Supply Chain Stability Index

Market volatility has always generated a level of uncertainty that supply chain organizations have had to contend with. But over the past decade, the list of market volatility drivers has grown. They now encompass a much wider set of events including cybersecurity; disease; environmental, social, and corporate governance (ESG); and geopolitical conflicts like the Russia-Ukraine war. Crisis mode has become the new normal, driving unprecedented variability in all aspects of the supply chain.

The KPMG Supply Chain Stability Index, in association with ASCM

KPMG has developed an index to measure the stability of supply chains. Using market data and advanced analytics, the Stability Index gauges how well organizations are dealing with the ups and downs of volatility and informs operations to make better decisions for what lies ahead.

Behind the index is a set of machine learning algorithms. Fueled by 14 years of data comprised of nearly 30 key variables and performance indicators, the Stability Index unlocks insights into new behaviors that companies are taking to adjust to the new norm of volatility.

According to the index, cost of freight and labor have been the predominant causes of stress in the supply chain, accounting for the majority of the variability in operations performance. This is followed by supply chain labor availability and supply disruption.

Explore the index to gain further insight into the stability of supply chains across the United States. The dashboard below provides an interactive interface that visualizes trends over time, drills down into granular levels of detail, and reveals root causes behind variability. There are three pages to help navigate:

• Supply Chain Stability Index provides overall index values over time.
• Contributing Factors decomposes the index into three sub-indices or factors - Freight and Labor, Capacity, and Supply.
• Supporting Variables provides access to the variables that drive each factor.

Hovering over a point on any charted line displays a window that provides further insight into what drives variability at a particular point in time.