For supply chain professionals, there is always a conundrum when balancing supply and demand. Traditional order management or enterprise resource planning (ERP) systems do their best to execute an optimal balance, and they can be sufficient during normal operations. But a sudden imbalance is enough to throw planning systems off, requiring a brand-new fulfillment plan. After the past few years, it seems almost clichéd to caution about the dangers of supply and demand disruptions. Today, disruptions occur so frequently that while each individual instance is unpredictable, the consequences in terms of product availability are entirely predictable.

A sudden demand spike can lead to stockouts of popular retail products, while a severe weather event can interrupt the flow of critical supplies to consumers. The responsibility to react clearly rests with planning and execution leaders. From fulfillment analysts and omni-channel commerce managers to customer success managers, diverse functions are tasked with ensuring that inventory is properly rebalanced following a disruption. Ultimately, their mission is to strike the right balance between customer satisfaction and profitable fulfillment. But most of the available tools today make that task difficult.

While we can’t avoid disruptions, it is possible to respond swiftly, strategically and profitably via intelligent inventory rebalancing — all within the time horizon of an existing plan.

The Shortfalls of Today’s Approach

Generally, these functions become aware of disruptions through a supplier, colleague or even a news article. At that point, they’re forced to navigate multiple systems and work through complex macro-based spreadsheets to find a potential solution. With these systems working in siloes, it’s difficult to communicate and coordinate across the organization. And manual analysis simply can’t manage the complexity of rebalancing, let alone keep pace with the urgent nature of this task.

The result? Scarce inventory is rarely allocated in the most profitable and strategic way, to optimize fill rates and service outcomes for top-priority customers — as well as maximize inventory utilization and return on investment.

Let’s explore this challenge through the lens of a consumer goods manufacturer. ABC Corporation currently has 100 units of inventory on-hand across four distribution centers (DCs) — more than enough to satisfy customer orders for 80 units. The initial allocation has been determined by the planning team, and the execution team understands how to optimally fulfill the orders.

Due to a storm, one DC that’s carrying 40 units of inventory is suddenly out of operation. To minimize stockouts and customer dissatisfaction, ABC Corp now must quickly address an unexpected shortfall of 20 units. This forces execution leaders to re-engage the planning team and wait for a new fulfillment plan. Planners must quickly decide which customer orders get filled, search for an alternate source of supply and analyze transportation capacity. All the while, they are losing valuable time, and margins, every minute.

Besides calculating shipping costs from one supply chain node to another one, intelligent rebalancing must consider the strategic importance of each customer, the probability of selling each item at full price, and real-world operational constraints such as the availability of labor and transportation assets. A new article in Science Direct notes that, for fashion apparel, there are typically a million binary variables to consider when re-allocating inventory. Clearly, traditional approaches based on spreadsheets and human cognition are going to fall short.

Imagine a Better, Smarter, More Profitable Way

The good news? Thanks to digitalization, planners now have access to all the data they need to make optimal decisions based on customer tiers and priorities, as well as allocation rules like first-in-first-out (FIFO) and fair share. The bad news? They probably lack the advanced decision tools, powered by sophisticated, purpose-built algorithms, to perform all the necessary analysis and make rapid, profitable decisions.

While many teams leverage an allocation engine from Blue Yonder, enabled by artificial intelligence (AI) and machine learning (ML), to make their initial allocation plans, rebalancing is often a mad scramble. Planners are forced to rely on manual processes, spreadsheets and best guesses to make incredibly complex trade-offs and decisions.

But today Blue Yonder offers a powerful new solution, fueled by AI and ML, that mirrors the high degree of analytic rigor used to originally plan inventory allocation using Blue Yonder Order Management. With the addition of this new solution, called Intelligent Rebalancer, Blue Yonder has become the only software provider with the unique capability to strategically react and recover from disruptions in both the planning and execution stages.

In my next blog post, I describe the specific capabilities and benefits of Intelligent Rebalancer — and how its cloud-native, microservices-based delivery model can help your team quickly improve its success in managing through uncertainty.

To learn more now, visit blueyonder.com.