The Steps to Conquering Your Supply Chain with Analytics

In a world overflowing with data, solutions that provide predictive and prescriptive analytics have become essential to supporting intelligent, efficient decision making. Fully realizing the benefits of advanced analytics is an evolution. Yet many don’t fully understand how to implement the baseline requirements to effectively leverage supply chain analytics including data manipulation techniques such as data cleansing and normalizing, visualization, statistical modeling, and machine learning. This article outlines a roadmap for how to sequence your supply chain analytics growth, including key insight on why it’s essential to start with a foundation of quality transportation visibility data.

The Race to the Best Customer Experience

Why are analytical capabilities so important to a well-run supply chain? In an age of instant access to information, goods, and services, customer expectations have raised the bar, resulting in a race to meet their needs. With the end consumer having more clout in the buying process, most companies accept that improving experience and becoming more customer centric is vital to success.

B2B and B2C consumers have shifted their mindset from buying a product to buying an experience, and quality service is no longer a differentiator. As it becomes even more difficult to compete, organizations are examining their entire supply chain to solve the puzzle. And it’s not an easy problem to solve. To tackle this challenge, the supply chain needs to run like a well-oiled machine with each piece of the process no matter how far up or downstream having a potential impact on the overall result. For organizations to improve their supply chain and transportation operations, they need to gain actionable and real-time insights through analytics.

The Path to Valuable Analytics

While many businesses see the benefit of an analytics-driven approach, it’s important to understand the foundational building blocks that must be in place to achieve valuable analytics. Gartner outlines the visibility journey in three stages of maturity: low, mid, and high. As organizations build on their visibility strategy—including extending modes, regions, and analytics capabilities—they move through these stages to reach an automated and prescriptive approach. While it can be tempting to jump to the high maturity stage, there are steps each organization needs to master first, and it all starts with real-time visibility powered by high-fidelity data.

Real-Time Visibility

Real-time visibility into shipments and tracking has become table stakes for the modern supply chain. While the ability to accurately track shipments hasn’t always been expected, it has quickly become a requirement for all modes of transportation and geography. It has become essential for organizations to have knowledge of exactly where orders and shipments are located at any given time and place. If your data is even an hour old, it’s completely useless.

Tracking via directly API integration with a carrier or ELD/telematics device is the best way to ensure access to high quality location data. With this knowledge at your fingertips, the amount of manual phone calls and emails to find a shipment are completely eliminated. Operations teams can focus on activities that add value or generate revenue rather than spending time trying to track down the location of a truck. It also provides the ability to offer more transparency to customers and supply chain partners, which has become an expectation. Most importantly, real-time access to high quality transportation data is the foundation for any advanced analytical capabilities.

Predictive Analytics

While real-time visibility is fundamental, it’s not enough to keep up with today’s demand. Let’s face it, your customers expect fully transparent, on-time and in full deliveries. The supply chain is complicated, and transportation is impacted by numerous unruly conditions—such as extreme weather, traffic, road constraints, limited driver hours of service, and unexpected geopolitical circumstances—that make it near impossible to predict an arrival time without techniques that analyze current data to make predictions about future.

World-class predictive analytics combines high-fidelity visibility tracking data with information about surrounding conditions to calculate an estimated time of arrival (ETA), which is updated in real-time as changes occur. To ensure these predictions are accurate, it’s important to have access to clean, normalized visibility data and a platform that leverages statistical modeling and machine learning to generate insights on future scenarios.

Why does this matter to your business? With a dynamic ETA, you can proactively adjust plans based on potential delays or unexpected changes to reduce inefficiencies and wasted costs, such as On Time In Full (OTIF) fines. If a shipment is running late, you can modify appointment times at the arrival location, allowing the dock crew to focus their efforts elsewhere instead of waiting for shipment.

Predictive analytics also enable proactive alerts and notifications when a shipment is nearing its delivery target. By geo-fencing a set timeframe, you can get notified when your truck is close, allowing the dock to allocate appropriate preparation resources.

Prescriptive Analytics

Once you’ve aggregated enough quality data, you can start to take advantage of prescriptive analytics. Prescriptive analytics identify different strategies to mitigate supply chain risks or eliminate inefficiencies. A solid baseline of historical data opens the door for organizations to reduce costs and improve efficiency even more by making informed improvements and optimizations to processes and operations.

With cleansed and normalized historical transportation data, you can take a broader look at your supply chain. This ultimately provides you the ability to identify trends, investigate points of weakness, and uncover the root cause of issues and pain points in your supply chain.

Magna International, a $40B global automotive manufacturer, which has produced more than 3.5 million vehicles including 29 different models for brands like BMW and Jaguar, analyzed their data to identify cost-saving improvements in their processes. By investigating historical data available through project44, they found that shipments were late two percent of the time and early 50 percent of the time, and both scenarios were causing missed dock appointments and impairing their JIS (just in sequence) manufacturing process. Having access to this information allowed them improve processes to ensure an additional 40 percent of their shipments arrived in the 30-minute appointment window, reducing dwell time and fines.

By breaking down data by carrier, you can find ways to improve certain partnerships, or find successful siloed practices and apply them with all of your carriers. Exploring trends in lane performance allows you to determine the most efficient routes. Prescriptive analytics can help organizations increase efficiency and reduce costs throughout the transportation process.

It All Starts with High-Fidelity Data

Analytics will become integral to running an efficient and competitive supply chain—if it’s not already. To leverage predictive or prescriptive analytics, and obtain accurate and actionable insights, visibility is not optional. Moreover, quality real-time data is vital. A comprehensive visibility solution with a large network and a cleansed, normalized and enriched data management platform will allow you to garner the insights needed to take action. Your results will only be as good as the data that’s analyzed. Without accurate data that’s updated in real time, you won’t be able to make effective decisions.

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