- Demand for fast fashion may be becoming overshadowed by a collective drive towards slower fashion, but the baseline expectation for fast delivery has only been superseded by demand for even faster delivery.
- Technological advancements – including some powered by artificial intelligence — have led to the birth of a brand-new generation of automation in the logistics and fulfilment space. Besides shortening delivery cycle-time, improving efficiency, minimising risks to health and safety, and reducing errors, the new wave of logistics automation is enabling real-time data-backed decision-making that impacts the complete organisation — from strategy planning to delivery execution.
- In this ongoing agility race, retail companies that are combining intelligence-powered automation with a scalable and flexible last-mile delivery strategy are driving industry-wide logistics and fulfilment transformation.
- Beyond a certain point, though, the return on investment in logistics will come from innovation rather than pure speed.
When Amazon launched Prime in 2005, most of us were awed by the concept of one or two-day deliveries – so taken in by the luxury that we never really looked under the hood. And when same-day shipping came to major cities, the sheer convenience was the aspect that most people remembered, rather than the industry-disrupting race for agility and automation that was taking place behind the scenes.
Today, that race continues. But is now powered by robotics, machine learning, and generative artificial intelligence (AI) instead of just raw logistical might and person-power. And beyond the headline-making speed, the front-runners are also using those technologies to execute on genuinely novel distribution strategies.
A deeper look at the history and the current standings in that race reveals the sheer amount of effort, investment, automation and re-alignment organisations have had to undertake to enable them to simply offer the faster delivery that so many of us take for granted – let alone the new unlocks that are fuelling the future of logistics. But when it comes to getting product to people, how fast is fast enough?
The new age of automation
Automation in logistics and fulfilment is no longer simply about expediting specific tasks or processes, but about adopting a digital-first strategy that drives unparalleled digital collaboration and unrivalled organisational agility, all in service of ensuring rapid product delivery.
In 2018, Chinese conglomerate JD.com opened the world’s first fully automated warehouse. Machines with robotic arms located, picked, sorted and transferred products to packaging machines via a series of conveyor belts. Once ready, a small army of sleek robots collected and lined up individual packages for distribution. The robots, powered by hi-tech controllers and sensors built by Japan-based start-up Mujin, did all the heavy lifting like a flawlessly-choreographed dance routine – and the entire facility was manned by a crew of 5, whose sole responsibility was to service the machines.
Since then, other warehouses and fulfilment centres have only become smarter and more sophisticated in their deployment of technology towards that same objective. More recently, Nike deployed 1000 cobots (collaborative robots) across its warehouses. These cobots help workers sort, pack and move products, improve order processing speed, reduce the risk of injuries, and increase productivity.
The case for automation in logistics and fulfilment, then, is clear: the more that companies can automate, the more and the faster they can deliver. And this means more than just raw throughput: besides increasing order processing speed through greater efficiency, automation also helps bridge certain gaps. For example, deliveries are not equally distributed throughout the day, so retailers must plan and build capacity for peak periods. It is not practical to hire staff in the warehouse for only peak hours, creating a gap that can be bridged via automation.
However, the agility story is not just a machine story. It is increasingly an intelligence story. Generative AI, machine learning, Internet of Things, and cloud computing are no longer tools of the future; they are powering the present and amplifying the impact of digital transformation in warehouses and distribution networks.
AI-powered automation, for example, is enabling customer-focused inventory optimisation, inventory-based pricing, supply chain optimisation, ideal delivery route determination and automated buying, planning, pricing, and promotion. All long-winded ways of saying that the route from stock to shelf (or to the consumer’s doorstep) has been streamlined to an extremely finite degree.
As an example, by combining machine learning algorithms with AI, robots use sales data to rearrange inventory optimally. Further, real-time inventory updates can trigger dynamic product price adjustment, request for raw material sourcing for fast-moving items or promotions for slow-moving items – demonstrating how technology deployed in distribution can help to realise benefits much further upstream.
Kevin Davis, MD at Last Mile Consulting and with previous experience as the Head of Logistics at Marks & Spencer (M&S) as well as the Head of EU Central Operations at Amazon, explains: “Automation helps support the highly variable customer demand and intense cost pressures that come with being in the retail space. AI helps ensure you have the right number of products in the right place at the right time by taking into consideration the end-to-end journey as opposed to just the immediate needs.
