By Johan du Toit, Strategic Sales Executive for SYSPRO Africa

Industry 4.0 is driving manufacturers to increasingly harness and use Big Data to enable responsive, optimised outputs. However, investment in technologies is irrelevant without data analytics to understand the internal and external factors impacting on a business. Big Data gets bigger every year, and manufacturing companies are increasingly able to collect data from a vast and growing number of sources, but manufacturers are struggling to manage the huge volumes of data available.

When combined with a tightening world economy, businesses are operating in an increasingly competitive and complex landscape, with multiple internal and external factors impacting business success. Having the right technology in place is crucial for gaining a competitive edge and adapting to changing industry dynamics.

How are you using the data you have?

Explosive growth in the volume and different types of data throughout manufacturing and the supply chain has created the need for businesses to invest in technology that can intelligently and rapidly analyse data to extract valuable and actionable business insights.

Manufacturing analytics relies upon the industrial internet of things (IIoT), machine learning, and edge computing to enable smarter, scalable factory solutions, and data as the blood in the arteries between them. Together, these technologies enable much more than they do separately. New AI capabilities also have the benefit of making data more usable, allowing manufacturers to dissect their data in ways that provide a comprehensive picture across the business.

Manufacturers need to leverage advanced analytics to access critical insights that allow them to engage in more data-driven decisions around sourcing, production, fulfilment, cost reduction, and other focus areas. As AI becomes increasingly sophisticated, manufacturers are now turning to analytics for predictive insights.

Where, when and how

Predictive analytics helps improve business decision-making by enabling manufacturers to analyse vast amounts of real-time data to identify potential events and opportunities before they happen. It’s no surprise then that predictive analytics usage is increasing across all types of industries and businesses. This can be attributed to the recognition of the strategy’s accuracy and success in improving efficiency, streamlining processes and the overall improvement of the bottom line.

Predictive analytics in manufacturing is especially useful. The most common of these is predictive maintenance, where an array of sensors record data points for manufacturing equipment performance, upload them in real time and use analytic modelling to identify small fluctuations in performance that may be early indicators of larger problems. These issues can then be proactively addressed before they result in equipment malfunctions or shutdowns. Equipment downtime is an immediate hit to the bottom line, so this use of predictive analytics enables much more control and planning over downtime, with a resultant cost benefit.

For many manufacturers, predictive analytics can also be useful for accurate demand forecasting and supply chain management, by analysing historical data. Too much inventory is inefficient and has an impact on holding costs, but too little, or too few of the right thing is equally problematic. The technology allows for much more informed inventory decision-making and ordering, facilitating the most effective just-in-time plan possible and delivering more accurate inventory management.

Forecasting consumer demand is another area where predictive analytics can empower demand forecasting through statistical algorithms. There are hundreds of factors at play in determining future purchasing habits of customers, but predictive analytics can guide decision making and highlight seasonal trends.

While no one can reliably predict the future, what predictive analytics does offer manufacturers is the capacity to offer much more informed decision-making and avoid or minimise costly downtime. By using big-data tactics and models, predictive analytics can provide a more complete and effective view of forthcoming potential risk factors, opportunities and recommendations for multiple areas across the business.

The ability to mitigate and manage risk is of crucial importance to manufacturers. Predictive analytics can forecast potential areas of risk by identifying trends and patterns in the company’s data and make predictions on how these risks might impact or affect the business. Companies can then use this data to identify and prioritise the most critical risks, assess the potential impact and design a suitable course of action.

How can ERP improve predictive analytic usage?

Predictive analytics is well suited to manufacturing, as the industry involves large amounts of data, repetitive tasks that can be automated, and solving multi-dimensional problems. This technology is the key to reducing downtime and increasing efficiency.

However, manufacturers looking to incorporate predictive analytics must prioritise collecting quality data through their ERP system to reap the benefits. Consolidating all data into a single source improves accessibility and is more likely to make the data representative, readable and accessible.

Not all ERP systems are created equal. Creating an environment of visibility across all operations in the business is necessary in today’s highly competitive environment, but it’s equally important to ensure that your ERP system doesn’t just deliver retrospective insights that don’t offer the opportunity for immediate action. This is where an ERP system that delivers predictive insights will provide powerful, actionable opportunities for agile and responsive behaviour.