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Data, data everywhere: The future of AI in logistics transportation
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Most shippers, carriers and logistics service providers understand the importance of data collection and data-driven decision-making. Data collected over time provides intelligence, enabling companies to enhance long-term decision-making. Meanwhile, real-time data can be used to make smart split-second decisions – like how to correct or replan when problems occur. 

Artificial intelligence is a potent tool that helps companies get the most from their data. This takes several forms. “Statistical AI” enables users to analyse huge quantities of information to find hidden patterns and make smart decisions. Meanwhile, companies can use past data to programme “symbolic AI” models, which can be used for “purpose-seeking” applications, such as process optimisation. Jonah Mcintire (pictured, above), Chief Network Officer at Transporeon, A Trimble Company, explores further…

Automation vs. AI – understanding the difference

Automation and AI are often spoken about in the same breath, as if they are synonymous. However, though they’re interlinked, there’s an important distinction between the two. Automation involves delegating mundane, often administrative, tasks to software. It’s clerical. On the other hand, true AI involves handing over decision-making power. Software is given set parameters, but it will use them to draw unexpected conclusions. Users can give AI varying degrees of freedom. A more cautious approach is to allow software to calculate options and make recommendations for a human to approve. However, it’s also possible for it to reach conclusions and make decisions autonomously, without even informing a human.

So, where can AI in logistics transportation have the most impact? The short answer is “everywhere”. In fact, forward-thinking shippers, carriers and logistics service providers are already integrating AI into their tech stacks.

There are a few considerations to keep in mind. AI is best used for decisions with concrete financial values that are easy to score and have discrete, well-known variables. Fast decision-making cycles are also important. Like humans, AI learns from experimentation. So, if a decision is only made annually, it will take decades for the software to gather enough data to get feedback. Realistically, you want AI models to analyse thousands of decisions per day. Ideally, players would use models trained not just with their own data, but with data gathered from across the industry. This collaborative (also known as “platform”) approach enables everyone to get ahead.

So, how AI can transform how companies utilise their data through autonomous procurement, real-time ETA tools and decarbonisation?

Real-time ETA tools 

The disconnect between shippers and carriers has long been a challenge in the logistics transportation industry. To enhance visibility, transparency and efficiency, we need to connect load receivers and load givers.

For example, predicting arrival times for loads has traditionally been a pain point for both shippers and carriers. Common causes of delay – like strikes, traffic jams and mechanical difficulties – can seem completely random to the human eye. But when an AI model analyses years’ worth of this data, hidden patterns do emerge. Typically – unless circumstances are truly unprecedented – AI is much better at predicting ETAs and with the help of an AI-assisted real-time ETA tool, companies can ensure they’re prepared to receive loads whenever they arrive.

Automating procurement and quotation

Spot buying is a perfect use case for symbolic AI, as companies have a set budget and clear constraints around lead times and carrier types. Beyond this, the structure of negotiations is relatively simple – participants can make an offer, wait for a response, make a counteroffer, accept an offer, or end a negotiation. This makes it easy for software to pursue its goals independently, saving thousands of manual administrative hours.

This is just one example. In the procurement space, statistical AI can also revolutionise tendering by using huge quantities of data to predict pricing. For example, instead of asking carriers to bid on a load tender, AI can present said tender – and a pricing offer – to a select number of carriers. If no carrier accepts the tendered load at the offered price, the AI can initiate additional tendering rounds as needed.

AI can also have a transformative effect for sellers of logistics services, enabling them to automatically serve customers with instant, accurate pricing for spot transports based on predicted market rates. With this ability, load takers can increase the volume of opportunities they quote for and ultimately win more new business.

Decarbonisation

The logistics transportation sector is under pressure to slash its carbon emissions. End-user customers are leaning on shippers to decarbonise. Meanwhile, shippers are putting the same pressure on carriers by contracting them based on their sustainability practices, offering longer freight contracts to environmentally responsible carriers, and even paying a premium for lower carbon transport.

With sustainability now affecting the bottom line, it’s no surprise that decarbonisation is rising to the top of the agenda for both shippers and carriers. So, how can AI help with all this?

The first thing to emphasise is that – unlike procurement – there’s often no single “right” answer when it comes to sustainability. Companies may have differing ideas of the optimum strategy, carefully balancing “cost vs. emissions” or “certainty vs. emissions”. However, once shippers, carriers and logistics service providers have decided on their risk appetite, AI can play a crucial role in helping them stick to their goals.

Companies typically adopt one of two mentalities. The first is a cap-and-trade strategy, where the company decides that it won’t tolerate more than X emissions. The second is a carbon tax, where a company decides to offset its emissions. For both of these strategies, shippers and carriers can factor “price per ton of emissions” into procurement events. Statistical AI can be a helpful decision-making tool. For example, when deciding which mode of transportation should be used for each shipment.

