The potential for artificial intelligence to transform supply chain costs and performance is immense. From forecasting and procurement to customer delivery and service, it’s a sector to which the data analytics, automation, and modelling capabilities of AI are ideally suited.

But despite the potential for a perfect partnership, AI adoption in supply chains remains low. What’s more, companies implementing AI solutions often seem to be doing so without a clear value proposition. Adoption is driven by hype, not business need.

The Chief Supply Chain Officer (CSCO) should be the driving force in achieving intelligent AI implementation through the supply chain. Successful adoption will require leaders who understand where to employ AI for the most significant impacts.

Why supply chain AI adoption is lagging

Adoption of AI in the sector is being delayed by several limiting factors. Foremost is the skillset gap. From the board to the CSPO, leadership needs to improve AI fluency in order to make the right implementation decisions. Competition is also fierce across the talent market for AI and data experts, with a premium on those experienced in the operational nuance of complex supply chains.

AI continues to receive criticism for not presenting its ‘thought process’. Poorly designed supply chain systems risk becoming ‘black boxes’, making unchallengeable decisions without giving stakeholders clear rationales. While GenAI can provide reasoning narratives, full explainability remains limited, especially for deep learning or complex statistical models.

Risk accompanies the introduction of any new technology, and is multiplied when rolled out across a complex supply chain. AI implementation requires meticulous design, with an emphasis on transparency. As AI expands into warehouse operations, maintenance, and planning, critical questions must be asked around whether systems should be modular or integrated end-to-end; and where governance for autonomous decision-making lies. 

A perception barrier to AI adoption also exists. By focusing on cost-cutting, and implementing point solutions to fix specific issues, organisations are overlooking AI’s strength in optimising value across the supply chain. By enhancing margins, resilience, and service levels, it can be a value creation enabler, not just an efficiency tool.

Where AI can benefit supply chain management

Overcoming these barriers presents opportunities to improve supply chain efficiencies, increase capacities, and lower costs, with use cases including:

Responding rapidly to market shifts

AI’s use of real-time data to predict, optimise, and adapt means it can prioritise inventory for high-margin products; guide dynamic pricing, allocation, and fulfilment; and circumvent bottlenecks.

Improving logistics decision-making

AI maps likely scenarios and plans responses, helping leadership to make faster, better-informed decisions. It uses learning loops to adapt, delivering smarter outcomes over time.

Bridging business and technology

GenAI improves accessibility and adds transparency by allowing users to interact directly with complex systems in natural language, making it a collaborative business partner.

Improving customer experience

By detecting patterns in customer churn, AI can drive operational improvements, while also performing sentiment analysis, aggregating high volumes of unstructured feedback to identify what issues influence customer satisfaction. 

Aligning stakeholders

For supply chain stakeholders, AI offers shared visibility on cross-function activities. By integrating planning and procurement through data, organisations can gain greater resilience and commercial agility.

Meeting ESG goals

AI tools can measure emissions, evaluate ethical sourcing, and ensure regional regulatory compliance to help reach sustainability targets.

The role of the CSPO in harnessing AI

With these advances, the role of the CSPO is evolving. What was once an operational and tactical job, focused on the movement of goods and management of supplier relationships, is now a broader strategic one, designing an AI-enabled central nervous system to create competitive advantage. 

To harness the potential of AI, CSCOs will need to be data-fluent: understanding how AI algorithms reason, and able to manage intelligent systems spanning automated procurement, real-time shipment routing, and inventory optimisation. They will oversee global supply chains via an AI dashboard, making fast, agile decisions based on multiple live information streams, and surfacing enterprise risk factors.

CSPOs will simultaneously have to spearhead a talent transformation, shifting teams from manual and operational roles to strategic ones where they work alongside machine intelligence. This means reskilling existing talent, creating cross-functional partnerships, and hiring data specialists who can apply their skills to supply chain challenges.

How the supply chain sector can reap the rewards of AI

The evolution of supply chains through AI is not a distant vision. It’s today’s reality, and it’s defining how organisations operate and compete. For the modern CSCO, AI is more than an efficiency tool. It is a catalyst for strategic leadership which enables smarter decisions, greater resilience, and customer engagement.

Realising the trechnology’s full potential goes beyond an adoption program. It demands thoughtful business strategy, cross-functional talent, and commitment to transparency and explainability. Companies that align AI initiatives with positive business outcomes and empower people to work with intelligent systems will unlock unprecedented value.

In an increasingly complex, volatile environment, AI-powered supply chains will be the difference-makers. The technology will turn risks into opportunities and supply chains into engines of innovation and growth.

• Camilla Gilone is a principal in Heidrick & Struggles’ London office and a member of the global Supply Chain & Operations Officers Practice, focusing on chief operations, supply chain, and procurement officer positions. 

• Jorge Gouveia de Oliveira is a partner in Heidrick & Struggles’ London and Lisbon offices and a member of the Industrial Practice, specialising in the mining & metals sector, and the CEO & Board of Directors Practice. 

The global supply chain is in constant motion. Organisations face persistent disruptions, evolving regulations, and urgent calls for greater sustainability and resilience. Artificial Intelligence (AI) is widely seen as a transformative solution to this ongoing turbulence. 

Yet, beyond the general buzz, a specific evolution – agentic AI – offers a profound, actionable pathway to not just automate, but to fundamentally rethink and intelligently orchestrate future supply chains. This involves thoughtfully integrating AI systems not merely as tools, but as true digital collaborators.

The evolving AI landscape: from automation to agency

Making a case for thoughtful AI integration means noting that AI already plays a key role in logistics.

Logistics organisations have used traditional AI for years, with machine learning algorithms acting as a reliable workhorse. It is deeply embedded in optimising routes, forecasting demand by analysing vast datasets, and automating routine warehouse tasks, forming a robust foundation for efficiency gains.

Generative AI, particularly Large Language Models (LLMs), however, have become more prominent recently. This form of AI excels at creating new content, ranging from drafting communications, generating insightful reports, and enhancing human-computer interaction through more natural language interfaces. While valuable, agentic AI introduces a new paradigm. These are not merely predictive models or content creators; agentic AI systems are proactive, goal-oriented digital agents. 

They possess defined profiles (identity, objectives, constraints), maintain relevant knowledge bases, utilise memory for context, and crucially, feature reasoning and planning capabilities. This empowers them to autonomously decompose complex tasks, make inferences, develop sophisticated action plans, and interact with software systems and data sources to execute them. 

They are designed as ideal digital collaborators, working synergistically alongside human experts.

The core triad: what powers agentic AI? 

Agentic AI is successful when powered by three key things. The first is intelligent algorithms, or agentic systems. These engines orchestrate complex, multi-step processes. Multi-agent systems, for instance, involve specialised agents collaborating on intricate workflows, effectively mirroring the dynamic teamwork of human expert teams.

Another important factor is the use of high-quality data. This is the lifeblood of agentic AI, seeing as it is critically dependent on it. Reliable, integrated, and semantically rich data are vital for sound decisions and meaningful ecosystem interaction. Comprehensive supply chain visibility and robust data integration are the non-negotiable bedrock of intelligence.

The last factor to guarantee successful agentic AI use is to program it with the knowledge that it elevates, not replaces, the human’s role. People define strategic goals, oversee agent performance, and critically, manage novel situations beyond predefined parameters. 

Effective agent design requires a “behavioural lens”: data scientists and supply chain professionals collaborate to translate nuanced human expertise, critical judgment, and adaptive decision-making logic into the AI’s operational blueprint. This transforms professionals from process executors into architects of intelligent workflows, focusing their unique ingenuity on higher-value strategic challenges and innovation.

Agentic AI in action: towards the proactive and resilient supply chain

Agentic AI promises to shift supply chains from a predominantly reactive firefighting mode to one of proactive orchestration and significantly enhanced resilience.

It can revolutionise planning and proactive responses. Traditional AI enhances forecasting, but agentic AI takes this further. 

It can autonomously generate, evaluate, and dynamically adapt operational plans from these predictions. Imagine professionals using natural language for complex queries, like, “What’s our optimal strategy for the upcoming Singapore port closure, factoring in inventory, alternative routes, lead times, and contractual obligations?” agentic systems could then provide detailed, actionable recommendations or initiate pre-approved adjustments. When unforeseen disruptions strike – geopolitical turmoil, climate-driven weather, or abrupt tariff changes – a coordinated team of digital agents can act.

Similarly, it can help in transforming operational efficiency and achieving visibility. Beyond planning, agentic AI can fundamentally transform daily operations. It enables dynamic inventory optimisation across the network, reacting to live demand signals and anticipated disruptions, not just historical trends. These systems can also autonomously coordinate complex logistics, intelligently managing assets and adapting to fluctuating capacity constraints. This cohesive, intelligent action is key for achieving genuine end-to-end traceability—vital in sensitive sectors like food and pharmaceuticals—and for constructing a robust, shock-resilient supply network.

Lastly, it can elevate supplier relationships and strategic cost management. While fully autonomous AI negotiation is still nascent, current agentic AI can readily automate routine procurement, manage supplier communications, and track contract performance. This frees human procurement specialists for strategic relationship building and complex negotiations. Sustainable cost savings then result from enhanced operational efficiency, significant waste reduction (e.g., through better demand-supply matching or optimised routing), and proactive risk mitigation, rather than being a standalone, short-sighted objective.

