Reference no: EM132241345

RESTORING CUSTOMER SERVICE IN A FABRICATION AND ASSEMBLY PLANT

Moog, Inc., began more than 50 years ago as a designer and supplier of aircraft and missile components. Today Moog’s motion-control technology enhances performance in a variety of markets and applications from commercial aircraft cockpits, to power-generation turbines, to Formula One racing, to medical infusion systems. In 2002, the Industrial Controls Division of Moog, Inc., was facing a crisis. This division designed and manufactured a variety of products for industrial applications, including precision control valves, hydraulic manifold systems, and electric motors and drives. The division was having significant problems meeting customer due dates and was in danger of losing market share to its competitors. Customers were demanding price reductions as the cost of materials and production continued to rise. Manufacturing cycle times averaged 16 weeks, whereas the market was demanding 2- to 4-week lead times. The manufacturing process involved fabrication, subassembly, final assembly, and test in a configure-to-order environment. Moog had already started a Lean manufacturing initiative with help from one of its largest customers, Boeing. The company had started a 5S housekeeping program, organized the floor into manufacturing cells, and begun working on reducing setup times. But the problem of poor customer service was not being resolved, and the number of projects (kaizens) needed to become truly lean was huge. More important, Moog management did not believe that it had time to wait for the benefits from its Lean initiative. The company needed better on-time delivery now. George Cameron, materials manager, was one of a number of managers from Moog who had attended a Factory Physics seminar and thought that it seemed like a good tool for understanding the principles of manufacturing and assisting with improvements. He decided to call in Factory Physics Inc., to perform an assessment of the one of Moog’s plants. The assessment provided a road map that would first focus on improving delivery by rearranging the existing variability buffers and then focus on reducing waste in the fabrication area. The basic idea was to insulate fabrication from assembly and test by putting an inventory buffer between the two. To some, this sounded like heresy! Inventory is waste. Why add waste to the process? But the rationale was simple—you do not operate on a gall bladder when the patient is bleeding to death! Stop the bleeding first. If the poor on-time delivery continued, it could have a very negative effect on revenue. Thus the first step was to perform Pareto analysis to determine the high-volume parts. Then a supermarket stock was created that fabrication would maintain using a ROP, ROQ pull system. Assembly and test would build the final product from the components in stock based on customer orders. The effect would be reduced lead time to the customer (now only the cycle time in assembly and test) and much greater customer service. The model was first piloted in the torque motor subassembly cell. Variability in supply and demand was buffered with an inventory of about 180 part numbers in the cell that was used to create over 1,000 unique torque motors. As the process became more stable, Moog started to lower inventory levels and change to a first-in, first-out (FIFO) process. The next step was to move back to parts supply to further reduce buffers as variability was reduced. Finally, once customer service improved, focus was shifted to the problems in fabrication. To avoid adding too much inventory, Moog wanted to avoid using a traditional supermarket controlled by kanban. Instead, the Factory Physics, Inc., team employed an early version of the Factory Physics CSUITE inventory optimizer to set inventory policies using a ROP, ROQ model, as seen in Figure 9-16. Notice how much more effective an optimal strategy is than using kanban, particularly for high fill rates. At a 98.2 percent fill rate, kanban requires more than $281,000 in inventory, whereas the optimal policy requires slightly more than $200,000 for 98.4 percent fill rate. A slightly different version of the tool (and what became the CSUITE inventory optimizer) made it extremely easy to quantify the tradeoffs among fill rate, inventory investment, and number of setups in the cell. Figure 9-17 illustrates this tradeoff plot. Here the three different curves represent different numbers of setups (orders/month). As the number of setups increases, the inventory investment needed to achieve the same fill rate decreases. This was a great help to Moog in deciding how much setup reduction was needed. Selecting a point on the plot then generated an optimal policy, which was used to set inventory order quantities and reorder points for each part number needed for the cell. Employees were then trained in basic Factory Physics principles and Lean manufacturing techniques during a one-week accelerated improvement workshop. One component of the workshop was the Factory Physics paper-house exercise, which was used to help supervisors and operators understand how and why a pull system works. Following the training, operators and management made changes on the shop floor to prepare the cell for a pull system. Page 329After learning about the importance of bottlenecks, the operators changed the way they ran the cell. Formerly, an operator would perform a task on 60 parts before moving them to the next station. The operators suggested reducing this move batch significantly. They also realized that keeping the bottleneck busy was not that difficult and implemented a simple rule—keep the electrical discharge machine (EDM) busy. They quickly realized that WIP needed to move quickly through the cell and that there needed to be a queue of work before the EDM. Because the EDM was a pretty sharp bottleneck, this was not terribly difficult. Nonetheless, these simple rules increased productivity by 7 percent at a time when lot sizes were being reduced. The results from this approach substantially improved customer service. Cycle times in the cell went from 12 to 3 days while improving on-time delivery from less than 50 percent to over 95 percent. Even better was the unexpected 7 percent boost in productivity. Although an inventory buffer had been added in the cell, the overall inventory levels dropped over 15 percent. After this initial success integrating Factory Physics science and Lean, the company moved to another subassembly cell and repeated the process. With both subassembly cells using Factory Physics WIP control and setting inventory levels using the Factory Physics inventory optimizer, cycle times to the customer went from 23 to 6 days. George Cameron summarized the challenges and lessons learned in a presentation to management the following year: The challenges were and are: 1. People have memory and want to return to “the way things used to be,” even in the face of a successful change. 2. The shop had to be convinced that working to fill a bin is just as important as filling a work order. 3. A system to regularly review inventory levels should be in place. The lessons learned: 1. Factory Physics modeling actually works! 2. It is relatively easy to quantify the inventory investment/fill-rate/setup-frequency tradeoff. 3. The employees understand the concepts. Streamline the process → smaller lots → shorter cycle times → FIFO → smoother consumption → less inventory.

Questions

1. Discuss the challenges and lessons learned in industry,

2. Relate an example of challenges and lesson learned in the BSG simulation (see how the challenges and lessons presented on page 329 can be applied to BSG) and

3. In a properly labeled and explained figure, provide one illustration (chart, table, graph, etc.) representing a process or concept (e.g. change model or inventory management or other).