Supply Chain Resilience using Confidential Computing

June 18, 2021

By: Alisa DiCaprio, Hanns-Christian Hanebeck, Sarah Lynch and David Wray


The pandemic introduced the global public to the very real problems of supply chain continuity. It also changed the way that supply chains approach resilience.

Together, these changes are leading to some fundamental shifts in the technologies being used for supply chain management. Of these, confidential computing is one that targets the single most important underlying problem in supply chain management: how to process business-sensitive data in use. By using confidential computing, it is now possible to protect sensitive data throughout all stages of its lifecycle, as well as provide technical assurances that it can’t be misused. This opens up several opportunities for the supply chain industry to build and take advantage of new collaborative solutions.

To translate this, we will go through two categories of use cases: aligning suppliers and managing products.

Resilience doesn’t come from working harder

Crisis is not new to supply chain management. Earthquakes, port strikes, road closures, cyberattacks and cranky customs officers are par for the course. Normally, these exceptions are local, and so are the solutions. In the beginning of the pandemic, this is exactly what happened. The explosion in job openings for supply chain professionals indicates that many companies immediately responded by working harder.

But as the pandemic has stretched into 2021, supply chains are looking at ways to work smarter.  Unfortunately, this isn’t always easy to do. The old and outdated systems currently being used aren’t designed to establish trust between counterparties or verify that an organization’s data will be protected. However, with the advent of confidential computing, it is now possible to build systems that aggregate data across multiple parties to build new solutions that align supply chain partners.

Below we outline several ideas on how this new collaboration can be implemented in highly innovative ways.

Use case I: Alignment With Supply Chain Partners

  • Cost reduction – Managing day-to-day operations in supplier relationships often necessitates a delicate balance between demands and incentives. Today’s supply chains experience high levels of inefficiency due to the inability to jointly manage cost. Suppliers are often unlikely to share cost data beyond contractual mandates simply because they fear price cuts. Likewise, related costs such as those incurred for transportation and logistics are often not reported, even when they could be optimized through collaboration.
  • Load tendering – The same applies when industry data is aggregated. Load tenders and cost per mile in transportation are instructive examples. Both can be obtained from a variety of sources to analyze not just who is over- or underpaid but also to identify key trends. It is further conceivable that we will witness the emergence of blockchain solutions that initiate transactions such as the procurement of transportation and warehousing services based on bids or auctions. This is a natural evolution and we already observe the emergence of load matching platforms and data aggregators.
  • Scorecarding – Monitoring supplier performance, or Scorecarding, is typically performed in ERP systems today, but it is hard to obtain data that allows for direct comparisons between different suppliers. If scorecards could be securely shared not just across all suppliers of a manufacturer but between several manufacturers, an entirely different picture of supplier performance would likely emerge. It would be possible to extract best-practices that all parties could directly benefit from.

Use case II: Managing Products and Demand

  • Secure data collection – An obvious example where collaboration is easy to achieve is the management of product features, observation of consumer behavior and collection of usage data. Confidential computing allows manufacturers to securely collect data about product usage in the field without intrusion of privacy or data leakage to improve the products they build. The resulting insights are incredibly valuable, especially when they are shared among all relevant parties in a supply chain.
  • Collaborative planning – Today, forecast accuracy typically ranges from 25% – 75%. Demand planning becomes substantially harder the farther we move away from the point of sale, as it is impossible for upstream suppliers to anticipate demand without downstream market knowledge. Leveraging confidential computing means everyone can pool data to derive accurate forecasts while remaining confident they aren’t giving away their competitive advantage.  This brings down inventory levels across the entire chain while also optimizing costs. The more parties that participate, the more benefits there are for everyone.
  • Inventory planning – Suppliers can also record planning, order, inventory, and production data securely on a blockchain and aggregate the data across multiple organizations on a secure confidential computing platform. All participants are assured confidentiality and anonymity in this way while they benefit from the results of analysis. The key to such a solution is that it must be trusted by all parties and guarantee that the underlying data is inaccessible to everyone, including malicious actors.

Can You Afford to Wait?

In 2021, we’re already referring to “the time before.” Solving local exceptions with brute force is part of that time. The emergence of confidential computing as a feasible technology for data sharing and processing means that supply chain management can evolve on a more fundamental level than ever before.

The use cases described here are just a peek at some of the changes that are happening as industry dynamics change with technology. The question may not be whether you can afford to get started today, but rather whether you can afford to wait any longer.

Want to learn more?

Here are some helpful resources to learn more about Confidential Computing and Conclave.