Navigating the frontier: embracing opportunity and mitigating risk

Although agentic AI offers immense potential for adaptive, efficient, and intelligent supply chains, users must be cautious and aware of its risks. 

One cannot expect an agent to produce a good output if its input is poor. The adage “garbage in, garbage out” is critical here; agent efficacy depends entirely on data quality, timeliness, and integrity.

Similarly, agentic AI can face agent-specific challenges. These include:

• “Hallucinations”: Poorly designed generative components can produce plausible but flawed outputs, leading to costly decisions.
• Lack of Explainability: Difficulty understanding an agent’s decision-making can hinder error correction, accountability, and trust-building – vital for SLA-governed environments.
• Unforeseen Consequences: Semi-autonomous actions could yield unintended negative outcomes if agents aren’t meticulously designed, constrained, and rigorously tested.

The solution is a human-centric, pragmatic approach focused on augmented intelligence. Agentic AI should be viewed as a powerful tool to amplify human capabilities, not blindly replace them. This means robust governance, rigorous testing (akin to critical software), and clearly defined roles for human oversight and intervention. 

Critical thinking, domain expertise, and the human touch remain indispensable. This is especially true when navigating unprecedented disruptions or making complex judgment calls when weighing strategic priorities and unquantifiable factors. A “reality check” is vital, focusing on clear use cases where agentic AI delivers measurable value for specific pain points like disruption management, regulatory compliance, or managing operational complexity.

Moving beyond hype to actionable intelligence

The conversation around artificial intelligence is often saturated with futuristic promises, yet agentic AI offers a clear path to tangible, present-day improvements within your supply chain. This isn’t about a distant technological horizon; it’s about deploying “digital collaborators” now to address persistent operational friction. 

Consider the immediate impact of agentic AI in coordinating swift responses to volatile demand surges across your multi-enterprise network, or its power to sift through millions of daily transactions to pinpoint that tiny fraction of critical shipments or processes that truly demand human insight, thereby unlocking unique competitive advantages. These are not abstract concepts but concrete operational advantages.

For supply chain leaders, the imperative is clear: move beyond observing the AI trend and begin a pragmatic exploration. Identify specific pain points where intelligent automation and collaborative AI can yield measurable results by focusing human talent where it’s most valuable. 

The future of the supply chain hinges on this intelligent augmentation – a dynamic partnership where human expertise is amplified by sophisticated agentive systems. This synergy is the key to navigating an ever-shifting global landscape, not with more buzzwords, but with enhanced operational control, resilience, and a distinct competitive edge.

AI and automation aren’t here to replace humans entirely. It is an important tool for businesses to incorporate into their strategies to remain competitive, but it is still crucial to preserve a human touch. 

One of the most important aspects of the new era of automation in logistics is how it is helping to mitigate supply chain disruptions and improve resilience, problems that have existed in the courier industry for years. Companies can utilise automation to make more informed decisions, improving overall efficiency. 

The power of automation and human fusion 

Modern supply chains are complex, with supply disruptions and ever evolving customer expectations. By fusing automation and a human touch, it can best utilise the strengths of both, creating a harmonious collaboration. Rather than viewing AI as a replacement for humans, it can be used to approach challenges as complementary partners. 

AI has a role in supply chain management. This is especially the in areas of demand forecasting. Here, advanced technologies analyse historical data and market trends to improve the demand accuracy. It is also vital for inventory management. In these cases, AI algorithms can dynamically balance inventory across locations to minimise stockouts and overstocks. By incorporating AI technology into these areas of the supply chain, it can transform supply chain management, optimising at scale. 

However, human judgment remains critical. AI may be powerful, but incorporating a human touch is crucial for effective strategic decision-making and ethical oversight. Humans provide contextual understanding with a long-term vision, inputting a strategy that AI can’t replicate to the same level. 

In crisis management, human adaptability is also so important. Unprecedented situations that require a human to reply and respond to customers need to be empathetic and understanding, helping customers feel heard and seen, as well as mapping out a response strategy that can’t be replicated by automation. 

Combining the two and integrating them into business strategies can help processes become efficient and seamless. 

Leadership in the era of AI

Leadership remains critical as AI technologies become integrated into logistics and planning. A strong leadership team can determine the correct steps to take in an evolving and competitive environment, along with the right level of emotional intelligence. 

Leaders must be able to lead people through disruption. Whereas AI can tackle the problem, leaders must communicate clearly with their teams and empower and reassure them. 

As technology becomes more advanced and is implemented into business strategies, leaders must understand their teams’ varying levels of comfort with new technologies. By having nuanced empathy for each member, leadership can acknowledge how jobs are shifting and help the team approach automation with enthusiasm and excitement. 

Automation should be designed to help speed up jobs, not replace them. Therefore, having skills of emotional intelligence cannot be replaced by AI. It is a critical skill in an increasingly AI-driven world. 

Ethical and cultural considerations 

AI systems can directly influence decision-making, customer experiences, setting standards, and the trust of the public. Therefore, having ethical and cultural considerations with the use of AI is important. 

AI bias can occur when the algorithm produces results that are systematically prejudiced by assumptions in the machine learning process. It can have severe business implications if not addressed or rectified. These can include reputational risk or being unable to meet compliance and regulation standards. AI is not advanced enough to overcome these hurdles by itself. 

We still have an ethical responsibility in the ways that we use AI, hoping to benefit the working environment and avoid any type of harm. It can have implications such as governance, long term viability, and social impact. 

By having a strong team, the human touch goes a long way to combat these issues that can occur. Fostering cross-functional teams that are well-trained with automation and regularly auditing and testing AI algorithms being implemented in business strategies can prevent this from becoming a problem in supply chain management. 

As AI and automation become integral to modern business strategies and supply chain management, it is clear that it is not a replacement for human intelligence. 

Only the human touch can deliver empathy and leadership, a role that remains paramount in logistics leaders. Automation has proven its value by enhancing supply chain resilience, but it’s the fusion of automation and human touch that creates an adaptive and forward-thinking business.

Essential industries like manufacturing, distribution, and building supplies have long relied on technology to navigate supply chain disruptions, rising costs, and labour shortages. 

In recent years, cloud computing and Internet of Things (IoT) have become vital tools for streamlining operations and building resilience in the face of supply chain uncertainty.

Now, with the development and advancement of the possibilities of AI, we’re seeing an even bigger impact of technology on supply chains. Traditional AI has proven its value by automating processes, analysing data and providing useful insights — helping businesses streamline operations and reduce inefficiencies. 

And with the rise of agentic AI, businesses can go a step further — and move beyond reactive crisis management towards AI driven predictive intelligence. The focus is no longer just on adapting to supply chain issues when they arise, but on the ability to anticipate and prevent disruptions before they occur. 

The rise of predictive intelligence

Predictive intelligence relies on advanced AI capabilities, such as machine learning (ML) and agentic AI, to analyse vast amounts of data in real time. These technologies go beyond traditional AI systems by enabling more adaptive and autonomous decision making. 

One of the most significant advantages of predictive intelligence is its ability to identify potential disruptions before they happen. By looking at historical data, market trends and external factors, AI can pick up on risks ahead of time like supplier delays, inventory shortages or demand fluctuations. This allows businesses to take preemptive action — whether that means changing production schedules, finding new suppliers, or reallocating their resources to prevent bottlenecks.

The role of agentic AI in supply chain optimisation

Unlike traditional AI, agentic AI can act autonomously within defined parameters, executing complex tasks without requiring constant human input. 

One example of this in action is the automation of supplier communications and decision making, where AI can handle tasks such as sending requests for quotes (RFQs), parsing responses to compare pricing and delivery times, and helping to select the best option — all without manual intervention. This not only streamlines procurement but also minimises delays – optimising the supply chain. 

Agentic AI is particularly effective in specialised domains like manufacturing and logistics, where efficiency and precision are key. By operating within defined boundaries — guided by ethical standards, policy constraints, and human oversight, it means that businesses can optimise operations without sacrificing control. 

A practical approach to AI adoption

For businesses considering agentic AI, taking a strategic and measured approach is essential. This starts with looking at existing data structures to make sure they align with the capabilities of an AI system. Then, working with trusted digital transformation experts, such as enterprise resource planning (ERP) specialists, can help streamline AI powered integrations while dealing with concerns around cybersecurity and adaptability. 

Working with such suppliers means software solutions deliver context based intelligence through an ecosystem of industry focused platforms, cloud technologies, and people centric AI capabilities – cognitive ERP. Gone are the days of transactional ERP and ahead lies a world of automated and increasingly cognitive business solutions to build a truly connected, efficient and resilient enterprise. 

Research shows that nearly two thirds (63%) of high growth companies have embedded AI into their ERP and supply chain management systems, while 58% of organisations have already integrated generative AI into their digital supply chain operations. Don’t get left behind.

The future of AI in supply chains

As businesses continue to embrace AI, the focus will shift from reactive resilience to predictive intelligence — not just withstanding disruptions, but anticipating and preventing them.

Using agentic AI and predictive analytics, companies can transform their supply chains into agile, proactive ecosystems that drive long term growth and innovation.

The path toward predictive intelligence requires collaboration, investment, and a commitment to continuous improvement. But for those willing to take the leap, the rewards are game changing — a supply chain that is not just resilient, but intelligent, cognitive, agile, and future proof. 

The pharmaceutical sector has seen significant growth in recent years—a trend that shows no signs of slowing down. The industry is already comparable in size to the gross domestic product (GDP) of countries like Spain, Mexico, and Australia, with revenue predicted to increase by 6.12 percent annually between 2023 and 2030. This expansion brings both challenges and innovative solutions to meet industry needs.

