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Nutrition Meets Innovation
Enhancing the food industry through cutting-edge technology and innovative solutions.
About
- One of the largest and fastest growing sectors.
- Accounts for around two-thirds of India’s overall retail market.
- Currently preferences are given to organic, gluten-free, dairy-free, vegan, nongenetically modified, superfoods etc.
- Basic preferences for the overall category includes fast food (Packaged and non packaged), soft drinks, energy drinks, bakery products etc.
Problem
When such an industry is growing and expanding itself into a huge scale , many problems arise. To cater such problems one needs proper solutions. Those prominent issues are catering evolving consumer preferences, cost optimization and ultimately enhancing revenue streams. Other significant problems lie in the “lack of comprehensive data utilization for demand forecasting, inventory management, and supply chain optimization.”
For instance, in the absence of robust big data analytics, a food and beverage company may struggle to accurately predict consumer demand patterns, leading to either overstocking or understocking of products. This can result in increased waste, operational costs, and missed revenue opportunities.Traditional data analysis method fails in:
- Real time insights of Market Trends.
- Tailoring marketing campaigns to specific consumer segments.
- Enhancing revenue streams.
Solution
- Personalized Power BI solutions.
- Implementing Data Warehousing.
- BI Interactive Dashboards and Reporting.
- Predictive Analytics for Demand Forecasting.
- Sustainability Analytics.
- Real-time Analytics for Operational Efficiency.
- Machine Learning for Product Innovation.
Goal
- Supply Chain Optimization- Helping in predicting customer demand more accurately by analyzing historical sales data, market trends, and external factors.
- Data Encryption- Addressing concerns related to data security.
- Sustainability and Waste Reduction- Data analytics can be used to identify areas where waste can be reduced in the production and distribution processes.
- Operational Efficiency- Big data analytics helps in streamlining internal processes, reducing operational costs, and improving overall efficiency.
Case
A multinational consumer goods company with high revenue figures operating in 190 countries was looking to create a culture and organization which is data intelligent and can predict the future. As they were adding more channels to market it meant more trucks on ground. The challenge for them was how to optimize their shipment to ensure that they were still sending optimum loaded trucks and keeping distribution costs low
Solution
Big data analytics helped them in combining the internal data on the transportation planning and the data coming from track and trace and giving it real time visibility It also helped in forecasting the time of arrival of a truck to their customers and optimizing their shipments. Also if their truck was moving from point A to B and somewhere midcourse along the route, they got an order of another shipment, say from somewhere in the vicinity of location B. Now rather than hiring a fresh truck, they can actually reroute the current truck in such a way that it first reaches point B, unloads the goods and then goes to the location near B, picks up the goods and returns.
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