Every day, the supply chain generates an enormous amount of data: picking times, inventory levels, supplier performance, error rates, shipping costs, and lead times. But what should companies do with all this data once it has been collected?
This is where supply chain business intelligence comes into play: the ability to collect, process, and visualize operational supply chain data so that it becomes actionable information that supports better, more informed decision-making.
The global supply chain analytics market was valued at USD 11.08 billion in 2025 and is projected to reach USD 13.03 billion by 2034, with a compound annual growth rate (CAGR) of 10.62% (Fortune Business Insights, 2026).
This is a clear sign that companies around the world are realizing that data visibility is now a critical competitive requirement. In this article, Alessio Pavan, our Operations Director and logistics digitalization expert, explains what business intelligence in the supply chain is and how analytics can drive decision-making for business improvement.
What is business intelligence in the supply chain?
Business intelligence (BI) is the set of technologies, processes, and tools that transform raw data into structured, visual information. Applied to the supply chain, logistics BI enables real-time monitoring of performance across every stage of the value chain, from procurement and production to warehousing and final distribution.
“Business intelligence should not be confused with simple reporting,” explains Alessio. “A report tells you what happened. A supply chain BI system goes further and also tells you why it happened, what is happening right now, and, in more advanced systems, what will most likely happen next.”
The four supply chain BI analytics models
Data analysis can deliver different levels of insight depending on the analytics models adopted. “Based on two key variables, namely analytical complexity and potential business value, we can identify four different analytics models: descriptive, diagnostic, predictive, and prescriptive,” explains our Operations Director.
- Descriptive analytics
Answers the question: “what happened?“. This category includes tools designed to graphically represent the current and historical state of business processes through dashboards and reports. It provides a concise view of key performance indicators (KPIs).
Example: the dashboard shows that over the last quarter, picking productivity increased by 18%, while the number of order lines picked per operator grew significantly. - Diagnostic analytics
Answers the question: “why did it happen?“. This type of analysis is used to identify the root cause of a specific event and determine the factors that led to the current situation. Understanding the causes behind trends and outcomes also helps organizations replicate and optimize the actions that generated positive results. A key feature supporting this model is drill-down analysis, which allows users to move from a high-level aggregated view of data to increasingly detailed information.
Example: the drill-down analysis reveals that the improvement is linked to a new slotting strategy that reduced operator travel distances and concentrated high-turnover items in the most accessible warehouse locations. - Predictive analytics
Answers the question: “what could happen in the future?”. Predictive analytics extracts information from existing datasets and converts raw data into insights that help forecast future trends. It is closely linked to data mining and machine learning, where systems leverage historical and current information to build predictive models.
Example: based on current trends and order growth, the system forecasts that the warehouse will be able to absorb a 25% increase in volume without requiring additional staff. - Prescriptive analytics
Answers the question: “what should I do next?“. Prescriptive analytics combines data analysis, mathematics, experimentation, simulation, and artificial intelligence to improve decision effectiveness. A particularly valuable component is machine learning, the branch of artificial intelligence based on the idea that systems can learn from data, identify patterns autonomously, and make decisions with minimal human intervention.
Example: The system recommends extending the slotting logic to additional warehouse areas and reallocating selected SKUs to further increase productivity while postponing investments in additional operational resources.
| Analytics model | Question answered | Techniques and tools | Supply chain example |
| Descriptive | What happened? | Dashboards, reports, KPIs, scorecards | Service level drops to 92% and delivery delays increase by 15% |
| Diagnostic | Why did it happen? | Drill-down analysis, correlations, root cause analysis | Delays are concentrated within a specific product family, with stockouts caused by demand forecasting errors |
| Predictive | What could happen? | Data mining, forecasting, statistical models, scenario simulations | Stockouts are expected to increase over the next two months, reducing service levels to 88% |
| Prescriptive | What should I do? | AI, machine learning, decision algorithms | Increase safety stock levels for the product by 20% and bring procurement activities forward |
Why does the supply chain need business intelligence software?
A supply chain without BI is a blind supply chain. “The data exists, but it is scattered across different systems such as ERP, APS, WMS, MES, and TMS, often in incompatible formats and accessible only to those who know the system that generated it. The result is that decisions are made too late, based on partial data, or worse, inaccurate data,” emphasizes Alessio.
