What Chip Shortages Can Teach Us about Supply Chain Meltdowns
President Biden’s efforts to address chip shortages are a manifestation of supply chain issues.
Let’s start with an example we can all relate to: Not long after the coronavirus forced millions of people worldwide to shelter in place, something inexplicable happened in retail outlets: They ran out of toilet paper. No matter how hard the stores searched, they simply could not find enough of the white stuff to keep shelves stocked and customers satisfied.
Daily supply needs did not fundamentally change because of COVID-19. The only difference was that consumers were buying for at-home use, instead of outsourcing this to professionals at work. Supply chain planners that had divided shipments between commercial and consumer buyers couldn’t adjust quickly enough to the new situation.
Shortage fears fueled by the media also led consumers to over-purchase, which actually helped to create the shortage. On March 12, 2020 alone, TP sales surged 734% from the same day a year earlier, according to NCSolutions. Manufacturers, impacted by their own quarantining, never did fully catch up to fickle demand, even though they were reportedly operating near capacity in late March.
In many ways, the shortages we experienced during the first year of the pandemic laid bare just how fragile our traditional supply chain processes are. Under normal circumstances, planning and logistics run just fine. But when unforeseen surges in demand or ill-timed government regulations come along, regimented supply chain processes can go into a tailspin. They struggle to adjust, which is what’s happened in the last 12 months. Not only did we run out of common household items, but we also began experiencing severe shortages of critical parts and materials for making the things that tend to power our economy—like semiconductors for computers and motor vehicles, rare-earth minerals for electronic devices and natural ingredients for medicines.
And of course, semiconductors. This is why President Biden signed an executive order directing a broad review of the nation’s supply chains to identify systemic weaknesses and find long-term solutions to avoid the kind of situation businesses and consumers are now facing. It is a laudable if overdue effort.
Those of who look back at this era in history will see the same conclusions that we are drawing now: the statistical analysis, forecasting, ordering, and delivery systems underlying today’s supply chains need much better data, insights, and automation to succeed.
Addressing Systemic Problems
The problem with the way we traditionally manage supply chains is that everything is based on historical consumption patterns. Supply chain professionals look at what’s been ordered over time, adjust for things like seasonality and promotions, add margins of error, and build analytical models that advise them on what they ship every few weeks and how they can optimize inventory.
For the most part, this just-in-time (JIT) inventory approach operates like clockwork—until market dynamics fundamentally change.
Analytical models are geared to solve known problems. If unforeseen issues or disruptions arise, those models lack the flexibility and agility to provide good results Complicating matters, most supply chain professionals use antiquated tools and technologies that aren’t well-suited for collecting, analyzing, and unlocking insights from multiple data sources.
Kearney research into supply chain resilience finds the top two priorities for businesses following the COVID-19 pandemic are their ability to adapt to changing demand and to better manage risk, especially in industries with highly complex supply chains, such as industrials, healthcare, and automotive. The only way to accomplish those goals is to move on from outdated tools and adopt automated technologies enabling real-time decisions—in other words, Decision Intelligence.
Built on machine learning (ML) and artificial intelligence (AI), Decision Intelligence tools allow planners to offload manually intensive, time-consuming tasks to machines so they can focus on more strategic matters. Planners no longer have to look for key information about the state of their operations. Technology does that for them by crawling internal and external data sources, applying ML and industry models, and coming up with relevant insights and recommendations to guide critical decision making.
The Need to Feed AIs
Now, it’s true that anything based on AI or ML is only as good as the data fed into it. And even the smartest technology will starve and stumble without a steady diet of good, clean, current, and relevant information.
Most businesses hesitate to share numbers about the state of their business. This is particularly true in times of crisis when they feel their existence might be in jeopardy. Manufacturers don’t like to publicize production lulls. Suppliers won’t talk about order drop-offs, and buyers won’t say what they’re buying.
Yet, without an accurate picture of how all those interconnected ecosystems’ parts are functioning, supply chain planners will always be at a serious disadvantage.
What’s needed is some sort of central repository for accurate industry-level data. It doesn’t require giving away competitive standing or secrets. Everything can be neutral or anonymized. But such an apparatus is sorely needed, because modern technology alone—as great as it may be—cannot possibly help planners predict or interpret rapidly- shifting market signals without actionable data.
As labor shortages, the Great Resignation, war in Ukraine, and the lingering impacts of COVID-19 have shown us, chaos can occur at any time. And most supply chain professionals are still applying outdated technology, tools, and processes to try and solve modern digital problems. That cannot reasonably continue.
As time moves on and other unexpected events arise, Decision Intelligence solutions will be vital in order for companies to adapt and thrive.