How big data is disrupting manufacturing

How Big Data is Disrupting Manufacturing_Data Analyst Exam

By looking at the core factors that determine process effectiveness, how can big data with advanced analytics on top of it clarify the value chain in manufacturing, and then help executives take action in order to make continuous improvements to the manufacturing process. Here are 10 ways on how big data is disrupting the manufacturing process:

i. In the biopharmaceutical industry's production process, further improving accuracy, quality and yield.

In the biopharmaceutical production process, manufacturers typically need to monitor more than 200 or more variables in order to ensure the purity of raw material ingredients, as well as to ensure that the drugs produced meet standards. One of the factors that makes the biopharmaceutical manufacturing process challenging is that yields can vary between 50 and 100 percent, with no immediate way to identify the cause. Using advanced analytics, manufacturers are able to track the nine variables that are most likely to affect yield variations. With the help of these tools, they were able to increase vaccine yields by 50 percent and save between $5 million and $10 million per year on a single vaccine variety.

II. Accelerate the integration of IT, manufacturing and operations to make the vision of Industry 4.0 a reality faster.

Industry 4.0 was proposed by the German government to promote automation in the manufacturing industry through the development of smart factories. Big data has been used to optimize production schedules based on constraints related to suppliers, customers, effective capacity, and costs. Manufacturers along the manufacturing value chain in highly regulated industries are making strides toward Industry 4.0 with the help of German suppliers and manufacturers, and as a result, these manufacturers are able to fully utilize their respective departments, and big data and advanced analytics are critical to success.

Three key areas where big data is helping to improve manufacturing performance

are: better predicting product demand and adjusting production capacity (46%), understanding factory performance across multiple metrics (45%), and providing faster service and support to consumers (39%). These figures are based on a recent survey by LNS Research and MESA International.

Four: Integrating Advanced Analytics in the Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) Framework for Continuous Improvement

Gain a deeper understanding of the process of working on a DMAIC-driven improvement program, as well as an in-depth appreciation of how the program impacts all other areas of manufacturing performance. Developments in this area are expected to lead to a more consumer-driven shift in the manufacturing process than has been the case in the past.

V. The ability to look more closely at supplier quality than ever before, and to predict supplier performance more accurately

Through the use of big data and advanced analytics, manufacturers are able to look at product quality and delivery accuracy in real time, and weigh how to allocate orders between suppliers based on time constraints. Control of product quality is prioritized over shipping schedules.

VI. Monitoring product compliance and tracing it back to a specific production facility is now possible

By equipping all equipment in the production center with sensors, operations managers can instantly see the status of each piece of equipment. Through advanced analytics, the working conditions, performance, and skill differences of each machine and its operator can be visualized. This data is critical to improving workflow in the production center.

VII. Sell only the customized models with the highest profit margins or produce the models that have the least impact on production capacity on a fixed-volume basis

For manufacturers with many complex models, customized or fixed-volume production can lead to higher gross margins, but it can also lead to a sharp increase in production costs if the production process is not properly planned. Using advanced analytics, a manufacturer can calculate a reasonable production plan to produce these customized or customized products with minimal impact on the current production plan, and then analyze the planning down to the level of equipment operation plans, personnel, and stores.

VIII. Integrate and prioritize quality management and compliance systems at the corporate level

It's time for manufacturers to take a more strategic look at product quality and compliance. The McKinsey article gives several examples of manufacturers using big data and analytics to help managers gain a deeper understanding of the parameters most relevant to product quality management and compliance through big data and analytics. Most of these parameters are at the enterprise level and do not exist only in product quality management or compliance departments.

9. Quantify the impact of daily production capacity on corporate financials, down to the level of the production facility

With big data and advanced analytics, a manufacturer's financials can be directly linked to daily production activity. By tracking each piece of production equipment, managers are able to understand how efficiently the plant is operating, and production planners and senior managers are better able to scale production.

X. By monitoring products, manufacturers are able to proactively advise customers on preventative maintenance to provide better service

Manufacturers are starting to produce more complex products that need to be equipped with on-board sensors and managed through an operating system. These sensors collect data on how the product is performing and send preventive maintenance notifications based on the situation. With big data and advanced analytics, these maintenance recommendations can be issued at the first sign of trouble, and consumers can get more value out of them. General Electric currently uses similar tactics on its engines and rigs.

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