As digital technologies like IoT, AI, and cloud computing reshape traditional processes, manufacturers have the opportunity to unlock new levels of efficiency, cost savings, and competitiveness. Central to this transformation is manufacturing analytics, a powerful technology that turns raw data into actionable insights.
Manufacturing analytics and business intelligence go beyond operational metrics, integrating real-time information across supply chains, quality control, and customer demands to drive smarter and faster decisions. Predictive maintenance, for example, enables manufacturers to forecast equipment failures and optimize maintenance schedules, minimizing downtime.
For business leaders, these capabilities mean identifying growth opportunities, mitigating risks, and delivering measurable ROI. This article explores the business relevance of manufacturing analytics, shares best practices for implementation, and highlights real-world use cases that demonstrate its transformative impact.
Whether you're looking to build and implement a custom manufacturing solution or optimize an existing one, understanding the big picture and ensuring your solution can handle large data volumes and deliver accurate analysis are critical first steps.
The rise of digital manufacturing, the Industrial Internet of Things (IIoT), and connected products is driving an unprecedented surge in data generation. On average, the manufacturing sector produces around 2 petabytes of data annually—almost twice as much as any other industry.
This data predominantly originates from supply chains, sourcing processes, factory operations, and stringent compliance and quality management stages. Notably, over one-third of manufacturers report that their data volumes have at least doubled in the past two years. Collecting and managing this data effectively is key to optimizing the impact of technology investments. If you need to speed up managing all your organization’s data, you can consider discussing with your custom manufacturing software development partner and see what would be the best course of action to scale up the project.
Manufacturing analytics transforms the way companies allocate and utilize resources. Many companies are collecting data just because they can, or because it might be needed for something at some point in the future. It’s very important to clearly define what data is needed, by whom, for what purpose, and to what degree of precision – to make sure that data is valuable for the overall analytics operations of the company. Data integration is key to provide a singular source of truth and can reduce the time needed to reconcile data from multiple sources.
Right before tapping into new software, sensors or platforms that might help to generate even more data it can be a good practice to start with:
Why are these steps important before adopting new technologies? They help manufacturers identify blockages and resolve data discrepancies. It’s not about the quantity of data but its quality. If you plan to leverage advanced technologies like LLMs and GenAI, ensuring data integrity is a critical first step.
Business innovation in manufacturing thrives on accurate, data-driven decision-making. Analytics enables companies to anticipate market needs by tracking trends in demand, customer preferences, and product performance.
Here are some of the benefits offered by manufacturing analytics:
For example, predictive models can highlight which products are most likely to succeed in specific markets or which production strategies will yield the highest return. By combining historical data with real-time insights, decision-makers can refine product portfolios, allocate R&D budgets more effectively, and prioritize high-impact projects.
Supply chains in manufacturing are increasingly complex and prone to disruptions. Analytics provides clarity by integrating data from suppliers, production facilities, and distribution networks. This ensures that decision-makers can anticipate and mitigate risks before they impact operations.
For example, advanced analytics can flag potential delays from key suppliers or suggest optimal inventory levels to balance cost and availability. This allows manufacturers to maintain steady production cycles, minimize waste, and adapt quickly to changing circumstances without compromising delivery timelines.
Another example is that manufacturers have a finite number of trucks, warehouses and drivers, and a lot more delivery addresses. For the supply chain it’s vital to have proper data that can guide you to decide how many drivers and trucks to send, how to keep transportation costs to a minimum, while also meeting the delivery demands.
Manufacturing analytics transforms data into a strategic asset, enabling companies to innovate, operate efficiently, and remain resilient.
Deciding whether to build a manufacturing analytics solution in-house or purchase an off-the-shelf platform is an important question for manufacturers aiming to maximize return on investment (ROI). Both approaches have their unique advantages, and the decision depends on factors like budget, resources, and specific business needs.
Purchasing an off-the-shelf analytics platform can provide a faster ROI, especially for businesses with limited internal resources. These platforms often come with pre-built features, user-friendly interfaces, and integration options that allow for quicker deployment.
For example, a mid-sized manufacturer looking to optimize supply chain operations could implement a ready-made solution like Microsoft Power BI or Tableau. These platforms enable immediate access to advanced data visualization and reporting, helping the company reduce costs and improve decision-making within weeks (especially if there’s enough clean historical data in the organization).
On the other hand, developing a custom analytics solution may offer higher long-term ROI for manufacturers with complex, unique requirements.
For instance, a large-scale automotive manufacturer might invest in building a proprietary analytics platform to integrate IoT data from connected assembly lines with predictive maintenance models. Although this approach requires significant upfront investment, the customization can lead to greater efficiency, better insights, and competitive advantages over time.
To determine the ROI, manufacturers must weigh the costs of development, deployment, and maintenance against the expected benefits. A “buy” strategy is often ideal for companies seeking rapid deployment with minimal customization needs, while a “make” approach suits organizations that prioritize flexibility and long-term scalability.
