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The tough decisions supply chain leaders are facing in 2024

By David Strauss, AVP, Strategic Partner Development, e2open

We’re three months into 2024, but it hasn’t been an easy start to the year for businesses. Thanks to geo-political conflict, the continued cost-of-living crisis, and the impact of extreme weather events, enterprises continue to face unprecedented challenges in supply and demand.

Securing inventories, meeting demanding ESG requirements, and keeping discerning customers happy are no easy feats when many supply chains still lack the visibility and flexibility to cope with changes.

Leaders should consider four crucial supply chain issues when drawing up any game plan for transformation this year.

1. Protectionism is the word on everybody’s lips – but will it produce resilience?

In a rush to reduce costs and increase efficiency, many organizations looked further afield in the last years, investing in offshore manufacturing, supplier networks, and supporting logistics infrastructure to produce and distribute goods. In theory, this approach makes it easier to fulfil orders quickly and lowers the risks associated with global supply chains. However, companies must remain cautious. These models have limited short-term flexibility due to the cost and time associated with repositioning production facilities and supplier and logistics networks closer to home, as well as geo-political uncertainty making it difficult to know who your friends are—or will be—in the long term. On the opposite side, nearshoring can lead to higher costs, which cannot always be passed on to the consumers, lest they generate a backlash and decrease demand.

Nearshoring should be viewed as a possible long-term strategy, with multiple backup supply routes and innovative supply sensing and routing technology to make the best real-time decisions. Realistically, it’s also important to recognize that supply chains will likely remain global because companies still need to source and distribute globally. While nearshoring may be one strategy in a complex supply chain, it will be one of many approaches. 

2. Leaders will discover ESG reporting and scalability are intertwined

Many businesses already understand the importance of reporting on crucial ESG metrics. They know there’s little time to waste addressing areas such as Scope 3 emissions, particularly from a reputational perspective. But when faced with tight budgets, achieving net zero is daunting. Given the vast nature of most supply networks, many leaders feel intimidated by the sheer scale of operations and struggle to decide where to start. Fortunately, it’s easier than ever for leaders to identify the ‘quick wins’ regarding sustainability within their supply network and to marry a strategy for net zero to longer-term plans for business growth. In other words, scalability and sustainability needn’t be mutually exclusive.

The key to success here is granular visibility and access to a wealth of data. Companies can lean on connected supply chain technologies that unite entire networks and help answer the questions: how many supplier tiers are in the supply chain? Who are the different suppliers, and what do they supply? What are the emissions of these materials?

Beyond the supply network, connecting all supply chain data – company-wide and from partners – in a single supply chain platform is also the foundation for scalability. Partners in sourcing and manufacturing, material and component flow, transport and logistics, cross-border regulations, and even product storage and distribution partners form a complex, far-flung supply chain. When all these partners, processes, and data exist in a collaborative space without silos, a company can model changes and optimise processes in real-time. Through this connectivity, companies can make, move, and sell goods as efficiently, cost-effectively, and sustainably as possible—at scale.

Fundamentally, a supply chain must be sustainable to be scalable. Therefore, every company must focus on supply chain connectivity to grow long-term.

Note: Any company operating across Europe or trading with Europe must meet the strict reporting standards of the Corporate Sustainability Reporting Directive (CSRD). But with network transparency, data, and the means to analyse it, making correlations between materials and their associated emissions is easier.  Seamless data sharing, connected suppliers, and collaboration between every stakeholder in the supply chain will be vital to success in the years ahead.

3. The evolution of the high street and the omnichannel experience

The role of retail stores is ever-evolving. Though the pandemic saw many consumers pivot to online shopping, customers are now also enjoying a return to in-person shopping experiences. But expectations have changed, and consumers are now more discerning than ever, enjoying multiple retail channels and expecting products to arrive on doorsteps within – or even less than – 24 hours. 2024 will signal the evolution of retail stores into fulfilment hubs. No longer reliant on offsite warehouses, stockrooms will increasingly be used as fulfilment locations, facilitating rapid orders, buy-online-pick-up-in-store (BOPIS), and click-and-collect.

