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How Data Analytics Can Help You Better Understand Your Supply Chain

Now's the time to build a data analytics strategy that gives you visibility into when your products will arrive — and helps you understand your supply chain's impact on your bottom line.

Dave Taddei, AllCloud

February 2, 2023

4 Min Read
chain connected by shipping containers
Alamy

We've all heard and seen the realities of the increase in shipping rates. In 2021, the domestic shipping rates for moving good by road and rail in the U.S. was up 23% from 2020. The real problem, though, is that even if you can afford that increase, the product and capacity might not be available. The demand clearly exists, but the supply on the transportation side to fulfill that demand may not.

Given that increase in both cost and demand, there has never been a more important time to build a data analytics strategy that helps you find ways to have visibility into when your products will arrive — and the true cost of not knowing.

Start by Understanding the Big Picture

The old phrase "information is power" has never been more true. With respect to product availability, this means having real-time details on the entire supply chain allows for rapid adjustments that have a meaningful impact to both revenue and margins. Shipping costs are an increasing piece of the profitability puzzle. The other side of the coin is the revenue. Are you now also losing revenue because of an inability to get your products on time — or at all? Or can you create a competitive advantage because you have products available? Your customers are still ordering, but if you can't fulfill those orders, what is the true downstream effect of that issue on your business?

Related:Top 10 Stories About Data Analytics and Management in 2022

People (your customers) will find another way to fill their demand, and those are customers that you might never get back. Seeing this issue as not a "this will pass" challenge but as a critical threat to your business is the first step. Building a data analytics strategy to solve these issues and make your data actionable is the next.

Define a Model for Demand Forecasting

When your business hinges on getting raw materials or products in and then shipping a product back out, it becomes a real challenge when you don't know if/when a particular shipment is going to arrive. These unknowns can become the reason for product delays and customer attrition — but they don't need to be. The cost and speed of creating predictive analytics and cloud data platforms have greatly decreased in the last 18 months. No longer do companies have to spend a small fortune to realize value from their data.

By understanding what factors are important — everything from economic financial models, to what's cyclical, to the movement of people geographically, to what's unique to your industry, you can build a demand model to see what you need to plan for in the next three to six months. That information, when coupled with the lead time on raw materials, the production time, and the shipping time, can become the backbone of your logistics strategy for what to order — and when.

Know the Actual Effects to Your Margin

We often call this "profit margin attribution." Without being able to understand what is contributing to expenses, it is nearly impossible to make changes. An analysis of your shipping spend tends to reveal key insights and the need to optimize that spend. Are you using the right vendor for the right shipments? Is a shipping lane, origination or destination location, customer, or product type causing a disproportionate negative or positive effect on margin? Are the characteristics of the products, such as weight, dimensional weight SLA, and distance from a distribution hub to name just a few, having an effect?

These insights and others could have a huge impact on how a vendor prices a shipment. Making the wrong decision related to your delivery could have a massive impact on your overall margins. We've had clients where there has been up to 20% cost savings potential simply due to selecting the wrong vendor. By understanding the shipping spend — and where the hidden costs are, you can determine which vendors should be used, for what products, and for what customers.

When looking to control unknown factors, it's not about getting to the ideal state. Instead, it's about continuous improvement and moving toward it. The immediate outcome is increasing revenue and reducing costs. The strategic outcome is that when you can meet the demand of your customers, you have a huge competitive advantage over everyone else in your industry.

Dave Taddei is SVP, Data Analytics North American Practice Lead, at AllCloud.

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