Agri-Food Supply Chain

Digital Challenges Faced by the Enterprises

In this article I will cover the data and decision making challenges faced by the businesses in the agri-food supply chain. Typically, these businesses are the farming enterprises, aggregators/intermediaries, and retailers/food processors in the supply chain. Farmers are at the upstream end of the supply chain who produce the food and we consumers are at the downstream end who consume the food either directly in a fresh form or in a processed form. There are other usages of the crops as well but for the sake of simplicity I am not covering it here. 

Let’s take a step back and look at our world. The climate change is real and food production systems have an impact on the environment. We at 1Point5, believe the world's food supply must become sustainable and sustainability encompasses both efficient use of natural resources and financial viability of the system. We also believe digital tools provide access to the information helping businesses take informed decisions and giving them the power to achieve sustainability goals.

Many businesses in the agri-food industry recognise the potential of data as a competitive advantage, driving innovation and generating business value. We also think that the businesses who are able to harness the opportunities that come with the digital transformation will be the winners of the future. 

However, many businesses struggle to extract true value from their data because:

• the data are often siloed; 

• the data is in different formats and unconnected across disparate systems; and

• The concept of “interoperability” across multiple software systems in agriculture is non-existent. 

The lack of end to end data integration across multiple systems, makes effective analysis from such data sources time consuming and expensive. 

In addition to the challenges in data analysis, the major retailers in the UK/Europe are committing to the Net Zero standard. The businesses having a software and data tooling will be in a position to show the carbon footprint of the supply chain transparently - to growers, retailers as well as the consumers. Others who do not have such tooling are going to struggle with it. 

So, what are the examples of the data silos. Let’s take a farmer use case - they typically use following:

• A farm recording software like Gatekeeper or MuddyBoots in the UK;

• Some software system provided by their tractor and/or combine manufacturer like JDLink;

• Weather forecasts or the weather station/sensor data feed like Davis or Pessl;

• Satellite or drone images and associated crop parameters;

• Precision Ag or decision support tools for yield prediction, pest and disease management like NIAB CUF model, Crop4Sight;

• On-line marketplace or a platform to market their produce;

• Financial/accounting data like Xero, QuickBooks; and

• Banking/fintech apps.

The enterprises in the supply chain have a dual challenge of data “integration” and “aggregation” across all of their farmers (suppliers). They typically have:

• On-farm data, aggregated from farm recording software systems for each of their supplier and their fields;

• Monitoring of their supply chain and risks - many a times this exists on paper or in Excel;

• Short term/medium/long term weather forecasts and historical weather datasets; 

• Financial and operational data from ERP systems like SAP, Oracle Financials or MS NAV;

• Quality data (in-field sampling as well as intake QC) - some of this can reside in the ERP system;

• Inventories - both field and store;

• Demand data from retailers/processors used to plan the loads or finalise contracts;

• Trading platform; 

• Logistics and transportation; and 

• BI tools - having access to business role specific Dashboards to take better, informed decisions. 

Agriculture is vast, heterogeneous and digitising the entire agri-food supply chain is a no mean task. These enterprises are still spending lot of time collecting this data using pen/paper and/or Excel, thus, making the procurement and grower management process administratively burdensome.

This article is purely to put out these challenges and emphasise the importance of a data integration so that enterprises can take informed decisions based on the data.