A looming economic recession. Record-low water levels and port congestion in the Mississippi River. Retailers predicting delayed holiday shipping. It’s a difficult time for supply chain leaders, yet consumer expectations for services and experiences remain high.
Overcoming disruptions requires an intentional, proactive approach to supply chain optimization. While organizations have more data than ever, many lack the tools needed to organize it quickly and efficiently. The result: disconnected and siloed data that never reaches its full potential. Data can provide the predictive and prescriptive insights needed to get ahead of supply chain constraints, yet traditional approaches to data management often impede, rather than enable, optimization.
A recent IDC study found that 34% of supply chain leaders are looking for ways to better integrate their supply chain applications. Where should they focus their investments to get ahead of constraints? The answer lies in a combination of automation, speed and intelligence integrated with data. This approach enables both predictive and prescriptive capabilities, helping supply chain leaders achieve a critical goal: a supply chain strategy that can foresee costly events and present trusted solutions.
A Data Strategy for Supply Chain Success: First Mile to the Last
Disruptions can occur in an instant. Timely, accurate data can offer retailers, manufacturers and businesses the insights they need to plan for disruptions proactively. While nearly every link in the supply chain produces data, it’s often disorganized, delayed and left untouched because leaders face timeliness requirements and lack complete visibility into the data’s supporting context, known as metadata. This means businesses can only react to disruptions as they arise.
The first mile—the first 120 days of the supply chain process—is where experts have the greatest opportunity to optimize efficiency, accuracy and delivery all the way into the last mile. Disruptions can happen anytime, but if the first mile isn’t communicating with the last mile, upfront challenges can become long-term detriments. This leads to stores with the wrong inventory—a growing issue during the 2022 holiday season. Affected retailers often choose to sell slow-moving inventory at reduced rates to make room for the right products, often too late, leading to lost profit.
According to supply chain leaders, current gaps, like lack of supply chain visibility and lack of coordination and collaboration between first and last mile, will get worse if they’re not addressed today. When the supply chain lacks agility, no amount of forewarning is enough. And agility is less valuable when your data doesn’t inform timely decision-making.
Supply chain optimization requires a more proactive and predictive approach in all aspects of the journey because demand patterns fluctuate daily. Leaders need to go beyond forecasting for demand and leverage their data to understand events in and around the supply chain so they can get ahead of disruptions.
Use Case: Supply Chain Success Achieved with Data
Consider this use case: One of the world’s largest food retailer consortiums often ran short of goods. They wanted to overcome this disruption through data but didn’t know where to start. Having the data wasn’t enough—the organization needed to understand how to use it to improve operations. The retailer prioritized investments in self-service, and a high degree of automation, speed and intelligence, to guard against disruptions.
After careful analysis, the retailer found they were lacking an end-to-end enterprise resource planning (ERP) and point-of-sale system. Embedding this technology empowered local store managers to control their inventory at shelf level with accurate data to improve on and inform shelf availability. They implemented a web interface that provided store managers a unified, end-to-end view of their sales, inventory, orders and deliveries. Replenishment was automated in real time with machine learning technology. With the system running, the company could sense demand shifts and improve on-shelf availability at over 1,500 stores.
Tasks that were once handled individually across systems were streamlined and accelerated, improving supply chain efficiency, eliminating many risks as they occurred, and preventing others from occurring at all. The organization gained a full picture of its retail business, from shelf levels to timelines for food arrival.
This use case can be applied to almost any supply chain, fulfilling the need to achieve a full view of inventory optimization, production footprint analysis, transportation route optimization, and more. Leveraging data to predict when disruptions might occur in the “first mile,” where grocery stock is ordered, for example, allows retailers to optimize the “last mile,” where grocery store shelves are stocked. From there, managers can shift sourcing requirements, enable inventory rebalancing in the network, or optimize product allocation in real time.
So, what made this approach so successful?
Supply Chain’s Future: Data, First Mile through Last Mile
While experts across industries understand data’s increasing value, industry leaders understand how to properly harness data to achieve lasting results from first mile through last mile.
Modern data management techniques in supply chain and logistics, such as smart data fabrics, AI and machine learning, are granting some organizations competitive advantages and leaving others behind the curve. These developments will help businesses model demand, automate predictable and repeatable situations, and even provide prescriptive analytics to inform how to solve problems in real-time—unburdening the user and improving competitive advantages.
Today’s leaders should identify their supply chain data goals and challenges and focus on the end goal: streamlining and simplifying existing data to create better outcomes. By gaining a holistic and comprehensive view of past, present and future, supply chain leaders and their organizations will become proactive rather than reactive to potential obstacles, saving time and money while improving customer trust.
Mark Holmes is senior advisor for supply chain with InterSystems, a provider of data solutions.
