AI as the Ultimate Disrupter in Logistics: How to Manage Last-Mile Costs?

AI in Logistics

Nowhere are efficiencies introduced by AI more visible than in the areas of vehicle routing, network planning, and predictive demand. Machine learning led innovations in these areas have made businesses more agile and dynamic permitting Uber and Grab to deliver outstanding customer experience. Better capacity planning, optimal route planning, dynamic charging, optimal allocation of resources and vehicles quickly to the in-demand areas to reduce customer wait times are just some examples of how AI is used today.

Last-mile/First Mile services optimization via Vehicle Routing Optimization

The Vehicle Routing optimization (VRO) has various applications in real life. The main objective of this problem is to design routes for vehicles that depart from a given number of different depots, need to go through several locations to deliver some service, and once the shift is over they return to a set location. The complexity of the operations differs by customer; you can add your business conditions, such as the load capacity of vehicles, the maximum distance they can travel per day or the duration of the working shift of your drivers. The goal of VRO given these conditions is to compute a route which minimizes the aggregate transport costs such as the total distance traveled number of vehicles used and/or the total transport time.

Making a comprehensive optimization roadmap for Logistics companies

Recently we partnered with POS Indonesia to provide a comprehensive vehicle routing optimization solution, starting from individual single-vehicle route optimization to countrywide multi-channel end-to-end logistics optimization. Based on the work we have performed with several regional companies in this sphere in the past few months our results indicate total savings of anywhere from 22% to 71% in terms of time, distance and fuel expenses when implementing the complete three-phase roadmap as illustrated in Figure 4.

  • Phase 1. Consists of simple local improvements in single-vehicle routing. This is the simplest phase but captures some of the highest value (anywhere from 15% for companies that are already using efficient vehicle routing protocols to 60% in companies which do not have optimized operations). The results of Phase 1 also serves as a benchmark for improvements in the company for Phase 2 and Phase 3
  • Phase 2 consists of extending the single-vehicle to multi-vehicle routing and fleet management for a certain locale. Savings in time and money of over 20% are not atypical in phase 2. Taken together with Phase 1 we have seen optimizations of up to 47%.
  • Phase 3 consists of combining the vehicle routing with capacity planning and demand prediction to make a comprehensive end-to-end routing platform for goods and services. While savings may not look at significant compared to Phases 1 and 2, Phase 3 benefits accrue more in the form of customer satisfaction. Faster end-to-end delivery, tracking and prediction is one of the surest ways to increase satisfaction of customers and gain an edge over competitors.

Case study: Savings from single-vehicle route optimization in Bandung and Jakarta city

Rosebay recently conducted field trials of its vehicle routing optimization solution in Bandung and Jakarta. Twenty sets of eight locations were chosen both in Jakarta and in Bandung. Figure X below is a single example from the twenty sets from Bandung. The noted markers represent real delivery locations of our partner logistics company. It should be noted that the un-optimized/control case represents the unsolicited route taken by delivery personnel.

Planning your AI/Digital Transformation journey

Many organizations have now been benefited with investments in artificial intelligence. While in western nations 15% have already started to use AI while other 31% plans to have them implemented in 2019, the figures are much lower in Asia (less than 5%). Rosebay is planning to hold workshops in cities around Asia in December and January, including in Jakarta, Kuala Lumpur, Ho Chi Minh City, and Phnom Penh to help companies understand the importance of AI and Big Data. There are three key ways where AI can contribute to commercial and business success:

  1. AI for Business growth
  2. AI for Optimizations/savings
  3. AI for Managing risk



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