314.51 million tones.
That’s the total foodgrain production in India from 2021-to 22, according to this recent report by the Ministry of Agriculture, Government of India. That’s an increase of 3.77 million tonnes compared to the previous year.
On the one hand, the country is witnessing an increase in foodgrain production, while on the other hand, India ranks 101st out of 116 countries on the 2021 Global Hunger Index. With a score of 27.5, India has a severe level of hunger. This rank can be attributed to many factors like economic distress, unemployment, lack of accessibility to food, and a broken public distribution system. While many of these factors are economic factors, one of the significant contributors to hunger in India is an inefficient agri-input supply chain.
The agri-input supply chain includes the distribution of fertilizers, seeds, additives, cleaning agents, and machinery to end consumers – the farmers. Various stakeholders are a part of the agri-input supply chain ecosystem – manufacturers, distributors, retailers, and consumers. Therefore, many stakeholders necessitate the need to make prompt data-driven decisions. This can only be done by incorporating technology into supply chain management. So, let’s discuss the top technology strategies we can implement to improve the supply chain inefficiencies in the agri-input supply sector.
Top technology strategies to improve the agri-input supply chain efficiency
1. Using IoT, blockchain, ICT, AI, and machine learning for traceability: The full potential of technology such as AI, ML, ICT, and IoT needs to be harnessed to generate sustainable productivity and authenticate genuine agri-input products. These technologies also help predict scenarios well in advance using the power of data analytics and automation.
For example, using the supply chain visibility solutions provided by Data for Decisions, the manufacturers are able to clearly identify the dead stock at every stakeholder level in the agri-input supply chain. The embedded AI capabilities in the solution further help with inventory optimization.
2. Combating counterfeit agri-inputs: According to this report, the Indian agriculture supply chain is flooded with 30% counterfeit pesticides by volume. This has led to several negative impacts such as – loss of harvest, contamination of the environment, deterioration of flora and fauna in the surrounding vicinity, health risk to humans, and exports getting affected to the international market.
Such a grave issue was combated with ease using technology by Tanzania, according to this report by ResearchGate. The technology-enabled the stakeholders in the supply chain management to authenticate the genuineness of agricultural inputs. Similarly, DforD’s AI-powered agriculture input supply chain solution helps easily detect and invalidate counterfeit agriculture input products. The powerful patented monitoring system and anti-counterfeit technology help increase the value of the agri-input supply chain by using QR codes. The QR codes track each input item by providing insights into the item’s journey – from the manufacturer’s plant to the end-user, i.e., the farmers.
Source: Data For Decisions
You can learn more about how Data For Decisions combats counterfeiting.
3. Smarter and effective decisions based on insightful data: Imagine this. The manufacturer of a particular seed, say brinjal, can supply the farmers well before the cropping season begins. This minimizes last-minute demand-supply alignment and reduces cost. Usually, the agri-input supply chain functions using traditional inventory techniques. Unfortunately, this leads to demand-supply mismatch, less competitiveness, and decreased productivity. But using technology as an enabler, manufacturers, distributors, and retailers can access data and actionable insights well in advance. This leads to increased productivity and better logistics and storage planning.
Source: McKinsey
4. Planning better to fight contamination of agri-inputs: Poor logistical and storage planning leads to contamination of agri-inputs. This affects the environment and leads to environmental hazards. The cost is not just ecological but also affects the health of end consumers. Such contamination can be contained by enabling smooth exchange and sharing of information. Also, by digitizing the supply chain data, we can enable quick and prompt decision-making, leading to more value creation in the agriculture ecosystem.
Conclusion
The major challenges confronting Indian agriculture include unsustainable usage of resources, declining farm productivity, rapidly growing demand for high-quality and safe food, stagnating farm incomes, and fragmented land holdings. These can be overcome through data and insights gained via the sustainable and scalable deployment of digital technologies and infrastructure in agriculture supply chain management.
Cutting-edge solutions by Data for Decisions is a sincere effort to improve the supply chain inefficiencies in the agri-input supply sector.