The future of AI in logistics transportation is collaborative

We’re at an important inflection point in the use of AI in logistics transportation. It’s poised to slash administrative work and help companies become more efficient and sustainable. But achieving this depends on effective data gathering and sharing. This is where cooperation between industry players comes in. To maximise positive outcomes for everyone, shippers, carriers and logistics service providers need collaborative digital platforms to share data to feed AI models. Looking ahead with this approach, we can significantly accelerate our progress towards reaching the industry’s digitalisation and decarbonisation goals.

Leveraging Artificial Intelligence in Logistics Planning: A game-changer for supply chain professionals?
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The supply chain domain, particularly logistics planning, has always been fraught with complexities and uncertainties. From fluctuating demand to unforeseen disruptions, supply chain professionals grapple with a myriad of challenges daily. However, the integration of Artificial Intelligence (AI) into logistics is providing them with the tools to navigate this intricate landscape more efficiently and effectively. Here’s how AI is revolutionising logistics planning…

  1. Demand Forecasting:
    • Function: AI algorithms, by analysing historical data, market trends, and even external factors like weather or local events, can predict demand with higher accuracy.
    • Benefit: Accurate demand forecasting allows supply chain professionals to optimise inventory levels, reduce carrying costs, and ensure timely fulfilment of orders.
  2. Route Optimisation:
    • Function: AI can process vast datasets related to traffic patterns, weather conditions, and roadwork disruptions to determine the most efficient routes for shipments.
    • Benefit: This results in faster delivery times, reduced fuel consumption, and lower operational costs.
  3. Dynamic Pricing in Freight:
    • Function: AI tools can adjust freight pricing in real-time based on various factors, including demand and capacity, fuel prices, and competitor rates.
    • Benefit: Dynamic pricing ensures that transportation costs are always optimised, leading to cost savings and enhanced profitability.
  4. Predictive Maintenance:
    • Function: AI can predict when machinery, vehicles, or equipment are likely to fail by analysing usage data and identifying patterns leading up to past breakdowns.
    • Benefit: Predictive maintenance reduces downtime, extends the lifespan of equipment, and ensures smooth operations.
  5. Automated Customer Service:
    • Function: AI-powered chatbots and virtual assistants can provide real-time updates on shipments, answer queries, and even process claims, all without human intervention.
    • Benefit: This enhances the customer experience and frees up human resources for more strategic tasks.
  6. Supplier Relationship Management:
    • Function: AI systems can analyse supplier performance data to identify which suppliers are consistently meeting criteria and which may need re-evaluation.
    • Benefit: This ensures that the supply chain is backed by reliable partners, reducing the risk of disruptions.
  7. Warehouse Management:
    • Function: AI-driven robots and systems can automate tasks such as picking, packing, and restocking. These systems can also predict which products will be in demand, optimising warehouse layout accordingly.
    • Benefit: Automation increases warehouse efficiency, reduces errors, and ensures that products are always available for dispatch.
  8. Risk Management:
    • Function: By analysing global events, market shifts, and historical disruptions, AI can forecast potential risks to the supply chain.
    • Benefit: Advanced warning allows professionals to implement contingency plans, ensuring the continuous flow of goods.

As AI continues to evolve, its role in streamlining and enhancing logistics planning becomes more pronounced. Supply chain professionals leveraging AI are better equipped to navigate the complexities of their domain, ensuring that goods move efficiently, cost-effectively, and punctually from origin to destination. The result is a more resilient, agile, and responsive supply chain, ready to meet the demands of today’s fast-paced world.

Learn more about how AI is impacting supply chain logistics at the Total Supply Chain Summit.

Image by Kevin Schwarz from Pixabay

Could AI-generated inventions soon be patented in the UK?
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With the rapid advancements in artificial intelligence technology, how are AI-generated inventions recognised when it comes to patents? Innovation funding and Patent Box experts ABGI UK look into where inventions created by AI systems currently stand in regards to intellectual property, and how potential changes will affect UK businesses…

As artificial intelligence becomes increasingly advanced, how is AI-generated innovation considered when it comes to intellectual property?

The issue is more pertinent than ever following the case earlier this year of Thaler v Comptroller General of Patents, Trade Marks and Designs. After Dr Stephem Thaler submitted two patents naming his AI machine “DABUS” as the inventor, the UK Intellectual Property Office withdrew the patents, citing that the machine did not meet the necessary criteria for an inventor. When taken to the UK Court of Appeal, the Court backed the IPO’s decision.

So why does the issue of AI in relation to patents continue to be a point of contention?

IPO Consultation is Launched: Can Artificial Intelligence Hold Ownership of New Patents?