2025 

As 2025 continues, the pharmaceutical industry will likely face a familiar challenge in the form of supply chain disruptions. Pharmaceutical leaders recently highlighted significant supply chain issues as one of their biggest concerns, citing increased pressures from raw material and labour shortages, geopolitical instability and extreme weather events. 

These supply chain challenges are an ever-growing threat to the industry as they not only cause shortages of medicines but can lead to an increase in counterfeiting and diversion. As such, it is more important than ever for pharmaceutical organisations to build resilient supply chains while also ensuring they are compliant with the ever-evolving regulatory landscape. 

Beyond regulatory requirements, there is a gap between consumer and patient demand for greater transparency—including product origin, materials, environmental impact, and recommended usage—and the data currently provided by pharmaceutical brands and supply chain stakeholders. By fully leveraging serialisation, aggregation, track-and-trace, and authentication solutions, many of which are already in place for the EU Falsified Medicines Directive (FMD) and the US Drug Supply Chain Security Act (DSCSA) compliance, pharmaceutical manufacturers can unlock valuable data to enhance supply chain effectiveness.

Partnering with an expert with deep industry knowledge can help pharmaceutical companies harness this data to improve supply chain transparency, streamline inventory management, strengthen connections with customers and patients, and protect brand reputation. Such experts can also help integrate the latest AI-driven innovations and machine learning capabilities, further enhancing predictive analytics, automation, and real-time decision-making across the supply chain.

Rise of counterfeit goods through pharmaceutical e-commerce

Counterfeiting and diversion continue to present a major challenge within the pharmaceutical industry, a problem exacerbated by the rise of online marketplaces. The growth of e-commerce in recent years has increased accessibility and convenience for consumers, providing an easy way to obtain medicine that otherwise would have been difficult to source from local vendors. However, the loosely regulated nature of online markets poses a threat to the legitimacy and safety of pharmaceutical goods.

The number of illicit online pharmacies has significantly increased in recent years, with many of these masking as trustworthy, easily accessible options for customers. For instance, a recent Royal Pharmaceutical Society investigation found that fraudulent internet pharmacies target vulnerable patients who are experiencing medication shortages, including hormone replacement therapy (HRT), ADHD medication, and obesity medications. Prescription medicine sales on these unregistered websites are unlawful and put patients’ health at risk. 

The uncertainty and danger instilled in online markets confirms the importance for pharmaceutical organisations to implement solutions that can help them combat the rising number of counterfeit and diverted products entering the pharmaceutical supply chain.

Regulatory environment: a key driver for supply chain innovation

Regulatory compliance remains a major driver of pharmaceutical supply chain innovation, including the established EU FMD and the US DSCSA. These regulations have been introduced to safeguard patients and improve the safety of the pharmaceutical supply chain and manufacturing process. Nevertheless, even with the implementation of these regulations, counterfeiting and drug diversion continue to plague the industry.

Adhering to regulations strengthens supply chain visibility for pharmaceutical and moves them closer to achieving true traceability. Enhancing the transparency of production and shipping procedures can also improve operational efficiency. In a time when the development of a strong pharmaceutical supply chain depends on traceability, authenticity and transparency, digital track-and-trace technologies are becoming the norm.

Leveraging technology to address supply chain issues

New technological developments are transforming multiple sectors. This includes the pharmaceutical industry, where technology is leading the way in helping improve supply chain resiliency in the face of disruptions. 

For example, the growing usage of artificial intelligence (AI) is set to have a major impact on the pharmaceutical supply chain. One notable development is AI-powered authentication technology, which uses machine vision and machine learning to create a digital blueprint of packaging artwork features—enabling precise product identification through pattern recognition. Additionally, AI will enhance real time monitoring and secure pharma supply chains by detecting irregularities and possible risks as they occur. 

Moreover, smart packaging technologies, such as Radio Frequency Identification (RFID) and Near Field Communication (NFC), are increasingly being adopted by pharmaceutical companies, for speciality drugs. Although this innovation has been present in the pharmaceutical industry since the early 2000s, it has become more affordable and accessible in recent years. These technologies, which can scan large quantities of products, boosting efficiency in warehouses and distribution centres, have become more affordable and accessible in recent years. RFID technologies, along with RFID readers, can scan large quantities of products, boosting operational efficiency in warehouses and distribution centres.

Beyond efficiency, smart packaging technologies provide access to invaluable insights that can inform future decision making. Once a company has implemented the latest technologies and solutions, it can start exchanging data and capturing end-to-end data about a product’s journey. For example, access to information like inventory levels, item status, and location can help pharmaceutical manufacturers optimise production and meet customer demand efficiently. Smart packaging technologies also enhance interconnectivity and collaboration across the value chain. By providing in depth visibility and actionable insights, these solutions align production and supply chain requirements, streamline internal processes, and boost efficiencies, ensuring a steady product flow and addressing drug shortages.

An uncertain future 

The pharmaceutical industry continues to face multifaceted supply chain challenges which compromise brand integrity, threaten patient safety and reduce revenue. To address this, businesses must go beyond regulatory compliance by leveraging advanced traceability and authentication technologies to unlock the power of their data. 

This approach will not only be crucial in enhancing digital connectivity and strengthening brand protection—it will also support pharmaceutical organisations in creating a more resilient supply chain to safeguard patient health.

Supply chain technology firm Flexport has announced  the launch of more than 20 tech and AI-powered products designed to transform global logistics. The tools aim to help logistics and supply chain organisations increase visibility into their operations, and are based on Flexport’s own reserves of proprietary logistics data. 

This new wave of AI-powered supply chain products and tools has made Flexport “the largest provider of AI tools for global supply chains,” according to Flexport CEO Ryan Petersen. “While many startups are emerging to provide AI tools for logistics, they lack the data required to train the AI models and struggle to sign up customers to use their technology. Our scale as one of the largest logistics providers in the world gives us huge advantages in both creating the technology and getting it into the hands of businesses operating in the real world.”

Highlights from the release 

The flagship product released this week is Flexport Intelligence, which allows businesses to ask questions in natural language and receive immediate insights about their supply chain performance. Flexport customers can use this AI-powered tool to build reports and create dashboards with no technical skills required, making it extremely easy for operations managers to take control of their global supply chains.

Another major announcement within the release is Flexport Control Tower. This product helps businesses use Flexport’s supply chain technology even for shipments where they’ve contracted another carrier or forwarder to move the freight.

Flexport “will build the smartest supply chains in the world”

The company argues that it is “uniquely positioned” to harness AI’s full potential, combining proprietary data, enterprise-scale operations, and AI-ready tech platforms to make new innovations easily accessible to customers.

“Flexport has been leveraging AI for years, but with the explosion of large language models and new open-source tech, we’re able to innovate faster than ever before,” said Petersen. “AI is incorporated throughout the new products and features you’ll see today. Our vision is to make global commerce as simple and reliable as flipping a light switch, and today’s technology release is the clearest sign that we’re well on our way to bringing that to life.”

Flexport Intelligence consolidates fragmented data into easily accessible, actionable insights. The tool allows businesses to ask complex logistics questions in simple terms and receive instant answers and interactive reports. The business is also enhancing Flexport Fulfillment’s AI Demand Planning and Inventory Placement solution. Using advanced AI models, Flexport can process a multitude of client and industry data points to optimize inventory distribution across the Flexport network, ensuring we allocate the right levels of inventory as close to consumer demand as possible. The company is also expanding its use of AI Voice Agents, deploying them to work with carriers to improve quality and efficiency in its behind-the-scenes operations.

“Flexport’s technology isn’t just about automation—it’s about augmentation,” said Sanne Manders, President of Flexport. “Our tools free up human ingenuity to tackle challenges AI alone can’t solve, delivering the highest quality service at the best value.”

Logistics, the most critical pillar in the Supply Chain, grapples with many challenges. From driver shortages to increasing road accidents, logistics operators are increasingly turning to AI-powered fleet management systems to enhance safety, improve visibility, and build more resilient, agile fleet operations.

In today’s complex global supply chain ecosystem, ensuring fleet safety and visibility is crucial to maintaining seamless operations. While fleet operations are a critical link in the supply chain, traditional technologies often fail to deliver real-time asset visibility and management, making it difficult to mitigate potential risks effectively. 

However, organisations are increasingly recognising the benefits of artificial intelligence (AI) technology in improving driver safety, operational visibility, and the overall efficiency and sustainability of supply chains. Research that we conducted last year—based on responses from the top Supply Chain leaders in the UK and EU—revealed that while only 33% of supply chain professionals currently use AI for fleet safety monitoring, 81% plan to implement AI solutions within the year. 

Organisations can improve accuracy, efficiency, and profitability in their supply chains by using AI-driven fleet safety systems with computer vision, predictive insights, and better operational control.

Speed and accuracy — the top priorities for goods delivery

Our survey revealed that 57% of supply chain professionals consider accuracy and timeliness the top priorities for goods delivery. This reflects broader consumer trends as today’s shoppers increasingly demand near-instant delivery and greater convenience when receiving their purchases. A recent Retail Week report, ‘Shopper Unlocked: Inside the Minds of 1,000 Consumers‘, highlights that delivery experiences now outweigh loyalty perks or alternative payment options in importance for shoppers. Real-time order tracking has also emerged as a key factor in customer satisfaction, underscoring the growing demand for transparency throughout the delivery process. Businesses that fail to meet these expectations risk losing customers to competitors and potentially damaging their brand’s reputation. 

