The spine of any company, regardless of size, is its supply chain, and organizations that harness the massive volumes of logistics data most efficiently gain significant competitive advantage.
Multinationals are already deploying artificial intelligence (AI) technologies to accelerate their supply chain analytics and turn that data into actionable intelligence that leaders can use to make decisions more quickly and with greater confidence.
IBM refers to AI-driven processes as cognitive analytics, which mimics human reason and understanding while drilling into enormous data sets to uncover trends and provide real-time insights and visualizations.
AI-driven technologies “can automatically sift through large amounts of data to help an organization improve forecasting, identify inefficiencies, respond better to customer needs, drive innovation and pursue breakthrough ideas,” notes IBM.
What Are the Features of Advanced Supply Chain Analytics?
The COVID-19 pandemic inspired global conversations about supply chain systems, and many industries struggled to manage the pandemic mandates with existing supply chain systems. As a result, analytics took on new urgency as businesses attempted to fix the damage.
Ramping up their supply chain analytics, according to Oracle Netsuite, resulted not only in better logistics decision-making but also organization-wide benefits such as:
- Integrating data across the enterprise. Advanced supply chain analytics uses data from virtually all company operations to optimize visibility in both directions.
- Collaborating with supply chain partners and customers to innovate processes and take advantage of Cloud computing to share insights and information.
- Growing more attentive to cyber risks as they integrate more software and connected processes.
- Moving more quickly to adopt cognitive analytics to accelerate detection of supply chain disruptions, mitigate the impact and develop strategies to prevent them in the future.
- Gaining long-range insights and understanding while enhancing the ability to deliver immediate results.
“As many organizations strive to be ‘data-driven,’ supply chain analytics represent a critical step toward this goal. Put simply, company leaders can make better decisions when armed with detailed supply chain information and reports,” it notes.
What Do Supply Chain Analytics Look Like in the Real World?
Companies use analytics to gain visibility into every link of their global supply chains. By integrating sales, inventory, logistics and other operational data, they can adjust quickly to immediate challenges, seize opportunities as they present themselves and make strategic decisions, according to CIO.
It used three companies to illustrate how multinationals use analytics to drive world-class supply chains:
- UPS’ business intelligence platform collects, organizes and analyzes billions of data points daily to manage its worldwide shipping network. The platform provides decision-makers with forward-looking predictive analytics that support real-time coordination of operations among more than 1,000 distribution centers in 120 countries, as well as its fleet of nearly 600 aircraft.
- Pepsico leverages machine learning and predictive analytics to let customers know when it’s time to reorder. Integrating customer, manufacturing, sales and supply chain data enables the company to forecast precisely when retailers should place orders to keep their shelves stocked.
- Data-driven decision-making supported the accelerated manufacturing and global distribution of its COVID-19 vaccine by big pharma giant Pfizer. By integrating end-to-end production and supply chain operations visibility, the company can predict disruptions before they occur and model potential solutions to avoid them.
“With the help of sophisticated predictive analytics tools and models, any organization can now use past and current data to reliably forecast trends and behaviors milliseconds, days, or years into the future,” notes the magazine.
What Is the Demand for Supply Chain Management Professionals?
According to research by FinancesOnline, supply chain managers list the top three technical priorities as data analytics, Internet of Things (a growing source of supply chain data) and cloud computing.
Those priorities, according to McKinsey & Company, are driven by three key operations that span the supply chain from sourcing to customer: managing supplier risk and incoming goods inventory and scenario planning; managing supplier risk management and incoming goods projections; and forecasting accuracy and optimization.
“For big data and advanced analytical tools to deliver greater benefits for more companies, those organizations need a more systematic approach to their adoption,” McKinsey notes.