How supply chain analytics can change your agri & food perspective
From retrospective to predictive, proactive and preventive
New technologies and applications, such as supply chain analytics, have a lot to offer to the agri & food industry. We present four applications of data analytics and related technologies that both contribute to business success and to solutions for urbanisation and the growing world food crisis.
Patrick Schunck and Naser Bakhshi, 12 December 2017
Data analytics, and more particularly supply chain analytics, is one of the most promising technological offerings for the agri & food industry. However, many businesses in this industry’s supply chain are not yet fully aware of its possibilities. When it comes to analytics, they will mostly use data from a retrospective point-of-view, whereas analytics can also be predictive, leading to proactivity and prevention. Based on public data (Big Data), your own data, and scenario analysis and planning, it is possible to make detailed forecasts.
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From ‘educated guess’ to real-time forecast
For instance, a large agricultural company needed demand & supply forecasts for all their products, on a monthly and quarterly basis. Yet their forecasts were always either too positive or too conservative. Deloitte discovered they were largely based on an ‘educated guess’ by the sales department and regional managers, rather than on actual data. So we built a fact-based forecasting model within 6 weeks’ time, using historical and other relevant data and supported by 6 forecasting methods, which can do near real-time forecasts for 3, 6 and 12 months ahead.
Traceability and sustainability
Data analytics and related technologies can also help to face one of the big challenges in agri & food: consumer-led disruption. Consumers are increasingly demanding detailed product information: is it healthy, organic, locally produced, and safe - in other words: is it sustainable and traceable? To guarantee the integrity and traceability of products, the blockchain technology can be applied. This is a digital, distributed transaction ledger with identical copies maintained on each of the network’s members’ computers. The links between blocks and content are protected by cryptography, so previous transactions cannot be destroyed or forged.
Optimising the food supply chain
Another useful application of data analysis is precision agriculture. Precision agriculture is a method for optimising crops, based on observing (e.g. drones), measuring and responding to inter- and intra-field variability in crops, with variables such as crop yield, terrain features, moisture levels, and nitrogen levels. One of its enablers is the Internet of Things. The data that is gathered is used for e.g. seeders and sprayers. Precision agriculture decreases waste on one side of the food supply chain. Along with solving waste in other parts of the food supply chain (e.g. end-users), this could actually end the growing world food problem.
While we are facing today’s challenges in agri & food, new ones glimmer on the horizon, such as the rise of the megalopolis. It is expected that in 2050, more than 60% of the global population will live in urban areas. This means the workforce in agriculture will shrink dramatically, which could threaten crop yield. However, we expect that new technologies, such as robotics and artificial intelligence, combined with new concepts such as vertical farming, bioprinting and plant-based meat alternatives, will solve this problem as well. Peter Diamandis' blog on disruptions in agri & food provides more insight in this.
The key is to stay informed, innovative, and forward-thinking – turning the potential of data analytics and other technologies into actual benefits.
More information about supply chain analytics?
Do you want to know more about supply chain analytics in agri & food? Please contact Patrick Schunck at +31 (0) 88 288 1671.
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