Supply Chain Data Analytics

Optimize your supply chain process with data and AI

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Streamline supply chain operations with advanced data analytics

Macroeconomic uncertainties, geopolitical strains, and market disruptions have strained global supply chains. As a result, companies are turning to intelligent supply chain operations equipped with predictive maintenance, real-time demand forecasting and route optimization. Sigmoid offers a comprehensive portfolio, including end-to-end services for data management to bring together siloed data sources and and AI/ML analytics that build connected supply chains and scale analytics in real-time. Our industry-specific accelerators powered by generative AI fuel predictive analytics for smarter decisions. With a collaborative and consulting-led approach, we customize data-driven decision-making for every stage — from procurement and logistics to inventory management.


Creating a transparent and resilient supply chain with analytics

Predictive supply chain analytics plays a critical role in helping companies minimize supply chain risks, help create granular visibility and improve inventory management. Learn more about how companies are leveraging analytics to drive sales and optimize operations.

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Supply chain data analytics

Data driven insights at every stage of supply chain operations

Operations management & monitoring

  • Leverage internal, market research data and advanced ML models that preempt market dynamics and seamlessly predict future demand at category and brand levels with high accuracy.

  • Real-time data analytics for end-to-end visibility across the entire supply chain network. This enhanced visibility enables proactive identification of potential disruptions, allowing for faster decision-making and risk mitigation.

  • Track critical metrics like on-time delivery, quality, lead times, and responsiveness to identify top performers, negotiate better terms, and mitigate supply chain risks.

Production planning & warehousing

  • Leverage sensor data analysis and ML-driven anomaly detection to prevent costly unplanned machine downtime and optimize maintenance schedules.

  • Utilize historical data, demand forecasts, and ML simulations to identify and optimize resource allocation to meet future needs and avoid potential bottlenecks.

  • Enhance inventory planning with ML to continuously evaluating and optimize reorder points and order quantities to consistently minimize carrying costs while ensuring that products are always in stock.

Fulfillment & logistics management

  • Lower fulfillment costs, improve delivery speed, and enhance the customer experience by leveraging real-time order, location, and inventory data to optimize order-picking processes and shipping strategies.

  • Preempt potential stockouts with ML and guide proactive replenishment strategies, ensuring products are available on the shelves when and where customers want them.

  • Improve carrier selection, and shipment tracking using historical and real-time data to reduce costs and maximize delivery efficiency across the supply chain.

Network and distribution management

  • Reduce delivery times, and enhance overall operational efficiency with data driven route planning by analyzing data on vehicle location, speed, and fuel consumption in real-time with IoT sensors.

  • Determine optimal replenishment quantities and distribution strategies by analyzing data from demand forecasts, inventory levels, and lead times to ensure product availability while minimizing stockouts.

  • Leverage GPS tracking, driver data, and vehicle diagnostics to optimize route planning and monitor driver performance for improved vehicle maintenance, and enhanced delivery efficiency.

Integrated KPIs dashboard for enhanced supply chain performance monitoring

Inadequate visibility into supply chain processes hampers the ability to anticipate demand fluctuations, address procurement bottlenecks, and optimize cost efficiencies. Sigmoid's supply chain data analytics solutions can equip businesses with a Supply Chain Control Tower- harnessing high-quality data, supply chain teams can gain real-time visibility into key performance indicators via intuitive dashboards across all facets of the supply chain.

Supply chain data analytics KPI dashboard

Maximizing on-shelf availability for CPGs

On-shelf availability (OSA) and the accuracy of reported inventory is a challenge which is increasingly becoming a primary focus area for a number of manufacturers. Find out how CPGs can solve pertinent problems with the quality of OSA using machine learning.

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Supply chain analytics for CPG

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Get 10% increased throughput and revenue growth!

Build a resilient supply chain with control towers and digital twins to improve operational efficiency.


Supply chain analytics enables organizations to gain insights into their supply chain processes, identify areas for improvement, and make data-driven decisions. By leveraging advanced analytics techniques, organizations can understand the performance of suppliers, logistics providers, and internal operations. It allows them to identify bottlenecks, optimize production schedules, and identify potential risks in the supply chain, such as disruptions due to weather or geopolitical events. Supply chain analytics can help you to improve customer service by providing organizations with better visibility into delivery schedules. Finally, analytics can be used to identify opportunities for cost savings by optimizing transportation routes and improving efficiency in the supply chain.

Supply chain data analytics can enhance the decision-making process by providing organizations with access to real-time data and insights. It provides greater visibility into the supply chain operations to identify patterns and make data-driven decisions. This visibility enables informed decisions on supply chain strategies, such as which suppliers to work with, what transportation modes to use, and how to optimize inventory levels. Supply chain data management leverages data to proactively identify potential risks whereas predictive analytics recommends steps to mitigate these risks. Furthermore, by analyzing delivery and customer feedback data, organizations can gain a better understanding of customer preferences and make informed decisions about delivery schedules and inventory management.

Demand planning and forecasting are key to modern supply chains as it supports key operational procedures like demand-driven material resource planning (DDMRP), inbound logistics, production, financial planning, and risk assessment. Predictive analytics in supply chain can help organizations estimate the expected demand for products, plan their production schedules and procurement activities accordingly. It reduces the likelihood of overstocking or understocking, saving higher storage costs and inefficient use of resources. It also improves efficiency of supply chain operations and inventory planning by building ML models that identify shifts in consumer behaviour and enable SKU-level optimization for eCommerce.

AI-powered supply chain helps in inventory optimization by providing real-time data and insights into inventory levels and demand patterns. Analyzing data such as historical sales trends, lead times, and supplier performance can help make data-driven decisions to optimize the inventory levels. This includes setting appropriate safety stock levels, identifying slow-moving or excess inventory, and forecasting demand to ensure that inventory levels are sufficient to meet customer demand.

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Get 10% increased throughput and revenue growth!

Build a resilient supply chain with control towers and digital twins to improve operational efficiency.