Supply chain analytics leverages data analytics to optimize and support supply chain operations.
As distribution networks expand, the demand for data professionals who can ensure seamless operations increases. This is where supply chain managers and analysts play a crucial role.
In this blog, we will delve into data analytics in supply chain management, examine various types used daily, and highlight its numerous benefits. Let’s get started!
What is Data Analytics in the Supply Chain Management?
Supply chains typically generate vast amounts of data. Supply chain analytics helps make sense of this data by uncovering patterns and generating insights.
These insights enable organizations to enhance product quality, delivery, customer experience, and ultimately, profitability. Analytics is the process of making data-driven decisions based on a summary of relevant, trusted data, often visualized through graphs, charts, and other means.
Types of Data Analytics in the Supply Chain Management
Different types of supply chain analytics include:
1.Descriptive Analytics
Provides visibility and a single source of truth across the supply chain for both internal and external systems and data.
2.Predictive Analytics
Helps organizations understand the most likely outcomes or future scenarios and their business implications. For example, predictive analytics can project and mitigate disruptions and risks.
3.Prescriptive Analytics
Assists organizations in solving problems and collaborating for maximum business value. This helps businesses work with logistic partners to reduce time and effort in mitigating disruptions.
4.Cognitive analytics
Enables organizations to answer complex questions in natural language, similar to how a person or team might respond. It helps companies think through complex issues, such as “How might we improve or optimize X?”
Importance of Data Analytics in the Supply Chain Management
Supply chain analytics helps organizations make smarter, quicker, and more efficient decisions. Benefits include:
1.Reducing Costs and Improving Margins
Access comprehensive data for a continuous integrated planning approach and real-time visibility into the data driving operational efficiency and actionable insights.
2.Better Understanding Risks
Identify known risks and predict future risks by spotting patterns and trends throughout the supply chain.
3.Increasing Accuracy in Planning
By analyzing customer data, supply chain analytics helps businesses better predict future demand, decide which products can be minimized when less profitable, and understand future customer needs.
4.Achieving a Lean Supply Chain
Monitor warehouse activities, partner responses, and customer needs for better-informed decisions.
5.Preparing for the Future
Advanced analytics for supply chain management processes both structured and unstructured data, providing timely alerts for optimal decisions. They build correlations and patterns among different sources to minimize risks at little cost and with less sustainability impact.
As AI becomes more commonplace in supply chain analytics, companies may experience further benefits. AI can rapidly and comprehensively read, understand, and correlate data from disparate sources, silos, and systems, providing real-time analysis and broader supply chain intelligence. This enables companies to become more efficient, avoid disruptions, and support new business models.
Key Features of Data Analytics in the Supply Chain Management
The supply chain is the most visible aspect of a business to customers and consumers. Effective supply chain analytics protect a company’s reputation and ensure long-term sustainability.
In “The Thinking Supply Chain,” IDC’s Simon Ellis identifies the five “Cs” of effective supply chain analytics of the future:
1.Connected
Access unstructured data from social media, structured data from the Internet of Things (IoT), and traditional data sets through ERP and B2B integration tools.
2.Collaborative
Enhance collaboration with suppliers using cloud-based commerce networks for multi-enterprise collaboration and engagement.
3.Cyber-Aware
Strengthen systems against cyber-intrusions and hacks, making it an enterprise-wide concern.
4.Cognitively Enabled
The AI platform serves as the control tower of the modern supply chain, coordinating decisions and actions across the chain. Most of the supply chain is automated and self-learning.
5.Comprehensive
Scale analytics capabilities with real-time data. Insights must be fast and comprehensive, as latency is unacceptable in the future supply chain.
Evolution of Data Analytics in the Supply Chain Management
Historically, supply chain analytics focused on statistical analysis and quantifiable performance indicators for demand planning and forecasting. Data was stored in spreadsheets from different supply chain participants.
By the 1990s, companies adopted electronic data interchange (EDI) and enterprise resource planning (ERP) systems to connect and exchange information among supply chain partners. These systems provided easier access to data for analysis, design, planning, and forecasting.
In the 2000s, businesses began using business intelligence and predictive analytic software solutions to gain deeper insights into supply chain performance and optimization.
Today’s challenge is how to best utilize the massive amounts of data generated within supply chain networks. As recently as 2017, a typical supply chain accessed 50 times more data than just five years earlier.
However, less than a quarter of this data was being analyzed. Approximately 20% of supply chain data is structured and easily analyzed, while 80% is unstructured or dark data. Organizations are now looking for ways to analyze this dark data effectively.
IDC estimated that by 2020, 50% of all business software would incorporate cognitive computing functions. AI not only correlates and interprets data across systems and sources but also enables real-time analysis of supply chain data and intelligence. Coupled with emerging blockchain technologies, companies will be able to proactively forecast and predict events.
FAQ: Data Analytics in Supply Chain Management
What are the challenges of data analytics in supply chain management?
Challenges in data analytics for supply chain management include integrating diverse data sources, ensuring data quality, processing real-time data, handling complex analyses, maintaining data security, and managing change.
Why is data quality important in supply chain management?
Data quality is crucial in supply chain management for accurate decision-making, operational efficiency, cost reduction, customer satisfaction, compliance, and effective risk management, ensuring smooth and reliable supply chain operations.
Which businesses should use data analytics in their supply chain management?
The following businesses should make use of data analytics to keep track of things in their SCM.
- Large Enterprises: Companies with complex and extensive supply chains, such as multinational corporations, can greatly benefit from data analytics. It helps them manage their vast networks of suppliers, distributors, and logistics partners more effectively.
- Retailers and E-Commerce Companies: Businesses in retail and e-commerce face fluctuating demand and inventory challenges. Data analytics helps them optimize inventory levels, forecast demand, and improve supply chain efficiency.
- Manufacturers: Manufacturers with intricate production processes and supply chains can use data analytics to streamline operations, improve production schedules, and enhance supplier collaboration.
- Logistics and Transportation Firms: Companies in logistics and transportation can use data analytics to optimize routes, reduce fuel costs, and enhance delivery performance. It helps them manage fleet operations and improve service levels.
- Consumer Goods Companies: Companies producing consumer goods need to manage supply chains that balance production and demand. Data analytics helps them forecast trends, manage inventory, and respond to market changes.
- Pharmaceuticals and Healthcare: Businesses in pharmaceuticals and healthcare require precise management of their supply chains to ensure timely delivery of critical products. Data analytics aids in inventory management, compliance, and tracking.
- Food and Beverage Industry: This industry benefits from data analytics by ensuring product quality, managing supply chain logistics, and responding to demand fluctuations effectively.
Data analytics is beneficial for any business aiming to enhance supply chain efficiency, reduce costs, and improve decision-making, regardless of size or industry.
Conclusion
Data analytics in supply chain management is crucial for optimizing operations and enhancing efficiency. By leveraging advanced analytics, businesses can make informed decisions, improve profitability, and stay competitive. For seamless integration of these technologies into your supply chain, consider exploring solutions from Qodenext.
