Innovating in Food Supply Chain: Big Data and Optimization Based Decision Making

Innovating in Food Supply Chain: Big Data and Optimization Based Decision Making

big data agriculture bThe use of big data as a strategic tool is increasing. And it’s no different in the food industry. As gathered in an article published in Innovation Excellence, 60% of the senior corporate executives from Fortune 1000 companies reported in a 2013 survey that they had recently implemented at least one big data initiative in their companies. Within the food supply chain, big data is gaining ground in areas such as security and traceability management, customer service, or production improvements. An analytical approach to the different sources of information that a company handles allows them optimization-based decision making, which in turn leads to revealing opportunity areas to innovation and an improved and sustainable supply chain. Here are examples of how big data is impacting food businesses.

 

More safety and better traceability

The latest years’ news on food frauds as well as different research about food contamination has proved how difficult and time consuming the traceability of food sources and the confirmation of food product authenticity are. SAP and Tru-ID, a new member of the SAP Startup Focus program, are working on DNA-based verification testing and product authenticity certification to introduce more transparency and accuracy into the food supply chain, helping companies identify the source of the adulteration among their suppliers. This tool shall permit food companies to increase the quality and safety of their products while reducing risks of contamination.

In a similar line of work, IBM has created a system that automatically identifies, contextualizes and displays data from multiple sources to determine contamination provenance, as indicated by the company in this article.

 

More sustainable agricultural systems

One of the companies that have made positive steps in this line is Intel. Working around the concept of precision farming, they want to create an accessible and reliable platform for worldwide scientists. As stated in this information published by TechRepublic, Intel is collaborating with academia and research institutions for applying big data analytic solutions to significant and world challenging problems.

agriculture bPrecision farming aims to increase the world’s food supply by measuring and responding to field variability for crops. The tools for doing so are sensors to monitor the crops, the implementation of smarter farm machines, cloud technology, GPS, webs compiling useful information, and the creation of specific software to analyze the collected data. This can lead to the increasing the farming capacity or a better use of hydraulic resources or risk related to weather conditions assessment, for instance.

A good example is Agralogics, the so-called “the Internet of food”. This software combines weather data, satellite images, and other operational insights to help schedule harvests and provide information for those growing or consuming foods, among other things.
Apart from Intel and Agralogics, Monsanto, Dupont Pioneer, John Deer and small farmers in different parts of the world are moving towards a data-based agriculture as well. They look for trust in data aggregation and predictive analytics, as mentioned in this other article published by Tech Republic.

Also IBM has put its confidence in the power of big data to create a more sustainable food value chain. They talk about smart food and, as referred in this post, they have their own project regarding the big data agriculture tendency: Deep Thunder by IBM Research. “Measurements of the weather and soil, including data from sensors dotting a farm, multi-spectral images of fields taken from satellites or airplanes, characteristics of irrigation systems, requirements for fertilizer and pesticide coupled with precise weather predictions can help optimize a farmer’s decisions about what to plant, when to plant, when to water, when to fertilize and when to harvest”, they say.

 

Better customer services

Under a completely different perspective, McDonald’s resorted to Big Data to change their organization into a more information-centric one that makes data-driven decisions. Since this decision was taken, McDonald’s tracks and analyzes vast amounts of data to better understand what is happening in their restaurants and identify and implement the best practices overall in their food establishments. This way, they are able to improve their company as well as their customers’ experience.

They started creating multidisciplinary leading teams to, finally, extend to the whole organization their data strategic model.

 

These examples show how more and better-quality circulating information can help to connect different areas of the food supply chain, making it shorter and more efficient, or improve specific links.

1Comment
  • Future fields: big data applications in precision agriculture - allfoodexperts®
    Posted at 13:38h, 11 November Reply

    […] Nowadays, the main challenges of food sector concerning farming are reducing waste, optimizing production and minimizing the use of precious resources such as water. Therefore, traditional agriculture processes are becoming insufficient for the future market. And analytics are key to improve the results and rethink the agricultural supply chain. […]

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