How to optimize your food supply chain, without an army of data scientists

This topic will be addresed March 18 as part of Feedstuffs 365 programming for the day.

Over the last decade the food and agriculture industry has seen a virtual explosion of information and data from sensors and IoT devices. There are satellite images of fields, real-time GPS information from carriers, the latest seed data, reams of historical harvest and commodities trading feeds, plus sensors relaying information at (almost) every stop along the supply chain path, from growers to consumer insights at retail shelves. This wealth of information provides better supply transparency and the ability to develop new business insights. While the good news is that we have an abundance of data like never before, the bad news is that we can’t hope to process it in a meaningful fashion. In theory, this wealth of data should result in better decisions, but for many organizations the data can be overwhelming, and the decision complexity is increasing. The classic paralysis through analysis.

Data overload is an old problem. Unfortunately, many of the standard technological solutions – such as data warehouses, are necessary but insufficient. As a result, organizations have turned to a more traditional solution: people. Defining a problem, making a decision, and formulating a solution that is impactful and makes use of the firehose of information, has become the realm of the data scientist. The new problem for many enterprise companies has been the cost and challenge of finding, hiring, and keeping qualified data scientists with the combined technical and industry knowledge. As their success in an organization grows, these specialist resources can become overloaded and turn into a massive bottleneck for innovation and growth. That’s the best case. Most food and agriculture companies simply can’t acquire data scientists they need. Many companies have fallen back to hiring AI consultancies or buying and adopting standardized package solutions that embed such approaches.

Because of this, organizations do the best they can to make sense of the data – using existing technology, and people at hand. Most firms can only focus on getting near-term answers rather than true insights and optimization.

For example:
Preliminary focus: When should I harvest a field crop?

True Insight: What seeds should I plant & where, to maximize the value of my acreage?

Preliminary focus: Which trucks are under-utilized?

True Insight: What is the best load-plan to fully utilize all my trucks?

Preliminary focus: How do I store my raw materials to maintain ingredient integrity?

True Insight: What sequence and quantity of materials should I maintain to optimize results?

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