McKinsey: How to use big data to improve manufacturing

McKinsey, a consultancy, published an article on how to make use of large data to improve manufacturing, giving in-depth analysis of how large data and advanced analysis makes biopharmaceutical, chemical and discrete manufacturing more rational. The article specifically mentions how manufacturers in the process-based industries use advanced analysis to increase production and reduce costs. Today, manufacturers can track a large number of data from production and sales processes. The McKinsey article analyzes several cases to illustrate how large data and advanced analysis applications and platforms can help with business decisions.

By looking at the core factors that determine process effectiveness, the large data and the advanced analysis on it how to clarify the value chain in manufacturing, and then help managers to take action in order to continue to improve the manufacturing process. Here are 10 ways to get big data to subvert the manufacturing process:

First, in the biopharmaceutical industry in the production process, further improve the accuracy, quality and output.

In the biopharmaceutical production process, manufacturers often need to monitor more than 200 variables to ensure the purity of the ingredients, while ensuring that the drugs produced meet the standards. One of the factors that makes the biopharmaceutical process challenging is that production will change between 50% and 100%, and the reason cannot be identified immediately. With advanced analysis, the manufacturer is able to track 9 variables that are most capable of affecting production changes. With the help of these measures, they have increased the production of vaccines by 50%, and the cost of saving a single vaccine species each year is 5 million to 10 million dollars.

Second, accelerate the integration of it, manufacturing and operations, so that 4.0 of the vision of industry to become a reality faster.

Industry 4.0 was proposed by the German Government to promote automation of the manufacturing industry through the development of intelligent factories. Large data has been used to optimize production schedules, based on constraints related to suppliers, customers, effective capacity, and cost. Manufacturers in the manufacturing value chain in highly regulated industries are making strides towards industrial 4.0 thanks to the help of German suppliers and manufacturers. At the same time, as an opportunity, the various departments of these vendors can fully play their respective functions, and large data and advanced analysis is critical to success.

Third, large data to help improve manufacturing performance in 3 main areas

They are: Better forecast product requirements and adjust capacity (46%), understand factory performance across multiple indicators (45%) and provide service and support to consumers faster (39%). The data are based on a recent survey of "LNS Research and Mesa International".

Iv. integrate advanced analysis into the Six Sigma Dmaic (definition, measurement, analysis, improvement and Control) framework for continuous improvement

Gain a deeper understanding of the work process of a dmaic-driven improvement plan, as well as an in-depth grasp of how the plan impacts all other areas of manufacturing performance. Compared with the past, this area of development is expected to lead to a more consumer-oriented production process direction.

Five, compared with the past, can be more detailed from the supplier quality level of scrutiny, and can more accurately predict the performance of suppliers

Through the application of large data and advanced analysis, the manufacturer can view the product quality and distribution accuracy in real time, and weigh how to distribute order production tasks between different suppliers according to the time urgency. Control of product quality takes precedence over delivery schedule.

Vi. monitoring of product compliance and traceability to specific production equipment

By equipping all equipment on the production center with sensors, the operations manager is able to immediately understand the status of each device. Through advanced analysis, the working conditions, performance and skill differences of each equipment and its operators can be reflected. This data is important for improving the workflow of the production center.

Seven, only the largest sales margin of the custom product model, or in the production way to produce the least impact on the capacity of the product model

For manufacturers with many complex product models, custom products or production products can lead to higher gross profit margins, but can also cause a sharp rise in production costs when the production process is not properly planned. With advanced analysis, the manufacturer is able to calculate a reasonable production plan to minimize the impact on the current production plan in the production of the customized or production product, and then to make the planning analysis specific to the equipment operation plan, personnel and store level.

Viii. integrating quality management and compliance systems into consideration and giving priority to both enterprise level

It is time for manufacturers to give a more strategic perspective on product quality and compliance. The McKinsey article gives several examples of manufacturers using large data and analysis, pointing out how to analyze the parameters that are most relevant to product quality management and compliance through large data and analysis, in order to help managers gain a deeper understanding. Most of these parameters are at the enterprise level, not just in product quality management or compliance departments.

Ix. quantify the impact of daily productivity on the financial situation of the enterprise and the production equipment level

Through large data and advanced analysis, the manufacturer's financial position and daily production activities can be directly linked. By tracking each production facility, managers are able to understand the operational efficiency of the plant, and production planners and senior managers can adjust the production scale better.

By monitoring products, manufacturers can proactively provide preventive maintenance advice to customers in order to provide better service

Manufacturers are starting to produce more complex products, with on-board sensors in their products and managed by the operating system. These sensors can collect data on the operation of the product and issue preventative maintenance notifications as appropriate. With large data and advanced analysis, these maintenance recommendations can be issued at the first time, and consumers can get more value from them. For now, GE has used similar techniques in its engines and rigs.

(Responsible editor: Mengyishan)