Every shipment is a gold mine of shipping data!
Information has become an essential element in competitive differentiation. Fortunately for logistics companies, every shipment is a fount of data — useful information to improve both operational efficiency and customer experience.
The traditional means of using data analytics is to confirm decisions already made. But if logistics firms want to drive forward and avoid stagnating in a status quo that will soon see them dropping behind the competition, they need to shift their mindset. Deriving accurate insight from data is the foundation for effective decision-making.
For forward-looking decisions, companies can use logistics data analysis in two main ways:
- Strategic planning: this looks at the long-term structure of the distribution network. It considering aspects such as warehouses, custom-built vehicles, and other investments with long requisition and authorization cycles.
- Operational planning: this forecasts network flows based on real-time data. That enables the daily or monthly scaling of capacity up or down.
Deploying data analytics for strategic planning can lower the risks of investing in storage and fleet capacity; consider seasonal factors and emerging freight-flow trends; generate market intelligence segmented by industry, region, and product category; reveal supply chain risks and provide resilience against disruption; among other benefits.
In terms of operational planning, the list of ways in which precise data analysis can be used to improve logistics and customer experience is long. A few examples: more precise capacity forecast and resource control, gaining individual customer insight and creating targeted customer value, ensuring sender and recipient satisfaction, continuous service improvement and innovation, optimized work schedules.
Of course, the more precise the analysis and the more relevant the shipping data, the more accurate will be your insights driving future decisions.
Here are three ways to capitalize on the value of your shipping data and logistics data analysis.
1. Increase Profitability
Shipping and supply chain expenses typically have the greatest impact on profitability. How much does it cost to service each customer? What do you need to measure to identify that cost? Can you set accurate benchmarks for that cost? Identifying these cost factors and establishing baseline measurements are the first steps in understanding how and where you can improve the margin on each shipment. This is particularly critical in an environment where customers expect free or very low cost shipping.
Measuring freight cost as a percentage of sales is a way to identify potential low-margin or money-losing customers. Other customer-specific costs such as accessorials or additional handling often go untracked or well understood. These same costs are often not considered on a per-customer basis. Each of these is an area where data analysis can help you improve margins.
Using information from your logistics data analysis can support strategic business decisions like comparing different shipping scenarios, or optimizing the location of suppliers and manufacturing and distribution points. Tap the data of shipping history to analyze the impact on logistics costs of changing suppliers. You can also use this data analyze the potential benefits of adding an additional fulfillment warehouse.
Shipping data can also bring to light hidden inefficiencies in logistical processes. This can simplify and speed up decision making to enable swifter response to customer demands or market changes.
2. Make Better Decisions
Data can also support better decision-making within the logistics function and other parts of the supply chain — and it goes beyond the obvious importance of choosing the cheapest carrier to minimize shipping costs.
A prime example is that it’s common for companies to pay for service levels they do not need. A quick review of shipment history often show a large percentage of orders sent at unnecessarily fast service levels. Most commonly, shipments are sent next-day or second-day air when ground service will get it there just as fast. Adding insult to injury is that the carriers know when this happens and purposely send these types of shipments through their ground network. It’s cheaper for the carrier, but they do not charge any less, of course. It’s on the shipper to figure out when this is happening and make better choices to prevent it.
The line between whether a shipment should be routed LTL or small parcel is another area where many shippers need to make better decisions. It’s because most have arbitrary cut-offs deciding when a shipment becomes large enough to ship with a common carrier. The problem is this choice is not a one-size-fits-all decision. Rates, lanes, and product type all go into determining the best way to ship something. They also illuminate opportunities for finding better ways when looking at the data closely.
The intent with these examples is also to show that getting value out of data is really a process. Logistics data analysis helps identify the problems. KPIs and dashboards can then be used to keep processes on target. Future decisions are improved by using benchmarks to measure improvements.
3. Plan Strategically and Utilize Resources
In the past, companies used data analytics to confirm decisions already made. Now, a shift in mindset is needed to forward-looking data analysis to drive future decisions. Strategic decision-making in the logistics supply chain often involves comparing “what-if” scenarios and other hypotheticals. The goal is to improving efficiency as well as utilization of resources.
Historical data like shipping patterns and costs — as well as seasonality — provide benchmarks that enable shippers to compare new shipping options and alternative strategies.
For example, determining the location of potential suppliers or other distribution points can be done by modeling shipping costs to and from those locations. Similarly, as shipping rates change (for better or worse), the cost impact can be determined with certainty and budgeted for under various scenarios.
Data can also enable better capacity planning for shippers. This can include optimizing warehouse personnel levels needed during the business peak season, or maximizing capacity utilization for equipment to improve the load factor on trailers.
Taking a few small steps to identify basic KPIs and other metrics that can help improve a logistics operation is not hard. Using the data already available within an operation will also lead to more and better ideas for how it can be used. Most importantly, as improvements come so will confidence — along with a willingness by the company to invest more to achieve bigger and better benefits.