In the conclusion of the case, the Court acknowledged that the law on inventorship continues to change and the Court remains open to further development. The Intellectual Property Office (IPO) subsequently launched a consultation into the issue on the 29th of October, stating  that “Artificial intelligence (AI) is playing an increasing role in both technical innovation and artistic creativity. Patents and copyright must provide the right incentives to AI development and innovation, while continuing to promote human creativity and innovation.” In other words, the government recognises that patent limitations on AI-generated inventions could hinder UK businesses and individuals, and is reviewing their treatment of AI in copyright and patents legislation to seek a balanced solution.

How Long Until the UK Names an AI System as Inventor on a Patent?

Concurrent to the investigation into patent protection for AI-devised inventions, the National AI Strategy was published this September, making the government’s ambition to become a global leader in artificial intelligence clear.

With the government keen to push AI and machine learning across UK industry sectors, the legal framework surrounding intellectual property rights such as patents could need to be adjusted to suit the changing scenario and reflect that the concept of “creations of the mind” may no longer apply exclusively to the inventions of humans.

Countries such as South Africa have recently granted successful patents to artificial intelligence systems; the recent IPO consultation confirms that the UK is determined not to be left behind in the technological race, and therefore changes to the UK Patents Act 1977 may occur sooner rather than later.

How Might This Change Impact Innovative UK Businesses?

One of the main ways in which a change in UK regulation regarding AI-held patents would positively impact UK businesses would be in regards to Patent Box eligibility.

The Patent Box regime was introduced in reaction to the relatively low number of patent applications submitted in the UK annually compared to many other countries, providing an incentive for UK companies to formalise the IP generated from UK-based R&D and commercialise their IP, repatriating the economic benefits back into the UK.

Aiming to increase the level of patenting of UK-developed IP and ensure that new and existing patents are developed in, manufactured and sold from the UK, the UK’s patent box regime is among the most favourable in the world. Profits earned from patents and intellectual property rights under the Patent Box regime benefit from a reduced tax rate of just 10%; with the imminent increase in the standard corporation tax rate in the UK from 19% to 25% in 2023, the tax advantage of Patent Box becomes even more significant.

If the change in legislation regarding AI-generated patents comes into effect, IP-protected AI innovation will also be eligible for Patent Box, creating the potential for huge savings on profits generated from AI-generated inventions.

UK companies should ensure all their intellectual property is structured to take advantage of Patent Box with immediate effect, including investigating AI creations for a potential shift in patent legislation – but what does this involve?

Get Ready For Change and Plan Ahead

  • Make sure you conduct IP reviews at regular intervals. For each element of IP considered for protection, establish a cost/benefit comparison to decide whether or not it’s worth protecting.
  • Review your R&D plans to establish whether any of your products, services or processes could be patented to receive the benefit of the Patent Box regime both now and in the case of a future reform.
  • Educate everyone involved in R&D about the importance of IP protection and the risks related to data leakage.
  • Keep a laboratory notebook recording R&D progress to prove precedence in the case of competing patent cases.
  • Look into Patent Box eligibility even if your patent is pending. Companies with pending patent applications can also qualify retrospectively for the 10% rate once the patent is granted, but the Company has to elect into the Scheme for the accounting period in which profits are generated. If companies are submitting patent applications to the UK IPO and inform the IPO that they are intending using the Patent Box scheme to improve the business benefits of the commercialisation, some patent attorneys believe the IPO will give the application preferential treatment and speed up the patent grant process – so by no means dismiss the idea of Patent Box eligibility until your patent is approved as it could mean missing out on enormous tax reductions.

How Can UK Businesses Elect into the Patent Box Scheme?

The idea behind Patent Box is simple enough, but electing into the scheme can be complex.

Getting advice from a specialist can ensure you’re making the most of the scheme, helping to provide clarity on areas such as the impact of your existing R&D on the calculation of relevant IP income, how to best manage tax benefits when combining R&D tax relief and Patent Box schemes, or on legislative changes and their impact on current or future patent box claims. Receiving guidance here will help identify key areas where new patent applications are needed and provide a clear patent strategy for the business moving forward in regards to areas of potential change such as patents from AI-generated innovation.

How Smart Technology is Helping the Manufacturing Industry
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In the world of manufacturing, the way things are produced has changed dramatically over the years. From the first moving assembly line created, through to the modern-day invention of Artificial Intelligence (AI), it’s safe to say that manufacturing methods have evolved quickly.

The secret to this progression is advances in technology. Not only has it allowed businesses to speed up production and increase efficiency it has brought greater profit margins too. In today’s market, it’s all about ‘smart technology’ or more accurately ‘enablers’.

Businesses now use technology to optimise their operations — from automated sales and distribution processes to energy management software. Here’s a list of the benefits smart technology can bring.

