The Human Side of Automation

Aug. 21, 2003
Automation with a human touch is the watchword for this manufacturing consultant. The approach often involves keeping more people in the process.

When he learned he was about to be interviewed by Automation World, Anand Sharma’s response was, “Do they really want to talk with me?” Sharma is founder, president and chief executive officer of Durham, N.C.-based TBM Consulting Group, a practice formed to help companies achieve manufacturing excellence using time-based techniques and Kaizen transformation process. He is not noted as a proponent of large automation projects, and, of course, as the title suggests, this magazine is all about automation.

“We talk about ‘autonomation,’ ” he says, “that is, the human side of automation. A lot of automation has been done to satisfy a fascination with technology. Often you can use 10 percent to 20 percent of the money and get most of the benefits without taking the human factor out of it. People should be making the critical decisions while machines take care of the work that is just drudgery.”

The term, “autonomation,” was coined by Taiichi Ohno of Toyota to translate the Japanese term jidoka. The literal translation is “automation with a human touch.” The operational application of this concept means separating machine work from human work and giving machines the capability to detect and immediately respond to production abnormalities. It seeks to simultaneously meet manufacturers’ dual need for the highest possible product quality and the most cost effective manufacturing process.

No decision about layout, or machinery, or worker training and movement can be made independent of each of the other factors that make up the production system. Although traditional production thinking might approve of making capital equipment purchase decisions based simply on time to recover the original investment, jidoka looks at how operators will work with machines, and how the machines can be made to work independently to perform simple, repetitive functions.

An example of this concept, says Sharma, is the Citizen Watch Co. “It originally had a long U-shaped cell with about 90 people—that is, 30 stations assembling watches in three shifts. It’s a fashion industry with rapid product change, so it was difficult to automate well,” Sharma notes.

In order to take cost out, Citizen’s engineers selected small pick-and-place robots coupled with simple riveting machines that cost an average of 00 to 00 per station. But as these units did not have much intelligence, the engineers then had to figure out how to make repairs when something goes wrong, Sharma relates.

For this, they applied the concept of jidoka. “They put in very simple mechanisms that stop a machine immediately if a problem occurs. They did this for under ,000 and put two people on each shift to fix the things that went wrong,” he explains. “A machine to do all this could have cost close to million, but by keeping six people, they could automate with smaller machines that also were easier and cheaper to change over to new products.”

Pull demand

Classic discrete manufacturing systems schedule production on a push system, that is, each machine or manual operation is given a place sequentially in line with operations that must precede or succeed it. If one operation has a shorter cycle than the one following, then a queue of work-in-process inventory can, and usually will, build up between the stations. Not only that, manufacturing departments often churn out whatever quantities they can produce for each product, and expect sales to get rid of them. This results in finished goods inventory build-up. Both of these scenarios are considered a waste of capital assets by TBM.

Sharma states that one of his first principles is to figure out how to connect manufacturing to actual customer demand and then reduce response time of the system to meet the expectations of customers. The first step is to execute the system manually, then automate where needed to get all the benefits of the investment. “The goal is not automation, but responsiveness,” he says. Look at the goal, not just at the technology.

When a system responds only to the customers’ pull, it requires that each component of the flow manufacturing system responds only to an actual demand signal from the successive operation. The system virtually links an actual customer’s buy signal through distribution, various manufacturing operations, all the way back to the supplier of components and raw materials.

One purpose of the pull system is to shut down an operation as soon as an abnormality occurs. The abnormality may be due to disruption in demand, machine breakdown or a quality problem. In any case, the pull system’s discipline will reveal the abnormality for immediate corrective action and root cause analysis to make the operation more robust.

People supply the intelligence in Sharma’s system, while machines do the repetitive and dangerous work. But if this approach is to be effective, companies must invest in their people. “We need to change the balance in U.S. manufacturing,” Sharma states, “and go from investing just in stuff to investing in people as well.”

Sharma authored “The Perfect Engine” with Patricia Moody (Free Press) in order to explain his ideas about “LeanSigma” methods for improving manufacturing responsiveness and profitability. While a reader may be justifiably suspicious about any system labeled “perfect,” the authors offer evidence that the system has worked.

LeanSigma combines the bias toward fast action from Lean Manufacturing, or kaizen, with the rigorous statistical analysis of Six Sigma.

Kaizen means “change for the better” in Japanese. The best English equivalent is “improvement.” The word in Japanese is made up of two characters, Kai (change) and Zen (good). The best use of kaizen is for breakthrough improvements and activities to sustain such improvements. Transformation into a Lean Enterprise requires changes in the way we do things. Kaizen is a culture of sustained continuous improvement focusing on eliminating waste in all systems and processes of an organization.

Black belt snobbery

Regarding Six Sigma, Sharma says, “There is an enormous and significant difference in implementation methods. Kaizen breakthrough methods produce results faster and drive culture change a lot better. Six Sigma takes too long and breeds a culture of elitism of black belts, although both Six Sigma and LeanSigma techniques focus on the same end result.”

LeanSigma roots out inefficiencies to create a culture of continuous improvement, tracked by consistent measurements. The transformation begins at the intuitive level, where trained operators and engineers are taught to look for waste and redundancies, and progresses to real-time improvements. The goal is to achieve productivity and growth that serve all constituents of the enterprise—reducing both defects and lead-time. Implementing LeanSigma Transformation, an organization gives its employees the tools to move to new levels of excellence.

Results of implementing LeanSigma include:

• Eliminating waste

• Ensuring that flow of products match market demand

• Developing highest quality at the lowest cost

• Re-designing processes to suit product volume.

When evaluating a manufacturing problem and considering a new automation project, it might just pay to think lean. Make sure that the automation will improve business profitability as well as smooth out production flow. This might just make heroes out of project team members.