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Use Cases, Applications, and Examples of AI in Logistics

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Written by Cem Dilmegani

“The article was originally published on AIMultiple

Artificial intelligence is transforming all industries and logistics is one of them. Logistics is the management of the flow of products between different locations. A global network of suppliers and customers complicates logistics operations and logistics companies contain both easy to automate tasks and complex processes that can benefit from AI/machine learning algorithms.

What does AI mean for logistics companies?

The technology offers a wide range of capabilities to logistics companies from autonomous machines to predictive analytics. According to Mckinsey research “AI adoption advances, but foundational barriers remain,” the logistics industry has adopted AI mostly for 4 business functions which are service operations, product and service development, marketing and sales, and supply chain management. These four business units cover 87% of AI adoption in logistics. Mckinsey estimates that logistics companies will generate $1.3-$2 trillion per year in economic value by adopting AI into their processes.

Source: Mckinsey

What are the applications of AI in logistics?

Planning

Logistics requires significant planning that requires coordinating suppliers, customers, and different units within the company. Machine learning solutions can facilitate planning activities as they are good at dealing with scenario analysis and numerical analytics, both of which are crucial for planning.

Demand Forecasting

AI capabilities enable organizations to use real-time data in their forecasting efforts. Therefore, AI-powered demand forecasting methods reduce error rates significantly compared to traditional forecasting methods such as ARIMA, AutoRegressive Integrated Moving Average, and exponential smoothing methods.

With improved accuracy in demand prediction,

  • manufacturers can better optimize the number of dispatched vehicles to local warehouses and reduce operational costs since they improve their manpower planning
  • local warehouses/ retailers can reduce the holding costs (opportunity cost of holding the item instead of investing the money elsewhere)
  • customers are less likely to experience stockouts that reduce customer satisfaction

Supply Planning

Artificial intelligence help businesses analyze demand in real-time so that organizations update their supply planning parameters dynamically to optimize supply chain flow. With dynamic supply planning, businesses use fewer resources since dynamic planning minimizes waste.

Automated Warehousing

According to the 2020 MHI Annual Industry Report, only 12% of businesses are using AI technology in their warehouses, but it is expected to reach 60+% in 6 years.

Source: MHI/Deloitte

Warehouse robots are another AI technology that is invested heavily to enhance businesses’ supply chain management. The warehouse robotics market was valued at USD 2.28 billion in 2016 and is expected to grow at a CAGR of 11.8% between 2017 and 2022.

For example, the retail giant Amazon has acquired Kiva Systems in 2012 and changed its name to Amazon Robotics in 2015.  Today, Amazon has 200,000 robots working in their warehouses. In 26 of Amazon’s 175 fulfillment centers, robots helping humans for picking, sorting, transporting, and stowing packages.

Damage Detection/Visual Inspection

Damaged products can lead to unsatisfied customers and churn. Computer vision technology enables businesses to identify damages. Businesses can determine the damage depth, the type of damage, and take action to reduce further damage.

Predictive Maintenance

Predictive maintenance is predicting potential machine failures in the factory by analyzing real-time data collected from IoT sensors in machines. Machine learning-powered analytics tools enhance predictive analytics and identify patterns in sensor data so that technicians can take action before the failure occurs.

Autonomous Things

Autonomous things are devices that work without human interaction with the help of AI. Autonomous things include self-driving vehicles, drones, and robotics. We should expect to see more autonomous devices in the logistics industry due to the industry’s suitability for AI.

Self-Driving Vehicles

Self-driving cars have the potential to transform logistics by decreasing heavy dependence on human drivers. Technologies such as platooning support drivers’ health and safety while reducing carbon emission and fuel usage of vehicles. Tesla, Google, and Mercedes Benz are investing heavily in the concept of autonomous vehicles, it is only a matter of time before autonomous trucks are seen on roads around the world. However, according to BCG estimations, only around 10 % of light trucks will drive autonomously by 2030.

Delivery Drones

For the logistics of products, delivery drones are useful machines when businesses deliver products to places where ground transfer is not possible, safe, reliable, or sustainable. Especially in the healthcare industry where pharmaceutical products have a short shelf life span, delivery drones can help businesses reduce wastage costs and prevent investments for costly storage facilities.

Analytics

Dynamic Pricing

Dynamic pricing is real-time pricing where the price of a product responds to changes in demand, supply, competition price, subsidiary product prices. Pricing software mostly uses machine learning algorithms to analyze customers’ historical data in real-team so that it can respond to demand fluctuations faster with adjusting prices.

Route optimization/Freight management

AI models help businesses to analyze existing routing, track route optimization. Route optimization uses shortest path algorithms in graph analytics discipline to identify the most efficient route for logistics trucks.

