According to a McKinsey report manufacturing is the most data-prolific industry generating 1.9 petabytes of data every year. Imagine a network of IoT devices like sensors, LiDAR, thermostats, automated belts, cameras, and much more, manufacturing setups are continuously streaming data in realtime. What technology can help you analyze this vast number of datasets in order to gain a clear interpretation of this data? The answer is Business Intelligence (BI) tools in manufacturing that refine your operational decision-making with extraordinary insights.
The Role of Business Intelligence in Manufacturing
As manufacturing transitions towards a data-driven strategic model, BI tools become the apparatus for the collection, integration, analysis, and representation of data originating from disparate sources. BI tools are essential for every stage of your manufacturing journey check this out:
Strategic planning
In the strategic planning phase, BI tools help to gather information on competitors and implement the strategies that are working for them. Procurement and production plans are optimized based on inventory status and market insights to predict demands. BI helps in answering the core questions like where we are and what needs to be changed to build a core plan.
Product development
BI helps validate a product or feature idea by reflecting on what has met success by analyzing customer feedback on similar ideas that were launched to the market. Testing, adjusting, and retesting of new products is also streamlined based on BI metrics and analysis reports. Collecting, analyzing, and representing data with BI tools helps to validate a product idea and develop a foolproof product roadmap.
Production optimization
BI tools gather data about various machineries in the production pipeline to analyze whether their performance is optimized or some bottlenecks are slowing down the workflow. Production managers can set up automatic monitoring for KPIs like equipment downtime, cycle times, and defect rates to get an overall picture of production performance with BI tools. This in turn also helps to identify equipment in bad health and take preemptive measures to restore it and save production breakdown.
Supply chain management
BI dashboards visualize the composite picture of the supply chain on a single screen. Data is collected and analyzed in real-time from suppliers, distributors, and logistics partners. BI allows for real-time monitoring of inventory levels, lead times, and transportation assisting with risk mitigation and maintaining overall efficiency of the supply chain.
Use Cases of Business Intelligence for Manufacturing
Inventory optimization
Meticulous inventory planning saves manufacturing companies from financial losses incurring from wastage due to overproduction or increased production lead time. BI provides accurate, real-time insights into stock levels, sales trends, and product demand. Production Planners leverage BI insights for demand forecasting of a product and adjust their inventory levels accordingly to minimize stockouts and reduce carrying costs.
Production scheduling
BI paints a real-time picture of machine utilization, employee availability, and material resources. Production Schedulers can optimizedly plan production schedules considering constraints like machine and employee availability. Quick adjustment of production schedules is also possible with the real-time visibility that BI offers into the production process.
Workforce management
BI tools help to track irregularities in timesheets, detect patterns for no-shows, and assess employee performance to identify gaps in workflow optimization or skills. Production Managers can spot the right talent with a specific skill set and deploy them in resource-depleted processes. Demand forecasting defines talent requirements, and the hiring team can be alerted beforehand for headhunting.
Quality management
BI tools are crucial for monitoring and quality tracking. Real-time data on product defects, production errors, and equipment performance helps manufacturers identify the root causes of quality issues and take preventive measures. BI also finds a significant role in SQMP (Supplier Quality Management Procedure) through audit scoring systems and generation of instant reports and non-conformances with supplier and stakeholders.
Energy efficiency and sustainability
BI tools help to monitor energy consumption and track energy usage patterns in manufacturing pipelines. Identifying bottlenecks with BI tools results in reducing non-productive time and increased energy efficiency. Tracking environmental performance and optimizing processes to reduce carbon emissions based on BI data helps to achieve sustainability and ensure compliance.
EMI: Specialized Software for Business Intelligence in Manufacturing
The manufacturing industry requires its own niche BI tools that ingest data types unique to manufacturing datapoints to analyze and generate insights that aid in decision-making related to industry workflows. These are high-tech production intelligence software developed exclusively for manufacturing, the Enterprise Manufacturing Intelligence (EMI) software. They ship with unique features like live monitoring for machine performance, or predictive maintenance. EMI also assists with supply chain tracking and route planning based on real-time data. Integrating EMI with the ERP system assists in inventory planning, scheduling, and production planning.
Components of a Business Intelligence in Manufacturing System
BI in manufacturing involves transforming raw data into valuable insights. Following are the key components a BI tool consists of:
Data collection
The first step in implementing BI is collecting data from various sources and integrating it into a centralized data warehouse or data lake. In the manufacturing industry data is collected from different sources such as machines, ERPs, CRMs, and inventory logs.
Data preparation
Data collected from various sources are both structured and unstructured in nature and of varying data types. Some of the collected data is either redundant or incomplete. To make them suitable for processing by a BI engine all data needs to be prepared and transformed into a uniform data type.
Data analytics
BI engines analyze data using advanced analytical techniques, such as predictive analytics, machine learning, and artificial intelligence, to uncover hidden patterns and trends.
Data visualization
Interactive dashboards represent the analyzed data in the form of visualization over a single screen making it easy to grasp the insights by general users.
Decision support
The reports provide actionable insights to decision-makers. This helps them to identify areas of improvement and implement strategies to enhance efficiency, reduce costs, and increase profitability.
Best Practices to Adopt Business Intelligence in Manufacturing
For a successful implementation of business intelligence in manufacturing stick to the following best practices:
Start with a clear business case
Get a clear understanding of the problems you hope to resolve with a BI system. Define the benefits and set up metrics for calculating the ROI from BI integration.
Prepare high data quality pipelines
BI involves processing vast amounts of data in real-time. High data quality and a consistent supply of data is crucial to the success of BI integration. The data from different IoT devices or shadow applications utilized across departments is unstructured or diverse in data type and requires data cleansing and preparation before it can be processed by a BI tool. Consider setting up a real-time data ingestion pipeline with ETL or ELT processes to collect, clean, and load data automatically.
Involve all stakeholder
BI is successful only with enterprises that have implemented robust data governance. To ensure this every stakeholder of an organization should be involved and trained to follow some practices and guidelines in creating, storing, and retrieving data. Different departments should act in coordination to implement and adopt the BI system.
Leverage modern tools and techniques
Consider adopting cloud-based BI solutions that ship with infinitely scalable computing power. Implement machine learning and deep learning algorithms within your BI solutions to get a broad context of your enterprise data and generate unprecedented insights. Build Generative BI solutions by integrating AI agents into your EMI software so that every non-technical employee can harness the benefits of the BI system based on natural language interactions.
Monitor and measure progress
Implementing BI is not the end of the journey, but continuous monitoring needs to be done to gauge the success of a BI system, and the challenges faced. Evaluate the success of BI implementation based on set metrics and make regular adjustments to ensure that the BI strategy aligns with the organization’s goals and objectives.
Conclusion
In a data rich sector like manufacturing BI tools can be pivotal in unearthing unique information and transforming the perspective of business decision making. From production planning to supply chain management BI can track patterns, identify bottlenecks, and influence process optimization. For a modern manufacturing enterprise, BI is a digital transformation technology that can help to uncover unprecedented information and set them ahead of the curve as a data-driven business.
