Recent research conducted by InterSystems highlights a critical challenge within supply chain organisations in the UK and Ireland: nearly half (47%) cite their dependency on manual processes for data collection and analysis as their primary technological hurdle. This reliance not only leads to inaccuracies and delays in accessing data but is also a significant barrier to the adoption of artificial intelligence (AI) and machine learning (ML), which almost one in five (19%) anticipate will be the trend that most impacts their supply chain.
Mark Holmes, Senior Advisor for Supply Chain, InterSystems
For AI and ML adoption to be successful, models must be fed healthy, unified data. This requires supply chain organisations to move away from manual data collection and analysis and adopt a robust data strategy to underpin their efforts. This data strategy will sit at the heart of AI and ML initiatives but will also play a bigger role in the business’ overall operational strategy.
Developing a smart data strategy
A smart data strategy should encapsulate three things: data collection, analysis, and integration into organisational operations. This is where technology like the smart data fabric comes in, helping supply chain businesses to do all three things and bring their data strategy to life.
Built on modern data platform technology, the smart data fabric creates a connective tissue by accessing, transforming, and harmonising data from multiple sources, on demand. In particular, smart data fabric technology allows supply chain businesses to leverage usable, trustworthy data to make faster, more accurate decisions.
With a wide range of analytics capabilities, including data exploration, business intelligence, and machine learning embedded directly into the platform, supply chain businesses will also gain new insights and power intelligent predictive and prescriptive services and applications faster and easier.
Once these solid data foundations are in place, supply chain organisations can begin to unlock the real potential of AI and ML to augment human decision-making.
Applying AI and ML across the supply chain
The use of AI and ML can deliver operational change across supply chain organisations, from improved demand sensing and forecasting, to optimised fulfilment. For instance, SPAR, the world’s largest food retailer consortium, has turned to ML to solve some of the difficulties it was experiencing in streamlining and optimising end-to-end fulfilment processes in stores across Austria.
Operating in the extremely fast moving food and beverage sector, and with more than 600 merchants in Austria, SPAR Austria required a better way to help managers of local stores control their inventory. It consequently deployed ML for real-time sensing of demand shifts to optimise replenishment and strengthen its supply chain network. This has significantly improved on shelf availability (OSA), demand forecasting, productivity, and time to decision. In turn, it also helped SPAR increase revenue and efficiencies.
ML can also be used for production planning optimisation, using different constraints including transportation cost, or component inventory allocation to improve fill rate and optimise product shelf-life, productivity, cost, and revenues. Additionally, with access to AI and ML-driven prescriptive and predictive insights, organisations will be able to reroute or resupply at the drop of a hat, helping to maintain operations, achieve on-time in-full (OTIF) metrics, and ensuring customer satisfaction.
The automation and optimisation of these different processes also has a material impact on those working in supply chain operations. It transforms their work from reactive to proactive efforts. With less time spent on processing, more time is freed up for strategic thinking to improve fill rates and lower transportation costs, for example, making their role more rewarding and value-adding.
The transformative potential of AI and ML in supply chain management hinges on a smart data strategy that moves beyond manual processes to a seamless integration of robust data collection, analysis, and usage. By adopting smart data fabric technology, supply chain organisations can resolve their primary technological hurdles, transitioning from reliance on inaccurate and delayed data to leveraging real-time, actionable insights that fuel AI and ML initiatives. This strategic shift not only enhances operational efficiency and decision-making but also paves the way for predictive and prescriptive capabilities that dramatically improve demand forecasting, inventory management, and overall supply chain responsiveness.
The success stories of companies like SPAR Austria demonstrate the profound impact of integrating AI and ML into supply chain operations. These technologies both optimise operational tasks and empower employees by shifting their roles from mundane, reactive tasks to strategic, proactive engagements that add significant value to their organisations. By adopting a smart data strategy and embracing these advanced technologies, supply chain businesses will realise benefits that extend beyond operational efficiencies to include improved customer satisfaction, increased revenue, and a stronger competitive edge.