Poor fleet visibility and driver shortages are two significant challenges hindering delivery precision in logistics and transportation. Accidents are another critical issue, causing product damage during transit, harming brand reputation, affecting driver well-being and availability, and driving up operational costs. 

Enhancing delivery precision with AI-driven insights

While data is at the core of effective fleet management and safety, many existing systems rely on older technologies like telematics and GPS. These technologies are often limited in their functionality and cannot provide the level of granularity that modern AI solutions based on vision can. Integrating such advanced tools into goods delivery processes can significantly enhance delivery precision and predictability by improving fleet visibility and driver engagement. 

Modern AI tools serve as powerful data mining assets for fleet managers, analysing large volumes of data from a wide range of sources to deliver actionable insights. For example, motion sensors such as accelerometers and gyroscopes, which track the speed and orientation of vehicles, can provide valuable insights into driving manoeuvres. When combined with data from computer vision systems, these insights offer fleet operators an accurate and holistic view of their operations, helping them to make proactive adjustments to routes or schedules and helping maintain fleet safety while ensuring the timely delivery of goods. By improving operational efficiency, businesses can provide real-time customer updates, ensure precise delivery timelines, and offer greater transparency—ultimately meeting consumers’ growing demand for reliability and visibility in the delivery process. 

Smarter fleet safety management

AI’s real time data analysis capabilities also play a crucial role in fleet safety. Research shows that human error accounts for over 70% of road accidents, with driver fatigue, excessive speed, and inattention cited as the most common contributing factors. AI systems work to minimise these errors by actively identifying and addressing safety risks. For instance, AI-powered video-telematics and analysis can detect dangerous driving habits such as speeding, distracted driving, and tailgating. By providing real-time alerts—which automatically factor in environmental and contextual data—to drivers and fleet managers alike, these systems offer accurate risk assessments, enabling corrective actions before an incident occurs.  

The advanced edge computing-based mapping and analysis technology integrated within modern AI solutions provides valuable insights, helping fleet operators make informed decisions about driver behaviour patterns. Not only that, but these insights also enable operators to draw unbiased conclusions in the event of an accident. According to a joint report by NETS, NHTSA and OSHA, road accidents cost employers nearly $60 billion annually. Leveraging advanced driver safety systems is critical to cutting down costs in the long run while enhancing fleet safety.

Furthermore, modern AI-powered fleet management solutions can also create comprehensive driver performance profiles using advanced driver-scoring systems to evaluate behaviour in detail. These insights allow fleet operators to positively reinforce good drivers and reward safe driving practices through incentive programs. Gamifying the driving experience in this way helps to enhance overall fleet performance and establishes a proactive safety culture within organisations. These insights also offer critical data to support driver exoneration in road accidents, ultimately helping reduce driver churn, a significant issue within this sector.

The benefits of these advanced technologies extend beyond the fleet itself. Improved safety and visibility directly impact the broader supply chain, driving greater efficiency, cutting operational costs, and reducing disruptions. 

AI-powered data driving safer and more efficient fleet operations

As transportation remains one of the most challenging aspects of supply chain management, ensuring fleet safety and visibility is more critical than ever. 

AI-powered solutions are revolutionising fleet management by delivering real-time insights that enhance efficiency, improve safety, and enable proactive decision-making. By leveraging AI-driven data analysis, logistics providers can overcome operational challenges and gain a competitive edge in an increasingly demanding industry. 

As businesses prioritise speed, accuracy, and transparency in goods delivery, AI will play a pivotal role in shaping the future of fleet management and supply chain resilience.

The construction industry is one of the sectors with the lowest level of digitisation to date. Given the scale of the sector, its considerable contribution to the global economy and its impact on the environment, it’s somewhat surprising that this should be the case. 

Few within the industry would doubt that an increase in digitisation would have many positive effects. The efficiency and sustainability of the construction supply chain in particular would benefit from this, according to the assessment of industry experts. A practical solution for this is offered by modern, software-based construction planning systems that rely on artificial intelligence (AI).

AI powered solutions to today’s challenges

No industry can currently ignore the issue of sustainability. The construction sector in particular has a lot of catching up to do here and must react accordingly. It’s a lesser-known fact that the construction industry is responsible for around 38% of global CO₂ emissions. Given its carbon footprint, arguably it has a duty to decarbonise to a more palatable level. 

Therefore, innovative and promising concepts that contribute to achieving climate protection goals are urgently needed.

An indispensable prerequisite for achieving this goal, is the consistent digitisation and automation of processes. The main goal here is to relieve the burden on supply chains and thereby also reduce CO₂ emissions. However, the construction industry in this country does not yet have the reputation of being a digitalisation pioneer. The processes are still very rigid. This is especially true when it comes to planning the requirements of building materials and designing logistics chains. 

Most processes are still based on paper documents, which hinders consistent processes. Waybills and delivery documents are often still signed by hand and can easily get lost. The relevant data must be entered manually into IT systems, which presents many sources of error. 

In addition, little attention is paid to the environmental friendliness and CO2 footprint of materials when selecting them. There is also usually no comprehensive overview of which building materials are needed in what quantities, where and when. This leads to complicated and often unnecessary transport routes for trucks and other vehicles. 

Material requirements planning: the key to efficiency and sustainability 

In order to increase the efficiency of workflows here, maximum transparency is required along the entire supply chain. Those responsible must carefully determine how to adjust the ordering of materials to actual requirements. 

This is the only way to effectively counteract excessive waste of valuable resources. According to GIRI somewhere between 10% and 25% of project costs are lost through errors. That’s a double-digit percentage of building materials ordered unnecessarily or used incorrectly. This leads to inefficiencies in construction, drives up costs and is an obstacle on the way to greater sustainability. However, optimised demand planning alone is not enough – attention should also be paid to where the material comes from. It is important to keep in mind that building materials such as concrete are produced in different regions and countries according to different standards. This has a significant impact on quality and therefore also influences the CO2 footprint. 

Therefore, it makes sense to purchase materials from regional suppliers. This shortens transport routes, which in turn conserves resources and minimises pollutant emissions. 

AI-based construction planning for maximum process efficiency

In order to adequately address all of those challenges, well-thought-out project planning tools are required that are optimised with regard to the processes in the construction industry.

The solutions work particularly effectively when they are equipped with AI. For example, the technology can independently recognise certain conflicts in the procurement workflows. This is the case, for example, when ordering building materials that do not match the type or quantity of the building in question. Here, AI ​​can raise the alarm and initiate an optimisation of the processes. The result? Significantly less material waste and unnecessary transport, which noticeably relieves the burden on the supply chain.

Another advantage of AI-based planning systems is that they can handle large amounts of data extremely well, analyse them comprehensively and draw the right conclusions from them. By way of example, large construction companies work on numerous different projects in parallel and cooperate with a large number of suppliers who are spread across the entire country. The AI-supported evaluation of relevant data makes it possible to quickly see which delivery partners are suitable for a specific construction project. 

This brings more efficiency to the supply chain. However, it is important to always keep an eye on the entire ecological balance. This includes the production of the materials, the type of transport and the delivery distance. It is possible that environmentally-friendly concrete with a longer journey proves to be more sustainable when compared to a conventionally produced building material from the neighbouring town. 

Such an overall view requires a large amount of valid data, which can be evaluated quickly and thoroughly using an AI solution, thus paving the way for sustainable and economically sensible decisions. 

Construction companies and suppliers benefit from demand forecasts

In addition, AI-supported planning tools based on big data analyses can be used to precisely predict future demand for building materials. Depending on the project workload, those responsible in the construction companies can realistically estimate how much concrete, gravel or wood will be needed in the coming weeks. 

This improves the coordination of logistics processes, optimises the use of budgets and makes a significant contribution to cost savings. On the other hand, the manufacturers of building materials also benefit; they can use demand forecasts to closely monitor the market and align their production capacities accordingly. 

The current share of renewable energies in the power grid can also have an influence on this. If, for instance, a lot of solar and wind energy is currently being fed into the grid, the production rate can be adjusted and increased accordingly. If there is less surplus energy, manufacturers can reduce production again. 

Ultimately, this increased flexibility has a positive effect on sustainability and climate protection. 

AI tools determine the future of the construction industry 

The analysis of large amounts of data and the targeted use of AI-based planning tools will significantly drive the digitalisation of the construction industry in the future – that much is certain. In this way, a variety of positive effects can be achieved, such as an efficient and sustainable supply chain.

Kick-started by the global pandemic, the transportation and logistics industry has seen a boom in digital transformation over the past five years. While we’ve started to see companies embrace general technology, however, a recent study from HERE Technologies and AWS found that just 19% of logistics companies are using AI for advanced use cases, such as demand forecasting.

In 2025 logistics companies should ensure they don’t fall behind their customers in other industries in putting to use advanced automation, powered by AI. AI-driven automation can streamline multiple complex processes for logistics organisations. These include inventory management and warehouse automation, which has the potential to transform functions across the supply chain. These efficiency gains will help businesses counter tight margins while staying agile. Perhaps most importantly, they will help logistics companies deliver better service in an increasingly demanding market. 

Embracing new, advanced technologies will increasingly be key to success for logistics companies. So, what steps do they need to take to see the benefits?

