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You know the term “logistical nightmare”? When organizing the moving pieces of an event or situation just seems to get way out of hand? Well, if you’re in the logistics industry, that’s exactly what you can’t let happen. The word “logistics,” in its ancient Greek roots, means “practical arithmetic.” It’s an industry that, with so many moving parts, has to be driven by data, because making a decision based on a gut feeling just isn’t an option.
While most people in the industry know they need to harness the power of Big Data (and may already be doing so), the biggest challenge is implementation. Dare we say, implementing business intelligence in logistics can be … a logistical nightmare? It’s because the data that needs to be aggregated and analyzed is more than just numbers. It’s images, phone calls from service centers, likes and shares from social media, aggregated data from other partners, etc.
In other words, it’s seemingly different forms of data that have to be brought together to make a complete picture.
If you’re not a huge company with millions to invest, such as Amazon or UPS, how do you take on the overwhelming task of implementing a Big Data solution to optimize your services, make better business decisions, and increase revenue?
Whether you deal with planes, trains, automobiles, or ships, B2B or directly with consumers, one place to start is with these four strategies for making Big Data solutions happen in the logistics industry.
Identifying goals and objectives should always be the first step, regardless of your industry. You’ve got to know what your goals are and what success looks like before you jump into anything. One of the biggest missteps you can make when implementing business intelligence (sometimes referred to as BI) solutions would be to start amassing large amounts of data with no clear picture as to why you’re collecting it and how you’re going to use it.
One of the challenges of collecting data in logistics is there are so many sources you could be collecting data from. Whether you have a large company’s budget or a small company’s limited budget, the last thing you want to do is waste time and money trying to collect data from a wide range of disparate sources.
Start by identifying what you hope to improve with the use of Big Data, and then identify what data needs to be collected and how that data will be analyzed. And if you’re not sure what the answer is, start with scouting out a consultant or agency that specializes in business intelligence solutions. Take advantage of their expertise to get started.
Can you see across your entire supply chain? Can you see at a glance how much of your fleet is docked, on the road, and in maintenance? If you don’t already have a Big Data solution employed, chances are that you can’t. End-to-end visibility is a very important yet hard to attain goal for everyone in the industry, and Big Data can help. Big Data can help make your blind spots visible again.
Blind spots can occur from specific events and points in the process where metrics are not being tracked or from a lack of integration points with trading partners and suppliers. To find your blind spots, you’ll need to map the individual workflows within your supply chain. Then, you will be able to look for areas of improvement in your processes. You can assess what kinds of visibility tools (such as data collection points) you’ll need to increase visibility in each workflow.
Collaborating with trading partners and suppliers to achieve end-to-end visibility will strengthen those relationships, help identify bottlenecks, and strengthen your contracts – not mention improve overall performance.
Your most important interactions can be your biggest opportunities for improvement. These are the places where it will be important to track and monitor workflows and data points.
In logistics, the actual point where goods are being transferred between companies or between modes of transit are key moments. For many logistics and parcel delivery services, the last mile is a particularly critical interaction – and one that can cause logistical issues and delays.
For Amazon, solving this problem meant crowdsourcing a portion of the last mile through a program they call Amazon Prime Now. Amazon Prime Now is a delivery service that offers home delivery within two-hour windows of time. Amazon achieves this feat by using crowdsourced delivery drivers and mobile app technology. As a result, they are able to provide a faster means for customers to get the products they’ve ordered.
Another way to optimize the last mile would be to study all of your parcel data and determine which routes are the most reliable and most cost effective.
What are your company’s key moments in the transportation and delivery process? Tracking them will most likely lead you to pivotal insights.
Beyond identifying systems and processes to improve, using BI should ultimately help you make better business decisions. Not just day-to-day decisions but predictions that will help your business save money and stay ahead of the competition.
In order to make better decisions, you’ve got to figure out where you can be more proactive in your decision making and what data you need to make that happen. In a nearly endless sea of data possibilities, go back to your goals and objectives to guide you.
Being able to spot trends and make predictions can impact your staffing levels, your inventory, and even the estimated delivery date and time of your customer orders. Amazon is a great example of this. They are able to analyze web traffic in order to anticipate customer demand and adjust inventory on a regular basis to meet the future needs of shoppers.
Using these four strategies for making Big Data solutions happen is a great place to start if you are feeling overwhelmed by the possibilities. These four strategies will help you align business goals with data tracking and analysis, as well as help you make better decisions and improve your operational efficiency and end-to-end visibility.
Contact us to talk about how we can help.