How smart manufacturing thrives with clear understanding in the shop

Smart manufacturing is a tool to achieve improved levels of production efficiency. When employees apply good judgment to the analytics derived from information systems, a manufacturing facility can make sweet music, kind of like an orchestra. nythzl/DigitalVision Vectors/Getty Images

What is smart manufacturing? When we strip away the hype about hardware and network configuration, we don’t find much information available. The industry performs poorly when defining the impact of smart manufacturing on production, safety, and employees. This failure jeopardizes the promise and potential of smart manufacturing to support better decision-making. I hope to explain a vision of smart manufacturing’s role with a simple analogy.

Smart manufacturing thrives when employees involved with production and design support smart manufacturing’s goals, understand how it works, and adopt a new collaborative culture focused on product. Smart manufacturing is a contact sport. It requires a lot of participation with others. It also demands employees have the knowledge and willingness to challenge the technology when the results are contrary to knowledge and experience.

Directing the Performers

Smart manufacturing is similar to an orchestra and conductor. As the conductor stands before the orchestra, his or her actions and direction are visible to all members. Musicians immediately understand the direction. Even those who aren’t the focus of a direction might respond in a subtle way to support the conductor’s goal. If the conductor directs the flute section to play louder, the horn section may instinctively play a bit quieter.

An orchestra conductor brings a vision to a performance. Composers write music, but the conductor defines how to deliver that composition to an audience. The conductor’s tools are a composition of musical notes, musicians, historical performances, and a vision. The conductor’s role is to instruct musicians in his vision, rehearse to eliminate mistakes, coordinate diverse instruments, and direct musicians during live performance.

Talented musicians understand the conductor’s instruction and know how to execute that instruction with their instruments. As an orchestra, musicians must all be in tune with each other, understand how to follow the conductor’s timing, and be able to read and interpret their sheet music. The conductor must coordinate and direct all the musicians in the orchestra to deliver a cohesive and enjoyable performance.

Correctly managed, smart manufacturing reflects and supports execution of the business’s goals and values. Much as in an orchestra, smart manufacturing uses data and mathematics to help employees tune and coordinate their talents. Reliable data and analysis support employees to ensure product quality, production efficiency, equipment availability, and safety.

Data-based Decisions

A smart manufacturing system offers timely feedback on the results of conditions and activities. This feedback allows employees to adjust their activities and control material and manufacturing activities for optimal results. One of the biggest challenges in smart manufacturing, however, is that algorithms aren’t visible and only execute the instructions they currently maintain. We don’t always understand their objectives. We can’t honestly even say we respect their competence. Many algorithms reduce complex problems to simplified scores. Most importantly, algorithms aren’t always objective, precise, and accurate.

Statistical analytics present us with models of a manufacturing activity. Models are simplifications of an activity. For example, let’s assume we want to find the stroke velocity that causes the fewest failures. Over time, we can collect data on several stroke velocities and correlate it to forming splits. After a while, we will have enough information to select the best -performing velocity.

Forming sheet metal components is a complex activity driven by many variables. What if we stepped out of statistics and began experimenting with additional variables? We might find that the failure rate of a given stroke velocity changes with a change in lubrication or the application of the lubricant. Perhaps we can identify a stroke velocity and lubricant combination that both reduces our failure rate and helps us meet our production volume requirements. In this case, our model failed to support both good decision-making and our business goals.

Manufacturers must work to maintain accurate and unbiased data. By its nature, data is biased to existing equipment and management practices. Analyzing historical data carries with it the biases of the past. Data should be scrubbed of implicit bias if it is to support objective decision-making based on the entire spectrum of possibilities. Likewise, when we replace equipment, data from the previous machine is likely no longer relevant, and the information probably should be archived or discarded.

The strength of smart manufacturing lies in mathematics. When we bring timely analysis to the plant floor, collaborative employees can adjust their respective activities to enhance the development of product. With this strength also comes risk.

Data isn’t always accurate. Analysis isn’t always correct. People can place too much faith in analytics at a cost to good judgment. The only way to remedy this is to support employee skills, encourage good work habits, demand cooperation across design and manufacturing, and teach employees the opportunities and risks inherent in smart manufacturing.