As is well-known, the pandemic has triggered a veritable boom in online shopping. Despite global retail sales falling by 3% in 2020, e-commerce volumes soared, rising by 27.6% for the year and reaching $4.3T. But with e-commerce’s continued growth come new challenges for retailers: keeping pace and managing huge amounts of orders, shipments and data efficiently. Combined with increasing consumer expectations and supply chain bottlenecks triggered by COVID-related lockdowns, all this creates enormous pressure for e-commerce logistics professionals.
To overcome these obstacles, AI, automation, and algorithms are proving to be critical assets in e-commerce logistics, which is inherently data-intensive and requires collaboration at various stages in the fulfillment process. Leveraging these disruptive technologies enables etailers to handle growing or fluctuating demand, satisfying customer expectations and, ultimately, become more competitive.
Thus, this article will explore the key applications of AI, automation, and algorithms in e-commerce logistics, and the benefits these disruptive technologies offer your business.
Artificial intelligence has traditionally been defined as a computer or robot able to perform complex tasks that would typically require human intuition. As it exists today, AI is described by computer scientists as being “narrow”. Since AI systems don’t currently possess lateral thinking skills, their current applications are limited to repetitive tasks, such as facial recognition, powering search engines, and aiding medical diagnoses. Existing AI systems are yet to have surpassed human intelligence, which is the ultimate goal of many researchers in the field.
Among the core competencies of AI are learning, reasoning, and perception, making it a fantastic tool for scenario-rationalization and optimal decision-making. As such, AI has a myriad of industrial use cases, from finance to the automotive sector. With its strong suite of analytical capabilities including data collection, storage, labeling, analysis, and decisions and feedback, AI has broad applications as a problem-solving tool in the complex world of e-commerce logistics.
E-commerce logistics is especially data-intensive and requires collaboration across multiple business units. Given its ability to leverage data to aid decision-making, AI is a powerful vehicle for harnessing critical predictive analytics, which ultimately enables effective supply chain management (SCM). AI can be deployed to forecast supply chain bottlenecks, demand spikes or dips, or to optimize logistics. This information can then be used to help e-commerce stakeholders decide which warehouse or carrier to use and when to replenish their stock.
Automation involves using technology to perform an industrial function without or with little human intervention. Companies implement automation to optimize the efficiency and productive output of industrial processes. It has broad use cases, ranging from manufacturing to defense, and is also particularly well-suited to e-commerce logistics.
There are two main subsets of automation in an industrial context: business process automation (BPA) and robotic process automation (RPA).
Also known as digital transformation, BPA is the tech-enabled automation of business processes. BPA involves managing data flows among other business processes to optimize performance and resource allocation and minimize costs. BPA supports knowledge workers in handling business-critical issues and aims for enterprise-wide efficiency.
In an e-commerce logistics environment, an example of BPA would be automated order entry. Processing orders manually can be complex and cumbersome due to the high volume of orders and the collaboration required across multiple teams, such as finance and logistics. Manual order entry also takes far longer and contributes to a higher error rate. The time spent correcting these errors contributes to declining revenues and delayed delivery. Automated solutions enable greater efficiency in order processing, minimizes costs, and increases overall ROI.
RPA optimizes an organization’s efficiency by automating mundane tasks that would have previously required human action. RPA is a machine learning-enabled software application that automates rules-based, repetitive actions.
In an e-commerce logistics environment, RPA can serve as a great inventory management tool. Automated RPA bots can oversee stock control and notify stakeholders of any likely stock-outs. This improves supply chain visibility and enables supply chain managers to identify issues before they become problems.
Another one of the principal applications of automation in e-commerce logistics is warehouse automation. The e-commerce boom has underlined the importance of efficient parcel sortation. Automated conveyor systems equipped with scanners can identify the bar codes anywhere on a package and send it in the appropriate direction. Similarly, automated pick-and-pack technologies are deployed to ensure maximum efficiency. Robotic arms equipped with sensors determine the dimensions of an object before safely grasping and packing it.
Algorithms are instruction manuals, providing computers with a step-by-step guide for carrying out a specific function and achieving the desired outcome. With any algorithm, there are 3 core components. In many ways, it is similar to following a recipe while cooking.
