Business Analytics Is Creating A New Era Of Manufacturing Intelligence

The rapid gains in analytics, big data, machine learning and Artificial Intelligence (AI) are fueling a new era of manufacturing. Based on the rapid gains in analytics applications, technologies and platforms, manufacturers can gain greater visibility across their supply chains, from the shop floor to the top floor of their companies.

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Manufacturing is among the most data-intensive industries there is. From recruiting, managing and improving supply chains to orchestrating customer orders across their production centers to delivering orders on time, manufacturing is, by nature, a very data-intensive process. Given the wealth of data inherent in manufacturing, there are many opportunities to improve production efficiency, flexibility and visibility based on the massive amount of data being generated in every manufacturing business. Combining semi-structured, unstructured and structured data into a common system of record that scales across an entire enterprise is what’s needed to excel in many manufacturing businesses, both now and in the future.

The Era Of Manufacturing Intelligence Has Arrived

The proliferation of data in manufacturing creates many opportunities to improve operations through the use of analytics applications and platforms. The same technologies that form the foundation of business analytics today are agile and flexible enough to excel in manufacturing.  Manufacturing intelligence, by definition, is the ability of analytics applications and platforms to capture, aggregate, analyze and provide line-of-business users with accurate insights into their manufacturing operations.

Scaling from descriptive to predictive, manufacturing intelligence is designed to meet the information needs of a wide variety of roles across manufacturing operations. The scope and scale of manufacturing intelligence is based on the need to monitor machines and processes at a very detailed level, so that viable maintenance and performance upgrades can be defined. Machine monitoring includes monitoring, managing and predicting production cycle times, including how efficient a given production process is, in addition to tracking machine failures.  Diagnostic uses of manufacturing intelligence include creating new metrics and key performance indicators (KPIs) for tracking production capacity utilization, production costs and variances, as well as providing data to streamline failure analysis. Predictive and prescriptive manufacturing intelligence are the areas of greatest interest to manufacturers today.  Gaining great insights into line and product team flexibility, determining predictive maintenance and optimizing production schedules using predictive and prescriptive analytics has the potential to revolutionize the manufacturing industry.

The study Industrial Analytics Report 2016/17 from the Digital Analytics Association e.V. Germany (DAAG), in collaboration with research firm IoT Analytics GmbH took a look at data usage in the manufacturing industry. The report provides a wealth of insights regarding how business analytics are enabling greater industrial analytics and manufacturing intelligence. The study specifically found that 79% of manufacturers consider the predictive and prescriptive maintenance of machines as the most important application for analytics and manufacturing intelligence in the next 1 – 3 years. 77% of respondents consider customer/marketing-related analytics and analysis of product usage in the field (76%) are the second- and third-most important. The following graphic provides an overview of the 13 most important applications of industrial analytics:

Manufacturing Intelligence Is Scaling, Providing New Metrics and KPIs

Every manufacturer creates a data ecosystem every day it opens for business and produces products. The foundational elements of every manufacturer’s data ecosystem need to be managed to their specific strengths, with the contributions they can collectively make to greater manufacturing intelligence. The most prolific sources of data include customers, especially when they’re collaborators in creating products, thanks to the data generated from their transactions. Suppliers, the production process itself and Internet of Things (IoT) initiatives are now in production for potential use in manufacturing intelligence. Industrie 4.0 initiatives are becoming more prevalent as well, creating even more data. All these sources of data are converging, making the conditions ideal for manufacturing intelligence to scale in the next three years.

Real-time integration and monitoring enable the analytical foundation of manufacturing intelligence to scale from providing relatively simple descriptive analytics to more advanced predictive and prescriptive metrics and KPIs. Real-time data obtained from Enterprise Resource Planning (ERP), Supply Chain Management (SCM), Customer Relationship Management (CRM) and other enterprise systems provide contextual intelligence not available before, and are accelerating the adoption of these key manufacturing metrics and KPIs:

Source: Using Big Data for Machine Learning Analytics in Manufacturing

Taking On The Toughest Compliance Challenges With Manufacturing Intelligence

Manufacturing intelligence can create a single, unified system of record for all activity on a given product. In industries with stringent quality control requirements, having a system of record regarding all activity can save thousands of hours of work per year, and millions of dollars in lost revenue. An example of how manufacturing intelligence is used for compliance includes medical device manufacturers supporting their track and trace initiatives that keep them in compliance with ISO 13485, ISO9001, Current Good Manufacturing Practice (CGMP) and FDA 21 CFR Part 820. The essence of track and trace is determining the current and past location of a given part, assembly or component. Manufacturing intelligence is ideal for this requirement, and is proving to be very valuable in assisting medical device manufacturers today.


Manufacturing intelligence is enabling a new era of manufacturing, one not constrained by a lack of real-time data or insights that help previous generations of global manufacturing back. By integrating enterprise-level systems and creating a unified system of record, manufacturing intelligence has the potential to revolutionize how products are produced, shipped, sold and serviced. There are many areas where manufacturing intelligence can bring greater business value, too. Take, for example, creating a service-based business that captures sensor data, analyzes it and provides preventative maintenance recommendations for keeping machines up and running. GE Oil and Gas is doing this today across its drilling platform business, relying on manufacturing intelligence as the catalyst for their future revenue growth.

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