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Home » Blogs » Think Tank » The Last Mile In Supply Chain is Longer Than Expected. How Can AI Fix it?

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The Last Mile In Supply Chain is Longer Than Expected. How Can AI Fix it?

A DELIVERY VAN DRIVES DOWN THE HIGHWAY

Photo: iStock.com/aapsky

June 30, 2023
Majaz Mohammed, SCB Contributor and Shashank Dubey, SCB Contributor

With the growth of e-commerce, and consumer preferences gravitating towards digital channels, retail stores have become a key hub in the omni-channel delivery and fulfillment ecosystem. The pandemic has further sped up this transition by amplifying the demand for online ordering, curbside pickup, and same-day deliveries. Last-mile capabilities are crucial to the success of retailers.

However, there are burdens and costs associated with expanding last-mile practices. Today, consumers expect the convenience of online ordering and same-day deliveries, without realizing the operational burdens and costs of these expanding last-mile practices. According to Insider Intelligence, last-mile delivery can account for up to 53% of the total shipping cost. Companies that eschew last-mile optimization risk falling behind their competitors.

Optimizing last-mile capabilities has become a crucial factor in the success of retailers. It involves analyzing and optimizing the following pivotal moments:
•     Planning — The time to plan execution is extremely brief once you receive an order. Forecasting demand, and positioning your inventory in the right location are crucial preparatory steps to successful last-mile delivery.
•    Order Receiving —  The clock starts ticking the moment a customer completes their online order. Beyond the order details, this is a crucial step for gathering customer details and preferences, then establishing order status and communications.
•    Shipping — Determining the optimal shipping location for the customer. Does each order ship separately, or in batches, at risk of delayed delivery? Is there a cost advantage to bundling and shipping different orders?
•    Routing — How do you minimize the delivery cost if you own your fleet? What route should the driver take from the store to the customer, especially if you deliver multiple orders? You need to assess the cost-per-shipment and cost-per-mile of various routes.
•    Delivery — What is the optimal delivery mode — in-store, curbside pickup, carrier delivery (FedEx, USPS, or UPS), or same-day delivery through a third-party service (DoorDash, Uber)?
•    Returns — On average, 16% of merchandise finds its way back to retailers, creating a significant dent in the profits. Digging deep into the reasons behind these returns and adopting preventive measures is crucial.

Complicating matters, customers also expect real-time order status, whether for a big-ticket item like a refrigerator or pizza delivery before the big game. The clock is always ticking. Getting these steps right requires investing in planning, technology, training, and constant optimization.

Using AI/ML for Better Decision-Making and Optimizing the Last Mile

Many companies collect data on sales, inventory, and fulfillment but often lack the tools to effectively analyze it and tweak their strategies, practices, or services accordingly. While everyone knows when their orders are backed up, they may need to realize when the time is right to scale up last-mile service capabilities. This necessitates a forward-looking approach to decision-making.

The most prominent challenge in last-mile delivery is the absence of valuable insights. Value creation hinges on these insights; without them, last-mile efforts risk hitting a dead end.

Can artificial intelligence help plug that knowledge gap and generate greater value for last-mile excellence and profitability?

The short answer is yes. AI can help integrate the enterprise data flow and generate customer insights that guide demand forecasting, order batching, cost optimization, hyper-personalization, warehouse inventory optimization and much more.

For example, machine learning (ML) algorithms can create a schedule to optimize warehouse order selection — determining the most efficient way to pack a cart. ML can assess the optimal delivery route and sequence drops to avoid empty miles. Perhaps ML’s greatest strength is that algorithms improve over time, based on feedback at each step of the last mile.

When used in collaboration, AI and ML can predict what products and services will be in greater demand so that businesses can maximize sales and growth opportunities while engaging fewer resources. Properly implemented, AI and ML can help companies decrease costs while growing profitability.

The Road Ahead: How do Organizations Kickstart a Resilient Last-Mile Strategy?

The last mile of the supply chain has become a defining aspect of modern retail. Businesses can transform their supply chain operations by harnessing the power of AI. And as AI continues to learn and adapt, its impact on supply chain performance will only grow, driving significant improvements in the years to come.

To initiate these efforts, enterprises must first acknowledge the growing importance of last-mile capabilities and be willing to invest in the technology, training, and optimization required to harness the full potential of AI. This will help them navigate the complexities of the last mile and capitalize on the opportunities in the ever-evolving retail landscape.

The challenge, however, lies in effectively implementing an AI tech stack that can handle each company's unique supply chain needs and customer base. Enterprises should circumvent the last-mile issue by partnering with firms specializing in supply chain optimization through AI. Such partners could offer pre-built accelerators that can be rapidly customized and deployed to address specific business requirements.

The successful implementation of last-mile optimization initiatives requires significant alterations to current processes, systems and practices. These changes have the potential to interrupt existing workflows, trigger resistance among employees, and create unanticipated challenges. Therefore, effective change management practices are crucial in the last mile of the supply chain because it involves optimizing multiple pivotal moments that affect stakeholder satisfaction and profitability. When embraced with the right change management approach and strategy, organizations can unlock and truly maximize the potential of AI in the last mile of the supply chain.

Shashank Dubey is chief revenue officer & co-founder, and Majaz Mohammed is senior director, supply chain, at Tredence Inc.

Last Mile Delivery Technology Artificial Intelligence Data Management (Big Data/IoT/Blockchain) Forecasting & Demand Planning Inventory Planning/ Optimization Supply Chain Planning & Optimization Transportation Management Business Strategy Alignment

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