Supply Chain Analytics: How It Works for Business 

Supply chain analytics is becoming a necessary tool for modern businesses that are interested in streamlining their operations, reducing costs, and gaining meaningful insights into every facet of their supply chains. For demand planning, inventory management, or any other activity, businesses can use advanced data analysis techniques to optimize efficiency, make better decisions, and keep up with the competition.

What is Supply Chain Analytics? 

Supply chain analytics refers to the use of advanced data analytical techniques to monitor, optimize, and predict processes within an enterprise's supply chain. These include procurement, warehousing, inventory control, transportation, and demand planning. By analyzing data for all these processes, businesses can make evidence-based decisions to improve performance, reduce costs, and make operations run smoothly. 

Based on research, cutting supply chain costs by 50% can double the profits of a company. Thus, the adoption of effective supply chain analytics is crucial for companies to stay competitive or become more competitive. 

Key Types of Supply Chain Analytics 

Supply chain analytics helps businesses make informed decisions by analyzing data to improve efficiency. The key types of supply chain analytics are descriptive, diagnostic, predictive, prescriptive, and cognitive, each serving a unique role in optimizing operations. 

Descriptive Analytics 

Descriptive analytics gives information about what has occurred in the past by aggregating past data into reports or visualizations. It provides answers to questions such as "What is our current inventory level?" or "How did our sales perform last quarter?" Descriptive analytics assists companies in comprehending trends, recognizing patterns, and examining their past performance. 

Diagnostic Analytics 

Diagnostic analytics is beyond descriptive analytics because it examines the "why" of what occurred in the past. It helps firms identify the root causes of issues, such as delayed shipments, inefficiencies, or inventory shortages. By understanding the reasons behind certain outcomes, firms can take remedial measures to perform better in the future. 

Predictive Analytics 

Predictive analytics applies statistical models and machine learning to forecast future trends and requirements. It predicts "what will happen" based on analysis of what has occurred, for instance, how consumer demand may shift in the coming months. This enables businesses to plan and prepare ahead of time for possible disruptions or opportunities. 

Prescriptive Analytics 

Prescriptive analytics provides actionable guidance based on predictive conclusions. Using optimization techniques, it suggests the best course of action for businesses, e.g., the best stock levels, the best routing for delivery, or the best suppliers to work with. Prescriptive analytics is designed to improve decision-making for improved supply chain performance. 

Cognitive Analytics 

Cognitive analytics applies AI and machine learning to analyze difficult, big data sets. It finds hidden patterns and trends that may not always be made transparent using more traditional methods. It helps organizations make better, fact-based decisions and automate processes for more efficiency and accuracy. 

How Supply Chain Analytics Works

Supply chain analytics helps organizations optimize their operations by analyzing data from various stages of the supply chain. It involves collecting, processing, and integrating data to provide valuable insights that drive smarter decision-making. 

Data Collection & Integration 

Effective supply chain analytics starts by collecting comprehensive data from all segments of the supply chain. That data can be collected from inside systems such as ERP and SCM or from outside vendors, logistics firms, and buyers. The collected data can either be structured (e.g., inventory levels, order data) or unstructured ( e.g., customer reviews, social media mentions). 

Once the data is collected, it gets consolidated into a single data repository, like a data warehouse, a cloud-based data warehouse, or a data lake. With integration, companies have a complete picture of their supply chain processes and are able to make decisions based on data. 

AI, ML & Automation in SCM 

Supply chain analytics are pioneered by artificial intelligence (AI) and machine learning (ML). These technologies help automate processes, process large amounts of data, and provide real-time analysis of supply chain activity. Machine learning algorithms can be used to predict demand, identify risks, plan routes, and even identify anomalies such as fraud or inefficiency. 

Automation of supply chain operations also leads to lower labor costs, greater accuracy, and faster decision-making. Companies can react quickly to changes in the market as well as prevent probable disruptions. 

Real-Time vs. Batch Processing 

Supply chain analytics can operate either in real-time or batch processing modes. Real-time processing provides instant results so that companies can make quick decisions in response to real-time occurrences. This is particularly beneficial in dynamic supply chains where fast decision-making is necessary to ensure smooth operations. 

Batch processing, on the other hand, involves the processing of volumes of data on a periodic basis (e.g., on a daily or weekly basis). Even though it's not as quick as real-time insights, it allows firms to examine trends over a longer period, expose patterns, and base strategic business decisions on complete data. 

