Machine learning in logistics can be responsible for analyzing data sets looking for better ways to deal with operations. That may be improving demand forecasting or accuracy, inventory optimization or responses in procurement.
While doing that, it also reduces time and money spent on those operations and help tracking the whole process. So, knowing that, how machine learning can impact your logistics?
Machine Learning in Logistics
There are innumerous ways an industry can benefit from machine learning in logistics. With the help of algorithms, the patterns in supply chain data often reveal the most influential factors of the operation.
So, using the technology, companies can discover ways to improve performance in tasks such as:
- Supply Chain Planning: balance of demand and supply, optimization of delivery process.
- Warehouse Management: optimization of inventory, avoiding over and under-stocking.
- Warehouse Analysis: monitoring of warehouse perimeter, automate barcode reading, track employees, prevent thefts and violations.
- Demand Prediction: predict demand and improve demand forecasting, analysis of factors that influence demand.
- Logistics Route Optimization: reduce costs of shipping, decide better routes.
- Supplier Selection: predictions for interaction with potential and existing suppliers, optimization of orders, faster deliveries.
With machine learning solutions, the whole process will be more efficient and profitable. Making your team gain time and insights from the operation. Those things combined will ensure growth and better development of your logistics.
Other uses of machine learning in logistics
Firstly, a machine learning integrated process with logistics will allow companies to access fundamental information about their operations, as billing amounts, account information, dates, addresses, and other parties involved.
But the uses aren’t limited to that. Machine learning can be part of the marketing process, for instance, dealing with e-mail. Freeing time for marketing professionals focusses on creative process.
Another possibility is the automatization of customer service. Chatbots can perform the task of call centers and take care of shipments, delivery requests and reordering. Besides, it’s easy to answer frequently asked questions.
Still outside the supply chain management, Machine Learning can make pricing more dynamic, responding to changes in supply and demand while considering market prices. That can happen trough analysis of historical data.
Finally, damage detection can be done by algorithms of machine learning trough computer vision solutions, detecting the type of damage and how much of the item was affected.
How to implement Machine Learning on your Supply Chain
Comprehend the Structure
Of course, changing the whole structure of your logistics isn’t easy. You need to plan and take actions accordingly to the needs of your company and logistics.
So, before you start using machine learning you must understand your supply chain. Evaluate the structure and find the core factors of operations. To find that, make a detailed analysis of the network considering the suppliers.
After that, it’s determinant to find the relations on the structure. How they function with each other and the way it affects the supply chain. That will lead to a diagnostic of the weaknesses of the system and where the major risks concentrate.
Establish objectives
To evaluate correctly how machine learning can have a positive effect on your logistics you need to establish clear objectives for your progression. It’s fundamental to have a plan defining goals and requirements of the operation.
Then you need to calculate Return of Investment and the Total Cost of Ownership to see the probability of gains in short and long terms, for this, predefined KPI’s will define the scope of problems in terms of machine learning.
Develop a consistent Machine Learning project
All that evaluation is necessary to build a consistent project that will be functional for your purposes. That takes into consideration aspects such as the team you need.
For instance, professionals from Data Science, development, business analysis are key to the operation with machine learning, because they will be able to interpret the information the algorithm extracts.
Also, is important to establish right metrics of success for each of the tasks and stack. That will be given by the problem statement in the early steps of the project.
The most important aspect, though, is to always train and retrain the model, that will ensure consistency and accuracy of the algorithms and make your project be successful.
In sum
Machine Learning can help your supply chain and logistics in a lot of different ways. It can help you interpret data and extract information and relations from aspects that wasn’t previously considered.
With that, you will be able to save money and free time of your employees, resulting in a more efficient operation that is more profitable and durable.
