Industry 4.0, or the fourth industrial revolution, is a term being used to define modern manufacturing’s utilization of automation and big data. How are companies making manufacturing data science a critical part of their success?
Conditions are ideal for manufacturing data science to be implemented: data is seemingly unlimited and accessible through cheap and abundant storage and inexpensive, scalable computing. Algorithms are open source. Customers are interested and willing to collaborate in product development. Data Scientist is the top job on Glassdoor.com for the past three years. Here are the ways companies are making manufacturing data science work for them and their customers:
Increased Revenue
Thirty three percent of companies cited increased revenue as the biggest benefit of manufacturing data science in a survey by the Digital Analytics Association of Germany. The more accurate forecasting of demand coupled with just-in-time manufacturing means matching supply to demand and avoiding both carrying expensive inventory and losing sales.
Increased Customer Satisfaction
The second biggest benefit of manufacturing data science cited by companies in the German survey is increased customer satisfaction. The collection and interpretation of customer insights means producing products more in-line with customer specifications and expectations. This product optimization means more appealing and more saleable products. Pricing optimization through analysis of material and production costs matched to customer purchase expectations means increased sales at the most viable price.
Machine Performance
Understanding why a machine fails and then being able to predict when a machine may fail helps companies reduce downtime on critical steps in their production process. Data science can discover new ways to manage costs and increase quality through improved machine performance.
Improved Quality
Warranty analysis gives valuable information on the quality and reliability of the product. These analyses are early warnings signs of product defects or customer requested improvements. Robotic implementation in some processes has removed the need for human intervention and human error, improving quality and, in some cases, increasing safety.
Sustainability
Manufacturers recognize by saving energy and reducing carbon emissions they not only help the environment but reduce costs. Through the use of manufacturing data science they can meet and exceed sustainability goals. Improved compliance through sensor detection and improved safety mean a healthier work environment as well as reduced costs. Predictive analytics in smart factory design shows how production and costs can be impacted by new machinery and new processes.
Challenges
AIMultiple, an artificial intelligence supplier, notes the biggest challenge for the implementation of manufacturing data science is ‘buy-in’ from top management. Data collection and organizing, and an up-to-date metadata repository is expensive and complicated. Hiring data scientists in an extremely competitive market is difficult. Making sense of the data and turning it into actionable insights is only impactful when processes are changed substantially. Top management must be committed to the changes manufacturing data science can bring to their organization.
