The Role of Predictive Analytics in Manufacturing


The global predictive analytics market is projected to grow from $12.49 billion in 2022 to a staggering $38.03 billion by 2028. Undoubtedly, predictive analytics has a key role to play across all industries but for manufacturers in particular, the technology has the potential to solve the complex manufacturing problems that face so many businesses today.

With its enormous amounts of data, multi-faceted challenges, complicated processes and never-ending pressure to do more for less, the manufacturing industry is ideally positioned to benefit from predictive analytics. By using historical and real-time data, which manufacturers certainly don’t have a shortage of, predictive analytics can predict future outcomes and scenarios, answering the questions, ‘what is going to happen?’ and ‘what is likely to happen?’. The technology turns data into insight, distilling the information needed to underpin faster, more effective, data-driven decision-making. What this does is help manufacturers to reduce costs, mitigate against risk, boost operational efficiency and ultimately, improve profitability.

With all this in mind, if manufacturers haven’t already, where should they be applying predictive analytics capabilities in their business and what can they hope to achieve by doing so?


Predictive maintenance

The most common application of predictive analytics in manufacturing is maintenance. The increase in smart devices and sensors brought together through the Internet of Things has led to more data being generated than ever before. But it’s harnessing this data that really makes all the difference, with the application of predictive analytics enabling manufacturers to use this data to develop an effective predictive maintenance strategy. With a predictive maintenance programme in place, adjusting and repairing assets before failure occurs, not only can manufacturers reduce unforeseen downtime, but they can maximise the lifespan of their assets too. And, by ensuring that asset use is optimised, manufacturers can also reduce their energy consumption.

All this leads to substantial cost and efficiency savings. The precision and timeliness of the data available, in conjunction with the ability to harness and analyse this data, facilitate the most precise and effective predictive maintenance programmes, increasing uptime, efficiency and, perhaps most importantly, productivity.


Quality assurance

Another area where predictive analytics can make a huge difference to manufacturers is quality assurance. So often carried out by humans, quality assurance processes can be both time-consuming and error-prone and regularly happen too late in the manufacturing process, leading to costly waste and rework. Predictive analytics can help in a number of ways. Are processes out of tolerance? Are yields affected? If quality analytics can help to stop production sooner, manufacturers can reduce or even prevent waste and rework.

Similarly, if there’s a change in raw materials or ingredients, for example, the application of predictive analytics can anticipate the impact that this might have on the end product, informing any production changes needed to ensure the quality of the end product isn’t affected. Again, this results in less waste for the manufacturer, increasing efficiency and saving money in the process.


Forecasting and planning

In their quest to become more agile and responsive, more manufacturers are realising the benefits that predictive analytics can bring to forecasting and planning. By taking into account a whole host of variables and factors, including customer feedback, historical buying patterns, seasonal variations, and market dynamics, to name but a few, predictive analytics can help to uncover previously hidden insight and foresight to better predict and forecast future demand. This allows manufacturers to plan accordingly, which has an impact on so many areas of the business.

Decision-making can be faster and more effective, underpinned by accurate demand planning insight. This makes for a more agile business, able to respond quickly and effectively to challenges and opportunities as they arise. Inventory levels are optimised thanks to more confident, accurate supply chain decisions. With the correct application of predictive analytics, manufacturers can ensure they’re never over or under-stocked, with the insight available to predict fluctuations in demand allowing them to optimise inventory. In fact, the benefits of wider supply chain management are evident, with greater precision of forecasting standing manufacturers in good stead to minimise friction across the supply chain for even greater efficiency and productivity, and shorter lead times.


 Plugging the labour gap

This insight into demand planning has a positive impact on predicting workforce demands too. With manufacturers all feeling the effects of labour and skills shortages, the ability to accurately predict demand is very useful when it comes to better-managing staffing levels in-line with production requirements. The right use of analytics enables manufacturers to fully understand how they can optimise staffing levels, as well as help to predict what additional resources or skill sets they might need in the future.

Additionally, predictive analytics can help to increase workforce efficiency and safety too. Analytical tools can help to uncover factors that perhaps weren’t considered as having an impact on efficiency in the past, factory temperature or type of shift pattern, for example. Taking into account all relevant variables, the use of predictive analytics can help manufacturers to optimise the staff that they have, building a working environment that is the most conducive to optimal productivity levels in the most efficient way possible.

The use of predictive analytics has the potential to change the way manufacturers do business. No longer just reserved for predictive maintenance strategies, applied in the right way, predictive analytics tools deliver new depths of business insight for manufacturers, furnishing them with the real-time prediction capabilities that lay the foundations for smarter, more efficient and more productive operations across the board.