AI can enable companies to have more precise forecasting and planning by taking into account a far more comprehensive set of variables that span the full product life cycle, including where the product might be returned and restocking costs.”
To do this in a scalable manner he explains, “It would require a lot of manual time and cost whereas AI can do it more efficiently.”
In essence: how well retail companies grasp, build and integrate AI into their systems and processes will determine how competitive or ahead of the curve they will be. Again: solving for a distribution problem represents an opportunity to unlock real change elsewhere.
How are new technologies altering the logistics and fulfilment landscape?
The picture of total automation I have just painted? That is not the reality for the majority of fashion businesses.
The vast majority of fashion companies largely still operate conventionally. While warehouse management, inventory management and routing may be tracked using separate software solutions, the majority of work such as loading, unloading trailers or sorting continues to be manually accomplished. But when we look deeper, there is still process happening – albeit unevenly – in these places, and the proportion of processes — especially those involving “heavy lifting” — that are now at least partially automated is changing.
As Davis explains, this unequal distribution of technology is common even amongst retail giants that, from the outside, are part of the same network: “In Amazon’s case, the main warehouse tends to be the most automated due to processing such large volumes each day, justifying the investment in the technologies. The sub-regional warehouses are less automated as they process only a fraction of the volume so automating a final delivery station to the same extent may not make sense as payback will take much longer.”
As technology investments, capabilities, requirements, and availability of labour range across companies, levels of automation also differ and span a broad spectrum. We would expect Amazon, for example, to lead the pack (even if individual warehouses don’t necessarily benefit from corporate initiatives) and this is largely true. But for a multitude of reasons, that range from using speed as a service differentiator to struggling to service pandemic-propelled high online order volume, different companies are prioritising the automation of their logistics and fulfilment process in unique orders – with the key difference being that larger companies have simply been able to tick more processes off their list.
Across the board, companies are also making use of AI to inform, build, shape and steer strategy. Understanding the potential impact of AI on business, in 2021, LVMH and Google Cloud announced a five-year alliance to integrate AI and machine learning technologies across every aspect of the business, as well as launching a DATA and AI Academy focused on accelerating innovation in the field. Among other benefits, AI will enable the company to create a data-backed detailed view of its consumer, predict demand, and craft an effective logistics strategy.
While companies of this size are obviously the ones that make the headlines, these benefits are also being realised at a cloud services level (hence Google’s involvement) making them accessible – likely sooner rather than later – to businesses of all sizes.
And while some believe that the true potential of AI has yet to be fully tapped, experts in logistics generally agree that AI has already yielded significant benefits in terms of driving agility. According to Jeppe Albertsen, Sales Development Executive from 3rd party logistics company Extensive, some of these benefits include:
- Shortened order processing times: AI helps streamline order processing by automating various tasks such as order sorting, inventory management, and picking optimisation. By analysing historical data and real-time information, AI algorithms can identify the most efficient routes for order fulfilment within the warehouse, reducing the time required to process orders.
- Improved demand prediction: AI can analyse vast amounts of customer and sales data to identify patterns and trends. By leveraging machine learning techniques, retailers can accurately forecast demand, anticipate customer preferences, and optimise inventory levels accordingly. This helps in preventing stockouts and reducing the time it takes to restock popular items.
- Optimised sales: AI can analyse customer data, including browsing patterns, purchase history, and demographics, to personalise product recommendations and marketing strategies. By delivering tailored suggestions and promotions to customers, AI-powered systems can drive sales and enhance customer satisfaction.
Based on a list they provided to me, Albertson believes these ecosystems enable retailers to have:
Besides AI integration, enterprise-level digital transformation impacts how companies automate. Tight integration between robots, cloud computing, Internet of Things, AI and machine learning enables companies to use predictive and real-time data analytics to inform systems that can automatically generate, modify or execute strategy.
- Seamless integration: smoother data flow and communication between different stages of the supply chain, minimising delays and improving overall efficiency.
