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Application and Benefits of Business Intelligence in Manufacturing


Businesses collect a huge volume of data daily from various sources like ERMs, e-commerce platforms, supply chains, and many other internal and external sources. In making use of this data, we make use of data-driven decisions, organizations need business intelligence (BI).

What is business intelligence?

It refers to a mix of business analytics, data mining, data visualization, data tools & best practices to help companies make more informed, data-driven decisions. When an organization has a comprehensive view of its business using its data and drives change, eliminates inefficiencies, and can quickly adapt to market changes, it can be said to have achieved modern business intelligence.

It encompasses data mining, process analysis, performance benchmarking & descriptive analysis and presents the processed data in easy-to-digest reports, performance measures, and trends for the management.

BI combines core concepts like

  • Data warehousing –  It stores data and information from various sources in an accessible centralized location.
  • Business analytics – Or data management tools that are used to mine and analyze the data from the data warehouse, like extraction, Integration, and Interpretation.
  • Business performance management (BPM) – It monitors and analyzes progress towards business goals.
  • Data visualization – Used to generate graphs, charts, and diagrams.
  • Reporting capabilities – Creates dashboards, finds trends, generates performance analytics & provides quick access to information.

What is Enterprise resource planning?

Enterprise resource planning (ERP) is a software system that is used by companies to manage daily business activities like accounting, project and risk management, compliance & supply chain management. ERP systems collate various business processes and enable the flow of data between these processes. These systems also help provide transparency into the business process by tracking all aspects of logistics, production, and finance. The current modern ERP software comes with embedded AI, and automation by offering an immersive user experience. ERP can help organizations outpace changes and respond quickly to market changes and resulting in greater business agility and operational success.

Benefits of business intelligence in the manufacturing sector

Inventory management – Inventory of raw material, work in progress & finished products are vital for a manufacturing unit. Real-time visibility of every item is essential to ensure that the production line does not stop because of raw material shortage or finished product surplus. BI can help track inventory across multiple jobs and shifts to ensure that balance is maintained for better planning and management.

BI can also generate predictive analysis to assess material requirements which can help manufacturers avoid over or under-stocking of raw material, which can significantly affect productivity and order fulfillment.

Quality management – The current modern manufacturing process has seen an integration of BI tools with ERP and quality management practices. Data generated from both manual and computer-related quality inspections makes it easier to sort out quality issues at an early stage and implement corrective measures to ensure that it does not occur in the future. BI can also help implement measures that ensure that manufacturing parameters are fed into the system to ensure that quality issues do not crop up. Quality inspections using BI can be implemented from the raw material to the finished product stage.

Improved data-driven decision-making – Based on key information and metrics that are generated using BI, informed decisions can be taken. Decision makers can analyze the performance of the company by comparing the past and current performance of the company. Further, BI helps make the right decisions at the right time while offering strategic data points.

Isolate downtime causes – BI solutions can be used to monitor machine performance using sensors and other embedded devices that continuously send critical, real-time data for analytics and monitoring. This helps in the analysis of uptime and downtime, machine performance, etc to isolate downtime causes if any. This also helps in identifying if any machinery needs an overhaul, or repairs and avoiding bottlenecks.

Increased visibility – In today’s digital world, large volumes of data are available from different sources and it’s easy to miss out on some vital information, which affects decision-making. Using BI, dashboards can be generated which are data visualization tools that can help in tracking, analyzing, and displaying KPIs, metrics, and other critical data points on screen for easier understanding and interpretation of raw data.

Boost productivity – BI automates almost all reporting processes using big data leading to a reduction in time, effort & costs, thereby increasing productivity. BI can also be used for predictive and prescriptive analysis. Predictive analytics utilizes historical data combined with statistical modeling, data mining, and machine learning to make predictions about future outcomes. This tool is used to identify opportunities and risks in business. Prescriptive analysis analyzes data and uses all possible permutations,

Continuous improvement – The use of business intelligence in the manufacturing process provides analysis of historical and real-time data which can be used for improving and benchmarking the overall business working. This can also include improvements in shop floor productivity and increasing overall business performance.

BI can have a huge and identifiable impact on the manufacturing sector. These tools help in inventory control, managing supply chains, identifying and removing operational bottlenecks & automating routine tasks. A Business intelligence software development services company can take this big data and provide dashboards with intelligent and powerful reports and analysis. Such services can help businesses identify issues, trends & opportunities and organize and structure data in a way that can create concise and actionable reports that can be used by all the stakeholders within the organization.