Business intelligence is a cornerstone for modern manufacturing businesses, providing insights that drive informed decision-making and improved performance.
Although analytics in manufacturing is often associated with production-focused areas, such as machine monitoring or maintenance optimization, business analytics focuses on improving decision-making in areas like sales, marketing, procurement, and profitability.
The scope of business intelligence in manufacturing includes several key areas: sales performance reporting, marketing effectiveness, materials procurement, logistics, and profitability tracking.
Each of these areas relies on a set of specific metrics and KPIs that provide deeper insights into the organization’s overall efficiency and financial health. For example:
Business analytics help manufacturing companies to eliminate data silos and create a unified view of performance and ensuring a high level of visibility empowers decision-makers to detect patterns and correlations that would otherwise go unnoticed when data is isolated in individual departments.
For example, a manufacturer might discover that a drop in profitability is linked to inefficiencies in the procurement process or that sales performance is being influenced by a shift in market dynamics captured through marketing analytics.
Ultimately, business intelligence serves as the glue that connects different parts of an organization, providing a comprehensive understanding of how various activities impact the overall business.
Business intelligence goes beyond the production line, addressing key areas like business operations, sales, marketing, materials procurement, and profitability tracking. Below, we explore each core area in detail.
Business operations analytics provides a deep understanding of how efficiently resources are being utilized and how processes are performing across the organization. This area of analytics focuses on insights related to operational efficiency, process optimization, and resource utilization, helping organizations pinpoint inefficiencies and identify opportunities for improvement.
Use Cases:
Sales Analytics focuses on understanding and optimizing the sales pipeline, identifying high-performing products, and forecasting future demand. For example a Sales Performance Reporting Power BI can be used to create a dashboard that leverages historical data and advanced models to inform strategic decisions related to budgeting and forecasting processes.
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Marketing analytics helps manufacturers assess the effectiveness of their marketing initiatives, track campaign performance, and understand customer behavior.
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Effective procurement analytics ensures that manufacturers have the right materials at the right time, without incurring unnecessary costs. This involves analyzing supplier performance, optimizing procurement costs, and enhancing order accuracy.
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Profitability tracking involves understanding cost structures, monitoring profit margins, and analyzing the financial health of the business. Gaining deeper insights into a company’s profits is something every organization can benefit from. This area is closely connected to all other operational aspects within manufacturing. From a manufacturing business intelligence software development perspective, enhancing profitability tracking improves the BI department's capabilities and directly influences other business functions. By building modern business analytics solutions and implementing them in your organization they eliminate manual workload and therefore help in efficiently identifying the most profitable products, regions, or customer segments and aligning strategies to maximize growth.
Use cases:
Manufacturing is a prime example of an industry that goes beyond simple production facilities, workers, and engineers.
Behind the scenes, there’s a dedicated team continuously monitoring metrics to ensure they align with the ongoing technological advancements happening in the manufacturing industry, driven by IoT solutions and AI implementations.
The implementation of business intelligence solutions in manufacturing can yield substantial returns for manufacturing organizations, translating data into actionable insights that drive strategic decisions. Moreover, the long-term strategic benefits extend beyond immediate financial gains, contributing to greater agility, enhanced customer understanding, and improved market positioning.
Calculating the ROI of business intelligence solutions involves measuring the financial and non-financial impacts on business performance.
While we could estimate the ROI improvement from reducing manual work for the BI department and lowering overall costs, we won’t provide a general figure because every company is unique, and the exact impact varies. However, as your manufacturing technology consulting partner, we encourage you to reach out to our team of experts. Together, we can analyze your specific case, identify relevant KPIs, and determine how much your metrics could improve with the right analytics technology in place.
What we can do is to provide some key areas that you can look into and that you can use to propose an ROI increase over the next quarters.
Key indicators include cost savings from process optimization, increased revenue through better sales forecasting, and efficiency gains achieved through resource utilization. For example, a manufacturer that uses analytics to optimize its supply chain can reduce inventory holding costs and improve order accuracy.
