Ever since the blockchain industry graduated from its Bitcoin-only beginnings, supply chain management has been named among the killer applications for smart contracts and Turing complete ledgers. It is easy to see why.
Designing, manufacturing, shipping, and retailing a single good in the global economy requires the cooperation of hundreds of different entities, many of them operating under different jurisdictions and standards of accountability. Elaborate legal agreements and escrow services aside, a manufacturer often simply has to trust their supplier that a certain batch of parts has been shipped on time and is being handled adequately on its way to its destination. Blockchain technology provides several ways to remove the need for blind trust from this relationship. Most of them involve the tracking of goods and services via hashed parcel IDs only the receiver can trail.
On top of that, CyberVein, with its immutable database functionality, adds an entirely new use-case scenario for distributed ledgers in the realm of supply chain management, one providing unprecedented levels of transparency and new tools for automation and economic forecasting. To understand how, we’ll need to dive a bit deeper into the world of large-scale industrial manufacturing.
Imagine that you’re in the business of assembling electric bicycles. You probably won’t be able to produce all parts of your bikes from scratch, and even if that were the case, you would still require raw materials which have to be mined and processed. Instead, you rely on a long list of suppliers for bike parts, batteries, electronic components, and the like.
While choosing your suppliers, you’ll conduct a market survey to decide which supplier can get you the best deal, but additionally, you’ll look for a vendor with the capacity to deliver the amounts you need, when you need them. Otherwise, your manufacturing process would get out of sync and become inefficient. This can very often be the trickiest part of a market survey. Calculating price-efficiency is relatively easy, compared to estimating the delivery capabilities of a potential supplier.
Today, the industry solves this mainly on the basis of pretty old school techniques, namely trust, reputation, and rumors. Often enough, however, this is far from being sufficient.
A manufacturer’s wet dream would be a crystal ball, disclosing to them how their suppliers stock might look like in a month, how many inbound and outbound shipments they receive, and calculate their supplying capacity on these grounds. Surprisingly, many suppliers are indeed willing to disclose this information to their clients, however not on a crystal-ball basis, but rather - again - on a simple trust basis.
Cybervein’s database network could remove the need for trust from this process. An inventory is, after all, a kind of database. Maintaining it on the CyberVein network and allowing clients view access is a no-brainer and could quite frankly be done on Google Sheets. The question, however, is how do the items listed in this inventory get there, and how does the client know that they can trust what they’re seeing?
Going back to our electronic bike factory, let’s say you want to know what the supplying capacity of an electric motor supplier in three months is going to be. Navigating to the supplier's inventory on the CyberVein network, you could see their projected stock. However, the numbers and items you would see there wouldn’t be entered by the supplier, but rather would arise from the suppliers actual trading activity. The motor supplier’s inbound shipments are, after all, someone else’s outbound shipments. If the motor supplier, for example, lists 1000 motors for next month, their parts will have to be ordered from someone else. Only after these orders are actually placed, the future part would appear as projected as from the expected delivery time. The motor supplier would have no way to artificially inflate numbers that are not reliant on actual orders.
However, this chain of accountability wouldn’t stop at the motor manufacturer’s suppliers. You, as bike factory owner, could backtrace each and every part up to the iron-ore produced by a mining facility on the other side of the globe and could rest assured that no data point would have been forged by any of the members of the supply chain.
The utility of such a universal supply chain database network would of course massively exceed the needs of the common bike manufacturer. As production processes get exceedingly automated, and just-in-time production lines become the norm, this level of industry-wide transparency will most probably emerge as the new industrial gold standard.
Going forward, with this sort of reliable data readily at hand, forecasting the behaviours of global markets and producing macroeconomic projections will become easier and more accurate than ever.
The CyberVein Team.