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Big data is not just limited to the Silicon Valley, but it has transformed the operation of most large-scale industries. Through predictive analytics, big data has made it possible to determine how a series of factors can potentially impact a company’s business. When it comes to supply chain management (SCM), dozens of variables define its success, and predictive analytics can make a significant difference. While each element of the supply chain is the focus of a distinctive management activity, as much as 90% of the supply chain demands some level of forecasting.

How then, can predictive analytics help you be at the top of your game as a supply chain manager? Besides aiding in cost reduction and improving customer behaviour, it can help you avoid risky ventures by forecasting future trends, thus keeping you a step ahead in running a business that is more accurate and profitable than ever before.

Until recently, supply chains had mostly relied upon quantifiable factors like statistical models and performance indicators. However, with the introduction of big data, predictive analytics has now started revolutionizing the industry by analyzing unstructured, rapidly growing datasets in real-time. Investing in data science courses can thus help supply chain managers to make better-informed decisions about their overall processes. The SCM of a particular product can be impacted by a variety of factors like machinery, vehicles, weather condition, workforce, etc. As a result, SCM experts have been working relentlessly on how each supply chain can be optimised to drive maximum profitability.

In a white paper published by the Journal of Business Logistics, it was stated that big data analytics could have a variety of prospective applications in Supply Chain Management which when applied, can help industries in outperforming their competitors. When there are a dozen variables that can directly impact operations, here is how predictive analytics can make a difference in the SCM workflow:

Making sense of massive volumes of data:

When it comes to large quantities of data, predictive analytics becomes indispensable as it not only makes it easy to collect data but also computes it efficiently to generate relevant insights. The various processes associated with a supply chain produce enormous amounts of data, and while easily accessible, this data is hardly useful when arranged into small samples. With predictive analytics, SCM professionals can create their high-performance computational systems that are tailored to the workflow of their organization. Once the operational data is arranged and analyzed on a global scale, it gives forth several kinds of patterns. These patterns can then be successfully used to predict several instances like the rise and fall in demand for a product, the number of items required for inventory management, etc.

Help avoid risky ventures:

The high degree of accuracy provided by predictive analytics enables SCM managers to identify fraudulent activities, thus helping them identify safer alternatives instead. It can also be used to focus on areas that had been previously neglected, to later provide a measurable boost to the company’s sales figures. For instance, an inventory management database only depicts a list of all the raw materials required for manufacturing. On the other hand, an inventory management system powered by predictive analytics can precisely predict which items of the inventory need to be replenished based on the history of their individual usage. Predictive analytics classifies data into patterns that are relevant for an organization’s computations. These patterns can thus be used to recognize different facets of the supply chain like data reusability, refinement of datasets, and operational analysis at each stage of operation.

Forecasting future trends:

By running computations on data collected from sources like customers, suppliers, and partners, predictive analytics can predict future trends. By analyzing these data using advanced analytical tools, SCM experts can pinpoint complex intricacies of consumers’ demand and can thus determine potential variations in supply and demand. For instance, a retail SCM professional can use predictive analytics to examine historical transactions to predict a possible surge in demand. A supply chain that is driven by analytics can also forecast structural shifts in the economy. These insights can then be used for planning inventory and production processes in a manner that can increase production and decrease spending on unnecessary items.

Save time and reduce cost:

One of the biggest reasons behind the spur of predictive analytics in SCM is that it helps professionals to reduce the time and expense of computations involved significantly. According to a study performed by Accenture Global Operations, the incorporation of big data and predictive analytics in a company’s operations can lead to a 4.25x improvement in the delivery time, and a 2.6x increase in the efficiency of the overall supply chain. Data-driven evaluations can thus significantly simplify previously complex processes. For instance, earlier it used to take SCM experts a significant amount of time to come up with practical solutions to operational problems, and even they involved human errors associated with calculation and evaluation. With predictive analyses, however, SCM professionals can quickly implement the advanced algorithms that are tailored to their specific needs and are free of error-causing inaccuracies. Further, even when an unexpected event takes place, it can be rerouted again within a few minutes, thus making sure that only accurate predictions have been made.

Aids in improving customer experience:

SCM personnel can also use predictive analysis to enhance their company’s customer service significantly. Since predictive analysis tools analyze the performance of the supply chain both as a whole and as separate links in the chain; professionals can now determine precisely which aspect of the supply chain can be worked upon to improve the customer experience. Further, if a problem happens to pop up in the workflow, professionals already have the in-depth knowledge that enables them to pinpoint the cause of the problem and thus come up with an immediate solution. Thus, predictive analytics analyses customer reviews and behaviour that helps professionals to make the potential changes for performance improvement.

When it comes to SCM, big data and predictive analytics have just started to make an impact in the field. Process management techniques enable every company to come up with relevant information on various processes in the production lifecycle. SCM professionals can thus use predictive analytics to improve their processes significantly, thus giving them a competitive advantage over their peers.