Supply chains generate massive amounts of structured and unstructured data, which, when used efficiently, can enable organizations to gain intelligent, actionable insights. Traditional supply chains, that do not make use of data analytics are siloed and slow-moving, with no data being exchanged between processes. This setup calls for a host of issues like poor logistics efficiency, limited transparency, high inventory holding costs, poor forecasting leading to stock-outs.
The use of supply chain analytics enables organizations to gather, analyze, and act upon the data generated by their supply value chains, and rid themselves of all these issues and create an agile, transparent, robust supply chain organization of the future to combat uncertainties.
What is Supply Chain Analytics?
Supply chain analytics is the ability to bring data-driven intelligence to the end-to-end supply value chain—planning, sourcing, manufacturing, delivering—reducing inefficiencies and improving productivity across processes.
Here are key aspects of what supply chain analytics entails:
- Demand Forecasting and Planning: Utilizing historical data and predictive analytics to forecast demand accurately. This aids in planning inventory levels, production schedules, and distribution strategies.
- Inventory Optimization: Analyzing inventory levels, turnover rates, and lead times to optimize stock levels. This minimizes holding costs while ensuring products are readily available to meet demand.
- Supplier Performance Analysis: Assessing the performance of suppliers based on various metrics, such as on-time delivery, quality, and adherence to contractual agreements.
- Cost Reduction Strategies: Identifying areas for cost reduction through data analysis. This may involve analyzing transportation costs, production inefficiencies, and procurement expenses.
- Risk Management: Employing analytics to identify and mitigate potential risks within the supply chain. This includes assessing geopolitical, economic, and operational risks.
- Real-Time Visibility: Implementing analytics tools that provide real-time visibility into the entire supply chain. This enhances responsiveness and allows for proactive decision-making.
- Performance Metrics Monitoring: Establishing key performance indicators (KPIs) and monitoring them regularly to evaluate the overall efficiency and effectiveness of the supply chain.
- Logistics and Route Optimization: Optimizing transportation routes and logistics operations through analytics. This minimizes transit times, reduces fuel costs, and improves overall logistics efficiency.
- Customer Satisfaction Enhancement: Analyzing customer feedback and order fulfillment data to enhance customer satisfaction. This includes improving order accuracy, reducing delivery times, and enhancing overall service quality.
- Continuous Improvement Initiatives: Implementing a data-driven approach for continuous improvement by identifying areas for refinement and innovation within the supply chain.
In essence, supply chain analytics empowers businesses to navigate the complexities of their supply chains, fostering efficiency, reducing costs, and ensuring a responsive and adaptive approach to the dynamic landscape of supply chain management.
Types of SCA and how it works:
- Descriptive Analytics:
- How it Works: Descriptive analytics involves examining historical data to understand past performance and identify trends. Businesses use this type of supply chain analytics to gain insights into factors such as inventory levels, demand patterns, and overall operational efficiency. By visualizing and summarizing historical data, businesses can make informed decisions based on a comprehensive understanding of past performance.
- Predictive Analytics:
- How it Works: Predictive analytics utilizes statistical algorithms and machine learning to forecast future trends and outcomes. In the context of the supply chain, businesses can predict demand fluctuations, identify potential risks, and optimize inventory levels. By analyzing patterns and correlations in data, organizations can proactively address challenges and capitalize on opportunities.
- Prescriptive Analytics:
- How it Works: Prescriptive analytics goes beyond predicting outcomes to provide actionable recommendations. In the supply chain, this involves suggesting optimal courses of action to improve efficiency and address challenges. By leveraging prescriptive analytics, businesses can make data-driven decisions to enhance overall supply chain performance.
- Diagnostic Analytics:
- How it Works: Diagnostic analytics focuses on understanding the root causes of specific outcomes or issues within the supply chain. By analyzing data to uncover the factors contributing to successes or failures, businesses can identify areas for improvement. Diagnostic analytics is crucial for addressing underlying issues and refining processes.
These four types of supply chain analytics collectively empower businesses to extract valuable insights from their data, enabling them to optimize processes, reduce costs, and enhance the overall efficiency of their business supply chain. By strategically applying these analytics types, organizations can proactively navigate the complexities of the supply chain, staying agile and responsive to market dynamics.
Key features of an effective supply chain
- End-to-End Visibility:
- Effective supply chains prioritize comprehensive visibility across all stages, from raw material sourcing to product delivery. This visibility, often facilitated by supply chain analytics, enables businesses to proactively manage and optimize each step, minimizing disruptions and enhancing overall efficiency.
- Collaborative Relationships:
- Successful supply chains foster strong collaborative relationships with suppliers, distributors, and other stakeholders. Collaboration ensures seamless communication, timely information sharing, and the ability to collectively respond to challenges, ultimately promoting agility and resilience.