Therefore, the business will be able to reduce shipping costs and speed up the shipping process. For example, Valerann‘s Smart Road System is an AI web-based traffic management platform that delivers information about road conditions to autonomous vehicles and users.

Back Office

Every business unit has back-office tasks and logistics are no different. For example, there are numerous logistics-related forms like a bill of lading from which structured data needs to be manually extracted. Most businesses do this manually.

Automating Manual Office Tasks

Hyperautomation, also referred to as intelligent business process automation, means using a combination of AI, robotic process automation (RPA), process mining, and other technologies to automate processes in an end-to-end manner. With these technologies, businesses can automate several back-office tasks such as

  1. Scheduling and tracking: AI systems can schedule transportation, organize pipelines for cargo, assign and manage various employees to particular stations, and track packages in the warehouse.
  2. Report generation: Logistics companies can use RPA tools to auto-generate regular reports that are required to inform managers and ensure everyone in the company is aligned. RPA solutions can easily auto-generate reports, analyze their contents and based on the contents, email them to relevant stakeholders.
  3. Invoice/bill of lading/rate sheet processing: These documents help communication between the buyers, suppliers, and logistics service providers. Document automation technologies can be used to increase the efficiency of processing these documents by automating data input, error reconciliation, and document processing.
  4. Email processing: Based on contents in auto-generated reports, RPA bots can analyze the content and sends emails to relevant stakeholders.

For more RPA and hyperautomation use cases for businesses’ back-office tasks, feel free to read our articles:

  • 60+ RPA applications
  • 10+ Hyperautomation applications
  • Supply chain automation

Customer Service Chatbot

Customer service plays an important role in logistics companies since customers will contact companies for any issue they experience in delivery. Customer service chatbots are capable of handling low-to-medium call center tasks such as:

  • requesting a delivery
  • amending an order
  • tracking shipment
  • responding to a FAQ

Chatbots are also valuable tech to analyze customer experience, chatbot analytics metrics enable businesses to understand their customers better so that they can enhance the customer journey they deliver.

 

Cem Dilmegani, Founder, AIMultiple

 

 

 

 

Cem is a high-tech industry analyst and he served as a tech consultant at McKinsey and as a tech entrepreneur at Hypatos, the document hyperautomation company. AIMultiple.com provides 1M enterprises with transparent, data-driven insights on enterprise technology.

Features

HOW FSI INCUMBENTS CAN STAY RELEVANT THROUGH THE GCC’S PAYMENTS EVOLUTION

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payment

By Luka Celic, Head of Payments Architecture – MENA, Endava

Banks and payment services providers (PSPs) have been the region’s engines of economic growth for as long as anyone can remember. It is therefore jarring to imagine that this dominance is now under threat. After all, venerable banks and credit card companies have elegantly embraced the Internet, mobile banking, and the cloud to deliver self service banking to millions of customers. But consumers, especially digital natives, have never been known for congratulating an industry for a job well done. Instead, with each convenience, their expectations only grow. The siege reality of the pandemic accelerated a shift in consumer behaviour, and Middle East banks and PSPs now face challenges on three fronts.

The first is FinTechs. from Saudi Arabia’s BNPL (buy now, pay later) pioneer Tamara and Qatar’s unbanked oriented platform cwallet, to online financial services, Klarna, tech startups have been able to tap into rapidly changing consumer markets. New companies find it easier to pivot. And like speed boats racing against aircraft carriers, they weaved effortlessly to fulfil a range of desires amid high smartphone connectivity rates and a range of other favourable market conditions. By one estimate from 2022, BNPL alone accounted for US$1.5 billion (or 4%) of the Middle East and Africa’s online retail market.

The second threat is open banking, which comes in many forms, but one example is the instant-payments platforms being introduced by central banks such as those in Saudi Arabia and the United Arab Emirates. To get a sense of how this could play out, we need only look to Europe, where players who once relied on payments through card schemes are now pivoting towards open banking enabled payments. Closer to home, Al Ansari Exchange recently announced its customers can now transfer money and settle bills via the recipient’s mobile number, enabled by the UAE’s Aani IPP.

And finally, comes big tech. To augment its e-wallet service, Apple has signed up to an open banking service in the UK. The open banking framework which banks enabled through their investments is being exploited by a Big Tech firm that has access to 34% of UK smartphone users. Unsurprisingly, this sparked a fierce antitrust complaint by UK’s banks. Other big names will surely follow as they continue to craft ways of offering the digital experiences that garnered them user loyalty in the first place.

THE BALANCE

Apple Wallet is aimed at blending payment methods, loyalty cards, and other services into a single experience. But such moves have raised regulators’ eyebrows regarding a lack of interoperability and the preservation of competitive markets. Hence, Apple’s open banking foray — a gesture to calm the nerves of a finance market that fears having to compete with a company armed with countless millions of user transactions from which to draw insights. The massive user bases of tech giants will give any FSI CEO goosebumps. How does a traditional bank lure an Apple user? Open banking initiatives open the door to greater competition and innovation, both of which are good for consumers. But the only way to ensure both is by building an ecosystem that balances innovation with regulatory oversight.

