AI Supply Chain: From Reactive to Anticipatory Logistics

Modern supply chain management extends far beyond the physical movement of goods. It is the nervous system of global commerce, one in which the precision of information is nearly as crucial as the product itself. This precision is difficult to achieve through siloed and manual reporting. A transition to a unified, intelligent and transparent supply chain is becoming fundamental for survival, and success.

This white paper arrives at a definitive moment when traditional, reactive models are straining under the weight of global volatility and soaring customer expectations around digitalisation and the need for real-time information. Yet it’s also a point in time when the convergence of artificial intelligence and advanced data analytics promises effective solutions to these challenges. 

The insights presented here outline a transformative journey – from establishing a single source of truth to deploying prescriptive analytics that anticipate challenges before they arise. As we move towards a future defined by end-to-end visibility and hyper-personalisation, the organisations that treat data as their most valuable asset will not only weather future storms but turn their logistics operations into a powerful competitive advantage. 

We hope this paper serves as your blueprint for that future.

Eric Siebering, Supply Chain Innovation Director

Jean-Pierre Le Pogam, AI Project Manager

Florent Martin, Data & Analysis Manager

Supply chain management (SCM) is in the midst of a fundamental transformation. Traditional, reactive models are no longer fit for purpose. Replacing them are modern and versatile SCM solutions that are powered by data analytics and artificial intelligence (AI). 

This paper argues that these new models are not merely tools for optimisation but essential infrastructure for building a customer-centric and truly intelligent supply chain that is agile and resilient.

One of the core challenges facing organisations today is the opportunity cost of having limited visibility over increasingly complex supply chains. A lack of end-to-end (end-to-end) clarity hampers a company’s ability to plan proactively, mitigate risks, keep customers satisfied and forestall financial losses. 

A second major challenge is the need to tailor solutions for customers whose experience of e-commerce is informing their increasingly high expectations of service from the third-party logistics (3PL) sector.

These realities are driving a strategic shift in SCM from basic, siloed reporting to the implementation of high-quality, actionable data analytics and specialised expertise. The journey towards such intelligent SCM rests upon three pillars:

Looking further into the future, the next strategic frontier for SCM lies in using AI to manage two key imperatives: sustainability and hyper-personalisation. This involves using data analytics to optimise routes, mutualise assets to reduce CO2 emissions and provide customers with the advanced, tailor-made transparency they now expect. 

Efforts are also ongoing to enhance SCM tools with predictive analytics capabilities so they can leverage historical data and statistical models as well as machine learning to move beyond alerts to anticipation. This way they can help foresee potential delays, demand spikes and market trends to enhance risk management and proactive decision-making before problems materialise.

At the centre of it all lies a clean, unified data structure. Companies that embrace a centralised, analytics-first platform can transform their supply chain from a necessary operational cost into a powerful, future-ready profit centre.

World commerce and its supply links have never been more complex. The past decade has seen global supply chains tested by a confluence of factors: globalisation and de-globalisation, sanctions and trade wars, a pandemic, the accelerating threat of climate change and, perhaps most crucially, rapidly evolving customer expectations around speed and transparency. Traditional, manually intensive SCM methods that rely on periodic reports, email chains and disconnected systems simply cannot keep pace with this dynamic environment.

The new imperative is agility and intelligence. By harnessing technology, particularly advanced AI-powered data analytics tools, 3PL providers and their customers can transform SCM practices through a powerful trifecta: improved demand forecasting, optimised inventory levels and precision logistics planning.

However, the path to intelligence is fraught with internal operational challenges. The single biggest bottleneck is data quality. Data is often siloed across disparate legacy infrastructure such as ERP, WMS and TMS systems. A further data challenge comes from the need to link together the non-standardised documents and identification systems used by a large ecosystem of external stakeholders (suppliers, customers, regulators, carriers, other 3PLs and the end-consumer). 

Creating a repository of clean data not only allows for certainty over the past and present, it also makes it easier to anticipate the future. Problems can be foreseen before they arise, and measures taken to ensure they never become problems at all. Think of it like an orchestra, led by a conductor and guided by a musical score, producing a perfectly coordinated performance. For supply chains, the conductor is the SCM and data is the score that together ensure the performance of a symphony. Without them, there would be cacophony and chaos.

The financial and operational case for adopting an intelligent SCM system is now indisputable. Current estimates show that approximately 50% of companies lack end-to-end supply chain visibility, hampering their ability to mitigate risk and respond effectively to disruptions. Given that supply chain disruptions cost organisations an estimated $184 billion annually, the lack of visibility is an expensive problem.

AI and data analytics-enabled SCM solutions provide the necessary structural solution. These tools optimise risk, lower operating costs and enhance efficiencies by enabling asset mutualisation and proactive decision-making. As importantly, they also dramatically improve customer experience through better product availability and reliable, transparent deliveries. 

