Considering its financial impact on so many global markets, the recent Suez Canal blockage understandably garnered significant attention. It is an interesting story, in part, because we operate within a truly global economy. While a focus on on-shoring or re-shoring are part of the solution, it is not as easy as saying make everything here. For some manufacturers, the most recent supply chain incident meant a stoppage of raw materials or needed components. For others, it meant finished goods were at a standstill, meaning missed deadlines, which, depending on contract language, can get costly in a hurry.
The real story goes well beyond the Suez Canal or any global shipping challenge for that matter. These incidents should be about what manufacturers can learn, and what actions they can take to address the undeniable shortfalls in current supply chains.
Below, Ahmer Inam, chief AI officer at Pactera EDGE, shares his insights on how properly utilized artificial intelligence could play a meaningful role in addressing these issues and lessening the impact of damaging incidents.
IW: In what ways could AI play a role?
Inam: Disruptions caused by the pandemic, and now this shipping crisis in the Suez Canal, clearly serve as a warning to companies across the board: Don’t wait for an unexpected crisis to wreak havoc on your business. The tools now exist to proactively plan and protect your interests. And most of those tools are powered by AI and data.
A proactive, data-driven approach to a disruption - like the supply chain chaos caused by the ship grounding in the Suez Canal - could significantly mitigate the business impact. Real time data about inventory, demand, projected demand, and alternate supplies, when run through properly trained AI Platforms can render surprisingly effective contingency plans.
Does your business know, at a drop of the hat, what cargo they have on the affected ship, for instance?
Does the business have an AI powered platform in place to simulate and forecast the impact of these supply side issues on the demand side? Can that platform conduct scenario planning exercises to inform and suggest critical business strategies for handling disruption?
Can that program then use connected partners and marketplaces to quickly source and fulfill products from other sources?
AI can play a role in limiting the damage caused by unexpected occurrences, leveraging real time data - both from internal and external ecosystems – to quickly run best case scenarios for getting out of the jam.
IW: What actions should companies be taking now to be better prepared going forward.
Inam: A good strategy for businesses is to develop and focus on the right key performance indicators (KPI). As an example, week-on-hand (WoH) is a fairly common and generally accepted supply chain KPI - but it is backward looking, whereas weeks-of-supply (WoS) is a forward-looking KPI that can help businesses plan better for what’s likely to come.
The numerator for this KPI would be forecasts generated by an AI/machine learning platform (factoring in multi-dimensional factors that impact demand) and the denominator would be holistic inventory positions (items in factory, on dock, on ship, on trucks, in warehouse etc.).
A focus on this KPI will accelerate an enterprise-wide transformation that will help ensure that a business has near real-time data of their inventory positions (with AI and AI-IoT enabled supply chain transformation) so that they can better plan to respond to global and local events (e.g., Suez Canal, shipping accidents, port strikes, hurricanes, broken trucks etc.) and their potential downstream supply side impact.
Businesses going forward need to implement a platform like this, and the good news is, such platforms are getting more robust and more available to a wider range of businesses.
Businesses don’t have to think of this as a boil-the-ocean transformation. They can establish more robust data sharing partnerships with their existing logistics providers (or use this as a mechanism to rationalize logistics providers to focus on those that can provide inventory positions data in near real-time). They can use current secure cloud data architectures for their vendors to share data with them and use AI platforms to generate predictions of likely events and their potential downstream business impact.
