What is supply chain analytics?

Analytics represent the ability to make data-driven decisions, based on a summary of relevant, trusted data, often using visualization in the form of graphs, charts and other means. Supply chains typically generate massive amounts of data. Supply chain analytics helps to make sense of all this data — uncovering patterns and generating insights.

Different types of supply chain analytics include:

  • Descriptive analytics. Provides visibility and a single source of truth across the supply chain, for both internal and external systems and data.
  • Predictive analytics. Helps an organization understand the most likely outcome or future scenario and its business implications. For example, using predictive analytics can project and mitigate disruptions and risks.
  • Prescriptive analytics. Helps organizations solve problems and collaborate for maximum business value. Helps businesses collaborate with logistic partners to reduce time and effort in mitigating disruptions.
  • Cognitive analytics. Helps an organization answer complex questions in natural language — in the way a person or team of people might respond to a question. It assists companies to think through a complex problem or issue, such as “How might we improve or optimize X?”

Supply chain analytics is also the foundation for applying cognitive technologies, such as artificial intelligence (AI), to the supply chain process. Cognitive technologies understand, reason, learn and interact like a human, but at enormous capacity and speed.

This advanced form of supply chain analytics is ushering in a new era of supply chain optimization. It 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.

Why is supply chain analytics important?

Supply chain analytics can help an organization make smarter, quicker and more efficient decisions. Benefits include the ability to:

  • Gain a significant return on investment. A recent Gartner survey revealed that 29 percent of surveyed organizations said they have achieved high levels of ROI by using analytics, compared with only four percent that achieved no ROI.⁴    
  • Better understand risks. Supply chain analytics can identify known risks and help to predict future risks by spotting patterns and trends throughout the supply chain.
  • Increase accuracy in planning. By analyzing customer data, supply chain analytics can help a business better predict future demand. It helps an organization decide what products can be minimized when they become less profitable or understand what customer needs will be after the initial order.
  • Achieve the lean supply chain. Companies can use supply chain analytics to monitor warehouse, partner responses and customer needs for better-informed decisions.
  • Prepare for the future. Companies are now offering advanced analytics for supply chain management. Advanced analytics can process both structured and unstructured data to give organizations an edge to get alerts on time to make the optimal decisions. It can build correlation and patterns among different sources to provide alerts that minimize risks at little cost and less sustainability impact.

As technologies such as AI become more commonplace in supply chain analytics, companies may see an explosion of further benefits. Information not previously processed because of the limitations of analyzing natural language data can now be analyzed in real time. AI can rapidly and comprehensively read, understand and correlate data from disparate sources, silos and systems. It can then provide real-time analysis based on interpretation of the data. Companies will have far broader supply chain intelligence. They can become more efficient and avoid disruptions — while supporting new business models.

Evolution of supply chain analytics

In the past, supply chain analytics was limited mostly to statistical analysis and quantifiable performance indicators for demand planning and forecasting. Data was stored in spreadsheets that came from different participants in the supply chain.

By the 1990s, companies were adopting Electronic Data Interchange (EDI) and Enterprise Resource Planning (ERP) systems to connect and exchange information among the supply chain partners. These systems provided easier access to data for analysis, along with assisting businesses in their designing, planning and forecasting.

In the 2000s, businesses began turning to business intelligence and predictive analytic software solutions. These solutions helped companies gain a more in-depth knowledge of how their supply chain networks were performing, how to make better decisions and how to optimize their networks.

The challenge today is how companies can best use the huge amounts of data generated in their supply chain networks. As recently as 2017, a typical supply chain accessed 50 times more data than just five years earlier.¹ However, less than a quarter of this data was being analyzed. Further, while approximately 20 percent of all supply chain data is structured and can be easily analyzed, 80 percent of supply chain data is unstructured or dark data.² Today’s organizations are looking for ways to best analyze this dark data.