Meanwhile, 3PL providers are leveraging technology with an aim to eventually offer forecasting capabilities and real-time visibility across the supply chain. For instance, FM Logistic has tested a solution that analyses historical data, including carrier performance, destinations and delivery types, to predict the probability of a delay before a shipment even leaves the warehouse.

An intelligent SCM is also the only viable path to managing escalating strategic demands, such as the call to adhere to global sustainability principles with the mandatory tracking and reduction of Scope 3 emissions. Systems that deploy analytics to accurately track environmental performance and optimise routes for CO2 reduction will be essential for meeting such regulatory requirements and key customer mandates. Furthermore, these systems support the trend towards hyper-personalisation, helping companies satisfy rising consumer expectations with features like advanced, tailor-made dashboards and precise delivery schedules. 

These capabilities are bringing about an industry-wide shift. Businesses now rank new technologies that increase the efficiency and visibility of supply chains as the main reason they are optimistic about the future; and 75% of manufacturers are actively looking to update their supply chain analytics and transition from rudimentary reporting to information powered by high-quality data analytics and expertise.

The logistics industry, despite its critical role in global commerce, has long been hampered by fragmented systems, inconsistent data and reactive operational models. If AI is to change this, it can only do so through the creation of a unified, high-quality data infrastructure. That in turn elevates supply chain management to an exercise aimed at proactive problem-solving resting on three key pillars.

The 3PL providers that transport and warehouse the world’s cargoes typically have to base key decisions on inconsistent metrics. This irregularity mainly stems from poor end-to-end visibility across convoluted supply chains, which leads to higher costs and inefficiencies. Adding to the problems are fragmented, poor-quality data resulting from siloed transport and warehouse management systems. These problems are further compounded for global operators who deal with widely differing data formats and compliance requirements between markets.

The old computing adage of “garbage in, garbage out” applies as much to AI as any other software technology. A logistics provider cannot use AI effectively without a unified source of clean data. This foundational step involves the creation of a global data lake, aggregating and standardising information from WMS and TMS, finance platforms and client interfaces. Such a data lake unifies the various streams of information in one place to provide end-to-end visibility across transport networks through dashboards. These dashboards enable real-time executive decision-making to enhance overall operational efficiencies.

AI can help create this lake. One of the great sources of data error in logistics, for example, is the fact that the same delivery address can be written in many different ways. Computers once had difficulty identifying instances when “Main St” and “Main Street” were the same location, but the stochastic models used by AI allow it to incorporate randomness and probability in order to accurately match and standardise inconsistently formatted addresses into a single “golden address”, significantly reducing the scope of duplication and error. Just as instruments need fine-tuning to make good music, clean and standardised data sets are foundational to the functioning of effective SCM systems.

Once this bedrock of solid data is in place, it becomes possible to link it to the data generated by ongoing operations. For example, many consignments contain elements that are hazardous and require special treatment in storage, delivery or both. Those that involve potentially hazardous chemicals, such as ethanol in a de-icing fluid, come with a Safety Data Sheet (SDS) that contains standardised yet often complex information about the chemical composition of the products. 

The 3PL industry is moving to automate such tasks. To give an example from FM Logistic, it uses a Robotic Process Automation (RPA) bot to extract SDS PDFs from incoming emails and send them to Gemini, an AI algorithm. Gemini pulls the critical data points and returns them to the bot, which then populates a WMS spreadsheet for human validation. 

This is just one example of the many sources of information aggregated by systems such as FM Logistic’s Control Tower, a tool designed to      bring centralised end-to-end visibility to end-to-end SCM. Its unified dashboard affords visibility over both upstream and downstream operations, allowing users to see flows and stocks in real time. One FM Logistic client, for example, ships to 120 countries from a single warehouse. Control Tower orchestrates the entire flow, synchronises documentation, enables booking, tracks deliveries, anticipates potential unforeseen events and optimises transport and costs.     

AI-powered systems’ descriptive analytics capabilities can uncover a range of trends, answering key questions such as how demand fluctuates within a specific time period and presenting the analysis through real-time dashboards. 

For instance, one Control Tower user has retail outlets in service stations across Poland. When the company experienced a sudden surge in demand for de-icer fluid after temperatures dipped unexpectedly and sharply, the dashboard quickly alerted the company through the modern equivalent of “blinking lights” that highlighted the affected outlets. This allowed the company to see the locations at which demand was most rapidly outstripping supply and prioritise new deliveries to those shops.

Additionally, AI-powered image analysis is proving especially helpful in diagnosis. During the picking stage in a warehouse, a camera can check the number of items or boxes on the trolley against the expected count in the WMS, instantly diagnosing and preventing picking errors before the shipment leaves the dock. It can also assess the consignment for signs of damage – in other words, adding another, preliminary layer of quality control.

