#resilientautonomousplanning

Perhaps you have Yoda on your planning team – he’s got centuries under his belt, he’s seen every conceivable shock in the multiverse and he’s basically a 99.99% accurate crystal ball so perhaps he can predict the next shock. But the vast majority of us mere mortal planners cannot, and kidding aside, Yoda isn’t recruitable!

The C-suite execs acknowledge it’s not realistic to predict a pandemic, an EU war or a continuously morphing trade war, however, they still expect their planners to be ready to mitigate these shocks and have a “resilient” plan that optimizes the business KPIs. The supply chain planners are stressed, the C-suite execs are stressed and the suppliers are stressed!

So what’s the solution?

Clearly, the solution is to actually envision and map out all potential shocks, rank them in order of likelihood, and simulate each one of the top priority ones in your supply chain digital twin. An AI Decision Model would subsequently recommend actions for each one of these scenarios, along with their impact on the business KPIs, and your planners can grade such actions and select which ones are most appropriate per geography, market segment, set of suppliers, set of SKUs etc.

Scenarios, Scenarios, Scenarios

If this brings to mind what military leaders do in their war game simulations, you’d be right on point. Even though, luckily, this is operational planning not an armed conflict,  it’s very much analogous to fighting against an invisible enemy such as a pandemic - causing factory shutdowns and container pile up at seaports, or climate-catastrophe-causing droughts impacting semiconductor fabs, or a crippling components/materials shortage.

Okay so you get it, the solution is a decision model trained on a multi-environment digital twin that can simulate numerous shock scenarios. That seems logical, we can get behind that.

But it’s still missing a key component.

So let's say the overall shock is, to take a very timely one, the war in eastern Europe. It’s clearly imposing global shortages of wheat.  As you can see in the price chart below, Wheat was a $618 $/Bu back in July 2021! On May 31st, it was $1103 $/Bu, and today it's at $774 $/Bu, which is understandable since commodities markets tend to price-in near term forecasted shortages.

Src: Trading Economics

That key component is the supply chain Knowledge Graph those external signals that capture the shocks in near-real-time as they manifest. Such signals such as the primary, secondary and tertiary commodities that impact a given industry vertical. They also include all the macro-economic signals that influence and interact with the industry specific signals such as interests rates, unemployment, CPI and PPI etc.

The more expansive and cross-industry a knowledge graph is, the more impactful its signals and predictive weights.

Role of Governments and Resilient Planning

The most obvious example of this is the latest baby formula shortages due to the closure of one of Abbott’s Similac factories in the US due to contamination. Clearly no one in the FDA simulated that scenario, and everyone was scrambling to find production capacity with other manufacturers to cover the shortage to feed American babies. Ultimately, it took the POTUS invoking the Defense Production Act to instruct the other manufacturers to increase production capacity to meet the formula demand. Luckily this intervention was possible this time, but clearly one can envision scenarios where it may not be, and it could unfortunately lead to loss of life!

That’s the importance of putting scenario-based resilient planning and risk mitigation decisioning at planners’ fingertips so they can see all recommended mitigation actions and their impact on the KPIs, and choose the best decision for the scenario at hand!

Conclusions

Supply chains are truly a matter of life or death and national security at this point, whether they affect infant formula, food commodity shortages, vaccines, silicon components, battery materials, or otherwise.