Acing the game via predictive analytics

Acing the game via predictive analytics

Modern supply chains offer unprecedented visibility. Regardless of time or channel, advanced tracking technologies have given companies a clear and comprehensive real-time picture of their goods’ locations at any moment. In an industry as interconnected and globalized as logistics, such visibility is indispensable; it’s business intelligence that allows companies to make choices based on instant access to up-to-date information.

But how do we get beyond the present to see into the future? If we could confidently know how an existing situation might cause cascading impact across our supply chains, how would we make decisions differently? The next evolution in logistics technology, predictive analytics, offers exciting answers to these questions.

Defining predictive analytics

The push for end-to-end supply chain visibility has generated an enormous amount of information. Including data points such as shipment dimensions, transportation mode, routing, transit time, and stop length, vast quantities of data tell millions of stories about how shipments have behaved. Similarly, disruptive events — such as inclement weather, heavy traffic, or civil unrest — create their own quantifiable data streams via news services, weather feeds, and government updates.

Predictive analytics overlays these two information sources by looking at how disruptive situations impacted past shipments and using its complex modeling to forecast the potential logistics consequences of current events.

Put another away, if we think of a shipment as creating patterns of behavior and of an event as creating patterns of effects, predictive analytics finds matches in past patterns to anticipate emergent event impacts on the downstream supply chain.

Transforming supply chains

Clearly, the implications of predictive logistics analytics are enormous. Until recently, companies could only find out about interrupted shipments after the fact through missed stops, late pickups, or deferred deliveries. Businesses could only react.

But because today’s supply chains are so globalized and interconnected, logistics disruptions now quickly radiate down the supply chain to become very costly. Especially when companies seek to minimize expenses and transit times — with tight windows to replenish inventories or sell products — the ability to foresee (and contain) potential problems in advance puts companies in a more proactive and less risky position.

The exact applications of predictive analytics depend on transit time and mode. In ocean, long distances and bottlenecking at ports can easily compound downstream disturbances, making careful planning more essential. For surface transportation, where there are more ways to circumvent developing problems, predictive models can help identify which options businesses should consider.

Putting out fires

Last year, the wildfire situation in California changed daily, causing significant disruptions to surface transportation (especially rail) moving through the area. By scrutinizing what had happened to historical surface shipments moving through wildfire-affected regions, predictive models could dynamically determine which current California (or California-scheduled) shipments would likely be interrupted before they were.

Companies could use this information to stay in control of their shipments by developing alternate routing or engaging different transportation modes. Working with a logistics provider to service transportation needs — including ocean, air, and surface transportation — helps supply chains stay in motion and avoid potential added costs and time overages that could have directly, or indirectly, resulted from any particular delayed shipment.

The same principles and practices also apply during other disruptions both dramatic, such as hurricanes or snowstorms, and less so, such as traffic jams or strikes.

Impact beyond logistics

Predictive analytics can also have practical uses aside from transportation. For example, if modeling suggests that shipments might be delayed or rerouted, you could change staffing and scheduling at affected warehouses and distribution centers. You might also alter ordering cadences for replenishing stock. With advanced knowledge of an upcoming impact, you can reallocate resources to minimize overhead in transportation-adjacent departments such as procurement, human resources, and finance.

Although all businesses can benefit from predictive analytics’ power to smooth out logistics, companies that seek to run a lean supply chain will reap the greatest rewards. Industries with tight turn times that cannot tolerate disruption, such as manufacturing or food and beverage, especially stand to gain from the risk-reducing effects of leveraging predictive data to guide transportation decisions.

Every day, the amount of transportation data to which statisticians have access only grows, meaning predictive engines are only getting stronger. As this pool of information increases, and as worldwide supply chains continue to become more complex and interlinked, having not only visibility but also intelligent forecasting built into your business becomes crucial.

Indeed, a holistic understanding of changing, real-time supply chain vulnerabilities and opportunities is only becoming more relevant; predictive analytics suggests a clear path forward toward that reality.

Rick Willbank is IT director, global forwarding at C.H. Robinson. Contact him via email at