- Real-time visibility: retailers can have real-time visibility into inventory levels, order statuses, and logistics processes. This transparency enables better coordination and decision-making, leading to faster and more accurate order fulfilment.
- Scalability and adaptability: An ecosystem provides flexibility to scale operations as the business grows. It allows retailers to add or modify modules within the ecosystem according to their evolving needs. This adaptability ensures that the system can keep up with increasing order volumes and changing market demands, supporting faster delivery capabilities.
- Reduced complexity: Managing multiple individual solutions from different vendors can be complex and time-consuming. By opting for a single ecosystem, retailers can simplify their IT infrastructure and minimise integration challenges. This streamlining of processes can contribute to faster delivery times.
Down the funnel: how automation flows from shipping to fulfilment.
Warehousing and shipping transformation go hand-in-hand. Transforming one without the other only creates a roadblock. That is to say, warehouse automation will lead to fast order processing, but ultimately, if first and last mile delivery strategies, processes and systems are not in place, order fulfilment speed improvement will be throttled.
Fashion companies are therefore optimising the order fulfilment process as a whole – end to end —to be faster and more cost-effective while enhancing reach, flexibility and integration.
There is no one size fits all strategy here, though the general trend shows a preference toward greater customer segmentation to help expedite delivery, malleable models to accommodate changing inventory or demand levels and a concerted effort to use existing resources optimally through better synchronisation between first and last mile delivery.
Davis adds some further insight: “For Amazon to deliver faster, it involves connecting first mile and last mile delivery very well, minimising hand-offs in-between. For example, typically when products are delivered from Scotland to Cornwall they will be transferred from the warehouse in Scotland to a main warehouse for the carrier – that could be located in the Midlands – and then from a sub-regional depot to another final depot, so about 3 steps are required to reach the customer. This is because the Scotland warehouse could not possibly sort to every single postcode. However, the more you can sort from the originating warehouse, the more quickly you can deliver the items by cutting out steps in the journey. To enable this journey successfully with strong customer engagement, integrated tracking and communication systems need to be in place. This is also true of the returns journey, which is often overlooked.”
Target seems to concur. Earlier this year, the North American retailer – revenues exceeding $100 billion in 2022 – announced plans to invest $100 million to drive next-day delivery countrywide by adding six new sorting centres by end of 2026. To help achieve this, the company uses its stores as hubs, from where e-commerce orders are sent to the company’s sortation centres. Here the products are sorted, routed and dispatched for delivery via the lowest-cost local carrier option. By supplementing increased automation with a more cost-effective shipping strategy that employs a combination of stores and fulfilment centres, the company expects to significantly shorten order processing and delivery time.
Some of the ways in which retailers are maximising reach and minimising delivery time include:
Expanding capacity strategically:
Instead of having one giant warehouse, companies servicing high order volumes use multiple large warehouses strategically located across the country serviced either via building their own future-forward logistics facilities or partnering with those companies that have the capabilities and expertise to do so. This enables companies to be closer to their customers and more agile as the load is shared across multiple warehouses, allowing them to reach a higher number of customers quickly.
Recently, Walmart announced the building of four next generation fulfilment centres over a period of three years. Through its partnership with logistics automation company Knapp, Walmart will reduce the number of steps taken to process e-commerce orders from 12 to five. According to the company’s website, “these four next generation FCs alone could provide 75% of the U.S. population with next- or two-day shipping on millions of items, including Marketplace items shipped by Walmart Fulfilment Services.”
However, increasing the number of warehouses or distribution centres also involves massive levels of investments. Companies must carefully evaluate customer geography, demand, and willingness to pay for expedited delivery to justify the expense.
Using micro-fulfilment centres:
Those retail companies that have brick-and-mortar presence can leverage existing stores as fulfilment centres, as Target demonstrated.
In his work, Davis helped UK-based multinational retailer M&S turn stores into micro-fulfilment centres or dark stores to provide faster delivery. By turning stores into “click and collect” centres, the company saves transportation costs by shipping an item from stores near the customer and is now able to offer 2-hour or same day delivery for a variety of items at select locations. Stores are selected not just based on proximity to customers, but also on the level of efficiency with which they operate and how quickly they can restock the items.
