Supply-chain managers face unprecedented disruption, not only from the upheavals that resulted from the pandemic, but also from the ongoing shifts in demand, rising customer expectations and external geopolitical factors. Two things have become apparent: These disruptions are not temporary, and they have brought to light shortcomings in the current supply-chain data model.
Strategies for addressing the fallout from these disruptions will become more focused as we prepare for an economic rebound, giving managers the twin challenge of recovering from the pandemic-driven disaster while also dealing with a potentially rapid rise in post-pandemic demand and consumer spending over the coming year. Now is the time to take a deep dive into how companies will address recovery while simultaneously taking advantage of new growth opportunities. The solution is not in returning to the pre-pandemic status quo, rather, it is in establishing a new data model. As is often the case, disaster drives innovation, and we are now seeing that innovation arise as more supply-chain organizations embrace a new model of decision intelligence to rewrite the rules of the game.
Decision Intelligence for a Better Outcome
Dealing with the recovery from the past year’s disruptions, the rise in demand to come, and finally, gaining the knowledge and insights required to survive future disruptions will require going beyond the traditional data model of historical analysis, siloed information and on-screen dashboards with a limited array of output and limited means of interpretation.
The next generation of decision making is already here. Decision intelligence delivers early and immediate access to data and trends, driven by artificial intelligence and forward-looking predictive analysis and with a middleware layer capable of abstracting multiple sources of data to provide a single, unified view, and all of it delivered with a natural language interface.
Decision intelligence, when delivered with a unified, natural language interface, solves many of the hidden vulnerabilities and challenges that were brought to light as a result of the past year’s disruptions. Decision-making support in normal circumstances may work and there was, until now, little incentive to move forward. The older model however, left many unprepared when faced with the unexpected.
Decision Making in an Unpredictable World
Gartner Inc.’s CORE (configure, optimize, respond, execute) model of planning imposes a universal model to all supply-chain decision making:
- Configuration of the supply chain to support strategies,
- Optimization to account for factors like limited resources (or in the case of the pandemic, a major and unexpected disruption),
- Response, which builds in a level of optionality and intelligence, and
Real-time insights are needed to achieve a successful CORE model in supply-chain planning. To achieve real-time insights, we further need an intelligent, interactive and real-time interface which can be used by decision-makers at all levels, not just data analysts. This can be achieved with a natural language processing facility that allows the system to build context so the user is capable of having a progressive conversation with the system rather than simply carrying out a series of one-off questions or commands. Furthermore, the abstraction layer of the back-end processing allows for transparent ingestion of data from multiple structured and unstructured data sources.
A responsive supply chain in today’s global ecosystem requires planners to close the long-standing gap between planning and execution. Those who are capable of closing that gap, anticipating the unexpected and building in a capacity to quickly and intelligently pivot based un unpredictable events or shifts in demand, are those which fared better during last year’s devastating supply-chain shortfalls, and will be better positioned to quickly accommodate the inevitable shifts in demand that will follow.
Ganesh Gandhieswaran is co-founder and CEO of ConverSight.ai, a decision intelligence platform.