Navigating the complex web of last-mile deliveries

The importance of last-mile delivery has increased significantly in recent years due to the rise of e-commerce and the changing expectations of customers.

The COVID-19 pandemic accelerated the shift towards online sales, with physical restrictions forcing businesses to adapt quickly. However, many companies, including those already in the e-commerce space, were ill-prepared for the massive surge in demand. What initially seemed like an advantage for e-commerce businesses quickly turned into a colossal challenge.

Consumers expected faster and more frequent deliveries, but the industry faced a range of issues, including a diminishing workforce, lack of inventory, manufacturing backlogs, insufficient technology to handle consumer needs, and a lack of analytics to predict variables affecting orders and deliveries. High uncertainty and complexity made last-mile delivery difficult to achieve.

Today, last-mile (LM) delivery is one of the fastest-growing, most expensive, and most complex processes in global supply chains. According to Straits Research, the global last-mile delivery market was valued at USD 40 billion in 2021 and is expected to grow at a CAGR of 13-15%. This resurgence in e-commerce volume will lead to a rapid supply chain transformation for last-mile delivery in the near future. According to the Capgemini Research Institute, last-mile delivery costs account for 41% of total logistics supply chain costs, with retailers absorbing a significant portion of these costs, often offering free delivery to consumers. However, this can hurt retailers’ profitability by up to 26% if they continue to operate with suboptimal delivery models.

Consumers have grown accustomed to faster and more frequent deliveries, and when their expectations are not met they’re likely to switch to competitors. Retailers face a difficult balancing act between meeting customer expectations and maintaining profitability. New fulfilment models will continue to evolve, and automation can help drive profitability. However, companies must continuously optimize their capabilities and infrastructure to maximize capacity and reduce the cost of last-mile deliveries.

Effective supply chain planning for last-mile delivery will play a pivotal role in helping organizations develop their business strategy, build infrastructure for the future, invest in technology, data, and analytics, hire the right talent, and augment workforces through automation and robotics to deliver a world-class last-mile experience to consumers. With the right technological mix, retailers can now care for their customers and their bottom lines.

The interconnected and complex web of last-mile delivery

While last-mile delivery may seem like a straightforward pick-up and drop-off process, it involves several critical components, both physical and technological. Physical components include the delivery hub infrastructure, availability of modern equipment, workforce capacity, transportation capacity, packaging, payment handling, and returns and exchange handling.

On the technology side, last-mile delivery also requires readiness in several areas, such as order handling, sorting, route optimization, cost optimization, capacity planning, real-time ETA estimation, payment processing, returns processing, labelling, fake detection, and customer service.

Relying on traditional demand and supply planning alone will not cut it in today’s fast-paced world of last-mile delivery

The FMCG (Fast Moving Consumer Goods) industry may provide some insight into e-commerce challenges, but relying on traditional planning methods will not suffice for last-mile delivery. When it comes to the last mile, the most important metric is the number of parcels delivered daily, without any backlog or delays, which ideally means that the delivery hub capacity is designed to deliver everything that lands on the hub from various sources on a given day.

Adding to this difficulty, hubs must calculate how many packages are slated for each day, depending on distances and capacity. When they’re overloaded on a Monday, they can expect knock-on effects throughout the week.

Unlike traditional demand forecasting, last-mile delivery hub capacity is determined by the SLA expectations of the organization and an estimation of the stock quantity that will arrive or land at the hub after orders are taken. This calculation requires complex supply chain planning and optimization. Advanced forecasting methods are used to determine the order pattern at the destination, and back calculations are performed to determine the landing (based on multiple sources). The aggregated landing plan is the summation of all the packages landing at the destination on a particular date from various sources across the country.

Selection of the source for a product involves planning inventory across the country, estimating buying plans from categories, and predicting seller activity on the marketplace, all while minimizing long-distance national transportation and maximizing local source selections. Additionally, e-commerce events, sales, promotions, and payday discounts can lead to higher orders and landing volumes on specific days of the month. However, maintaining an infinite capacity to clear peak loads in a day comes at an excessive cost.

To balance SLA and cost, optimizers are used to derive reliable delivery hub capacities. SLA targets are established using historical data, customer expectations, and inflexion points where customers abandon their carts due to the high estimated time of arrival. Balancing the cost and SLA requirements is a crucial part of determining optimal delivery hub capacities.

This gives an overview of some of the challenges involved in last-mile delivery. Stay tuned for part II of Navigating the complex web of last-mile delivery, where we’ll dive into the solutions.