Quick Response Manufacturing (QRM) Principles Enable Promega Corporation to Aid in Development of New Coronavirus Test
Promega Corporation of Madison, Wisconsin was recently recognized by its customer, Utah-based Co-Diagnostics, Inc. for the support Promega custom manufacturing provided in the rapid development and launch of the new Logix Smart™ COVID-19 Test. This test is now approved and available in Europe as an in vitro diagnostic and continues to advance toward emergency use clearance as an in vitro diagnostic in the US as well as India. Co-Diagnostics used the Promega PCR Optimization Kit to refine its custom master mix for coronavirus testing. The Promega Custom Operations team then manufactured, QC tested, dispensed and packed the customized PCR assay reagents under the highest quality standards IN LESS THAN 10 BUSINESS DAYS.
Promega Corporation is a member of the Center for Quick Response Manufacturing (QRM) at the University of Wisconsin-Madison, and company leaders credit the use of QRM lead time reduction principles for dramatic improvements in their ability to respond quickly to unique customer orders and requests. The QRM Center is a partnership between industry, faculty and students dedicated to the development and implementation of lead time reduction principles. According to Promega Senior Manager Kristina Pearson, who oversaw implementation of QRM at the company, “As Promega’s custom manufacturing business continues to grow, we understand that lead time reduction is critical for market success. Based on our objectives, QRM principles were a natural fit to meet our goals.”
Working directly with the QRM Center’s students and staff, Promega designed both office and product finishing cells to address the need to assess and evaluate inquiries, process orders and answer questions rapidly with a high level of customer service.
As Pearson explained, “A unique aspect of QRM is the focus not only on the manufacturing shop floor but also in the office area. Most manufacturing principles focus solely on shop floor efficiencies. Our initial evaluation helped us realize some key inefficiencies in our office processes. We worked with a graduate student from the QRM Center to help us analyze our functional roles, using various modeling programs, to create a solution tailored to our business. As a result, we moved cross-functional roles into a single co-located office space. By co-locating staff, the team is able to quickly talk with each other in an open office area to get questions answered instantaneously instead of waiting for someone to respond to email.”
“In addition,” Pearson said, “Promega created dedicated office and lab spaces to build additional capacity for our custom business. Our team was involved with office and lab space layouts. We also worked with the QRM Center to help us design our new spaces in order to maximize the area for lead time reduction.”
As a result of these efforts, Dwight Egan, CEO at Co-Diagnostics said, “Promega proved to be an invaluable partner, enabling us to rapidly deliver high-quality diagnostic solutions using our CoPrimer™ technology. Our business model demands that we work with a manufacturer that can re-prioritize quickly, enabling a truly rapid response to emerging infectious diseases, and Promega provides us with that high level of service. Their dedication to customer support was instrumental in bringing a detection solution to the market.”
According to Promega’s Pearson, as a result of the projects conducted with the QRM Center, the company has been able to reduce its lead time for basic custom orders from 15 working days to 10 working days. In addition, the extra capacity built into lab and office space design with the Center’s help allows the company to meet requests in as little as five business days for emergencies like pandemic responses.
To learn more about Quick Response Office Cells like the one implemented at Promega, consider attending an upcoming “How to Design Office Cells to Reduce Lead Times for Custom Products” workshop.
QRM Center Blog
Can the OEE Metric Be Used with QRM?
OEE stands for Overall Equipment Effectiveness. It is a popular metric associated with Lean Manufacturing, so you may be wondering whether it can be utilized with Quick Response
Manufacturing (QRM) or other manufacturing improvement philosophies. This question frequently comes up at QRM workshops. This blog post describes briefly what OEE is and where it works well, analyzes why it motivates behaviors that are inconsistent with QRM principles, and finally suggests what portions of the metric could fit with a QRM initiative.
What is OEE?
OEE is a way to measure the productivity of a machine or a production line (see oee.com for more detailed information). For a single machine, it is calculated as follows:
OEE = Availability x Performance x Quality
Availability is a ratio of the Actual Operating Time over the Planned Production Time. Thus, this factor considers planned and unplanned machine stoppages, including setup/changeover time, adjustments, and breakdowns. For example, if you work a one-shift operation, 40 hours per week and had actual production time on the machine of 28 hours after subtracting machine setups and downtime, your Availability would be 70%. The Performance factor accounts for the speed of the operation. It is found by taking the Ideal Cycle Time (the fastest possible time per piece that could be achieved) and dividing it by the actual cycle time (total operating time divided by the number of pieces produced). Thus, if you have a machine with an ideal cycle time of 60 seconds, it could ideally produce 480 parts in 8 hours; if you only made 360 parts in that time, your Performance would be 75%. This factor captures influences such as slow operator pace, waiting for an operator to tend a machine, and minor machine jams that are quickly resolved but nonetheless increase the operation’s cycle time. The Quality factor is the number of good pieces produced the first time divided by the total number of pieces. Any piece that is scrapped or requires rework decrease the result. For example, if 100 pieces were produced but 5 were reworked and 3 were scrapped, the Quality would be 92%. To complete the calculations, OEE is the product of these three factors: 70% x 75% x 92% = 48.3%.
