The Advantage of Real-Time Analytics in a Complex Supply Chain 


It’s been a rough few years for global supply chains. Since the start of 2020, 72% of senior-level supply chain executives reported a negative effect, and 57% said they were impacted by serious disruptions. 


First, the COVID lockdowns slowed — or in some cases, halted — the availability of both finished goods and raw materials. Next, many supply chains experienced demand shocks resulting from consumers stockpiling months’ worth of goods, from toilet paper to disinfectant wipes.  


And once people realized that staying home would be the new normal, they began to spend their stimulus money or savings from the services they were no longer using to make an unprecedented amount of purchases. Air fryers and pressure cookers for all the meals they were now preparing at home. A new, comfy work-from-home wardrobe. Every type of manufactured good needed to support newfound hobbies. More unexpected demand that exceeded the economy’s ability to produce and fulfill. 


Supply chains barely had a chance to recover from lingering pandemic-related issues and new supply challenges resulting from Russia’s invasion of Ukraine before they were bombarded with new disruptors: inflationary pressures and a recession.  

The only constant for today’s global supply chains is disruption.

The lean and just-in-time strategies that worked so efficiently in the past had been rendered largely ineffective during the pandemic. Yet excess stock and inventory can kill a business during a recession.  


For retailers in particular, getting products into the hands of demanding consumers has its own unique challenges. Fast fulfillment is essential:

67% of organizations consider meeting customer expectations for speed of delivery to be a critical force impacting the structure and flow of their supply chains over the next 12-18 months. 


In addition to customer expectations, ever-increasing costs, reliance on suppliers that are facing their own difficulties, and last-mile delivery challenges, the need for optimized communication across the supply chain will give businesses the ability to get ahead of disruptions by using real-time supply chain analytics. This ‘live data’ does more than just look impressive in colorful dashboards. It’s data that drives real time analytics which in turn drives decisions, actions and entire workflows. 


Leveraging real-time analytics and AI for actionable visibility 

The world’s leading companies have turned to analytics and dashboards for increased visibility. A recent McKinsey survey found that 67% of respondents implemented digital dashboards for end-to-end supply chain visibility. And those companies were twice as likely as others to avoid supply chain problems caused by the disruptions of early 2022. 


What is real-time analytics? 

Real-time analytics is the process of applying logic and mathematics to data for analysis as soon as it is captured, resulting in the ability to make better decisions faster. In the supply chain, real-time analytics provides insights and drives decisions based on the immense amounts of data associated with the procurement, processing and distribution of goods.  

Gartner divides analytics into four key categories: descriptive, diagnostic, predictive and prescriptive.

Ahead, we explore what insights each area can uncover and how to use those insights to create value in the supply chain. 


Descriptive analytics 

At a high-level, dashboards and data visualization tools can show companies what happened or what is happening. Live monitoring of inventory, suppliers, costs, sales, warehouse KPIs, and other critical events provides an at-a-glance view of performance, plus trends over time. Descriptive analytics help companies identify metrics and patterns such as key inventory trends, supplier spend, frequent bottlenecks or item cost. 


Diagnostic analytics 

Based on drill-down capabilities or data mining, diagnostic analytics enable further exploration to understand the ‘why’ behind the data. What stores are on track to meet their sales targets? Why are customer shipments delayed? What’s causing low inventory turn? This more detailed level of analysis allows companies to recognize when it may be time to scale operations, minimize costs or optimize processes. 


Predictive analytics 

This is where analytics begin to evolve from visual dashboards to analytics-driven, decision-support tools. Through techniques like predictive modeling, forecasting, machine learning and multivariate statistics, predictive analytics highlight what will or what could happen — for instance, if we cannot find a reliable source for raw materials, if tougher trade regulations are enacted or if the warehouse labor market continues to shrink.   


Prescriptive analytics 

While the concept behind supply chain analytics has existed for over 100 years, many companies still lack end-to-end visibility and actionable insights, or the decision intelligence or process orchestration that analytics should enable. This is where prescriptive analytics comes in: to answer the question “what should I do now?”. When combined with predictive analytics, companies can understand what they should do in order to realize a desired outcome, based on data. Actionable insights determine the best time to launch a new product or the ideal pick, pack and ship process flow for a specific fulfillment center.  


Warehouse fulfillment orchestration is one emerging use case integrating predictive and prescriptive analytics with AI to continuously drive optimal decisions and actions. By combining the power of real-time analytics with smart robotic automation, companies can instantaneously model and execute the best possible workflows for any fulfillment process (e.g., multi-variant SKU handling, dock-to-stock processing of high-volume inventory, palletizing, picking and packing).  


Because every new scenario is assessed in real time and analyzed based on precise historical data, the actions driven are the right fit at that exact moment to ensure the highest probable accuracy, efficiency, and speed.  


The bottom line 

In a challenging economic environment, shedding light on supply chain trends and bottlenecks via real-time analytics can improve decision-making and drive growth. Companies should go beyond visual dashboards to analytics-driven decision intelligence and process orchestration to truly gain an advantage.

Integrating real-time analytics with AI and automation can also boost speed and productivity in specific supply chain functional domains, such as fulfillment orchestration in the warehouse.  

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