Supply Chain Trends In 2023

Supply Chain Trends In 2023
By HERE Technologies

As companies accumulate more and more data from their logistics and supply chain operations, the lack of location data makes it difficult to effectively coordinate supply chain activities. However, when logistics and operations data is combined with location data, companies can improve the efficiency, accuracy, and reliability of their supply chains. Using machine learning in location analytics can identify transportation patterns and predict future planning. In today’s fast-paced supply chain environment, quick decision-making is crucial, and location data is an important tool for achieving this. By integrating location data with other enterprise data, companies can gain real-time insights into current supply chain conditions and future planning. This information can be used to provide context and predict future events, ultimately benefiting supply chain planning processes.

Predictive Supply Chains That Improve Decision-making 

The effective use of predictive analytics in supply chain planning requires large amounts of varied data, including location and logistics information. Combining these types of data can result in more accurate predictions for estimated arrival times, which can benefit logistics operators, shippers, and customers by promoting certainty and building confidence. The analysis of this data can also reveal downstream implications of delays and other issues, allowing managers to adjust labor schedules and contingency plans as needed. Artificial intelligence and machine learning can help predict future conditions based on past behavior, while real-time data integration, including weather information, can help manage changing conditions. Location data can add further clarity to predictive processes and support effective supply chain planning.

Building More Sustainable Supply Chains 

Sustainability became a major concern for transportation and logistics companies about two years before the pandemic, with shippers requiring carriers to disclose their carbon efficiency during bidding processes. During the pandemic, many companies put sustainability on hold, but now they are eager to catch up. Location intelligence can play a crucial role in helping supply chains meet their sustainability goals, such as minimizing emissions by optimizing routes and planning for recharging electric vehicles. Companies that don’t prioritize sustainability risk losing customers, facing higher costs, and becoming non-compliant with regulations as they evolve. Governments are also considering emissions pricing and increasing fossil fuel taxation, while some countries are already introducing bans on the production of internal combustion engines.

Digital Twins That Deliver Value 

According to Coppelmans, the increasing use of predictive analytics will make supply chains more flexible in 2023. Digital twins, which use AI and machine learning to create virtual supply chain models that can analyze problems, predict future impacts, and suggest reaction plans, will be a major use case. By using location intelligence, logistics operators can track shipments and predict their arrival times. Digital twins can provide a control tower approach to shipments, allowing them to be redirected or moved to other transportation options if problems arise.

Integrating enterprise data with location intelligence will help enterprises more efficiently manage their operations, particularly in last-mile delivery operations. Location systems provide electronic timestamps for shipment arrivals, streamlining processes surrounding penalties and claims for late deliveries. Optimizing production and transportation processes by merging operational and location data will ultimately generate higher levels of customer satisfaction for logistics providers and their customers, as well as additional revenue streams.