An example is Intel, which recognized that to continue delivering quality products to its clients, it needed a clear cost-saving plan. As part of this plan, the company decided to reduce its headcount by 15,000. While this news is unfortunate, the decision was based on clear, actionable data made possible by the robust business intelligence infrastructure they had established.
Other quantifiable benefits might include reductions in production downtime due to data-driven scheduling, improvements in marketing campaign efficiency by targeting the right customer segments, or revenue growth through the identification of cross-selling and up-selling opportunities.
To better understand these metrics, we have prepared a few case studies that analyze the real-world impact of technology on business intelligence in manufacturing.
Danone enhanced sales planning, financial forecasting, and decision-making processes across 11 countries by implementing a comprehensive Business Intelligence (BI) platform. This solution provided near-real-time insights, enabling better data-driven decisions.
The project supported multiple business areas, including sales, purchasing, finance, marketing, and operations, across the FMCG and manufacturing industries.
Danone needed a unified Business Intelligence solution for its sales and finance departments across 11 countries. The company faced the challenge of providing quick access to essential KPIs for business users in regions with varying market conditions. Multiple business areas, including sales, finance, and operations, had to be integrated to streamline decision-making processes.
Danone redesigned their entire business analytics architecture, migrating multiple technologies to Microsoft Azure. The solution integrated internal systems (SAP, Navision, and others) with external sources such as market data, sell-through and sell-out information, and Salesforce Automation solutions.
The solution optimized sales force effectiveness, reduced stock shortages, and enabled top management to make better, data-driven decisions. As a result, Danone saw enhanced decision-making capabilities based on full analyses of income, costs, and effective margin across customers and products.
Hochland accelerated decision-making by up to 200% by switching over 100 users from Excel to Power BI. Together with their manufacturing custom software development partner, they implemented a cloud-based, self-service solution, integrating data from 26 different sources into a unified data warehouse. The project significantly enhanced Hochland's business intelligence capabilities, supporting faster, more informed decisions across multiple departments.
Hochland sought a technology partner to modernize its data infrastructure. The core goals were to create an easily expandable database, speed up data delivery to decision-makers, and improve report accessibility and transparency. Hochland needed a scalable solution that would integrate numerous data sources into a single, consistent data model and allow for self-service analytics across the organization.
Hochland, implemented a solution based on Microsoft SQL Server and Azure Analysis Services, creating a scalable data warehouse hosted in the cloud. This unified 26 data sources, providing a single source of truth for the organization. By using Power BI, Hochland was able to perform ad-hoc analyses and generate new reports with ease. The project followed the Scrum framework, ensuring continuous alignment with business needs.
Within the first few months, over 100 employees transitioned from Excel to Power BI, dramatically improving data processing times and enabling self-service business intelligence. Six new, complex Power BI reports were created for different business areas, and the integration of 26 data sources into a standardized model provided a robust foundation for decision-making in sales, planning, and operations. Hochland now benefits from a scalable, cloud-based solution that enhances transparency and efficiency across the organization.
DSI Underground improved its sales performance reporting and financial planning by implementing a comprehensive data management solution. This transformation allowed the company to streamline data consolidation, budgeting, and reporting across more than two dozen entities in different regions, boosting decision-making efficiency and accuracy.
As DSI Underground grew rapidly across multiple regions (Europe, APAC, LATAM, and NA), managing data became increasingly complex.
The company faced challenges with integrating data from various ERP and local systems across multiple time zones, which hampered the efficiency of budgeting, forecasting, and sales performance reporting processes.
The lack of a unified data management system made it difficult to gain timely and actionable insights.
To address these challenges, DSI Underground together with its manufacturing custom solutions development partner from Poland implemented a solution that involved separate integration instances for each entity, ensuring data was consolidated efficiently across all regions.
The solution included dedicated data marts for each entity and a robust data consolidation process. A tabular data model was developed to structure the data, and Power BI was used to provide interactive dashboards and reports, allowing business users to easily access and analyze sales performance.
The new system improved DSI Underground's ability to manage data across multiple entities and regions. This led to significant improvements in the efficiency of their budgeting and forecasting processes, resulting in more accurate financial planning.
Sales performance reporting was streamlined, providing better insights and enhancing decision-making.