Modernise infrastructure

Logistics companies must make substantial investments in modernising infrastructure, operations, and workforce skills. A Neos Network’s study revealed that 63% of UK logistics companies report a shortage of digital skills. Therefore, a successful AI implementation will, however, require not only technology upgrades. Companies will also need a strategic focus on upskilling personnel to manage and optimise these new systems.

Integrated cloud-based platforms will be essential, enabling companies to unify their logistics needs into a single, centralised dashboard. 

Intelligent document processing is a logical first step towards implementing AI-powered technology. The pocess can deliver immediate value in this document-heavy industry, creating quick business wins with minimal investment. Businesses in the logistics sector handle large volumes of documents such as invoices, shipping documents, and customs forms. AI-powered intelligent document processing can extract data from these documents accurately, reducing manual effort and errors. AI like this can reduce costs, increase transparency and accuracy, and allow for faster, data-driven decision-making. 

Focus on becoming greener

Green logistics will become a key focus as companies strive to align with sustainability goals. Motivated by regulatory pressures and consumer demand, logistics companies will further integrate eco-friendly practices across their operations. This will become increasingly important as these sustainability regulations become less flexible over time.

The HERE Technologies and AWS study also revealed that over 60% of logistics companies lack defined sustainability goals. The study highlighted this as an opportunity for organisations to leverage technology to streamline fuel usage and routes. Not only did this address environmental concerns, but it also reduced spending on fuel.

By integrating eco-friendly digital practices into their supply chain and operations, businesses can reduce their carbon footprint, lower energy consumption, and minimise waste. One example is using AI-powered technology to speed up customs clearance through Intelligent Document Processing (IDP). IDP enables fast-moving consumer goods (FMCG) manufacturers to accelerate their trucks through border customs, cutting clearance times by over 90%. Portumna Pastry Ltd, a pastry supplier based in Ireland, leveraged AI-powered IDP to reduce their customs clearance time at the EU/UK border from one hour to only five minutes.

Purpose-built AI can significantly optimise energy usage, streamline logistics operations, and minimise emissions. By enhancing operational efficiency, it not only predicts maintenance needs but also reduces delays across the supply chain, ensuring smoother and more sustainable processes.

Mistakes in freight forwarding documents can stop a shipment in its tracks at the border, grinding operations to a halt and causing delays for customers while fuel and demurrage costs rack up. To keep logistics smooth and efficient, AI-powered intelligent document processing can extract exactly the right information from huge volumes of documentation, delivering the right information to the right systems at the right time.

Embrace real-time monitoring of the supply chain

Finally, logistics businesses can rely on real-time monitoring to improve transparency across the supply chain. Using IoT and data analytics in monitoring can provide end-to-end visibility, ensuring a more customer-centric and resilient supply chain.

Access to real-time AI-powered insights gives businesses the ability to proactively address challenges like delays, shortages, or changes in demand, minimising the impact of these challenges on customers. Embracing AI-powered digital tools will enable businesses to adapt more quickly and reduce costs, even as the market remains volatile.

The opportunity is immense: by adopting AI-powered, digital solutions, logistics businesses can significantly increase operational efficiency, streamline processes, and ensure better decision-making. Into 2025, leveraging these technologies will not only drive efficiency but also position logistics companies at the forefront of innovation, enabling a more sustainable and responsive supply chain for the future.

At this year’s LogiPharma 2024 event, we caught up with some of the pharmaceutical supply chain sector’s leading executives to learn more about them, their analysis of the trends shaping the industry, and how their organisations are responding to the challenges ahead. 

Sankalp Raviprolu is an experienced supply chain advisor who works closely with clients to drive operational excellence, build differentiated digital capabilities, and enhance supply chain strategies across manufacturers, wholesale distributors, and retailers. At this year’s event, we spoke to him about the pain points facing the industry and how he leverages his expertise in technology, advanced data science, and AI to achieve his goals.

ZS is a management consulting and technology firm that partners with companies to improve life and how we live it. ZS’s supply chain and manufacturing team has deep domain and industry expertise. They help their clients optimise planning and operations, design agile and resilient supply chains, achieve supply chain visibility and digitise manufacturing using data, analytics and technology.

Inside LogiPharma 2024

1. What is the value of events like LogiPharma 2024? How important is this conference in the supply chain pharma calendar?

Events like LogiPharma provide industry practitioners, academics, and thought leaders a forum to collaborate, share perspectives, and learn best practices in a dynamic setting. 

These events create a unique opportunity for logistics solution providers, technology partners, and product companies to demonstrate their latest innovations to a relevant and engaged audience within the pharma logistics industry. The opportunity to network and form partnerships is invaluable in solving some of the industry’s biggest challenges.

2. What are the biggest takeaways from this year’s LogiPharma for you?

The pharmaceutical logistics industry is evolving rapidly. If there was ever a time to embrace data, technology, and strategic partnerships, it’s now. We’re seeing a meteoric rise of new modalities such as cell and gene therapies and radiopharma therapies. In this new context, supply chain operations must become even more adept and agile. 

End-to-end visibility solutions are moving from descriptive and predictive models towards prescriptive and autonomous supply chain solutions. Track-and-trace devices, temperature monitoring sensors, advanced data platforms, and robust partner ecosystem—including 3PLs and logistic providers—are pivotal in achieving supply chain excellence from raw material at source to the point of care for the patient. Carbon emission tracking, network orchestration, and n-tier supplier visibility are no longer “nice-to-haves” but essential elements in identifying and addressing the weakest links in supply chains before they become larger disruptions.

The global supply chain

3. How would you sum up where the pharma supply chain space finds itself today? 

The pharmaceutical supply chain has experienced significant challenges in recent years including supply shortages, volatile demand, and geopolitical shifts. The industry now faces a more complex landscape with an increasing number of modalities across biologics, small molecules, and advanced therapies lined up. To navigate this environment, supply chains must ensure that the right product is delivered at the right time, at the right quality, and to the right patient—every single time.

The Drug Supply Chain Security Act (DSCSA) is a major milestone in establishing traceability and accountability within the pharma value chain, enhancing transparency across various parties involved.

4. What do you feel are the biggest lessons supply chains have learnt over the past few years and how well equipped is the modern-day supply chain now to deal with ‘black swan’ events like the ones we’ve seen recently?

The key lesson is that there is no one-size-fits-all approach to supply chain management. Each supply chain is unique and the metrics we use to measure, monitor, and react must evolve with patient needs and product complexities. Organisations must prioritise flexibility and resilience, recognizing that the frameworks they use today may need to be rapidly adapted tomorrow.

During the pandemic, for example, companies that had already invested in advanced scenario planning and predictive analytics capabilities were able to adjust more quickly to sudden changes in demand and supply chain disruptions compared to those that relied on traditional models.

Sustainability in the pharma supply chain 

5. Sustainability is an important item on most Chief Supply Chain Officers’ agenda. Amid government legislation and changing customer demands, is a sustainable supply chain a non-negotiable in today’s world?

Sustainability is no longer just a corporate initiative; it is a non-negotiable requirement for Chief Supply Chain Officers (CSCOs). Sustainability is intertwined with every decision made throughout the supply chain, from sourcing raw materials to carriers delivering products to patients. Governments and consumers are increasing pressure on companies to demonstrate sustainable practices and failing to do so not only risks reputational damage, but can also impact compliance and operational efficiency.

Driving digital transformation

6. How are you navigating the world of generative AI?

Generative AI is revolutionising logistics and supply chain operations by transforming how we interact with data and how we work every day. 

It enables faster decision-making through synthesising vast sets of data both structured and unstructured, thus allowing companies to anticipate disruptions and optimise their supply chains in near real-time. AI can also facilitate personalised supply chain strategies, adapting to the unique needs of different therapeutic areas and modalities.

For instance, supplt chain managers are using AI-driven models to optimise cold chain logistics for temperature-sensitive therapies. This ensures the integrity of products from the point of manufacturing to the patient point of care.

7. What should CSCOs consider before starting the gen AI journey?

The first step for CSCOs is to identify and prioritise specific business cases that can benefit from generative AI. 

There is a lot of hype surrounding AI. However, organisations should adopt a structured, value-driven approach to separate reality from the hype. Experimenting with pilot projects, learning quickly from failures, and scaling successful initiatives is the best way to ensure AI adoption yields measurable outcomes. It’s essential to focus on the problem AI is solving rather than implementing technology for technology’s sake.

8. What are the biggest challenges or hesitations you’re seeing companies have around AI? What should organisations look for in technology to hedge against these concerns?

One of the biggest challenges companies face when adopting AI is trust in the data. AI models are only as good as the data designers use to train them. As a result, many organisations struggle with fragmented, incomplete, or inaccurate data. 

Additionally, concerns about the ethical use of AI and transparency in decision-making are common. To overcome these challenges, companies should invest in robust data governance frameworks and ensure that AI tools are designed to be explainable and accountable. Finally, AI adoption in the context of pharma and controlled environments also comes with some nuances. These require careful consideration and navigation in GxP environments.

The future of the pharma supply chain

9. How exciting a future does the pharmaceutical supply chain have? 

There is no better time than now to be involved in the pharmaceutical supply chain. With years of digitization across core capabilities and transactional systems, organisations are now poised to leverage their digital assets (data) to turbocharge their supply chains to build agility, anti-fragility and resilience. 

By embracing predictive insights and data-driven decision intelligence platforms, supply chains can become a true competitive advantage. They can enable faster, more efficient operations that deliver value to patients and the business alike.