Practical problems solved by logistics business intelligence software:
- fragmented visibility: each department sees only its own portion of the process. BI consolidates data from all systems into a single access point, providing end-to-end visibility across the supply chain.
- reactive rather than proactive decisions: without predictive analytics, companies can only respond after a problem has already occurred. Stockouts, delivery delays, and excess inventory can be anticipated rather than chased.
- KPIs that are not monitored or poorly monitored: many companies do not know their picking accuracy rate, OTIF performance, or average lead time. Without clear metrics, continuous improvement is impossible.
- slow, manual reporting: extracting data from the ERP, pasting it into Excel, and building reports often consumes hours every week, only to produce insights that become outdated almost immediately. A BI system updates information automatically and in real time.
Supply chain BI and available tools
The supply chain business intelligence market is generally divided into two main categories.
General-purpose BI tools such as Microsoft Power BI, Tableau, and Qlik are flexible platforms that can be used across multiple business contexts. However, they require specific configuration for logistics processes, technical expertise to build data models, and custom integration with operational systems such as WMS, ERP, and MES platforms.
Vertical BI modules integrated into logistics systems, on the other hand, are native solutions already connected to warehouse or production workflows. They provide logistics-specific KPIs and preconfigured dashboards without requiring complex data-engineering projects. This approach is particularly suitable for companies seeking rapid results without building a data warehouse from scratch.
“Without attention to data governance, SCP leaders will likely struggle to achieve their expected return on key technology investments.” ⁓ Eva Dawkins, Director Analyst, Gartner Supply Chain Practice
Stesi’s logistics business intelligence module
Stesi’s silwa Business Intelligence Application Module is a data analytics tool that integrates directly with warehouse management (WMS) or manufacturing execution systems (MES). It interactively analyzes supply chain KPIs to support decision-making and process optimization.
Within logistics and warehousing, it analyzes areas such as historical inventory levels for items, lots, and serial numbers, operator movements, order fulfillment capability, carrier on-time performance, and more. Each of these areas can then be explored in greater detail through drill-down functionality. Within manufacturing, the silwa BI module analyzes metrics such as OEE (Overall Equipment Effectiveness). Detailed views provide separate visibility into machine Availability, Performance, and Quality.
“Business Intelligence creates value when the information collected is transformed into concrete actions.” continues Alessio. “While descriptive and diagnostic models help organizations understand supply chain performance, predictive and prescriptive models enable direct intervention in operational processes.”
According to Mordor Intelligence forecasts (2026), prescriptive analytics is growing at a CAGR of 27.4%, as companies increasingly seek automated recommendations rather than simple descriptive reporting.
FAQ
What is supply chain business intelligence?
Supply chain business intelligence is the application of BI tools and methodologies to logistics and manufacturing processes. It enables organizations to collect data from different systems such as APS, ERP, MES, WMS, and TMS, aggregate it into dashboards and reports, and transform it into actionable insights for performance monitoring, inefficiency identification, and data-driven decision-making.
Will AI replace the supply chain analyst?
No. AI will not replace the supply chain analyst, but it will enhance their capabilities. For example, AI can analyze thousands of historical records to identify anomalies, predict potential delays, and recommend optimal inventory levels. The analyst remains responsible for interpreting the results, assessing business impact, and making decisions aligned with the company’s strategy.
What is the difference between statistics and data mining?
Statistics starts with hypotheses and models that are used to analyze and interpret data. Data mining explores large datasets to identify patterns, correlations, and hidden insights, even when there is no clearly defined initial question.
What does a Supply Chain Analyst do?
A Supply Chain Analyst collects, analyzes, and interprets data related to demand, inventory, procurement, production, and transportation to support more effective decision-making. The objective is to identify inefficiencies, optimize processes, and improve supply chain performance through data-driven analysis and insights.
How can Business Intelligence improve the supply chain?
Business Intelligence transforms large volumes of data into accessible information presented through dashboards and reports. This enables real-time KPI monitoring, rapid identification of critical issues, improved forecasting accuracy, and faster, more informed decision-making throughout the supply chain.
Supply Chain Analyst vs Data Analyst: what are the differences?
Both roles work with data, but they focus on different objectives. A Data Analyst generally operates across a variety of business functions and supports multiple departments. A Supply Chain Analyst applies analytical expertise specifically to logistics, planning, procurement, and manufacturing processes.
