Enhancing supply chain analytics with AI-powered business intelligence

Supply chains generate enormous data that can help businesses understand what’s working and not working in the current system. They can pinpoint the factors slowing down the supply chain ecosystem and offer insights to improve efficiency by plugging those gaps.

But growing data volumes, an emerging tech landscape, and a shifting geopolitical scenario render traditional Business Intelligence (BI) tools incapable of leveraging data power.

This poses a question: How do businesses utilize data to gain a competitive edge by empowering their BI solutions with AI?

In this blog, we look at how AI transforms BI capabilities and how business organizations can enhance their supply chain efficiency with AI-powered business intelligence.

AI meets BI: revolutionizing business intelligence

Business Intelligence (BI) involves collecting, integrating, and analyzing business data to support better decision-making. BI tools help business organizations gain insights from their data to identify trends, monitor KPIs, and make informed business decisions.

Adding Generative AI to this mix boosts data analysis through advanced predictive models, natural language processing and personalized, automated insights. 

This new approach automates and enhances traditional BI processes while unlocking new opportunities for data-driven decisions across industries.

What constitutes AI-powered BI? 

Insight generation: AI models automatically identify patterns, trends, and anomalies in data, providing deeper insights without extensive manual analysis.

Recommendations: They propose actionable recommendations for business decisions, such as optimizing inventory levels or targeting specific customer segments.

Automated response using NLP: Users can interact with BI systems using natural language, asking questions in everyday language and receiving responses that include data visualizations and summaries.

Narrative generation: It generates narratives and explanations accompanying data visualizations, making reports more accessible and understandable to non-technical stakeholders.

Dynamic dashboards: It creates and updates dashboards dynamically based on user queries and new data inputs, enabling interactivity and real-time analysis.

Custom reports: Generates custom reports tailored to specific user needs, including appropriate visualizations and context-specific explanations.

Predictive models: Leverages generative models to forecast future trends based on historical data.

Prescriptive analytics: Provides recommendations on the best actions based on predictive insights.

Reimagining supply chains with the power of AI-enabled business intelligence 

Let’s look at some of the functional use cases of the supply chain where AI-powered BI is used!

Demand forecasting and planning: Predictive models help ensure precise demand forecasts by accurately analyzing historical sales data and market trends. By incorporating key performance indicators such as growth patterns, behaviour trends, and seasonality, businesses can apply these models to various use cases, enabling better decision-making and more efficient resource management.

In a retail context, this can help retailers predict demand and replenish stock on time, avoiding overstocking and stockouts. Optimizing inventory management with AI helps streamline the supply chain, boost customer satisfaction, and foster repeat business.

Inventory optimization: A comprehensive view of current and in-process inventory, combined with advanced forecasting techniques, helps make demand predictions more accurate and improves inventory management.

Using time-series analysis, regression models, and machine learning, businesses can easily anticipate demand changes, avoiding problems like understocking or overstocking. This reduces inventory issues and makes better use of capital, leading to more efficient operations overall.

By analyzing demand patterns and lead times, businesses can set appropriate safety stock levels to ensure longer coverage, balance inventory levels, and enhance operational efficiency.

Supplier selection and risk assessment: Evaluating suppliers by looking at their past performance, financial stability, and reputation gives an informed approach to choosing the right partners. Businesses can understand potential vulnerabilities and make smarter, more strategic supplier decisions by considering factors like delivery reliability, financial health, and external risks such as market or geopolitical changes.

By analyzing these factors, they can uncover potential risks like delays and financial instability and choose reliable, low-risk suppliers.

Route optimization: This new approach to BI facilitates real-time analysis of traffic conditions, weather patterns, and fuel costs by integrating data from GPS, traffic monitoring systems, and weather APIs.

By continuously monitoring this information, businesses can quickly identify delays caused by congestion or adverse weather and adjust routes accordingly. Meanwhile, evaluating fuel costs in real time helps businesses adopt the most cost-effective delivery strategies, improving route efficiency and reducing operational costs.

Scenario analysis and simulation: Sophisticated modelling and simulation techniques enable businesses to anticipate and plan for various scenarios, such as seasonal demand shifts or competitive actions.

Companies can proactively adjust their strategies by analyzing trends and factors that influence demand, improving decision-making and risk management.

Customer service and satisfaction: Real-time monitoring of shipment processes, combined with natural language analysis, swiftly identify issues like inefficient transportation or inadequate packing.

This proactive approach helps address potential quality problems before they impact customer satisfaction, ensuring high service standards.

Quality control and predictive maintenance: Real-time data analysis enables continuous monitoring, allowing companies to detect potential quality issues early. By tracking key metrics like production defects, delivery times, and material consistency—and applying techniques like anomaly detection—businesses can identify and resolve issues proactively, ensuring quality consistency and timely interventions.

Assessing elements such as transportation conditions and packaging practices also empowers businesses to take corrective actions promptly, upholding high-quality standards and minimizing disruptions.

Cost reduction and efficiency: Advanced forecasting and analysis techniques optimize supply chain costs by minimizing stockouts and excess inventory risks. 
Improved route planning reduces fuel and transportation expenses, while better quality management cuts costs related to errors and delays. This comprehensive approach drives significant cost savings and enhances overall operational efficiency.

How can organizations leverage AI to elevate their BI capabilities?

Let’s explore how leading global organizations benefit from empowering their BI capabilities with generative AI.

Supply chain data analytics platform for an FMCG Conglomerate: A leading agro-industrial food production and distribution conglomerate wanted a centralized supply chain solution that would provide actionable insights and a single source of truth for data analytics.

They partnered with Nagarro to create a solution that allowed tracking the data gaps and improving data quality, ensuring increased data accuracy and the value of the insights.

The client leveraged Power BI’s AI features for interactive and intuitive reports and dashboards. They use Quick Insights to uncover trends and Q&A for intuitive visualizations using NLP queries. They witnessed improved data accessibility and improved their decision-making, ensuring tangible business gains. 

Freight analytics and optimized inventory management for a leading plywood manufacturer: A leading plywood manufacturer sought to enhance its BI capabilities with AI for optimized inventory and freight analysis.

Partnering with Nagarro, they aimed to build an intelligent solution to identify profitable and loss-making routes, empowering their sales team for strategic contract renegotiations. Nagarro developed an automated system that reduced manual freight estimation errors, generated accurate forecasts, and ensured precise freight charges for future orders.

Leveraging Qlik’s AI-driven features, including Insight Advisor, Nagarro enabled advanced analytics and intuitive visualizations to pinpoint freight variances. The solution’s NLP capabilities allowed for conversational data exploration, accelerating insights into key freight trends and optimization strategies.

Nagarro’s approach to intelligent supply chain analytics

We combined our data engineering and AI capabilities to build a solid AI-powered BI offering that enables leading brands across industries to make optimal business decisions by tapping into the true potential of their enterprise data. Our exhaustive data offering and in-house accelerator SupplyView enable us to create tailored and scalable solutions. 

What’s SupplyView?

SupplyView is a cutting-edge data analytics accelerator built to supercharge supply chain forecasting and intelligence. This robust solution simplifies cloud data warehousing and analytics with a pre-built, adaptable enterprise data model crafted exclusively for supply chain needs, serving as a unified, reliable source of truth.

With effortless integration across diverse data sources and dynamic BI dashboards, SupplyView provides deep insights into critical supply chain KPIs. It empowers businesses to save time, streamline operations, and accelerate their path to sustainable, resilient growth.

Ready to elevate your supply chain? Start exploring our AI solutions today for a smarter, more efficient future.