Manufacturing

4 Ways to Eliminate the Challenges of Data-Driven Manufacturing

“Data-driven manufacturing is the “next wave that will drive efficient and responsive production systems,” Harvard Business School professor Willy Shih and Siemens EVP Helmuth Ludwig told Harvard Business Review. As companies attempt to implement data-driven manufacturing, they’ll have to make a number of system changes and upgrades, the pair said.

Here are 4  things manufacturers will need to fully optimize and implement data-driven manufacturing.

1. Systems that can more quickly respond to market demands. Manufacturers will need systems that better capitalize on customer and market-driven manufacturing. They will need to do more than simply capture the data, they’ll need to know how to apply it. Systems and solutions will need to be able to instantly process information to set production goals.

Manufacturers will need to leverage that data to drive their strategies and to convert those insights into actions. “Investing in data visualization, action planning, brain-storming, and execution tools can greatly increase the likelihood of success,” Katie Easterly said in a blog post for analytics platform Vennli.

“Most production systems will employ a combination of time-triggered and event-triggered control mechanisms, and managers need to ask how they will design and operate these hybrid systems effectively.”

2. Engineers that can design and operate hybrid systems. While most of today’s production systems are planned, the future of data-driven manufacturing will be less predictable, often guided by customer demand and “event-triggered” controls. Shih and Ludwig say manufacturing engineers will have to work with new methods and tools to ensure system stability during fluctuating demand. They will need to design and operate systems that can quickly scale up or down to meet customer-driven demand.

“The reality is that most production systems will employ a combination of time-triggered and event-triggered control mechanisms, and managers need to ask how they will design and operate these hybrid systems effectively,” says Shih and Ludwig.

3. Data systems that can share information throughout the company. Manufacturers will need systems that can allow information to be shared throughout the organization so that everyone from design to distribution is using the same platform. This will require some manufacturers to eliminate long-held organizational silos that have been established between departments.

A 2015 report from mobile app developer Catavolt revealed that information silos continue to be a major data challenge for manufacturing organizations. Catavolt says in a blog post that many manufacturers still rely on manual processes for data integration and have ineffective communication between various departments. The goal isn’t to destroy organizational silos but to eliminate the problems that they cause. “In order for a company to work effectively, decisions need to be made across various silos,” Catavolt says

4. More powerful systems with better security. Manufacturers will need more computing power to handle the movement of industrial control systems into the cloud. They will also need to be able to integrate legacy systems with new data-driven solutions.

The increasing use of devices and IoT will also expose systems to unauthorized access by attackers as many legacy systems were designed at a time when industrial security was less of a concern. With new vulnerabilities to attack through the internet and internet-enabled devices, manufacturers will have to invest in more powerful systems with better security controls. “These gateways will need sufficient computing power to handle networking and security tasks, and that they will cost a little more than what many proponents think,” says Shih and Ludwig.


Craig Guillot

Craig Guillot is a business writer based in New Orleans, La. His work has appeared in Wall Street Journal, Entrepreneur, CNNMoney.com and CNBC.com. You can read more about his work at www.craigdguillot.com.

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Craig Guillot

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