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Stricter shipper requirements drive automation of exception management

( Photo: Shutterstock )

As supply chains have grown more complex and performance standards have tightened, exceptions have increased in frequency and cost, highlighting the need for predictive and automated exception management.

The ‘Amazon effect’ of widespread consumer expectations of fast, inexpensive shipping has rippled outward and put increased pressure on supply chain participants further removed from the consumer. Systematically removing the slack from logistics networks in order to move goods more quickly has improved performance but also reduced the margin of error for suppliers and carriers.

Walmart and Kroger have tightened their requirements for on-time and in-full delivery, narrowing delivery windows and increasing fines for non-compliance. When Walmart first introduced its supplier performance metrics, full truckload shipments had to arrive on-time and in-full 75 percent of the time, but the retailer has raised the bar each year, increasing its supplier performance metrics to 87 percent for 2019. Late shipments incur a charge equal to 3 percent of the shipment’s value, and Target charges even more, fining suppliers 5 percent for late or partial deliveries.

“As these sanctions become the norm across the industry, they could create real pain for suppliers,” wrote a team of analysts led by Christoph Kunze in a 2018 McKinsey & Company report. “Our analysis suggests that penalties could add up to more than $5 billion a year across the U.S., if the consumer-packaged-goods (CPG) sector doesn’t improve its current delivery performance. Individual CPG players could see their margins cut by a full percentage point.”

It is imperative for CPG suppliers to understand their customers’ pain points and requirements and tailor their processes to specific networks, facilities and customer relationships.

“Slync’s technology integrates exception management and customer relationship management so that actionable events and recommendations are generated according to specific customer’s requirements, the history of transactions with that customer and the value of the customer relationship,” said Chris Kirchner, CEO of Slync.

At the same time that retailers are leveraging data analytics to drive speed and precision in their supply chains, they are optimizing for inventory. Visibility at the level of the SKU allows retailers to build increasingly complex orders, reducing inventory days of supply while growing sales revenue and widening gross margins.

Complex orders are similar to the previous examples of ever-stricter performance metrics because in both cases shipper behavior is ratcheting up pressure on upstream supply chain partners. The chart below shows how dramatically fill rates drop as order complexity increases, from a fill rate of approximately 60 percent for an order with 50 SKUs to approximately 30 percent for an order with 100 SKUs.

 ( Chart: McKinsey )
( Chart: McKinsey )

The bottom line is that as large retailers transform and accelerate their supply chains, their suppliers have to keep up or pay. Third-party logistics providers (3PLs) like intermodal marketing companies, freight forwarders and freight brokerages have begun developing predictive tools that use machine learning algorithms to forecast everything from demand and orders to estimated times of arrival and probability of delay or damage.

“Even the smartest forecasting technologies can only work with good data, and only deliver results if the organization acts on their information,” Kunze wrote. “Therefore, companies also need to ensure they have effective collaboration and data sharing with suppliers and customers and should set an aspiration for ‘no-touch’ planning to seamlessly translate forecasts into production and deployment schedules.”

Companies with exposure to supply chain risk have spent enormous amounts of time and energy working to minimize exceptions in an era of higher expectations, but they still occur, and managing exceptions after they happen is often costly. Manual communications processes slow resolution and introduce error, while incomplete visibility obscures liability and root cause.

Even digital 3PLs like Flexport and Convoy have operations teams staffed by humans to deal with exceptions and unforeseen problems with the shipments they’re handling. That makes exception management one of the most costly functions in their organizations, reducing net margins across the enterprise.

Slync’s software-as-a-service platform serves as an integration layer that pulls data from disparate supply chain partners, assembles and analyzes it to provide real time analytics and recommendations. Slync’s exception management tool automatically performs actions – like sending emails or ordering replacement shipments for damaged or lost goods – based on configurable rules.

“As exceptions occur on our platform and actions are automatically taken to move the exception toward resolution, all relevant supply chain partners have real-time visibility into the nature of the problem and the steps taken to address it,” Kirchner explained. “Our tools reduce time-to-decision and help collaborators stay on the same page without constantly going back and forth by phone and email.”

John Paul Hampstead

John Paul conducts research on multimodal freight markets and holds a Ph.D. in English literature from the University of Michigan. Prior to building a research team at FreightWaves, JP spent two years on the editorial side covering trucking markets, freight brokerage, and M&A.