Metcalfe’s law states that the value of a network is proportional to the square of the number of connected users of the system. In short, each new user does not just make the network more valuable; they make the network much, much, much more valuable. The implication is also that there are network effects – it is a winner take all environment; as one network becomes more dominant, it kills the competitors.
But there is a type of solution known as multi-enterprise supply chain networks (MSCN). These are networks. Metcalf’s law does not apply to these supply chain networks. Yes, these supply chain networks advantages do improve as more companies and users join. But it is not the winner take all environment implied by Metcalf’s law.
A multi-enterprise supply chain networks (MSCN) is a key technology for improved collaboration across an extended supply chain. A MSCN is a collaborative solution for supply chain processes built on a many-to-many architecture which supports a community of trading partners and third-party data feeds. MSCN solutions provide supply chain visibility, network-based applications, and network analytics across an extended supply chain. MSCN solutions have distinctive advantages when it comes to supplier onboarding, communication, partner management, and being able to provide unique analytics.
As one examines the MSCN market – one notices many MSCN suppliers. And the number of suppliers is not shrinking – it is growing. Christine Barnhart, the vice president of Christine Barnhart at MSCN vendor Nulogy, points out that these networks tend to grow up as solutions for industries and thus have data elements and functionality particularly relevant to that industry.
Nulogy, for example, is a platform for collaboration between consumer goods brands and their copacker and comanufacturing partners. The data elements include the ability for the Brand to share a demand forecast with their copacker/comanufactruing partners; the ability for copackers/comanufacturers to share their available production capacity with the brands, views into inventory on site or in transit, bill of material collaboration (BOM) with BOM substitution logic, and the ability for a Brand to get views into the status of their production order. Then the site also does the kind of purchase order and invoicing collaboration that EDI solutions do.
Chris Castle, the vice president of product management at Nulogy, said that collaboration in the consumer goods industry was more even handed than in industries like automotive. In automotive, the OEM says “jump” and the suppliers asks “how high?”. In contrast, their platform is designed to provide benefits for both brands and their partners. Forecast collaboration and visibility to potential exceptions, for example, allows copackers/comanufacturers to run their operations more efficiently.
Contrast The solution from Nulogy with the MSCN solution from TraceGains. TraceGains’ solution digitizes the food ingredient supply chain. They capture product attributes such as nation of origin, FDA data, whether a product is a genetically modified organism (GMO), has allergens associated with it, and other ingredient attributes as well. The solution also understands which factories are authorized to process or use specified ingredients, and for which products they have those ingredients are approved. The solution is used to help digitize research & development, sourcing, regulatory compliance, and product quality. In short, the TraceGains solution is capturing different data attributes, and working with different supply chain groups in an organization than what the solution from Nulogy supports.
Then there is FourKites. A large share of their business is with food & beverage companies. Their solutions ingests real-time GPS information on the location of an inbound or outbound shipments. They provide predicted times of arrivals for those shipments. The solution provides value for companies trying to avoid OTIF fines (on time and in full), helps logistics executives manage their warehouses more efficiently, and can help food companies turn around the trucks of carriers more quickly (and thus make the manufacturer a preferred customer when trucking capacity is in short supply). So again, a different type of data is ingested, and it is used by different supply chain people in the supply chain.
Supply chain management has so many different dimensions, and there are so many different responsibilities, that specifying a standard set of data types and data fields to be exchanged is not realistic. Different data leads to different types of visibility, for different people, in a muti-echelon supply chain. Without some standardization, network effects are not realistic. Thus, there continues to be a need for diverse types of supply chain networks. And networks are in coopetition – competition and cooperation – where data from one network ends up being used in other networks.