Power BI’s interactive reports allowed key stakeholders to access real-time data, improving the overall accessibility and visualization of the company’s performance metrics.
Business intelligence can drive substantial value for manufacturers but often face several obstacles that need to be addressed for success.
These challenges range from technical difficulties in data integration to organizational resistance to adopting data-driven approaches. Below are common challenges and practical solutions for overcoming them.
A key challenge for business intelligence solutions implementation in manufacturing is integrating data from various systems like ERP, CRM, and legacy tools. Different data formats, inconsistent definitions, and data silos make it difficult to get a unified view of business performance.
Solution: Start with a clear data integration strategy. Use standardized data models and automated tools like ETL (Extract, Transform, Load) pipelines or APIs to sync data in real time. Ensure data accuracy by establishing regular audits and validation processes and adopt a governance framework to maintain consistency.
Shifting to data-driven decision-making can face pushbacks, especially in companies with long-standing processes. Teams may be hesitant to trust analytics or change their workflows.
Solution: Focus on building a data-driven culture, starting with strong leadership support. Demonstrate the value of analytics through small, successful projects that show quick wins.
Even with good infrastructure, the value of analytics is limited if teams can’t effectively interpret and act on the insights.
Solution: Offer targeted training that helps teams learn to use analytics tools and understand the available data. Encourage collaboration between business and technical teams, and establish a community or center of excellence to provide ongoing support and mentorship.
A thoughtfully designed data architecture is the backbone of successful business analytics. It ensures that organizations can efficiently gather, integrate, and analyze data from various sources, providing actionable insights that inform decisions across key business functions.
The main challenge lies in creating a system that can handle increasing data volumes and complexity while remaining flexible enough to integrate new data sources and adapt to the business needs.
Cloud-based data architectures have become the go-to solution for businesses due to their scalability, cost efficiency, and ability to support diverse data types and analytical workloads. When applied to BI solutions, these architectures typically consist of distinct layers, each with its unique function:
For manufacturing business intelligence solutions to drive real value, the data architecture must be designed for growth and agility. Here are key considerations:
Data is probably the most important asset of any organization regardless of their industry. Protecting it and handling it properly should always be a priority. It is also important to have quality data at hand that can help people from all departments make data-driven and informed decisions on the spot to avoid any potential operational challenges.
Data integration plays an important role in business intelligence. To support effective decision-making, you need to combine information from multiple systems into a unified and accurate format.
The key challenge isn’t just collecting data—it’s turning it into a consistent, actionable format that provides a complete view of business performance.
Most organizations rely on multiple systems like ERP, CRM, and SCM, each with critical business data. To create a cohesive view:
High-quality data is critical for sound decision-making. Key strategies to maintain data integrity include:
Master Data Management (MDM) consolidates key business data (e.g., customers, products) into a single, reliable source of truth. This unified view enables better decision-making:
Most of the strategies above are probably golden standards of data handling, security and integration, there are other potential best practices that can be used for your particular case, but in order to receive tailored advice you can reach out to our team of manufacturing technology development consultants and find out what are the key best practices that you have to take into consideration for your project.
Advanced analytics techniques such as segmentation, clustering, and predictive modeling play an important role in enhancing business intelligence in manufacturing by uncovering hidden patterns and providing actionable insights. Here are some techniques that you might want to consider when using advanced analytics for your manufacturing operational needs:
Employing these advanced analytics techniques enables organizations to gain a deeper understanding of their operations, boost profitability, and make proactive decisions that drive business success.
Using advanced analytics solutions for business intelligence in manufacturing goes beyond improving efficiency—it provides real-time insights that empower manufacturers to make smarter decisions across sales, operations, and profitability.
Critical decisions—such as new product launches, waste reduction, cost-saving strategies, material changes, and even headcount adjustments rely heavily on accurate and actionable data. If this data is skewed or mishandled when developing custom software solutions, it can lead to poor decisions that may significantly impact your business.
If you're looking for support, you can schedule a free consultation call with our team. As a company specializing in building custom software for manufacturing, we can guide you through the do's and don'ts of using and implementing business analytics and advanced business intelligence tools, ensuring the numbers work for you—not the other way around.
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