Manufacturing companies today operate in an increasingly complex and competitive environment in which rapid decision-making and strategic use of data have become key success factors. Many decision makers, however, face critical issues inherited from a traditional approach to business intelligence, based almost exclusively on historical reporting.
Among the main limitations found in manufacturing companies are data fragmentation and the presence of information silos: production, quality and supply chain information is often distributed in separate, non-communicating systems, preventing a unified view. This leads to slow and often reactive decisions, as access to information requires complex steps such as requests to IT and the generation of static reports.
In addition, the prevalence of descriptive reporting limits the ability to predict future problems or opportunities, leaving companies unprepared for changes in demand or unexpected failures. Finally, the lack of self-service tools forces managers and analysts with limited technical skills to depend on IT support, with little exploitation of available information potential.
These critical issues highlight the need for a paradigm shift in BI: it is no longer enough to “snapshot” the past with monthly reports, we need to leverage data in an integrated and intelligent way to actively support business decisions.
Not surprisingly, industry analysts stress the urgency of evolving BI systems.
Gartner, for example, notes that already one-third of companies have implemented Decision Intelligence solutions and predicts that by 2027, 50 percent of business decisions will be automated or supported by AI agents based on these systems.¹
In other words, advanced analytics and artificial intelligence will become intrinsic components of decision-making processes.
Moving from simple reporting to Decision Intelligence, in short, is the concrete answer to business needs.
From Traditional BI to Decision Intelligence
Decision Intelligence represents the most mature landing place in the evolution of BI, in which the focus shifts from data to decision making. In essence, we move from a purely descriptive BI to a decision-making approach in which analytic systems in addition to producing reports, provide recommendations, automate repetitive decisions, and close the loop between insight and action.
This leap is enabled by the pervasive integration of AI and machine learning algorithms into data analysis processes, as well as explicit decision modeling: companies encode decision logics (business rules, predictive models, optimization criteria) into platforms that can suggest or execute actions based on the input data.
For manufacturing companies, adopting a Decision Intelligence paradigm means, for example, being able to automatically identify an anomaly in the production line and immediately trigger corrective actions, or recalculating production and purchasing plans in real time based on updated demand forecasts. However, all this requires flexible data infrastructures and advanced analytical technologies-first, there is a need to integrate data dispersed across many systems . This is where the concept of data fabric comes in.
Data fabric: an integrated fabric of enterprise data
The data fabric is a modern architecture that integrates data from heterogeneous sources across the enterprise and facilitates access and use of information wherever it resides. It uses automation and active metadata to overcome the rigidity of traditional ETL approaches, creating an information fabric that connects databases, data lakes, and cloud applications.
In manufacturing, this enables a unified view in real time without duplicating data. Solutions such as SAP Data Intelligence and SAP Datasphere enable orchestration and virtualization of data from multiple systems, eliminating silos and laying the foundation for advanced analytics and intelligent decisions.
Decision Intelligence: from data to action
As anticipated, Decision Intelligence represents the natural evolution of proactive BI. If the data fabric provides the unified infrastructure, Decision Intelligence is its strategic application: a set of methodologies and technologies that aim to improve decisions by combining data, advanced analytics, and human knowledge. A Decision Intelligence system does more than just show what is happening via dashboards: it primarily helps determine what to do in response to a given situation, possibly automating the decision itself when appropriate.
In practical terms, this spans fields such as prescriptive analytics (analytics that suggests optimal actions) and the orchestration of end-to-end decision-making processes. For example, an algorithm can analyze production and quality data to recommend adjustments to machinery, or a software agent can autonomously reorder materials to a supplier when inventory falls below a threshold, all based on predefined logic and predictive models. The goal is to make decisions more scientific, faster and repeatable, minimizing subjective intuition and downtime in the transition from analysis to action.
SAP is also moving its tools in the direction of more advanced decision support. For example, we find the concept in the SAP Business Technology Platform: SAP S/4HANA is an enabler of embedded analytics capabilities and predictive indicators that support real-time operational decisions; similarly, solutions such as SAP Integrated Business Planning for supply chain use advanced algorithms to automatically optimize production and procurement plans. Even the market’s leading vendors, in short, are increasingly aiming to provide decision-making tools in which analytics, AI and automation work together to bridge the last mile between insight and action.
Augmented BI: AI to support insights
Augmented BI integrates AI and machine learning into traditional platforms to automate tasks such as data cleansing, anomaly detection and insight generation via natural language. This allows enabled users to quickly get explanations and suggestions, making BI proactive and accessible even without advanced technical skills. In manufacturing companies, augmented BI enables, for example, anomaly detection and fault prediction in real time.
Leading BI vendors are integrating advanced features such as AI, machine learning and automation into their products. SAP Analytics Cloud (SAC), for example, offers smart insights, smart discovery and digital assistant tools that facilitate analysis even for nonspecialist users. These platforms go beyond traditional reporting with predictive and prescriptive insights that optimize operations and support business decisions. In SAC, predictive models can be easily created and results displayed in interactive dashboards to reduce manual work and promote greater accessibility to data.
Self-service BI: democratizing data analysis
Self-service BI has long since changed the way manufacturing companies manage data and eliminated dependence on IT and analysts for reporting. Today, line, plant and logistics managers can access data and create reports independently through intuitive interfaces to quickly respond to operational needs. The adoption of self-service tools accelerates decision making and spreads the data culture in the company, as well as reducing the load on IT. In this way, BI becomes more democratic and accessible to all.
Suites such as SAP BusinessObjects BI and SAP Analytics Cloud embody this evolution with self-service exploration, visualization and analysis capabilities, even for users with limited technical skills. The ability to model data, create customized dashboards and collaborate in real time , for example, enables plant managers to directly analyze production data, identify inefficiencies and simulate scenarios without intermediaries. To fully reap the benefits, however, investment in training and data governance is needed to lay a solid foundation for autonomous and reliable information management.
Artificial intelligence and decision intelligence: a structural link
The evolution toward Decision Intelligence would be virtually unfeasible without the pervasive integration of artificial intelligence techniques. The two dimensions are deeply interconnected: AI provides the algorithms and computational capabilities to model, automate, and optimize decision-making processes; Decision Intelligence, in turn, provides the methodological and operational framework to translate these capabilities into concrete decisions that can be tracked and improved over time.
Specifically, AI is used to enhance each stage of the decision cycle: in the descriptive stage, it automates data collection and cleansing; in the predictive stage, it generates models that can estimate future outcomes; and in the prescriptive stage, it suggests or executes optimal actions based on constraints and objectives. The joint use of AI and Decision Intelligence enables companies to move from a decision-support approach to a decision-engineering approach, where choices are no longer just influenced by data: they are systemically designed and automated. This convergence of AI and Decision Intelligence marks the ultimate overtaking of BI as a passive reporting tool, transforming it into an active asset for adaptive business process management.
Decision Intelligence: business data finally at value
Today, thanks to the integration of data, technology, and people, information is available when and where it is needed: for data-savvy companies, the old information silos are just a bad memory. AI is no longer an interesting novelty with good potential: it has already evolved into a concrete presence that automates processes and enhances decision-making capabilities across all levels of the enterprise.
For those who manage data, it is time to move beyond standard reports: new BI solutions, such as those offered by SAP, enable them to anticipate scenarios, involve more people in decision making, and respond quickly to market challenges. However, technology alone is not enough: the real leap is made by investing in data skills and culture. The adoption of Decision Intelligence thus becomes the concrete path to building a manufacturing that is more agile, resilient and ready for future challenges, where the union of human and artificial intelligence really makes a difference.
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Source ¹: Gartner.com
