Cognitive analytics is a field of analytics that tries to mimic the human brain by draw inferences from existing data and patterns, draws conclusions based on existing knowledge bases and then inserts this back into the knowledge base for future inferences – a self learning feedback loop.
Scaling to leverage data. Machine learning and artificial intelligence has several applications in the supply chain. The most obvious ones are the uses of this technology in the operations of tasks, trucks, machinery, auto order, etc. These all benefit in terms of economics and throughput, since they cost less than the human options, make less mistakes and can operate for longer periods.
But the real benefit is in the supply chain management area. This is an area where the same issues happen daily, they just appear differently. For example, delays are fundamentally similar, but where and when they occur is almost random. In addition, the data is now there to predict these, if only we had the human capacity to process that data real time. The major benefit of this technology in the supply chain is the ability to pair it with humans, learn from the human operation and scale that for the next event so the human administrates less and innovates more…meaning the human drives the agility from point number one.
Triggers for Cognitive analytics
Factors propel the transition to a cognitive supply chain, are
- Digital technologies, including the IoT, big data, the cloud, blockchain, and bots. These help collect, store, process, and share information securely across the value chain. Also, coming into play: artificial intelligence, machine learning, cognitive analytics, and techniques that learn patterns and context from structured and unstructured data to get the best possible outcomes
- Skill sets are evolving from functional expertise to functional-plus-digital expertise.
- Operating models that evolve from decentralized and shared services centers (SSC) to scalable hybrid global business services (GBS) with global, regional and local centers pushing cost, service and cash goals.
Cognitive Supply Chain
The cognitive supply chain differs from its transactional predecessor in a significant way. Unlike transactional supply chains, a cognitive supply chain will be a digitally led, yet process-centric – as opposed to being merely digitally enabled. This new model is already evolving, as digital technologies with embedded analytics converge to capture, store, process, and share data. Scalable, more flexible operating models as well as new skill sets are natural enablers of this evolution.
The transition is exciting and aspirational, but it won’t be simple. It entails change management at every level and impacts operating models, skill sets, and the digital tools now in place. By driving exception-based issue resolution, for example, companies can establish processes and systems that foster greater productivity. And in a world of information overload, eliminating noise from signals can promote insights and encourage meaningful actions that lead to improvements.
One way to manage change quickly is through digital nudging. The concept owes its origin to behavioral economists, who believe that with subtle encouragement—so-called “nudges”—they can direct people’s decisions. Digital nudging uses technologies to do the same thing at scale. Here are some examples in a supply chain context:
- Planners get real-time alerts on service risks, enabling prompt decision making
- Planners get daily priority lists that highlight critical exceptions
- Before planners start their day, LED screens indicate if nightly scheduled jobs have run and data is up to date
- Real-time, role-based visualizations show critical measures such as inventory outside-tolerance limits, safety stock gaps, master-data quality, and audit compliance
- Automated root-cause assignments of service misses and excess inventory encourage meaningful actions