Beyond delivery hubs, these stores also operate as hubs for returns, a cumbersome and increasingly environmentally damaging problem for the fashion industry. As the company is planning for growth in general for online order delivery, this tactic helps to increase capacity and ensure more efficient use of warehousing. Forecasting enables the company to place popular items where they need to be so that certain brands are sent direct to customer, increasing sales without the need for warehousing. In addition, micro-fulfilment centres provide flexible capacity expansion to manage demand peaks driven by seasons, sales or holidays without the need for a separate warehouse.
Since stores were not originally designed to be fulfilment centres, this tactic comes with its own set of challenges as well.
Partnering with 3PL and smart locker providers:
While order processing is typically in-house, it is not necessary to invest massive amounts in distribution when third party logistics companies (3PL) companies can do what is needed.
Retailers can also partner with companies for providing smart lockers. Widely known as PUDO (picking up deliveries and dropping off returns) lockers, they enable more efficient delivery. Instead of dropping off individual items to a plethora of different locations, a 3PL company drops off multiple packages at a single location that customers can access at their convenience. As 3PL companies service multiple vendors, they can fill a vehicle very quickly compared to fashion retailers, saving on hold time.
Besides raising efficiency and reducing infrastructure and operating cost for the company, it also increases flexibility since multiple drop times can be accommodated. From a customer perspective, delivery can be planned and scheduled as per convenience, which also saves fashion companies redelivery costs.
According to Davis, companies can see as much as 50% cost savings, if not more, depending on volume, through better carrier pricing from increased trailer fills.
Besides capitalising on their strategic advantage, operating with a more flexible model enables retail companies to be adaptive and agile – necessary traits to foster an environment that is conducive to inspire innovation and absorb automation.
India-based fashion e-commerce company Myntra (part of Walmart-owned Flipkart, which is the country’s largest e-commerce business) employs a network of more than 20,000 small neighbourhood retail stores, called Kiranas, to complete last mile delivery.
The hyperlocal approach enables Myntra to service difficult to reach areas, thereby increasing the number of consumers it services quickly. While cost of labour may be a deterrent to replicating the system elsewhere, it serves as an example of how innovative thinking can drive large-scale impact.
Fashion retailers may also benefit by learning how companies outside the industry are pioneering quick-commerce. For example, India-based e-grocery start-up Zepto, promises delivery within 10 minutes. The company has partnered with e-logistics provider Zypp Electric to complete last mile delivery. The company leverages strategically placed micro-warehouses and dark stores (brick-and-mortar stores that are used solely to stock orders for fulfilment purposes and not for public sale) to deliver on its promise.
How does AI-powered automation impact the returns journey?
Reverse logistics, or the returns journey, has become a looming concern because it hasn’t always been treated with the importance it should have been, given the impact of returns. According to a 2023 survey of US-based apparel brands and retailers by Coresight Research, average return rate for online apparel orders exceeds 24%, translating into a potential processing cost of more than $25 billion to the industry.
Currently, this aspect of the customer journey is filled with a lot of inefficiency as returns often tend to be handled by separate teams and need to be processed and checked before the refund is initiated. Although fashion companies partner with 3PL companies or have a dedicated warehouse to process returns, processing time often causes significant delay. In addition, forecasting returns is a challenge as retailers offer products from multiple vendors.
To minimise returns, AI can be used to analyse customer data and feedback on products to assess likelihood of return for some products. Using this data along with other parameters, companies can plan ahead to redirect fast-moving products to the appropriate retailer or warehouse.
Alternatively, fast-moving items can be sent directly to their final destination. To do so, the product must be labelled correctly, which takes a lot of work to enable, an area where automation can help expedite the process.
What does faster fulfilment mean for fashion companies?
The benefit of faster order fulfilment from a customer perspective is obvious. But, what does faster mean from a business perspective?
Beyond technologies shaping the landscape, shape-shifting fulfilment and delivery models are the need of the hour. All of which come at a cost: human, economical and environmental. The topics merit separate coverage, which we delve into in our follow-up Interline article on Logistics Automation: Is Faster Better?