Artificial Intelligence 

Artificial intelligence (AI) is a computer science that can help the capabilities of humans. Voice recognition for example, allows processes to be carried out without being manually entered into a computer. AI also uses algorithms that can record and react to changes in data to help businesses achieve more and increase efficiency. 

Blockchain

Block chain is essentially a programme that helps keep track of goods, logs transactions and manages supply chains, following a ‘chain-like’ process. For businesses, this information is essential – allowing them to record data and deliver real-time analytics on their stock and supply chain without manual input. 

The Industrial Internet of Things 

The collection of data has now become a significant priority for businesses looking to gain a deeper insight into their production processes. With the Industrial Internet of Things (IIoT), companies can ensure that every device, machine and process is connected through data communication systems. This gives them a greater understanding of their business and can look at ways of enhancing efficiencies and increasing profits. 

Industrial Robotics

An extension of AI, industrial robotics have now become a focal element of the manufacturing process. Modern robotics can now carry out a range of tasks, whilst reducing the risk of injury to workers. Although robotics is a modern invention, they’re intelligent enough to learn human tasks.

More recently ‘collaborative robots’ or ‘cobots’ have been designed to work together with humans. Cobots have become prominent in the automotive industries to help build vehicles. 

Digital Twin

Manufacturers can create a ‘digital twin’ when creating a new product — this allows them to virtually forecast its cost and production. Using this technology, they can evaluate production, visualise products in different environments, track and monitor systems and troubleshoot equipment. This results in a more streamlined development process. 

Condition monitoring

Broken or machinery that needs repair can have significant impact on production. With condition monitoring, businesses can monitor a range of performance conditions, including vibration, temperature, pressure and oil condition. This can help manufacturers prevent breakdowns in equipment by noticing changes and faults at an early stage.

Cyber security

The rise in technology means businesses are open to digital malfunction, including the risk of cyber-attacks — which have been common in several industries. Cyber security is important as it protects computer systems from theft or damage to their software and electronic data. As the manufacturing sector is the third most hit sector in the UK for cyber-attacks, companies to ensure they are adequately protected.

A ‘smart’ way of working

As ‘smart technology’ continues to improve the way manufacturers can do business, evidence suggests that introducing new methods can have a positive impact on a company’s output and profit margins. With energy at the heart of manufacturing processes, it’s important that energy supply is efficient and automated. If you’re switching from oil to gas, such as Liquid Petroleum Gas (LPG) or Liquefied Natural Gas (LNG), speak to an expert for help with the process of becoming greener. 

Carrefour optimises supply chain with Artificial Intelligence
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Carrefour has become the first French retailer to use artificial intelligence to optimise inventory management and reduce waste by integrating software developed by SAS into its supply chain.

Drawing on the €2 billion annual investment budget included in the “Carrefour 2022” transformation plan, mainly for IT and digital technology projects, Carrefour Supply Chain ultimately selected the Viya solution.

Following the completion of an 18-month trial, the SAS platform will be used to collect and process data from stores, warehouses and e-commerce websites to better predict demand and refine supplier orders. Carrefour says more intelligent supply chain management will ultimately reduce stock outages and overstocking in stores and warehouses.

The integration represents the first time an AI solution has been integrated into the supply chain for food products – and, shortly, non-food products – in the French retail sector.

SAS Viya provides Carrefour with a multi-channel distribution and inventory optimisation solution to reduce waste and create value. The aim of this far-reaching project is to create a unique online and physical environment in which recognised and loyal customers are guaranteed the most suitable offer anytime and anywhere.

AI will also enable people in new occupations – such as data scientists for data processing and demand planners for business expertise – to work in parallel on forward planning tasks. Optimising the work of the supply and planning teams, the SAS platform can process a variety of data from the Carrefour Supply Chain information system.

It will also make teams more agile by allowing them to integrate new working methods and continuously improve their forecasting processes. The SAS open AI solution will also offer Carrefour experts an opportunity to develop their own bespoke algorithms to meet their specific needs.

“The deployment of the SAS platform will help us turn the corner in optimising our supply chain,” said Franck Noël-Fontana, Forecast Director at Carrefour France. “By freeing up time for teams, artificial intelligence will allow them to focus on developing differentiated forecasting strategies and better meeting customer expectations while reducing waste.”

For its part, SAS will use its technological and business expertise to benefit Carrefour teams, supported by the solutions integration expertise of its long-standing partner Capgemini.

“Beyond the operational benefits of a fluid and efficient supply chain, this project will also improve the Carrefour customer experience,” said Boualem Alouache, Retail Director SAS France. “Based on artificial intelligence and machine learning, this Big Data project will optimise each step in the supply chain and increase customer satisfaction. We are proud to be driving the success of this project, with Capgemini’s support at the integration phase.”