In today’s landscape, marked by substantial bottlenecks, constraints, and unpredictable spikes in demand, numerous supply chain organisations are eager to integrate artificial intelligence (AI) into their operations. Their goal is to harness AI to generate actionable insights, enabling them to sift through extensive data swiftly and make decisions more efficiently. This integration is crucial for quickly capitalising on opportunities and mitigating disruptions.
Supply chain experts, including analysts at Gartner, agree that AI platforms are most effective when they utilise a blend of AI techniques, such as machine learning (ML), rules, or optimisation, rather than depending solely on one approach. For AI technology to be effective, it requires consistent data from all facets of the supply chain, including logistics, warehousing, and transportation. This data must also be harmonised and standardised, achieving unified data. In essence, AI forms a critical component of the decision intelligence that organisations strive for as they progress towards higher levels of digital maturity and ultimately, digital transformation.
However, this integration poses a significant challenge, especially when dealing with enormous volumes of data that are often highly disparate and in varied formats. Supply chains are complex, with numerous fluctuating variables that need to be analysed accurately and almost instantaneously to enable AI-driven orchestration.
Time for connective tissue technology
A data strategy, encompassing three key elements, must form the foundation of all AI and decision intelligence ambitions. These elements are:
- Unified data – Harmonising and normalising disparate sources and formats — relational, non-relational, streaming, etc.
- Real-time data – Ingesting, processing, and analysing data in real time without delay and at scale, without moving or copying the data for analysis
- Intelligent processes – Interoperability, enabling seamless, accurate, connected supply chain orchestration and AI-enabled intelligent business processes
Supply chain businesses must ask themselves how they can achieve each of these effectively, while also ensuring data quality. The answer lies in using the right technology.
The entire data strategy must be underpinned by modern data platform technology. The right solutions help not only to collect data but also to analyse and make it usable and actionable for a wide variety of business applications. In particular, technology that creates a connective tissue within the organisation does this by accessing, transforming, and harmonising data from multiple sources, on demand. This approach allows supply chain businesses to leverage usable, trustworthy data to make faster, more accurate decisions.
The value of this capability cannot be underestimated. Too many organisations in all sectors have lost significant amounts of money through ill-advised attempts to leapfrog the stages of digital maturity. They have assumed they can use advanced simulation technologies, automated decision-making, advanced orchestration, and predictive capabilities without first laying the right foundation – unified data.
Even though organisations rightly proceed with AI implementation on a use case basis, learning from the first implementation, they still need to resolve the problems of inconsistent, inaccurate, and incomplete data. It is poor quality data, a lack of data governance, and a lack of trust in the data outcomes that prevents organisations from moving forward to advanced supply chain orchestration.
Laying the foundation for AI and analytics
By adopting a data management platform with embedded analytics capabilities, including data exploration, business intelligence, and ML, supply chain businesses will gain new insights and the decision intelligence they seek, with predictive and prescriptive services showing them what is likely to happen and what their options are.
Having established robust data foundations, supply chain organisations can begin to unlock the full potential of AI, ML, and other advanced technologies to enhance human decision-making. This advancement will propel them further along the digital maturity spectrum. Leaders in the supply chain sector will then be equipped to accelerate desired business outcomes, fostering efficiencies in a supply chain that is markedly more flexible. This agility will empower them to swiftly reroute or resupply at the drop of a hat.
Whether the focus is on improving forecasting of supply and demand, optimising inventory allocation, or achieving near-perfect on-time in-full (OTIF), the use of AI in decision intelligence can enable supply chain leaders to see, understand, optimise, and act to attain an agile and resilient supply chain model.
Primed to maximise the power of AI
The cornerstone of this transition is the effective collection, analysis, and operational infusion of data, all supported by modern data platform technology. As organisations continue to identify how to get ahead this year, laying down robust data foundations and utilising the right tools will ensure supply chain organisations are optimally positioned to fully tap into the capabilities of AI and ML. This will enable supply chain leaders to cultivate a supply chain model that is not only more agile and resilient but also highly responsive, capable of addressing current needs and equipped for future growth.
