For over 30 years the leading pallet and sawmill magazine in America.
PART II Finishing the Quality Circle: Quality Control Strategies for Lumber and Wooden Pallet Companies
Dr. Charles Ray expands on the initial quality control strategies outlined in the December 2009 issue. His comments may change the way you look at quality in your operation.
By Dr. Charles Ray
Date Posted: 2/1/2010
Quality doesn’t happen by accident. Consistency produces quality. And it doesn’t have to cost a lot if you build the right policies into your organization and make sure that everyone follows those procedures.
In the December issue of the Pallet Enterprise, I outlined a number of variables that factor into the development of quality in a pallet or lumber company. These factors make up the Quality Circle where one aspect leads to another creating a culture of consistency and excellence.
The Quality Circle starts with Obligation and then moves to Specification, Communication, Observation, Information, Variation, Documentation, and finally Repetition. My previous article covered the first four components. Now, let’s take a look at the rest of the Quality Circle.
The previous component is “Observation”, which must take place if you want to ensure that everyone is doing the right thing to improve the overall process of your organization. However, that improvement will follow only if that observation is turned into useful information. There is a difference between facts and information. If the observer reports that “the stacker jammed twelve times today” but nothing is done to determine the cause of the jams, then no useful information has been developed and no beneficial quality improvement will be gained.
There are many methods and logical tools that help information develop, most of which are not used frequently enough. They’re not difficult to apply; they’re just out of our daily routine and considered a nuisance out of lack of procedural discipline on the plant floor. In companies that apply formal quality systems such as ISO-9000 formal procedures are developed and enforced with the hope that these different types of “thinking tools” are applied in appropriate circumstances. The easiest to use is the
1.) “Why did the stacker keep jamming?” Corners of the pallets kept catching the angle iron on the edge of the stacker. 2.) “Why?” A deckboard on the pallets that jam slightly overhangs the stringer. 3.) “Why is that?” Because those deckboards seem to be out of alignment. 4.) “Why are certain boards being misaligned?” Because the problem boards are slightly warped and come out of the board slot at an angle. 5.) “Why do we have warped boards in the pallet machine?” Because there is a new employee working the pallet nailer who has not been instructed to sort out warped boards. So the root cause of the problem is found to be incomplete employee training, not requiring a call to the maintenance man to fix the stacker but a simple instruction to the operator.
Sometimes the problem is more complex, and requires a group of people to work together. In this case, simple diagramming of the process is sometimes helpful in focusing the investigation. Scott Leavengood and Jim Reeb of Oregon State Wood Products Extension demonstrate the use of Flowcharts and Cause-and-Effect diagrams in the case of lumber size control in their series on statistical process control; their cause-and-effect diagram is included here as Figure 5 for illustration.
The biggest problem we face in developing information about product and process quality is recognizing the impact of variation on the results we are seeing. Quality problems seemingly come and go at random intervals. In fact, that is exactly what happens. Processes run with natural variation; the fact that McDonald’s burgers are more uniform than Burger King’s is because the manufacturing process of the McDonald’s burgers allows less natural variation than the BK process.
Mills are the same way; variation ebbs and flows as a function of the design of the process, the time of day, and the mental state of the operators, among other things. Advanced statistical tools, called “control charts”, can be implemented to track process variation and to identify those times when the process is “out-of-control”. But an easier first step is simply to get in the habit of compiling process data to help explain any part of the process or product variation.
For instance, I frequently ask lumber mills about moisture content variation. Most softwood mills utilize automated moisture meters with computer print-outs to display information on the average moisture content of their lumber and its range of variation out of the dry-kiln.
Most hardwood mills that dry lumber track the daily average moisture content in their kilns and unload the charge when that average gets down to their target. However, the variation in moisture content is much more important than average moisture content for quality control purposes; any customer problems related to lumber moisture content are not due to the average, but how variable the lumber moisture is.
Many sawmills do not actively attempt to control this variation. In failing to do so, they miss the opportunity to shorten their kiln schedules or to reduce their percentage of non-conforming lumber. In the same way, pallet mills that do not collect data on various processes miss many critical opportunities.
For instance, mills could keep pallet delivery times to the same customer, by driver, to determine if one particular driver or route is more dependable than another. Recycle mills could collect and analyze load sortation data to determine which sources regularly provide the most profitable combination of pallets, and how variable the unload times are from that source. In fact, this area of incoming raw material quality is one that we’ll discuss in a little more detail.
At the receiving end of pallet mills, especially those that purchase lumber cants to saw board stock, it is common for the incoming cant material to be quite variable from supplier to supplier. When asked, managers often reply something to the effect that “beggars can’t be choosy”…in other words, they believe that because a pallet cant or pallet stock is one of the lowest value products to a sawmill, that the pallet producer should be happy to get whatever shows up at his door. And in fact, that is the attitude some sawmill managers may take toward their pallet lumber customer. However, more progressive sawmill managers recognize an opportunity to respond to customer opportunities whenever it occurs, even if it happens to be a pallet mill.