When it comes to using artificial intelligence (AI) in road transport, it no longer falls into the category of “too new to try”. Instead, it is a market-tested technology that is driving results across the supply chain, particularly the logistics sector. 

In fact, advancements in AI have ushered in a new era of transformative technology, as well as speculation about how it will revolutionise the world — not least in the world of transport and logistics. 

While AI’s potential might not be immediately obvious to everyone, its impact in this sector is already profound. From enhancing safety to reducing costs, AI is driving significant improvements across the board and streamlining workflows for fleet managers and drivers alike.

Driving safety forward

AI’s integration into existing technologies opens the door for significant improvements in driver safety, and dashcams are an excellent example of this. When enhanced with AI, dashcams can monitor driver behaviour in real-time, helping to prevent incidents and save lives.

For instance, AI can detect distractions such as a driver picking up their phone or signs of fatigue, automatically issuing alerts that prompt drivers to refocus or take necessary breaks. These interventions go beyond passive observation and instead, actively contribute to preventing accidents.

Wholesale food distribution giant, Sysco, reported significant results since implementing Samsara’s AI safety system, including a 40% decrease in on-road accidents within just three months. In addition, it also witnessed a 17% reduction in harsh driving events and a 15% reduction in insured costs year-on-year – and they’re not alone.

M Group Services, which equipped over 8,500 of its vehicles with dual-facing AI dashcams, saw an almost 30% reduction in incidents per million miles.

AI is facilitating never-before-seen levels of safety, and its potential for growth in this area is exponential. As more businesses integrate these technologies into their logistics functions, our roads will become safer, meaning that all drivers will reap the benefits of this new standard of road safety.

Returns on AI investment

Not only does AI save lives, it also helps to mitigate risk and reduce costs. By minimising accidents and monitoring driving behaviours, AI can save businesses money in terms of repairs, replacing damaged vehicles and hefty insurance premiums.

Moreover, by crunching much larger amounts of data than humans can, AI can spot anomalies such as excessive fuel use among particular drivers, vehicles, or routes, and identify changes that can lead to greater efficiency.

Beyond cutting costs out on the roads, AI is saving time and money in the back offices too. By automating workflows and removing the need for human intervention on more menial, time-consuming tasks, AI has become a real game changer for an industry where many are often still reliant on manual, pen-and-paper based processes.

Another impactful application of AI is predictive maintenance. By using real-time data and performance history, AI allows businesses to forecast when a vehicle is likely to need maintenance. As a result, fleet managers can get the most value for their money as AI allows them to carry out maintenance exactly when needed.

A glimpse into the future

The benefits of AI are constantly evolving and embracing this technology early on will provide significant long-term benefits to businesses. In the next five years, AI is poised to seamlessly connect all parts of the supply chain, as fleet leaders increasingly turn to AI not only to cut costs and improve safety in logistics, but ultimately to remain competitive. 

Samsara’s new State of Connected Operations report found that, of the 1,500 operations leaders surveyed, more than half (51%) reported that their organisation is already using AI in some capacity.  Of those leaders, 90% also said that their employees feel positive about the technology. While this is a promising start, there’s still a great deal of progress to be made to ensure fleet organisations are fully realising AI’s potential across all operations.

Trust and AI

However, with so much happening at pace, it’s crucial to make the right preparations and to stay focused on deploying AI responsibly. Issues such as privacy, data quality, and data protection are all potential obstacles to the implementation of AI, which could undermine trust if left unaddressed.

In fact, the top three barriers to implementing AI technology solutions are data quality and availability (41%), privacy and security concerns (40%) and integration with existing infrastructure (39%).

Of those already using AI, our research shows that the majority report that their organisation is currently deploying privacy and data protection measures (58%), establishing AI ethics and principles (57%) and making efforts to mitigate bias in AI (54%). 

These priorities indicate that organisations are taking a measured and thoughtful approach to AI implementation, focusing on addressing legal and ethical risk before tackling logistics and rollout.

Data fuels AI 

Ultimately, one thing has to be understood from the outset: AI is fuelled by data. The better the data, the better the outcomes. So investing in AI also means fleets also need to invest in digital management platforms capable of providing accurate real-time data.

For those already on the digital transformation journey, AI is opening up a world of possibilities. For those yet to start, AI will increasingly be built into applications as standard.

As a general management consultancy, ScottMadden seeks to provide high-quality, objective advice and support to help clients solve challenging problems. The company is renowned for the quality of its services, as well as the integrity of its relationships.

To ScottMadden, clients are at the heart of its strategy. In supply chain, the team simplifies enterprise strategy for supply chain management while identifying opportunities to reduce costs through improved category and inventory management​. A recent project that showcases ScottMadden’s supply chain intent was its work with a large, midwestern investor-owned utility needed to improve its supply chain organisation’s strategy, governance, and operations. 

ScottMadden

It was revealed that costs were high and the organisation could not provide the strategic direction needed for efficient planning, sourcing, and materials management across the company. This is where ScottMadden came in. ScottMadden were engaged to help articulate one clear enterprise strategy for supply chain management which senior executives could support, drive, and monitor.

Given the disruption that recent global events have caused, it has become clearer than ever before how easily supply chains can be disrupted. Through ScottMadden’s five-step approach, they can partner with companies to develop a supply chain business intelligence tool to ensure its customers have the data-driven insights needed to safeguard their supply chain.

Amidst ASCM CONNECT 2024, ScottMadden partners Jason Davis and Jason Payton reveal ScottMadden’s approach to supply chain management in the middle of technological change and digital disruption.

Would you be able to give me a brief introduction to your role and the company you work for?

ScottMadden: “We are partners with ScottMadden, a general management consulting firm with over 40 years of experience. ScottMadden delivers a broad array of consulting services ranging from strategic planning through implementation across many industries, business units, and functions. In supply chain, we have assisted our clients with improving their operating models and transforming functions in procurement, materials management, and logistics.”

ASCM CONNECT 2024

What is the value of events like ASCM CONNECT 2024: North America? How important is this conference in the supply chain calendar?

ScottMadden: “ASCM CONNECT provides a valuable forum for supply chain professionals to gather and discuss challenges and solutions. One of the many benefits is the ability to learn from organisations and solution providers of all sizes and focus areas. For ScottMadden, ASCM CONNECT is a very important event and will continue to be a focus for us in the future.”

Global supply chain

Given the backdrop of the global disruption over the past few years (COVID, wars, inflation etc.), how would you sum up where the supply chain space finds itself today?

ScottMadden: “It’s well documented that today’s supply chain landscape is marked by increased complexity and uncertainty. The COVID-19 pandemic disrupted global supply chains, revealing vulnerabilities in just-in-time inventory models and overreliance on single-source suppliers, particularly in critical industries like healthcare and technology. Geopolitical tensions, such as wars and trade disputes, exacerbated these disruptions, leading to increased protectionism and the reshoring of some manufacturing activities.

“Inflation and price volatility have further complicated supply chain management by impacting the cost of raw materials, transportation, and labour. These trends have put pressure on profit margins, forcing companies to rethink pricing strategies and cost management. Additionally, climate change and other sustainability priorities have prompted businesses to seek more resilient and environmentally friendly supply chain practices, although these initiatives often come with their own hurdles.”

“Despite this environment, executives and stakeholders recognise that supply chain is a strategic lever for companies. Meeting ever-increasing customer and stakeholder demands in this challenging environment requires supply chain organisations to consider new strategies and practices. Companies are evaluating emerging solutions (e.g., generative AI, advanced analytics), implementing strategies to strengthen supplier diversity, risk management, and supplier relationships, and redesigning processes to improve visibility, agility, and resilience. These improvements will help ensure success and a promising future for supply chain.”

Sustainability

Sustainability is an important item on most Chief Supply Chain Officers’ agenda. Amid government legislation and changing customer demands, is a sustainable supply a non-negotiable in today’s world?

ScottMadden: “Regulations and increasing consumer demand for ethical and environmentally friendly products are major drivers for sustainability. Sustainability is less of an option and more of a fundamental requirement in today’s business environment.”

“Global regulations are increasingly mandating sustainable practices, with regulations targeting carbon emissions, waste management, and resource usage. Failure to comply with these regulations can lead to significant financial penalties, reputational damage, or worse.”

“Customers are increasingly prioritising sustainability in their purchasing decisions. Consideration of sustainability may be more weighted when procuring certain types of materials, services, or for end-users. For example, in the Consumer Packaged Goods industry, customers may be more sensitive to the consideration of sustainability of product ingredients. This shift includes B2B relationships, where businesses scrutinise their suppliers’ environmental and ethical standards. Sustainable supply chains are indeed becoming non-negotiable. In addition to the required compliance with regulations, this transition feeds brand loyalty and, when executed effectively, operational efficiencies.”

Digital transformation

What should CSCOs out there do first? What needs to be considered before starting the gen AI journey?

ScottMadden: “First, ScottMadden recommends clarifying the business problem that could be solved with generative AI. All organisations have a list of improvement opportunities that can be refined into specific use cases. Supply chain organisations should evaluate and prioritise these use cases based on data availability, quality, and business context. Identifying one or two high-value use cases for a pilot is a good way to begin the generative AI journey.”

“Executing the pilot involves forming the right team, selecting the best-fit technology solution, transforming the data, and iterating the solution until the desired result is achieved. Companies can use Pilots not only to deliver on the use case but to also build components of the technology and data infrastructure needed for future pilots or use cases. The key benefit of a pilot, in addition to addressing a high-value use case, is the learning that results from the data transformation and technology configuration. These lessons can be applied to additional use cases, setting the foundation for a robust data structure and enabling the delivery of solutions at scale that build on one another.”

