If you follow supply chain metrics regularly, you’ve likely been riding a roller coaster of emotions.
From a global pandemic to the war in Ukraine, rising geopolitical tensions between the U.S. and China, and ongoing labor issues leading to product shortages and long lead times, it’s tough to be a supply chain manager right now.
For example, in 2021, manufacturers were still catching up to meet production demand, leading to longer production lead times and clogged ports. The resulting blockages strained already scarce resources, compounding problems for manufacturers and less product for consumers. Ports had barely gotten back on track when the war in Ukraine broke out, bottlenecking shipments from the war-torn country and complicating trade routes.
When less product is available, costs rise as demand outpaces supply. As a result, prices spike due to volatility.
By most measures, the supply chain is finally returning to “normal.”
Costs are slowly dropping, supplies are mostly back to pre-pandemic levels, and manufacturers are cranking out products to meet demand. The supply chain has largely recovered, though the ongoing war in Ukraine, constant risk of cyberattacks, and other geopolitical tension still looms over the world’s supply lines.
Additionally, companies and manufacturers have reduced lead times and bolstered supply chain stability in a few ways. More companies are leaning on artificial intelligence and machine learning to spot trends and increase efficiency with less labor.
Other companies are tightening supply chains by bringing more production closer to home. Unlike offshoring, which allows companies to take advantage of lower manufacturing labor costs overseas, nearshoring brings jobs and production back to the U.S. (or North America) to shorten shipping distances.
To the average person, the supply chain is returning to normal, but what does the data say?
If you look at several popular indexes, the data shows supply chain stress at or near pre-pandemic levels.
When the pandemic began, everything was thrown into flux, making it difficult to use predictive analytics to decide what the next week, let alone months, would look like. Material lead times crept up, then exploded as the world paused. Companies faced higher prices while navigating long wait times for desperately needed raw materials and finished goods.
Today presents a different story, with nearly every economic region showing positive signs, including Europe, North America, and Asia. In a perfect world, conditions would ebb and flow near zero. When the system is near zero stress, prices are stable, everyone operates efficiently, and it’s easier to forecast future needs. Problems may arise the further the index moves away from the standard deviation.
But what indexes do companies look at, and what factors are considered when figuring out supply chain stress, stability, or volatility? Turns out, we have a few options to choose from that offer a good idea of what conditions look like.
One of the indices used to track volatility is the GEP Global Supply Chain Volatility Index, which uses surveys sent out to about 27,000 companies globally to track supply, demand, labor, transportation, and more.
Like most indexes, it uses a standard zero deviation as its starting point, the weighs six sub-indices to determine how the supply chain is doing. According to the GEP data, volatility has steadily dropped since the end of 2022 and now sits at a healthy .32.
Why is volatility returning to normal levels? Companies are less worried about not getting materials and products, so there is less stockpiling. There are also fewer backlogs and item shortages, and transport costs have dropped below the standard deviation line, resulting in some underutilized capacity.
The data is encouraging but also allows for more conversation between manufacturers, suppliers, and distributors to address possible stockpiling or shortages.
This index has only been around since May 2022 but has collected and published data stretching as far back as 1997.
Launched by the Federal Reserve Bank of New York, the Global Supply Chain Pressure Index (GSCPI) uses more than two dozen transportation and manufacturing variables to paint a picture of global supply chain conditions. Like GEP’s Volatility Index, it uses surveys gathered from seven economies alongside BLS data and information from the Harpex Index, Baltic Dry Index, and others to track delivery times, inventory levels, and pricing.
According to the GSCPI, supply chain pressure is as low as in late 2019, at the beginning of the COVID pandemic. In fact, most of the index’s data suggests the supply chain has returned to near pre-pandemic levels.
Launched in late 2022 by KPMG and the Association for Supply Chain Management (ASCM), this index uses machine learning and market-level data to determine supply chain health.
Despite being less than a year old, its data stretches back to 2008, providing enhanced views into industry trends. The Supply Chain Stability Index also includes data tied to global events, including disease, cyber-attacks, geopolitical tensions, and environmental, social, or governance (ESG) issues.
Like the other indexes on this list, the Supply Chain Stability Index shows improvement after peaking in the middle of 2022. The ongoing war in Ukraine, the COVID pandemic, and the impact of the Uyghur Forced Labor Prevention Act (UFLPA) led to soaring supply chain stress during the spring and summer of last year.
According to the data, labor and freight have caused most of the stress, with supply and capacity forming less than 10%. In KPMG’s estimate, labor will likely continue to impact both the production process and delivery of goods to customers.
It might seem redundant to use three similar charts, but having access to up-to-date information is critical for data scientists to make better decisions.
Forecasting and planning require incredible amounts of skill and data – data these charts readily share. The indexes compile vast amounts of information from around the world to fully dissect previous events and predict future conditions.
With this data, companies can see how earlier events affected the supply chain and use historical data to make better predictions. The indexes also track and measure supply and demand, though the data isn’t displayed in real-time.
Indexes can help solve some industry mysteries but aren’t the only data available. Sometimes, the metrics are better suited to help companies confirm trends within their own data analytics, helping them make better decisions on a micro level. Depending on their internal metrics, companies can compare internal and external data to understand what issues are impacting the business.
The short answer to this question is no, but the long answer is more nuanced.
No one can predict the future. With the threat of a recession constantly looming, companies are trying to read the tea leaves to determine if an economic downturn could occur.
As great as having a pile of supply chain data is, it can only tell you how previous problems impacted the industry. Instead of using the data to predict the future, align historical metrics alongside internal data to see how previous downturns affected business and what to do to avoid a similar result if it happens again.
One thing to consider is that earlier metrics don’t always predict what the future will look like. Experts said a recession was likely to begin in 2022, but consumer demand has stayed strong, even with labor concerns shaking pieces of the economy.
It’s easier to find and create efficiencies in the supply chain when you understand how the data and indexes work together.
Statistical analysis highlights areas where more attention is needed and can find new opportunities for improvement – but many factors determine a company’s or industry’s health. Indexes are only composite pictures and include variables that may or may not always apply to your situation.
Good data helps make sense of the world around you, even if it doesn’t always have all the answers. The goal is to make the most informed decision possible and adapt to whatever situation you find yourself in.
No one can predict the future, but with data analysis and a little intuition, we may be one step closer.
Subscribe to the Kris-Tech Blog