Summary

Supply chains are complex, and with unforeseen events like Covid-19, efficiency is crucial. Due to this desire for a combination of collaborative optimisation and data privacy/ confidentiality, Multi-Party Computation has vast applications within Global Supply Chains. 

Introduction

Supply chains are complex, and with unforeseen events like Covid-19, efficiency is crucial. With high demand for consumer goods and a lack of supply in terms of transport (50% of air cargo is transported via passenger planes), optimising the Supply chain is of critical importance.

Computation problems within Supply Chain Collaboration often involve optimisation. For example, finding and utilizing spare capacity, cargo routing, maximising cargo revenues or minimising transport costs. The issue when it comes to supply chain collaboration is that competitive information could be revealed, thus leading to a loss of competitive advantage. Trusting third parties can be difficult, as companies do not know whether the third party is biased towards a specific party, whether they are adequately protecting their data or selling their data. Furthermore, in the event that one party decides that they don’t trust the third party, the collaboration could be halted - leading to a loss of efficiency. 

Due to this desire for a combination of collaborative optimisation and data privacy/confidentiality, Multi-Party Computation has vast applications within Global Supply Chains. 

How does Multi-Party Computation work?

Secure Multi-Party Computation (commonly known as MPC) is a cryptographic technique that allows multiple parties to collaborate on sensitive data without the need to reveal their data. It enables calculations on a joint data set, without parties exchanging or combining datasets. The data input from an individual party remains hidden from other parties and the result of the analysis is only known to pre-specified parties. With MPC, one can run computations on multiple data sources as if the data were centralized, without the data ever being brought to a central place. This makes it possible, for example, to combine data from multiple sources in an analysis, without the need to entrust this data to a “third party”, which can be highly beneficial from a data privacy/confidentiality point of view. 

Example: Air Cargo Routing

One example of how MPC could be applied within the supply chain is in the Aviation industry for freight management purposes. In the air cargo industry it is common practice for freight handlers to work in alliances. However, since alliance partners are also competitors it is not possible for alliance members to provide access to each other’s real-time capacities and tariff information. 

However, capacity and tariff information is needed when a partner wishes to route a shipment through multiple hubs in order to reach its destination. Current practice is that the actual capacity and tariff information for a specific shipment is retrieved manually. Using multi-party computation enables alliance partners to find the most optimal route in terms of cost and available capacity automatically by:

●  Using capacity planning information for route planning purposes without sharing the underlying data
●  Blind auctioning of capacity across members
●  Real-time freight quote generation.

This would also simplify the technological approach, as there would be no central storage, hence there would be fewer liabilities and less costly controls, reducing the implementation time from months (or years) to weeks. This would allow for auctioning or optimisation without disclosing the underlying information of members like their real-time capacity and prices during the optimisation process. Only the “winning” or optimal route is revealed, allowing those participating in the alliance to preserve their privacy.

To conclude

Parties working within the Aviation Supply Chain can rapidly deploy the Roseman Labs solution, without the traditional burden
of having to use a “trusted third party”. The proprietary information held by each party remains at the source, and only pre-agreed statistics are shared. This solution could revolutionise collaborative optimisation within Supply Chains globally, at a critical time when efficiency is more important than ever.

Sources

Image credit: Patrick Campanale on Unsplash, https://unsplash.com/photos/oCsQLKENz34
'Secure multi-party computation for supply chain collaboration', Romanov, Danila, TU Delft, 2021-07-01