Let them Grow!

Border closures, quarantines, and other sanitary measures restrict people’s access to sources of food, especially in countries hit hard by the virus or already affected by high levels of food insecurity. Food production faces an unprecedented challenge showing several simultaneous features: 

  • Fall of wholesale demand from large buyers such as hotels and restaurants, 
  • Flight to quality resulting in high demand for branded and premium food,
  • Difficulties in maintaining production capacity due to worker shortage,
  • Disrupted global supply chains and logistics systems: for example, the price of transport to China has increased as containers had to arrive empty in Europe due to the lack of goods to export to China.

 

Appellation d’origine confinée

UK chartered workers from central and eastern Europe. In France, authorities relaxed product quality policy (involving labels and registered designations of origin – AOC) for the production of famed cheeses such as Bleu d’Auvergne and Comté, to limit milk surpluses at a time when production reaches its seasonal peak. In Ontario, some 500 farms have been asked to dump 5 million litres of milk a week to maintain prices..

Food and Beverage companies take action to maintain production levels. Danone, and Nestle have announced they would not reduce staff, pay salaries in full even where factories have ceased to operate, while Lactalis is actively looking for new hires. « The minute we saw this crisis spread we were trying to improve inventory levels at all levels of manufacturing: […] input materials, products in the working process, and also finished goods. » said Mark Schneider, Nestlé’s CEO.

Big data technologies and predictive algorithmics can help better manage the need for stockpiles in industries used to just-in-time, especially for perishable goods. According, to a report by McKinsey, “Retailers that use machine-learning technology for replenishment have seen its impact in many ways—for instance, reductions of up to 80 percent in out-of-stock rates, declines of more than 10 percent in write-offs and days of inventory on hand, and gross-margin increases of up to 9 percent.” The same recipe could bring the same benefit up in the production chain.

 

Data for food

Agriculture faces a bigger challenge for the years to come: how can the demand for food cope with 2Bn more people, and a larger share of them requesting access to sophisticated meals,  while also preserving the environment ?

Data analysis can help. More and more, farmers are becoming process experts who need to take a lot of variables into account, such as weather, soil moisture levels and nutrient content, and threats to the health of his crops. With the right tools and algorithms he can optimise his yield and maximise efficiency, but he needs data…

John Deere, the world’s largest manufacturer of agricultural equipment decided 20 years ago to fit all its mobile machines with (GPS). Today the company has more than 150 people working on “Data”. More is needed: from Tulips in the Netherlands to poultry in Singapore, agriculture is following other industries in setting itself apart from soil constraints by bringing all necessary ingredients in places where space, light (provided by custom designed LEDs), water and nutrients are finely controlled to optimize yield.

Disruption may also come from artificial meat where (yet-to-be-reached) economies of scale could also help temper the environmental impact of the highly desired protein-rich diet of the riches.

 

Winter is coming

The process of understanding how our eating habits respond to the Covid shock is only starting and the impact the economic crisis and continued lockdown may have on our diet is yet to be evaluated. But better data management and analytics models are a game changer to better navigate the crisis. According to a report by McKinsey : “A large, diversified industrials player followed this playbook and drove $500+ million in savings for its manufacturing and R&D functions by using advanced analytics to quickly identify opportunities and inefficiencies across sites.”

Looking to make better use of data to find quick wins in the food processing value chain ?


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