Planning for peak season used to be simpler: retailers braced for a holiday rush, carriers ramped up to handle more volume and by January, everyone exhaled. Those days are over. Today, peak season arrives earlier, lingers longer, and puts more pressure than ever before to meet service demands. That’s especially challenging in healthcare, where on-time delivery is essential for patient care. In many cases, a life could be on the line.
An earlier and longer peak season compounds the impactof many challenges that supply chain professionals already face, including managing costs, improving operational efficiency, and scaling labor to meet demand. Higher volumes during peak season can also exacerbate supply chain disruptions due to weather such as hurricanes and blizzards.
Today, supply chain professionals have an opportunity to turn these operational pressures into a strategic advantage. The right data analytics can empower you with actionable intelligence that brings more precision to your performance during this challenging season. To get started, let’s discuss why peak season is expanding and then how data analytics can help you more effectively manage the change.
Why Peak Season Is Expanding
Peak season continues to arrive earlier than it has in years past, and we can expect that trend to continue as the industry adapts to various external factors. This change in timing began with the rise of e-commerce, as consumers flooded carrier networks with more volume than ever before. At the same time, consumer demand for ever-faster delivery began to grow. While two- or three-day delivery was once the norm, overnight and even same-day service became a growing expectation. The traditional holiday rush only added to the challenges.
With capacity strained, carriers collaborated with retailers to redistribute sales volume. Rather than wait for the holidays, retailers began to launch major promotional events earlier in the year. By pulling consumer demand forward, the result was a peak season that started earlier and ultimately lasted longer.
The beginning of peak seasons also can shift from year to year, so it is important to recognize the shift and impact to be proactive in preparation and planning. Today, peak season generally kicks off in early fall and stretches well into January, driven by the post-holiday return and exchange cycle. When peak season finally ends, carriers begin planning for the next one to begin.
In effect, it’s a year-round effort no longer limited to the holiday season. And the impact isn’t confined to the retail industry alone. An earlier and longer peak season affects all industries that rely on carrier networks.
To better manage the challenge,logistics professionals can rely on a powerful combination of prescriptive and predictive analytics. First, you can leverage prescriptive analytics to understand how you’ve handled peak season disruptions in the past. Then, use predictive analytics to leverage those insights as the foundation for proactively forecasting and addressing future disruptions. Let’s take a closer look.
Prescriptive Analytics: Building the Foundation
Every peak season generates exceptions. Weather disrupts routes, shipments get delayed and volumes spike and strain carrier capacity. Prescriptive analytics examine these exceptions and other historical data to reveal patterns. You see what happened, why it happened, how your team responded, and the result.
This allows your team to identify what worked, and what didn’t. The lessons learned can then be codified into actionable standard operating procedures (SOPs). You can look at historical data to determine which lanes, markets or regions experienced higher disruption rates during peak season. Then, use that data to optimize your logistics, updating your playbook to prepare for similar scenarios next peak season.
For example, let’s say your organization historically sees disruptions during hurricane season, as your shipments move through the Gulf Coast region. Using prescriptive analytics, you can see exactly how your organization managed the impact of past storms. As a result, you’ll have the foundation to prebuild a contingency plan that won’t have to be invented under pressure.
This historical perspective is especially important as peak season now stretches earlier into summer and later into January. As the window for potential disruption grows, prescriptive analytics give you a structured way to mine a longer history of exceptions to build a richer, more reliable foundation for future planning.
Think of prescriptive analytics as your institutional memory put to work. You’ll turn historical insights into timely action, continuously refining SOPs in anticipation of the next peak season. The approach can make the difference between knowing in advance how to respond effectively versus improvising in the moment.
Predictive Analytics: Forecasting the Future
While prescriptive analytics build the foundation, predictive analytics apply those insights forward. By understanding historical volume patterns during an earlier and longer peak season, supply chain professionals are in a better position to anticipate where and when demand surges are likely to occur. That forecast can then drive critical logistics decisions well before the surge — including how to position inventory, reserve carrier capacity, reroute shipments and plan labor needs.
In effect, predictive analytics shrink the window between signal and response, giving organizations the ability to anticipate and address potential disruptions before they occur. This is especially important during peak season, when carrier capacity is already under greater pressure. By combining risk identification with pre-planned responses, you’ll have a powerful tool to be decisive rather than reactive when disruptions occur.
Let’s return to the hurricane example mentioned earlier. While prescriptive analytics reveals which rerouting decisions were most effective, predictive analytics leverages that foundation to build an alternative route in advance of the next storm.
Creating a Comprehensive Approach
Together, prescriptive and predictive analytics are key tacticsfor creating supply chain visibility, leveraging robust technology paired with logistics expertise to help drive actionable insights that better inform your peak season planning. The cycle is continuous, with each peak season generating new lessons learned that inform the prescriptive foundation and sharpen predictive modeling for next peak season.
If you haven’t already, it’s important to begin your peak season planning today. One of the most common mistakes that supply chain professionals make is waiting too long to prepare. Think about everyday operations with a peak season lens; if you have a solid foundation to handle everyday pressures, peak season can be less stressful.
Next, be sure to update your plan every year. Take a look at last year’s data and account for any changes year-over-year that could impact your operations, such as carrier service commitments and surcharges. Plan ahead for additional labor expenses, carrier capacity constraints and peak season surcharges.
As part of your efforts, develop what-if scenarios and contingency plans, rather than react to disruptions as they happen. For example, hurricane season can impact not only the Southeast, but also destinations thousands of miles away that rely on the same carrier networks. Using data analytics to prebuild alternative routing scenarios can make the difference in on-time delivery.
Once peak season is underway, be intentional and proactive with customer and carrier relationships. Close collaboration and ongoing communication are keys to success, particularly during peak season disruptions.
With that season arriving earlier and lasting longer, the time to plan is today. And prescriptive and predictive analytics are the way.
Emily Gallo is Senior Vice President and General Manager, OptiFreight Logistics at Cardinal Health.
This article originally appeared in the July/August, 2026 issue of PARCEL.
