Supply chain analytics allows companies to unify, analyze and act upon all the data churned out by their complex business networks. It’s the best way to avoid major supply chain disruptions and gain real and long-term value.
Descriptive analytics helps eliminate wasteful spending by optimizing inventory reorder points based on historical sales trends. It also avoids excess inventory carrying costs and lost revenue due to stockouts.
A business needs to collect and analyze data from all supply chain touchpoints in order to understand their performance. But managing the resulting data can be cumbersome, particularly for companies with global operations. Fortunately, robust data analytics tools make it easier to gather and assess data for effective management practices.
Descriptive analytics presents raw datasets in an easy-to-grasp format. For example, a dashboard might show historical metrics like delivery speed or inventory turnover. It also allows analysts to use a variety of statistical analysis techniques to identify relationships and correlations in data sets.
For example, a dimensional analysis could reveal that a supplier’s delivery delays are related to local weather conditions and that this correlation is stronger during colder months. This insight could be used to develop more proactive strategies, such as locating alternative sources for the materials that are currently being sourced from this vendor. This is called diagnostic analytics, and it aims to unearth the causes of issues faced in SCM processes.
Using predictive analytics, supply chain managers can anticipate future demand and performance. This allows companies to adjust production schedules, staffing needs and procurement processes before problems arise.
To use predictive analytics, data must be collected from a variety of sources, including point-of-sale terminals and distribution centers, ERP systems and physical warehousing. Once this information is consolidated and integrated, data visualization tools can be used to create easy-to-read dashboards and reports.
This helps analysts spot patterns and trends, such as a decrease in sales from one region or a sudden increase in unreadable bar codes from a particular supplier. More sophisticated models, like cognitive analytics, can explore these relationships in depth and discover unexpected correlations. These insights can lead to improved productivity, lower costs and fewer supply chain disruptions. This is why many organizations are leveraging artificial intelligence solutions to help streamline their supply chains. These technologies are able to process enormous amounts of raw operational data and identify complex relationships in seconds.
Supply chains depend on a number of factors for success, including procuring raw materials and component parts, managing inventory levels and production processes, and ultimately getting high-quality products to customers in a timely manner. Companies use data analytics tools to help them better understand and optimize these complex operations.
Supply chain analytics is an essential tool for achieving long-term business success. These tools are used to collect and analyze data across your entire supply chain, from sourcing and manufacturing to logistics and customer service. They provide valuable insights that improve operational efficiency.
Data analytics uses algorithms to turn raw data into a readable format for analysis. It’s often used in conjunction with data visualization to create graphical representations of the data. The key is to combine these technologies into a single platform to deliver value to your organization. This requires a tech stack that integrates disparate data sources using secure data pipelines and allows analysts to build reports and visualizations collaboratively.
Supply chains rely on many different types of data to function properly. From raw materials and component parts to shipping providers and warehouses, these systems create a web of connections that impact strategic business decisions. Managing this data manually is impossible and inefficient, but supply chain analytics can help companies gather and assess all relevant information.
Cyberattacks are increasingly targeting supply chains and resulting in costly disruptions. According to the Identity Theft Resource Center, phishing attacks on supply chains already account for more breaches than malware and other threats combined.
Procurement teams should consider a formal cybersecurity supply chain risk management (C-SCRM) program that includes all policies, processes, and procedures regarding the business and cybersecurity aspects of collaborating with third parties. These agreements should clearly define roles and responsibilities for both the business and the cybersecurity aspects of the relationship, and ensure accountability in case of a breach or attack. They should also include a minimum cyber standard for all suppliers and encourage them to adopt those standards.