Top Benefits for Businesses

Supply chain analytics offers businesses numerous advantages, including cost reduction, improved efficiency, and enhanced decision-making. By leveraging data-driven insights, companies can optimize their supply chain processes and deliver better value to customers. 

Cost Reduction & Efficiency 

Supply chain analytics allows firms to reduce costs and enhance efficiency by defining inefficiencies and streamlining operations. Through optimizing the inventory level, eliminating excess inventory, and optimizing logistics, firms are able to reduce operating costs and increase profit margins. 

Risk Mitigation & Resilience 

Predictive analytics helps businesses to foresee possible disruptions and risks, such as transport delays, supply shortages, or changes in the market. By identifying these risks early enough, businesses can take preventive measures to minimize the impact and create a stronger supply chain. 

Increased Customer Satisfaction 

Supply chain analytics increases customer satisfaction by guaranteeing timely delivery and appropriate quantities of products. Through the optimization of the level of inventory, firms can avoid stockouts, backorders and offer a consistent service to the consumers. 

Sustainability & Efficient Use of Resources 

Supply chain analytics supports sustainability by promoting the efficient use of resources, reducing waste, and minimizing environmental impact, helping businesses meet regulatory standards and consumer expectations while also lowering costs. 

Implementation & Best Practices 

While the benefits of supply chain analytics are clear, establishing a strong system will not be easy. Companies need access to quality data, appropriate tools, and seasoned experts to decode and interpret data appropriately. Fulfilling these could be possible with clear-cut goals, data unification, and a sound strategy in place for leveraging analytics throughout the supply chain. 

Choosing the right tools 

In selecting supply chain analytics tools, businesses should look for platforms that handle large sets of data, provide real-time feedback, and possess sophisticated predictive and prescriptive capabilities. Integrated tools with existing infrastructures (e.g., ERP, SCM) will provide a seamless experience. 

Measuring ROI 

To measure the ROI of supply chain analytics, firms need to track changes in key performance indicators (KPIs) such as inventory turnover, order fulfillment accuracy, and cost savings. Firms can determine if their analytics initiatives are yielding real benefits by tracking these metrics on a continuous basis. 

Future Trends Influencing Supply Chain Analytics 

As technology advances, supply chain analytics continues to evolve, bringing in smarter, more transparent, and sustainable practices. These future trends are shaping the way businesses manage and optimize their supply chains for long-term success. 

Blockchain & Transparency 

Blockchain technology is helping to increase supply chain transparency by providing secure and tamper-evident records of product flow and transactions. This instills trust among stakeholders and makes goods traceable end-to-end across the supply chain. 

IoT & Smart Supply Chains 

The Internet of Things (IoT) is enabling intelligent supply chains by connecting physical devices, such as sensors and RFID tags, to the internet. This provides real-time data on inventory, shipments, and equipment performance, improving visibility and decision-making. 

Sustainability Analytics 

Sustainability is increasingly becoming a key area of focus for supply chain management. Organizations are using analytics to reduce carbon footprints, increase energy efficiencies, and implement more sustainable practices throughout their supply chains. 

Industry-Specific Usage 

Different industries leverage supply chain analytics to address their unique challenges and improve performance. From enhancing customer experience to ensuring the timely delivery of critical supplies, analytics supports smarter, data-driven decisions across sectors. 

Retail & E-Commerce 

Supply chain analytics for the retail and e-commerce sectors maximizes inventory levels, rationalizes order fulfillment, and improves customer satisfaction. Customer buying patterns and demand patterns-based supply chain analytics place the right products at the right moment. 

Manufacturing 

Supply chain analytics helps manufacturers in better demand forecasting, better supplier performance monitoring, and efficient production planning. Supply chain analytics reduces lead times, minimizes waste, and maximizes overall manufacturing productivity. 

Healthcare 

Supply chain analytics is crucial in the healthcare industry for ensuring the timely availability of medical equipment and supplies. It also maximizes the delivery of drugs and optimizes inventory management, leading to better patient outcomes and reduced costs. 

Transform Your Supply Chain with TurnB’s Smart Analytics 

Supply chain analytics is a game-changer for businesses when looking to increase efficiency, reduce costs, and stay ahead in an ever-changing market. With the right tools and strategy, businesses can unlock the true potential of their supply chains, make data-driven decisions, and build more resilient operations. 

At TurnB, we specialize in providing data-centric analytics solutions that empower companies like yours to streamline supply chain operations and gain valuable insights into every activity of your logistics and operations. Let's transform your supply chain with real-time analytics that deliver results together.