FROM REACTIVE TO READY: USING DATA ANALYTICS TO MANAGE AN EARLIER PEAK SEASON
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By Emily Gallo
lanning 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 impact of 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.
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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.