Companies are often hesitant to share data to other parties. This hesitancy is also the case in logistics, where companies think that sharing the information will give other companies an edge over them. But when combining all the available data, logistic processes could be organized faster, more efficient and more sustainable for all parties.
Different companies and organizations are trying to combine the data of companies and show them their advantages. This data comes from a number of different type of sources: Browsing patterns, business forecasts, traffic and weather data, and the traditional data from operational system. The challenge is here to combine and analyze large streams of data that are ever increasing (Lebied 2017).
A great example where sharing information can be profitable is the container shipping industry. A shipping company will use data analytics to predict the arrival of a container in the port. This arrival time is constantly updated, as ships are several days late on a regular basis. If this information is shared with a transport company, the trucks will be waiting in the port at the right time.
Another example is the last mile delivery of a supply chain. It is known to be a very inefficient process, where people are often not home, homes are difficult to reach, and the number of packages has increased a lot since the start of the corona crisis. An example that could improve the process is better tracking of the package. Customer will know very detailed when the package arrives, and the company can use it to optimize delivery strategies (Lebied, 2017).
The advantages of data driven logistics is better efficiency and less costs. The amount of kilometers driven is decreasing due to better capacity planning, which makes logistics also more sustainable (TNO, n.d.). Besides, the data and its analysis can be sold to other companies in the supply chain, who are willing to pay for it.
The amount of data that is used in supply chains is increasing constantly. This data is valuable, and I think that with the examples provided, logistics can be optimized a lot further. Especially the sustainable savings with less emissions is now very relevant. Decisions will be more and more based on more and more data, and companies will have to manage all the incoming streams of data. Companies that will use this data effectively and efficiently, will have a significant competitive advantage over the others.
References:
Anon, Data Driven Logistics: De Toekomst Van Logistiek. TNO. Available at: https://www.tno.nl/nl/aandachtsgebieden/mobiliteit-logistiek/roadmaps/smart-and-safe-traffic-and-transport/smart-mobility-and-logistics/data-driven-logistics/ [Accessed October 10, 2021].
Lebied, M., 2017. 5 examples of how big data in logistics transforms the supply chain. Datapine. Available at: https://www.datapine.com/blog/how-big-data-logistics-transform-supply-chain/ [Accessed October 10, 2021].
Thank for this article Wim. It was a really interesting read.
With more and more complexities being added to logistic industry, the nest best alternative to derive solutions is data. Complexities arrive in many different forms, shapes and sizes, anywhere between new location, orders, technical and logistical difficulties to plain simple more customer base.
In order to be efficient, we have to make efficient decision throughout the logistic supply chain. That means making the right decision of supply of order, timings, route allocations and much more. This can only be done through historical data and the predictions and insight that we can draw of that data.
With new technological advancements in data analysis, the insights and predictions can be drawn very easily and are much more accurate. The insights can be on the customer base density, different orders and routes and predictions can be made such as demand, type of orders etc.