies must explore new ways to maximise the productivity and profitability of their processes. There’s one substantial asset that manufacturers have not yet optimized, their own data. Industries generate huge volumes of data, but many have failed to make use of this supply of potential information. Traditionally, manufacturers have trailed behind other industries in their IT capabilities. However, due to cheaper computational power and quickly advancing analytics opportunities, process manufacturers can put that data to use, gathering information from several data sources and taking advantage of machine learning packages to expose new ways to optimize their processes from the tracing of raw materials to the sale of their finished goods. Advanced analytics developments also enable manufacturers solve previously unsolvable problems and reveal those that they were not aware of, such as unknown bottlenecks or unprofitable production lines.

That’s the first and undoubtedly oldest rule in the manufacturing industry. It used to be that the best way to manage was hoping someone on the plant floor, using a combination of instinct and experience, would see the indications of a machine or process about to go down and repair it in time. However, with more complex machinery to keep track of, continued pressure to increase uptime and productivity, and growing demand for flexible operations, hope is no longer a viable strategy. Companies can maximize the operating time of critical assets and machines by analysing big data to predict their failure. Predictive maintenance systems gather historical data (structured and unstructured, machine- and non-machine-based) to produce insights that can’t be observed with conventional techniques. With the use of advanced analytics, companies can define the circumstances that typically cause a machine to break and monitor input parameters so they can intervene before breakage happens, or be ready to replace it when it does thus minimizing downtime. Yield/throughput analytics can be used to confirm that each individual machine is as efficient as possible when they are functioning, assisting to increase their yields and throughput and decrease the amount of energy they consume. Profit maximization analytics, can analyse the thousands of parameters and circumstances that have an influence on the total profitability of a supply chain (from raw materials purchasing to final sales), offering information on how best to exploit given process conditions. In conclusion, the benefits of data analytics to all environments including the manufacturing industry are clearly evident. These advanced analytics approaches can deliver significant margin improvements to all organisations.

They can also help enhance ongoing continuous improvement efforts at a time when manufacturers have seemingly exhausted all other options for increasing productivity, which is what every company is striving towards. Additionally, they offer a lever for competitive advantage, even for companies with overcapacity, by helping them better manage their production systems and optimally reallocate resources in real time. The use of data analytics is the future with the way technology is so rapidly advancing. It is what every company should be looking to going forward, to gain as much of an edge over their competition as possible in their respective markets.