- Data-Driven Decision-Making:
- An effective supply chain leverages data and analytics for informed decision-making. By utilizing real-time data, businesses can make strategic choices related to inventory management, demand forecasting, and resource allocation, optimizing operational outcomes.
- Agile Responsiveness:
- Agility is a cornerstone of effective supply chains. The ability to adapt quickly to changes in demand, market conditions, or unforeseen disruptions is crucial. Agile supply chains can adjust production, distribution, and other processes promptly, maintaining a competitive edge.
- Continuous Improvement Culture:
- Sustainable excellence in supply chain management requires a commitment to continuous improvement. Businesses with effective supply chains foster a culture of ongoing evaluation, innovation, and refinement. This ensures the adaptation of best practices and the ability to stay ahead in a dynamic business environment.
Incorporating these key features into a business supply chain not only enhances operational efficiency but also equips organizations to navigate the complexities of the modern marketplace with resilience and strategic foresight.
Supply chain analytics enables organizations to overcome the following supply chain challenges:
- Rigid supply value chain
- Lack of harmonization between planning and execution
- Absence of real-time data visibility and no central view of data across all supply chain processes
- Production line imbalance, resulting in missed production deadlines
- Sub-par batch sizes
- Suboptimal asset management
- Price volatility
- Incorrect holding of safety stock levels, leading to excess holding costs or frequent stock-outs
- Lack of flexibility in logistics and distribution
- High cost to serve
- Inadequate customer service, leading to unhappy customers
Supply chain analytics can be broken down into:
- Demand Forecasting
- Sales and Operations Planning
- Capacity Planning
- Procurement Analytics
- Spend Analytics
- Supplier Management
- Shop Floor Analytics
- Supply Chain Digitization
- AI- and ML-based Optimization
- IIoT Analytics
- Predictive Maintenance
- Transportation and Logistics Management
- Network Optimization
- Inventory Optimization
- Warehouse Optimization
- Reverse Logistics Management
- Supply Chain Visibility
Why is Supply Chain Analytics Important for Business?
Unlike the traditional siloed and slow-moving supply chain, supply chain analytics enables all processes to seamlessly exchange information and create a flexible, optimally integrated network that works in tandem.
Supply chain analytics enhances decision making and optimizes the entire value chain by:
- Optimizing Demand Forecasting – By analyzing datasets like POS, order history, competitor strategies, and external impact creators, organizations can improve their demand forecast accuracy. This enables them to optimize inventory levels, reduce lead times, and improve supply chain flexibility.
- Optimizing Production Planning – An agile supply chain makes it easy for the organization to implement changes basis ongoing sales. Bringing about changes in production planning basis latest sales data saves on unnecessary transportation and inventory holding costs and improves overall efficiency.
- Improving Reaction Time – Supply chains are complex, interwoven processes that require time and effort to implement any changes. By creating a flexible supply chain, it becomes easier for organizations to react to bottlenecks and unforeseen issues.
- Reducing Expenses – With optimized sales and operations processes and real-time analysis of the market, supply chain analytics enables organizations to save on unnecessary inventory holding and transportation costs.
All these steps in turn lead to optimization of productivity, budgets, and profitability across the supply value chain.
Speak with Quantzig’s analytics experts to learn how we can help you derive intelligent, actionable insights from complex, unstructured data with minimum effort to drive competitive readiness, market excellence, and success.Get Started
Quantzig – The Solution to all Your Supply Chain Challenges
In a world that is rapidly becoming data-driven, improving supply chain management capabilities has become imperative to companies in order to stay in the competition. Market volatility, dynamic demands, supplier management, and excess spends — all these challenges arise due to lack of robust data-backed supply chain management. Supply chain analytics, therefore, acts as a nexus between industries and the gigantic troves of supply chain information lying at their disposal.
Quantzig’s AI-powered supply chain analytics solutions enable you to gain continuous visibility into all supply chain processes to identify inefficiencies, reduce wastage, spends, and optimize inventory, and make supply chain processes transparent, intelligent, and more data driven. Moreover, importance of data analytics in supply chain are designed to help businesses identify new opportunities within the supply value chain and generate additional returns and maximize revenues.
Continually achieve synchronization and alignment among all supply chain functions and processes, make better-informed decisions, and revolutionize your end-to-end supply chain organization with Quantzig.
Create data-driven plans to meet demands, reduce time to market, optimize operations efficiency, and meet budgets.
- Demand Forecasting – Make intelligent supply chain decisions by accurately forecasting future customer demand. Optimize sales and operations planning, capacity planning, capital expenditure, and mitigate risks and unnecessary holding costs.
- Sales and Operations Planning – Align your supply planning with demand forecasts and create a data-driven, informed production plan that helps improve inventory management, optimize budget forecasting, enhance product lifecycle management, and increase revenue share.
