Custom coaters, have you ever wondered how well your business is performing relative to your competition? Or, how rapidly your competitors are investing in new technology? Why some competitors can consistently bid jobs at lower rates with faster delivery times? Which new equipment investments could have the biggest payoff for your business?
Because your business is unique, you need to have customized answers to these and other strategic questions. Perhaps you should investigate manufacturing process benchmarking.
What is manufacturing process benchmarking? Manufacturing process benchmarking is a process that allows a company to make quantitative comparisons between its performance and the performance of a group of similar manufacturing companies. Each benchmarking study is designed around two key variables that reflect the objectives of the company commissioning the work:
- The practices, metrics and measures that will be studied.
- The composition of the comparison group.
Manufacturing benchmarking originated in the statistical quality control techniques pioneered after World War II by W. Edward Deming and Joseph Juran. Later, the arrival of computerized manufacturing made benchmarking easier, and books like The Machine That Changed the World turned a trend into a tide. Many U.S. industries now use these sophisticated benchmarking techniques routinely.
Until recently, benchmarking studies have largely focused on business practices, using measures such as productivity, revenues, costs, financial ratios or inventory turns. Specialized studies often focused on the outcomes critical to the performance of a specific business unit. For instance, a customer service unit may be interested in the number of customers handled, response time or customer satisfaction measures.
Manufacturing process benchmarking is different because it focuses primarily on the activities that take place during manufacturing operations and measures levels of efficiency and productivity. Typical measures of interest to manufacturers include size and age of equipment, material throughput, labor requirements and degree of computerized operation. Information about how a company compares with the rest of the industry in these areas can lay the foundation for targeting improvements.
Just as important as the practices, metrics and measures that are studied in benchmarking, is the composition of the comparison group. The comparison group can be drawn as narrowly or as widely as desired. For instance, a company can choose to compare itself with other companies that use the same manufacturing process or those that serve a similar market. Factors such as total revenues or geographical location are sometimes used to define the comparison group. Alternatively, the company can choose to position itself against companies already operating in a market that the sponsoring company is trying to enter. The selection criteria for the comparison group can extend to the nature of the manufacturing output, such as the cost or volume of products produced.
What kind of information is generated? Benchmarking information usually falls into one of three categories: practices, measures and metrics.
Practice queries deal with operations, processes or activities that a company may use during the normal course of business. Frequently these queries require yes/no answers. For instance, a practices query for a coater might be, "Do you perform metal cleaning prior to coating using aqueous-based solutions?" The group response to this type of question is usually given as a percentage. For example, the output data to the example practice query is likely to be, "X% of the comparison group uses aqueous-based cleaning fluids for metal cleaning." Practice queries are useful in assessing the extent that a certain manufacturing operation or business practice has penetrated an industry. If the benchmark study is repeated, a manufacturer can determine how rapidly a particular practice is being adopted by the competitors.
Measure queries also deal with operations, processes or activities, but they usually have a quantitative element to them. Questions such as, "How many hours annually do your coating lines operate?" and "What percentage of the output of the coating line is rejected for quality reasons?" are typical of measure queries. The resulting data can be presented in several different forms. One approach is to aggregate the data into different size groups and report the percentage of the comparison group that falls into each size group. An example of aggregated data would be, "30% of the comparison group reports reject rates of less than 1%; 45% of the group reports 1-2% reject rates; and 25% of the group reports reject rates of greater than 2%." The numbers in this example are fictitious, but they would give a hypothetical manufacturer with a reject rate of 2.2% some idea of how it compares with the rest of the industry.
Perhaps an even richer presentation of this data would be the spectrum approach. In this methodology, the reject rates for individual companies are listed, beginning with the best and progressing to the worst. Next, the list is segmented according to the size of the comparison group. In a group of 40 participants, the value for the top 10% cut would be the fourth value on the list. The value for the top 25% cut would be the tenth value on the list, and so on. An illustration of the spectrum display of data is shown in Figure 1 (below). The star indicates the position of our hypothetical manufacturer with the 2.2 % reject rate.
Using the spectrum display, our hypothetical manufacturer has a much clearer picture of its position in the industry. (Initially, almost every company believes it is performing above average.) For instance, our hypothetical manufacturer can see that most of the comparison group is achieving reject rates similar to or slightly lower than its shop. By simply emphasizing quality and consistency, it is likely that our hypothetical manufacturer can become average. However, there is a significant portion of the industry that has found a way to reduce costs by significantly reducing rejects, in some cases by an order of magnitude. These best practices companies, those found in the upper 10-20%, must be doing something that is fundamentally different from most other companies in the comparison group, and it is obviously working.
If it wishes to keep its costs competitive, our hypothetical manufacturer will pay attention to what new equipment, practices or techniques have allowed other coaters to achieve this breakthrough. Once you know what you are looking for, there are many ways to find this information. Other benchmarking questions and networking among industry contacts offer valuable starting points.
Metrics. The third type of information generated from a benchmarking study involves metrics. Metrics generally focus on issues related to productivity or efficiency and are mathematical composites of raw input data.
