Consumers are seeking greater transparency and easy access to data about the origin and supply chain of their foods, otherwise known as traceability. There are many drivers of this trend including (but not limited to) a desire to avoid foods which are:

  • Unsafe due to bacteria or pesticides;

  • Grown using slave labor; or

  • Grown on recently deforested land.

Some consumers are also looking for a guarantee of positive traits such as foods which are organic or nutritious. Ensuring a food meets these expectations requires a capacity to trace its origin. Traceability solutions use a database or ledger to record the origin of commodities, particularly data which reflects consumer concerns.

 

These consumer pressures are particularly acute for Fast Moving Consumer Goods (FMCG) such as Nestle, Unilever and Mars. For these firms, meeting consumer expectations can build brand equity while also reducing reputational risk and creating a competitive edge. FMCGs have been leading implementers of traceability solutions. 

 

Because FMCG companies buy their commodities from trading firms, it is usually the traders that ultimately implement traceability databases at the behest of their clients. Traceability is closely associated with certification. Certifications include independent organic and fair trade standards (such as Rainforest Alliance), as well as internal standards. While certification sets the standard, traceability is used to determine if it is being met.

 

In South East Asia, traceability has its origins in cocoa and palm oil. Cocoa traceability was driven by concerns about slavery in West Africa, and palm oil by deforestation. Many of the leading traceability solutions started in one of these two crops but are now used on a range of commodities including rubber and coffee.

 

We observe two approach to traceability being practiced in the region. The first is simpler, called farmer mapping while the second, batch traceability, requires greater investment of time and funding.

 

Farmer mapping is the most common starting point. The trader typically engages a contractor to map their farmer suppliers in a region and store the data in a database. The data typically includes personal information, plot location, plot size and certification details. The database becomes a “white list” of suppliers from which the trader (and ultimately the FMCG) buy their raw materials.

 

Batch traceability goes a step further and includes in the database specific details of each shipment of goods purchased from each farmer. Each batch is given a unique code (such as a QR code), which is maintained throughout the supply chain. Batch traceability is significantly more complex than farmer mapping, as data needs to be added to the database in real time by at each step along the value chain by a range of different parties.

 

Even the (simpler) mapping option requires a range of activities by multiple stakeholders; survey teams, certification fees, and farmer training. The buyer also needs to consider the cost of dealing with any supplier farmers who don’t comply with the agreed upon standard. Compared to other digital business models in this series, the digital element is a relatively small part of a much larger business model that the contractor provides. Batch traceability carries other additional costs including batch tags and payments to intermediary traders to record data at each step.

 

With consideration to the full range of costs, only some products have proved to be viable targets, and traceability is working best under the following conditions:

  • Where consumer pressure is greatest;

  • In contract farming schemes, where implementation costs are lower;

  • Where food safety risks are particularly high; and

  • When the value of the goods is particularly high.

In these scenarios, the premium provided by the end buyer is adequate to meet the costs of traceability; and if required, certification.

 

Both traders and traceability contractors are exploring other ways to monetize the data in traceability systems, including selling it to financial institutions to help assess loan applications from farmers. In fact, traceability is evolving from a service targeting FMCGs. We are likely to see both traders and traceability contractors increasingly monetizing traceability data with not just banks, but input manufacturers and logistics providers as well.  The movement towards making data available to a wider range of parties will see the sector evolve from traceability systems that target an end buyer, to “data exchanges” that sell data on a value chain to multiple data consumers. These systems raise a number of data protection risks but could ultimately make smallholder value chains more efficient.

 

Of the six solutions covered in the series, the traceability model stands out because contractors typically deliver large complex traceability projects rather than selling a unitized service within the value chain. The future of this model is dependent on changing consumer sentiments and the potential for contractors to shift into a broader data exchange offering.

Comment from Leesa, General Partner at R3I Ventures

"In any risk driven business model there are two levers for disruption:

  1. Reduce the level of information risk in an industry - empowering the change of who, what, where, and how the decision is made in the value chain.

  2. Align the incentives in the industry

By its origin, traceability platforms are designed to achieve both:

  • 1. Empowering transparency along the value chain to reduce the level of information risk.

  • 2. Realigning the incentives e.g. if there is an irrefutable source of origin for a product, this reduces the risk for any buyer/third party, encourages a consumer to potentially continue to buy or buy more, gives a financier the accurate information in order to insure/fund at lesser risk premium, and for a farmer, it can provide a stamp of “legitimacy” and “provenance” that can command higher margins, brand loyalty and increased market share.

The issue is that this model demands a desire for transparency of the value chain, willingness of the ecosystem to subscribe to it, and evidence of the same side, cross side network effects.

To invest in a business with this model, I would need to see:

  1. A level of ecosystem critical mass re user adoption.

  2. Evidence of same side and cross side network effect

  3. Demonstrated traction of user adoption across each of the main user groups

  4. Demonstrated willingness to pay from the “Customer”

  5. Barriers to entry and exit from fast followers"

Comment from Pierre, Sustainability and Supply Chain Strategist, Optel Group

"The farmer data collection and farm mapping, that the piece qualifies as ‘farmer mapping’ could certainly help in building a trustworthy supplier list.  However, this will not give the assurance that the supplies comply with the market requirements and expectations as it is a static database.

Tracing raw materials from those ‘compliant farms’ throughout the supply chain is the only way to guarantee product integrity and ensure the lack of contamination and substitution with non-compliant products. On the other hand, a basic traceability solution will only track and trace the movement of the product along the supply chain without providing much additional insight.

This was the reason why we merged both databases when building GeoTraceability in 2011. A solution that guarantees the integrity of the products origin and its evolution while carrying production, contextual and impact data. In the future, farmers could receive a payment for sharing his or her data to downstream supply chain actors and other stakeholders. The farmer keeps the ownership of its data and accepts to share the information with organizations paying a fee on a data exchange. Everyone wins, a trader or brand owner does not have to support alone the investment of gathering data and complying with data privacy regulation, and farmers are incentivized to participate in the digital booming economy.

 

The great thing is that multiple organizations could access the same farmer data set and pay to use it: a trader, a brand owner, a bank, an agri-dealer, a NGO, the government, etc. This will increase the revenue streams, distribute costs and benefits among stakeholders and maintain the system."

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