It isn’t hard to name a recent product recall or scandal that’s affected thousands, if not millions, of consumers worldwide. Consider the Takata airbag recalls that rocked the automobile industry and allegations of child slavery in chocolate supply chains. Supply chains have not only become more vulnerable to geopolitical uncertainties and natural disasters, but also human missteps that can raise safety or ethical concerns. 

Full traceability is the gold standard in supply chain management. The ability to trace the processing history, origin of materials and final destination of products isn’t only needed to mitigate supply chain risks and enhance product safety, but also to enable sustainable and ethical production. In the case of chocolate producers, traceability could benefit all supply chain players by enabling the detection of unethical practices, increasing demand visibility and enabling sustainability certifications.

But this is easier said than done. Most businesses can hardly reliably trace the products they produce and source beyond a few upstream and downstream supply chain tiers, given that today’s supply chains are complex networks spanning dozens of countries and actors. Fortunately, tech can help. 

Tech to the rescue

Producers who want to make their supply chains more transparent and resilient may develop and deploy their own traceability technology or forge partnerships with tech companies. In the food industry, companies may form industry consortiums, or large retailers  like Walmart and processors like Tyson Foods may take the lead in collaborating with IT companies. In the fashion industry, fast-growing IT companies such as TextileGenesis work with the likes of H&M, Lenzing and Bestseller to lead traceability initiatives. 

Modern traceability technologies promise to improve supply chain management by increasing visibility, simplifying recalls and verifying sustainable supplier practices. For these reasons, traceability initiative leaders such as Walmart or Tyson aim to have all players in target supply chains adopt their traceability technology, or even position it as the industry standard. 

In practice, technology can only fulfil its promise if all (or most) producers and processors involved from raw materials to finished good are on board. After all, being able to identify the origin of the wheat and cheese in a pizza – but not the farmers of tomatoes and dairy cows – won’t make the pizza a fully traceable product. Having the right technology is but a first baby step. 

This implies that there are substantial network effects in the adoption of traceability technologies: the more supply chain participants use such a technology, the more benefits the supply chain as a whole can reap. In the context of supply chains, we refer to such a network effects as the supply chain effect. This effect has profound implications for traceability initiative leaders designing a technology dissemination strategy. The question then is: Where and how to start getting all suppliers and producers to adopt the technology?

Towards a dissemination strategy 

To jumpstart technology adoption, traceability initiative leaders tend to target a set of early adopter companies in the supply chain network, which we’ll call the “seed set”, to use the technology and influence other firms to do so. Engagement with the seed set may include pilot programmes, subsidies or cost-sharing incentives, which can be costly. Traceability leaders must thus identify the lowest-cost seed set to disseminate the technology to the entire network. 

To address this challenge, we formulated the problem as an integer program that accounts for the supply chain effect and the diffusion process of the technology in the supply chain network. While this problem can be solved with standard integer programming solvers, the size of modern supply chain networks (which can involve thousands of companies) makes this a very expensive approach. 

We propose a reformulation of the integer program into a linear program based on key structural properties of supply chain networks, specifically their typically “tree-like” structure. By leveraging this property, we can reduce the size of the linear program and use powerful linear programming solvers to solve the problem effectively.

Emerging patterns

As part of our study, we conducted a series of large-scale numerical experiments to show how supply chain network structures influence diffusion. We found that overall, the complexity of disseminating technology across the entire supply chain stems from the supply chain effect and the network’s intricate structure.

Structurally, we found that the level of overlap between individual supply chains within the network is a crucial predictor of the seed set size. We introduced a measure of this overlap, termed “clustering”, and found that more clustered networks tend to require a larger seed set, which suggests an increased effort to disseminate the technology. This finding, which might seem counterintuitive, shows the workings of the supply chain effect. 

We also found two types of early adopters in the seed set: starter and helper firms. Their roles can be illustrated through the analogy of a forest fire: A fire (technology diffusion) begins with starter trees (starter firms) and continues to spread, thanks to helper trees (helper firms) that connect overlapping forests (supply chain groups). In other words, starter firms that adopt traceability technology early in the diffusion process “jumpstart” diffusion, while helper firms that adopt the technology at a later stage keep the diffusion process “moving” along the supply chain network. 

From a managerial perspective, understanding the roles of starters and helpers can help managers tailor their diffusion strategy according to the structure of their supply chain network. Companies are organised as groups of nodes within a network, which are characterised by their network modularity – a measure of how connected different groups are. A very modular network has tightly connected groups of nodes that are loosely connected with other groups, and vice versa. 

When a supply chain network has tightly connected groups, but with little overlap with other neighbouring groups in the network, the benefits of using helper firms to activate diffusion across groups is limited. On the other hand, when groups in networks are loosely connected, with high levels of overlap with neighbouring groups, there is little need for helpers. However, when groups are moderately connected with moderate overlaps with other groups, helpers can be useful in activating transfers. In such a scenario, traceability initiative leaders can place helpers at strategic junctures between different parts of the network in the later parts of the diffusion process to ensure broad diffusion.

Traceability in practice

Our framework sheds light on two critical questions: How does the network structure affect the size of the seed set? What are the different roles that seed set firms play in the diffusion process?

Answering these questions can help traceability initiative leaders estimate the effort required to disseminate their technology and determine ways to engage with different firms in the supply chain network. It can inform their traceability technology diffusion strategy, for example, by answering questions like which product categories (and corresponding supply chain networks) tend to require smaller seed sets and which networks are more likely to need helper firms to promote diffusion. 

In the supply chain landscape, our model is the first to integrate the notion of networks to understand technology diffusion in complex supply chains, bridging the fields of computer science and technology diffusion. Nonetheless, leaders don’t need to solve complex equations to apply this knowledge. We offer practice-oriented heuristics that provide a quick estimate of the cost of seeding and which companies to seed. Our suggested heuristic mimics the strategy of first seeding starter firms and then seeding helper firms as diffusion propagates.

Due to the supply chain effect described earlier, a whole-ecosystem approach to traceability is needed to address challenges that require the coordination of dozens or hundreds of firms, such as adopting sustainable supply chain practices. Indeed, a supply chain can only really be “circular” if all companies in the network adopt a consistent set of practices and technologies. 

Even if traceability may seem like an unsurmountable goal, there are success stories. Consider how Tony’s Chocolonely offers chocolates that are slavery-free, and with minimal impact on deforestation, thanks to sustainable sourcing – made possible with its traceable cocoa supply chain.