It’s a simple proposition: if you don’t have supplies, whether that’s raw materials or finished products in a warehouse, then you don’t have a business. Despite the obvious nature of this statement, though, too many companies lack an overall grasp of their supply chain, leaving their businesses vulnerable – but they’re ready to change.

According to a 2016 Honeywell Process Solutions-KRC Research study, 67% of manufacturing executives are investing in data systems to reduce costs and stabilize their operations. These leaders recognize that the more they know about upcoming supply trends, the better they can compete in a saturated marketplace.

Visualizing The Future

One of the most effective ways to understand upcoming business trends is by creating visual representations of current and predicted operational data. Such data visualization is at the heart of business intelligence (BI) and companies are mastering the use of such data to increase day-to-day efficiency and gain a competitive edge. Until recently, however, most companies relied primarily on retrospective analysis. Predictive analytics, which use past patterns to identify future events, is a newer – and more powerful – tool.

What businesses noted over the past several decades was that, though manufacturing and processing were increasingly automated, supply chain issues like procurement remained a hands-on process. With the growth of AI, however, manufacturers now have the ability to provide forward-looking assessments of supply chain activity. That allows businesses to identify potential materials shortages, common political and economic issues that influence product availability or shipping lanes, and to source alternative routes or suppliers to stabilize their business in the short term.

Inside The Revolution

So what have businesses gained from predictive analytics? One of the top benefits is that they’ve been able to reduce freight costs by developing collaborative supply chains. If more products arrive along the same delivery systems or are shipped along less congested supply lines – or those with less sociopolitical conflict to interrupt chipping – that can protect the materials and offer financial and physical insurance for businesses.

And predictive analytics don’t just cover upstream supply chains. Rather, they can also help identify consumption patterns; will this product be in demand in stores in 6 to 12 months? How will demand vary across regions? Knowing how much of a given item to produce and where to ship it is central to businesses that want to maximize profits. For this strategy to succeed, however, manufacturers need those on the distribution side to adopt similar systems and provide integrated data. Right now, though, the majority of downstream sales venues don’t have this capability, though they’re likely to adopt such tech in the near future, as it will benefit retailers as well.

Predictive analytics for supply chains has the potential to transform how we think about manufacturing, decrease the cost of common products, and stabilize the availability of key goods, from medications to food products and the broader set of consumer goods. Though the technology is concentrated on the manufacturing side, this is a case in which the advantages stemming from new industrial technology trickle down – and up to suppliers – to everyone’s benefit.