{"id":14100,"date":"2019-05-13T09:54:41","date_gmt":"2019-05-13T07:54:41","guid":{"rendered":"https:\/\/sprint-project.com\/?p=14100"},"modified":"2024-02-23T17:35:08","modified_gmt":"2024-02-23T16:35:08","slug":"machine-learning-une-revolution-pour-la-supply-chain","status":"publish","type":"post","link":"https:\/\/www.sprint-project.com\/en\/avis-dexpert\/2019\/05\/machine-learning-une-revolution-pour-la-supply-chain\/","title":{"rendered":"Machine learning: a revolution for the supply chain"},"content":{"rendered":"

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.\u00a0<\/strong><\/p>\n

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Improving the supply chain through\u00a0machine learning<\/em>\u00a0predictive<\/h2>\n

Forecast activity volumes and useful resources<\/h3>\n

Supply Chain activities by nature produce a lot of data related to\u00a0delivery rounds<\/strong>, storage of goods or\u00a0preparation of commands<\/a>. Thanks to the'artificial intelligence<\/strong>, this information can now be used predictively to optimize the supply chain.<\/p>\n

Demand Forecast<\/h3>\n

In distribution, algorithms\u00a0machine learning<\/em><\/strong>\u00a0today 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.<\/p>\n

The\u00a0machine learning<\/em>, 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\u00a0manage your stocks<\/strong>\u00a0and 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.<\/p>\n

Help with the decision<\/h3>\n

In the warehouse, the preparation of orders is subject to\u00a0peak loads<\/strong>\u00a0important. 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\u00a0artificial intelligence solutions<\/strong>\u00a0and of\u00a0machine learning<\/em>.<\/p>\n

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.<\/p>\n

At the end of the chain, these decisions have a significant impact on\u00a0transport costs<\/strong>\u00a0and customer satisfaction which can be delivered without waiting for the next collection. In the same way, the optimization of\u00a0goods collection circuit <\/strong>allows the order picker to avoid unnecessary journeys and thus be faster and more efficient.<\/p>\n

Estimation of the necessary resource<\/h3>\n

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\u00a0logistics site performance<\/strong>.<\/p>\n

Implement predictive maintenance<\/h3>\n

Through\u00a0smart sensors<\/strong>, Supply Chain companies have new ways to extend the life of their transport vehicles and warehouse management equipment. THE\u00a0data from IoT<\/strong>\u00a0make it possible to detect the factors which have an influence on the longevity of machines and to imagine new models of use. Thanks to\u00a0machine learning<\/em>, we can more easily measure theoverall equipment efficiency<\/strong>\u00a0(OEE \u2013 Overall Equipment Effectiveness), which is a key performance indicator for the supply chain.<\/p>\n

Have real-time visibility across the entire chain<\/h3>\n

To gain end-to-end visibility across all\u00a0supply chain operations<\/strong>, real-time monitoring is essential. Combined with the implementation of IoT sensors and the use of advanced analytics\u00a0machine learning<\/em>, the establishment of a digital platform whose data is accessible in real time will allow a much more complete analysis.<\/p>\n

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A good algorithm, yes, but above all data!<\/h3>\n

To study behaviors in detail and establish\u00a0forecast models<\/strong>\u00a0reliable, artificial intelligence must however be able to have large volumes of information to analyze. This is the condition\u00a0sine qua non<\/em>\u00a0the proper functioning of the algorithms. Three weeks of history does not allow a monitoring application to function correctly.\u00a0machine learning<\/em>, which would not have enough material to analyze.<\/p>\n

The\u00a0data storage<\/strong>\u00a0Big Data is therefore a real challenge for businesses. It is necessary to be able to :<\/p>\n