Territory planning – SLA and transportation capacity as a function of historical density
Taking a one-size-fits-all approach to service level agreements is very likely doomed to fail in last-mile logistics. When trying to answer questions like how much last-mile transportation capacity do I need? What should be its geographical distribution? and can I offer a better Service Level Agreement (SLA) and stay profitable, you need to develop a nuanced understanding of demand and supply in different geographical areas.
Use the power of historical data to project demand and our simulation tools to find answers to the above questions and control the cost-to-serve while pushing the boundaries of the offered SLAs.
Real-time capacity analysis – The power behind demand shaping
There are your plans and there is reality. When delivery orders start to accumulate, use a fully automated process that is taking into account both real-time and projected demand in order to keep your commitments at bay and never over-promise. Moreover, the same process, combined with a dynamic pricing engine, is the behind-the-scenes mechanism to incentivize customers to make logistically-friendly time-related decisions and shape the demand curve.
Route optimization – meet recipients’ expectations while minimizing operating expenditure
Once you have all the orders in hand, use a powerful route optimization engine that incorporates street-level routing, driver and vehicle constraints as well as customer time windows to produce the most optimal daily routes. Our sophisticated AI constantly learns by analyzing traffic patterns and data gathered from the fleet driver’s historical service time for future planning.
AI, ML and Hyper-automation
Data-Driven decision making
Logistics is a numbers game. Developing a deep understanding of delivery demand and transportation capacity, the cost elements of every step in the supply chain, and the performance of all stakeholders involved, is the key to put yourself on a path for continuous improvement.
Use a data platform that gives you both the real-time view and the historical perspective and bubble to the surface the most important and relevant information that drives better decision making.
Let the machines learn and act
With advances in artificial intelligence (AI), today’s machines can automatically analyze your data, draw conclusions in many dimensions and orchestrate end-to-end processes.
Use the power of hyperautomation to optimize the following processes:
Capacity management, residual capacity, and pricing incentives – Forecast demand based on historical data and real-time activity have a minute-by-minute update to your delivery residual capacity and find the optimal incentive, transaction-by-transaction, to shape demand.
Stakeholders, performance, and planning – Use continuous learning of the behavior and performance of all stakeholders and understand that performance in the context of time and geography to improve operational assumptions and drive better planning.
Milkman’s Last-mile delivery orchestration platform
Commerce and logistics integration at the point-of-sale is enabling dynamic choice of convenience and cost.
Deep business insights are driving better planning of last-mile logistics and transportation.
Effective communication based on the customer’s unique needs and preferences drives a better experience from purchase to delivery.