FROM INCUMBENT TO INNOVATOR

Yes, smaller businesses have freedom of movement that larger incumbents do not. But that does not mean that there are no paths for banks and PSPs. There are, in fact, several strategies that larger FSI companies can employ to capitalise on the open banking revolution.

The first of these is collaborating to create ecosystems that provide users with frictionless experiences. Established FSIs already have access to a wealth of information about their customers and must now consider how to integrate data sources to create highly streamlined and frictionless workflows. A customer applying for a loan could then see their details auto populated, and credit history already accounted — all without the hassle of lengthy phone calls, application forms, or submission requests. In an age when instant is everything, it’s easy to see why the former approach could foster loyalty, while the latter would only serve to drive customers towards more capable competitors.

Card companies and issuer banks could also work with acquirers to smooth out the rough landscape that has arisen from the advent of digital payments. Acquirers traditionally acted on behalf of the merchants that accepted payment methods to recoup funds from the PSP through the issuing bank. This system has served the industry well, but with more payment methods emerging, acquirers have branched out into mobile wallets, QR codes, and gateway services. Gradually the relevance of established players has dwindled as their lack of representation at the critical checkpoint has diminished their significance. Incumbents must work to turn back the tide by recognising that acceptance and acceptance ownership are becoming increasingly important for maintaining market relevance.

Another strategy is diversification. Veteran FSIs may feel like they’ve lost ground to nimble start-ups and Neo Banks, but history shows value in patience — established FSI players now benefit from the investments of early innovators, and double down on payments innovations which have already shown the most promise. Moreover, if they diversify their portfolios through acquisitions, innovations, and partnerships, they can secure their future. Mastercard presents an excellent example with their US$200m investment into MTM payments. This single move has given the company access to MTM’s 290 million strong subscriber base, allowing these customers to become familiar with Mastercard products before getting entrenched with mobile wallet alternatives.

WHO’S ON TOP?

If we look at the rise of BNPL services, we see an origin story with — at least — major supporting roles for large card providers. But open banking has sidelined them in just a few years. BlackBerry was a stock market darling just five years before it sought a buyer. Traditional FSI players must innovate; they must collaborate with emerging disruptors; they must diversify. They can survive and thrive if they do these things — after all, they already have much of the infrastructure, and experience required for success. Middle East banks and PSPs have the existing user bases, so they have the scale to get out in front in the era of open banking. All they lack is the kind of compelling use cases that will entice the banking public. PSPs and their issuers could offer embedded payments, for example. The right services at the right time will be warmly received by consumers, no matter the scale of the offering institution, so there is every reason to believe that incumbents will come out on top against FinTech and Big Tech.

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Features

SEC paves way to approve spot ethereum ETFs

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ETF

By Simon Peters, Crypto Analyst at eToro

Ethereum spot ETFs took a significant step forward to being available to US investors last week with approval of the 19b-4 applications, allowing US exchanges (namely Cboe BZX, NYSE Arca and Nasdaq) to list and trade ethereum spot ETFs.

On the back of this, ethereum has been one of the best performing cryptoassets this week, gaining 19%.

According to a recent survey by eToro with retail investors in the UAE, over 74% respondents agreed that the prospect of an ethereum ETF will significantly influence their decision to increase, decrease or maintain their current ethereum allocation.
Focus now turns to the S-1 registration statements from the ETF issuers, as these still need to be approved by the SEC before the ethereum spot ETFs can actually launch and investors can buy them.

As to when the S-1s will be approved we have to wait and see. It could be weeks or months unfortunately.

Nevertheless, with the 19b-4s out of the way, it could be an opportunity now for savvy crypto investors to buy ethereum in anticipation of the S-1s being approved, frontrunning the ETFs going live and the billions of dollars potentially flowing into these.

We’ve seen what happened when the bitcoin spot ETFs went live, with the bitcoin price going to a new all-time high in the months after. Could the same happen with ethereum? The all-time high for ethereum is $4870, set back in 2021. We’re currently at $3650, about 35% away.

We’re also going into a macroeconomic climate with potentially looser financial conditions, i.e. interest rate cuts and a slowdown of quantitative tightening, conditions where risk assets such as crypto tend to perform well price-wise.

