Today’s increasingly complex and volatile markets have made supply chain agility and resilience a top priority for organisations around the globe. In response, businesses took the decision to deploy traditional AI solutions in a bid to optimise supply chain processes and improve decision-making. The subsequent arrival of Generative AI (GenAI) technologies is now prompting many to redouble their AI efforts.
Capable of modifying plans and resource allocations using real-time data, the advanced capabilities of GenAI enables organisations to simulate various demand scenarios, optimise inventory and logistics routes, proactively identify and mitigate potential supply chain disruptions, and more.
However, embedding GenAI into existing supply chain business processes isn’t easy. According to Gartner, while 72% of supply chain organisations say they have deployed GenAI many confirm they are struggling to unlock its full potential.
It’s a revelation that serves to emphasise why, when it comes to successfully adopting and realising value from AI, organisations must first ensure they have the right technical architecture in place.
AI supply chain challenges
According to the Project Management Institute, between 70-80% of AI initiatives end in failure. A statistic that highlights how difficult it can be to plug GenAI and machine learning tools and technologies into industry specific contexts.
The successful implementation of AI in supply chain management is highly reliant on the acquisition of data from a variety of sources. However, the sheer complexity of supply chains means that getting the technical architecture right represents one of the biggest barriers that organisations will need to confront.
As many organisations are discovering, point solutions for supply chain processes are not fit for purpose when it comes to delivering up the data AI needs. As a result, businesses that rely on point solutions and batch processes find they are unable to give AI the right quality of data it needs, quickly enough. In addition to which, organisations will find it difficult to leverage the end-to-end data their AI tooling needs to generate value-add near real-time decisioning support or data-driven optimisations.
Since GenAI tools are only ever as powerful as their input data, let’s explore three ways in which organisations can improve their supply chain technology architecture and enhance the potential of their AI deployments.
1. Initiate an AI-tuned data model
Companies can often struggle with disconnected data foundations, poor data quality and multiple siloed systems of record. All of which results in incomplete or outdated information that will hinder the effective implementation of AI.
To address this challenge and facilitate the successful adoption of AI, organisations will need to initiate a well-defined and AI-tuned common data model that is designed to bridge the gap between disparate data sources and support the seamless flow of information. Providing a standardised framework that defines how data is structured and interconnected across various systems and applications, this model will deliver the unified schema needed to ensure data is consistent and interoperable. Something that is essential for enabling systems to communicate effectively.
Structured in way that allows first party AI/ML to leverage data with greater speed and accuracy, the AI-tuned data model both facilitates faster recommendations and root cause analyses and enables easier integration with external AI agents and applications. Something that is essential for enabling the seamless use of data across systems that is essential for supporting more responsive and automated supply chains at scale.
For example, with an AI-tuned common data model in play, organisations can utilise AI agents to understand how a natural disaster in one part of the world will impact specific purchase orders and shipments, evaluate what options are available, and generate recommendations.
2. Rehearsals and tuning: enabling a studio environment
AI models often need a degree of experimentation and fine tuning to maximise their value. Activating a dedicated studio will enable the organisation’s data scientists to experiment and undertake the development of live applications at scale.
Providing a safe space where data scientists can explore ways to connect to relevant internal and third-party data the studio environment makes it possible to build, test and refine AI models without impacting live operations.
Enabling rapid prototyping and iteration, enabling a studio environment makes it easier to customise AI and ML models more precisely to specific business needs and address specific use cases, outcomes and scenarios. All of which is essential for generating additional end-to-end value from AI implementations.
3. Scenario modelling
Today’s GenAI tools are capable of creating ‘what if’ scenario simulations that include potential course of action suggestions should things go awry. Providing a powerful augmentation to supply chain analytics, GenAI can test strategic options, predict potential disruptions on the horizon and support improved decision making and operational planning processes. All of which gives planners greater autonomy and agility when it comes to managing daily operations.
By embedding AI deeply into the processes of key strategic supply chain processes like planning, demand and supply, organisations will be able to model thousands of scenarios in a matter of minutes and adapt much faster to disruption or changing demand. Doing so effectively, however depends on having the technical capacity to deliver integrated rapid modelling capabilities that are finely tuned to specific business goals or accurate AI-driven demand signals.
By expanding AI capabilities to support fully integrated demand and supply planning, organisations can break down silos and enable seamless collaboration between supply partners. Ultimately, this will enable the seamless and automated cross-functional orchestration of the supply chain which can be consistently optimised and fully aligned to changing market conditions and customer demand.
Alongside making it possible to navigate challenges and opportunities as these arise, engaging in integrated scenario modelling is an essential start point for analysing current baseline operations and identifying ways to enhance efficiency, resilience and overall performance.
