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I was visiting a contract precision machining plant that had completed a first-pass implementation of visual controls for its operations. The shop floor consisted of rows of sophisticated CNC lathes and machin- ing centers, most of which were leased and thus relatively new, and well maintained. Appropriately in an operation like this, the visual con- trols were focused on material availability, scheduling, and equipment downtime. The plant Lean leader indicated, not surprisingly, that most downtime occurred when changing over from one job or job family to the next. The new job’s material and tooling had to be loaded, the new program called up on the machine, and the setup tested before the job could be started, the first piece checked, and the machine put into automated operation. Surprisingly, there were no expectations by job for how long a changeover should take, nor was there standardized work defining the sequence of steps for any given changeover.

Each machine had a daily log sheet covering both shifts. Operators recorded each job, how long it took to complete, number of pieces produced, and other information. A preprinted Pareto chart on the log sheet listed reasons for downtime. Over the course of the two-shift day, operators filled in segments of a bar chart representing the amount of downtime they experienced based on the listed reasons.

Looking at one of the log sheets, by far the longest bar, and thus the most lost time, was for “setup and changeover.” I observed that it was equivalent to explaining why people were hospitalized by the category “illness and injury.” It tells you everything, but in such a general way as to provide no information on which you can act. And in this case, if you did want to act, you would have to rework the Pareto entries, trying to recreate the situation at the time each incident of downtime occurred. Not only is it a waste of time, but also fallible memory is likely to pro- duce inaccurate or incomplete information. Reason codes may seem neat and efficient. In the practice of process improvement, they are a bad idea.

Proximity of Visual Controls

Another advantage of visuals completed by hand is that they usually are (and should be) close to the process whose performance they reflect. That means it is easy to go look at and verify that what is shown on the visual corresponds to physical reality in the production process itself. This ease of verification is another example of assessing quality of the information in the visual control.

Sometimes it is possible to make the same verification with IT systems, but often the relative scarcity of computers in the production area makes it less convenient to perform a go-see verification or quality check. Worse, the computerized system encourages managing the production process from a computer screen in an office somewhere removed from the actual produc- tion area. Interestingly, this problem can also occur in supervisors’ offices even when these are little more than a stand-up desk and a computer. When you are focused on a screen, you are not focused on the process. Either vio- lates the Lean management system’s three-part prescription for focusing on process: “Go to the place, look at the process, talk with the people.”

Flexibility of Visual Controls

IT systems provide powerful analytical tools, without doubt. However, often the analyses (that is, the questions addressed by the automated systems) are those programmed into the reports that are accessible on the system. Typically, only the questions addressed in the report can be answered by it. In contrast, questions prompted by entries on a visual control chart can often be addressed, at least initially, right where the control is posted. When that is not the case, for example, when a new defect has surfaced or sup- plies have been exhausted and not replenished, a new control can be easily drawn up to track and address it.

For example, when a piece of equipment starts malfunctioning, it is easy to draw up a simple downtime log to record instances, durations, and observable causes or symptoms that can be tallied up or Pareto- charted. Or, when a process starts to produce rejects, it is a simple matter to start tallying them. Pareto-charting the defects and then taking action on the findings come next, with no programming or transcription of data required. By contrast, someone with specialized knowledge is usually required to reprogram an analysis in an automated system. It might mean getting support from IT, often not a quick proposition, or getting help from a technical professional in the area, probably already working on a

full plate of assignments. Or automated tracking, specifically because it is automatic, can produce nicely printed charts from, say, automated bar code scans of completed units. When actual volume is less than expected, nobody is there to record the miss and the reason why. Or it happens at the end of the day, as in: “Let’s see, why did we miss that pitch seven hours ago?” Long batches of time of this sort often produce less than accu- rate and less than useful information.

Visual Controls and the “Fingerprint Factor”

Perhaps it should not be so, but often people in a production operation look past posted computer-generated reports, even graphic ones, of leading performance problems. That is especially true when the data are reported in table format. Managers often do not realize that it takes a trained eye to read and interpret information presented in these ways, and production operators often have not had that kind of training. As an example, I recently saw a daily quality report, an Excel spreadsheet printed in tiny type (to fit on one page), posted on a shop floor start-up meeting board. I asked if anybody used or could even read it. The answer: “No, not really.” It is a very different story when the operators themselves have been involved in recording the data, or working through it in start-up meetings led by their team leader.

Hand-created data, especially when you know whose hands created it, including the real possibility that the hand was or could have been yours, have a much lower intimidation value than the crisp, precise-looking man- agement document someone has posted on your team’s information board. This fingerprint factor is important. It helps draw people to the information and conclusions from records they have had a recognizable part in creating. The same is not the case with impersonal, computer-generated materials, even those with the slickest graphics.

The Power of Networks

There are two aspects of information reporting where IT solutions win hands down. IT networks are excellent tools for broadcasting information to dispersed locations. A network can readily broadcast the sequence of units starting into the final assembly process, so that subassembly opera- tions can produce to the discrete unit in exactly the needed sequence (Case Study 4.10). A version of this is at work every time a customer places an order at Taco Bell. No finished inventory needs to be held; instead, every order is custom-produced in the proper sequence.

This is a particular application of the golf ball method of signaling devel- oped in Japanese factories. The difference is that instead of color-coded golf balls arriving at a subassembly station to signal the model or type of unit starting into final assembly, an electronic signal arrives on a screen or prints as a label at the beginning of the subassembly process—just like an order for a bean burrito with no cheese. These are flexible and powerful applica- tions, as long as the server does not go down!