Use Cases of AI in Logistics

The logistics industry is being revolutionized by artificial intelligence (AI). It may sound like a cliche, a hype, or a buzzword, but it’s true. Logistics has had a huge impact on AI, and advances during this time could lead to a better quality of service.

AI offers a wide range of possibilities for logistics companies, from self-service machines to predictive analytics. AI transforms warehouse processes such as information gathering and analysis, and inventory processes, and enables businesses to increase efficiency and increase revenue. Warehouse management uses AI to predict demand, modify orders, and reroute products in transit. Mckinsey predicts that the logistics industry will generate $1.3 trillion to $2 trillion in economic value per year through AI.

For this reason, we decided to analyze the progressive role of AI in the logistics industry. Let’s take a look at some reasons to maintain a logistics system based on artificial intelligence.

Supply Chain Optimization

Supply chain optimization uses data analytics to find the best mix of factories and distribution centers to meet customer demand. A good supply chain enables business efficiency and responsiveness, ensuring that customers get what they want, when and where they want it. This benefits your business and contributes to the sustainability of your supply chain.

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There are three phases to a successful supply chain optimization process:

• Supply Chain Design Describes network design processes. For example, where the storage facilities are located and how product flows between them, as well as strategic objectives such as forecasting demand, staging supply, and planning and scheduling manufacturing operations.
• Supply Chain Planning Strategic supply chain distribution plans, inventory plans, and facility coordination must be developed to optimize the delivery of goods, services, and information from suppliers to customers and to balance supply and demand.
• Supply Chain Execution It focuses on execution-oriented applications and systems: warehouse and inventory management, transportation management, global trade management, and other execution applications such as real-time decision support, supply chain visualization, and order management systems.

Product segmentation

Due to the nature of the product, a separate supply chain may be required. Important product characteristics for supply chain segmentation include:

• Temperature Range: There are three main temperature ranges for food: frozen (about -18 to -25°C), chilled (about +2 to +5°C), and normal temperature. These are often used as the basis for segmented supply chains, often with a mix of refrigerated and ambient goods.
• Bulk Processing: Certain items (liquids, powders, granules, etc.) are best suited for bulk processing and require special storage, handling, and transportation facilities.
• Hazardous Materials: Hazardous materials may require a separate supply chain to provide all necessary safety measures.
• Value: Commodity prices are relevant for segmentation reasons because they affect the cost of inventory management in the supply chain. For example, low-value goods can be stored in multiple locations near customers, while high-value goods can be centrally managed to reduce safety stock.
• Variety: Certain products, by their nature, are sold in a variety of ways. For example, his one line of shirts might include different collar sizes, colors, and sleeve lengths.

Demand forecasting

Demand forecasting is a means of predicting what customer demand will be in the future and how it will affect a company’s supply chain. Critical to business health, continuity, and growth, it empowers leaders to make the right decisions at the right time.

Demand Forecast plays a key role in effective supply chain management, ensuring timely inventory replenishment, improved capacity management, and optimal sales and revenue. It also improves decision-making and management while accelerating future growth and expansion plans.

Accurate forecasting of demand relies on a detailed analysis of the factors that affect an organization’s utility infrastructure. From historical sales patterns to specific events in the retail calendar (promotions, holidays, etc.). With real-time data, demand forecasting has become more accurate and easier. Worth noting is the error reduction with standard forecasting methods such as ARIMA, Prophet, AutoRegressive Integrated Moving Average, and Exponential Smoothing.

Transportation Network

For retailers, road transport to deliver to stores is a major part of logistics costs. Companies often conduct route planning optimization studies to reduce these costs and improve network efficiency. Requires collaboration between continuous improvement engineers and transportation teams who manage operations on a day-to-day basis. Based on your analysis, you can suggest possible improvements (additional branch groupings, root merges) and evaluate their operational feasibility with your team.

Last mile delivery optimization

Last Mile Logistics refers to the final stage of the delivery process from the fulfillment center or facility to the end user. In most cases, last mile logistics use packages or small packages carriers to deliver products to consumers. According to McKinsey and Company, parcel shipping is worth more than $83 billion and the growing e-commerce market value will double in about 10 years in a mature market.

Additionally, shippers of all sizes recognize last-mile logistics as a foundation for growth and profitability. Last Mile Logistics enables shippers to get more products to consumers faster and at a lower cost. This is an important aspect of both e-commerce and omnichannel supply chains. With AI technology, routes can be organized to deliver packages with the minimum number of drivers using optimization models.

Conclusion

As you can see, there are many AI use cases in logistics such as supply chain optimization, product segmentation, demand forecasting, and transportation networks,Last mile delivery optimization, and etc. This powerful technology is evolving specifically to improve logistics and supply chains. AI enables routine tasks that would otherwise take a long time to automate.

In logistics, the most interesting thing about AI is that there are many more use cases and companies that will impact your business. Technology is having a general impact on how we ship and increased collaboration between logistics companies and start-ups will no doubt lead to progress in the years and decades to come.