Given how data-intensive e-commerce logistics is, algorithms are a great tool for satisfying the end consumer and driving business value. Many organizations are encumbered with multiple data sets and, as such, are reluctant to handle new sources of data. Algorithms are the key for unlocking the powerful insights their data provides them with and making powerful decisions on the back of it.
These qualities give algorithms a suite of multi-sector applications. Financial analysts deploy algorithms to predict future capital markets activity by analyzing asset return movements and modeling for future risk and volatility. Similarly, in e-commerce logistics, algorithms are a tremendous SCM asset. Heuristic algorithms, which are designed to solve problems at speed, can be deployed in computational logistics to optimize efficiency when it comes to order fulfillment and last-mile delivery. In addition to ensuring timely delivery, algorithms are also used to ensure the ready supply of the desired product.
AI, automation, and algorithms already have a myriad of use cases in e-commerce logistics.
AI enables stakeholders to harness critical predictive analytics, especially when it comes to forecasting supply and demand and anticipating supply chain problems. This has the overall effect of maximizing supply chain efficiency, which in turn, keeps costs down, and the end consumer satisfied.
Automation enables greater efficiency in the packaging and sortation processes in warehouses and fulfillment centers. More efficiency in these large, complex facilities directly translates to more timely, organized delivery to the end consumer.
In densely-populated urban centers, traffic poses a major challenge to those on the fulfillment end. Algorithms can be used to identify optimal distribution routes, which will enable distributors to reach the end consumer safely and promptly. With nearly half of all online shoppers saying same-day delivery would persuade them to shop online, being able to offer timely fulfillment based on advanced algorithms will give any e-commerce business an edge.
Of course, these technologies don’t come without any downside. However, as the graphic below shows, the opportunities that AI, automation and algorithms present for etailers, usually outweigh the potential risks.
Below we will look in more detail at the specific use cases of these technologies in e-commerce logistics.
The success of e-commerce logistics operations hinges on extensive planning and collaboration between different teams throughout the entire fulfillment process. Advanced AI systems and algorithms enable significant supply and demand forecasting through powerful data-driven, predictive analytics. Leveraging these insights empowers logistics professionals to maximize supply chain efficiency by tackling issues before they become problems.
As such, AI-driven demand forecasting tools significantly minimize error rates compared to traditional forecasting methods, outperforming traditional methods such as Auto-Regressive Integrated Moving Average (ARIMA) in terms of accuracy and value of insights. This enables optimal resource allocation, especially from a distribution standpoint. For example, etailers can dispatch the correct number of vehicles to local warehouses, which reduces operational costs thanks to enhanced personnel planning capabilities. Local warehouses and fulfillment centers can also minimize their holding costs - the opportunity cost of keeping an item with low sales in stock. AI-driven demand modeling is also a great tool for avoiding stock-outs, greatly improving the end consumer’s experience.
AI systems draw on a range of data sources to inform supply chain management decisions. When it comes to handling goods, they gather detailed insights such as the size, weight, and fragility of a product, not to mention any potential hazards it poses. Similarly, where fulfillment is concerned, they track the relevant information about the sender and recipient’s addresses to begin calculating distance and lead time. AI systems can also compare different carriers in terms of pricing and quality. An example for the latter would be looking at a carrier’s history of complaints about speed or damaged products. The technology is also able to unlock key predictive analytics when it comes to specific parcel hub proximity and their future capacity for storage and sortation of goods. This enables proper planning and shrewd decision-making.
Palantir, an American Big Data analytics giant, has recently formed a joint venture with the Boston-based enterprise AI software firm DataRobot. The two software players are collaborating on an AI-driven demand modeling platform to optimize for real-time solutions for demand forecasting problems. Ritu Jyoti, an AI and Automation expert at IDC (a global leader in market research), believes the forecasting powers unlocked by this partnership will limit lost sales and stock-outs, reducing overall warehousing costs. Similarly, the British e-commerce grocery retailer Ocado leverages AI and Big Data to offer a more personalized shopping experience, greatly enhancing CX. Machine learning algorithms use predictive analytics to suggest which products the end consumer might need to replenish. They also use AI systems in a demand forecasting capacity to predict sales for over 58,000 of their products.