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Features

Harnessing AI and big data to transform Middle East’s retail industry landscape

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unifonic

By Saeed Alajou, Senior Sales Director, Enterprise Business

With the increasing dominance of technological advancements in the current era, the global retail industry is witnessing a massive shift in its operations. As the industry embraces a varied range of cutting-edge technologies such as artificial intelligence (AI) and big data analytics, it is redefining customer expectations and the conventional concepts of business operations. According to recent studies, The global artificial intelligence (AI) in retail market size is projected to grow from $9.36 billion in 2024 to $85.07 billion by 2032, at a CAGR of 31.8% from 2024 to 2032. This transformative wave is compelling companies to harness the potential of these cutting-edge technologies to maintain their competitive edge.

One of the most evident trends in this era is the convergence of eCommerce, AI and data analytics, which is driving the evolution of the retail landscape worldwide. In the current omnichannel retail landscape, consumers expect consistency and continuity across various touchpoints, pushing industry players to integrate conversational AI. This integration ensures a seamless experience; for example, customers can begin a conversation with a chatbot while browsing online and effortlessly continue it via a mobile app when they visit a physical store.

However, the potential of the omnichannel approach and conversational AI platforms is not limited to supporting customers. They also provide retailers with valuable insights into customer behaviour across different channels. Conversational AI platforms can generate a vast amount of data from customer interactions, offering retailers valuable insights into consumer preferences, trends, and pain points. By analysing this data, retailers can uncover patterns, identify emerging trends, and optimise their product offerings and marketing strategies accordingly.

Furthermore, AI-driven analytics enable retailers to gauge customer sentiment, allowing them to address issues and enhance satisfaction proactively. These data-driven insights empower retailers to make informed decisions and stay ahead of the curve. Reflecting the vast potential of AI, the retail sector in the Middle East is rapidly adopting this technology, becoming a leading industry in AI investment. Reports indicate that AI spending in the Middle East and Africa (MEA) reached USD 3 billion and is expected to grow to USD 6.4 billion by 2026, with a compound annual growth rate (CAGR) of 29.7 per cent.

The innovation of chatbots and virtual assistants has accelerated the integration of AI technologies in retail, revolutionising customer interactions by adding a human-like touch to digital engagements. These tools enhance the purchasing journey, making it more intuitive and responsive, providing customised and real-time recommendations based on consumer sentiment. However, retailers need to manage expectations of scalability and ensure AI complements rather than replaces human interactions.

Furthermore, integrating big data into retail operations helps understand customer behaviour and preferences. Retailers can leverage vast amounts of data to gain insights into customer needs and tailor their offerings accordingly. By analysing customer-generated data, businesses can conduct predictive analysis to anticipate trends and make informed decisions, keeping them ahead of the curve in offering products and services that resonate with their target audience.

When it comes to the impact of AI integration in the retail sector, one key segment where it is significantly visible is the supply chain. By integrating big data analytics, retailers are achieving more efficiency in their supply chain operations. Predictive analytics powered by AI aids in forecasting demand, optimising inventory levels, reducing waste, and ensuring products are available when and where customers need them. This enhances operational efficiency and customer satisfaction by minimising stockouts and delays.

AI integration supports a customer-centric approach in retail, and it positions technology as a key facilitator in meeting customer demand. Advanced technologies can identify and replicate demographic needs and pinpoint where investment is required to add value. The integration of various AI tools including price-matching technologies, pay-per-click advertising optimisation, and predictive analytics, aids the retailers in focusing on perfecting the customer journey, ensuring a seamless and enjoyable experience from the start to finish.

Although AI is widely embraced across the industry regardless of company size, delivering the best customer service requires empowering employees with the right tools and knowledge. When employees are equipped with AI-driven insights, they can provide more personalised and efficient service, enhancing the overall customer experience. This empowerment also promotes a culture of innovation and continuous improvement within the organization.

Additionally, data integration and integrity are crucial for the effectiveness of AI and big data. Retailers must implement systems that can integrate data from various sources, ensuring that all information is accurate, consistent, and up to date. This collaborative approach allows retailers to offer a unified brand experience across all channels while maintaining data boundaries and complying with privacy regulations.

This widespread adoption of AI technologies in the industry underscores the importance of establishing a robust and adaptable regulatory framework. Given the growing concerns about data privacy and ethical use, retailers must ensure responsible and secure handling of customer data. Stagnant regulations can lead to compliance issues and erode customer trust, and this necessitates current and customer-aligned regulations to maintain a trustworthy data environment.

Another challenge in AI integration is utilising AI and big data to experiment with new ideas and strategies. In retail, embracing calculated risks is crucial for innovation and growth, viewing risks as learning opportunities. Being responsive to evolving customer needs allows retailers to navigate uncertainties and capitalise on opportunities for success.

With AI projected to contribute up to USD 320 billion to the Middle East’s economy by 2030, the region is increasing its investment in technology. This emphasises the need for a holistic approach in retail, integrating AI, big data, and a customer-centric mindset to thrive in the market. The industry players can maintain their competitive edge by focusing on efficiency in supply chain operations, understanding consumer behaviour, and empowering employees.

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