We will also see a rise in the use of point-of-sale (POS) information in supply chain management. This will be vital for tracking customers’ buying habits and anticipating their needs. Going one step further than simply forecasting the orders large retailers are likely to place, companies will analyse the audience to whom they are selling. Through supply chain connectivity and collaboration, organizations can respond to near-term supply and demand signals, such as impending shortages caused by extreme weather or increased product purchases.

4. Leaders should take advantage of all that AI has to offer – but ensure it’s using quality data

Data and an integrated AI strategy will be fundamental to building flexible, resilient supply chains. It’s not necessarily about adopting the latest AI models the industry offers. Instead, leaders should use the right form of AI for the job and then feed the AI model with the right data – and lots of it. Data should draw from internal operations and the wider value chain of partners, suppliers, distributors, transportation providers, customers, and more to paint the most accurate picture of the network. A digital platform that brings these insights together under one ‘umbrella’ lowers entry barriers and grants AI systems the knowledge to make more accurate recommendations. It’s simple: the more data points you bring together, the better the performance of your AI solutions will be.

Photo by krakenimages on Unsplash

Data, data everywhere: The future of AI in logistics transportation

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.


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?

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

The transformative impact of AI on the logistics and supply chain sectors

Artificial Intelligence (AI) is revolutionising industries worldwide, and its impact on the logistics and supply chain sectors cannot be overstated. From streamlining operations to enhancing efficiency, AI-powered technologies are transforming how goods are transported, stored, and delivered.

This article explores the ways in which AI is impacting the logistics and supply chain sectors, revolutionising the way businesses manage their operations…

  1. Demand Forecasting and Inventory Management

AI algorithms are improving demand forecasting accuracy, enabling businesses to optimise their inventory management. By analysing historical sales data, market trends, and external factors, AI systems can predict future demand patterns with greater precision. This enables businesses to adjust inventory levels, reduce stockouts, minimise excess inventory, and improve supply chain efficiency.

  1. Route Optimisation and Fleet Management

AI-powered logistics systems are enhancing route planning and optimization, improving efficiency and reducing costs. These systems analyse factors such as traffic conditions, weather patterns, and delivery constraints to identify the most efficient routes. AI algorithms can also optimize fleet management by considering factors like vehicle capacity, fuel consumption, and driver availability, ensuring optimal resource utilisation.

  1. Warehouse Automation and Robotics

AI-driven automation and robotics are revolutionising warehouse operations. Autonomous robots equipped with AI algorithms can handle tasks such as inventory picking, packing, and sorting with speed and precision. AI-powered warehouse management systems optimise storage space, monitor inventory levels, and facilitate efficient order fulfillment. This automation improves accuracy, reduces labor costs, and enhances overall productivity in the supply chain.

  1. Supply Chain Visibility and Transparency

AI technologies provide real-time visibility and transparency across the supply chain. Through the integration of data from various sources, such as GPS trackers, sensors, and RFID tags, AI systems can track shipments, monitor conditions, and provide accurate delivery estimates. This visibility enables proactive issue resolution, better customer service, and improved decision-making throughout the supply chain.

  1. Risk Management and Mitigation

AI algorithms help identify potential risks and mitigate disruptions in the logistics and supply chain sectors. By analysing data from multiple sources, such as weather forecasts, social media feeds, and historical patterns, AI systems can predict and proactively address potential risks. This allows businesses to take preventive measures, adjust logistics plans, and minimize the impact of unforeseen events on supply chain operations.

  1. Enhanced Customer Experience

AI-powered technologies enhance the overall customer experience in logistics and supply chain operations. Personalized recommendations, real-time tracking, and delivery notifications improve customer satisfaction. AI-driven chatbots and virtual assistants provide prompt and accurate customer support, enhancing engagement and resolving queries efficiently. These technologies contribute to building customer loyalty and driving business growth.

