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Data Lakes: Jumping in at the Deep End

Data is at the heart of the Fourth Industrial Revolution and we are right in the middle of it. When talking to people in the transportation and logistics industry, it is clear that data is an important subject. Many companies have data high on their list of priorities. As they should. But which data? And what do you do with it?

Data Keeps Growing

Transportation and logistics companies operate with a narrow profit margin. Because of this there is relatively little room for investments and payback times need to be short. As a result often only small parts of processes are digitized and automated. Over time you end up with multiple small solutions that are used next to each other. This doesn’t have to be a big issue, but usually these solutions are not properly connected or integrated. Data from different processes accumulates in different places and the challenge is then to properly collect and process it. This makes it harder to have full visibility and control of your operation. And we haven’t even talked about data from sources outside of your own organization. It’s a big data lake.

Growing with Data

For properly and effectively analyzing data, you need to collect it and make it available. Take for example planning data from different excel sheets, or information on the cost or the profit per trip. If you have full visibility on your complete transportation planning, it is possible to optimize routes, loads, and capacity. The same work can be done while driving less kilometers. Trips that have a cost that is higher than the yield can be outsourced to a third party. These are just a few examples. Increase efficiency, lower cost, increase profit margins, and grow your business.

If you want your organization to be scalable, it is important to have your data management in order. It starts with your own systems and the correct mindset of the people that work with them. But it doesn’t end there. A lot of the data you use comes from outside your own organization and that data should be of good quality as well. This means not only educating your own staff, but also educating your customers and suppliers when it comes to providing full, correct, and complete data. Keep in mind: Garbage IN is Garbage OUT. If you have the wrong data, you will take the wrong decisions. And you want to take better decisions. And grow…

Jumping in the Deep End

Next to a change in mindset in your own organization, at your customer, and at suppliers, you also need data analysis capabilities. I’m not only talking about systems, but also about people. You need specialists for that and often these skills are not available within your own organization. Involve experts from outside your organization then.

You also need the right software, and above all the correct connection between the software and your business processes. Together you look at the issues you are facing, the available data, and at what improvements are possible. You enter the data lake together. You jump in the deep end and start swimming. Your starting point is where you have good quality data already available. The next step is to see which data you need to swim further into the lake towards your next data-driven improvement. You then make sure that data becomes available in your system(s) moving forward.

Stories from other Swimmers

A large Dutch retailer, who has two different supermarket chains and various distribution centers across the Netherlands also jumped in the deep end. They have started transforming the individual excel based transportation planning at their distribution centers into a central planning, supported by Advanced Planning System (APS) software.

They started by mapping out all transportation planning processes. All available data was checked and validated to make sure the quality was good. Steps were taken to improve data where needed and to start collecting data where necessary data was missing. After the setup of the APS, based on both processes and data, the roll out of the new planning system was started across all distribution centers in a phased approach.

Optimizing planning in 4 steps

With this approach the retailer has optimized their transportation planning processes.

Step 1: In each distribution center the transportation planning is automated, to give planners more control. The right data is collected and stored.

Step 2: The separate planning operations of all distribution centers is centralized, to create full visibility of all transportation needs and related capacity. Resources can easily be shared, and utilization goes up.

Step 3: The data lake is filled with increasing amounts of data. Good quality data for smooth swimming.

Step 4: The first of many optimizations is realized. The “new” high quality historic data is combined with the correct algorithms and forecasting models, resulting in a forecast of future capacity needs. These improvements are continuous. The more high-quality data you collect, the higher the quality of your forecasts.

One of the main benefits for this retailer is the increase in quality of the forecasting of future transportation capacity. The sooner you know you need to hire additional capacity, the lower your cost. And that’s just the start. They are still swimming and with strong strokes.

The post Data Lakes: Jumping in at the Deep End appeared first on This Complex World.

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