- Capacity Planning – Cut unnecessary costs and optimize the capacity of manufacturing, inventory, warehouse, and distribution networks based on demand forecasting.
Reduce the total ownership cost of sourced products, streamline spending, improve supplier management, and optimally source the right products to meet demands.
- Procurement Analytics – Optimize your procurement spends, manage resources, improve compliance, and successfully manage supplier relationships.
- Spend Analytics – Identify, collect, categorize, and analyze spend data to optimize procurement costs and eliminate inefficiencies.
- Supplier Management – Create a cost-efficient, reliable supplier network to obtain a seamless quality of service and optimize return on investment. Cut costs, alleviate risks, and drive continuous improvement by tracking, benchmarking, and managing supplier performances.
Optimize the manufacturing process, enhance end-to-end visibility, manage assets, and meet production planning budgets and timelines.
- Shop Floor Analytics – Adopt AI and automation on the shop floor to reduce unexpected downtime, achieve better yields, improve product quality, and transform operations on the shop floor.
- Supply Chain Digitization – Implement digital transformation across the supply value chain to create a unified, integrated sequence of processes that lead to reduced operational costs, accelerated lead times, better planning and production, and across–the–board visibility.
- AI- and ML-based Optimization – Continually analyze supply chain data to find new patterns to improve demand forecast accuracy, production planning, inventory levels, supplier quality, and transportation management.
- IIoT Analytics – Use industry internet of things to enable a continuous exchange of data and information between processes, devices, and systems to check real-time inventory levels, prevent stock-outs, monitor assets, and improve supply chain performance, visibility, and reliability.
- Predictive Maintenance – Use IoT to monitor assets, predict equipment failure, adopt preventive measures, and plan repairs.
Create better transportation and inventory plans, optimize routes, eliminate bottlenecks, and reduce lead time.
- Transportation and Logistics Management – Amalgamate supply chain transportation and logistics with automation and IoT to drive operational transparency, manage fleet performance, optimize routes and networks, rectify product/delivery errors, and enhance customer satisfaction.
- Inventory Optimization – Effectively manage inventory levels to meet predetermined service levels while minimizing inventory holding costs to maximize manufacturing efficiency, increase profitability, and enhance customer satisfaction.
- Warehouse Optimization – Develop lean warehouses and an agile supply value chain with automation and data-driven planning. Optimize fleet management, eliminate bottlenecks, track shipments and orders in real-time to create data-driven operational efficiency.
- Reverse Logistics Management – Create policies, processes, and infrastructure to efficiently manage returns while reducing waste, optimizing inventory, and lowering operating expenses.
Enable complete visibility of the supply chain process to exercise greater control, decrease reaction time, and optimize supply chain productivity.
- Supply Chain Visibility – Enable end-to-end supply chain visibility with the help of artificial intelligence, big data, and IoT. Track products in transit, remove costly inefficiencies, obtain greater inventory control, improve customer experience, and make smarter business decisions.
Future of Supply Chain Analytics
The importance of data analytics in supply chain will be characterized by three key factors—customer-centricity, inter-operability, and agility.
- AI and ML – Artificial intelligence- and machine learning-based optimizations will become more and more prevalent in modern supply chains, especially in demand forecasting, production planning, warehousing, transportation, and predictive maintenance.
- IIoT – The more systems and processes interact with each other, the more data flows between them. Internet of things enables this exchange of data and information between processes, devices, and systems. The use of IIoT in supply value chains will enable organizations to monitor their assets and plan repairs, check inventory levels in real time and prevent stock-outs or additional holding costs, improve supply chain performance, and increase visibility and reliability of supply networks.
- Blockchain – A global supply chain is complex, vast, and interwoven. Keeping track of thousands of records generated via every interaction and transaction is a tedious process. The use of blockchain technology will enable organizations to keep track of the source of each interaction and transaction, thus increasing transparency while ensuring security.
- Supply Chain Digitization – Supply chain digitization is radically transforming supply chains with a huge impact on product manufacturing and delivery. More and more organizations will opt for supply chain digitization to enhance end-to-end supply chain visibility, increase flexibility, and reduce costly inefficiencies.
Request a free proposal to know how you can leverage our advanced supply chain analytics solutions to revolutionize your supply chain organization.Request free proposal
Supply Chain Analytics Use Cases – The Business Transformation
- Capacity Planning and Network Optimization Solutions Helped an American CPG Firm Reduce Production Costs by 8%
- IT Supply Chain Management Helped a German IT and Consultancy Brand Reduce Supply Chain Management Costs by 35%
- Supply Chain Optimization Helped a Personal Protective Equipment Manufacturer to Mitigate the Bullwhip Effect on Supply Chain Operations
- Leading Meat Processing Company Optimizes Supply Chain Performance and Improves Agility with Quantzig’s Food Supply Chain Management