To use the OEE metric on a production line, it needs to be calculated only on the bottleneck station. The calculation will be the same as described in the paragraph above, but the OEE found for the bottleneck is the OEE for the entire production line. It is not necessary to calculate OEE for any other machines on the line because they are not controlling the production rate of the line. Improvements in OEE on non-bottleneck stations would only serve to create more work in process (WIP); the throughput of the line would be unchanged.
Where Does OEE Work Well?
The OEE metric works well in manufacturing environments that are high-volume, make-to-stock, with little or no product variety, or where production lead time is not a high priority. It is very effective for discovering where capacity is being lost to non-productive activities. For cost-sensitive products, it can help a company to determine where losses in throughput are occurring, helping to maximize output and reduce production costs.
Why OEE Motivates Behaviors Inconsistent with QRM Principles
Unfortunately, if a company is implementing quick response manufacturing, the OEE metric can lead production operators, supervisors, or managers to make decisions or exhibit behaviors that have a detrimental effect on production flow time.
First, the Performance factor motivates speed, so the incentive is for the operator or machine to go as fast as possible. This can best be achieved if the company runs large batches. Large batches lead to much longer flow times through the system. In a high mix, low volume environment, it may be difficult to determine which machine or workstation is the bottleneck, or the bottleneck may move around as the product mix changes. If OEE is applied to multiple machines within the production line or work cell, it can lead to excess inventory being produced, rather than increasing the throughput of the system. Another potential problem is that it could create quality problems that may not be caught within the facility but will end up causing problems for the customer down the road. Because the operators are motivated to work as fast as possible, they may cut corners that are not obvious but still problematic. One example would be cutting the cycle time short on a compression or transfer molding process for rubber components. A shortened cycle may not affect the look of the parts but will affect the material properties and expected life span when the component is put to use.
Another concern with the Performance factor in a high-mix, low-volume and custom production environment is that it can be difficult or impossible to define the ideal cycle time. With significant product variety, the cycle time will regularly change, making it much more challenging to define the ideal scenario and to measure it as a variety of jobs are performed.
Second, the Availability factor may cause operators to push through and continue production even if the machine would perform better if a stoppage were made. If a worn cutting tool needs to be replaced or the machine needs to be lubricated or a mold needs to be sprayed with mold release, the operator may not want to stop because it will hurt the Availability factor (or if it is a short stop, the Performance factor). The Availability factor can also lead to Overproduction because the goal is to keep the machine or workstation highly utilized. QRM advises that machines (resources) should be operated at 70 to 80% utilization in order to reduce flow times. The higher the machine utilization, the greater the increase in flow time through the system.
Third, the Quality factor may also encourage large batches since there tends to be a greater amount of scrap or rework when starting up a new job or model number.
Finally, the use of OEE may lead to difficult trade-offs or gamesmanship between the three factors. If faster speed will produce a big increase in Performance and only a small impact on Quality, the operator or supervisor may choose to make this trade-off. If skipping preventive maintenance will increase OEE and the resulting machine breakdown is not counted against the department, then people may choose to push the limits.
Can Any Part of OEE Work with QRM?
I have spent quite a bit of time thinking about whether there are aspects of OEE that could be helpful for QRM. Some of the underlying influences (downtime, setup, making defective products) help to identify where there is wasted capacity. Reducing these influences through setup reduction, quality improvement, and preventive maintenance can free up capacity. However, for QRM, the capacity that is gained should not necessarily be allocated to production. The newly freed capacity can be used instead to enable smaller lot sizes through more frequent setups or to provide planned spare capacity. QRM principle #2 recommends operating at 80% (or even 70%) capacity on critical resources. If the newly freed capacity is on a bottleneck resource and it is filled with production activity, the throughput of the line will increase, but the manufacturing critical path times (flow times) will also increase.
For QRM, manufacturing critical path time (MCT) should be the primary performance metric. Secondary metrics can also be utilized. Thus, measuring downtime, setup time, and time lost due to quality problems can be advantageous to free up capacity on a highly utilized resource. OEE, however, should not be used as a secondary metric because it will motivate behaviors and decisions that will increase MCT rather than reduce it.
Posted September 25, 2020