For instance, if a pallet mill supervisor recorded data on how many #1, #2, and reject boards were produced from each load of cants or pallet shook from different sawmills, it might be recognized that loads from sawmill A typically resulted in 50% #1 boards, 35% #2 boards, and 15% rejects, while loads from sawmill B typically resulted in 65% #1 boards, 30% #2 boards, and 5% rejects.
The pallet mill supervisor could then calculate how much value is in that difference, and it could offer sawmill A a portion of that difference if it improves its percentages to the desired targets. One hardwood sawmill manager I spoke with said that since pallet cants are 25-45% of his board footage production, an incentive as small as $20/mbf could significantly improve his bottom line and would be taken seriously. He could simply visually sort the cants produced for that pallet lumber customer to include the “nicest” ones; the pallet mill manager could determine if the sortation had the desired effect by tallying the board production from the trial load; and if effective, the arrangement could be extended to future loads with the sawmill getting its increase and the pallet mill realizing the remainder of the benefit.
The only drawback to this concept is that the sawmill must have an outlet for those lower-grade cants or pallet stock. In practice, this product could be shipped to other pallet mills in the area that have not recognized and acted upon this opportunity. The net effect is that the quality-conscious pallet mill is rewarded in better on-grade pallet stock, the sawmill is rewarded for its extra effort, and the competing pallet mills in the area are left complaining about “beggars can’t be choosy.”
Understanding variation in all its forms, and its impact on the operation, is a core competency of those operations striving for quality consistency in their products and services. Ultimately, the study of variation in our plant data leads us to concepts such as the standard deviation of a process, by which we can understand how reducing the variability of our process will allow us to center our process better on the target, as well as reduce the cost of doing so. Figure 6 is a graphical representation of a process that has an average of 24, with a standard deviation of 26, at period one (the first curve on the plane), that is improved over 10 periods to a process with an average of 25 and a standard deviation of 0.25 (the last curve on the plane). The wide range of the early curves indicate that even though the plant is targeting 25 for this particular quality attribute, the actual results obtained range from about zero to fifty with an average of 24. By the time the tenth iteration of the process improvement has been implemented, the plant is now averaging the targeted 25, and actual results obtained only range from about 24 to 26. In other words, the process is producing far more consistent results at period 10 than it was at period one. These results were obtained by first understanding how much variation was in this particular process and then by reducing or eliminating all the things that caused that variation to occur. At period 10, then, the quality quotient of the process is far better than it was at the beginning of the investigation.
The next area of quality control is one that is highly focused on by international quality systems such as ISO 9000 and quality control evaluations such as the Malcolm Baldrige National Quality award. The reason for the high degree of attention to documentation is that its use is very valuable in reducing the variability of a delivered product by standardizing the processes and procedures by which that product is manufactured.
Many companies that are serious about improving their quality develop Standard Operating Procedures (SOP’s) throughout their plant, beginning with the processes that are routinely the most troublesome and working through other processes until practically every action that can be altered in some way by an operator are standardized.
Documentation is another potential tool in helping supervisors develop their communication skills. Less experienced supervisors can be assigned the documentation of processes in their area and the task of confirming their documentation and communicating it to their crews.
More experienced supervisors can be challenged to oversee the documentation of their processes by delegating the documentation work to the crew members and helping them validate and perfect the documentation. The examples of great documentation opportunities are almost endless.
One excellent application of documentation combined with communication I recently ran across was in a pallet mill that had developed a maintenance checklist for each forklift operator, requiring the exact same items to be inspected before each day or work. However, the performance of these same items required the same equipment to be used, and time was being wasted by forklift operators all waiting to use hoses, fluids, or air guns at the same time.
The area supervisor solved this problem by developing and posting in clear sight a forklift maintenance schedule with staggered start times for each forklift; in this way, each forklift had exactly the same items inspected each day, no items were skipped due to the operator’s impatience with a waiting line, and the inspection process proceeded each day with minimal or no delay.
Besides reducing variation and operational error, another benefit of documenting your process can be derived by sharing this documentation with suppliers and customers. One example would be the pallet cant/board example discussed earlier; documentation of how the pallet boards were sampled and recorded over time would give the potential sawmill partner confidence that his efforts at improving pallet cant or shook selection would be accurately recognized and rewarded. Another great example would be with pallet load inspections; if a supervisor checked each outgoing load for a specified list of quality items, and this documentation was copied and sent along with the load paperwork, the customer’s perception of you as a quality supplier would be likely to increase over time.
The final step in the circle of quality control is repetition. All good intentions and actions have limited impact. They must be repeatedly reinforced, day after day. Quality operation must be reflected continuously as a reflection of your company’s culture of quality, or your employees will tend to slack up if they see that management has stopped paying attention.
And do it all over again…
So rather than getting hung up on the technical side of quality control and decide that its just too much to bother with, view quality instead as “Consistency” and strive to achieve it in whatever way fits your company culture. Apply the eight aspects of the Quality Circle we’ve discussed in these two articles… Obligation, Specification, Communication, Observation, Information, Variation, Documentation, and Repetition…in whatever ways that make sense to you. And make it a perpetual journey. Remember, Quality is sometimes hard to define, but “you know it when you see it”. And so does your customer.