The impact of AI on the logistics and supply chain sectors is transformative, revolutionizing traditional practices and improving operational efficiency. From demand forecasting and inventory management to route optimization, warehouse automation, and supply chain visibility, AI technologies offer unprecedented opportunities for businesses to streamline operations, reduce costs, and enhance customer satisfaction.

As AI continues to evolve, we can expect further advancements in the logistics and supply chain sectors, shaping a future where efficiency, speed, and reliability become the hallmarks of successful operations. Embracing AI-powered solutions will be crucial for businesses aiming to thrive in the ever-evolving landscape of logistics and supply chain management.

5 Minutes With… Amy Heineike, VP of Engineering at 7bridges

In the latest instalment of our supply chain industry executive interview series we spoke to 7bridges VP of Engineering Amy Heineike about managing geopolitical and economic shocks, the importance of adaptability, the impact of AI tools and how corporate sustainability will be a big focus in 2023…

What have been the biggest challenges the Supply Chain industry has faced over the past 12 months?

We continue to jump between major economic and geopolitical shocks.  The war in Ukraine, escalating fuel prices, sky high inflation, widespread strikes, extreme weather, and the rumblings of recession.

Businesses have needed to respond quickly, bring down their costs, and look hard at how to weather the storms.

And what have been the biggest opportunities?

They say necessity is the mother of invention.  We’ve seen a shift towards the circular economy, interest in creating demand driven supply chains, and a renewed commitment to lowering emissions.  Ambitious goals that require prioritising smarter supply chains.

What is the biggest priority for the Supply Chain industry in 2022?

Adaptability.  Our past doesn’t tell us enough about the future – we need evidence on what is happening, options for how to respond, and the ability to implement decisions quickly.

What are the main trends you are expecting to see in the market in 2023?

Firstly, we’re expecting to see everyone needing to calculate their emissions, particularly as Scope 3 and the Corporate Sustainability Reporting Directive have gotten the green light.  This will require gathering a lot of data, but also building processes that will last into the future.

Secondly, we expect to see supply chain teams having closer relationships with the commercial side of businesses. Supply chain leaders are going to be much more involved in strategy and there will be more collaboration between those parts of the business. Don’t be surprised if supply chain is a hot topic for the board next year.

What technology is going to have the biggest impact on the market this year?

My career has been in building data and AI technology tools – so I’m probably biased here!  It’s hard to overstate the magnitude of change in the past few years – in the tooling for wrangling data at scale, as well as the types of models and computation that can be used. They are incredible foundations for technology to continue to rapidly improve in this market.

AI and automation have the opportunity to drive enormous change, and make it much more tractable for teams to manage increasingly complex modern supply chains in flexible ways.

Adaptability requires that we have options available to choose from, but playing out how those choices will ripple through a supply chain is really hard without some heavy computations, and it’s daunting if we don’t have automation to enact those changes.   For supply chains that have the tech in hand, we’ll see them making the most of a lot of opportunities – experimenting, learning and adapting – having smarter supply chains that benefit them and their customers.

In 2025 we’ll all be talking about…?

Smarter, demand-driven, data-powered supply chains. In this age of data, what is going to matter most is how you use it to make your supply chain (and your business) smarter. We’ve seen incredible things happen for our customers who are using data to make decisions, simulate scenarios and get real visibility of what happens in their supply chain.

Which person in, or associated with, the Supply Chain industry would you most like to meet?

As someone who’s learning as much as possible about the industry I really enjoyed reading Christopher Mim’s book “Arriving Today” which gave a deep dive introduction into the people and tech at different stages of a global supply chain.  It would be fun to discuss how the last two years may have refined his thoughts on future trends.

What’s the most surprising thing you’ve learnt about the Supply Chain sector?