Sometimes the cause of delays or customer dissatisfaction can be less clear. Here too, dashboards like FM Logistic’s Control Tower can be of diagnostic assistance. For example, delivery carriers are expected to both manually record and photograph a delivery to confirm that it has taken place within an agreed time window. On occasion, a carrier may not fill in the manual proof of delivery (PoD) record accurately in order to disguise a late arrival, but Control Tower uses AI image analysis to validate those records against the submitted photographs to confirm that the data on both ends match. Likewise, it can identify photographs that do not fit the expected format.     

Meanwhile, in B2B settings, by digitising the PoD and making it available on the portal almost immediately, Control Tower allows users to invoice customers much faster with the data coming from FM Logistic’s digital customer portal called My-SCM, which offers unique access to all data collected and published internally, helping to improve cash flows and allowing for any queries, complaints or returns from the end customer to be expedited. 

The ultimate goal of an intelligent supply chain is to move beyond anticipation into the realm of action. In other words, the SCM system will not just identify an impending bottleneck but recommend or even initiate the most effective way to clear it.

Indeed, one of the first uses of AI in the logistics industry has been to optimise picking patterns, and this is an ongoing application of prescriptive analytics: by analysing which items are often purchased together, such as de-icing fluid and windscreen cloths for applying it, or shoes and socks, the system recommends that these products be located in close proximity to one another to optimise the “picking path” for workers and maximise warehouse efficiency.

This same logic is now being applied to inventory management via the My-SCM interface; by predicting a spike in demand for a specific stock-keeping unit (SKU) based on market trends or macro indicators, the system can recommend increasing stock levels before the surge occurs, ensuring that the “opportunity cost” of a stock-out is avoided. 

Similarly, prescriptive analytics are used to ensure the efficiency of fleets by calculating and suggesting optimal delivery routes. They do so by turning data on key factors, such as loads, schedules, traffic, demand trends and maintenance status, into concrete recommendations to help logistics operators reduce fuel use, avoid delays and keep vehicles and drivers productively utilised and travel fewer empty miles. 

Such prescriptive intelligence also extends to customer service, where Generative AI is being used to handle the “information layer” of the delivery experience. By using models like Gemini via the Gmail API, the system can automatically retrieve specific order data from My-SCM to draft accurate, customised responses to customer inquiries. This provides near-instantaneous feedback for the client while allowing human agents to focus on complex problem-solving rather than rote data retrieval.

Going back to the orchestra analogy, systems like Control Tower serve as the symphony’s conductor. First, order retention enables shipment consolidation, leading to optimised delivery routes and reduced costs. This is followed by the use of real-time data layers from tools like My-SCM to offer full visibility over the various components of the orchestra (in this case, the partners, carriers, warehouses and customers) to synchronise execution. Together, these tools can facilitate real-time decisions, such as reallocating warehouse staff to a different zone to meet a sudden priority, or deciding whether to hold a shipment to allow for a late-running consolidation. 

These examples illustrate how companies can rely on data-powered technology to delegate micro-decisions to cutting-edge platforms. In so doing they can ensure that their logistics operations remain agile enough to turn global volatility into a distinct competitive advantage.

As we have seen, FM Logistic’s My-SCM and Control Tower systems are leading examples of platforms based on these pillars. These technology- and data-driven solutions are specifically designed to provide end-to-end visibility across the entire distribution chain. By consolidating, standardising and sharing information from all systems and partners on one platform, these tools help companies transition from a reactive problem-solving mode – constantly chasing missing information or reacting to customer calls – to proactive, informed decision-making.

If My-SCM is the visibility tool, the Control Tower is the decision-maker.

My-SCM helps clients transition to a unified digital cockpit offering end-to-end visibility.

To manage today’s dynamic, constantly evolving supply chain, companies must transition from a reactive model reliant on manual processes and fragmented data to a smart and unified data-driven strategy. The core message is clear: in the digital economy, the quality of information determines the quality of execution.

AI and data analytics are the essential tools that can provide the necessary end-to-end visibility, predictability and agility required to thrive in a volatile world. Unifying data sources in a structured data lake, ensuring the quality of that data and then regulating access to it must be viewed as a mandatory endeavour to enable the use of tech-driven platforms, which allow companies to consolidate and standardise information across their complex ecosystems.

By embracing this centralised, analytics-first model, organisations achieve multiple strategic benefits. They not only de-risk their operations through proactive identification and issue-resolution but also meet a key customer requirement for real-time transparency and reliability. This foundation creates the basis for meeting compliance requirements, adopting sustainable logistics practices and making timely strategic business decisions, all of which can help position a company at the forefront of the next wave of supply chain optimisation.

For stakeholders evaluating their path forward, the recommendation is to better structure their data platforms, which will help to maximise their returns on investments in AI-powered, future-ready solutions that treat data as their most valuable asset. Because, companies that adopt a data and AI-driven smart SCM platform, and leverage the flexibility and coverage of an asset-light 3PL model, can turn their supply chain from a source of operational vulnerability into a powerful, future-proof competitive advantage.