Tech challenges

What are the biggest challenges or hesitations you’re seeing companies have around AI? What should organisations look for in technology to hedge against these concerns?

ScottMadden: “The most common challenge related to generative AI is data security. Generative AI enables relatively easy access to many users, which creates risk for organisations.

“Selecting the best-fit technology for investment is another key challenge. In supply chain, as well as other functions, many technology solutions with AI functionality have emerged, raising questions about what to use and how to deploy, manage, and govern these solutions.

“Determining how to implement a scalable data solution is another major hurdle. The issue of data governance has existed for many years. Investing the required time and resources in data integrity and infrastructure has been a daunting task for many organisations, often resulting in deferred or unsuccessful data-related initiatives.

“As described previously, we recommend executing a pilot to address one or two high-value use cases. This approach provides solutions to important business problems and enables learning that can be applied to other improvement opportunities and used to build a scalable data-driven operating model.”

Future

How can the supply chain industry take that next step and what strategies can be implemented to push the industry forward even further?

ScottMadden: “Supply chain leaders should lean into digital transformation, resilience, and sustainability. A solid approach to digital transformation (e.g., data analytics and AI) will enable resilience, sustainability, and effectiveness. By embracing digital changes early and incrementally, supply chain organisations can ensure a ‘seat at the table’ when enterprise strategies are discussed. Similarly, considering data skills to the Supply Chain workforce will help prepare organisations for an increasingly digital future”

Are there any exciting projects that you’re currently working on or any past ones that you’re proud of that you’d like to highlight?

ScottMadden: “Some of our most recent work has involved using data analytics and AI to help shape materials management strategies, resulting in significant opportunities to drive savings and increase agility and scalability. We have been able to leverage proven inventory management methodologies to identify these opportunities more quickly and efficiently, which enables faster implementation for our clients. We have also applied related techniques to warehouse design and network optimisation. The exciting part is that we are just scratching the surface with these solutions.”

Learn more about ScottMadden here.

GenAI is a game-changer for procurement.

It has the potential to redefine procurement’s entire operating model and significantly accelerate the function’s efficiency.

To a Chief Procurement Officer, saving both time and money is like music to their ears. However, that’s not to say that GenAI has all the answers and doesn’t come without challenges.

In this exclusive article, Suyash Deo, Functional SME Data Analytics and AI at Softweb Solutions, tells us more about how GenAI and conversational AI represent revolutionary advancements in technology, poised to transform traditional supply chain operations.

Would you be able to give me a brief introduction to your role and the company you work for?

Suyash Deo: “I serve as a Functional SME in AI and Data Analytics at Softweb Solutions. With 13 years at Softweb, I’ve managed key accounts in supply chain and manufacturing, driving their digital transformation.

“Softweb Solutions, an Avnet company, is a global system integrator headquartered in Dallas, with offices in Chicago and a development unit in India. For over two decades, we’ve been bridging technology gaps in digital supply chains through data and AI-driven strategies. We’re closely aligned with Hyperscalers like Microsoft and AWS, and enterprise platforms like Salesforce and SAP.”

ASCM CONNECT 2024

What is the value of events like ASCM CONNECT 2024: North America? How important is this conference in the supply chain calendar?

Suyash Deo: “ASCM CONNECT 2024 stands out as a premier event, offering a unique platform for professionals in the supply chain industry to collaborate and connect. While we attend many industry events, the networking opportunities at ASCM CONNECT are truly unmatched.”

What are the biggest takeaways from this year’s ASCM CONNECT for you?

Suyash Deo: “At Softweb, we had the opportunity to showcase our Generative AI framework, Needle, along with our AI agent capabilities. The response from the audience was overwhelmingly positive, particularly in seeing how AI agents can revolutionize traditional operations. Additionally, the event offered valuable networking opportunities and insights from industry experts, making it a highly enriching experience for us.”

Global supply chain

How would you describe where the supply chain space finds itself today?

Suyash Deo: “The supply chain sector has undoubtedly been among the hardest hit by recent global disruptions. These challenges have exposed vulnerabilities and push the industry to rethink its approach.

“We’re now moving away from the traditional, linear supply chains toward more interconnected, technology-driven networks. There’s a strong shift towards data-driven strategies and an ‘AI-first’ mindset, which is rapidly becoming the norm in organisations aiming to future-proof their operations. The focus has shifted to creating smarter, more resilient supply chains that are better equipped to handle the uncertainties of today’s global landscape. This evolution is what we’re now considering the ‘new normal’ for supply chains.”

Digital transformation

Where are generative and conversational AI having the biggest impact in the supply chain? What types of supply chain and procurement decisions are now possible, or much easier to make, with the rise of AI and LLMs? How does this compare to just a couple of years ago?

Suyash Deo: “At ASCM Connect, we showcased our GenAI Framework, Needle. This cutting-edge tool integrates with multiple data sources and enterprise systems through its AI agent. During the demo, we illustrated how Needle can generate a Bill of Materials (BOM) from natural chat conversations with clients. It captures key information—whether it’s an image, web link, or text—and uses that data to determine specific requirements for electronic boards and parts. Additionally, Needle can recommend alternative parts for aging inventory, create new opportunities in Salesforce, notify account managers, and validate parts availability.

“This AI-driven approach has optimised the traditional procurement process by over 40% and significantly improved customer experience. Looking back a few years, procurement involved a cumbersome chain of emails and forms, extensive data exchanges, and a high level of human dependency, all of which increased the risk of errors. Needle modernises and streamlines these processes, delivering greater efficiency and accuracy.”

What should CSCOs out there do first? What needs to be considered before starting the gen AI journey?

Suyash Deo: “GenAI technology has the potential to unlock numerous opportunities and create significant value for organisations when implemented effectively. However, we’ve noticed that many clients begin with proofs of concept (POCs). This is before then abandoning full-scale implementation due to inadequate planning.

“In our Data 360 program, we address this challenge by starting with a thorough data readiness assessment and mapping out all overlapping workflows. While we always adhere to a well-defined AI strategy, our approach ensures that it is tailored to fit each client’s existing ecosystem and use cases.”

Biggest challenges

What are the biggest challenges or hesitations you’re seeing companies have around AI? What should organisations look for in technology to hedge against these concerns?

Suyash Deo: “During our workshops, we encounter various concerns from stakeholders. This is much like the apprehensions someone might have when riding a jet ski for the first time.

“However, one common worry is, ‘We do not want to share our data with the LLM.’

• To address this, we implement our Needle framework directly within the client’s cloud environment.
• This setup ensures that clients maintain full control over their data sources, user interactions with the AI model, and the resources it generates.

“Additionally, we provide a comprehensive overview of the GenAI thread landscape from an enterprise perspective and explain how Needle integrates within it. We can also discuss specific cases in detail to address any further concerns.”  

What are the underlying issues in how companies are currently storing and looking at their supply chain data? Why is this a problem and how can they overcome those challenges with generative AI and knowledge graphs?

Suyash Deo: “We frequently encounter this question during our workshops. As I mentioned earlier, GenAI or even traditional AI should come later in the process. The first and most critical step is building a solid data foundation.

“Many systems operate in silos, a number of applications run on local servers, and it’s not uncommon to find data managed through XLS and CSV files. This fragmented setup makes it difficult to ensure data availability for successful AI implementation.

“We always recommend focusing on strengthening the foundational data layer first. Once that’s in place, companies gain the flexibility to fully explore and leverage the true potential of AI.”

Future

Are there any exciting projects that you’re currently working on or any past ones that you’re proud of that you’d like to highlight?

Suyash Deo: “We’re involved in some exciting projects, especially in the areas of AI, data analytics, and digital transformation. Let me share two examples.

“Recently, we partnered with a leading supply chain company to develop a robust solution called POLAR. It’s a cloud-based web platform specifically designed to manage supply chain operations across different business units in the Asia-Pacific region. POLAR includes modules for planning, operations, logistics, reporting, and analytics, all customised to meet the needs of various roles within each unit.”

Challenges:

“They were facing numerous challenges managing their complex supply chain operations across diverse business units in the Asia-Pacific region. These includes Slow SAP data retrieval, which hindered real-time decision-making, forecasting future operations was also challenging due to the difficulty in analysing historical trends effectively.

Solution

“Our developed web platform designed specifically to manage supply chain operations across diverse business units in the Asia-Pacific region. Built leveraging Azure services, it offers robust features, including modules for planning, operations, logistics, reporting, and analytics.

“This resulted into greater impact in form of;

• Improved Operational Efficiency: Automated processes, reducing time and effort in managing supply chain operations.
  • Enhanced Data Accuracy: Eliminated manual errors, resulting in more accurate reporting and decision-making.
  • Faster Decision-Making: Real-time SAP access and historical comparisons enabled quicker, informed decisions.
  • Seamless Integration: Integration with SAP and third-party systems optimized the entire supply chain.
  • Future Scalability: The cloud-based platform supports long-term growth and adaptability for the business.

Project FSP

“We have also built Full-Service Portal (FSP) to streamline their product service    and ordering for end client.   

“Portal also covered critical modules, including product listing, order processing, and BOM (Bill of Materials) management, to enhance operational efficiency across regions.

