In a sector where margins are shrinking year after year with ever more demanding customers, logistics is a strategic function for distribution. Optimization is required at all levels of the logistics chain to enable the finest possible management of stocks, transport and order preparation. In this context, machine learning and the data on which it is based constitute a real opportunity. What are the points of logistics improvement enabled by artificial intelligence (AI)? What conditions must be met so that the algorithms are as efficient as possible? Explanations.
Improving the supply chain through machine learning predictive
Forecast activity volumes and useful resources
Supply Chain activities by nature produce a lot of data related to delivery rounds, storage of goods or preparation of commands. Thanks to the'artificial intelligence, this information can now be used predictively to optimize the supply chain.
Demand Forecast
In distribution, algorithms machine learning today offer unrivaled prediction capabilities to estimate business developments. They make it possible to assess the combined influence of a multitude of internal or external factors on demand, while considering the particularities of each sector.
The machine learning, or machine learning, relies in particular on data transmitted to sellers by distribution partners. It is used in particular to detect criteria having an impact on demand without one previously suspecting it. Armed with this knowledge, the distributor can manage your stocks and its merchandise flows as accurately as possible in order to market the right products at the right time, what's more, on suitable channels and in sufficient quantities.
Help with the decision
In the warehouse, the preparation of orders is subject to peak loads important. Most often seeking to limit internal resources to optimize costs, team managers will have to call on temporary workers during high consumption activities. To help novice operators make the right decisions and be productive more quickly, managers can use artificial intelligence solutions and of machine learning.
Let's take the example of an operator responsible for stacking packages on pallets. Based on the dimensions of the packages, the algorithm can indicate to the operator the best positioning of the boxes on the pallet (lying down, on the edge, vertical, etc.) to optimize loading and reduce the number of pallets to be shipped.
At the end of the chain, these decisions have a significant impact on transport costs and customer satisfaction which can be delivered without waiting for the next collection. In the same way, the optimization of goods collection circuit allows the order picker to avoid unnecessary journeys and thus be faster and more efficient.
Estimation of the necessary resource
When it comes to planning, AI can help warehouse managers predict the number of operators needed to absorb peaks in activity. Based on the period and demand forecasts, it is possible to precisely calculate the additional resources required and thus increase the logistics site performance.
Implement predictive maintenance
Through smart sensors, Supply Chain companies have new ways to extend the life of their transport vehicles and warehouse management equipment. THE data from IoT make it possible to detect the factors which have an influence on the longevity of machines and to imagine new models of use. Thanks to machine learning, we can more easily measure theoverall equipment efficiency (OEE – Overall Equipment Effectiveness), which is a key performance indicator for the supply chain.
Have real-time visibility across the entire chain
To gain end-to-end visibility across all supply chain operations, real-time monitoring is essential. Combined with the implementation of IoT sensors and the use of advanced analytics machine learning, the establishment of a digital platform whose data is accessible in real time will allow a much more complete analysis.
A good algorithm, yes, but above all data!
To study behaviors in detail and establish forecast models reliable, artificial intelligence must however be able to have large volumes of information to analyze. This is the condition sine qua non the proper functioning of the algorithms. Three weeks of history does not allow a monitoring application to function correctly. machine learning, which would not have enough material to analyze.
The data storage Big Data is therefore a real challenge for businesses. It is necessary to be able to :
- record and retain a sufficient quantity of data to ensure the performance of the algorithms;
- maintain access to information over time;
- guarantee the data security without exploding conservation costs.
In order for the data to be usable in real time, it will also be necessary to work on the history and succeed in giving meaning to certain information that has become obsolete. If a reference no longer exists, for example, you will have to consider the behavior of your family rather than trying to exploit the reference as such.
To ensure that you can fully use the AI engines and benefit from their predictive functions, Supply Chain companies must start storing their data without delay. And, to do this, establish as quickly as possible sufficient bases to be exploited in the future.
Artificial intelligence is already a reality in the Supply Chain. Its algorithms make it possible to concretely improve the performance of companies in terms of inventory management or increasing turnover thanks to machine learning. To make the most of its contribution, however, it is important to have a solid database available. If you have not yet started building your database, build your archives without further delay.
Designed in compliance with regulatory obligations related to data protection (in particular the GDPR), the Generix Group Data Lake solution will allow you to collect and store your data securely. Want to know more about our offer ? Consult our experts !
Featured image source: Amazon warehouse robots
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