Summary
The global supply chain analytics market is estimated to attain a valuation of $32.18 billion by 2030, with a CAGR of 20.32%, and the U.S. market valued at $7.1 billion in 2023, doubling from 2018. Supply chain analytics plays a vital role, enabling informed decisions and offering benefits such as cost reduction and risk management. The operational mechanism involves data consolidation, real-time visibility enhancement, and collaboration among stakeholders for continuous improvement. Effective analytics attributes include data visualizations, security emphasis, comprehensive analysis, digital modeling, and intuitive, collaborative access. Future trends indicate a surge in supply chain innovation, AI integration, and IoT proliferation for enhanced insights into digitized operations.
Supply chain analytics is like the brain behind the scenes, using data analysis to fine-tune and support the intricate dance of supply chain operations. In today’s world, supply chains play a crucial role in shaping our modern economy, not just by delivering fancy gadgets to consumers but also by ensuring that we have the basics like fuel and food on our tables.
As distribution networks become more complex, the demand for data-savvy professionals who can keep everything running smoothly is on the rise. This is where supply chain managers and analysts step in.
This article is your guide to understanding this field of analytics. We’ll dive into the different types used in everyday operations and explore the various benefits it brings to the table.
What is Supply Chain Analytics?
The term “supply chain analytics” describes the methods used by businesses to acquire knowledge and derive value from the massive volumes of data about the sourcing, manufacturing, and distribution of products. It is an integral part of supply chain management (SCM).
Although this field has existed for more than a century, its foundational mathematical models, data structures, and software have changed considerably over time. Better statistical methods, predictive modeling, and machine learning have all contributed to the advancement of mathematical models. Cloud computing, complex event processing (CEP), and the Internet of Things (loT) have all altered data infrastructure. Applications such as enterprise resource planning (ERP), warehouse management, logistics, and enterprise asset management have broadened their scope to provide information that spans traditional application silos.
The industry is foreseen to witness strong growth in the coming years. As per a recent Kings Research report, the supply chain analytics market is projected to accrue revenue of $32.18 billion by 2030, growing at a robust compound annual growth rate (CAGR) of 20.32% from 2023 to 2030. Now that we know what it means, let’s understand why this field is important.
Why is Supply Chain Analytics Important?
The significance of supply chain analytics lies in its ability to enable organizations to make informed, efficient decisions promptly. This translates into tangible benefits, including:
- Cost Reduction and Margin Improvement: Access to comprehensive data fosters a continuous integrated planning approach, offering real-time visibility into diverse data sets that drive operational efficiency and actionable insights.
- Risk Management: It identifies known risks and predicts future ones by detecting patterns and trends across the supply chain.
- Enhanced Planning Accuracy: Analysis of customer data allows for better prediction of future demand, enabling organizations to optimize product portfolios based on profitability and anticipate customer needs post-initial orders.
- Lean Supply Chain Achievements: Utilizing supply chain analytics to monitor warehouse operations, partner responses, and customer needs facilitates well-informed decision-making for a leaner supply chain.
- Future Preparedness: Advanced analytics in supply chain management processes both structured and unstructured data, ensuring timely alerts for optimal decision-making. It establishes correlations and patterns among various sources, providing cost-effective risk mitigation strategies.
As technologies like AI become more commonplace, companies can anticipate a surge in additional benefits. The ability of AI to process natural language data in real-time, previously limited, will lead to comprehensive supply chain intelligence. This not only enhances efficiency and minimizes disruptions but also supports the adoption of new business models.
How Does Supply Chain Analytics Work?
Supply chain analytics operates by consolidating data from various applications, infrastructure, third-party sources, and emerging technologies like IoT. It aims to enhance decision-making across the strategic, tactical, and operational aspects of supply chain management. This process improves real-time visibility into supply chain processes, aligning planning and execution to impact customers and the bottom line. Increased visibility also enhances supply chain network flexibility, aiding decision-makers in evaluating tradeoffs between cost and customer service.
The creation of this analytics typically begins with data scientists focusing on specific business aspects such as cash flow, inventory, waste, and service levels. These experts seek correlations between data elements to build predictive models optimizing supply chain output. Successful models are deployed into production by data engineers, ensuring scalability and performance. Collaboration between data scientists, data engineers, and business users refines the presentation and practical implementation of these analytics. Continuous improvement occurs by correlating the performance of production models with the business value they deliver over time.
6 Key Attributes of Effective Supply Chain Analytics
- Data Visualizations: Analysts utilize visual representations like bar charts, histograms, and line graphs to present raw datasets in an easily understandable format, offering insights into different aspects of supply chain operations.
- Security: Given the sensitivity of operational data within supply chains, employing industry-standard security practices and robust access controls is crucial to safeguard information.
- Comprehensive Analysis: Effective analytics encompasses all data-producing elements of the process, extending beyond raw materials and shipping providers. This ensures that analyses consider all relationships and correlations impacting strategic business decisions.
- Digital Modeling (Digital Twin): A digital modeling system, often termed the “digital twin”, replicates physical supply chain processes. This allows analysts to iterate and experiment before making significant investments in adjusting physical systems.
- Internal and External Data Integration: Beyond internal integrations with systems like inventory management, analysts should incorporate external data sources such as public weather datasets or social media feeds, influencing their supply chain dynamics.
- Intuitive, Collaborative Access: While maintaining secure access controls, the sharing of data among analysts should be facilitated to enhance collaboration and iteration in improving models and visualizations. This not only fosters creativity but also reduces duplicated efforts, especially with generalizable data pipelines.
The Future of Supply Chain Analytics
The supply chain has become a focal point for innovation, offering opportunities for cost reduction and improved customer experiences. In the U.S., this field of analytics garnered a valuation of $7.1 billion in 2023, doubling in size from 2018. While some smaller companies may find prescriptive and cognitive analytics challenging due to resource constraints, the landscape is evolving, making these advanced analytics more accessible. Leading providers are integrating AI into supply chain software for emerging businesses. As operations digitize and IoT devices proliferate, supply chain analytics will tap into an expanding data pool, necessitating AI technologies to extract actionable insights from the wealth of information generated.
