Swiss Supply Chain Pressure Index

Several movements of the index align with known shocks, supporting its construct validity. The sharp spike in spring 2020 reflects the first COVID wave and China's factory shutdowns. The sustained climb afterwards captures additional shutdowns, Ever Given blockage of the Suez Canal in March 2021, and Russia’s invasion of Ukraine in February 2022. Although the war in Ukraine continued, supply chain pressure declined from autumn 2022 onward, likely reflecting gradual adaptation to the wartime environment and the phasing out of COVID-19 disruptions. The years 2024 and 2025 reflect lower supply chain pressure compared to the preceding turbulent period, with the index settling into a stable, slightly-below-average range. In 2026, the index returns above its long-run average for the first time in roughly three years, likely reflecting the 2026 Iran war and associated disruptions to shipping around the Strait of Hormuz.

01Background

Global supply chains are vulnerable to disruptions caused by geopolitical shocks, climate extremes, health crises, and shortages resulting from geopolitical tensions or export restrictions. Examples include shipping disruptions during the ongoing Iran war, restrictions on Panama Canal traffic due to drought, the disruptions caused by the pandemic, and shortages of critical materials following trade and export restrictions. These disruptions can have severe operational and financial consequences. The increasing complexity of modern supply chains, driven by global sourcing, outsourcing, multiple tiers, and geographically dispersed networks, can further increase firms’ exposure and vulnerability (Wagner & Bode, 2009; Bode & Wagner, 2015).

Recent approaches quantify the pressure on supply chains resulting from economic, geopolitical, and environmental vulnerabilities using global and national indices (Benigno et al., 2022; Friesenbichler et al., 2026). Building on these methodologies, the Swiss Supply Chain Pressure Index (SSCPI) offers a systematic way to measure pressure on Swiss supply chains. This is significant given the potential impact of such pressures on firms and the Swiss economy. The SSCPI is expressed in standard deviations from its long-run average since 2019. Positive values indicate above-average pressure, and negative values indicate below-average pressure. The index reports changes relative to one month, three months, and one year earlier.

02Data

Data collection is closely aligned with the Austrian Supply Chain Pressure Index (Friesenbichler et al., 2026). Following this approach, we collect two types of data.

Primarily, Swiss industry perceptions are exploited via manager surveys. Sub-indices of the Swiss Purchasing Managers’ Index (PMI) approximate supplier delivery times, backlog of orders, and stock of purchases in industrial production. Survey data from the Swiss Economic Institute (KOF) indicate shortages in material and equipment for the manufacturing and construction sector. While manager perceptions provide essential industry perspectives, survey data are exposed to potential biases (e.g., Bertrand & Mullainathan, 2001). Thus, global and Europe-specific transport price data are included to reflect more objective scarcity signals in the market. Bulk shipping costs are captured by the Baltic Exchange Dry Index. Container shipping is approximated by the cost of shipping goods from North America (New York) and Asia (Shanghai) to Europe (Rotterdam). Likewise, air cargo is approximated by the cost of shipping goods from North America (JFK/ ORD/ LAX) and Asia (PVG/ HKG/ ICN) to Europe (FRA). The port of Rotterdam is chosen as it is the biggest cargo seaport in Europe and well connected to Switzerland via the Rhine River system, road, and rail. Frankfurt is the biggest cargo airport in Europe and sits close to Switzerland in both geographic and economic terms.

All series enter in seasonally adjusted form so that recurring patterns are not mistaken for genuine pressure. Series for which a seasonally adjusted version is available are sourced directly as such. The remaining transportation cost series are adjusted using the X-13ARIMA-SEATS procedure (U.S. Census Bureau, 2025). The collected data are displayed in Table 1.

Table 1: Variables and data sources for index construction

03Methodology

The methodology is based on the Global Supply Chain Pressure Index (Benigno et al., 2022) and the Austrian Supply Chain Pressure Index (Friesenbichler et al., 2026), and proceeds in three steps.

1. Data gaps are addressed to obtain consistent monthly time series beginning in 2019. This base year was chosen because it precedes several global crises (e.g., COVID-19 pandemic, Russia’s invasion of Ukraine) and because most underlying series extend back to it.
   • Where needed, leading observations are imputed using ARIMAX models with correlated series as regressors, estimated in state-space form using the Kalman filter, with the leading values recovered from the smoother. The KOF manager perception series, which begins in April 2021, is backcast to 2019 using the PMI delivery times, order backlog, and quantity of purchases series; similarly, the first six months of the ICN-FRA air freight route are backcast from the PVG-FRA and HKG-FRA routes.
   • The KOF measure of Swiss manufacturing material and equipment shortages, available only at quarterly frequency, is disaggregated to monthly frequency using the Chow and Lin (1971) procedure, with the Swiss PMI delivery times, order backlog, and quantity of purchases as related monthly indicators.
   • Finally, a single isolated interior gap is filled by linear interpolation between adjacent months. This was the PVG-FRA route in April 2022, which was missing during the Shanghai lockdown.
2. The variables are demand-adjusted to isolate supply-side restrictions and avoid measuring pressure due to demand spikes. Therefore, the SSCPI subcomponents are regressed on current and lagged national data of firms’ new orders. This procedure is shown in Equation (1) for the manager perception variables and in Equation (2) for the transportation cost variables. The resulting residuals are interpreted as the variation not explained by the included demand indicators and are subsequently used in the final index construction.
   $$\text{ManagerPerception}_t^{CH} = \beta_0 + \sum_{l=0}^{2} \beta_l^{CH}\, NO_{t-l}^{CH} + \varepsilon_t$$

   (1)
   $$\text{TransportationCost}_t^{Global} = \beta_0 + \sum_{l=0}^{2} \beta_l^{CH}\, NO_{t-l}^{CH} + \sum_{l=0}^{2} \beta_l^{USA}\, NO_{t-l}^{USA} + \sum_{l=0}^{2} \beta_l^{CHN}\, NO_{t-l}^{CHN} + u_t$$

   (2)

   Note: t=time in months, NO=New Orders

3. To extract their common underlying trend, the adjusted values are analyzed via principal component analysis (PCA). The first component, which covers the largest proportion of the shared variance, is identified as the Swiss Supply Chain Pressure Index.

04Publishing

Due to data constraints, the SSCPI for a given month is published at the beginning of the second following month (e.g., February's value is released in early April). Because the index is re-estimated over the full sample at each release, historical values are subject to revision when new data are added.

05Acknowledgements

We gratefully acknowledge the support of the KOF Swiss Economic Institute at ETH Zurich for the Swiss Supply Chain Pressure Index, including the provision of underlying data. We particularly thank the KOF Business Tendency Surveys team and Klaus Abberger for their support.