Ultimately, the decision hinges on the alignment of the solution with the company’s operational goals, available resources, and growth strategy.
With the wide range of analytics tools available on the market, starting with tools like Power BI—often seen as a more advanced, constantly evolving version of Excel—can be a practical first step.
If your needs are unique, you can build custom implementations on top of your existing capabilities. While the strategy of whether buying or building ultimately depends on you and your technical team, seeking a second opinion from a custom manufacturing software development partner can provide valuable insights to guide your decision.
Successfully adopting manufacturing analytics requires more than just investing in technology—it’s about building a framework that aligns with your business goals, empowers teams, and ensures reliable data. The following best practices can help manufacturers get the most value out of their analytics initiatives.
Before diving into analytics, it’s crucial to identify what you want to achieve. Start with specific goals that address key business priorities, such as improving production efficiency, reducing waste, or enhancing product quality. Clear objectives ensure that your analytics efforts remain focused and deliver measurable results.
For example, instead of broadly aiming to "reduce costs," you might set a goal to decrease energy consumption by 10% over the next six months. With well-defined targets, it becomes easier to choose the right metrics and tools to track progress.
Launching analytics across your entire operation at once can be overwhelming and risky. Instead, start with a pilot project focused on a specific area, such as predictive maintenance or energy efficiency. Prove the concept, refine your approach, and use the lessons learned to expand analytics capabilities incrementally.
For example, a small-scale pilot might involve optimizing a single production line. Once successful, the same techniques can be applied across other lines or even different facilities.
Conduct a comprehensive assessment of the various data types available across systems used by different departments. This evaluation should account for applications tied to acquisitions, tools for accounts payable, payroll, and other administrative functions, as well as niche solutions that may have been implemented over time.
For example, a custom tool designed years ago to track niche production metrics might still be in use, yet its data is rarely integrated into broader analytics efforts. Including such overlooked sources ensures no valuable data is left untapped, helping to build a holistic and accurate foundation for analytics initiatives.
Aggregate data from different data warehouses into a single, cloud-based data warehouse or data lake. This is important to make sure that your employees don’t waste time on looking for data in multiple sources, it also helps them determine to predict different production or operational metrics that can aid you to make informed decisions.
The success of manufacturing analytics depends on selecting the right technology stack to fit your business needs. This could range from off-the-shelf tools like Microsoft Power BI to customized analytics platforms tailored to your operations.
Key considerations include scalability, integration capabilities, and user-friendliness. For instance, if your operations involve IoT-enabled equipment, you’ll want a solution that can seamlessly ingest and analyze real-time data from sensors while integrating with your existing systems, such as ERP or MES platforms.
AI and machine learning (ML) offer transformative possibilities for manufacturing analytics, enabling deeper insights and more proactive decision-making. These technologies excel at processing large datasets, uncovering patterns, and providing predictive and prescriptive recommendations.
For example, manufacturers can use ML algorithms to predict machine failures before they occur, reducing downtime and maintenance costs. Similarly, AI-driven quality control systems can analyze production images in real time, identifying defects that might escape human detection. Integrating AI and ML into your analytics strategy ensures you stay ahead of inefficiencies and unlock greater operational potential.
Analytics is only as good as the data it’s built on. Poor data quality—whether due to inaccuracies, inconsistencies, or gaps—can lead to unreliable insights and flawed decisions. To address this, establish strong data governance practices, such as defining clear data standards, automating data validation processes, and regularly cleaning datasets.
For example, ensure that IoT sensors are calibrated correctly to avoid discrepancies in equipment performance metrics, or align inventory data across systems to prevent mismatches in supply chain reporting.
Manufacturing analytics initiatives succeed when they involve more than just the IT or data teams. Bringing together operations, production, and business leadership ensures that analytics is both practical and aligned with strategic goals.
For instance, machine operators can provide context for production data, while business leaders can highlight areas where insights can directly impact profitability. This collaboration helps bridge the gap between technical analysis and actionable outcomes.
Manufacturing environments are constantly evolving, and analytics solutions must evolve with them. Regularly review your analytics processes, tools, and outcomes to ensure they remain effective. Use feedback loops to refine models, adjust metrics, or address new business challenges as they arise.
For example, as your company adopts more IoT devices or expands operations, analytics solutions may need to scale or incorporate new data sources to maintain their relevance and accuracy.
Manufacturing analytics has moved beyond theory into practice, delivering measurable results across various aspects of production, supply chain management, and business operations. Below are examples of how analytics is solving real-world challenges and driving tangible benefits for manufacturers.
Tikkurila, a leading Nordic paint company, built an IoT Predictive Maintenance platform with the goal of optimizing costs of production, line maintenance, and minimizing downtime.
