Shippers face two external factors that impact their costs, and traditionally they have had very little control over them – shipping rates and available capacity. Big data, though, is starting to change this. More companies are now using the millions of data points their supply chains are generating to improve their operational metrics, including shipping expense and asset utilization, to deliver better customer service and improved relations with carrier partners. It’s all part of the move to incorporate predictive analytics.
The concept of predictive analytics is not new – in many ways it’s the 21st century way to handle demand forecasting. According to SAS, “predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.”
The benefits of predictive analytics over previous methods is the ability to leverage the millions of data points now available within the supply chain, and in doing so, improve the overall efficiency of the operation.
McKinsey & Company advised caution to those who believe they can simply plug in a data scientist and gain instant benefits. “Supply chain managers – even those with a high degree of technical skill – have little or no experience with the data analysis techniques used by data scientists. As a result, they often lack the vision to see what might be possible with big data analytics,” it wrote in a recent report. “Second (and perhaps more significantly), most companies lack a structured process to explore, evaluate and capture big data opportunities in their supply chains.”
Both challenges can be overcome, and in so doing, organizations benefit. McKinsey pointed out that using predictive analytics can expand datasets beyond what is traditionally acquired on an enterprise resource planning or supply chain management system. These datasets provide new insights throughout the supply chain, from front-line operations to carrier delivery of the shipment.
Shippers and carriers will each use predictive analytics differently, of course, and each can benefit. Third party-logistics (3PL) providers are now able to help as well, bringing data insights from each party in the relationship to improve the overall structure for all. Carriers may be focused on data accumulated along routes (including competing rates along that route), operational data, and even maintenance-related data. Shippers may be more concerned with data related to material costs and inventory buildup, unaware of how improved inventory flow could reduce transportation costs. Carriers could better price services if they had insight into a shipper’s specific needs, such as timing of shipments. Improving the timing of shipments could reduce both on-hand inventory costs and shipping rates, if only the parties had collective insight.
“Organizations can modify how and where they use resources to better prepare for future events,” explained a white paper from Research Optimus. “It creates a framework for connecting the dots between trends, patterns and associations in data to help businesses respond proactively to future developments.”
Using predictive analytics within a transportation management system (TMS) setting can help identify these types of future developments. The data can identify past trends to allow planners to adjust schedules and routines for future events. In the retail world, suppliers who have access to real-time sales can more accurately predict where merchandise will need to be located. The same philosophy applies for nearly every supplier and end customer. When a 3PL is brought into the equation, costs can be driven even lower through its ability to tap into data from the entire supply chain.
Predictive analytics also helps shippers proactively locate inventory to take advantage of trends and to gain insights into customer and supplier behaviors that can influence future decisions. Most importantly, predictive analytics and big data is helping shippers predict supply and demand so that deliveries can be made on time, at the right time, and at the right cost.
An often-overlooked ability of predictive analytics is the opportunity to bring together various departments within a single organization. Transportation planners are often reactionary to a company’s needs – a decision is made, and it is the planner’s job to carry out the transportation part at the best price. But, if that planner had insight into inventory plans or sales forecasts, he or she could better forecast transport needs by adjusting schedules accordingly and with enough notice to ensure capacity is available at the best price possible.
Many trucking companies already utilize predictive analytics for things like fuel consumption and GPS tracking, but it is often done without the input of shippers. Again, third-party logistics companies can leverage their own relationships with shippers and carriers to bring the parties together to build an even more effective system that drives costs lower for all.
“Big data is already helping leading organizations transform the performance of their supply chains,” McKinsey said. “Today, such approaches are the exception rather than the norm, however. Lack of capabilities and the lack of a structured approach to supply chain big data is holding many companies back. For big data and advanced analytical tools to deliver greater benefits for more companies, those organizations need a more systematic approach to their adoption.”
Ultimately, the success of any predictive analytics program demands on human input – it’s the age-old Garbage In, Garbage Out concept. What does your business want to gain? What are the pain points? Where can the data be found and who will analyze it?
“If the company has a good supply chain performance management system in place, it should already have an understanding of where it lags behind current practices and which supply chain improvements would pay the biggest dividend,” McKinsey wrote. “Perhaps forecast accuracy is poor in certain regions, for example, or delivery performance is poor in certain markets, or there are too many stock-outs for a particular retailer. A review of current performance levels should produce a list of a dozen or so priority issues where big data could enable improvements.”
The good news is that companies can leverage the expertise of 3PLs so they don’t have to invest in large big data teams or expensive systems to accomplish these savings. The growing use of predictive analytics within the supply chain is driving down costs for shippers and carriers.