For instance, in benchmarking studies conducted by the Performance Bench-marking Service in Ann Arbor, Michigan, one particularly useful metric has turned out to be the Value Added per Full-Time Equivalent Employee. Value Added is the term applied to the total revenues minus purchased goods and services. A participating company can generally supply its revenues, purchases and employment, but frequently it has no solid indication of how efficiently it uses its work force. The Value Added number itself has little meaning, but when it is tied to the consumption of different resources, it offers many valuable insights. Value Added can be linked to facility size or capital invested, as well as to employment. With these metrics, an individual manufacturing operation can determine how efficiently it is using its resources compared with the competition. Is the company's work force significantly larger than competitors' for the amount of product produced? Can more work reasonably be put through a shop of this size? Are competitors investing more heavily in new equipment?
What are the benefits? When a company agrees to participate in a bench-marking study, the usual expectation is that it will receive what amounts to a report card on past performance. Frequently, staff members have some trepidation about how their performance will be portrayed.
In reality, benchmarking data frequently produces a much broader understanding of the company's competitive position and yields an array of benefits that are totally unanticipated at the time the study is undertaken. Benchmarking results have led to new corporate strategies and have been incorporated in the design of manufacturing improvement programs have provided justification for new equipment purchases, and have supported documentation required for ISO 9000 certification.
How important is the comparison group? Selection of the comparison group is particularly important in manufacturing process benchmarking where the emphasis is on what is happening on the shop floor. It is important that the manufacturers in the group be as similar as possible. For instance, asking process-oriented questions of a finishing group that includes both platers and coaters will yield data of limited value to either. While platers and coaters may respond to general business questions, their cost structures and efficiencies may not be comparable. Questions about equipment, throughput and operational practices will produce different answers from the two types of finishers or result in high rates of no-answers.
Within the coating industry, there is another key distinction between custom and captive operations. Their cost structures and operational practices may differ considerably. More importantly, captive operations frequently do not have access to data that is specific to their coating operations. Captive coating operations may share individual workers with other manufacturing operations. Overhead assessments may represent more of an accounting assignment decision, rather than the actual costs incurred by the operation. Obtaining accurate revenue data for a captive coating operation is a challenge when books are maintained at the company level.
When considering participation in a benchmarking study, a participant should ask how the comparison group is going to be selected. After the study is completed, the participant is not likely to receive the names of the companies in the comparison group, but should expect some profile information such as company size, geographical location breakdown, industries served and average piece price. This information is useful in assessing the fit of the group norms with those of a particular plant.
What about confidentiality considerations? For a benchmarking study to produce meaningful results, all participants must be willing to supply full and accurate data. By its very nature, this data is sensitive and proprietary, and companies are justifiably concerned about the confidentiality of the information they provide.
There are two areas where a company needs to maintain vigilance over its data. The first has to do with who sees the data submission and the final report. The risk is greatest when the data can be linked to the company's identity by an unauthorized party. Use of a company code name or number during data processing frequently helps to minimize problems in this area. A company should inquire about the data handling practices that will be used in its study and also about the distribution of its customized report. Access to a company's customized report should not be available to another participant or to an industry group. When a company's data is aggregated with data from the rest of the comparison group, the risk is reduced to negligible levels, so a general report focusing on norms of the comparison group presents little threat and is a suitable vehicle for communication with outside parties.
A second consideration involves the size of the comparison group. The comparison group needs to be large enough so that best practices information (metrics and measures related to the top 5 or 10% of the group) does not reveal proprietary data. For instance, if the comparison group consisted of only 10 companies, the best practices data for the top 10% would reveal the actual performance data of the best performer. Generally, comparison groups with a minimum of 20-40 participants are needed to obtain detailed best practices information that will not compromise the integrity of the data from any single participant.
Why should custom coaters be interested? Taratec Corporation conducted a new manufacturing process benchmarking study specifically for custom coaters in the fall of 1998. This study included business, operational and equipment measures and allowed individual custom coaters to gain an understanding of their strengths and weaknesses on more than 100 practices, measures and metrics. In addition to identifying areas needing improvement, this study allowed individual coaters to quantify the magnitude of improvement required to reach the best practices of the industry.
Selection of the practices, measurements and metrics included in the bench-marking study was a critical step that required an intimate understanding of the information needs of the custom coater. It is easy to ask many questions. The hard part is asking the right questions. The final selection of appropriate queries was done under the guidance of a Technical Steering Committee led by Sal Lovano of Taratec. Mr. Lovano has more than 30 years of coatings experience. Other committee members selected for their understanding of the coating process and their business acumen included: Dale Buhr, president, Industrial Powder Coatings and Bill Kaufmann, president, Powder Finishers, Inc. and chairman of PCI's Custom Coaters Group.
This is an opportunity for custom coaters to get detailed information about their individual company's performance at group rates. The study took three to four months to complete. Participation from a minimum of 40 custom coaters ensured confidentiality and valid results.