With operations in over eleven countries, the company wanted to reduce downtime and the costs of maintenance for their production lines. They were also aiming to reduce product return rate and warranty claims.
The solution helped Tikkurila to reduce downtime during production cycles and reduce product warranty claims and increase final product quality.
It also allowed them to identify any modifications of manufacturing parameters and insufficient quality of ingredients.
They identified and mitigated the effects of changes in maintenance schedules and workloads.
Siemens AG’s Electric Motor Plant in Bad Neustadt faced challenges in maintaining high standards of quality control while managing costs in their CNC production. Leveraging Industrial Edge applications, Siemens transformed its approach to quality management, reducing costs, minimizing defects, and improving efficiency.
Quality management in CNC production was a resource-intensive process. Inspecting finished workpieces required specialized measuring machines, significant storage space, and time-consuming logistics. Randomized inspections posed risks of defective workpieces slipping through, leading to costly rework and delays. Additional challenges included:
To address these issues, Siemens implemented a suite of Industrial Edge applications designed for real-time monitoring and AI-powered quality control:
The implementation of these solutions delivered measurable improvements:
Unilever is addressing the challenge of excess and aging inventory by leveraging advanced technology to streamline operations and adopt a circular approach for extending product shelf life. Through partnerships and innovative solutions, the company has improved inventory turnover, reduced waste, and strengthened its sustainability commitments.
Excess and aging inventory posed significant issues for Unilever, leading to inefficiencies, wasted resources, and a negative environmental impact. Key challenges included:
Unilever implemented a multi-year strategy beginning in early 2023, in partnership with Spoiler Alert, leveraging a digital discounting and pricing intelligence platform. The solution focused on:
Unilever’s technology-driven approach has delivered significant benefits:
Unilever’s integration of pricing intelligence and advanced analytics highlights its commitment to balancing sustainability with operational efficiency, setting a benchmark for the consumer goods industry
As Industry 4.0 technologies continue to evolve, manufacturing analytics is poised to play an even greater role in shaping the future of the sector. Emerging trends, advancing technologies, and changing business priorities will redefine how manufacturers use data to create value. Here’s what lies ahead.
Artificial intelligence (AI) and machine learning (ML) are transforming manufacturing analytics by enabling more sophisticated predictive and prescriptive insights. These technologies can process massive amounts of data, uncover hidden patterns, and make recommendations in real-time.
For instance, ML models will not only predict when equipment might fail but also suggest the most cost-effective maintenance actions. Similarly, AI-driven simulations could help manufacturers test production changes virtually, reducing risks and speeding up innovation cycles.
As IoT adoption grows, manufacturers are increasingly turning to edge computing to process data closer to the source—whether on the production floor or within supply chain nodes. This reduces latency and enables real-time decision-making without relying on centralized cloud infrastructure.
For example, edge-enabled analytics can instantly adjust robotic assembly line operations in response to sensor data, ensuring precision and minimizing waste. This level of responsiveness will be critical for manufacturers operating in fast-paced or highly customized production environments.
Sustainability is becoming a central priority for manufacturers, and analytics will play a vital role in achieving these goals. From tracking carbon footprints to optimizing resource usage, data-driven insights will help companies meet regulatory requirements and align with environmental, social, and governance (ESG) objectives.
For example, analytics might integrate data from energy systems, water usage meters, and material recycling efforts to provide a comprehensive view of a factory’s sustainability performance. These insights can inform long-term investments in green technologies and more efficient production processes.
As analytics platforms become more user-friendly, access to data-driven decision-making is expanding beyond data scientists to include business leaders, engineers, and even operators. Low-code and no-code tools, along with enhanced data visualization capabilities, are empowering more stakeholders to leverage analytics effectively.
For example, shop-floor workers might use an intuitive dashboard to monitor equipment health, while executives rely on predictive models to assess market trends—all from the same platform.
As manufacturers rely more heavily on analytics, the importance of robust cybersecurity measures will grow. Ensuring the integrity of data and protecting systems from cyber threats will be essential to maintain trust and prevent disruptions.
Future analytics platforms will likely incorporate advanced security protocols, such as blockchain for tamper-proof data logs or AI-driven threat detection systems that can identify and respond to vulnerabilities in real-time.
The journey to adopting analytics may start small, but its potential impact is vast. When implemented thoughtfully—with clear objectives, the right technology, and a focus on quality data—analytics becomes more than a tool; it becomes a strategic asset. Businesses can make informed decisions faster, reduce costs, and adapt to an ever-changing landscape with confidence.
Whether your focus is on reducing operational inefficiencies, meeting customer expectations, or advancing sustainability goals, manufacturing analytics provides a practical and scalable path forward.
However, when building and implementing a PoC or trying to scale up and finish a project before deadline you can consider scheduling a call with our team of experts, as your custom manufacturing software development partner we will help you expedite the project like we already did for plenty of our clients from manufacturing.
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