Part 2: Data, Dashboards, and KPIs for Improving Logistics Management


Data is pouring out of your logistics operation — it’s time to use it

This is part two of a two-part post. Here are more ways data can be used to improve a logistics operation.

  • Better Decision-Making

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, which we covered in Part One.

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 will often show a large percentage of orders are 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 (like anything over 200 lbs.) to decide 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, and opportunities for finding better ways are illuminated when the data is looked at closely.

The intent with these examples is also to show that getting value out of data is really a process. Data analysis helps identify the problems. KPIs and dashboards can then be used to keep processes on target. And future decisions are improved by using benchmarks to measure improvements.

  • Strategic Planning and Resource Utilization

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, with the goal of 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.

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