In the previous article we presented the current difficulties in the transport and logistics sector. But how can Data Science and Artificial Intelligence help in mitigating these pain points?
To answer this question, it is necessary to have a vision (albeit simplified) of how the sector works. Follow with us with a basic view of 4 stages. In every stage, Data Analysis and Artificial Intelligence play an important role.
- Company / Warehouse
All sectors of a company have Data Analytics and AI tools at their disposal to help in their work. We give three examples of solutions which can support companies.
BI (Business Intelligence)
A Business Intelligence system aims to help managers make better decisions faster. The system allows companies to organize data, analyze them and extract valuable insights for management in a wide range of areas: Finance, Sales, Marketing, HR or… Logistics. One of the visible faces of BI are the intuitive dashboards where the KPIs defined by the management can be consulted anywhere. Regarding this sector in particular, it allows a company to have a global view of its trucks location or the status of a specific commodity.
It is possible, for example, to analyze orders and understand through analytics tools if a customer is about to leave our company – making it possible to intervene in a timely manner. The methodology is called Churn Analysis.
Robotic Process Automation
It consists of using software with artificial intelligence skills, to deal with high volumes of repetitive tasks automatically, releasing human resources to other activities. In case you are not aware of, transport and logistics companies are among those that produce higher volume of documentation, either electronic and paper. There is a whole range of documents whose handling represents a drain of time and money: invoices, receipts, contracts, transport and shipping guides, delivery proofs, return documents, load insurance, transhipment documents, damaged goods … Data Analytics and AI solutions allow not only to scan information, but also to work the data so that they allow the generation of insights that support managers.
In transport and logistics, warehouses are a central component. Their management presents many challenges: Where to place the goods? Which ones should be closer to the exits? Where to place perishable goods? How to organize the merchandise in order to be easily found? How to deal with space faults in exceptional situations? Next, we present an example where data analysis and artificial intelligence are very useful.
ARIMA (autoregressive integrated moving average)
Methodology that uses data recorded over time in order to allow data forecast in the future. A simple example can be found in the analysis of weather data of previous years to forecast storms in the future.
Likewise, this technique can also be used in stock / warehouse management. If warehouse data are scanned, it is possible to analyze them in retrospect and detect patterns that allow to infer, for example, if in a given period (e.g. in the next quarter) there will be excessive traffic in the warehouse or the opposite. This allows for a better resource management.
- Loading
Placing the merchandise in a TIR truck, on a ship or an airplane is not easy at al. Different means of transport have distinct effects on the load. Some transport requires refrigeration systems. The load must be accommodated so as not to put the driving conditions at risk (for example, the weight should be balanced). Goods placement and volume vary and all have to coexist in a limited space. If we give it a second thought, there are many variables to consider. In these cases artificial intelligence and data analysis can be paramount.
Knapsack
Many of the solutions designed for placing items in limited spaces emerged from the analysis of the “Knapsack Problem”. In this problem, the goal is to place items of different weights and values in a small space (knapsack) so that the greatest possible VALUE can be placed inside. And Artificial Intelligence tools can help solve this problem by supporting transport professionals, for example, in situations of container loading or better use of warehouse space. By maximizing the value of the transported load, companies become more competitive.
IoT
The Internet of Things (IoT) refers to the growing proliferation of devices capable of collecting and sharing data with each other over the Internet.
Its application is visible in various situations, from connected cars to reduce accidents in cities, smart homes, wearables or smart cities. The transport sector is among those with the most investment in IoT.
Let’s look at the application of IoT in transport through a problem. A road transport company wants to know exactly where a certain load is located and what is the temperature inside the trailer. This is possible by connecting GPS, Active RFID and WIFI systems, which in turn transmit the information to the cloud. The data can then be accessed from any location with suitable devices (e.g., a mobile phone or a tablet).
In the next article we will present some more solutions that make life easier for logistics and transport managers.
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