Key Features:

• Product Listing and Search: A robust system for listing products with extensive search and filter options, enabling users to find products efficiently.
• Quoting System: FSP introduced a quoting module that allowed users to request quotes through multiple channels, track quote progress, and place orders based on quoted products.
• BOM (Bill of Materials) Management: The BOM module enabled users to create BOMs through various options, track product availability and pricing, and manage quotes and orders for BOM products seamlessly.  

Results and Impact

• Improved Efficiency: The system streamlined product listings, order management, and quoting processes, improving response times and customer satisfaction.
• Global Reach: The platform’s capabilities supported FSP’s operations across the Americas, APAC, and EMEA, enhancing its global presence.
• Enhanced Customer Experience: Customers could easily navigate through product listings, request quotes, and track orders, leading to a better user experience.
• BOM Optimisation: The BOM management feature enabled more accurate tracking of product availability and pricing, reducing delays in production and order fulfilment.

“This solution helped FSP rapidly respond to market changes, ensuring greater flexibility and operational agility across its global footprint.”

Supply chains have never needed more help – from geopolitical conflicts to extreme weather, the challenges facing supply chains across every industry are becoming much more pronounced. As with almost every other industry, AI technology could be perfectly poised to support supply chain professionals to overcome these challenges – but how?

AI technology isn’t simply a plug-and-play solution, however. To get the most out of AI, supply chain professionals need to abide by a few guiding principles.

1. AI should augment humans

First thing’s first: the achievements of AI in the past couple of years are nothing short of incredible, which is why it’s easy to forget the things that machines cannot provide, which I call the three C’s: context, collaboration and conscience. Models cannot derive meaning from context, critical in so many areas of the supply chain, nor can they work together to solve problems, including addressing issues like sustainability or human rights in supply chains.

This is why AI should augment humans. The most powerful combination is for humans and AI to work together, a belief reflected in a Workday survey of decision-makers, 93% of whom believe in the importance of keeping the human in the loop when AI is making significant decisions.

2. AI needs to fuse with heuristics and optimisation

AI can model problems at scale to produce more precise recommendations, such as greater demand forecast accuracy or better predictions of on-time delivery. Precision is also a benefit of optimisation, a field of AI familiar to many in supply chains for its ability to make the best use of resources within constraints to specific objectives, such as cost minimisation. Scale, though, can be a challenge: optimising a supply network can involve 200 million interdependent variables, slowing down even the fastest optimisation solver. Instead, some turn to heuristics, a problem-solving model that utilises a practical solution, or best practice, to produce a quick and feasible course of action good enough for the situation.

A fusion of the heuristics and AI can “warm start” an optimisation model, creatively combining the strengths of each approach to achieve an equilibrium of speed, precision and cost-effectiveness. Supply chain professionals should keep their hands on the wheel and remember that the most elegant solution is one that uses the right model for the right problem at the right time, no more, no less.

3. Concurrency and AI can transform supply chain management

Supply chains connect many functions across a company and beyond, which is why optimising one link doesn’t optimise the entire chain. For example, AI can greatly increase the accuracy of forecasts, but we want more than highly-efficient silos. The power of AI on its own is not enough.

The real breakthrough is not from AI but with concurrency, which integrates AI in the workflow to align decision-making across the supply chain for faster response. We want AI for its ability to predict with greater precision, speed, and elegance, and we need concurrency to connect supply chains for better, faster response, no matter what the conditions are. The bottom line is that AI embedded in concurrency leverages predictions while absorbing the volatility we cannot predict from the inevitable disruptions our supply chains will always face.

4. Democratise the power of AI

For AI to realise its potential, everyone must be able to use it. We will always need experts to explore new ways to apply AI, but empowering supply chain practitioners to adopt it themselves is crucial to realising its true power within the supply chain industry. For this reason, the best solutions are the ones which don’t require technical proficiency in AI or data science in order to use in your day-to-day role.

If solutions are designed for someone with supply chain context and business knowledge, they can “consume” the results of a model without knowing how to build it. Democratising AI in this way ensures its use, so choose to work with a provider who allows you to start from where you are and evolve.

5. Build Trust in Your AI

Many AI solutions come in a black box that even data scientists struggle to unpack. This is bad for visibility, but it can also be bad for adoption; supply chain professionals are ultimately responsible for their forecasts and, if they can’t explain how an AI platform is helping them to make their forecasts, they might think twice before trusting it. In fact, researchers have found that humans are more forgiving of what they perceive to be error on the part of fellow humans than they are from machines, a trait that can lead to them to develop “algorithm aversion.”

One approach to overcoming this aversion is state-of-the-art techniques that make black box AI models more understandable. For example, explainability techniques such as feature attribution methods can be used in demand sensing to help a planner see how adding a signal like weather affects predictions. Creating AI solutions that we can understand goes hand in hand with democratisation and, ultimately, will help improve adoption across the supply chain industry.

It’s clear that AI is transformative for the supply chain, and it’s fascinating to envision an industry augmented by this exciting technology. As we ramp up our use of AI, though, we need to remember that the trick to getting the most out of it is by adopting a human-centred approach. When AI is embedded across the end-to-end supply chain, expertly fusing the best techniques available, we can reimagine what is possible in our supply chains.

Blue Yonder’s 2024 Supply Chain Executive Survey reveals the many shared challenges and trends that continue to impact businesses worldwide. Once again, supply chain disruptions, rising costs and sustainability pressures proved top of mind for many. Unsurprisingly, the combined impact of all these issues has served to hit the bottom line, with almost half (46%) of global organisations confirming that profit margins are down.

To address these challenges and regain a degree of control, supply chain leaders are turning to innovative technology solutions to elevate end-to-end capabilities and make their supply chains more resilient, sustainable and agile. 

Let’s take a look at what they are doing, and why.

The top twin challenges: disruptions and rising costs

Dealing with disruptions remains the number-one challenge facing today’s supply chain teams. In addition to raw material shortages (48%), delivery lags from suppliers (47%), labour shortages (44%) and transportation capacity restrictions (41%), senior executives cited issues resulting from extreme weather conditions, shipping route changes and geopolitical unrest. 

Asked to evaluate how these disruptions had impacted their business, senior decision makers identified a number of negative outcomes including delays for customers (42%), production stoppages (42%), regulatory compliance issues (39%), reputational or financial damage (38%) and an inability to meet customer demand (38%).

In addition to managing frequent disruptions, supply chain organisations also confirm that soaring cost inflation is putting profitability under pressure. Globally, rising transportation costs (38%) and raw material costs (34%) were identified as the two most significant issues generating supply chain stress.

These combined challenges have had a significant commercial impact, with almost half (46%) of organisations globally reporting a decline in profit margins. Firms operating in the US appear particularly hit by these ongoing supply chain vulnerabilities, with 60% reporting decreased margins.

In response, many firms are doubling down on sustainability initiatives in a bid to reduce waste and excess and, by doing so, gain greater control of their cost base.

Sustainability: a strategic investment

Maximising sustainability across the supply chain is now a key focus for supply chain firms, with nearly half (44%) increasing their investment in sustainability initiatives last year. 

Reducing waste and excess – including production, inventory and raw materials – was the top sustainability goal for 57% of senior executives. Meanwhile, 55% said their primary focus was increasing transportation efficiency, optimising fuel usage, redesigning network and transportation routes and creating greener fleets.

In addition to the pursuit of initiatives that boost efficiency and reduce the consumption of resources, and thereby lower operational costs, supply chain organisations are also looking to improve supplier sustainability (46%), enhance the returns process (34%), and innovate product design for enhanced reuse/circularity (34%).

To accelerate these sustainability initiatives, and optimise supply chain decision-making at scale, more and more organisations are increasing their investment in AI technologies. The reason – so they can improve supply chain efficiencies (53%), reduce disruptions (37%) and increase profitability (29%).

AI and supply chains: navigating uncertainty with enhanced insight and agility

The survey shows the extent to which supply chain organisations are already harnessing AI technologies to drive efficiencies, augment decision-making, and speed up how they pivot in the face of unexpected or disruptive events. 

Over half of supply chain organisations have deployed AI and ML solutions to improve the performance of supply chain activities such as planning (56%), transportation (53%) and order management (50%). Furthermore, 80% currently have generative AI initiatives underway, having either fully (12%) or partially (33%) completed these implementations or are running pilot programmes (35%).

Asked to evaluate the success of their generative AI programmes to date, 91% of firms stated these technologies are having a measurably positive impact when it comes to optimising supply chain processes and enabling improved decision-making.

So much so, that 86% of all survey respondents confirm they plan to increase their investments in AI, ML and generative AI in the coming year. The top motivations for these investments include boosting supply chain visibility (43%), driving automation (40%), optimising supply chain planning (35%), elevating supplier and partner collaboration (27%) and improving last mile delivery (25%).

Reimagining the future: digitalising the supply chain

Persistent and ongoing challenges are spurring organisations to digitally transform their supply chains so they can synchronise decision making in near real time and leverage data to drive smarter end-to-end resource planning and utilisation.

Changing how organisations execute their sustainability strategies, manage unexpected events and empower their personnel with insights – such as disruption predictions and course-correction recommendations – supply chain decision-makers are increasingly relying on AI technologies to power their organisation’s future success. 

Looking ahead, supply chain executives have clear strategic investment objectives in their sights. In the coming year their top functional areas for targeted advanced technology adoption include transport management systems (49%), advanced warehouse management systems (41%), sales and operations planning (37%) and integrated demand and supply planning capabilities (34%).