Warehouse robotics is steadily emerging as a business-critical asset in e-commerce logistics, especially for the successful set up of micro-fulfilment centers. Without efficient sorting and packing technology within warehouses, timely distribution and last-mile delivery would also slacken, damaging an e-commerce brand’s overall reputation and potentially limiting business returns. The global warehouse robotics market surpassed a $3.97B valuation in 2020. Analysts broadly predict the market to be worth $7.63 by 2026, sustaining a Current Annual Growth Rate (CAGR) of 11.54% over the next 5 years. This boom is underpinned by rising IIoT (the Industrial Internet of Things) integration into e-commerce logistics supply chains, especially in warehouses and fulfilment centers. This enables:
COVID-19 has elevated the role of robotics and automation in warehouses. The widespread implementation of social distancing measures has broadened the scope for automated processes in warehouses and fulfilment centers. As such, many e-commerce and logistics businesses are capitalizing on this trend. However, many leading players in the e-commerce logistics space were adopting automation well before the pandemic.
For example, in 2015 DHL collaborated with Beumer Group - a German intralogistics manufacturer - to design an innovative parcel automation and sortation system for its €140M Brussels logistics hub. A sophisticated system of 1,650 conveyor elements with a total of 6.6km of Beumer Group conveyor units in addition to 15 camera stations for 5-sided reading and 18 cameras for bottom reading at induction units with a total of 94 camera units was set up. Similarly, a computerized maintenance control (CMC) system was implemented in 6 of the sorters. Cross-belt loops were integrated with sensors to track and trace all the items moving during the automated sortation process. Additionally, shortest-path algorithms were deployed during the sortation process to send items on the fastest possible route through the facility. As a result, it now only took 6.5 minutes to sort an item to its destination and the shipment rate was increased from 12,000 to 32,500 parcels per hour.
AI systems and algorithms are important assets in optimizing for efficiency in the last-mile delivery stage of fulfilment, especially when it comes to partnering with carriers. Codept matches clients with ideal shipping providers and carriers using advanced algorithms. Their algorithm leverages data ranging from the recipient’s address, to product information and the promised delivery date. This is then used to select the optimal carrier and organize shipments within seconds.
Algorithms can also help cut costs on the distribution end. For example, one of Codept’s clients saved around €0,20 per parcel by using their carrier selection algorithm. If they were shipping a volume of 500K parcels per year, their annual savings would reach €100K. Finally, an internal study also showed that timely delivery improves consumer satisfaction, which can lead to a Net Promoter Score increase of up to 21%.
SaaS and other IT solutions in the logistics sector are already leveraging AI and algorithms to optimize for efficiency across their operations. They typically work on the back-end, hidden from the user, before providing them with actionable recommendations, or directly assisting them with automated processes and actions.
Codept helps e-commerce logistics businesses leverage disruptive technologies to deliver best-in-class customer experience (CX). This enables them to meet the rising expectations of delivery partners and the end consumer and crucially maximize ROI while managing multiple nodes of inventory. Codept achieves this through automated shipment optimization, Service Level Agreement (SLA) monitoring, and item-based order tracking with an early warning system in case of a delay. Similarly, its fully automated order tracking system allows for proactive case management, meaning issues can be tackled before they become problems. The company also automates parcel optimization based on shipping rules which you can self-configure, resulting in fewer clarification cases and liabilities for clients. Moreover, Codept’s AI systems process the feedback from the performance data of your individual orders, such as transit time, costs, and customer satisfaction, which is subsequently relayed to the system for process optimization.
AI, automation, and algorithms are proving to be critical assets in e-commerce logistics in the success of e-commerce logistics operations. Not only do these disruptive technologies maximize output and efficiency in warehouses and fulfilment centers, but they also minimize error rates and distribution delays. This all results in reduced costs, CX improvements, and return business. With e-commerce’s growth set to continue, leveraging these technologies will enable e-commerce logistics businesses to gain a competitive advantage.
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