Dr Behzad Hezarkhani: Associate Professor
Introduction:
Supply chains are increasingly influx. International expanse of out supply chains makes us vulnerable to many risks. In this project, we explore the global supply chains using international trade data. The trade data is available at UN Comtrade website.ÌýComtrade collects the customs data of the world. Each country records its imports and exports records at the HS code levels of commodities.
We usually think of the trade of a commodity from an origin to a destination at a point of time as a singular value. However, there are two values pertaining to this notion. At one end, the country of origin registers the export of the commodity towards the destination (export value). At the other end, the country of destination registers the import of the commodity from the source (import value). These values together constitute the "mirror trade data".Ìý
The reasons for mismatch of the mirror trade data values are diverse, but among other reasons, one can imagine that what is exported from a country of origin at one time, will not be arriving to the destination country at the exact same time, and the distance between the partners of trade exacerbate this. In this project, we especially explore this feature of mirror trade data to draw insights on the trade lead-time, i.e., how long it takes for the commodities to move from their origins to destinations.
Objectives:
We use the mirror trade date to analyze global logistics and supply chains. We examine the relation between unit value and average lead time. We explore the connection between mode of transport and lead times which varies over time with changes of modes. Through case studies, we show how to interpret changes of lead time with the market forces. We analyze the correlation between the lead time patterns of groups of commodities and countries. We assess the impact of global events, such as Covid 19 and war in Ukraine on the global lead times of commodities. We further elaborate on possibilities to use mirror trade data in SCM.
Our work can help make global supply chains more visible and allow policy makers to understand and react to the changing patterns and disruptions, eventually making supply chains more resilient.
Outcomes:
We developed techniques to forecast trade lead times of various commodities amongst different countries. Using optimization techniques, we track changes in lead time patterns and interpret them with regard to external factors.
The figure above is an example of our analysis for HS code 7318 (screws, bolts, and nuts) from China to EU and the UK. As can be seen, the lead time for this commodity has been increasing from 1 month in 2018 to about 2 months in recent years. This show a significant increase in the time it takes to moves this commodity from China. We see similar patterns across many commodities.
Finally, in terms of policy impact, the project has generated collaboration between ÃÛÌÒTV and Department of Business and Trade (DBT) and the momentum towards building a national dashboard for UK global supply chains. Notably, Professor Hezarkhani was invited to join the Government Office for Science’s Future of Global Supply Chains Expert Advisory Group.
Ìý