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Agrifood Chain: Using data and digital twins to simulate scenarios

Between July and October 2021, torrential rains drenched northern parts of China, flooding major agricultural provinces such as Hebei, Henan, and Shandong. The floods affected millions of acres of cropland, causing billions of dollars in food losses and greatly increasing prices for consumers.

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Extreme weather and the pandemic have caused fertilizer prices to increase, adding to global inflationary pressures. The ongoing conflict in Ukraine is further compounding the situation, with the Food and Agriculture Organization of the United Nations stating that the global supply gap from potential shortfalls in agricultural exports from Russia and Ukraine could raise international food and feed prices by more than 20 percent.

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To respond to agricultural supply chain shocks caused by these kinds of notable events, agricultural companies can consider implementing the next generation of end-to-end supply planning. This approach involves the integration of data sources to enable real-time monitoring; simulation to produce various supply chain scenarios; deployment of appropriate optimization algorithms for real-time responses to shocks; and full automation of the first three steps to produce automatic response systems.

Data integration and real-time monitoring

Providing seamless, real-time access to data relevant to stakeholders involved in supply chain planning is a key consideration on the journey toward automated response systems. While internal company data sets may offer reliable information on logistics, transactions, and inventory, many organizations lack deeper visibility throughout their entire supply chain due to underutilization of the vast amounts of freely available external data.

Integrating public and private data sources may be a powerful foundation on which to mimic reality. Models of the real world are often referred to as “digital twins” of an organization’s physical supply chain. With the right data, digital twins could include all key elements of the supply chain, from farm-level production and harvesting to inventory and points of sale. But digital twins may also be built for specific supply chain stages where visibility may currently be low.
Take, for example, a digital twin of global corn exposure to notable anomalies. Climate anomaly insights (for example, “rainfall in this region is higher than it has been in the past 25 years”) combined with text-based insights from news outlets (for example, “the number of articles related to flooding or inundation in this region has increased 60-fold compared with the past five years”) and satellite-derived field information (“one million hectares of corn are grown on 50,000 individual fields in the affected region”) could provide insights on the types of supply chain disruptions that may be likely to occur further downstream.

Using digital twins to simulate scenarios

Digital twins of physical supply chains provide a strong basis for simulating a variety of scenarios, such as changes in climate or government policy.

Take crop yield forecasting, which is the art of predicting crop yields and agricultural production before the harvest actually takes place—typically a couple of months before the end of a growing season. Fluctuations in crop yields have implications for downstream activities such as harvesting logistics and inventory planning.
Simulating crop yields could be achieved via mechanistic models (in which future plant growth is predicted based on underlying processes such as photosynthesis and respiration), as well as by deep learning–based approaches (which aim to predict yields based on high-resolution satellite imagery).

After having simulated different scenarios related to crop yields or factory outages, organizations can choose from a range of optimization methods that can help them make informed decisions on topics such as how to best reroute trucks around flooded areas or how to determine appropriate inventory levels.

In addition to optimizing for cost-related factors, optimization models could incorporate rigorous sustainability constraints. For example, one company that was engaged in sourcing crop residues (such as straw) from grain crops applied constraints to promote soil health, including leaving enough residue on farms to return nutrients to the ground.

Choosing the appropriate approach may depend on factors such as data availability, cost, and the complexity involved in implementing optimization models. More advanced optimization approaches capable of handling uncertainty may allow better decisions to be made based on information updates from sources such as satellite imagery or news text.

Automating supply chain decisions across the entire organization

While the level of automation may vary depending on the type of agricultural supply chain, there are prerequisites for operating a technology-driven supply chain planning system that is capable of identifying and driving potential evaluation:

  • Easily accessible real-time updates. This requires strong integration of various data feeds and delivery of real-time changes in the appropriate format.
  • Coordinated execution management. Planning and execution of responses should be coordinated within the planning system.
  • Change management and trust. Company employees must be able to trust the system, and mindsets and behaviors may need to be adapted to promote system use.
  • Technology capabilities.
  • Rapid error handling. Automated systems should identify and automatically correct deviations from expected values.
  • Real-time updates of projections. Automated systems should be able to produce updated projections for supply, demand, and inventory—as well as resulting costs, shortages, and stock levels—at appropriate time intervals.
  • Prescriptive decision response. The system should have an intelligent engine that can produce optimal recommendations based on trade-offs and can plan for adjustments to deal with problems such as sudden supplier shortages, quality issues at a site, sudden demand spikes, and unexpected orders.

Building automated planning systems that can handle high levels of uncertainty could potentially lead to improved preparedness against food security risks and higher cost savings for organizations affected by global supply chain shocks.