How much hard work and sweat still goes into coordinating supply chains.  The heroics and difficult decisions that are being made every day when, as consumers, we’re blissfully unaware.

You go to the bar at the Total Supply Chain Summit – what’s your tipple of choice?

I’d love a pint of a good English Cider.  Do you have those?

What’s the most exciting thing about your job?

I really like to geek out on data, and the picture of the world it paints for us.  I like being around talented people who want to imagine whats possible, and who have ideas that surprise me and that are really deeply useful.

And what’s the most challenging?

Supply chains are complex beasts. We want to make sure that the software we build enables that complexity to be managed, but without it being overwhelming.  That requires a lot of careful thought!

Peaky Blinders or Stranger Things?

The Marvelous Mrs Maisel.

Could AI-generated inventions soon be patented in the UK?

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

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. 


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. 

82% of supply chain managers frustrated by AI systems during COVID-19 pandemic

82% of supply chain decision-makers have been left frustrated by AI-powered systems and tools during the COVID-19 crisis.

That’s according to Secondmind-commissioned independent global research, carried out by Censuswide, to survey over 500 supply chain planners and managers across Europe and the USA to learn how AI was helping or hindering their decision-making. 

The results show that despite the frustration, belief in AI’s potential is strong – 90% agree that AI-powered tools and software will help them make better decisions by 2025 and over half (59%) strongly agree that AI will transform supply chains for the better in the next five years.

The managers surveyed cited a number of factors hindering the ability of AI systems to deliver value, all of which fell into two categories:

  • Data: a lack of reliable data to feed into AI systems (37%), historic data becoming ‘meaningless’ in times of unprecedented change (19%) and the need to spend significantly more time on manually analysing and interpreting data (50%) were concerns at a time when accuracy and speed were of the essence.
  • Organisational: a third of respondents (34%) said their leadership teams lack an understanding of what is currently needed on the ground to make faster, data-driven decisions. Furthermore, rigid processes and internal structures prevented over two in five planners and managers from quickly responding to changing market conditions (41%).

The supply chain planners and managers surveyed believe that a third of their time (on average 2.83 hours daily) is spent on manual tasks that could easily be automated. As frustrations with current AI systems emerged during the pandemic, 50% said they spent significantly more time manually analysing and interpreting data to assist strategic and operational decisions.

The decision-makers surveyed stated these data pain points are holding them back from working on higher value initiatives that could contribute towards building more resilient supply chains, such as: 

  • Proactively preparing scenarios and plans for future unexpected ‘black swan’ events (30%)
  • Spending more time on proactive and in-depth planning for major events such as Christmas and Black Friday (41%)
  • Conducting more in-depth analysis, using their experience and expertise (51%)

The majority of managers who use AI systems want their domain expertise to factor into the decision-making process. Desirable capabilities for AI systems included: the ability to modify AI-generated forecasts using the decision-maker’s own judgement ( 53%), AI that can learn from humans when historic data is unreliable (47%) and AI that could show what data or contextual information that impacted a forecast (39%).

Of those who believed AI alone was not enough to inform effective decision-making, the reasons cited were that human intuition cannot be replicated by a machine (62%), there will always be some events that a machine can’t predict (59%) and expertise developed from years on the job is critical in decision-making (51%).

Vishal Chatrath, CEO and Co-Founder, Secondmind, said: “COVID-19 has been a wake-up call for businesses operating in global supply chains as they prepare to rapidly accelerate the implementation and deployment of AI in the coming years. For AI to realise its potential, it will be critical for organisations to deploy systems that can cope with sparse or incomplete data environments and promote the effective collaboration between people and AI. Our report shows how much people benefit from AI, but also how much AI needs people. A collaborative approach to decision-making that combines the right skills and capabilities for each task is essential, particularly when systems are disrupted during uncertain times and unpredictable events.”

Carrefour optimises supply chain with Artificial Intelligence

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.”