Studies are pointing to cognitive technologies or artificial intelligence as the next frontier in supply chain analytics. AI solutions go beyond information retention and process automation. AI software can think, reason and learn in a human-like manner. AI can also process tremendous amounts of data and information — both structured and unstructured data — and provide summaries and analyses of that information in an instant.

IDC estimates that by 2020, 50 percent of all business software will incorporate some cognitive computing functions.³ AI not only provides a platform for powerfully correlating and interpreting data from across systems and sources — it also allows organizations to analyze supply chain data and intelligence in real time. Coupled with emerging blockchain technologies, companies in the future will be able to proactively forecast and predict events.

Key features of effective supply chain analytics

The supply chain is the most obvious face of the business for customers and consumers. The better a company can perform supply chain analytics, the better it protects its business reputation and long-term sustainability.

IDC’s Simon Ellis in The Thinking Supply Chain identifies the five “Cs” of the effective supply chain analytics of the future:

  • Connected. Being able to access unstructured data from social media, structured data from the Internet of Things (IoT) and more traditional data sets available through traditional ERP and B2B integration tools.
  • Collaborative. Improving collaboration with suppliers increasingly means the use of cloud-based commerce networks to enable multi-enterprise collaboration and engagement.
  • Cyber-aware. The supply chain must harden its systems from cyber-intrusions and hacks, which should be an enterprise-wide concern.
  • Cognitively enabled. The AI platform becomes the modern supply chain's control tower by collating, coordinating and conducting decisions and actions across the chain. Most of the supply chain is automated and self-learning.
  • Comprehensive. Analytics capabilities must be scaled with data in real time. Insights will be comprehensive and fast. Latency is unacceptable in the supply chain of the future.

In today’s supply chain networks, effective analytics requires the ability to become more customer centric — responding quickly while maintaining accuracy and integrity. Businesses are looking for supply chain analytical solutions that can quickly analyze huge amounts of data from disparate data sources, including unstructured and natural language-based data. Finally, supply chain analytics are being asked to predict an increasing number of supply chain variables — including external forces such as weather, war, workers and regulations.

Using software for supply chain analytics

With supply chain analytics becoming so complicated, many types of software have been developed to optimize supply chain performance. Software products cover the gamut — from supplying timely and accurate supply chain information to monitoring sales.

For instance, IBM has developed many software products to increase the effectiveness of supply chain analytics, with some of the software even using AI technologies. With AI capabilities, supply chain software can actually learn an ever-fluctuating production flow and can anticipate the need for changes. IBM products include:

IBM Sterling Supply Chain Insights with Watson

Uses AI capabilities to help a business cut through data noise and get the insights to confidently act faster.

Watson Supply Chain Fast Start

Is an agile workshop engagement to accelerate a company’s journey toward a supply chain powered by AI.

IBM Sterling Supply Chain Business Network

Enables companies to see all relevant transactions from a single dashboard. The software can quickly pinpoint and assess issues without IT involvement, allowing resolution in minutes not hours.

IBM Planning Analytics

Helps companies to automate planning, budgeting, forecasting and analysis processes to drive efficiency and create timely, reliable plans.

Case studies and blogs about supply chain analytics

Lenovo uses IBM Sterling Supply Chain Insights with Watson


Shrinks its average response time to supply chain disruptions from days to minutes — up to 90 percent faster than before.

Data analytics for a smarter supply chain

Data analytics

How can you apply data analytics to improve your operations and outcomes?



1. “The path to a thinking supply chain,” Simon Ellis, John Santagate, IDC Technology Spotlight, Aug 2018 (PDF, 1.2 MB)

2. “The AI journey: Artificial intelligence and the supply chain,” IBM Watson Supply Chain

3. “Creating a thinking supply chain for the cognitive era,” Matt McGovern, Watson Customer Engagement, 27 Mar 2017

4. “Why supply chain analytics is a must have,” Christy Pettey, Gartner, 14 May 2015