How Predictive Analytics is Transforming Supply Chain Management

"Predictive analytics can be used for determining events or outcomes before they happen. This is critical in keeping companies one step ahead of the competition." - Chad Sauter, Conway MacKenzie

By John Edwards

Gaining an Edge with Predictive Analytics

By John Edwards

 Put away the crystal ball, predictive analytics provides accurate forecasts of supply chain and procurement trends and conditions.

Businesses no longer need a crystal ball to predict the future. Today, thanks to predictive analytics technology, a supply chain or procurement manager can forecast key trends and conditions far more accurately and reliably than any run-of-the-mill psychic.

"Predictive analytics by definition looks at data to identify patterns that can be used to predict future outcomes and trends," says Regenia Sanders, vice president and supply chain practice leader at SSA & Company, a business management consulting firm in Aspen, Colo.

Predictive analytics encompasses many different technologies and methods, such as statistical modeling, data mining and various mathematical techniques, to scrutinize current and past data and predict what should happen at a specific time based on the supplied parameters. For supply chain and procurement managers, predictive models exploit patterns found in historical and transactional data to spot both risks and opportunities. Models can be configured to capture relationships among multiple behavior factors to enable the assessment of either the potential or danger associated with a particular set of conditions, thus guiding decision-making for various categories of supply chain and procurement events.

"If applied appropriately, predictive analytics can enable companies to identify and respond to new opportunities and challenges more quickly," says Matt Clark, chief operating officer of Corcentric, a financial process automation services provider headquartered in McLean, Va.

Prediction Power

Predictive analytics is being used by a growing number of electronics companies to build better supply chain and procurement forecasts, says Chad Sauter, a director at Conway MacKenzie, a business consulting and financial advisory firm based in Birmingham, Mich.

"Companies are combining standard historical demand data along with other criteria, such as weather, location, number of local web searches, seasonality and promotional uplift probabilities, to generate a more precise demand plan," Sauter explains. "This in turn translates to a supply plan designed to bring the customer their desired product, to their desired location, at their desired time."

Supply chain and procurement leaders who embrace predictive analytics can expect to advance their decision-making skills while gaining the ability to guide, optimize and automate decisions to meet specific business goals and redefine organizational processes in real-time, Clark says. "Predictive analytics can also be used for determining events or outcomes before they happen," he notes. "This is critical in keeping companies one step ahead of the industry and the competition."

Predictive analytics also enables users to quickly anticipate behaviors from different angles. "In supply chains, it is used in forecasting and demand planning, looking at historical customer order patterns to better predict future demand patterns, which can be used in supply, inventory and capacity planning," Sanders says. "In procurement, there are applications in spend analytics that look at historical spending to provide insights into future spending requirements to better formulate business expectations that can help in forecasting products purchased from suppliers, particularly those with long lead times."

Rising sales of predictive analytics tools show that businesses are beginning to recognize the technology's potential for slashing costs and revealing new opportunities. According to a 2017 report from Sarasota, Fla-.based Zion Research, the global predictive analytics market, valued at approximately $2.5 billion in 2014, is expected to reach approximately $7.8 billion by 2020, growing at a compound annual growth rate (CAGR) of around 17.0 percent between 2015 and 2020.

Innovation and Agility

Predicative analytics has a huge potential for innovating supply chains, Sauter says. "Consider, for example, that the internal operations of any company deploying predictive analytics will benefit from [applying it to] machine maintenance." Better maintained machinery is less likely to fail, keeping supply chains humming. Similar types of predictive analytics-driven enhancements can pop-up throughout the supply chain over time, delivering performance gains, improved reliability and other benefits to all participants. "Predictive analytics can increase efficiencies at every step of the supply chain, from sub-suppliers through point of sale," Sauter notes.

Predictive analytics also allows businesses to peek around corners and uncover potential opportunities as well as lurking challenges. "When companies are able to quickly identify risks, they are allowed more time to efficiently manage the risk before it creates a larger issue," Clark says. "Being armed with this kind of insight helps business leaders spend more time on growing their companies and looking for new ways to innovate."

Predictive analytics also helps executives and managers better understand the relationships between demand, supply and supply chain policies, says Scott Nalick, global supply chain industry consultant for SAS Institute, an analytics software provider headquartered in Cary, N.C.

"With this improved visibility, a company can become more agile and responsive to better anticipate unexpected events and plan for contingencies to maximize efficiencies," he notes. "Similar kinds of predictive analytics can drive insights for risk in sourcing and supplier relationship management; for manufacturing efficiencies for assets and equipment uptime in production; for fulfillment in order management, warehousing and transportation; and for the cost of service in the field for warranty and service parts." With predictive analytics, "supply chains become more flexible, extendable and customizable," Nalick adds.

Better visibility to customer demand leads to more effective inventory planning, which can help lower the amount of inventory kept on hand, Sanders notes. "It can also help influence better negotiated contracts with more detail on expected volumes and consumption rates," he says. "Additionally, access to this type of customer insight and market intelligence can improve sourcing strategies and supplier relationship management."

When all parties use predictive analytics, the technology can transform a supplier relationship from a purely transactional arrangement into an alliance that's more like a strategic business partnership.

According to Sauter, the savings generated by predictive analytics flow back through the entire supply chain as suppliers become focused on the exact quantities demanded at the right time and shipped to the correct location with little waste.

"Companies can work through demand analytics, finished goods optimization, replenishment planning, network planning and transportation analytics to make sure that they have the shipped the correct quantity, at the correct time, via the most cost effective method, to achieve the best price and margin," Sauter says. "This efficiency is automatically translated upstream through the supply chain via the improved forecast and purchase order demand signals."

Getting Started

"Any business considering the use of predictive analytics needs to assess where their organizations are with respect to the organizational processes and learning required to incorporate predictive analytics into their operation," Sauter says. "This includes a detailed assessment of their people, processes and technology, as they will all be affected by this transformation."

To harness predictive analytics' full potential, supply chain and procurement leaders need to make sure they have a solid foundation in place, Clark adds. "This includes a business foundation and a technology foundation," he notes.

"Invest first in simple analytics tools and personnel that can interpret data and translate it to deliver business value," Sanders says. "Start with data cleansing to ensure the accuracy of your data."

"It’s also important to make predictive analytics part of a repeatable process, so that it’s easy to gather data from different sources and ensure data quality that leads to better analysis," Nalik says.

"Companies must have a business plan in place that outlines the goals they wish to achieve, both short-term and long-term," Clark adds. "These business goals do not have to be set in stone, but they will better inform how the predictive analytics will be used in forecasting future plans."